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Use of an evapotranspiration model and a Geographic Information System (GIS) to estimate the trasvase system in the Sant...

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USE OF AN EVAPOTRANSPIRATION MODEL AND A GEOGRAPHIC INFORMATION SYSTEM (GIS) TO ESTIMATE THE IRRIGATION POTENTIAL OF THE TRASVASE SYSTEM IN THE SANTA ELENA PENINSULA, GUAYAS, ECUADOR By CAMILO CORNEJO A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2003

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To my Family

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ACKNOWLEDGMENTS This thesis work would not have been completed without the help of several people whom I wish to thank. First, I thank my advisor Dr. Dorota Z. Haman for all her help and support, interest, knowledge, problem solving and advice. Thanks go to my supervisory committee, whose comments and edits contribute substantially to my research and to this document. I would also like to thank to thank to all the people from ESPOL and CEDEGE in Guayaquil, Ecuador for helping me whenever I needed information for my research. Special thanks go to my friends, who always help me when needed. Finally, I would like to thank the very special people in my life, my girlfriend and my family, for their support. iii

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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES............................................................................................................vii LIST OF FIGURES.............................................................................................................x LIST OF OBJECTS.........................................................................................................xiii LIST OF ABBREVIATIONS..........................................................................................xiv ABSTRACT.......................................................................................................................xv CHAPTER 1 LITERATURE REVIEW.............................................................................................1 Significance of Irrigation in Agriculture......................................................................1 Reference Evapotranspiration.......................................................................................3 Use of FAO Penman-Monteith to Estimate Reference Evapotranspiration.................4 Actual Crop Evapotranspiration...................................................................................5 Computerized Crop Water Use Simulations.................................................................5 Irrigation Efficiency......................................................................................................7 Irrigation Techniques..................................................................................................10 Application of GIS to Irrigation Management............................................................11 GIS Data Quality Analysis.........................................................................................12 Perceptions about Irrigation........................................................................................15 2 INTRODUCTION AND PROJECT AREA REVIEW..............................................17 Introduction.................................................................................................................17 Irrigated Area..............................................................................................................17 Agriculture and Irrigation...........................................................................................19 On-Farm Technologies...............................................................................................20 Policy..........................................................................................................................20 Actual Situation and Projections................................................................................21 Characteristics of the Santa Elena Peninsula..............................................................23 Meteorological Data...................................................................................................27 Climatic Classifications..............................................................................................33 Soils............................................................................................................................36 iv

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3 WEATHER DATA ANALYSIS FOR CROPWAT MODEL...................................39 Weather Stations Distribution.....................................................................................40 Estimating Missing Climatic Data..............................................................................41 Estimating Weather Data Sets for the Santa Elena Peninsula....................................45 4 GEOGRAPHIC INFORMATION SISTEM..............................................................52 Introduction.................................................................................................................52 Mapping Systems........................................................................................................54 ArcGIS........................................................................................................................55 Original Maps.............................................................................................................56 Data Quality Problems with the Santa Elena Peninsula Data Set..............................59 GIS Layers Created or Edited for the Project from the Original Maps......................65 Creation of Evapotranspiration Surface Maps............................................................71 5 WATER AVAILABILITY AND ITS USE IN THE SANTA ELENA PENINSULA..............................................................................................................74 Infrastructure...............................................................................................................74 TRASVASE Santa Elena............................................................................................74 Water Loss from the Canals and Dams to Evaporation..............................................79 Irrigation Technology used in the Santa Elena Peninsula..........................................82 Water Consumption....................................................................................................83 Reference Evapotranspiration Surface Maps..............................................................84 Agricultural Production in the Santa Elena Peninsula................................................91 6 METHODOLOGY.....................................................................................................94 Evapotranspiration......................................................................................................94 Open Water Evaporation............................................................................................95 Crop Water Requirement............................................................................................96 Crop Irrigation Requirement.......................................................................................99 Scenarios.....................................................................................................................99 7 RESULTS AND DISCUSSION...............................................................................103 Conclusion................................................................................................................113 Suggestions for Future Work....................................................................................114 APPENDIX A MAPS.......................................................................................................................116 B AVERAGE WEATHER DATA...............................................................................124 C CROPWAT REFERENCE EVAPOTRANSPIRATION TABLES.........................130 v

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D ECOCROP SELECTION CRITERIA TABLES......................................................137 E TROPICAL CROPS.................................................................................................148 F IRRIGATION REQUIREMENTS...........................................................................167 G PROGRAM TO CALCULATE CROP IRRIGATION REQUIREMENT..............172 LIST OF REFERENCES.................................................................................................175 BIOGRAPHICAL SKETCH...........................................................................................180 vi

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LIST OF TABLES Table page 1-1 Conveyance efficiency (Ec).......................................................................................8 1-2 Field application efficiency (Ea)................................................................................8 2-1 Minor watersheds.....................................................................................................26 2-2 Basins that start in the Coastal Mountain Range......................................................26 2-3 Climate types............................................................................................................34 2-4 Kppen climate classification for the SEP...............................................................35 2-5 Soils..........................................................................................................................36 3-1 Distances among stations (m) and elevation (mmsl)...............................................40 3-2 Regression analysis method.....................................................................................48 3-3 Creating new values.................................................................................................49 4-1 Comparison of interpolation methods......................................................................69 5-1 Main dams................................................................................................................77 5-2 Approximated surface areas of the canals................................................................80 5-3 Approximated surface areas of the dams.................................................................80 5-4 Canal description......................................................................................................81 5-5 Chongn-Daular-Cerecita pressurized system, Zone I (2001).................................82 5-6 Chongn-Cerecita-Playas canal, Zone I (2001).......................................................82 5-7 El Azcar-Ro Verde canal, Zone II (2001).............................................................83 5-8 Crop growing period................................................................................................85 5-9 Crop coefficients......................................................................................................89 vii

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5-10 Crops planted and projected increase in the Santa Elena Peninsula........................93 6-1 Chongn-San Isidro, Zone I, crop water requirements (CWR)..............................97 6-2 San Isidro-Playas, Zone I, crop water requirements (CWR)...................................97 6-3 Chongn-El Azcar, Zone II, crop water requirements (CWR).............................98 7-1 Scenario A, Zone II.................................................................................................104 7-2 Scenario A, Zone I.................................................................................................105 7-3 Total area that can be irrigated under different scenarios......................................106 7-4 Areas that could be irrigated during dry season in the SEP, Scenario A................109 7-5 Areas covered by different buffers of the canals in the SEP..................................111 7-6 Comparison of areas that could be irrigated according different sources..............111 B-1 Available weather data sets....................................................................................124 B-2 Chongn weather station........................................................................................125 B-3 Playas weather station............................................................................................126 B-4 El Azcar weather station......................................................................................127 B-5 San Isidro weather station......................................................................................128 B-6 Suspiro weather station..........................................................................................129 C-1 Reference evapotranspiration Chongn.................................................................130 C-2 Reference evapotranspiration El Azcar................................................................131 C-3 Reference evapotranspiration Playas.....................................................................132 C-4 Reference evapotranspiration San Isidro................................................................133 C-5 Reference evapotranspiration Suspiro....................................................................134 C-6 Open water evaporation values per canal...............................................................136 C-7 Open water evaporation from dams.......................................................................136 F-1 Chongn-San Isidro, 50% efficiency.....................................................................167 F-2 San Isidro-Playas, 50% efficiency.........................................................................167 viii

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F-3 Chongn-El Azcar, 50% efficiency.....................................................................168 F-4 Chongn San Isidro, 70% efficiency...................................................................168 F-5 San Isidro-Playas, 70% efficiency.........................................................................169 F-6 Chongn-El Azcar, 70% efficiency.....................................................................169 F-7 Chongn-San Isidro, 90% efficiency.....................................................................170 F-8 San Isidro-Playas, 90% efficiency.........................................................................170 F-9 Chongn-El Azcar, 90% efficiency.....................................................................171 ix

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LIST OF FIGURES Figure page 1-1 Dual crop coefficient curve........................................................................................6 2-1 Canal in construction, TRASVASE Santa Elena.....................................................22 2-2 Landscape of the Santa Elena Peninsula..................................................................24 2-3 Location of the Santa Elena Peninsula.....................................................................25 2-4 Javita River, an intermittent river at SEP.................................................................25 2-5 Historical average precipitation in the Santa Elena Peninsula.................................31 2-7 Papadakis climate classification...............................................................................34 3-1 Weather stations.......................................................................................................41 3-2 Chongn vs. El Azcar.............................................................................................51 3-3 Chongn vs. El Suspiro............................................................................................51 3-4 Chongn vs. Playas..................................................................................................51 4-1 Soil types on Santa Elena Peninsula, original map..................................................57 4-2 Kppen climate classification of Santa Elena Peninsula.........................................57 4-3 Dams location on Santa Elena Peninsula.................................................................58 4-4 Canals and other features.........................................................................................59 4-5 Errors in the hydrology maps of the SEP.................................................................61 4-6 Overlap error............................................................................................................63 4-7 Main soil types layer created for the Santa Elena Peninsula....................................65 4-8 Ecological zones.......................................................................................................66 4-9 Canals.......................................................................................................................67 x

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4-10 Actual farm locations...............................................................................................68 4-11 Surface maps of weather data for January................................................................72 5-1 Daule-Peripa Dam....................................................................................................74 5-2 Hydroelectric plant, Proyecto de Propsito Multiple Jaime Rolds Aguilera......75 5-3 Chongn Dam..........................................................................................................76 5-4 Daule pumping station.............................................................................................76 5-5 Zone II potabilization plant......................................................................................78 5-6 Canal.........................................................................................................................78 5-7 Canal San Rafael, TRASVASE project...................................................................79 5-8 Trapezoidal canal.....................................................................................................81 5-9.1 Average reference evapotranspiration for the SEP I...............................................86 5-9.2 Average reference evapotranspiration for the SEP II..............................................87 5-10 Agricultural Production in the Santa Elena Peninsula.............................................92 6-1 Evaporation from canals of the TRASVASE system...............................................96 6-2 Irrigation zones in the Santa Elena Peninsula........................................................100 7-1 Buffers from the canals in the Santa Elena Peninsula............................................110 A-1 Maximum annual precipitation isohyets................................................................116 A-2 Minimum annual precipitation isohyets.................................................................117 A-3 Average annual precipitation isohyets...................................................................118 A-4 Complete map of soils in the Santa Elena Peninsula.............................................119 A-5 Santa Elena farms...................................................................................................120 A-6 Chongn farms.......................................................................................................121 A-7 Cerecita farms........................................................................................................122 A-8 Azcar-Rio Verde farms........................................................................................123 C-1 Surface maps used to create a reference evapotranspiration map..........................135 xi

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E-1 Aloe plantation, Santa Elena Peninsula..................................................................148 E-2 Plantain in the Santa Elena Peninsula....................................................................152 E-3 Grapes.....................................................................................................................154 E-4 Mangos...................................................................................................................155 E-5 Onion plantation and onions, Santa Elena Peninsula.............................................157 E-6 Papaya plantation in the Santa Elena Peninsula.....................................................158 G-1 Program to calculate crop irrigation requirement, input table and graph...............173 G-2 Program to calculate crop irrigation requirement, results table.............................174 xii

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LIST OF OBJECTS Objects 1. Program to calculate water requirement in the Santa Elena Peninsula 2. PDF version of the user manual for the water requirement program 3. Microsoft Word 2000 version of the user manual for the water requirement program xiii

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LIST OF ABBREVIATIONS CEDEGE Commission for the Development of the Guayas River Basin, Ecuador http://www.cedege.gov.ec CWR Crop Water Requirement CIR Crop Irrigation Requirement ESPOL Polytechnic School of the Littoral, Ecuador http://www.espol.edu.ec ETo Reference evapotranspiration ETc Actual evapotranspiration FAO Food and Agricultural Organization http://www.fao.org FGDC Federal Geographic Data Committee http://www.fgdc.gov GIS Geographic Information System GPS Global Positioning System IDW Inverse Distance Weighted IGM Geographic Military Institute, Ecuador http://www.igm.gov.ec ISO International Organization for Standardization Kc Crop coefficient SEP Santa Elena Peninsula USDA United States Department of Agriculture http://www.usda.gov WCD World Commission on Dams http://www.dams.org/ xiv

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science USE OF AN EVAPOTRANSPIRATION MODEL AND A GEOGRAPHIC INFORMATION SYSTEM (GIS) TO ESTIMATE THE IRRIGATION POTENTIAL OF THE TRASVASE SYSTEM IN THE SANTA ELENA PENINSULA, GUAYAS, ECUADOR By Camilo Cornejo May 2003 Chair: Dorota Z. Haman Cochair: Jonathan D. Jordan Major Department: Agricultural and Biological Engineering Irrigated agriculture produces more than 40 % of the world food supply, using 20 % of the agricultural land in developing countries. Food production is important, especially in developing countries like Ecuador. The TRASVASE irrigation system was constructed to provide water for irrigation to the Santa Elena Peninsula in Ecuador. However, this project performs below expectations. One of the limitations is that the total area that this irrigation system could irrigate has not been determined. Available geographic, climatic, and soils and land use data were summarized for the Santa Elena Peninsula using a Geographic Information System. The total area that can be irrigated was calculated based on the evapotranspiration concept used by CROPWAT software from UN/FAO. Evapotranspiration is a sum of the water evaporation from the soil and plant surfaces, and transpiration from the plant leaves. xv

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Calculation of evapotranspiration uses weather parameters like air temperature, relative humidity, solar radiation, and wind speed. Taking into consideration the water used by the potable water treatment plants and the water loss thru evaporation and seepage from the canals and dams, total water available for irrigation can be calculated. This total available water divided by the crop water requirement gives the total area that the TRASVASE system could irrigate. To cover a wide range of possible variations in irrigation technology and crops planted in the area, nine scenarios were tested. The variables were three levels of in-field water application efficiency (50 %, 70 %, and 90 %); and three levels of the crop water requirement (high, low, and a mixture of high and low). Results of this project show that with an in-field application efficiency of 90 % and low-water-requirement crops, 15,506 hectares could be irrigated. However, with 50 % application efficiency and high-water-requirement crops, the area is reduced to 7,700 hectares. It is obvious that very efficient irrigation technologies must be used in the Santa Elena Peninsula to optimize the use of water. Good management and maintenance of those irrigation systems are also needed. Agricultural production has to be planned to minimize water use and to increase the total area to be irrigated. xvi

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CHAPTER 1 LITERATURE REVIEW Significance of Irrigation in Agriculture Irrigation is a process that uses more than two-thirds of the Earths renewable water resources and feeds one-third of the Earths population (Stanhill 2002). Some 2.4 billion people depend directly on irrigated agriculture for food and employment. Irrigated agriculture thus plays an essential role in meeting the basic needs of billions of people in developing countries (FAO 1996). Although water resources are still ample on a global scale, serious water shortages are developing in the arid and semi-arid regions (Hall 1999). There is a need to focus attention on the growing problem of water scarcity in relation to food production. The World Food Summit of November 1996, drew attention to the importance of water as a vital resource for future development (FAO 1996). A major part of the developed global water resources is used for food production. The estimated minimum water requirement per capita is 1,200 m 3 annually (50m 3 for domestic use and 1,150 m 3 for food production) (FAO 1996). Sustainable food production depends on judicious use of water resources as fresh water for human consumption and agriculture become increasingly scarce. To meet future food demands and growing competition for clean water, a more effective use of water in both irrigated and rainfed agriculture will be essential (Smith 2000). Options to increase water-use efficiency include harvesting rainfall, reducing irrigation water losses, and adopting cultural practices that increase production per unit of water. 1

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2 Irrigation is an obvious option to increase and stabilize crop production. Major investments have been made in irrigation over the past 30 years by diverting surface water and extracting groundwater. The irrigated areas in the world have, over a period of 30 years, increased by 25 % (mainly during a period of accelerated growth in the 1970s and early 1980s) (FAO 1993). A major constraint to the understanding of the use of water is the difficulty associated with its measurement and quantification. Measurement and data collection of discharge in canals is difficult and fraught with potential errors. Necessary conditions for the optimal performance of regional water delivery systems include well-defined water rights; infrastructure capable of providing the service embodied in the water rights, and assigned responsibilities for all aspects of system operation (Perry 1995). One or more of those conditions may be missing in some regional systems at the start of irrigation deliveries. In other systems problems may develop over time with changes in land ownership, cropping patterns, and the volume of water available for delivery in the system. Problems with cost recovery and inadequate maintenance also can reduce the efficiency of regional water-delivery systems. Water use for crop production is depending on the interaction of climatic parameters that determine crop evapotranspiration and water supply from rain (Smith 2000). Compilation, processing, and analysis of meteorological information for crop water use and crop production are therefore key elements in developing strategies to optimize the use of water for crop production and to introduce effective water-management practices. Estimating crop water use from climatic data is essential to, better water-use efficiency.

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3 Because most of the Earths irrigated land is in the underdeveloped world (where food, water, and skilled manpower are in short supply), it is important to use the simplest, cheapest, and most practical meteorological method to improve crop water-use efficiency in irrigation. Stanhill (2002) says that in these regions use of standard, correctly sited and maintained evaporation pans operating within a national network can provide the basis for a scheduling method in which the use of empirical crop coefficients is accepted. These coefficients reflect the local economic as well as agronomic, climatologic and hydrological (water quality) situation (Stanhill 2002). However, the literature often contradicts. Hillel (1997) said: the use of evaporation pans has several shortcomings. Smith (2000) stated that agro-meteorology would play a key role in the looming global water crisis. Appropriate strategies and policies need to be defined, including strengthening of national use of climatic data for planning and managing of sustainable agriculture and for drought mitigation. The limitations of currently available methods for measuring rates of evaporation from natural and agricultural surfaces are well known; as is the resulting lack of information (local and global) on this major element in the hydrological cycle. A practical method (suitable for routine use in meteorological station networks) is to use calculations based on other meteorological measurements, like those used by the Penman-Monteith method (Stanhill 2002). Reference Evapotranspiration Several definitions of reference evapotranspiration ET o have been formulated. Jensen (1993) defined ET o as the rate at which water, if available, would be removed from the soil and plant surface. Pereira et al. (1999) stated that Duke simplified the definition of ET o to the water used by a well-watered reference crop, such alfalfa, which

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4 fully covers the soil surface. The modified Penman combination equation is used to compute ET o as it is considered to be a satisfactory estimation equation when daily estimates of ET o are desired (Jensen et al. 1990). Use of FAO Penman-Monteith to Estimate Reference Evapotranspiration This approach was introduced by Penman in 1948 to estimate open-water evaporation (Penman 1948); and extended by Monteith in 1965 to directly estimate evaporation from vegetation-covered surfaces (Monteith 1965). It is now the recommended method by the FAO to calculate reference crop evapotranspiration (Allen et al. 1998). Studies showed the superior performance of the PenmanMonteith approach, in both arid and humid climates, and convincingly confirmed the sound underlying concepts of the method. Based on these findings, the method was recommended by the FAO Panel of Experts (convened in 1990) for adoption as a new standard for reference crop evapotranspiration estimates (Hall 1999). The use of the Penman-Monteith equation in irrigation practice requires empirical coefficients to modifyin general to reduce but sometimes to increasethe estimates of reference crop evapotranspiration (Stanhill 2002). Use of FAO PenmanMonteith with limited climatic data. The limited availability of the full range of climatic data (particularly data on sunshine, humidity and wind) has often prevented the use of the combination methods and resulted in the use of empirical methods (which require only temperature, pan, or radiation data). This has contributed to the confusing use of different ET o methods and conflicting evapotranspiration values. To overcome this constraint and to further use of a single method, additional studies have been undertaken to provide recommendations on the

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5 using FAO Penman-Monteith when no humidity, radiation or wind data are available. As a result, procedures are presented to estimate humidity and radiation from maximum/minimum temperature data and to adopt global estimates for wind speed. The availability of worldwide climatic databases further facilitates the adoption of values from nearby stations. Such procedures have proven to perform better than any of the alternative empirical formulas; and will largely improve transparency of calculated evapotranspiration values (Smith et al. 1996). Actual Crop Evapotranspiration Procedures for estimating crop evapotranspiration have been well established by Doorenbos and Pruitt (1977), using a series of recommended crop coefficient values (K c ) to determine ET crop (ET c ) from reference evapotranspiration (ET o ), as follows: ET c = K c ET o (1-1) This formula represents the single crop coefficient. Crop evapotranspiration (ET c ) refers to evapotranspiration of a disease-free crop, grown in very large fields, not short of water and fertilizer. Estimation of ET c is essential for computing the soil water balance and irrigation scheduling. ET c is governed by weather and crop condition (Smith, 2000). The specific wetting (irrigation) events are taken into account (spikes in Figure 1-1). Computerized Crop Water Use Simulations Practical procedures and criteria need to be defined to enhance the introduction and application of effective water use practices for crop production. The introduction of computerized procedures linked to digital databases and geographic information systems (GIS) will greatly enhance the use of appropriate planning and management techniques for water use in irrigated and rainfed agriculture. Computerized procedures greatly facilitate the estimation of crop water requirements from climatic data and allow

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6 the development of standardized information and criteria for planning and management of rainfed and irrigated agriculture. Figure 1-1. Dual crop coefficient curve Figure 1-1 shows the crop coefficient divided in different stages according to crop development. The FAO-CROPWAT program (Smith 1992) incorporates procedures for reference crop evapotranspiration and crop water requirements and allows the simulation of crop water use under various climate (CLIMWAT 1994), crop and soil conditions. As a decision support system CROPWATs main functions include: (1) the calculation of reference evapotranspiration according to the FAO Penman-Monteith method; (2) crop water requirements using revised crop coefficients (FAO Paper 56, compared to the data from FAO Paper 49) and crop growth periods; (3) effective rainfall and irrigation requirements; (4) scheme irrigation water supply for a given cropping pattern; (5) daily water balance computations (Smith 1992).

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7 Irrigation Efficiency Classical overall irrigation system efficiency (E o ) is defined as the volume of water used beneficially (net crop evapotranspiration) divided by the volume of water diverted (Keller et al. 1996) Keller et al. (1996) defines effective efficiency (E E ) as the ratio of net crop evapotranspiration divided by the net volume of water delivered to a field (V s ). The volume of water that becomes usable surface runoff or deep percolation is subtracted from the total volume delivered when calculating the denominator ratio. Irrigation efficiency has a tremendous impact on agricultural water demands. Understanding how irrigation efficiency fits into estimation of water requirements is essential. Zadalis, et al (1997) consider the effective rainfall in their definition of efficiency. The mean irrigation efficiency for each system is defined by the ratio of the net volume actually used by the crops and the volume released at the head of the main canal: E E = (ET c R e )/V s (1-2) where ET c is the estimated water used by crops, R e is the effective rainfall, and V s is the volume of water delivered to each network or canal (Zalidis et al. 1997). The most common way to express the efficiency of irrigation systems is to subdivide it into conveyance and application efficiencies. The conveyance efficiency (Ec), which represents the efficiency of water transport in canals or pipes in the field. The field application efficiency (Ea), which represents the efficiency of water application in the field. The conveyance efficiency (Ec) mainly depends on the length of the canals, the soil type or permeability of the canal banks and the condition of the canals (Brouwer & Prins

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8 1989). In large irrigation schemes more water is lost than in small schemes, due to a longer canal system. When water is conveyed in pipes, Ec mainly depends on pipe leakage and is usually close to 100 % for new systems. Table 1-1 provides some indicative values of the conveyance efficiency (Ec), considering the length of the canals and the soil type in which the canals are dug. The level of maintenance is not taken into consideration: bad maintenance may lower the values of Table 1-1, by as much as 50 % (Brouwer & Prins 1989). Table 1-1. Conveyance efficiency (Ec) Percent Efficiency (%) of conveyance (canal length in meters) Earthen canals Lined canals Sand Loam Clay Long (> 2000) 60 70 80 95 Medium (200-2000) 70 75 85 95 Short (< 200) 80 85 90 95 The field application efficiency (Ea) mainly depends on the irrigation method and the level of farmer discipline. Some indicative values of the average field application efficiency (Ea) are given in Table 1-2. Lack of discipline may lower the values found in Table 1-2 (Brouwer & Prins 1989). Table 1-2. Field application efficiency (Ea) Irrigation methods Application efficiency (%) Surface irrigation (border, furrow, basin) 50-60 Sprinkler irrigation 60-80 Drip irrigation 80-up Once the conveyance and field application efficiency have been determined, the scheme irrigation efficiency (E) can be calculated, using the following formula (Brouwer & Prins 1989): 100EaEcE (1-3)

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9 with E = scheme irrigation efficiency (%) Ec = conveyance efficiency (%) Ea = field application efficiency (%) According to FAO a scheme irrigation efficiency of 50 % is good; 40 % is reasonable, while a scheme Irrigation efficiency of 20 % is poor. It should be kept in mind that the values mentioned above are only indicative values (Brouwer & Prins 1989). Water productivity increases with improvements in agronomic practices and in water supply and management, both regionally and at the farm level. Water supply reliability also is important, as optimal investments in seeds, fertilizer, and land preparation are less likely to be made when the timing of farm level water deliveries is uncertain (Brouwer 1988). Improving agricultural water efficiency is particularly important for improving the productivity of large irrigation schemes. The recent promotion of participatory irrigation management or turnover needs to be supported by other measures such as technological innovations, for example, the development of effective water metering of canal systems to enable cost recovery measures to be introduced (Brouwer 1988). Under irrigated conditions, priorities need to be set for reducing losses of irrigation water and for increasing effectiveness of irrigation management. Considerable amounts of water diverted for irrigation are not effectively used for crop production. It is estimated that, on average, only 45 % is used by the crop, with an estimated 15 % lost in the water conveyance system, 15 % in the field channels and at least 25 % in inefficient field applications (FAO 1994). This number depends on the type of irrigation system. For example, in Arizona, farmers have increased irrigation efficiency from 50 % in the 1980s to 95 % in 1995 by adopting sub-surface drip methods. This change in technology

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10 results in other benefits such as reduced power consumption, reduced fertilizer and herbicide use and higher yields (Wichelns 2001). Irrigation Techniques An adequate water supply is important for plant growth. When rainfall is not sufficient, the plants must receive additional water from irrigation. Various methods can be used to supply irrigation water to the plants. Whatever irrigation method is being chosen, its purpose is always to attain a better crop and a higher yield. Surface irrigation. Surface irrigation is the application of water by gravity flow to the surface of the field. Either the entire field is flooded (basin irrigation) or the water is fed into small channels (furrows) or strips of land (borders). Sprinkler irrigation. Sprinkler irrigation is similar to natural rainfall. Water is pumped through a pipe system and then sprayed onto the crops through rotating sprinkler heads (Izuno & Haman 1987). Micro irrigation. Consists of drip irrigation and micro-sprinkler systems. Drip irrigation. With drip irrigation, water is conveyed under pressure through a pipe system to the fields, where it drips slowly onto the soil through emitters or drippers that are located close to the plants. Only the immediate root zone of each plant is wetted. Therefore this can be a very efficient method of irrigation. Drip irrigation is sometimes called trickle irrigation (Izuno and Haman 1987). Microsprinkler. Also known as micro-spray, is an irrigation method that falls into the trickle category, characterized by the application of water to the soil surface as a small spray or mist. Discharge rates are generally less than 30 gal/hr (Izuno and Haman 1987).

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11 Application of GIS to Irrigation Management GIS have potentially considerable application to irrigation water management, especially in regions where there are poorly defined procedures for irrigation water management data collection, processing and analysis. The possibility of using GIS to identify crop areas, plan irrigation schedules and quantify performance offer exciting possibilities for research (Ray and Dadhwal 2001). The tools necessary to create a good GIS in irrigation are the availability of weather data and how it is spatially distributed over the study area. Also important are the techniques to be used to interpolate the climatic data, evapotranspiration, and other calculated variables. The availability of weather data of acceptable spatial resolution for large-scale irrigation scheduling is an important factor to consider in planning the development and management of irrigation information systems throughout the world (Hashmi et al. 1994). The spatial distribution of the available weather data is important. It is of special concern in developing countries where the availability of weather stations is limited. The recommended maximum distance between points (weather stations) for least dense networks is 150 km, for the intermediate network, 50 km for the densest network, 30km (Gandin 1970). Once the data is collected and analyzed using statistics, a surface map can be created using GIS. There are many interpolation methods; however, inverse-square-distance interpolation technique appears to be the most accurate method of interpolation irrespective of number of data points. Hashmi et al. (1994) has also used the inverse-square-distance approach to interpolate ET values.

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12 GIS Data Quality Analysis An important step when working with GIS is data quality analysis. The International Organization for Standardization supplies an acceptable definition of data quality using accepted terminology from the quality field. The International Organization for Standardization (ISO) is a federation of national standards bodies. ISO's working groups from most of the world's nations forge international agreements, which are published as International Standards. These standards are documented agreements containing technical specifications or other precise criteria to be used consistently as rules, guidelines, or definitions of characteristics, to ensure that materials, products, processes and services are fit for their purpose. Like other ISO standards, ISO quality standards are frequently updated to reflect advances in quality methodology. Among the many ISO standards is ISO 8402: Quality Management and Quality Assurance Vocabulary. ISO 8402 provides a formal definition of quality as: The totality of characteristics of an entity that bear on its ability to satisfy stated and implied needs (Marcey, et al. 1998). Thus, data can be defined to be of the required quality if it satisfies the requirements stated in a particular specification and the specification reflects the implied needs of the user. Therefore, an acceptable level of quality has been achieved if the data conforms to a defined specification and the specification correctly reflects the intended use. Structured analysis of these characteristics, together with careful planning, should provide a data quality assessment that reveals key data quality problems, root causes for the problems, and solutions for improving both conformance and utility.

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13 Data Quality Attributes A set of characteristics, or data quality attributes, is required for the objective and measurable assessment of data quality. Commonly used attributes to measure data quality include accuracy, completeness, consistency, reliability, timeliness, uniqueness, and validity (Chrisman, & McGranaghan 2000). Among other technical issues in GIS, accuracy is perhaps the most important, it covers concerns for data quality, error, uncertainty, scale, resolution and precision in spatial data and affects the ways in which it can be used and interpreted All spatial data is inaccurate to some degree but it is generally represented in the computer to high precision Data Quality Components Recently a National Standard for Digital Cartographic Data (http://www.fgdc.gov/) was developed by a coordinated national effort in the U.S. (Chrisman, & McGranaghan 2000). This is a standard model to be used for describing digital data accuracy Similar standards are being adopted in other countries This standard identifies several components of data quality: Positional accuracy Attribute accuracy Logical consistency Completeness Lineage Accuracy Defined as the closeness of results, computations or estimates to true values (or values accepted to be true). Since spatial data is usually a generalization of the real world, it is often difficult to identify a true value, and we work instead with values that are accepted to be true, e.g., in measuring the accuracy of a contour in a digital database, we compare to the contour as drawn on the source map, since the contour does not exist as a

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14 real line on the surface of the earth. The accuracy of the database may have little relationship to the accuracy of products computed from the database, e.g. the accuracy of a slope, aspect or watershed computed from a Digital Elevation Model (DEM) is not easily related to the accuracy of the elevations in the DEM itself. Attribute Accuracy, defined as the closeness of attribute values to their true value, has to be noted that while location does not change with time, attributes often do. Attribute accuracy must be analyzed in different ways depending on the nature of the data Positional Accuracy Defined as the closeness of locational information (usually coordinates) to the true position. Conventionally, maps are accurate to roughly one line width or 0.5 mm, equivalent to 12 m on 1:24,000, or 125 m on 1:250,000 maps. To test positional accuracy one of the following options can be used as an independent source of higher accuracy: a larger scale map, the Global Positioning System (GPS), raw survey data, internal evidence. GIS Data Entry GIS data typically are created from hard-copy source data. The process often is called "digitizing," because the source data are converted to a computerized (digital) format. Human digitizers can compound errors in source data as well as introduce new errors (Korte 2000). Although manual digitizing is used less often today, it was the predominant digitizing method in the 1980s. In this process maps are affixed to digitizing tables, registered to a GIS coordinate system and "traced" into a GIS (Korte 2000). Here also are many opportunities for error, because the process is subject to visual and mental mistakes, fatigue, distraction and involuntary muscle movements.

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15 In addition, the "set up" of a map on a digitizing table or a scanned raster image can produce errors. Source Data Only recently has it become commonplace to collect GIS data directly in the field. Data collection can be done using field survey instruments that download data directly into GISs or via GPS receivers that directly interface with GIS software on portable PCs. These techniques can eliminate the need for GIS source data (Korte 2000). During the last 20 years, GIS data most often have been digitized from several sources, including hard-copy maps, rectified aerial photography and satellite imagery. Hard-copy maps (e.g., paper, vellum and plastic film) may contain unintended production errors as well as unavoidable or even intended errors in presentation (Korte 2000). Controlling GIS Errors GIS data errors are almost inevitable, but their negative effects can be kept to a minimum. Many errors can be avoided through proper selection and "scrubbing" of source data before they are digitized. Data scrubbing includes organizing, reviewing and preparing the source materials to be digitized. The data should be clean, legible and free of ambiguity. "Owners" of source data should be consulted as needed to clear up questions that arise (Marcey et al. 1998). Perceptions about Irrigation Irrigation is perceived by some to be costly, and thus financially and economically questionable due to low world prices for the grain crops most commonly found on irrigated land. It has also been criticized as environmentally unfriendly due to water logging, soil salinization and unsatisfactory resettlement programs. To some extent irrigation suffers from excessive expectations. For example, a review of World Bank

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16 experience (Jones 1995) shows that irrigation projects yielded overall positive economic rates of returns with an average of 15 %, higher than the opportunity cost of capital and greater than the average for other non-irrigated agricultural projects. The actual achievements were however, lower than the rates of return predicted at appraisal. The need to manage water holistically has become a familiar message to all working in water resources. This has helped to focus on the cross-cutting nature of the resource and the need to optimize allocation between different users that depend on water for irrigation, drinking water supply, industry, power and between users and the environment.

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CHAPTER 2 INTRODUCTION AND PROJECT AREA REVIEW Introduction Large dams and irrigation projects such as the TRASVASE in the Santa Elena Peninsula, Ecuador consists of a nested set of sub-systems involving a dam as source of supply, an irrigation system (including canals and on-farm irrigation application technology), an agricultural system (including crop production processes), and a wider rural socio-economic system and agricultural market (WCD 2002). The performance indicators for large dam irrigation projects include: Physical performance on water delivery, area irrigated and cropping intensity; Copping patterns and yields, as well as the value of production Irrigated Area Large dam projects usually fall short of area actually irrigated, and to a lesser extent the intensity with which areas are actually irrigated. Poor performance is most noticeable during the earlier periods of project life, as the average achievement of irrigated area targets compared with what was planned for each period increases over time from around 70 % in year five to approximately 100 % by year 30 (WCD 2002). The under achievement of targets for irrigated area development for large dams has a number of causes. Institutional failures have often been the primary causes, including inadequate distribution channels, overly centralized systems of canal administration, divided institutional responsibility for main system and tertiary level system, and inadequate allocation of financing for tertiary canal development. Technical causes 17

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18 include delays in construction, inadequate surveys and hydrological assumptions, inadequate attention to drainage, and over-optimistic projections of cropping patterns, yields and irrigation efficiencies. Under achievements includes also the late realization that some areas were not economically viable. In addition, a mismatch between the static assumptions of the planning agency and the dynamic nature of the incentives that govern actual farmer behavior has meant that projections quickly become outdated (WCD 2002). Lower yields are often observed for crops specified in planning documents that emphasize food grain production for growing populations than for the crops actually selected by farmers. This occurs as farmers respond to the market incentives offered by higher-value crops such as seasonal or longer-term orchard based crops and allocate available resources to these crops. This implies higher-than-expected gross value of production per unit of area, with the caution that such increases have varied with the long-term real price trend of the relevant agricultural commodities (Vermillion 1997). But when changes in cropping patterns are combined with shortfalls in area developed and cropping intensity, the end result is often a shortfall in agricultural production from the scheme as a whole. Gross value of production is higher where the shift to higher-value crops offsets the shortfall in area or intensity targets. Lower than expected crop yields have been caused by agronomic factors, including cultivation practices, poor seed quality, pest attack and adverse weather conditions, and by lack of labor or financial resources. Physical factors such as poor drainage, uneven or unsuitable land, inefficient and unreliable irrigation application, and salinity also hinder agricultural production. The efficiency of water use affects not only production but also demand and supply of irrigated water (WCD 2002).

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19 A general pattern of shortfalls and variability in agricultural production from irrigation projects in developing countries is also revealed by other sources. In the 1990 World Bank OED study on irrigation cited earlier, 15 of 21 projects had lower than planned agricultural production at completion. Evaluations of 192 irrigation projects approved between 1961 and 1984 by the World Bank indicated that only 67 % performed satisfactorily against their targets. Agriculture and Irrigation Efforts to promote sustainable water management practices have necessarily focused on the agricultural sector as the largest consumer of freshwater. Governments have several objectives in deciding the nature and extent of inputs in agriculture. These include achieving food security, generating employment, alleviating poverty and producing export crops to earn foreign exchange. Irrigation represents one of the inputs to enhance livelihoods and achieve economic objectives in the agricultural sector with subsequent effects for rural development (Vermillion 1997). Irrigated agriculture has contributed to growth in agricultural production worldwide, although inefficient use of water, inadequate maintenance of physical systems and institutional and other problems have often led to poor performance. Emphasis on large-scale irrigation facilitated consolidation of land and in some cases brought prosperity for farmers with access to irrigation and markets (World Bank 1990). In the absence of good quality control and effective maintenance the canal linings often have not achieved the predicted improvements in water savings and reliability of supply. In most irrigation systems, particularly those with long conveyance lengths, a disproportionate amount of water is lost as seepage in canals and never reaches the farmlands.

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20 Inadequate maintenance is a feature of a number of irrigation systems in developing countries. An impact evaluation of 21 irrigation projects by the World Bank concluded that a common source of poor performance was premature deterioration of water control structures. Often poor maintenance reduces irrigation potential and affects the performance of systems (World Bank 1990). On-Farm Technologies A number of technologies exist for improving water use efficiency and, hence, the productivity of water in irrigation systems. Sprinkler system and micro-irrigation methods, such as micro-sprinkler and drip systems, provide an opportunity to obtain higher efficiencies than those available in surface irrigation. For these pressurized systems, field application efficiencies are typically in the range of 70 % (Cornish 1998). The output produced with a given amount of water is increased by allowing for more frequent and smaller irrigation inputs, improved uniformity of watering and reduced water losses. Policy Policy and management initiatives are fundamental to raising productivity per unit of land and water and increasing returns to labor. They are often interlinked and require political commitment and institutional co-ordination. Agricultural support programs tend to be developed and implemented in relative isolation from irrigation systems. Typically there is weak co-ordination between agencies responsible for agricultural activities (such as extension services, land consolidation, credit and marketing) and those responsible for irrigation development. Price incentives are also inadequate to raise productivity and the outcome is a significant gap between potential and actual yields. In the absence of better opportunities from agriculture, many farmers seek off-farm employment. Incentives to

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21 enhance production are necessary and can result from a more integrated set of agricultural support measures and the involvement of joint ventures that provide capital resources and market access to smallholder farmers. Appropriate arrangements need to be introduced for such joint ventures to ensure an equitable share of benefits (WCD 2002). One of the major contributors to poor performance of large irrigation systems is the centralized and bureaucratic nature of system management, characterized by low levels of accountability and lack of active user participation. The structure of farmer involvement varies from transfer of assets to a range of joint-management models. As yet, there is no general evidence to suggest that irrigation performance has improved as a result of transfer alone, although there are promising examples indicating that decentralization may be a required, but not sufficient measure to improve performance (Vermillion 1997). Experience has shown that in order to be effective, a strong policy framework is required, providing clear powers and responsibilities for the farmers organizations (Bandaragoda 1999). Water rights and trading are highly contentious issues. Win-win situations occur for farmers when they trade a part of their water to replace lost income while at the same time being able to finance water use efficiency gains from their remaining water allocation. The formulation of national policies and strengthening of the national capacities to implement effectively such national policies in better water use is essential (Smith 2000). Actual Situation and Projections The construction of the TRASVASE irrigation system in the Santa Elena Peninsula (SEP) was designed to intensify the agricultural use of the land in this Ecuadorian region, but after several years of functioning the improvement is not as significant and viable as expected.

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22 The construction of the TRASVASE was started in 1989 by CEDEGE 1 After being operative for nine years, the project has not come close to the expected land use. Less than 25 % or 6,512 hectares are being under agricultural production in the Santa Elena Peninsula. According to CEDEGE projections the total land capacity of the TRASVASE irrigation system is 23,066 hectares (CEDEGE 2001), but producers organizations do not think that the theoretical number given by CEDEGE is the real area that can be irrigated with available water. These organizations of producers say that 16,000 ha to 17,000 ha will be the maximum area that the TRASVASE project could irrigate at any time (El Comercio 2001). The analysis of the irrigation capacity is one of the main points of this research. Figure 2-1. Canal in construction, TRASVASE Santa Elena Some factors can be cited to try to identify the problems that have caused slow progress of the TRASVASE project. The climate variability, soil composition, land tenure, and commercialization problems are among the principal constrains. The Peninsula has surprisingly reduced solar radiation. It is estimated that the area has less than 600 hours per year of total solar radiation. The relatively frequent and strong El Nio effect, with the last one in 1997, and the next one expected in Ecuador by the 1 CEDEGE, Comision de Estudios para el Desarrollo de la Cuenca del Rio Guayas, in English, Commission for the Development of the Guayas River Basin.

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23 rainy season of 2002, has also a strong impact on agricultural production. Other problem associated with the climate in the peninsula is the high relative humidity (RH) that results in the spread of fungi and bacteria that attack the crops in the Peninsula. Soils in the Santa Elena Peninsula are of marine origin, mostly semiarid and of low natural fertility, with high clay content. All those factors require especial soil management techniques that are not always followed by the agricultural producers in the SEP due to their high cost. There are other lesser soil problems related to contents of lithium, sodium, and potassium (CESUR 1995). Characteristics of the Santa Elena Peninsula Administrative Jurisdiction Administratively, the Santa Elena Peninsula is included within the Guayas province. The area under this administration is 6,050 km 2 and represents about 30.5 % of the Guayas province (19,841 km 2 ) and 2.13 % of the total area of the Republic of Ecuador (CEDEX 1984). Geography Ecuador is traditionally divided into four natural regions, a scheme that is followed in this document: The Pacific coastal region (in Ecuador called the costa) includes the lower, western slopes of the Andes (below 1000 m elevation). The Andes Mountains above 1,000 m, which occupy the central portion of the country, know as the Sierra. Amazon lowlands east of the Andes, are referred to as the Oriente, including the lower, eastern slopes of the Andes up to 1,000 m. The Galapagos Islands, is the last region, is a volcanic archipelago in the Pacific Ocean 1,000 km west of the mainland (CESUR 1995).

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24 The coastal region of Ecuador is about 150 km wide from the base of the Andes to the Pacific coastline. A relatively low coastal range of mountains extends parallel to and just inland from the coast, from the city of Esmeraldas in the north to Guayaquil in the south, a distance of about 350 km. The summits of the coastal mountains are mostly between 400 and 600 m elevation, but a few isolated peaks are above 800 m. The coastal range is fairly continuous throughout its length, but is known by different local names: from north to south Mache, Chindul, Jama, Colonche, and Chongn (CESUR 1995). Between the coastal range and the Andes, south of equator, is the broad, nearly level Guayas River basin. At the mouth of the Guayas River lies Guayaquil, Ecuadors largest city and principal port. The estuary of the Guayas River empties into the Gulf of Guayaquil, the largest embayment of the Pacific Ocean on the South American coast. The Santa Elena Peninsula extends west and south of Guayaquil (CESUR 1995). Figure 2-2. Landscape of the Santa Elena Peninsula The Santa Elena Peninsula (SEP) is located at the southwest of the Guayas hydrographic basin, in the Ecuadorian Coast, west of Guayaquil. The main coordinates of the SEP are Latitude 2 o 12 South, Longitude 79 o 53 West (Figure 2-3). Its boundary is to the north the Manab province, to the south and west is the Pacific Ocean and to the east is the Guayas River basin, which is separated by the Chongn-Colonche mountain range (CEDEX 1984).

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25 Figure 2-3. Location of the Santa Elena Peninsula Hydrology Most of the description of the Santa Elena Peninsula was made by the Center for Study and Experimentation in Public Works, in Spanish, Centro de Estudio y Experimentacion de Obras Publicas (CEDEX), with base in Spain. Figure 2-4. Javita River, an intermittent river at SEP. The Chongn-Colonche mountain range divides the hydrologic system of the Santa Elena Peninsula (SEP) from the Guayas River basin, specifically from the Daule River

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26 sub-basin. The minor watersheds created by the Chongn-Colonche mountain range are indicated in the Table 2-1 (CEDEX 1984). Table 2-1. Minor watersheds Minor Watersheds in the Santa Elena Peninsula Basin Area (km 2 ) Area (%) SEP (%) Regime 53.29 65.98 81.88 137.52 161.29 800.00 1,050.80 631.42 588.00 517.61 1.4 1.7 2.1 3.5 4.1 20.6 27.1 16.2 16.1 8.2 0.9 1.1 1.4 2.3 2.7 13.3 17.4 10.4 9.7 5.2 Permanent Permanent Permanent Permanent Intermittent Intermittent Intermittent Intermittent Intermittent Permanent Oln Manglaralto Atravezado Valdivia Grande Javita Zapotal Grande Chongn # 20 Total 3,887.79 100 64 Table 2-2. Basins that start in the Coastal Mountain Range Coastal Mountain Range Basins Basin Area (km2) Area (%) SEP (%) Regime 80.24 166.40 310.71 140.45 362.70 319.80 295.21 152.32 179.06 154.82 3.7 7.7 14.4 6.5 16.7 14.8 13.6 7.1 8.3 7.2 1.3 2.8 5.2 2.3 6.1 5.5 4.9 2.5 3.0 2.6 Ephemeral Ephemeral Ephemeral Ephemeral Ephemeral Ephemeral Ephemeral Ephemeral Ephemeral Ephemeral La Mata Asagmanes Salado Engabao Zona Engunga El Mate San Miguel Arenas # 18 # 19 Total 3,887.79 100 64 Climate A large variety and range of climatic regimes are found in Ecuador, and this variety has a major effect on the extent of the diverse flora of the country. The climatic regimes found in Ecuador are influenced by its geographical position astride the equator, the general circulation of the atmosphere, the position and movements of the ocean currents,

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27 and by orographic effects produced by the abrupt topography of the Andes as well as the smaller coastal ranges. The climatic characteristics in the Santa Elena Peninsula (SEP) are very specific. This is especially true for conditions in the adjacent Guayas River area, especially regarding the precipitation. The main factors affecting the climate conditions in the SEP are two currents of the Pacific Ocean: the Humboldt cold current and El Nio warm current and the displacements of water and air at the inter-tropical convergence zone. Between the months of January and April, the warm current of El Nio moves from Panama to the South along the Pacific coast and close to the SEP encounters the cold waters of the Humboldt Current. This encounter results in rapid cooling of the air, releasing the moisture when colliding with the mountains (Caadas 1983). The Ecuadorian Andes create a bigger barrier increasing the effects of the inter-tropical convergence zone. The temperatures on the Peninsula are characteristically very constant all year around. The winds come mostly from the South. Meteorological Data The location of the weather stations in the Peninsula is presented in the Chapter 3 (Figure 3-1). The registered parameters are: precipitation, temperature, relative humidity, cloud coverage, evaporation (A Pan), and wind speed. However, not all this data is complete for all the stations. Temperature Due to Ecuadors position on the equator, the day length changes very little throughout the year every day has about 12 hours of sunlight, varying no more than about 30 minutes at any point in the country. On the equator, the total amount of solar radiation reaches a maximum at the equinoxes; this is only 13 % higher than the minimum amount

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28 of radiation intercepted at the solstices. A consequence of this relative annual constancy in solar radiation is the low seasonal variation in mean air temperature at the equatorial latitudes. From month to month, the mean temperatures at all sites in Ecuador are relatively constant; monthly means do not vary more than 3 C at any site, and at many sites vary less than 1 C. In contrast, the daily fluctuations in temperature over 24-hour periods are much more pronounced; the circadian cycle of temperature change is therefore much more important than the annual change in mean temperature. Daily temperature fluctuation at mid-to upper elevations in the Andes is often 20 C or more. In the lowlands, the daily fluctuation in temperature is generally much less, closer to about 10 C. The daily maximum and minimum do have significant annual variation at some sites, for example, at high elevations freezing temperatures are more prevalent during the dry season due to clear skies (Sarmiento 1986). Temperature in Ecuador varies rather predictably with altitude. At sea level in coastal Ecuador, the mean annual temperature is about 25 C. On moist tropical mountains, following the adiabatic lapse rate, temperature decreases at about 0.5 C for each increase of 100 m in altitude. The lapse rate, as determined from climatic records at various elevations, is slightly different for the western slopes versus the eastern slopes of the Andes (Caadas 1983). The average annual temperature is between 23.1 o C for Salinas and 25.7 o C for El Azcar where the coastal influence is smaller. From the available historical data, the maximum value recorded was 36 o C in Playas (February) and a minimum of 15.6 o C in the same station (October). It is appropriate to note that the rainy season is from January to April and this is also the time of the highest temperatures. Here also, daily variations in

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29 temperature are more significant than the monthly variations. However, daily variation on average is no larger than 5 o C. More detailed information of data from the weather stations can be found in Chapter 3, Weather Data. Precipitation In contrast to the constancy of temperature regimes in Ecuador, rainfall regimes vary enormously from place to place, in both the annual amount of precipitation and in the patterns of seasonal distribution of rainfall. Different patterns of rainfall are found in the Coastal, Andean, and Amazonian regions of continental Ecuador, and in the Galapagos Islands; variation also occurs from north to south in each main geographical region, and on a local scale according to topography and other factors. The Inter Tropical Convergence Zone (ITCZ) shifts from a position at about 10 N latitude at the June solstice, to about 5 S latitude at the December solstice. Therefore, the ITCZ passes over Ecuador twice during the year on its northward and southward oscillations. The shifts in the ITCZ produce a bimodal distribution of rainfall at Andean localities in Ecuador, with two rainy periods and two drier periods during the year. In the coastal region of Ecuador, annual rainfall patterns are under the influence of the two principal ocean currents in the Pacific, near the shore of northwestern South America (Caadas 1983). These include the cold Humboldt Current, which flows northward along the coast of Chile, Peru, and southern Ecuador, and turns eastward at about the equator and flows past the Galapagos Islands. The second is the warm equatorial current that flows southward from the Gulf of Panama, along the Pacific coast of Colombia, and meets the Humboldt Current near the equator along the north-central coast of Ecuador (Caadas 1983).

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30 The Humboldt Current brings arid conditions to the adjacent coast, as the cool oceanic air passes over the relatively warmer landmass. Another effect of the Humboldt Current is the overcast skiesthe low clouds, known locally as garua (Figure 2-6)that form a layer about 600 m above sea level and cover most of western Ecuador throughout the day during the dry season (Sarmiento 1986). The warm equatorial current that bathes the northwest coast of Ecuador brings with it moist air and rainfall. During most years, the warm equatorial current pushes farther to the south of the equator for a few months, December to April (Figure 2-5) generally, bringing rainfall and warm, moist air to the areas of the central and southern Ecuadorian coast that are under the influence of the dry, cool Humboldt Current the remainder of the year. This phenomenon is known locally as El Nio (the Christ Child) because the annual rains usually begin in midto late December, around Christmas (Caadas 1983). Due to the annual southward incursion of the warm equatorial current, most of coastal Ecuador, as well as the Galapagos Islands, have a unimodal pattern of precipitation, with one rainy season extending from December to April, and a long dry season from May to December. The length and intensity of the dry season vary at different sites in the coastal region (Sarmiento 1986). The most arid region within the Santa Elena Peninsula is the zone of Santa Elena, where the city Salinas shows an annual average precipitation of only 112 mm, 96 % of which is concentrated in the period from January through April. The topography around Salinas constitutes of valleys and small hills, no higher than 100 m (CEDEX 1984). The North section of the SEP is mountainous, with a medium elevation of 600 m. The effect of the mountain range makes the precipitation increase considerably. The

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31 effect of the humid winds coming from the ocean results in the more uniform distribution of the rains throughout the year. In El Suspiro, at 456 m of elevation, the average amount of rainfall for the January-April period is approximately 60 % of the annual total (CEDEX 1984). In the region from Nuevo River to the Chongn River, where the Chongn-Colonche mountain range has altitudes from 200 to 500 m, the weather stations have registered precipitation of approximately 550 mm. The presence of the mountain range in this zone adds for higher rainfall. The rainfall in the January April period represents 85 % of the annual total (CEDEX 1984). Historical Precipitation by Weather Station in the Santa Elena Peninsula050100150200250300350JanFebMarAprMayJunJulAugSepOctNovDecPrecipitation (mm) Chongon Playas San Isidro Suspiro El Azucar Figure 2-5. Historical average precipitation in the Santa Elena Peninsula According to the precipitation pattern (Figure 2-5) for five weather stations in the Santa Elena Peninsula the months that require supplementary irrigation are from May thru November. The period for which weather is available for each location is presented in Appendix B.

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32 In the Cerecitas savanna the effect of the orography is less accentuated, however the precipitation is greater than in the areas located to the South and Southwest of this savanna. The average rainfall over Cerecita is 463 mm, with 91 % captured in the period from January to April (CEDEX 1984). The semi-arid zone from Colonche to Progreso is a valley; this is not affected by the orographic precipitations. In Figures A-1, A-2, and A-3 (Appendix A), the isohyets (isohyet is a line drawn on a map connecting points that receive equal amounts of rainfall) for the region are presented. At irregular intervals, but averaging about every seven years, the El Nio phenomenon is much stronger than normal along the Pacific coast of South America. During El Nio years, the warm equatorial waters push much farther south into coastal Peruvian waters, displacing the cold Humboldt Current, bringing heavy rains to the Peruvian desert as well as coastal Ecuador. The warm water conditions may last for more than a year before the Humboldt Current again brings dry weather to the coast. The heavy rains associated with El Nio cause flooding in coastal Ecuador and destroy roads, bridges, houses, and crops. The last two major El Nio events were during 1982 and 1997. Relative Humidity The weather in the Santa Elena Peninsula is highly modified by the relative humidity (Figure 2-6). This relative humidity is presented in the form of fog and low clouds that cover the skies over the Peninsula most of the year (rainy and dry seasons). Winds The highest winds speeds were registered in Salinas, where the monthly average for each year surpass the 300 km/day, except in February and April.

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33 The dominant winds came from the Southwest, with a frequency of 50 %, followed by the winds coming from the West. The winds from the North are least frequent. Historical Relative Humidity by Weather Station in the Santa Elena Peninsula6065707580859095JanFebMarAprMayJunJulAugSepOctNovDecRelative Humidity (%) Chongon Playas San Isidro Suspiro El Azucar Figure 2-6. Historical average relative humidity in the Santa Elena Peninsula Sunshine This term refers to the number of hours of effective sunshine impacting the Earth surface, also described as the impact of the rays from the sun. Sunshine varies respect to the Latitude of the location. At the Equator the average sunshine should be 12 hours per day all year around. However, cloudiness in the Santa Elena Peninsula affects negatively the amount of hours of light impacting the soil, reducing evaporation and photosynthesis. Climatic Classifications Papadakis Climatic Classification The Papadakis method considers characteristics of the winter and summer, the temperature regime and the humidity balance to classify the climates (Figure 2-3.). In Table 2-3, the classification is listed, showing that the Santa Elena Peninsula (SEP) is divided in to three climatic zones: desert-monsoon in Salinas, going through the semi-arid-monsoon of Playas, Ancn and Azcar; into the dry-monsoon of Manglaralto.

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34 Table 2-3. Climate types Weather Station Climate Type Playas Azcar El Suspiro Chongn San Isidro Tropical, equatorial, semiarid, (Eq, mo) with 9 dry months. Tropical, equatorial, semiarid, (Eq, mo) with 10 dry months. Tropical, equatorial, semiarid, (Eq, mo) with 10 dry months Tropical, equatorial, warm, (Eq, mo) with 8 dry months Tropical, equatorial, semiarid, (Eq, mo) with 10 dry months (CEDEX 1984) Figure 2-7. Papadakis climate classification Kppen Climatic Classification Dr. Vladimir Kppen of Austria devised a climate classification system in 1918 based on the average annual temperature and total precipitation data for areas around the world. It was the most widely used and recognized climate classification system for many decades. Most revised climate classification systems are based on Dr. Kppens initial system (CEDEX 1984). A shorthand version was produced using letters to designate 5 broad climatic groups, with further subdividing in subgroups distinguished by seasonal characteristics of temperature and precipitation.

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35 Table 2-4. Kppen climate classification for the SEP Weather Station Avg. annual Temp o C Avg. Temp. coldest month (t 1 ) Avg. Temp. warmest month (t 12 ) Precip. (P)(mm) K/2 =1/2(20tm+280) Climatic Formula Playas Chongn Azcar El Suspiro San Isidro 24.3 25.3 25.7 23.4 23.1 22.0 23.9 24.3 21.4 20.8 26.5 26.6 27.5 26.1 26.0 362.3 1118.9 278.0 530.3 245.9 383 393 397 374 371 BWhai-desert AW tropical-savannah BWhai-desert AW tropical-savannah BWhai-desert A = tropical rainy climate, average temp > 18 o C no rainy season, large annual rainfall ppt > evap B = dry climate, evap > ppt no surplus water = no perennial streams AW = prairie BW = desert TS = savannah h = medium annual temperature > 18 o C (dry and warm climate) a = medium temperature of the warmest month > 22 o C i = annual temperature variation (t 12 -t 1 ) < 5 o C\ To determine the boundaries of each of the climate types, Kppen uses temperature and precipitation points. Average temperatures of the coldest (t 1 ) and warmest (t 12 ) months are needed to define the limits among the different climates. Kppens climatic formula is a brief description of the climate, especially air temperature and precipitation, including seasonal tendencies of these variables (CEDEX 1984). From the BW climate (desert), with very scarce precipitation year-round, gradually moves to the BS (steppe) with a rainy season that allows fast vegetation growing. From the steppe it goes to the AW or prairie where the rainfall concentrates during the summer but few are scattered year around; resulting in a tropical savannah.

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36 Soils Marine sediments are the parental materi al of the Santa Elena Peninsula (SEP) soils. Where this parental material stayed in situ it created residual soils in hills; but if it was deposited in lower lands, it cr eated alluvial soil in the valleys. Based on the United States Department of Agriculture (USDA) soil classification the following soil orders were obtained for the SEP. Table 2-5. Soils Units Area (ha) Individual Units Entisols Inceptisols Aridisols Vertisols Alfisols Mollisols 149,615 99,695 99,075 12,442 4,905 1,070 Associated units Aridisols/Aridisols Inceptisols/Inceptisols Inceptisols/Vertisols Vertisols/Aridisols Aridisols/Vertisols Inceptisols/Mollisols Aridisols/Entisols Inceptisols/Entisols/Aridisols 67,288 55,230 13,910 9,630 10,500 7,425 13,065 3,180 Total 546,970 The Entisols, Inceptisols and Aridisols or ders account for approximately 95 % of the area of the SEP. It can be said that in general the soils in the Santa Elena Pe ninsula have a great variability in texture, ranging from clay to si lt, and they are more superficial in the hills than in the valleys. In the hills and mountains the parental material can be found just a few centimeters below the surface. The erosion is very high in the hills and mountains; this is especially due to the deforestati on and destruction of the vegetation cover.

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37 Following is a more detailed description of the soils units in the Santa Elena Peninsula: Residual Soils Residual soils were developed in situ from marine clays, they are the oldest soils in the Peninsula and can be found in hills and mountains. The residual soils are less thick when the slope increases. These soils have been classified in the four following units (CEDEX 1984): Soils with Cambic horizons, normally found in locations with high slopes (> 20 %). They have a superficial horizon A from 10 to 20 cm with silt and clay contents, sometimes it consists totally of clay. The next horizon is cambic B with a thickness from 20 to 30 cm its texture has high variability. The C layer is similar to the parental material. All these soils correspond to the Typic Ustropepts and Typic Cambortid, however, if the clay content is greater than 35 % in the first 50 cm they are classified as Vertic. Very eroded soils can be classified as Paralithic and Lihic depending if is the horizon C or the parental material the one that shows in the first 50 cm. Soils with Argilic horizons, located in small hills with slopes less than 10 %. The A 2 superficial horizon is very eroded with 1cm; it contains silt and clay. The next is the Bt layer from 15 to 25 cm; in some cases with gravel and rocks (10 cm diameter). They correspond to the Vertic Paleargid and Vertic Paleustalf. Soils with high content of clay can be found in low hills and some times in high mountains for which the parental material is marine clay. They show cracks up to 80 cm deep and 2 cm wide, and their horizons are considered clay up to 70 cm with black spots coming from carbonates. The C-horizon also has a high content of clay. These soils are classified as Pellusters, Chromusters and Torrerts. Soils without defined horizons are located in degraded, rocky zones and also regions with strong slopes, and are grouped in the Ustorthens and Torriorthents units. Aluvial Soils Sandy non-saline soils, located close to the sea and in some riverbeds. Classified as Ustipsamment and Torripsamment. Non-saline soils with contents of silt and clay (40 %) correspond to the Ustifluvents and Torrifluvents units.

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38 Saline soils located in the mangroves close to the sea and in some rivers. Soils with more that 15 % of interchangeable sodium, Entisols.

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CHAPTER 3 WEATHER DATA ANALYSIS FOR CROPWAT MODEL Air temperature and soil temperature, along with water availability and soil, and relative humidity are key factors for agriculture and forestry systems. Under many situations temperature is the factor that determines which crops or trees can be grown in a given area, seed germination, growth rates, rates of maturation or ripening, and yield. At a global scale, the major pattern of vegetation is defined by latitudinal gradients. At continental and regional scales, elevation modifies the latitudinal gradients according to adiabatic lapse rates. Because of the large area and coarse spatial resolution of these scales, temperature regimes appear smooth and simple interpolation can be adequate for characterizing patterns. Most simulation models assume homogeneous conditions over the space they are representing. In fact, general conditions often do not exceed more than a few kilometers square. The weather data across the SEP was difficult to interpolate over several square kilometers between weather stations (Ashrat et al. 1995). Several interpolation procedures are available, ranging from simple linear interpolation techniques and triangulated networks, to more sophisticated distance weighing or kriging techniques. It must be remembered that the only information used by any interpolation procedure is the location of known values (van der Goot 1997). Most of the weather data information that is used in this project was provided by CEDEGE. For this project the weather data available comes from 5 different weather stations, the parameters recorded are (Appendix B, Table B-1): 39

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40 Maximum and minimum air Temperatures, in Celsius degrees ( o C) Maximum and minimum Relative Humidity (RH), in percentage (%) Wind speed (m/s) Sunshine (hours/day) The periods for which the data are available are listed in Appendix B (Table B-1) In addition to limited years of data, all the stations have some missing data for different periods of time. Because of these missing data several methods to fill those gaps were analyzed. Solar radiation and wind speed was also provided from some weather stations but they had too many missing data points to be useful. Weather Stations Distribution The maximum distance between points for least dense networks of weather stations is 150 km, for intermediate networks is 50 km and for most dense networks is around 30 km (Gandin 1970). In the Santa Elena Peninsula (SEP) the distances between the stations are shown in Table 3-1. Considering the above recommendations, the distribution of weather stations in the SEP can be considered an intermediate network. Table 3-1. Distances among stations (m) and elevation (mmsl) Station name El Azcar Playas Chongn San Isidro El Suspiro mmsl El Azcar 27,000 4,560 35,500 56,000 35 Playas 72,200 57,000 70,350 24 Chongn 32,500 54,500 41 San Isidro 25,000 35 El Suspiro 35 The spatial location of the weather stations is shown in Figure 3-1. Spatial distribution is not very good, but at least, there are weather stations in each of the ecologic zones of the Santa Elena Peninsula. The small differences in elevation over the main sea level among stations permits the use of more simple interpolation methods (Table 3-1). In flat areas such as the Santa Elena Peninsula the effect of elevation will be

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41 a small factor when interpolating the weather data. However, since this is a coastal area the effect of the wind and ocean is accounted for in the CROPWAT software to calculate evapotranspiration. Figure 3-1. Weather stations Estimating Missing Climatic Data The calculation of the reference evapotranspiration (ET o ) with the Penman-Monteith method requires mean daily, ten-day or monthly maximum and minimum air temperature (T max and T min ), actual vapor pressure (e a ), net radiation, and wind speed measured at 2 m. If some of the required weather data are missing or cannot be calculated, it is strongly recommended that the user estimates the missing climatic data with one of the following procedures and use the FAO Penman-Monteith method for the calculation of ET o

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42 Estimating Missing Humidity Data Where humidity data are lacking or are of questionable quality, an estimate of actual vapor pressure, e a can be obtained by assuming that dew point temperature (T dew ) is near the daily minimum temperature (T min ). This statement implicitly assumes that at sunrise, when the air temperature is close to T min that the air is nearly saturated with water vapor and the relative humidity is nearly 100%. If T min is used to represent T dew then (Allen, et al. 1998): (3-1) The relationship T dew T min holds for locations where the cover crop of the station is well watered. However, particularly for arid regions, the air might not be saturated when its temperature is at its minimum. Hence, T min might be greater than T dew and a further calibration may be required to estimate dew point temperatures. After sunrise, evaporation of the dew will once again humidify the air and will increase the value measured for T dew during the daytime (Allen, et al. 1998). Estimating Missing Radiation Data Net radiation measuring devices, requiring professional control, have not been used in the agro meteorological stations managed by CEDEGE. In the absence of a direct measurement, long wave and net radiation can be derived from more commonly observed weather parameters, i.e., solar radiation or sunshine hours, air temperature and vapor pressure. Solar radiation data can be derived from air temperature differences, the difference between the maximum and minimum air temperature is related to the degree of cloud

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43 cover in a location. Therefore, the difference between the maximum and minimum air temperature (T max T min ) can be used as an indicator of the fraction of extraterrestrial radiation that reaches the earth's surface, principle that has been used by Hargreaves and Samani to develop estimates of ET o using only air temperature data (Allen, et al., 1998). The Hargreaves' radiation formula, adjusted and validated at several weather stations in a variety of climate conditions, becomes: (3-2) where R a = extraterrestrial radiation (MJ m 2 /d), T max = maximum air temperature (C), T min = minimum air temperature (C), k Rs = adjustment coefficient (0.16.19) (C -0.5 ). The square root of the temperature difference is closely related to the existing daily solar radiation in a given location. The adjustment coefficient k Rs is empirical and differs for interior or coastal regions: For 'interior' locations, where land mass dominates and air masses are not strongly influenced by a large water body, k Rs = 0.16; For 'coastal' locations, situated on or adjacent to the coast of a large land mass and where air masses are influenced by a nearby water body, k Rs = 0.19. The fraction of extraterrestrial radiation that reaches the earth's surface, R s /R a ranges from about 0.25 on a day with dense cloud cover to about 0.75 on a cloudless day with clear sky. CROPWAT uses the location (coordinates) of each weather station to find the best coefficient for each station. The temperature difference method is recommended for locations where it is not appropriate to import radiation data from a regional station, either because homogeneous climate conditions do not occur, or because data for the region are lacking.

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44 Missing Wind Speed Data Importing wind speed data from a nearby station, as for radiation data, relies on the fact that the airflow above a 'homogeneous' region may have relatively large variations through the course of a day but small variations when referring to longer periods or the total for the day. Data from a nearby station may be imported where air masses are of the same origin or where the same fronts govern airflows in the region and where the relief is similar. When importing wind speed data from another station, the regional climate, and trends in variation of other meteorological parameters and relief should be compared. Strong winds are often associated with low relative humidity, and light winds are common with high relative humidity. Thus, trends in variation of daily maximum and minimum relative humidity should be similar in both locations. Imported wind speed data can be used when making monthly estimates of evapotranspiration. In the case of the Santa Elena Peninsula (SEP) a correlation comparison was made among all the weather station available in the area, but none of them show a good correlation coefficient (R 2 > 0.70), and because of that no data were used from one station to another. As the variation in wind speed average over monthly periods is relatively small and fluctuates around average values, monthly values of wind speed may be estimated. The 'average' wind speed estimates may be selected from information available for the regional climate, but should take seasonal changes into account. Where no wind data are available within the region, a value of 2 m/s can be used as a temporary estimate. This value is the average over 2000 weather stations around the globe.

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45 For this project the wind data came from historical data from the available weather stations. This data had some gaps, however, once the wind speed data were used in CROPWAT using both the FAO world average and the available data the latest produced values closer to the reality of the zone. Minimum Data Requirements Many of the above procedures rely upon maximum and minimum air temperature measurements. Unfortunately, there is no dependable way to estimate air temperature when it is missing. Therefore it is assumed that maximum and minimum daily air temperature data are the minimum data requirements necessary to apply the FAO Penman-Monteith method. Estimating Weather Data Sets for the Santa Elena Peninsula To find a way to complete the missing meteorological data for the Chongn (used as an example) weather station, two methods were used. The first one is Regression Analysis, and the second one is Compositional Data. The results were tested against each other to find which one fits better to this situation. One month (March) with 31 observations of weather data from each one of the five (5) stations was used to test the available methods to estimate missing weather data. The variables to be used are Maximum Temperature (Tmax) and Minimum Temperature (Tmin), and Maximum Humidity (Hmax) and Minimum Humidity (Hmin). In this document the Maximum Temperature is used as an example of what was done with all datasets. Regression Analysis Quite often data sets containing a weather variable Y i observed at a given station are incomplete due to short interruptions in observations. When data are missing, it may

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46 be appropriate to complete these data sets from observations X i from another nearby and reliable station. However, to use portions of data set X i to replace data set Y i both data sets X i and Y i must be homogeneous. The procedure of completing data sets is applied after the test for homogeneity and needed correction for no homogeneity has been performed (Allen et al. 1998). The substitution procedure proposed herein consists of using an appropriate regression analysis. Procedure: 1. Select a nearby weather station for which the data set length covers all periods for which data are missing (in this case, data from three stations were tested to find if the data of one of those have any relationship with the Chongn weather station data). 2. Characterize the data sets from the nearby station (Azcar, Suspiro, Playas), X i and of the station having missing data (Chongn), Y i by computing the mean and the standard deviation S x for the data set X i : (3-3) (3-4) and the mean Y and standard deviation S y for data set Y i : (3-5) (3-6)

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47 for the periods when the data in both data sets are present (in this case March 2001), where x i and y i are individual observations from data sets X i and Y i and n is the number of observations in each set. 3. Perform a regression of y (Play_Tmax, Azu_Tmax, and Sus_Tmax) on x (Cho_Tmax) for the periods when the data in both data sets are present (March, 2001): (3-7) with where a and b are empirical regression constants, and cov xy is the covariance between X i and Y i Plot all points x i and y i and the regression line for the range of observed values. If deviations from the regression line increase as y increases then substitution is not recommended because this indicates that the two sites have a different behavior relative to the particular weather variable, and they may not be homogeneous (Allen et al, 1998). Another nearby station should be selected. 4. Compute the correlation coefficient r: (3-8)

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48 Both a high r 2 (r 2 > 0.7) and a value for b that is within the range (0.7 < b < 1.3) indicate good conditions and perhaps sufficient homogeneity for replacing missing data in the incomplete data series (Allen et al, 1998). These parameters r 2 and b can be used as criteria for selecting the best nearby station. 5. Compute the data for the missing periods k = n+1, n+2..., m using the regression equation characterized by the parameters a and b, thus (3-9) 6. The complete data set with dimension m will now be Y j = y i (j = i = 1,...,n) (j = k = n + 1, n + 2,...,m) Results: After running the regression analysis of Chongn versus each one of the other three stations the output in Table 3-2, was obtain. Table 3-2. Regression analysis method Station r 2 F Azcar (Figure 3-2) 0.0718 2.2443 Suspiro (Figure 3-3) 0.0145 0.4262 Playas (Figure 3-4) 0.0801 2.5257 The result can also be look at in the scatter plots created for each of the comparisons among the weather stations (Figures 3-2 to 3-4). Since at least an r 2 of 0.7 is needed, the data from any of these stations cannot be used to complete the missing data from Chongn weather station. It was concluded that there is no relation between Chongn and any other existing weather station in the area.

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49 Compositional Data Any vector x with non-negative elements x 1 , x D representing proportions of some whole is subject to the obvious constraint x 1 ++ x D = 1 Compositional data, consisting of such vectors of proportions, play an important role in many disciplines and often display appreciable variability from vector to vector. This concept can be used to estimate missing weather data (Aitchison, 1986). Procedure: 1. Find the daily average for a given variable (Maximum Temperature, Tmax) from many years of a given month (this month will be May). 2. For each daily value calculate the percentage value compared to one (one equals the sum of the daily values for a given month). 3. Once these values are calculated new values can be created for May 2001, by multiplying the number for the day with the missing data (obtained in step 2) by the sum of the daily data of May 2001 (sum = 846). Table 3-3. Creating new values Cho_avg_may % Cho_may Cho_crt_may 31.4 0.031126 26.3 Formulas: = (31.4*1.0)/1008.8 Missing daily value = 0.031126*846 4. Now a regression analysis is used to test how these new values fit to the weather data. Results: The compositional data method gives an r2 of 0.46 and a F value of 25.13 for the Chongn station for May 2001 with 3 days of created data compared with the

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50 daily historic values for this month. A better r 2 value was obtained, but it did not meet the r 2 = 0.7 mark. Using Data from Other Years to Replace Missing Meteorological Data Special means were employed to maintain serially complete files of weather data when long segments of missing meteorological data were found. The majority of these situations occurred at stations that were not operated during the evening or on weekends, but in some instances a station would be shut down for several weeks or even longer. When these situations occurred, the gaps in the data were filled with data from other years, for the same days of the year. Averaged data from other years for the same time periods were selected to fill the gap. Conclusion Since none of the statistical methods tried to complete the missing weather data for the weather stations in the Santa Elena Peninsula were successful, the only option available was to use the data from other years from the same station to complete the missing data, and make all computation just with the currently available data. When possible and available, better weather data should be used to calculate crop water use. Chongon vs. El Azucar05101520253035400510152025303540Maximum Temperature ChongonMaximum Temperature El Azucar

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51 Figure 3-2. Chongn vs. El Azcar Chongon vs. El Suspiro05101520253035400102030Maximum Temperature ChongonMaximum Temperature El Suspiro 40 Figure 3-3. Chongn vs. El Suspiro Chongon vs. Playas05101520253035400510152025303540Maximum Temperature ChongonMaximum Temperature Playas Figure 3-4. Chongn vs. Playas

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CHAPTER 4 GEOGRAPHIC INFORMATION SISTEM Introduction Geographic Information System (GIS) technology is about 30 years old. However for the most part, it is still often used just to make maps. However, GIS can do much more. Using GIS databases, more upto-date information can be obtained or information that was unavailable before can be estimated and complex analyses performed. This information can result in a better understanding of a place, can help make the best choices, or prepare for future events and conditions (Mitchel 1999). Many countries and organizations are still building their GIS databases, as in the case of Ecuador and more specifically CEDEGE. This process of creating GIS databases has been difficult and cumbersome. Now, new easy to use software employing graphic interfaces is removing that obstacle. The most common geographic analyses that can be done with a GIS are (McCoy & Johnston 2001): Mapping where things are Mapping the maximum and minimum values Mapping density Finding what is inside (intersection analysis) Finding what is nearby (proximity analysis) Mapping change (overlay analysis) The steps for a good geographic information system analysis are (McCoy & Johnston 2001): 52

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53 Stating the problem. Stating the problem defines what information is needed, and it is often in the form of a question. Being as specific as possible about this statement will help when trying to decide how to approach the analysis, which method to use, and how to present the results. Other factors that influence the analysis are how it will be used and who will use it. Understanding the data. The type or data and features to be used in the project will help determine the specific method to be used. Choosing a method. There are almost always two or three ways of getting the information that is needed. Often one method is quicker and gives more general information. Others may require more detailed data and more processing time and effort, but provide more precise results. To decide which method to use the level of precision to answer the problem has to be again evaluated. Processing the data. Once the method has been selected, the necessary steps in the GIS have to be performed. Reviewing the results. The results of the analysis can be displayed as a map, values in a table, or a chart. It has to be decided which information to include in the maps, and how to group the values to best present the information. Looking at the results will help in the decision making process, deciding what information is valid or useful, or whether the analysis should be rerun using different parameters or even a different method. GIS makes it relatively easy to make these changes and create new output. The results using different methods can be compared to decide which one presents the most needed information and produces it in an efficient way.

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54 Mapping Systems The mapping systems today range from display only systems like electronic atlases to full featured geographic information systems. The dividing lines between one type of system and the next are not sharply defined. The systems do differ in a number of important ways: how they link geographic locations with information about those locations (topology and relational database management), the accuracy with which they specify geographic locations (positional accuracy), the level of analysis they perform, and the way they present information as graphic drawings (Mitchel 1999). Electronic atlases, for instance, allow displaying pictures of geographic areas on the computer screen. They provide limited information about the geographic areas, and limited ability to alter the graphics. Without any tools for analyzing the information, these systems are most useful for providing graphics that can be used in presentations and reports. They can also be used as reference tools (Mitchel 1999). Unlike electronic atlases, thematic mapping systems have the capacity to create graphic displays using information stored in spreadsheet or database. These systems are especially useful for creating graphic presentations. Each map produced is based on a theme, such as population or income, and uses color, patterns, shading, and symbols of various sizes to show the relative value of the information stored for that theme, at each geographic location. Street-based mapping systems are more sophisticated than electronic atlases and thematic mappers. They relationally link information to geographic locations. Street-based mapping systems can display address locations on street maps as points, and can plan travel routes via topological information.

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55 More sophisticated mapping systems can import database or spreadsheet files or provide direct access to outside information sources. Some mapping systems let the user create and manage tabular information, use tabular information to create charts and graphs, and even analyze information statistically. ArcGIS ArcGIS (Environmental Systems Research Institute, ESRI) desktop is a group of tools to develop and edit digital maps, and also allow some modeling (Breslin 1999). The tools used in this project are described in this section (Ormsby 2001). ArcMap. It is an application for displaying maps and investigating them, for analyzing, maps to answer geographic question and producing maps that make analysis persuasive. In ArcMap, maps can be made from layers of spatial data, colors and symbols, query attributes can be selected, analyze spatial relationships analyzed, and map layouts designed. The ArcMap interface contains a list of the layers in the map, a display area for viewing the map, and menus and tools for working with the map. ArcCatalog. This tool is used to browse spatial files on the computers hard drive, on a network, or on the Internet. The program can be used to search the spatial data, preview it, and add it to ArcMap. ArcCatalog also has tools for creating and viewing metadata (information about spatial data, such as who created it and when, its intended use, its accuracy, etc). Spatial analyst. It is an application used to create raster (cell-based) surfaces, query them, and do overlay analysis on them. It can also be used to derive new surfaces from other raster or vector layers. For example, a slope surface can be derived from an elevation surface or a population density surface from population points.

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56 Geostatistical analyst. It is a tool that can create continuous surfaces from a small number of points by predicting the values of unsampled locations. ArcPress. This is an application that improves map printing speed and renders high-quality maps without requiring additional memory or hardware. Original Maps The Instituto Geografico Militar (IGM) publishes the official maps of Ecuador. Topographic maps of the country at 1:1,000,000 and 1:500,000 scales are available, and a series of topographic sheets at 1:50,000 scale, published gradually during the past 20 years, now covers most of the country, except remote areas of the Amazon basin and parts of the Andean slopes. The IGM has also published thematic maps at 1:1,000,000 scale, including geologic, soils, bioclimatic, and life zones maps. A branch of the IGM, the Centro de Levantamientos Integrados de Recursos Naturales por Sensores Remotos (CLIRSEN), that operates a Landsat and SPOT satellite image receiving station near the Cotopaxi volcano, carries out geographic and natural resources studies using remote sensing data, and sells the satellite imagery to other users. Soils The original layer containing the different types of soils in the Santa Elena Peninsula (SEP) contain a large number of various soils that was too complex to use in a model using CROPWAT. This original map is presented in Figure 4-1, a complete view of this map is shown in Figure A-4. CROPWAT model from FAO/UN (Food and Agricultural Organization of the United Nations) uses only three soil texture groups of clay, silt, and sand to calculate irrigation schedules.

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57 Figure 4-1. Soil types on Santa Elena Peninsula, original map Ecological Zones Figure 4-2. Kppen climate classification of Santa Elena Peninsula

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58 The Peninsula was subdivided into three zones (Figure 4-2) based on the climate data presented in Chapter 2. According to the Kppen method the Santa Elena Peninsula has the following ecological zones: Desert, Steppe, Savannah. Dams Figure 4-3. Dams location on Santa Elena Peninsula The geographic location and area of the main dams that constitute the TRASVASE irrigation system is presented on the layer in Figure 4-3. The information in this layer was used to calculate the capacity of the system, evaporation and their storage efficiencies.

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59 The three dams that are presented in Figure 4-3 that are part of this project are: Chongn, El Azcar, and De Cola. Also, the projected areas for El Azcar and Velasco Ibarra dams are shown in this layer. Canals A layer containing geographic locations of the canals, length and materials used in their construction is also available and is presented in Figure 4-5. This layer also contains roads and drainage information that will not be used in this project. Figure 4-4. Canals and other features Data Quality Problems with the Santa Elena Peninsula Data Set Data quality problems in the data set available from the Santa Elena Peninsula during the creation of the geographic information system (GIS) for this area. Lineage,

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60 unclosed polygons, lines that overshoot or undershoot junctions, labeling problems, missing metadata, and maps in different projection systems. Lineage It is important to create a record of the data sources and of the operations that created the database: How was it digitized, from what documents? When was the data collected? What agency collected the data? What steps were used to process the data? The matching of final results, after calculation, can be a good indicator of the initial data accuracy. In the case of the data for the Santa Elena Peninsula even when one institution digitized the maps, the original hard copy maps came from multiple agencies and in different projections and scales. This made the process of interpolation very difficult and introduced an imbedded error. Some of the agencies that collected and digitized the data for the SEP are: ESPOL, CEDEGE, and IGM. This resulted in a set of data with different projections, scales, and different information in the metadata. In addition the weather and agricultural databases (CEDEGE, ESPOL) have certain problems. One major problem is that these databases do not describe the time when the information was gathered and how the data was collected. The most serious errors resulted from the missing data, especially weather data. Accuracy Overshoots and undershoots may be used as a measure of positional accuracy. These are presented in few layers for this project, but most of the errors occur in the

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61 elevation contours, and the hydrology maps (Figure 4-5) where logical consistency errors are also presented. Logical consistency refers to the internal consistency of the data structure, particularly it applies to topological consistency related to the polygon closure and polygon labeling. Figure 4-5. Errors in the hydrology maps of the SEP Many of the polygons in the maps that were available for the Santa Elena Peninsula have more than one label. One example is the ecological zones map, where up to three labels can be found. Some labels are not accurate or longer than required. Large

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62 descriptions of each attribute in the table make it difficult to read and interpret the labels, and are difficult to place in the map itself. All of that required significant changes during data preparation. To correct these errors the databases related to each of the maps presenting this type of error (Kppen, Papadakis, ecological zones soils, canals) were edited, adding or subtracting data fields as needed to create layer suitable for use in a GIS. Map scale is the other source of error. Cartographers and photogrammetrists work to accepted levels of accuracy for a given map scale. Locations of map features may disagree with actual ground locations, although the error likely will fall within specified tolerances. Scales from 1:5,000 to 1:50,000 are found for the maps available for the SEP and the combination of different scales added error to the final maps produced for this project. Although it is not possible to eliminate this error it should be recorded in the metadata for future reference, this was done for all the maps produced, using ArcCatalog. Precision The accuracy and precision errors can be located in the Santa Elena Peninsula data, overlapping the soils map, and the ecological zones (Kppen) layer (red line) shows the difference between the two maps. A shift to the northwest in the ecological zones layer can be identified. Georeferencing between this vector layers was used to correct the ecological zones layer. The soil layer was used as a reference layer because all its metadata were known and correct. Completeness Completeness is the degree to which the data exhausts the universe of possible items: are all possible objects included within the database? In the case of the data

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63 available for this project this aspect relates to a logical consistency. Basically, it does not matter if there is a lot of information in a database if that information cannot be used in an efficient manner. Figure 4-6. Overlap error Metadata Errors Coordinate transformation introduces error, particularly if the projection of the original document is unknown, or if the source map has poor horizontal control. The digital maps of the SEP were that the maps were in different projections or did not have any stated projection; however this is not something difficult to correct. ArcGIS contains a feature that allows setting the desired coordinate system for a map. The coordinate system selected for the maps of the SEP was the Universal Transverse Mercator (UTM) Zone 17 South. It is important to note that bad metadata can be a source of spatial error,

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64 especially when trying to overlay different layers. The most accepted standard for metadata is giving by the Federal Geographic Committee (2000) in the Content Standard for Digital Spatial Metadata. Manual Digitizing Manual digitizing was the method predominantly used to create the digital maps of the different attributes for the Santa Elena Peninsula (SEP). This digitizing method can introduce more errors in the final product, especially when done without proper trained people and without supervision and quality control (Figure 4-5). The digitizing process was made in ESPOL (Polytechnic School of the Littoral) Guayaquil, Ecuador, in most of the cases by students and people with little practice in digitizing. Source Data for the Santa Elena Peninsula The main problem with the SEP maps is related to many different data sources. Starting with maps created by various national and international agencies, and digitized in different manners. Several databases (climatic, agricultural production, water use, water consumption, etc.) the collected with unknown methods by different institutions. In addition, there was difficulty to compare this data against other sources because of the few studies completed in the area of the Peninsula. Controlling GIS Errors Data entry procedures should be thoroughly planned, organized and managed to produce consistent, repeatable results. Nonetheless, a thorough, disciplined quality review and revision process also is needed to catch and eliminate data entry errors. All production and quality control procedures should be documented, and all personnel should be trained in these procedures. Moreover, the work itself should be documented, including a record of what was done, who did it, when was it done, who checked it, what

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65 errors were found, and how they were corrected. GIS data should not be provided without metadata indicating the source, accuracy and specifics of how the data were entered. GIS Layers Created or Edited for the Project from the Original Maps Soils The new soil map divides all the soils into three groups (silt, sand and clay). The distribution of these soils within the Peninsula is presented in Figure 4-7. The merge tool in the Geoprocessing wizard from Arc View 3.2 was used to simplify this layer. The original soil map is presented in Figure 4-1. Figure 4-7. Main soil types layer created for the Santa Elena Peninsula Ecological Zones This layer shows the ecological division in the Santa Elena Peninsula. According to the Kppen method the Peninsula is divided in desert, steppe and savannah.

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66 The canals of the TRASVASE irrigation system cross the different ecological zones. The clipping geoprocessing tool available in Arc View 3.2 was used to clip the canals to the ecological zones (Figure 4-8). Figure 4-8. Ecological zones Canals This figure represents several layers; ecological zones, canals, and the dams in the area of interest. The roads, and drainage lines were deleted to create this layer. The layers were also clipped using the Kppen Ecological Zones. The different materials of the canals where maintained but join in one canal per ecological zone, this was done to facilitate the evaporation calculation in the later phase on in the project.

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67 Figure 4-9. Canals Existing Farm Locations A layer that shows the geographic locations of most of the farms close to the canals (Figure 4-9) was also created. More detailed maps of the farms locations can be seen in Appendix A (Figures A-5 to A-8). The farms are grouped in five regions: Santa Elena, Chongn, Cerecita, and El Azcar Rio Verde. This layer shows the total areas of the farms, however that is not the actual area under agricultural production. As information about crops produced in each farm becomes available, these layers will be used to create maps that will show the total area that can be irrigated in each zone according to the weather parameters and crop water requirements. These concepts will be explained in the following Chapters.

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68 Figure 4-10. Actual farm locations Weather Stations Location Known coordinates of each one of the weather stations were used to create a Microsoft Access database. An ID and Station Name fields were added for better identification of the stations and to be used to link or join this table with others in Arc View 3.2. With the Access file a shape file was created in Arc View 3.2 so it can be displayed as a layer in the GIS (Figure 2-5 in Chapter 2). Weather Data There are a number of commonly used interpolation techniques described in the literature, such as simple average, Thiessen polygon, classical polynomial interpolation, inverse distance, multi quadratic interpolation, optimal interpolation, kriging and others. In this study the inverse distance interpolation technique was chosen for its simplicity.

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69 Researchers have used diverse statistical and geostatistical models to generate temperature surfaces from point sampling locations. The simplest technique uses the nearest measurements. Trend surfaces, inverse distance weighted interpolation (IDW), and thin plate spline, all have been used to interpolate temperature measurements over global, continental, and broad regional scales. These models, assume the underlying surface is smooth as it is the topography found in the Santa Elena Peninsula. Inverse Distance Interpolation. As is obvious for the name of the interpolation technique, the weighting factor is inversely proportional to the distance. The weights of this interpolation technique are solely a function of the distance between the point of interest and the sampling points. Table 4-1. Comparison of interpolation methods Method Advantages Disadvantages Bi-linear interpolation Simple, conservative Smoothing Polynomial trend surface Designed degree of smoothing Unstable near edges Inverse square distance weighting Preserves high frequencies Outliers Kriging (variogram) Uses variance of data Directional effects Spline interpolation Optimal fit Strong edge effects Laplacian fitting Good fitting, smooth decay at edges Smoothing The inverse distance technique does not take advantage of spatial correlation structure explicitly. However, for climate data these correlation structures tend to be linear and it is a good guess to assume that the inverse distance weighting would work fairly well. The Inverse Distance Weighted interpolation method was used to create the surface maps (Figure 4-11) with the Geostatistical Analysis Tool from ArcGIS for the weather

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70 variables: maximum, minimum and medium temperature, and maximum, minimum and medium relative humidity. Examples are the surface maps for the month of January (Figure 4-11). Inverse Distance Weighted (IDW) interpolation explicitly implements the assumption that places that are close to one another are more alike than those that are farther apart. To predict a value for any unmeasured location, IDW will use the measured values surrounding the prediction location, with values closest to the prediction location having more influence on the predicted value than those farther away (Johnston, et al. 2001). IDW assumes that each measured point has a local influence that diminishes with distance, and weights the point closer to the prediction location greater than those farther away (Johnston, et al. 2001). SSiiio1 (4-1) Z(S o ) value to predict for location S o N number of measured points weight, these weights will increase with distance Z(S i ) observed value at the location S i The formula to determine the weights is (Johnston, et al., 2001): 1ipiopioidd (4-2) 11ii As the distance becomes larger, the weight is reduced by a factor of p. The d io value is the distance between S o and S i The Inverse Distance Weighted method includes a power (p) parameter. This p parameter influences the weighting of the measured locations value on the prediction

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71 locations value; that is, as the distance increases between the measured sample locations and the prediction location, the weight that the measured point will have on the prediction will decrease exponentially (Johnston, et al. 2001). The optimal p value is determined by minimizing the root-mean-square prediction error (RMSPE). The RMSPE is a summary statistic quantifying the error of the prediction surface (Johnston, et al. 2001). After one map for each weather variable was created for each month of the year they were classified into 5 classes because the variation in the data was small. To reclassify those surface maps the Equal Interval method was used. In the Equal Interval method the range of possible values is divided into equal-sized intervals. Because there are usually fewer endpoints at the extremes, the numbers of values are less in the extreme classes. This method is used in data ranges as percentages (relative humidity or temperature) (McCoy, and Johnston 2001). Creation of Evapotranspiration Surface Maps For this study, the climatic information used to make interpolation is based on inverse distance weighted method of 5 stations in the Santa Elena Peninsula (SEP). While interpolation of a value at reach cell in the study area using 5 meteorological stations is technically easy, some important questions can be raised. Are the stations representative of the areas around them? How large or small an area do they represent? How does the spatial feature in question change over space? Is it continuous or discontinuous and abrupt?

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72 Figure 4-11. Surface maps of weather data for January

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73 Characterization of climate data for a study area typically relies upon a series of measurements at discrete locations. Spatial interpolation of these discrete data into a continuous surface is generally the first step for use with other GIS data layers. These surface maps layers were used to determine relationships between stations and to identify the agricultural production zones affected by each of the weather stations. Weather parameters affect evapotranspiration and as a result they modify irrigation requirements. Later in this document (Chapter 5, Figures 5-9.1 and 5-9.2) reference evapotranspiration surface maps are presented to show the monthly variations within the Santa Elena Peninsula.

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CHAPTER 5 WATER AVAILABILITY AND ITS USE IN THE SANTA ELENA PENINSULA Infrastructure In the TRASVASE irrigation system the water is used for irrigation and also for human consumption, and as in any irrigation systems there are losses. To calculate the total available water for irrigation the amount used for potable water and the losses of the system have to be calculated. TRASVASE Santa Elena Daule Peripa Dam This dam (6,000 million cubic meter) works to control floods, regulate water flow, control salinity levels, and produce hydroelectric power. Because of that its name is Proyecto de Propsito Multiple Jaime Rolds Aguilera, in English, Multi-purpose Project Jaime Rolds Aguilera (Figures 5-1 and 5-2). Figure 5-1. Daule-Peripa Dam 74

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75 Figure 5-2. Hydroelectric plant, Proyecto de Propsito Multiple Jaime Rolds Aguilera. The area directly influenced by this project is 50,000 ha in the Daule Valley. Indirectly 42,000 ha are projected to be irrigated in the Santa Elena Peninsula. Another 50,000 ha (500 m3/year) are projected to be irrigated from the same Dam in the Manabi province (CEDEGE 2001). History of the Project The Santa Elena Peninsula (SEP) has suffered a water crisis for more than 100 years. Most people agree that deforestation is the main cause, converting what was once tropical forest to a near desert. To mitigate the drought conditions, and to use this land for agricultural production the TRASVASE project was built. In 1992 the first hydraulic structures were put together to start the TRASVASE Daule-Santa Elena. Following that, the Chongn Dam (Figure 5-3), the Chongn irrigation canal, the Chongn Cerecita irrigation canal and the irrigation infrastructure in the Chongn, Daular and Cerecita irrigation zones were built; total project covering approximately 5,000 ha (CEDEGE 2001).

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76 Figure 5-3. Chongn Dam In order to promote the use of most efficient irrigation techniques, and demonstrate the agricultural potential in the SEP, pressurized irrigation systems were installed, at Chongn and Cerecita, consisting of pumping stations, subterranean conveyance pipes, and portable sprinkler systems. Figure 5-4. Daule pumping station At the end of 1995 the construction of the Daule-Chongn canal was completed assuring constant water supply to the Chongn Dam, the Chongn-Cerecita-Playas canal and the Daule Pumping Station. Sixty-one kilometers of canals were lined with concrete,

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77 and the 6.5 km Cerro Azl tunnel was finished. It was estimated that this system will allow the irrigation of 15,000 ha of land (CEDEGE 2001). In 1998 the first part of the project was finished. In that part the water from the Chongn Dam was conducted to El Azcar Dam and from here to the Ro Verde irrigation zone. To accomplish this the following structures were build: the Chongn Pumping Station, a 3 km pressurized conveyance pipeline, and 40 km of canals lined with high-density polyethylene. The El Azcar Dam was refurbished, and the Cola dam in San Juan (Playas) was built to regulate the water flow. Table 5-1. Main dams Dams Volume (10 6 m 3 ) Surface (km 2 ) Water level (m) Max Min Max Min Max Min Chongn 273.6 148.5 25.7 16.9 51 45 El Azcar 53.8 25 14 8.5 45 42 De Cola 2.44 1.4 0.4 0.5 26.5 24.8 In the last part of this project the Sube y Baja Dam, the Sube y Baja-Javita, Afaye-Atahualpa, and Villangota and Azcar-Zapotal canals will be built. Other important and complementary projects were constructions of two potabilization plants and wastewater treatment plants in 2000. Potabilization Plants The water from the TRASVASE is now also used to supply two water potabilization plants for two of the larger towns in the Peninsula. One of those is the Santa Elena plant (Irrigation Zone I), which supplies water for the cities of Santa Elena and Salinas. The second one provides water to Playas (Irrigation Zone II). The flow water required by the plants are 1.6 m 3 /sec and 0.55 m 3 /sec respectively (CEDEGE 2001).

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78 Figure 5-5. Zone II potabilization plant The construction of sanitary canals in the main cities and towns of the Peninsula is included in the CEDEGE plans for the SEP. Also planned are controls for prevention of surface and groundwater contamination. Transmission Canals The TRASVASE irrigation system uses canals and pipes to deliver the water to its users. The seepage loss from the canals constitutes a substantial percentage of the usable water. Irrigation canals lose water through seepage and evaporation (Chahar 2000). The seepage loss from canals is governed by hydraulic conductivity of the subsoil, canal geometry, location of water table relative to the canal, and several other factors (Burton et al. 1999). Figure 5-6. Canal Transmission canals are used in the TRASVASE system to convey water from the source to a distribution canal. Often the area to be irrigated lies very far from the source, and hence requires long transmission canals.

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79 Figure 5-7. Canal San Rafael, TRASVASE project Normally, there should be no withdrawal from a transmission canal. In the case of the TRASVASE this occurs because the secondary (delivery) canals have not been completed and water from the transmission canals is used to supply irrigation water to the adjacent land. The canals of the TRASVASE system are lined with various materials, from reinforced concrete, to polyethylene, to the original material of the riverbed. To calculate the seepage losses of each section of the canal we need to know the characteristics of each of these materials. However, in this study the average number estimated by CEDEGE (7 %) was used for calculation of seepage losses. Water Loss from the Canals and Dams to Evaporation The main causes of loss of stored water are seepage through a leaking basin or dam wall, and evaporation from the surface. Many methods have been developed for controlling both, but few are economically attractive (Hudson 1987). Evaporation from open water can easily reach 7 mm per day in arid or semi-arid countries. This equals 5 cm per week and 20 cm per month (Brouwer, et al. 1992). The amount of water lost by evaporation can be considerable, particularly in reservoirs, which are large and shallow. Therefore, irrigation from shallow lakes and reservoirs should be started as soon as possible after the rainy season.

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80 Evaporation from the dams and canals (Appendix C, Tables C-6 and C-7) has to be calculated based on the available weather data and the surface area of the dams and canals (Tables 5-2 and 5-3) that are part of the TRASVASE irrigation system. Table 5-2. Approximated surface areas of the canals Canal Length (m) Width (m) Surface Area (m 2 ) Chongn-Cerecita 37,520 8.6 322,672 Cerecita-Playas 18,309 7.7 140,979 Chongn-Sube y Baja 19,600 8.3 162,680 Azcar-Rio Verde 19,863 12.5 248,288 Table 5-3. Approximated surface areas of the dams Surface (m 2 *10 4 ) Dams Max Min Average Chongn 25.7 16.8 21.25 El Azcar 14 8.5 11.25 De Cola 0.3 0.5 0.40 A well-designed and constructed canal system transports water from the source to the farmers fields with a minimum amount of water loss. However, water losses will occur and can seriously reduce the efficiency of water delivery. Water may be lost by seepage, leakage, or both (Hoevenaars, et al. 1992). Seepage Water that seeps through the bed and sides of a canal will be lost for irrigation. This so-called seepage loss can be significant where a canal is constructed from material which has a high permeability: water seeps quickly through a sandy soil and slowly through a clay soil, and so canals constructed in sandy soils will have more seepage losses than canals in clay soils. The results of seepage through the sides of a canal can sometimes be very obvious, such as when fields adjacent to a canal become very wet, and even have standing water. Seepage loss through the canal bed is difficult to detect

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81 because water goes down and does not appear on the nearby ground surface (Hoevenaars et al., 1992). Figure 5-8. Trapezoidal canal Leakage Water may also be lost by leakage. This water does not seep, but flows through larger openings in the canal bed or sides (Hoevenaars, et al. 1992). Seepage around structures, leading to severe leakages Gates which are not tightly sealed Cracked concrete canal linings, or joints that are not tightly sealed Torn asphalt or plastic lining Leakage often starts on a small scale, but the moment that water has found a way through a canal embankment a hole will develop through which water will leak. If the leakage is not stopped in time, the tunnel becomes larger and the canal bank may be washed away at a certain moment. In the case of a lined canal, the canal foundation may be undermined after some time and the canal will collapse. Table 5-4. Canal description Canal Length (m) Q (m 3 /sec) Lining Daule-Chongn (tunnel) 32,723 44 Concrete Chongn-Cerecita 24,494 12 Concrete Cerecita-Playas 31,741 9 Concrete Chongn-Sube y Baja 19,600 9.2 Polyurethane Azcar-Rio Verde 19,863 5.5 Polyurethane

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82 Irrigation Efficiency The application efficiencies considered for this project were within those defined by FAO (Table 1-2). The in-field irrigation efficiency values considered were 50 %, 70 % and 90 %. Using an ample range of efficiencies assures the inclusion of a wide range of conditions where irrigation is applied to the field. Irrigation Technology used in the Santa Elena Peninsula In the period 2000 CEDEGE conducted a survey to the agricultural producers in the SEP. The results from this survey show the adoption of the latest irrigation technology by large farmers that have the capital to implement the technology (Tables 5-5 to 5-7). Table 5-5. Chongn-Daular-Cerecita pressurized system, Zone I (2001) System Area % Drip 19.17 Sprinkler 18.63 Microsprinkler 9.89 Water hose 11.41 Other 2.28 Not using irrigation 38.02 In this zone consisting of 2,780 ha cultivated (Table 5-5) the percentage of farmers not using any type of irrigation system is highest compared to the other regions. This corresponds to the area owned by small farmers. Table 5-6. Chongn-Cerecita-Playas canal, Zone I (2001) System Area % Drip 27.78 Sprinkler 8.89 Microsprinkler 21.11 Water hose 11.11 Not using irrigation 31.11

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83 More than half (57 %) of the area in this zone of 3,175 ha under agricultural production (Table 5-6) is covered by irrigation systems that theoretically will have a field application efficiency of 70 % of higher. Table 5-7. El Azcar-Ro Verde canal, Zone II (2001) System Area % Drip 35.4 Sprinkler 7.96 Microsprinkler 3.54 Surface 13.27 Not using irrigation 39.82 The conditions in this area (565 ha cultivated) (Zone II) differ from the previous two areas (Zone I) because in Zone II the water is scarcer and the irrigation systems used should be more efficient to overcome that deficit. This can explain the higher use of drip systems over sprinklers. Water Consumption CROPWAT is meant as a practical tool to help agro-meteorologists, agronomists and irrigation engineers to carry out standard calculations for evapotranspiration and crop water use studies and more specifically, the design and management of irrigation schemes. It allows the development of recommendations for improved irrigation practices, the planning of irrigation schedules under varying water supply conditions, and the assessment of production under rain fed conditions or deficit irrigation. Calculations of crop water requirements (CWR) and irrigation requirements are carried out with inputs of climatic and crop data. Standard crop data are included in the program and climatic data can be obtained for 144 countries through the CLIMWAT database. Ecuador is included in this database, however, the data for the Santa Elena Peninsula is for weather stations that do not represent accurately the weather in this area.

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84 The development of irrigation schedules and evaluation of rain fed and irrigation practices are based on a daily soil-water balance using various options for water supply and irrigation management conditions. Scheme water supply is calculated according to the cropping pattern provided for the model. Reference Evapotranspiration Surface Maps The first step to calculate CWR is to calculate reference evapotranspiration (ET o ). The process starts by entering historical average weather data for the Santa Elena Peninsula into CROPWAT (FAO/UN). This is a process that requires careful quality control since each value has to be entered manually into the system, and errors are easily made. All weather data (maximum and minimum temperatures and relative humidity, available wind speed data, and sunshine) for each station was introduced to the CROPWAT database. Missing parameters like wind speed data (for some months and stations) and solar radiation were calculated using the same program. Once all the data was complete, the reference evapotranspiration was calculated using the Penman-Monteith method. To create the ET o surface maps, the output from CROPWAT had to be entered to an ArcMap (ESRI) database to georeference the ET o data set. Inverse distance weighting (IDW) interpolation method (Chapter 4) was selected to interpolate the reference evapotranspiration data between the weather stations. The average monthly surface maps of ET o for the Santa Elena Peninsula (Figures 5-9.1 and 5-9.2) present the variation in reference evapotranspiration in the months of June to November considered to be the dry season, and December to April (or May) considered to be the wet season.

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85 These ET o maps plus the precipitation distribution data (Chapter 2) were used later on in this project (Chapter 6) to determine the crops irrigation requirements in different irrigation zones. Crop water requirement (CWR). A specific crop grown in a sunny and hot climate needs more water per day than the same crop grown in a cloudy and cooler climate. The highest crop water needs are thus found in areas that are hot, dry, windy and sunny. The lowest values are found when it is cool, humid and cloudy with little or no wind (Brouwer & Heibloem 1985). The crop type impact on the crop water need is important in two ways: The crop type influences on the daily water needs of a fully-grown crop Duration of the total growing season depends on the crop. Data on the duration of the total growing season of the various crops grown in an area can best be obtained locally. These data may be obtained from, for example, the seed supplier, the Extension Service, the Irrigation Department or Ministry of Agriculture. Table 5-8. Crop growing period Crop Total growing period (days) Crop Total growing period (days) Banana 300-365 Onion green 70-95 Bean green 75-90 Onion dry 150-210 Bean dry 95-110 Pepper 120-210 Citrus 240-365 Potato 105-145 Cucumber 105-130 Rice 90-150 Grain/small 150-165. Sorghum 120-130 Maize sweet 80-110 Soybean 135-150 Maize grain 125-180 Squash 95-120 Melon 120-160 Sunflower 125-130 Tomato 135-180 Modified from Irrigation Water Management, Training manual no. 3, FAO

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86 Figure 5-9.1. Average reference evapotranspiration for the SEP I

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87 Figure 5-9.2. Average reference evapotranspiration for the SEP II

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88 When the plants are very small, the evaporation from the soil will be more important than the transpiration. When the plants are fully-grown, the transpiration is a larger component of total water loss than the evaporation. At planting and during the initial stage, the evaporation is more important than the transpiration and the evapotranspiration or crop water need, during the initial stage, is estimated at 50 percent of the crop water need during the mid season stage, when the crop is fully developed. During the so-called crop development stage, the crop water need gradually increases from 50 percent of the maximum crop water need. The maximum crop water need is reached at the end of the crop development stage, which is the beginning of the mid-season stage. To estimate the water use of the cultivated area in the Santa Elena Peninsula (SEP) some concepts have to be defined first to understand the calculations made to obtain the crop water requirement (CWR) values. To calculate these values the CROPWAT software from FAO was used. Crop coefficients. While reference crop evapotranspiration (ET o ) accounts for variations in weather and offers a measure of the evaporative demand of the atmosphere, crop coefficients account for the difference between the crop evapotranspiration (ET c ) and ET o Because evapotranspiration (ET) is the sum of evaporation (E) from soil and plant surfaces and transpiration (T), which is vaporization that occurs inside of the plant leaves, it is often easier to consider them together as ET (Stroosnijder 1987). When not limited by water availability, both transpiration and evaporation are limited by the availability of

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89 energy to vaporize water. Therefore, solar radiation interception by the foliage and soil has a big effect on the ET rate (Allen et al. 1998). Table 5-9. Crop coefficients Crop K c ini K c mid K c end Maximum Crop Height Small Vegetables 0.7 1.05 0.95 Garlic 1.00 0.70 0.3 Onions dry 1.05 0.75 0.4 green 1.00 1.00 0.3 Vegetables Solanum Family (Solanaceae) 0.6 1.15 0.80 Sweet Peppers (bell) 1.05 0.90 0.7 Tomato 1.15 0.70-0.90 0.6 Vegetables Cucumber Family (Cucurbitaceae) 0.5 1.00 0.80 Cantaloupe 0.5 0.85 0.60 0.3 Cucumber Fresh Market 0.6 1.00 0.75 0.3 Pumpkin, Winter Squash 1.00 0.80 0.4 Watermelon 0.4 1.00 0.75 0.4 Roots and Tubers 0.5 1.10 0.95 Potato 1.15 0.75 0.6 Legumes (Leguminosae) 0.4 1.15 0.55 Beans, green 0.5 1.05 0.90 0.4 Perennial Vegetables (with winter dormancy and initially bare or mulched soil) 0.5 1.00 0.80 Asparagus 0.5 0.95 0.30 0.2-0.8 Cereals 0.3 1.15 0.4 Maize, Field (grain) (field corn) 1.20 0.60-0.35 2 Maize, Sweet (sweet corn) 1.15 1.05 1.5 Rice 1.05 1.20 0.90-0.60 1 Forages Grazing Pasture Extensive Grazing 0.30 0.75 0.75 0.10 Tropical Fruits and Trees Banana 1 st year 0.50 1.10 1.00 3 2 nd year 1.00 1.20 1.10 4 Cacao 1.00 1.05 1.05 3 Coffee bare ground cover 0.90 0.95 0.95 2-3

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90 with weeds 1.05 1.10 1.10 2-3 Pineapple bare soil 0.50 0.30 0.30 0.6-1.2 with grass cover 0.50 0.50 0.50 0.6-1.2 *continuation Table 5-9 Crop K c ini K c mid K c end Maximum Crop Height Grapes and Berries Grapes Table or Raisin 0.30 0.85 0.45 2 Wine 0.30 0.70 0.45 1.5-2 Fruit Trees Avocado, no ground cover 0.60 0.85 0.75 3 Citrus, no ground cover 70% canopy 0.70 0.65 0.70 4 50% canopy 0.65 0.60 0.65 3 20% canopy 0.50 0.45 0.55 2 Citrus, with active ground cover or weeds 70% canopy 0.75 0.70 0.75 4 50% canopy 0.80 0.80 0.80 3 20% canopy 0.85 0.85 0.85 2 Based on Tabulated K c (Table 12), Paper # 56 FAO (Allen et al. 1998). As a crop canopy develops, the ratio of T to E increases until most of the ET comes from T and E is a minor component. This occurs because the light interception by the foliage increases until most light is intercepted before it reaches the soil. Commercial irrigation schedules typically begin their computation with published regional crop coefficients. These coefficients, when multiplied by reference crop evapotranspiration, are used to calculate crop evapotranspiration. These regional crop coefficients are based on a certain reference crop, soil type and irrigation management practice. In conventional agriculture irrigation scheduling, crop coefficients can be modified as needed during the growing season, because there is constant feedback based on field observations of crop and soil conditions.

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91 Agricultural Production in the Santa Elena Peninsula Ecuador, with its climatic conditions, could be completely self-sufficient in food production and even produced for export. However, today most of the subtropical fruits (deciduous, grapes, citrus, etc.) are imported from Chile or California. Many areas of the country are considered very dry (less than 500 mm of water a year) and are therefore not included in the agricultural production cycle. Ecuador has several state water projects that were built many years ago, however, most are not in operation. According to CEDEGE (2001) the TRASVASE Daule-Santa Elena Project is the largest and most modern irrigation project in Ecuador. Until 1994 there was no source of water for irrigation in the SEP and the few wells sunk had a low capacity and were mostly saline. On completion of the first part of the project, about 15,000 hectares were brought into the production potential and today, with completion of the second part, the entire 40,000 hectares are suited for irrigated crops. However, in 1995, a total of about 2,000 hectares were under cultivation and the farmers of the peninsula (large and small) did not in fact know where to invest and how to utilize the potential of the zone. The agricultural production in the Santa Elena Peninsula has been limited to few crops. Even those few crops were planted without any suitability study. Crops like mangos were planted in zones with heavy soils, and pronounced slopes often without a good drainage. In other farms, cocoa trees were planted in flat and low land, and then El Nio came in 1997, flooding most of the area and killing the plantations. Stories like these can be found throughout the Peninsula. In addition, poor market study and lack of governmental policies to control the planted area affected the price in the international markets, because of an excessive offer of the product (Mango, Onion, Passion Fruit).

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92 The large farms in the Santa Elena Peninsula are mainly focused towards export products, and only the excess or products that do not meet the international standards are left for the local (internal) market. The main market for onion is Colombia and Peru, while passion fruit is transformed into concentrate and exported to the U.S. and European markets. Tomato is used by the local industry, and pineapple is exported as fresh and canned fruit. Bananas were first planted in the SEP because it was thought that the dry conditions in the area would help to control the fungus Mycosphaerella fijinsis or Sigatoka. But even though the Santa Elena Peninsula is considered a dry area because of the soil characteristics and the reduced rainfall, it has a high relative humidity (average > 80 %), and relative humidity promotes the development of fungus. As a result, the banana planted area is being reduced. Plantain is far more resistant and resilient than banana to Sigatoka. Yields from 5 crops (Figure 5-10) were obtained from CEDEGE in 2001. Production in the SEP01020304050607080BananaPineappleTomatoPassion F.OnionTON/Ha Figure 5-10. Agricultural Production in the Santa Elena Peninsula In 2000 CEDEGE conducted a survey to the farmers using water from the TRASVASE project in the areas adjacent to the canals, dams, and covered by the pressurized irrigation zones, the results about land use are presented in Table 5-10. The

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93 data for 200o is the actual area under production, and the data for 2001 are the estimated increase in area. Table 5-10. Crops planted and projected increase in the Santa Elena Peninsula Crops planted and projected in the Santa Elena Peninsula Zone I Zone II Chongn-Daular Chongn-Playas El Azcar-Rio Verde Crops 2000 2001 2000 2001 2000 2001 Asparagus 20 Avocado 5 22 40 Banana 60 Beans 4 8 Black Pepper 1 Cassava 6 Plum 11 Citrus 74 2 26 Cocoa 241 127 492 60 2 54 Corn 364 155 244 168 263 Cotton 16 Cucumber 14 1 Flowers 21 Grape 8 6 7 Grass 198 22 5 20 8 Green pepper 11 19 19 Guava 24 201 100 Guanabana 28 40 Hot pepper 24 4 Lime 451 20 178 Mango 852 89 1343 171 Melon 51 15 12 Onion 75 213 70 132 38 Palm 4 Papaya 10 13 16 50 Pineapple 45 Pitahaya 10 Plantain 184 25 388 6 Rice 44 8 9 Soya 8 Teak 105 100 Tobacco 10 Tomato 5 15 Watermelon 23 14 12 14 Wheat 11 Other 19 37 14 17

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CHAPTER 6 METHODOLOGY Water available from the TRASVASE project is mainly utilized for irrigation. One of the objectives of this project was to determine the total area that can be irrigated using this water. The extent of irrigated land was determined by calculating the crop water requirement of crops grown in the area using CROPWAT, a model developed by FAO/UN. This model requires monthly averages of weather parameters that were calculated from the available data from the Santa Elena Peninsula. CROPWAT was reviewed in Chapter 5. To calculate water requirement (CWR) for each crop the required data are: evapotranspiration, crop coefficients, crop area, and planting dates. Weather data for the CROPWAT were based on the monthly averages calculated from the data provided by CEDEGE. The variables (Figure C-1) entered were average temperature ( o C), average relative humidity (RH), average wind speed (m/s or km/day), and daily average sunshine (hr/day). Built into the CROPWAT are the processes to calculate missing weather parameters as explained in Estimating missing climatic data section in Chapter 3. This technique was used to calculate the solar radiation (MJ/m 2 /d). Evapotranspiration CROPWAT calculates the evapotranspiration (ET o ) values for each month for every weather station in the Santa Elena Peninsula based on the provided weather data. These evapotranspiration values (ET o ) are presented in Appendix C (Tables C-1 to C-5). 94

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95 A revised method for estimating reference crop evapotranspiration, adopting the approach of Penman-Monteith as recommended by the FAO Expert Consultation in Rome is used in this model. Further details on the methodology are provided in the Irrigation and Drainage Paper No 56: "Crop Evapotranspiration" (Allen, et al. 1998). Open Water Evaporation In Tables 5-1 and 5-3 the approximated surface areas based on available dimensions for all canals and dams in the TRASVASE project are presented. Average evapotranspiration values calculated using CROPWAT for the same zones used to calculate crop water requirement (CWR), were used to estimate the evaporation from the canals and dams. The open water evaporation per month has been calculated with the following equation developed by Smith (1996): E = k w ET o (6-1) where: E: Open water evaporation ET o : Penman Monteith reference evapotranspiration k w : correction factor for open water evaporation Reference evapotranspiration values came from CROPWAT, and the correction factor used for open water evapotranspiration is 1.3, based on Smith (1996) and recommended by FAO as a global average for open water evaporation when local data is not available. The value of 1.3, therefore, is an arbitrary value valid only under average conditions. The monthly evaporation values for each canal, and dam are presented in Appendix E (Tables E-1 and E-2 respectively). Since evaporation is directly proportional to the evapotranspiration data these values are the highest in the month of April followed by

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96 November for all the weather stations. The monthly changes in evaporation in cubic meters from the different canals are presented here (Figure 6-1). Evaporation from canals40060080010001200JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberWater (cubic meters) Chongon-Cerecita Cerecita-Playas Chongon-Sube y Baja Azucar-Rio Verde Figure 6-1. Evaporation from canals of the TRASVASE system Crop Water Requirement Once crop water requirements (CWR) were calculated, for the known location of the weather stations, the average values were calculated for the areas surrounding the specific canals and dams. These areas were: Chongn-Cerecita canal influenced by the Chongn and San Isidro weather stations, Cerecita-Playas canal under the influence of the San Isidro and Playas stations, and De Cola dam influenced by Playas weather station. Chongn dam under the Chongn station, and Chongn-Sube y Baja canal is influenced by Chongn and El Azcar weather stations, El Azcar dam is under the influence of El Azcar weather station as it is the El Azcar-Rio Verde canal.

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97 Table 6-1. Chongn-San Isidro, Zone I, crop water requirements (CWR) Crop Water Requirements (CWR) Chongn-San Isidro, Zone I (mm) Jun Jul 63.2 73.9 Plantain 50.8 Month Jan Feb Mar Apr May Aug Sep Oct Nov Dec Avocado 84.2 78.6 74.9 70.7 75.2 72.9 Asparagus 63.4 79.5 90.9 61.1 50.7 49.5 57.2 100.7 93.0 97.3 97.1 15.7 Citrus 71.2 71.1 69.4 66.5 84.3 60.3 59.2 59.7 62.1 64.8 66.7 58.1 Grapes-t 40.6 40.6 39.6 38.0 49.7 55.3 74.0 77.6 81.2 84.8 82.2 45.7 Mango 91.1 91.2 89.2 88.8 122.0 95.4 97.3 100.5 103.7 104.3 99.5 78.7 Onion 74.1 77.9 92.9 92.2 38.7 Pineapple 49.1 40.3 29.7 28.5 19.1 Potato 57.5 89.2 114.0 100.5 24.9 Melon 55.4 89.5 105.0 91.5 Watermelon 54.9 68.6 100.0 89.0 S-Pepper 74.7 85.2 105.0 102.0 B-Pepper 62.6 77.0 102.4 98.5 74.1 77.9 92.9 91.0 *t = table, S= sweet, B=black Table 6-2. San Isidro-Playas, Zone I, crop water requirements (CWR) Crop Water Requirements (CWR) San Isidro-Playas, Zone I (mm) Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avocado 77.7 91.5 104.7 97.3 78.1 85.1 89.4 86.0 Asparagus 75.5 93.8 107.2 72.2 Plantain 62.5 62.7 61.5 70.8 123.1 111.8 115.7 114.6 18.5 Citrus 87.5 87.9 86.2 82.3 103.1 72.5 70.3 70.4 58.8 76.6 79.2 69.2 Grapes-t 50.0 50.2 49.2 47.0 60.7 66.5 88.0 91.6 95.7 100.2 97.6 54.4 Mango 112.1 112.8 110.8 109.9 149.1 114.9 115.7 118.6 123.5 123.3 119.3 93.8 Onion 90.6 93.7 110.4 108.8 45.6 Pineapple 60.3 49.8 36.9 35.3 21.2 Potato 70.7 110.2 141.6 124.4 30.6 Melon 65.4 105.5 124.1 108.6 Watermelon 64.8 100.9 118.2 105.7 S-Pepper 88.2 100.4 124.1 121.1 B-Pepper 76.9 95.3 127.2 121.9 90.6 93.7 110.4 107.5 Monthly crop water requirements (mm) were calculated for 13 crops: avocado, asparagus, citrus, grapes, mango, melon, onion, pineapple, plantain, potato, black pepper, sweet pepper, and watermelon. The calculations were done for three areas: Chongn-San Isidro, San Isidro-Playas, and Chongn-El Azcar.

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98 Table 6-3. Chongn-El Azcar, Zone II, crop water requirements (CWR) Crop Water Requirements (CWR) Chongn-El Azcar, Zone II (mm) Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avocado 64.0 75.7 87.4 81.9 77.8 72.6 76.0 72.6 Asparagus 64.2 79.3 89.6 61.5 Plantain 51.5 51.9 51.4 59.7 104.6 95.4 98.4 96.7 15.5 Citrus 72.1 72.7 72.0 69.3 87.6 61.9 59.8 59.4 61.2 63.6 65.6 57.7 Grapes-t 41.2 41.5 41.1 39.6 51.6 56.7 74.8 77.3 80.1 83.1 81.0 45.2 Mango 92.7 93.5 92.5 92.5 126.7 98.0 98.4 100.2 102.1 102.4 97.9 78.1 Onion 77.0 79.9 93.9 91.8 38.2 Pineapple 49.7 41.2 30.8 29.7 9.6 Potato 58.3 91.5 118.8 104.8 25.9 Melon 55.2 88.2 103.0 90.1 Watermelon 54.7 67.3 98.1 87.7 S-Pepper 74.5 83.9 103.0 102.1 B-Pepper 63.4 78.9 106.2 102.7 77.0 79.9 93.9 90.7 CROPWAT was used to calculate reference evapotranspiration (ET o ) for each weather station (Appendix C); crop information was used to calculate actual crop evapotranspiration for 13 crops under consideration (Appendix E). This crop data consists of crop coefficient, date when the crop is planted on the field, and percentage of the total area occupied by the crop. In this case the program was run for each crop and the area covered for each crop was considered 100 % of the total area. The crop coefficients came from those already into the CROPWAT database (CLIMWAT 1994) and for those not in CLIMWAT the crop coefficients where entered from the data in Table 5-12 also obtained from FAO/UN. The planting dates for each crop were entered following the recommendations of ESPOL (Polytechnic School of the Littoral, Guayaquil) (Appendix D) that takes into consideration seasonal effects and in some other cases market advantages.

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99 Crop Irrigation Requirement Crop irrigation requirement (CIR) is the total water needed for the crop (CWR) multiplied by an in-field irrigation application efficiency factor. These irrigation application efficiency factors were taken from the estimates given by FAO (Table 1-2). The values used for application efficiency in this study are 50 %, 70 % and 90 % efficiency. Fifty percent application efficiency represents a well-managed surface irrigation system or poor maintained and administered sprinklers or micro-irrigation systems. Seventy percent efficiency could be well maintained and managed sprinklers systems, or poorly managed micro-irrigation systems. Ninety percent efficiency represents well-managed and maintained micro-irrigation systems. Various scenarios were analyzed. Calculations of water requirement for land planted with crops with high irrigation requirements, low irrigation requirements, and a mixture of high and low irrigation requirement were made to be used in this project, for each of the efficiencies described above. These data are presented in Appendix F. Scenarios To facilitate the calculation of the total water resource for certain types of production, and to follow Kppen ecological classification and the climatic parameters associated with each classification (Chapter 2, Climatic Classification Section), the TRASVASE system was divided in two main zones (Figure 6-2). Zone I: Chongn-Cerecita, Cerecita-Playas canals, Daular, Cerecita, and Chongn irrigation zones, Zone I (Playas, Table 5-2) potabilization plant, and the Chongn, San Isidro, and Playas weather stations. Zone II: Chongn-Sube y Baja, El Azcar-Rio Verde canals, El Azcar and Chongn dams, Zone II (Santa Elena, Table 5-2) potabilization plant, and Chongn, and El Azcar weather stations.

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100 A map using ArcMap (ESRI) was created to show the two zones, the yellow areas represent Zone I, and the green areas represent Zone II (Figure 6-2). Figure 6-2. Irrigation zones in the Santa Elena Peninsula Nine scenarios were created to calculate the water available for agricultural production and the area that can be irrigated with that amount of water. The water inputs into the system were: water from the dams. The water available for irrigation was calculated by subtracting seepage losses of the system; water used in the water treatment plants, and evaporation losses from the canals and dams. The only changing parameters among the scenarios are crop types and the types of irrigation system that affects in-field efficiency. Scenario A, a surface irrigation system with an in-field efficiency of 50 %, and all the 13 crops with available crop water requirement (CWR) data. These 13 crops are:

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101 avocado, asparagus, citrus, grapes, mango, melon, onion, pineapple, plantain, potato, black pepper, sweet pepper, and watermelon; evenly distributed to cover 100 % of the area, the same procedure was repeater for scenarios B and C. Scenario B, a sprinkler irrigation system with an in-field efficiency of 70 %, all the 13 crops with available crop water requirement (CWR) data. Scenario C, a micro-irrigation system with an in-field efficiency of 90 %, all the 13 crops with available crop water requirement (CWR) data. Scenarios A, B, and C, all assume 100 % replacement of evapotranspiration in the month (August) of the highest evapotranspirational demand. Scenario AA, surface irrigation system with an in-field efficiency of 50 %, avocado, plantain, citrus, grapes, and mango, considered as high water requirement crops (CWR). These crops are in this group for their monthly evapotranspirational demand and also because the extended growth period they have. The same crops are used in scenarios BA, and CA. Scenario BA, sprinkler irrigation system with an in-field efficiency of 70 %, and high water requirement crops (CWR). Scenario CA, micro-irrigation system with an in-field efficiency of 90 %, and high water requirement crops (CWR). Scenarios AA, BA, and CA, all assume 100 % replacement of evapotranspiration in the month (August) with the highest evapotranspirational demand. Scenario AB, surface irrigation system with an in-field efficiency of 50 %, asparagus, melon, onion, potato, black pepper, sweet pepper, and watermelon are considered low water requirement crops (CWR), these crops have a short growth period

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102 and their evaporatranspirational demand is relatively small compared to those crops considered as high irrigation requirement crops. These low water requirement crops are used in scenarios BB, and CB. Scenario BB, sprinkler irrigation system with an in-field efficiency of 70 %, and low water requirement crops (CWR). Scenario CB, micro-irrigation system with an in-field efficiency of 90 %, and low water requirement crops (CWR). Scenarios AB, BB, and CB, also assume 100 % replacement of evapotranspiration in the month (August), the month with the highest water requirement. To calculate the output of the different scenarios quickly a spreadsheet program was developed using Excel. The program is based on the crop water requirement data produced by CROPWAT. Inputs required by the program are crop area (percentage of total area), and in-field irrigation efficiency (i.e. 50 % surface irrigation, 70 % sprinklers, and 90 % micro-irrigation). The output is given in millimeters (mm) of water per month for each one of the crops selected. To select a given crop an area (percentage have to be entered in the spreadsheet, to deselect a crop a zero (0) has to be entered for that specific crop.

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CHAPTER 7 RESULTS AND DISCUSSION The results for this project are based on the multiple scenarios described in Methodology (Chapter 6). In this Chapter, the outcome of each scenario will be discussed and compared to the other scenarios. As presented in Chapter 2, there are two months (April and August) with the highest evapotranspiration values. However, April is at the end of the rainy season and will still receive some rainfall (Figure B-1) and only supplemental irrigation may be required at this time. In addition, heavy soil texture (clays) (Figure 4-5) results in some water retention that can be used by the crops at the beginning of the dry season lowering irrigation requirement. The month of August is in the dry season with at least two dry months before it. During this time all the available water likely comes from irrigation. As a result, August is clearly the month of the highest irrigation demand and is used in this study to determine the maximum area that can be irrigated with the water from the dams of the TRASVASE system. A complete explanation of how these results were obtained is presented for the following scenario (Scenario A). The total irrigated area consists of Zone I and II. For Zone II (Table 7-1) the water comes from the El Azcar-Rio Verde and Chongn-Sube y Baja canals, and El Azcar dam (Table 7-1). There are 13 crops (avocado, asparagus, citrus, grapes, mango, melon, onion, pineapple, plantain, potato, black pepper, sweet pepper, and watermelon) planted in the zone. These crops represent a mixture of crop 103

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104 irrigation requirement (CIR) from high to low. However, not all the crops are growing during the month of August according to the recommendations by ESPOL and CEDEGE. In-field irrigation efficiency (application efficiency) is assumed to be 50 % with 100 % evapotranspiration replacement. Fifty percent efficiency indicates surface irrigation or poor management of pressurized irrigation systems. Zero (0) precipitation in the area is assumed. Table 7-1. Scenario A, Zone II Water Lost Evaporation from m 3 Canals: Chongn Sube y Baja 502 El Azcar Rio Verde 690 Dams: El Azcar 41,828 Potabilization Zone II 1,073,600 Seepage losses from canals: 7% 210,000 TOTAL water lost 1,326,620 Available Water CIR/zone m 3 m 3 /ha Irrigation Zones El Azcar 2,000,000 1,080 Canals Chongn Sube y Baja 1,000,000 1,080 El Azcar Rio Verde 2,000,000 1,080 TOTAL available water 5,000,000 Area that can be irrigated at 50% application efficiency is 3,401ha. The amount of water that is available for irrigation is calculated as a difference between total water available from the dams and various losses in the system. The losses in Zone II include: potabilization plant, seepage losses from the canals, and evaporation from the canals and the dams. The difference between available water and losses is divided by the monthly crop irrigation requirement (CIR) value (Table F-3) that was multiplied by the application efficiency factor (0.5 for 50 % efficiency, in this case). The parameters in each scenario can be defined as follows: evaporation (canals and/or dams) refers to the potential evaporation from an open water body (Appendix C, Tables C-6 and

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105 C-7) expressed in cubic meters (m 3 ). Potabilization is the water used in the water treatment plants (Zones I and II, defined in Chapter 6) estimating 12 working hours per day for 30 days. Seepage losses (Chapter 5) are calculated for the canals in each zone using 7 % (a value provided by CEDEGE 2 ). Available water is the total water available from the dams in cubic meters (m 3 ) minus losses in canals or conveyance losses in pressurized irrigation systems. Crop irrigation requirement or CIR (Appendix F. Tables F-1 to F-3) is expressed in cubic meters (m 3 ). The total area that can be irrigated is calculated in hectares (1 ha = 10,000 m 2 ) from the water available from the system and the CIR. Table 7-2. Scenario A, Zone I Water Lost Evaporation from m 3 Canals: Chongn Cerecita 1,064 Cerecita-Playas 528 Potabilization Zone I 712,800 Seepage losses from canals: 7% 350,000 TOTAL water lost 1,064,392 Available Water CIR/zone m 3 m 3 /ha Irrigation Zones Chongn 648,000 1,080 Daular I 617,040 1,080 Daular II 816,480 1,080 Cerecita I 479,520 1,080 Cerecita II 1,089,360 1,080 Canals Chongn Cerecita 2,500,000 1,080 Cerecita-Playas 2,500,000 1,280 TOTAL available water 8,044,872 Area that can be irrigated at 50% application efficiency is 6,662ha. The same procedure was conducted for Scenario A Zone I (Table 7-2). The differences are in the water sources, Chongn-Cerecita, Cerecita playas canals, and 2 CEDEGE, Comision de Estudios para el Desarrollo de la Cuenca del Rio Guayas, in English, Commission for the Development of the Guayas River Basin.

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106 conveyance pipe systems for Chongn, Daular I and II, Cerecita I and II irrigation. The losses are seepage from the canals, evaporation from canals, and the water used in the Zone II potabilization plant. In Scenario A, the areas calculated for Zones I and II were added to calculate the total area that could be irrigated under the given conditions for this scenario (Chapter 6, Scenario A). The total area becomes 10,063 ha for the month of August that is the month with the highest evapotranspiration. If some water stress would be permitted, for example 80 % or 90 % replacement, more area could be irrigated. Depending on the planting schedule, more area might be irrigated in other months than August since the calculations were performed for the month with the highest crop irrigation requirement (CIR). Table 7-3. Total area that can be irrigated under different scenarios Area that can be irrigated by scenario (ha) Scenario Zone I Zone II Total Area Efficiency (%) Crop water requirement Water replacement (%) A 6,662 3,401 10,063 50 Mix: high and low 100 B 7,683 3,950 11,633 70 Mix: high and low 100 C 9,049 4,650 13,699 90 Mix: high and low 100 AA 4,961 2,739 7,700 50 High 100 BA 5,847 3,161 9,008 70 High 100 CA 6,912 3,733 10,645 90 High 100 AB 7,382 3,988 11,370 50 Low 100 BB 8,519 4,603 13,122 70 Low 100 CB 10,064 5,442 15,506 90 Low 100 All the scenarios follow the same procedures. The only differences are the crops used to calculate crop irrigation requirement and the levels of in-field irrigation efficiency. The results from the other scenarios are summarized in Table 7-3. The parameters to be considered in this discussion are the irrigation efficiency, and the water requirement

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107 (Tables 6-1 to 6-3) by crops in each scenario. The crop irrigation requirements (CIR) calculated for the scenarios are presented in Appendix F. Scenario B has the same crops as Scenario A. The difference is the in-field efficiency of the irrigation system is increased from 50 % to 70 %. Under these conditions the total irrigable area increases by almost 1,600 ha to 11,633 ha. This is a good indicator how important are the changes in irrigation technology and irrigation management skills to improve the application efficiency of the irrigation systems in the Santa Elena Peninsula. Scenario C has the same 13 crops (Appendix E) as scenarios A and B, but is assuming a high application efficiency (90 %) of the irrigation system. This positively affects the area that can be irrigated for Zone I as well as for Zone II reaching a maximum of 13,699 ha. These three scenarios are calculated for August, the month with the highest crop irrigation requirement. This means that for other months with a smaller CIR there is some excess water in the system and this should be considered when planning the planting chronogram for the entire season (year).More area can be irrigated during months with less total crop irrigation requirement, and annual crops with short growing season can be planted at that time to take advantage of this excess water. For perennial crops the area is constant. In some cases, or in the first few years of the plantation, intercropping systems can be used in some fields. That will allow more agricultural production within the same area and better use of available water. Scenario AA uses high irrigation requirement crops (CIR) avocado, plantain, citrus, grapes, and mango; a low in-field irrigation efficiency of 50 % that can be

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108 considered as surface irrigation. According to the data obtained by ESPOL 3 (Tables 5-7 to 5-9) up to 13 % of the farmers use an irrigation system with this efficiency (50 %) in the El Azcar-Rio Verde area (Zone II). Almost 30 % less area can be irrigated under this scenario as compared to Scenario A. This area reduction may be required, if the majority of the area within the TRASVASE irrigation system is dedicated to the crops with high water requirement, under low efficiency irrigation. Scenario BA is a scenario with the same crops as Scenario AA but with an efficiency increased to 70 %. That gives almost a 20 % increase in total area (9,008 ha) compared to the previous scenario. A mixture of high efficiency (micro-irrigation) and low efficiency (surface) irrigation methods can create a 70 % overall efficiency. The entire area irrigated using well design and managed sprinkler systems would also result in similar application efficiency. Scenario CA considers the following combination of parameters: 90 % in-field efficiency, 100 % replacement of water lost, and high CIR crops. The total area that can be irrigated increases to 10,645 ha, similar to that achieved under Scenario A, using all 13 crops instead of just 5 with high irrigation requirement (CIR) as in this scenario. However, scenario A assumed lower application efficiency. Scenario AB is a scenario that uses crops with low water requirements (asparagus, melon, onion, potato, black pepper, sweet pepper, and watermelon), and irrigation efficiency of 50 %. Even using a low efficiency or poor managed irrigation system, the low water consumption by the crops compensates the high losses increasing the total area that can be irrigated under this scenario to 11,370 ha (Table 7-3). This area is greater that 3 ESPOL, Escuela Superior Politecnica del Litoral, in English, Littoral Polytechnic School.

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109 the one for Scenario CA that considered a highly efficient irrigation system and the crops with high water requirements. Scenario BB, considers a 70 % efficiency irrigation system and low water consumption crops, with zero precipitation, and low CIR crops. According to ESPOL among the different irrigation zones the use of systems with this application efficiency varies from 10 % to 20 % (Tables 5-7 to 5-9). Sprinkler irrigation systems are considered to be in this application efficiency (70 %) range. Scenario CB consists of asparagus, melon, onion, potato, black pepper, sweet pepper, and watermelon (low water requirement crops), 90 % in-field irrigation efficiency, and assumes 100 % replacement of water lost to evapotranspiration, with no precipitation. The total area that can be irrigated reaches 15,506 ha (Table 7-3). Of all the scenarios this one permits the irrigation of the largest amount of land. CEDEGE considers that the total area that can be irrigated is 23,066 ha for the entire TRASVASE project (Chapter 2, Actual Situation and Projections). This is 7,560 ha (48 %) more that the last, most conservative scenario (CB). Table 7-4. Areas that could be irrigated during dry season in the SEP, Scenario A Areas that could be irrigated (ha) Scenario A Month Zone II Zone I Total June 7,245 14,504 21,749 July 5,992 11,195 17,187 August 4,626 8,568 13,194 September 6,290 12,123 18,413 October 6,411 11,703 18,114 November 7,512 13,713 21,225

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110 The total areas that could be irrigated during the dry season (June, July, August, September, October, November) in the Santa Elena Peninsula and compared to the values to the values given by CEDEGE and the farmers (Chapter 2) are given in Figure 7-4. June and November are close to the numbers given by CEDEGE (Table 7-5). However, especially for August the crops will be subject of stress if the same area (23,066 ha) is maintained in this month. In Figure 7-1 the buffers created at each side from the canals represent the areas that could be irrigated under different circumstances. Figure 7-1. Buffers from the canals in the Santa Elena Peninsula

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111 Apart of water limitation there are other limiting factors such as the cost of conveying water far away from the main canals and also the higher losses associated with this process. The areas that theoretically could be irrigated assuming different buffers at each side of the canals are presented in Table 7-5, comparing those values to three of the scenarios for this project. The three selected scenarios represent 70 % application efficiency and three crop selections (Table 7-6). Table 7-5 Areas covered by different buffers of the canals in the SEP Buffer Size (Meters at each side of the canal) Area (ha) Areas by scenario (ha) 250 7,600 B11,633 500 15,200 BA9,008 1,000 30,400 BC13,122 Comparing the values given by CEDEGE, the associations of farmers, and the values obtained in this project (Table 7-6), it can be noticed that the values calculated (Chapter 7) in this project are closer to those given by the farmers association, and are approximately 50% of the area that can be irrigated according to CEDEGE estimates. Table 7-6. Comparison of areas that could be irrigated according different sources Comparison of areas that could be irrigated according different sources (Chapter 7) 70% application efficiency (Chapter 2) Scenarios (ha) B BA BC CEDEGE Farmers Assoc. Actual Mix of crops High irrigation Low irrigation Predicted (ha) Predicted (ha) Production (ha) Requirement Requirement 23,000 16,000-17,000 6,900 11,633 9,008 13,122 The question is whether the values obtained in this project are better than those given by CEDEGE. To assess that, the data and procedures used in this project had to be analyzed.

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112 CEDEGE used data from the same weather stations as used for this project, but also included the stations of Salinas and Guayaquil. Salinas is located in the driest area of the Peninsula and in the shoreline. The Guayaquil weather station is located in Guayaquils International Airport. Because of the use of these two weather stations extreme data can be introduced in the calculation of evapotranspiration (ET o ). Also, when the TRASVASE system was originally planned, the two water treatment plants were not included in the original plan. The total area to be irrigated after the water treatment plants where included in the system was not re-calculated to correct for the water lost. These water treatment plants alone use approximately 1,700,000 cubic meters of water per month, assuming 12 hours of operation per day. This amount of water would allow us to irrigated additional 1,150ha for scenario B, 920ha for scenario BA, and 2,160ha for scenario BC. In this project different procedures were used to calculate missing values of solar radiation and wind speed (Chapter 3). The use of those procedures can result in slight overestimation of the radiation values and at the end would affect the reference evapotranspiration data obtained in this project (Appendix C). The wind speed data were estimated for all stations. However, calculating reference evapotranspiration using zero wind speed, the variation in ET o is less than 0.4mm/day in average for most of the weather stations, except those closest to the sea (Playas, El Suspiro) having a small effect in the actual evapotranspiration. As a result, it can be assumed that an error in wind speed estimation would not have a significant effect on overall ET o The use of 13 crops to calculate the crop and irrigation water requirements is more precise than the single theoretical crop coefficient that CEDEGE used to calculate agricultural water consumption in the Santa Elena Peninsula.

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This increases the accuracy of the results obtained in this project compared to those of CEDEGE. Conclusion The results of this project are a reflection of the available data. Limitations in number of years of weather data available and the quality of these data affected the final calculations. The assumptions adopted to replace or complete the missing weather data can also inflict some inaccuracy in the final outcome of this work. Some of those assumptions (open water evaporation, crop coefficients) are on a global scale without considering local variations, however, this values are widely accepted and are also used by other institutions working in the Santa Elena Peninsula. Other assumptions (wind speed, solar radiation) do account for local variation reducing the margin of error. The quality of information provided by CEDEGE regarding the water conveyance capacity of the TRASVASE system, and the losses in the canals has an effect on the estimation of the total area that the irrigation system could maintain under agricultural production, and these data were not verified in this project. Based on the knowledge of the total area that can be irrigated with the TRASVASE project, plans to stimulate and to increased agricultural production in the Santa Elena Peninsula should be developed. Use of efficient and well-managed irrigation systems will be a key factor to achieve the crop production goals of the TRASVASE project. Further geospatial, and weather network data and GIS-based planning are needed to refine agricultural planning, particularly for new permutations of crop type, rotation, irrigation efficiency, and water-distribution policies. Cooperation among institutions is fundamental to increase agricultural production in the SEP. This agricultural production will have to be concordant with the water 113

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114 necessities of the inhabitants of the Peninsula. Reforms in policy and thinking are necessary to produce development in this area. Land tenure problems have to be solved. Equal access to credit and markets is needed for the small farmers in order to produce and compete in the local and international markets. The farmers also need to be organized within communities and have common goals to succeed. Different experiences in irrigation systems worldwide (Chapter 1) show that not just the engineering part of an irrigation system has to be implemented but also the social, economical and political components need to be considered. Suggestions for Future Work This project used all the available data to estimate the total area that could be irrigated with the TRASVASE project in the Santa Elena Peninsula. To implement any study high quality information is needed. More effort has to focus in the collection of high quality information for the Santa Elena Peninsula. For example, the installation of additional weather stations, and improvement in quality of the weather data acquired would be beneficial for further evaluations. The creation of an organization in charge of storage and management all the digital spatial information for the study area is recommended, to avoid the duplication of efforts, to maximize the use of scarce resources and to improve the quality of the data. More precise records of water delivered into the TRASVASE system are necessary for better estimation of water availability data. More studies related to crop production and water use by crops are needed. The factors affecting the quality of the soil have to be studied. Erosion, salinity, drainage, and soil and water pollution have to be considered in future work. The use of water for aquaculture, especially shrimp farms, has to be measured.

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115 Timber production in the SEP can help to increase the area that could be irrigated by the TRASVASE system, although no practical studies have been conducted in this area, this option has to be considered, as well as studies for other crops. Erosion can become an alarming problem for the Santa Elena Peninsula since most of the area is deforested and there are little efforts to plant trees or to adopt other practices to reduce erosion. In addition, soils of marine origin in the SEP can cause salinity problems for agricultural production if the farmers do not adopt adequate practices. Soil and water pollution can be caused by erosion, and salinization, however, over application of chemicals used in agricultural production can also pollute the water and soil. Studies to determine the risks and actual situation in the Peninsula are needed. All the institutions, public and private, related to the development of the Santa Elena Peninsula should work together with common goals to accelerate the progress of this area.

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APPENDIX A MAPS Figure A-1. Maximum annual precipitation isohyets 116

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117 Figure A-2. Minimum annual precipitation isohyets

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118 Figure A-3. Average annual precipitation isohyets

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119 Figure A-4. Complete map of soils in the Santa Elena Peninsula

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120 Figure A-5. Santa Elena farms

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121 Figure A-6. Chongn farms

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122 Figure A-7. Cerecita farms

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123 Figure A-8. Azcar-Rio Verde farms

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APPENDIX B AVERAGE WEATHER DATA Table B-1. Available weather data sets Available Weather Data Sets Weather station Periods Chongn 1991-2000 El Azcar 1966-1991 1995-2000 Playas 1962-1978 1995-2000 San Isidro 1991-1998 Suspiro 1962-1969 1991-1996 Chongn weather station is located in the CEDEGE experimental station at 24 m of elevation. The parameters registered in this station are temperature, relative humidity, wind speed, hour of light, precipitation, and evaporation, this data is different from all the others in the Santa Elena Peninsula since this station is close to Guayaquil where more rainfall is found. Playas weather station is located near to the coastal line. Its climate is influenced by the winds coming from the ocean. The evaporation is highest on this station, and the precipitation is scarce. El Azcar weather station is located in the CEDEGE research station in El Azcar. This station started its operation again in 2000. The parameters collected as the same as those shown in this table that collects historical data for this station. El Suspiro and San Isidro were under CEDEGE control but they are not operational at this moment. 124

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Table B-2. Chongn weather station Chongn 1991 2000 LATITUDE: 2 14' S LONGITUDE: 80 04' E ELEVATION: 24 m TEMPERATURE (C) RELATIVE HUM. (%) WIND (m/sec) LIGHT PRECIP EVAP MONTH Avg. Avg. Day Night H/month mm mm January 27.0 82 0.9 1.0 102.4 58.3 71.5 February 27.2 87 0.6 0.5 79.7 238.5 69.7 March 28.1 86 0.7 0.5 99.9 220.2 82.8 April 27.5 86 0.7 0.4 126.8 114.7 80.1 May 26.9 84 0.7 0.5 119.0 68.9 104.8 June 26.5 81 0.9 0.7 81.8 4.2 99.3 July 24.8 81 1.0 0.7 86.6 0.0 97.4 August 24.9 77 1.1 1.0 134.2 1.1 107.7 September 25.3 78 1.2 1.0 89.4 1.7 88.7 October 26.4 75 1.0 1.0 120.2 4.0 90.5 November 26.5 73 1.2 1.0 116.4 1.1 83.3 December 28.0 71 1.0 1.3 74.7 51.5 75.0 125 CEDEGE (2001). N/D: no data

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Table B-3. Playas weather station Playas 1963 1978 LATITUDE: 2 37' S LONGITUDE: 80 23' E ELEVATION: 41m MONTH TEMPERAT URE (C) RELATIVE HUM. (%) WIND (m/sec) LIGHT PRECIP EVAP. Avg Avg Day Night h/day mm mm January 25.7 77 3.1 N/D 5.3 97.5 N/D February 26.3 78 3.1 N/D 5.2 83.9 N/D March 26.6 79 2.9 N/D 5.8 135.7 N/D April 26.2 77 3.1 N/D 6.5 38.7 N/D May 25.2 79 3.4 N/D 5.7 10.3 N/D June 24.0 79 3.4 N/D 4.2 5.6 N/D July 24.9 80 3.7 N/D 3.6 2.1 N/D August 22.4 79 4.2 N/D 3.6 1.2 N/D September 22.3 78 4.3 N/D 3.9 2.5 N/D October 22.7 78 3.6 N/D 3.6 2.8 N/D November 23.2 79 4.1 N/D 4.8 0.9 N/D December 24.3 78 3.9 N/D 6.1 3.2 N/D 126 CEDEGE (2001). N/D: no data

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Table B-4. El Azcar weather station El Azcar 1974 1991 LATITUDE: 2S LONGITUDE: 80W ELEVACION: 50m MONTH TEMP. (C) RELATIVE HUM. (%) WIND (m/sec) LIGHT PRECIP. EVAP. Avg Avg. Night Day h/mo mm mm January 25.8 89 N/D 1.4 98.3 49.8 151.8 February 25.9 89 N/D 1.2 100.3 71.3 118.8 March 26.2 88 N/D 1.5 151.5 63.3 138.4 April 26.0 89 N/D 1.2 120.2 20.8 139.2 May 25.3 88 N/D 1.4 139.6 2.2 144.7 June 23.8 89 N/D 1.5 87.4 1.1 115.4 July 23.3 90 N/D 1.6 82.6 0.4 107.7 August 22.9 89 N/D 1.6 89.9 0.2 134.4 September 23.5 90 N/D 1.6 100.6 0.3 137.2 October 23.5 90 N/D 1.7 67.1 1.0 136.0 November 24.1 89 N/D 1.9 85.7 4.4 153.6 December 25.2 89 N/D 1.5 126.6 8.1 149.6 127

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Table B-5. San Isidro weather station San Isidro 1991 1998 LATITUDE: 2S LONGITUDE: 80W ELEVACION: 35m MONTH TEMP. (C) RELATIVE HUM. (%) WIND (m/sec) LIGHT PRECIP. EVAP. Avg Avg. Night Day h/mo mm mm January 26.4 87 0.2 0.5 N/D 95 99 February 26.4 90 0.1 0.3 N/D 313 67 March 26.5 91 0.1 0.2 N/D 419 100 April 26.6 90 0.1 0.3 N/D 192 84 May 26.1 87 0.3 0.4 N/D 108 87 June 25.5 86 0.4 0.5 N/D 10 89 July 25.6 86 0.5 0.5 N/D 109 90 August 25.2 86 0.5 0.6 N/D 0 71 September 25.3 86 0.6 0.8 N/D 36 87 October 25.2 84 0.8 0.8 N/D 20 97 November 25.6 85 0.4 0.8 N/D 175 94 December 26.0 85 0.9 0.7 N/D 263 82 128 CEDEGE (2001). N/D: no data

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Table B-6. Suspiro weather station Suspiro 1962-1969/1991-1996 LATITUDE: 2S LONGITUDE: 80W ELEVACION: 35m MONTH TEMP. (C) RELATIVE HUM. (%) WIND (m/sec) LIGHT PRECIP. EVAP. Avg Avg. Night Day h/mo mm mm January 25.9 80 N/D N/D N/D 39 161 February 26.1 81 N/D N/D N/D 125 130 March 26.4 81 N/D N/D N/D 92 173 April 26.0 77 N/D N/D N/D 18 131 May 25.2 82 N/D N/D N/D 11 151 June 23.2 88 N/D N/D N/D 12 117 July 22.0 90 N/D N/D N/D 17 77 August 21.4 89 N/D N/D N/D 14 59 September 21.7 88 N/D N/D N/D 6 71 October 21.6 91 N/D N/D N/D 38 96 November 21.9 90 N/D N/D N/D 10 121 December 24.5 82 N/D N/D N/D 26 164 129 CEDEGE (2001). N/D: no data

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APPENDIX C CROPWAT REFERENCE EVAPOTRANSPIRATION TABLES Table C-1. Reference evapotranspiration Chongn 11/1/2002 CropWat 4 Windows Ver 4.3 *********************************************************************** Climate and ETo (grass) Data (based on 9 years of data) *********************************************************************** Data Source: C:\CROPWATW\CLIMATE\CHONGON.PEM ----------------------------------------------------------------------Country : Ecuador Station : Chongn Altitude: 24 meter(s) above M.S.L. Latitude: -2.23 Deg. (South) Longitude: 80.07 Deg. (East) ----------------------------------------------------------------------Month MaxTemp MiniTemp Humidity Wind Spd. SunShine Solar Rad. ETo (deg.C) (deg.C) (%) (Km/d) (Hours) (MJ/m2/d(mm/d) ----------------------------------------------------------------------January 31.2 19.0 79.5 77.8 3.3 14.3 3.29 February 30.7 19.1 80.1 51.8 2.8 13.9 3.08 March 30.7 19.3 79.2 60.5 3.2 14.6 3.24 April 31.0 19.5 79.6 60.5 4.2 15.5 3.35 May 30.5 19.3 78.3 60.5 3.8 13.9 3.02 June 30.4 18.7 78.9 77.8 2.7 11.8 2.77 July 30.8 18.5 78.9 86.4 2.8 12.1 2.90 August 30.8 20.0 79.0 95.0 4.3 15.1 3.43 September 30.8 19.3 79.5 103.7 3.0 13.9 3.34 October 30.6 19.8 78.2 86.4 3.9 15.5 3.52 November 30.8 19.9 79.1 103.7 3.9 15.2 3.54 December 30.4 19.3 78.1 86.4 2.4 12.7 3.06 ----------------------------------------------------------------------Average 30.7 19.3 79.0 79.2 3.4 14.1 3.21 ----------------------------------------------------------------------Pen-Mon equation was used in ETo calculations with the following values for Angstrom's Coefficients: a = 0.25 b = 0.5 *********************************************************************** C:\CROPWATW\REPORTS\CHONGON2.TXT 130

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131 Table C-2. Reference evapotranspiration El Azcar 11/1/2002 CropWat 4 Windows Ver 4.3 *********************************************************************** Climate and ETo (grass) Data Azcar (based on 30 years of data) *********************************************************************** Data Source: C:\CROPWATW\CLIMATE\AZUCAR.PEM ----------------------------------------------------------------------Country : Ecuador Station : Azcar Altitude: 35 meter(s) above M.S.L. Latitude: -2.25 Deg. (South) Longitude: 80.58 Deg. (East) ----------------------------------------------------------------------Month MaxTemp MiniTemp Humidity Wind Spd. SunShine Solar Rad. ETo (deg.C) (deg.C) (%) (Km/d) (Hours) (MJ/m2/d)(mm/d) ----------------------------------------------------------------------January 31.5 21.3 81.4 121.0 3.2 14.1 3.45 February 31.4 21.4 82.5 103.7 3.6 15.1 3.53 March 31.9 21.4 83.4 129.6 4.9 17.2 3.98 April 32.2 20.6 82.3 103.7 4.0 15.2 3.57 May 30.6 20.9 81.9 121.0 4.5 14.9 3.40 June 29.4 19.0 84.9 129.6 2.9 12.1 2.84 July 28.8 19.3 87.2 138.2 2.7 12.0 2.74 August 28.1 18.7 88.3 138.2 2.9 13.1 2.86 September 28.0 19.0 88.8 138.2 3.4 14.5 3.08 October 29.5 19.0 87.5 146.9 2.2 12.9 3.05 November 28.4 19.0 87.5 164.2 2.9 13.7 3.09 December 30.5 20.1 85.9 129.6 4.1 15.3 3.45 ----------------------------------------------------------------------Average 30.0 20.0 85.1 130.3 3.4 14.2 3.25 ----------------------------------------------------------------------Pen-Mon equation was used in ETo calculations with the following values for Angstrom's Coefficients: a = 0.25 b = 0.5 *********************************************************************** C:\CROPWATW\REPORTS\AZUCAR2.TXT

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132 Table C-3. Reference evapotranspiration Playas 11/1/2002 CropWat 4 Windows Ver 4.3 *********************************************************************** Climate and ETo (grass) Data (based on 21 years of data) *********************************************************************** Data Source: C:\CROPWATW\CLIMATE\PLAYAS.PEM ----------------------------------------------------------------------Country : Ecuador Station : Playas Altitude: 41 meter(s) above M.S.L. Latitude: -2.62 Deg. (South) Longitude: 80.38 Deg. (East) ----------------------------------------------------------------------Month MaxTemp MiniTemp Humidity Wind Spd. SunShine Solar Rad. ETo (deg.C) (deg.C) (%) (Km/d) (Hours) (MJ/m2/d)(mm/d) ----------------------------------------------------------------------January 30.2 25.5 77.1 267.8 5.3 17.4 4.47 February 33.5 28.7 76.7 267.8 5.2 17.7 4.97 March 27.0 5.0 78.1 250.6 5.8 18.7 4.76 April 29.3 23.2 73.1 267.8 6.5 19.0 4.74 May 33.6 29.9 79.0 293.8 5.7 16.6 4.67 June 30.5 24.3 74.3 293.8 4.2 13.8 4.14 July 28.5 23.9 79.0 319.7 3.6 13.2 3.63 August 30.6 25.6 74.0 362.9 3.6 14.1 4.51 September 33.3 28.9 79.7 371.5 3.9 15.3 4.64 October 33.2 26.6 76.5 311.0 3.6 15.1 4.67 November 30.3 25.3 75.3 354.2 4.8 16.7 4.74 December 28.9 24.2 78.2 337.0 6.1 18.4 4.52 ----------------------------------------------------------------------Average 30.7 24.3 76.8 308.2 4.9 16.3 4.54 ----------------------------------------------------------------------Pen-Mon equation was used in ETo calculations with the following values for Angstrom's Coefficients: a = 0.25 b = 0.5 *********************************************************************** C:\CROPWATW\REPORTS\PLAYAS2.TXT

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133 Table C-4. Reference evapotranspiration San Isidro 11/1/2002 CropWat 4 Windows Ver 4.3 *********************************************************************** Climate and ETo (grass) Data (based on 7 years of data) *********************************************************************** Data Source: C:\CROPWATW\CLIMATE\SNISIDRO.PEM ----------------------------------------------------------------------Country : Ecuador Station : San Isidro Altitude: 35 meter(s) above M.S.L. Latitude: -2.25 Deg. (South) Longitude: 80.58 Deg. (East) ----------------------------------------------------------------------Month MaxTemp MiniTemp Humidity Wind Spd. SunShine Solar Rad. ETo (deg.C) (deg.C) (%) (Km/d) (Hours) (MJ/m2/d)(mm/d) ----------------------------------------------------------------------January 31.0 20.2 85.3 43.2 4.3 15.8 3.32 February 31.3 20.7 85.3 25.9 4.2 16.1 3.36 March 31.4 20.4 85.8 17.3 4.8 17.1 3.50 April 31.4 20.9 85.6 25.9 5.5 17.5 3.56 May 31.3 20.9 85.1 34.6 4.7 15.2 3.13 June 31.5 20.8 84.8 43.2 3.2 12.5 2.71 July 31.2 20.1 85.2 43.2 2.6 11.9 2.60 August 31.7 20.5 85.1 51.8 2.6 12.6 2.83 September 31.3 20.5 85.0 69.1 2.9 13.8 3.11 October 31.5 20.9 84.7 69.1 2.6 13.5 3.10 November 31.4 20.8 84.7 69.1 3.8 15.1 3.34 December 31.7 20.6 84.5 60.5 5.1 16.8 3.59 ----------------------------------------------------------------------Average 31.4 20.6 85.1 46.1 3.9 14.8 3.18 ----------------------------------------------------------------------Pen-Mon equation was used in ETo calculations with the following values for Angstrom's Coefficients: a = 0.25 b = 0.5 *********************************************************************** C:\CROPWATW\REPORTS\ISIDRO2.TXT

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134 Table C-5. Reference evapotranspiration Suspiro 11/1/2002 CropWat 4 Windows Ver 4.3 *********************************************************************** Climate and ETo (grass) Data (based on 13 years of data) *********************************************************************** Data Source: C:\CROPWATW\CLIMATE\SUSPIRO.PEM ----------------------------------------------------------------------Country : Ecuador Station : Suspiro Altitude: 35 meter(s) above M.S.L. Latitude: -2.25 Deg. (South) Longitude: 80.58 Deg. (East) ----------------------------------------------------------------------Month MaxTemp MiniTemp Humidity Wind Spd. SunShine Solar Rad. ETo (deg.C) (deg.C) (%) (Km/d) (Hours) (MJ/m2/d)(mm/d) ----------------------------------------------------------------------January 31.6 20.3 75.3 164.2 3.2 14.1 3.86 February 31.3 21.4 75.6 146.9 3.6 15.1 3.88 March 32.1 21.0 78.3 146.9 4.9 17.2 4.20 April 31.7 20.2 77.0 155.5 4.0 15.2 3.90 May 30.4 21.4 77.0 129.6 4.5 14.9 3.54 June 28.8 19.3 78.3 112.3 2.9 12.1 2.88 July 27.9 18.6 84.7 86.4 2.7 12.0 2.59 August 26.2 17.9 88.0 129.6 2.9 13.1 2.70 September 25.8 17.0 87.3 121.0 3.4 14.5 2.92 October 25.5 17.9 87.5 129.6 2.2 12.9 2.69 November 26.3 18.3 86.3 86.4 2.9 13.7 2.80 December 30.0 20.2 83.5 167.0 4.1 15.3 3.58 ----------------------------------------------------------------------Average 29.0 19.5 81.6 131.3 3.4 14.2 3.30 ----------------------------------------------------------------------Pen-Mon equation was used in ETo calculations with the following values for Angstrom's Coefficients: a = 0.25 b = 0.5 *********************************************************************** C:\CROPWATW\REPORTS\SUSPIRO2.TXT

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135 Figure C-1. Surface maps used to create a reference evapotranspiration map

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136 Table C-6. Open water evaporation values per canal Month Chongn-Cerecita (m3) Cerecita-Playas (m3) Chongn-Sube y Baja (m3) Azcar-Rio Verde (m3) January 1064 528 531 740 February 1037 565 517 725 March 1085 560 541 792 April 1112 563 555 759 May 990 529 494 705 June 882 465 440 616 July 885 423 441 619 August 1008 498 502 690 September 1038 526 518 705 October 1066 527 531 721 November 1107 548 552 728 December 1070 550 534 714 Table C-7. Open water evaporation from dams Month Chongn (m3) El Azcar (m3) De Cola (m3) January 90,886 50,456 2,324 February 85,085 51,626 2,584 March 89,505 58,208 2,475 April 92,544 52,211 2,465 May 83,428 49,725 2,428 June 76,521 41,535 2,153 July 80,113 40,073 1,888 August 94,754 41,828 2,345 September 92,268 45,045 2,413 October 97,240 44,606 2,428 November 97,793 45,191 2,465 December 84,533 50,456 2,350

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APPENDIX D ECOCROP SELECTION CRITERIA TABLES The information in ECOCROP permits the identification of 1,710 plant species whose most important climate and soil requirements match the information on soil and climate entered by the user. It also permits the identification of plant species for certain uses. ECOCROP can be used as a library of crop environmental requirements and it can provide plant species attribute files on crop environmental requirements to be compared with soil and climate maps in Agro-ecological zoning (AEZ) databases or Geographical Information System (GIS) map-based display. A list of 60 crops was selected as potential crops for the SEP following workshops and studies conducted by ESPOL and CEDEGE (2001). Following parameters were considered: Crops actually being grow in the Santa Elena Peninsula Market analysis for these crops Ecology and adaptability to the zone Discussion with the agricultural producers From the list of 60 crops, following a process of elimination using ECOCROP, expert advises from international market consultants, and producers a final list of 20 crops was produced. Thirteen crops from that list are used to calculate water use in this project. In this section the output given by ECOCROP is presented. These 13 crops were selected because were considered the ones with better economical future. 137

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Citrullus lanatus Herb. Annual Industry Commercial ND ND C. reticulata Tree Perennial Fruit Garden, commercial 60 365 138 ECOCROP SELECTION CRITERIA (PRODUCTION) DESCRIPTION CYCLE (days) SELECTED CROPS FORM TYPE USES PRODUCTION SYSTEMMIN MAX Psidium guajava L. Bush Perennial Fruit, lumber ND 150 365 Abelmoschus spp Herb. Annual Vegetable ND 50 180 Algarrobo Bush, tree Perennial Medicine. Aromatic, Industry ND 210 365 Ficus carica L. Bush, tree Perennial Fruit, Medicine. Aromatic, Industry Garden 120 300 Cucurbita spp Climbing Annual Fruit, Medicine. Aromatic, Industry, Forages, Vegetable Garden, commercial 80 140 Annona cherimola Bush, tree Perennial Fruit Garden, commercial 210 270 Anacardium occidentale Bush, tree Perennial Fruit, Medicine, Industry ND 190 260 C. sinensis Tree Perennial Fruit Commercial 180 365 Aloe spp Herb. Perennial Medicine, Industry Garden 120 150 Lycopersicon esculentum Herb. Annual Aromatic, Industry, Forages, Vegetable ND 70 150 Carica papaya L. Herb. Perennial Fruit, Medicine, Aromatic Garden, commercial 330 365 Cucumis sativus Herb. Annual Fruit, Medicine. Aromatic, Industry, Vegetable Garden, commercial 40 180 Persea spp Tree Perennial Fruit, lumber ND ND ND Climbing, Fruit, Medicine. Aromatic,

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139Piper nigrum L. Climbing Perennial Medicine, Aromatic ND 180 270 Allium sativum L. Herb. Biannual Vegetable, Medicine. Aromatic ND ND ND Ananas comosus Merr. Herb. Perennial Fruit ND 330 365 Cucumis melo L. Herb., Climbing Annual Fruit, Industry ND 50 120 Vitis spp. Bush, Climbing Perennial Fruit, Medicine, Industry Commercial 160 270 C. aurantifolia Tree Perennial Fruit, Aromatic ND ND ND Asparagus officinalis L. Herb. Perennial Vegetable, Medicine, Industry Commercial 210 270 Allium cepa Herb. Biannual Vegetable, Medicine, Industry Garden, commercial 85 175 Musa spp Herb. Perennial Fruit ND ND ND Capsicum spp ND ND ND ND ND ND

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140 ECOCROP SELECTION CRITERIA (SOIL) SOIL THICKNESS (cm) SOIL TEXTURE SOIL pH OPTIMUM ABSOLUTE SELECTED CROPS OPTIM ABSOLTOPTIMUM ABSOLUTE MIN MAX MIN MAX Psidium guajava L. 50-150 20-50 Medium, organic Heavily, medium, light 5.5 7.5 4 8.5 Abelmoschus spp 20-50 20-50 Heavily, medium, light, organic Heavily, medium, light 5.5 7 4.5 8.7 Algarrobo 20-50 20-50 Medium, light Medium, light 6 7.5 5 9 Ficus carica L. >150 50-150 Medium Heavily, medium, light 6 7 4.3 8.6 Cucurbita spp >150 20-50 Heavily, medium, light, organic Heavily, medium, light 5.5 7.5 5 8.5 Annona cherimola >150 20-50 Medium, light Heavily, medium, light 7 8 4.3 8.5 Anacardium occidentale >150 50-150 Medium, light Heavily, medium, light 4.5 6.5 3.8 8.7 C. sinensis 150 50-150 Medium, light Heavily, medium, light 5 6 4 8.3 Aloe spp 50-150 20-50 Light Medium, light 6.5 7 6 7.5 Lycopersicon esculentum 20-50 20-50 Medium, organic Heavily, medium, light 5.5 6.8 5 7.5 Carica papaya L. >150 50-150 Medium, organic Heavily, medium, light 5.5 7 4.5 8 Cucumis sativus 50-150 20-50 Medium, organic Heavily, medium, light 6 7.5 4.5 8.7 Persea spp >150 >150 Medium Medium 5 5.8 4.5 7 Citrullus lanatus >150 50-150 Medium Light 6 7 5.5 7.5

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141C. reticulata >150 50-150 Medium, light Heavily, medium, light 6 6.8 5.5 8.3 Piper nigrum L. >150 50-150 Heavily, medium Heavily, medium, light 6 7 5 7.5 Allium sativum L. 50-150 20-50 Medium, light Heavily, medium, light 6 6.6 5 8.5 Ananas comosus Merr. 50-150 20-50 Medium, light Heavily, medium, light 4.5 8 3.5 9 Cucumis melo L. 50-150 50-150 Medium, organic Heavily, medium, light 6 7.5 5 8.7 Vitis spp >150 20-50 Medium, organic Heavily, medium, light 5.5 7.5 4.5 8.5 C. aurantifolia 50-150 20-50 Medium, light Heavily, medium, light 5.5 6.5 5 7.5 Asparagus officinalis L. 50-150 50-150 Medium, organic Medium, light 6 6.7 4.5 8.2 Allium cepa 50-150 20-50 Medium, organic Organic 6 7 4.3 8.3 Musa spp ND ND ND ND ND ND ND ND Capsicum spp ND ND ND ND ND ND ND ND

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C. reticulata 0 35 0 45 2300 THS, StSA, SbH, SSV, SSI Piper nigrum L. 0 15 0 20 2000 THS, TH 142ECOCROP SELECTION CRITERIA (CLIMATE II) LATITUD (decimal degrees) ELEVATION (m) OPTIMUM ABSOLUTE ABSOLUTE SELECTED CROPS MIN MAX MIN MAX MAX CLIMATIC ZONE Psidium guajava L. 0 20 0 35 2000 THS, TH, StSa, SbH, SSV, SSI Abelmoschus spp 0 35 0 40 1000 THS, TH, StSa, SbH, SSV, SSI Algarrobo 27 42 25 45 1000 StSa, SbSV, TO Ficus carica L. 30 50 25 53 1200 THS, TH, StSa, SbH, SSV; SSI, TO, TC, THI, TSI Cucurbita spp 10 20 0 50 2000 THS, TH, StSa, SbH, SSV; SSI, TO, TC, THI, TSI Annona cherimola 0 15 0 25 1000 THS, TH, SSV Anacardium occidentale 0 25 0 30 1200 THS, StSa, SSV, SSI C. sinensis 0 40 0 40 2100 THS, TH, StSa, SbH, SHV, SSI Aloe spp 20 40 20 40 2000 StSa, SbSV Lycopersicon esculentum 0 0 0 0 2400 THS; TH; StSr, SbH, SSV, SSI, TO, TC, THI, TSI Carica papaya L. 0 30 0 32 2100 THS, TH Cucumis sativus 0 40 0 40 2000 THS; TH; StSr, SbH, SSV, SSI, TO, TC, THI, TSI Persea spp 0 42 0 42 2800 THS, TH Citrullus lanatus 20 43 10 43 1000 THS, TH, SbH, SHV, TO

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143Allium sativum L. 0 60 0 60 2200 THS; TH; StSr, SbH, SSV, SSI, TO, TC, THI, TSI Ananas comosus Merr. 0 25 0 33 1800 THS, TH, SbH Cucumis melo L. 0 0 0 0 1000 THS; TH; StSr, SbH, SSV, SSI, TO, TC, THI, TSI Vitis spp 20 45 0 50 2000 THS; StSr, SSV, TO, TC C. aurantifolia ND ND ND ND ND THS Asparagus officinalis L. 0 60 0 60 2600 THS; TH; StSr, SbH, SSV, SSI, TO, TC, THI, TSI Allium cepa 0 60 0 60 2000 THS; TH; StSr, SbH, SSV, SSI, TO, TC, THI, TSI Musa spp ND ND ND ND ND ND Capsicum spp ND ND ND ND ND ND

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Cucumis sativus 18 32 6 38 1000 1200400 4300 High luminosityHigh luminosityHigh luminosityOpen sky 144ECOCROP SELECTION CRITERIA (CLIMATE I) TEMPERATURE ( C) PRECIPITATION (mm) RADIATION OPTIMUM ABSOLUTEOPTIMUM ABSOLUTEOPTIMUM ABSOLUTE SELECTED CROPS MIN MAX MINMAXMIN MAXMINMAXMIN MAX MIN MAX Psidium guajava L. 20 33 10 45 1000 3000400 5000 High luminosityOpen sky High luminosityCloudy sky Abelmoschus spp 20 30 12 35 600 1200300 2500 Open sky Open sky High luminosityCloudy skyAlgarrobo 20 32 10 39 400 1000200 2000 High luminosityOpen sky High luminosityCloudy skyFicus carica L. 16 26 4 38 700 1500300 2700 High luminosityOpen sky High luminosityShadow Cucurbita spp 20 30 9 38 600 1000450 2700 High luminosityHigh luminosityHigh luminosityCloudy skyAnnona cherimola 23 30 11 41 800 1200570 4000 High luminosityOpen sky High luminosityCloudy skyAnacardium occidentale 15 35 5 46 750 1600400 4000 High luminosityHigh luminosityHigh luminosityOpen sky C. sinensis 20 30 13 38 1200 2000450 2700 High luminosityHigh luminosityHigh luminosityOpen sky Aloe spp 18 26 10 30 500 600 300 800 High luminosityHigh luminosityHigh luminosityCloudy skyLycopersicon esculentum 20 27 7 35 600 1300400 1800 Open sky Open sky High luminosityCloudy skyCarica papaya L. 21 30 12 44 1500 250010003000 High luminosityOpen sky High luminosityCloudy sky

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145Persea spp 18 40 12 45 500 2000300 2500 High luminosityHigh luminosityOpen sky High luminosityCitrullus lanatus 20 35 15 40 500 700 400 1800 Open sky High luminosityCloudy skyHigh luminosityC. reticulata 23 34 12 38 1200 1800300 4000 High luminosityHigh luminosityHigh luminosityOpen sky Piper nigrum L. 22 35 10 40 2500 400020005500 Open sky Shadow Open sky Shadow Allium sativum L. 18 30 7 35 750 1600500 2700 Open sky Open sky High luminosityCloudy skyAnanas comosus Merr. 21 30 10 36 800 2500550 3500 Open sky Cloudy skyHigh luminosityShadow Cucumis melo L. 18 30 9 35 1000 1300900 2500 High luminosityHigh luminosityHigh luminosityOpen sky Vitis spp 18 30 10 38 700 850 400 1200 High luminosityHigh luminosityHigh luminosityOpen sky C. aurantifolia 20 28 12 32 1200 1500750 2300 High luminosityHigh luminosityHigh luminosityOpen sky Asparagus officinalis L. 15 30 6 38 800 1200500 4000 High luminosityHigh luminosityHigh luminosityCloudy skyAllium cepa 12 25 4 30 350 600 300 2800 Open sky Open sky Cloudy skyHigh luminosityMusa spp ND ND ND ND ND ND ND ND ND ND ND ND Capsicum spp ND ND ND ND ND ND ND ND ND ND ND ND

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146 ECOCROP SELECTION CRITERIA (FERTILITY/DRAINAGE) FERTILITY SALINITY DRAINAGE SELECTED CROPS OPTIM ABSOLTOPTIMABSOLT OPTIM ABSOLT SHORT DAYSPsidium guajava L. High Low <4ds/m 4-10 ds/mgood (dry season) good (dry season), excessive (very dry) <12 hr Abelmoschus spp High Moderate <4ds/m <4ds/m good (dry season) good (dry season) <12 hr Algarrobo Moderate Low <4ds/m 4-10 ds/mgood (dry season) good (dry season), excessive (very dry) <12 hr Ficus carica L. Moderate Low <4ds/m 4-10 ds/mgood (dry season) good (dry season), excessive (very dry) <12hr, 12-14 hr, >14 hr Cucurbita spp High Low <4ds/m <4ds/m good (dry season) good (dry season) <12hr, 12-14 hr, >14 hr Annona cherimola High Moderate <4ds/m <4ds/m good (dry season) good (dry season) <12 hr Anacardium occidentale Moderate Low <4ds/m <4ds/m good (dry season) good (dry season), excessive (very dry) <12hr, 12-14 hr, >14 hr C. sinensis Moderate Low <4ds/m <4ds/m good (dry season) good (dry season) <12hr, 12-14 hr, >14 hr Aloe spp Moderate Low <4ds/m <4ds/m good (dry season) good (dry season), excessive (very dry) <12 hr Lycopersicon esculentum High Moderate <4ds/m <4ds/m good (dry season) good (dry season) <12hr, 12-14 hr, >14 hr Carica papaya L. High Moderate <4ds/m <4ds/m good (dry season) good (dry season), excessive (very dry) <12hr, 12-14 hr, >14 hr

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147Cucumis sativus High Moderate <4ds/m <4ds/m good (dry season) good (dry season) <12hr, 12-14 hr, >14 hr Persea spp Moderate Moderate <4ds/m <4ds/m good (dry season) good (dry season) ND Citrullus lanatus High Low <4ds/m 4-10 ds/mgood (dry season) good (dry season), excessive (very dry) >14 hr C. reticulata High Moderate <4ds/m <4ds/m good (dry season) good (dry season), excessive (very dry) <12 hr, 12-14 hr Piper nigrum L. High Moderate <4ds/m <4ds/m good (dry season) good (dry season) <12hr, 12-14 hr, >14 hr Allium sativum L. High Low <4ds/m <4ds/m good (dry season) good (dry season) >14 hr Ananas comosus Merr. Moderate Moderate <4ds/m <4ds/m good (dry season) good (dry season) <12 hr Cucumis melo L. Moderate Low <4ds/m <4ds/m good (dry season) good (dry season) <12hr, 12-14 hr, >14 hr Vitis spp Moderate Low <4ds/m <4ds/m good (dry season) good (dry season) <12hr, 12-14 hr, >14 hr C. aurantifolia High Moderate <4ds/m <4ds/m good (dry season) good (dry season), excessive (very dry) <12hr, 12-14 hr, >14 hr Asparagus officinalis L. High Moderate <4ds/m 4-10 ds/mgood (dry season) good (dry season) <12hr, 12-14 hr, >14 hr Allium cepa Moderate Low <4ds/m 4-10 ds/mgood (dry season) good (dry season) <12hr, 12-14 hr, >14 hr Musa spp ND ND ND ND ND ND ND Capsicum spp ND ND ND ND ND ND ND

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APPENDIX E TROPICAL CROPS Aloe Aloe spp. Varieties: Aloe barbadensis Aloe vera Aloe arborensis Aloe saponaria Aloe ferro Aloe perryi Aloe is an evergreen perennial native to Europe and the Mediterranean. It escaped cultivation and spread throughout the world. It is now found in deserts and jungles, temperate and cold climates. Cultivation is fairly easy; the plant prefers light, sandy, well-drained soil and a very sunny location (Atherton 1997). Figure E-1. Aloe plantation, Santa Elena Peninsula Asparagus Asparagus officinalis L. Varieties: Many new asparagus varieties are now available. All male hybrids are more productive and do not produce seed which sprouts to become a weed (Phillips 1990). Soils: Well-drained soils are a must for successful production, and very sandy soils are preferred. Good drainage is important in control for crown rot disease of asparagus. Commercial plantings of asparagus should not be made in soil that is heavier than a sandy loam. An ideal site includes a sandy loam soil with good drainage and aeration, 148

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149 water table below 1.2 m, and a pH of 6.8.5. Sites which retain standing water for more than 8 hours after a heavy rain should be avoided (Mullen 1998). Climate: Asparagus grows best when growing conditions include high light intensity, warm days, cool nights, low relative humidity, and adequate soil moisture. Compared to most other vegetables, asparagus is relatively winter hardy, with higher heat, drought, and salt tolerances (Cantaluppi & Precheur 1993). Spear initiation and root growth begin when the soil temperature is above 15 degrees C. Sandy soils warm earlier in the spring and encourage early spear production, while irrigation cools the soil and retards spear production. Optimum productivity occurs at 20 degrees C in the day and 15 degrees C at night. High daytime temperatures during harvest will loosen the spear tip and develop fiber in the stem, both of which reduce crop quality (Phillips 1990). Cashew Nut Anacardium occidentale Cashew, a native of Brazil is a major crop for the tropics. It is a hardy crop, which does well in areas considered relatively dry and marginal for many economic crops. The crop also requires minimal care and skills from the farmer. The cashew fruit consists of two distinct parts, a fleshy stalk in the form of a pear, also called cashew apple, with a brilliant yellow or red skin, which can measure from 5 to 10 cm; and a nut of greybrownish color, in the shape of a kidney, which hangs from the lower end of the stalk or "apple" and which is the true nut called cashew, very rich in carbohydrates and Vitamin A. Of the stalk or "apple", juices, syrups, preserves, wine or liquors are obtained. But its main commercial use is the cashew nut itself; shelled, roasted and salted forming an ingredient as snack and the confectionery industry (delicacies, chocolate, etc).

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150 Climate: Cashew is hardy and drought resistant, and can grow better in areas with rainfall between 500 mm and 1,500 mm per year. Cashew is well suited to a seasonally wet/dry tropical climate. The area selected for cashew production should be frost-free. Mean daily temperatures of less than 25 C will limit the growth and productivity of cashew trees. It is generally grown under rain fed conditions on soils of low fertility (Rieger 1990). Cucumber Cucumis sativus Cucumbers are a warm season crop and susceptible to frost damage. Low humidity is favorable to cucumber production because of lower incidences of fruit and foliar diseases. Extremely high temperatures may cause light green fruit color and bitterness in many cucumber varieties. Climate: Cucumbers are very tender, warm-season plants that grow best in temperatures from 18.3 C to 23.9 C with a minimum temperature of 15.6 C and a maximum of 32.2 C. Cucumber seeds germinate in soils at temperatures from 15.6 C to 35 C. Seeds do not germinate well at temperatures below 15.6 C. Cucumber plants are very susceptible to chilling injury in the field; prolonged temperatures below 12.8 C cause chilling injury to plants (pitting, water-soaked spots, and decay). Cucumber seed is relatively vigorous, and stand establishment is not generally a problem if appropriate soil preparation, temperature, and soil moisture conditions are met (Schrader 2002). Soils: cucumbers are planted on a wide variety of soils. Lighter soils are usually selected for earlier maturing fields. Cucumbers are a deep-rooted crop that grows best on deep, fertile, well-drained soils. Very light soils that have excessive drainage and poor moisture-holding potential should be avoided. Cucumbers are fairly salt tolerant. Research has shown yield reduction of 10 percent starting at 3 dS/m (Schrader 2002).

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151 Irrigation: cucumbers require frequent irrigation during the growing period. Too little soil moisture, particularly when fruit is filling, can cause poorly shaped and curved cucumbers. Fields should be maintained at or near field capacity to avoid plant stress and to keep plants growing at a constant rate. The use of tensiometers to monitor soil moisture and leaching is recommended. Banana and Plantain Musa spp. Bananas and plantains belong to the family Musaceae, genus Musa. This family is important not only for fruit production, but its other useful plants. Musa spp. have provide man with food, clothing, tools, and shelter prior to recorded history. Banana cultivars (Rieger 1990): Gros Michel, (AAA genome) Formerly the most widely cultivated banana in the western hemisphere, it has now been phased out due to susceptibility to Panama disease (Fusarium wilt). It has produced several clones and has been used as the parent for newer cultivars. Male sterile. Giant Cavendish, (AAA genome; syn.s Mons Mari, Williams, Williams Hybrid, Grand Naine) Similar to Gros Michel, this is a medium-sized (3 m) plant producing fairly large fruits with thicker skin to withstand bruising. Grown in Columbia, Australia, Martinique, Hawaii, and Ecuador. Triploid; male and female sterile. Dwarf Cavendish, (AAA genome) A small (4 ft) plant bearing medium sized fruit with thin skin. Grown in East Africa, South Africa, and the Canary Islands. Red bananas are found in both Cavendish groups. Lady finger, from the Sucrier group (AA genome), produces small (4 inch), very sweet fruits with thin skin. Common in Latin America and Australia.

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152 Plantain cultivars: Maricongo, Common Dwarf, Pelipita, Saba (generally AAB, ABB, and BBB genomes) are the leading cultivars. In Florida, 'Macho' is grown as a dooryard cultivar. Figure E-2. Plantain in the Santa Elena Peninsula Soils: Deep, well drained alluvial soils are best, but bananas can tolerate a wide variety of soil conditions. Bananas require heavy fertilization for adequate yield 90 kg N/acre and up to 220-270 kg K/acre are used. Climate: The banana is adapted to hot, wet, tropical lowlands. However, in South and East Africa, banana cultivation may extend to 1500 m above sea level. Mean temperature should be 25 C, and about 100 mm rain/month are required, with dry seasons no longer than 3 months. Frost kills plants to the ground, although the corm usually survives (Rieger 1990). Citrus fruits Citrus spp. The genus Citrus belongs to the Rutaceae family, sub-family Aurantoideae. This family contains many edible species, some distantly related such as White Sapote (Casimiroa edulis Llave & Lex.) and Wampee (Clausena lansium Skeels.). The literature on citrus usually recognizes each economically important type as a pecies, yielding the following (Rieger 1990):

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153 C. aurantifolia (limes). The two main cultivars include the Key (syn. Mexican), and Tahiti (syn. Persian). The latter is sometimes given species status as C. latifolia (Tanaka) or Citrus x tahiti (C. Campbell). C. macrophylla is a lime-like fruit used as a rootstock for lemons in California. C. sinensis (the sweet oranges). This is a widely accepted name for this group, containing many cultivars: Navel oranges are unique in that cultivars have a secondary ovary embedded within the usual ovary, giving a small fruitlet at the stylar end of the fruit at maturity; a fruit-within-a-fruit. Washington' is a major cultivar, but there are dozens. C. reticulata (mandarin, satsuma, or tangerine). This is probably a "real" species. Due to the success of breeding with these types, many cultivars and hybrids have been produced or formed naturally, some erroneously given species status. Common cultivars include: Dancy, Clementine or Algerian, Owari (a satsuma), Cleopatra (common mandarin rootstock). C. paradisi (the grapefruit). This is a relatively recent species (since 1700's) of unknown origin. It probably derives from Caribbean Forbidden Fruit, and was introduced to other places from there. Cultivars include: Duncan, Marsh, Red-blush, and Thompson (syn. Pink Marsh). Hybrids include the tangelos and citrumelos; the latter are used as rootstocks. Soils: Citrus is adapted to a wide variety of soil types and conditions. Trees are grown from almost pure sand, to organic muck, to loamy, heavy soils. Climate: Citrus fruit obtains highest internal quality in subtropical humid climates. However, with irrigation, it also grows well in Mediterranean climates, like California,

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154 achieving the best external quality. In the tropics, citrus accumulates less sugar and acid, and the peel usually remains green; also, bloom is not synchronized, so several stages of maturity are present on the tree at any given time, causing some immature fruit to be harvested (Rieger 1990). Cold hardiness is the major limiting factor for citrus production in subtropical areas. Fruit are killed by 30 minutes at to C; larger fruit are more cold tolerant due to greater thermal mass. Fruit freeze from the stem end to the button, and mildly frozen fruit can be salvaged for juice. Leaves and stems are killed by a few minutes at C to C, depending on stage of acclimation, species, and age of tissue. Citrus has no chilling requirement, and does not attain a truly dormant state, but becomes quiescent at temperatures below 13 C (Rieger 1990). Grapes Vitis spp. Grapes belong to the Vitaceae family. The genus Vitis is broadly distributed, largely between 25 and 50 N latitude in eastern Asia, Europe, the Middle East, and North America. Additionally, a few species of Vitis are found in the tropics: Mexico, Guatemala, the Caribbean, and northern South America (Rieger 1990). V. vinifera L., "Old world grape", "European grape". This is the major species of grape, accounting for >90 % of world production. It was probably domesticated more than 5000 years ago in the Middle East. Figure E-3. Grapes

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155 Soils: Grapes are adapted to a wide variety of soil conditions, from high pH and salt, to acidic and clayey. Rootstocks allow adaptation to various soil situations. However, deep, well-drained, light textured soils are best for wine grapes. Highly fertile soils are unsuited to high quality wine production, since vigor and yield must be controlled. Irrigation is detrimental to grape internal quality, and sometimes illegal for wine grapes, but is beneficial for table and raisin grapes where high yields are desired. Climate: Vinifera grapes can be generally characterized as requiring a long growing season, relatively high summer temperatures, low humidity, a ripening season free of rainfall, and mild winter temperatures. Cold hardiness is a major limiting factor for vinifera grapes. Damage to primary buds occurs at to C, and trunks may be injured or killed below C. Internal quality and hence wine quality is affected by summer temperature. Humidity is another limiting factor for vinifera grape culture, due to disease susceptibility. Grapes cannot tolerate high RH or rain during harvest (Rieger 1990). Mango Mangifera indica L. The Mango, Mangifera indica L., is the most economically important fruit in the Anacardiaceae (poison ivy family). Other important members of this family include cashew and pistachio. Figure E-4. Mangos

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156 Cultivars: Hundreds of mango cultivars exist throughout the world. However, in the western hemisphere, a few cultivars derived from a breeding program in Florida are the most popular for international trade. Locally, many cultivars are used, and often seedling trees are grown as a backyard food source. Tommy Atkins, the most common mango in the US; medium sized (0.5 kg), beautiful exterior but firm, finely fibrous, and low in flavor compared to others. This one sells on eye-appeal. Keitt, among the largest of major cultivars, about 0.75 kg (up to 1.2 kg). Yellow/green skin color makes it less popular in the US, but the yellow-gold flesh is fiberless and full-flavored. Kent, red blush to skin, medium sized (0.55 kg), and relatively round. Fiberless, rich-flavored flesh may have a turpentiny aftertaste. Haden, the old, anthracnose-prone mainstay of Florida market; now replaced by others, but 90 % of Hawaiian production, and grown in Ecuador (Guayaquil area) and western Mexico. Small (< 0.5 kg) and relatively round, the red skin color is excellent when grown in hot, sunny, dry climates. Firm flesh, almost fiberless. Soils: Mango is adapted to many soil types, but it requires adequate drainage. Excess fertility delays and reduces fruiting. Can be grown on soil with high pH. In Florida, trees are grown on limestone gravel, and still do not develop iron deficiency. Climate: Adapted to hot, tropical lowlands, to 1,000 m best quality in monsoon climates, i.e., those with a distinct wet and dry season. Leaves and fruit are injured by mild frost ( to C), but wood is not killed unless temperatures drop to below C.

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157 Onion Allium cepa Varieties: Onions are often grouped according to taste. The two main types of onions are strong flavored (American) and mild or sweet (sometimes called European). Each has three distinct colors yellow, white, and red. Generally, the American onion produces bulbs of smaller size, denser texture, stronger flavor, and better keeping quality than European types. Globe varieties tend to keep longer in storage (Riofrio & Wittmeyer 2000). Figure E-5. Onion plantation and onions, Santa Elena Peninsula Soils: Onions grow best in a loose, well-drained soil with high fertility and plenty of organic matter. Avoid heavier soils such as clay and silt loams, unless they are modified with organic matter to improve aeration and drainage. Onions are sensitive to highly acid soils and grow best when the pH is between 6.2 and 6.8 (Riofrio & Wittmeyer 2000). Climate: The onion is adapted to a wide range of temperatures and is frost-tolerant. Best production is obtained when cool temperatures prevail over an extended period of time, permitting considerable foliage and root development before bulbing starts. After bulbing begins, high temperature and low relative humidity extending into the harvest and curing period are desirable. A constant supply of adequate moisture is necessary for best results. For onions started from plants, light mulch will help conserve moisture for uniform growth (Riofrio & Wittmeyer 2000).

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158 Papaya Carica papaya L. Papaya belongs to a small family of only two genera, the Caricaceae. This family is often lumped into the Passifloraceae or passion fruit family by some. Carica papaya L., the papaya of commerce, is called "Paw-Paw" in some English-speaking countries; however, this is not to be confused with the North American Annonaceous species Asimina triloba Dunal. Carica pentagona Heilborn, the Babaco, is similar to papaya but smaller (<3 m), producing 5-angled fruits reaching 0.3 m in length with few or no seeds (Fruit Facts 2001). Cultivars: Solo or Sunrise solo, Introduced to Hawaii from Barbados in 1911, was the first major commercial cultivar. The name derives from the relatively small size of the fruit, it can be eaten by one person, as opposed to the more common family-sized fruits. Very high quality, smaller fruit; female plants produce, rounder, shallowly furrowed fruits, hermaphrodites produce pear-shaped fruits. It has been used as a parent in breeding programs, and produced newer cultivars such as Kapoho solo, Waimanalo, Higgins, and Wilder. As of 1999, all of the Solo papaya grown in Hawaii is transgenic; it has a gene inserted to confer resistance to papaya ringspot virus (Rieger 1990). Figure E-6. Papaya plantation in the Santa Elena Peninsula

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159 Soils: Papaya growth is best in soils with pH 5.5.7, rich in organic matter. However, fruits may be poor quality on highly fertile soils when vegetative growth is excessive. Climate: Papaya requires a tropical climate with high rainfall and temperature for proper fruit maturation. Plants are damaged by frost, and completely killed by temperatures close to C. High wind also causes damage by fruit loss or uprooting (Fruit Facts 2001). Pineapple Ananas comosus Merr. The pineapple, Ananas comosus Merr., is a member of the Bromeliaceae family, a large, diverse family of 2000 species. The bromeliad family contains hundreds of taxa used as ornamentals in greenhouses or sub-tropical areas: Billbergia, Vresia, Nidularium, Pitcairnia, Tillandsia, Tillandsia usneoides is "spanish moss" native to the Gulf States. Formerly, the pineapple was named A. sativus, Bromelia ananas, or B. comosus (Fruit Facts 2001). Cultivars: Smooth Cayenne, selected and cultivated by Venezuelan indians for its large, juicy fruit and lack of spines on leaves. Subject to diseases, and having poor shipping quality, it has been suplanted by superior sports in many areas. Hilo was selected in Hawaii, and St. Michael in Azores. Used for canned slices and fresh market. Fruit weight 2 kg. Red Spanish, The major fresh cultivar in the Caribbean, the plants are spiny, but disease resistant and fruits ship well. Roundish in shape, orange-red, 1.5 kg/fruit, and fewer eyes than Smooth (Rieger 1990). Soils: Well-drained sandy loams, with pH 4.5.5 are best. Fumigation is practiced routinely, since nematodes are serious problems in most growing areas.

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160 Climate: Pineapples are resticted to tropical lowlands, with temperatures of 18-35 C and precipitation of 1,100 mm distributed in spring and fall. Humidity is usually high. Pineapple is relatively drought-tolerant, and can be grown in areas receiving as little as 600 mm/yr. Alternatively, 3,800 mm/yr are tolerated if drainage is adequate. If climate during ripening is too cool, fruit are too acid, and if climate is too warm, fruit may be insipidly sweet. Cherimoya Annona cherimola The cherimoya is regarded by many as being among the best of tropical fruits. The cherimoya has a texture of a soft, non-gritty pear and a delicate, highly appealing fruit flavor with little acidity. Cherimoyas usually are eaten fresh; however they are excellent in ice cream and sherbets (Fruit Facts 2001). Soils: The most critical soil requirement is that of good drainage. Sandy loam or decomposed granite is preferred, but cherimoyas will succeed on many soil types with pH 5 to 8 (Fruit Facts 2001). Climate: All of the species grown for fruit require a tropical or semitropical climate except for the pawpaw which is native to temperate North America. Moreover, all but the cherimoya are better adapted to wet tropical conditions. The cherimoyas home is the highland tropics which are often characterized as areas of eternal spring with temperatures seldom straying from the 15s (C). There are wet and dry seasons with typical annual rainfalls being about 1,200 mm (Fruit Facts 2001). The cherimoya is adaptable to Mediterranean climates. In addition to San Diego and Santa Barbara and Ventura counties in the United States, significant commercial plantings have been made in Chile, Spain, Peru, Israel, New Zealand, Australia and Italy.

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161 The cherimoya requires a relatively frost-free environment similar to lemons (short periods of C for mature trees of hardy varieties). Some chilling seems beneficial (50 to 100 hours between 1 C and 5 C). However, a sunny location is needed since sufficient heat is required to develop a good flavor (inland, protection from extremely hot temperatures and dry winds is more important). In California most varieties do well extending 2 to 8 km inland from the ocean. Further inland, care must be exercised in selecting a variety that will do well. The cherimoya will not tolerate prolonged high humidity, such as is encountered in Florida (Fruit Facts 2001). Avocado Persea spp. Species: Guatemalan (Persea nubigena var. guatamalensis L. Wms.), Mexican (P. americana var. drymifolia Blake), West Indian (P. americana Mill. var. americana ). Hybrid forms exist between all three types (National Department of Agriculture 2000). Soil: Avocado trees like loose, decomposed granite or sandy loam best. They will not survive in locations with poor drainage. The trees grow well on hillsides and should never be planted in stream beds. They are tolerant of acid or alkaline soil (National Department of Agriculture 2000). Climate: The 3 best-known avocado races each has specific climatic requirements as a result of adapting to their original environment. West Indian cultivars originated in the humid, tropical lowlands of Central America and are best adapted to continuous hot, humid conditions with a high summer rainfall. Like all avocado cultivars they are, however, extremely sensitive to drought and do not tolerate frost well (minimum temperature of 1.5 C). The optimum temperature for growth is 25 to 28 C. The humidity should preferably be above 60 % (National Department of Agriculture 2000).

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162 The Mexican races originated in the cool, subtropical highland forests of Mexico and mature trees can withstand temperatures of to C. They should not be planted in areas prone to frost in August and September, because flowers are damaged easily by frost. A humidity range of 45 to 60 % should suffice. The optimum temperature for growth is 20 to 24 C (National Department of Agriculture 2000). Guatemalan cultivars originated from the tropical highlands of Guatemala and require a cool, tropical climate without any extremes of temperature or humidity. The trees can withstand light frost, down to C, but the flowers are very sensitive to frost. High temperatures of about 38 C, especially if combined with low humidity, could cause flower and fruit drop. A humidity level of 65 % or higher is required (National Department of Agriculture 2000). Tomato Lycopersicon esculentum Soils: A wide variety of soil textures are used for fresh-market tomato production. Sandy soils are preferred for early plantings (Hochmuth 2001). This is because planting can be done in sandy soils more easily during wet weather. Sand also warms more rapidly in the spring, promoting early growth. Loam and clay loam soils, however, are generally more productive than sand. Clay soil may be used, provided it is well drained and irrigated with care (Tomato Production Guide for Florida 2002). Climate: The tomato is a warm-season vegetable crop that is sensitive to frost at any stage of growth. The optimum soil temperature for seed germination is 20 C or above; seed germination below 16 C is very slow. Optimal production temperatures are between 21 C and 27 C. These temperatures are ideal for vegetative growth, fruit set, and development. With adequate soil moisture, tomato plants can tolerate temperatures in excess of 38 C, although fruit set is adversely affected. Tomato fruit development and

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163 quality are reduced when day temperatures fall below 20 C, and plants undergo chilling injury when night temperatures fall below 10 C (Tomato Production Guide for Florida 2002). Melon (Cantaloupe) Cucumis melo L Soils: Melons prefer well-drained soils. Sandy or silt loams are sometimes selected for the earliest crop. Heavier soils are preferred because of their greater water holding capacity, which slows the onset of vine collapse. Beds should be left cloddy to allow maturing melons to develop with minimal soil contact and good aeration (Mayberry 1996). Cantaloupes grow best on soils that hold water well and have good air and water infiltration rates. Soil should have a pH of 5.8 to 6.6. Cantaloupes are sensitive to cold temperatures, and even a mild frost can injure the crop (Mayberry 1996). Climate: The best average temperature range for cantaloupe production during the growing season is between 20 C and 30 C; temperatures above 30 C or below 15 C will slow the growth and maturation of the crop (Orzolek 2002). Irrigation: Cantaloupes require a constant supply of moisture during the growing season. However, excess water at any time during crop growth, especially as fruit reaches maturity, can cause the fruit to crack, which will reduce crop yields and fruit quality (Orzolek 2002). Guava Psidium guajava L The place of origin of the guava is uncertain, but it is believed to be an area extending from southern Mexico into or through Central America. It has been spread by man, birds and other animals to all warm areas of tropical America and in the West Indies (since 1526).

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164 The guava is an evergreen tree reaching a height of 3 m. The trunk is slender with a greenish-brown scaly bark which peels off in thin flakes. The white flowers are either solitary or in groups of two or three, arising from the leaf axils of younger branches. The fruit is round, ovoid or pearshaped berry, 5 cm or more in diameter and 4 cm long. It has a thin greenish-yellow skin and a flesh of varying thickness which may be white, yellow-pink or red. The outer layer of flesh is a finely granular pulp; the inside is softer pulp with many small hard seeds. Some varieties are seedless. The flavor is variable and is distinguished by a characteristic and penetrating musky aroma of varying intensity. Guava, a native of tropical America is well distributed throughout the tropics and subtropics. Soils: the guava is a hardy plant which grows in most soil types. Loam and alluvial types of soil is most ideal. The guava will tolerate many soil conditions, but will produce better in rich soils high in organic matter. They also prefer a well-drained soil in the pH range of 5 to 7. The tree will take temporary water logging but will not tolerate salty soils. Although the guava can tolerate low moisture condition, availability of water constantly will promote fast growth and leaf flushes. A warm, humid condition is most optimum for guavas. Guavas have survived dry summers with no water, although they do best with regular deep watering. The ground should be allowed to dry to a depth of several inches before watering again. Lack of moisture will delay bloom and cause the fruit to drop. Fig Ficus carica L. Climate: the fig grows best and produces the best quality fruit in Mediterranean and dryer warm-temperate climates. Rains during fruit development and ripening can

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165 cause the fruits to split. With extra care figs will also grow in wetter, cooler areas. Diseases limit utility in tropical climates. Fully dormant trees are hardy to C to C, but plants in active growth can be damaged at 0 C. Figs require full sun all day to ripen palatable fruits. Trees become enormous, and will shade out anything growing beneath. The succulent trunk and branches are unusually sensitive to heat and sun damage, and should be whitewashed if particularly exposed. Roots are greedy, traveling far beyond the tree canopy. In areas with short (less than 120 days between frosts), cool summers, espalier trees against a south-facing, light-colored wall to take advantage of the reflected heat. In coastal climates, grow in the warmest location, against a sunny wall or in a heat trap (Rieger 1990). Irrigation: young fig tees should be watered regularly until fully established. Is recommended to water mature trees deeply at least every one or two weeks. Desert gardeners may have to water more frequently. Mulch the soil around the trees to conserve moisture. Also, drought-stressed trees will not produce fruit and are more susceptible to nematode damage. Recently planted trees are particularly susceptible to water deficits, often runt out, and die (Rieger 1990). Watermelon Citrullus lanatus Watermelon is a warm-season crop related to cantaloupe, squash, cucumber and pumpkin. Soils: most well drained soil, whether clayey or sandy, can be managed to produce a good crop of watermelon. The best soils, however, are sandy loams that have not been in cucurbit (cantaloupe, cucumber, squash, etc.) production for a minimum of five years (Boyham 2001).

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166 Irrigation: 250 mm of timely rains or irrigations on a deep, sandy soil produce a good crop of watermelons. Growers with limited irrigation capabilities can often increase yields with only one or two irrigations. Inadequate moisture at planting results in poor and uneven emergence. Moisture shortage at bloom results in poor fruit set and misshapen fruit. Moisture stress close to harvest greatly reduces melon size and results in rapid vine decline. When irrigating, apply one to two inches of water (Roberts 2002).

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167 B-Pepper 115.4 143.0190.8182.9136.0140.7165.7161.3 APPENDIX F IRRIGATION REQUIREMENTS Table F-1. Chongn-San Isidro, 50% efficiency Table F-2. San Isidro-Playas, 50% efficiency Irrigation Requirement (mm) San Isidro-Playas, 50% irrigation efficiency Month Jan Feb MarchAprilMayJuneJuly Aug Sept Oct Nov Dec Avocado 116.6 137.3157.1146.0117.2127.7134.1129.1 Asparagus 113.3140.8160.8 108.4 Plantain 93.8 94.2 92.4106.3184.7167.8173.6172.0 27.8 Citrus 131.3 131.9129.4123.5154.7108.8105.6105.7 88.3 115.0 118.8103.8Grapes-T 75.0 75.3 73.9 70.6 91.1 99.8132.0137.5143.7 150.3 146.5 81.7Mango 168.2 169.3166.3164.9223.8172.3173.6177.9185.3 185.0 179.0140.7Onion 136.0140.7165.7163.3 68.4 Pineapple 90.5 74.8 55.4 53.0 31.9 Potato 106.1 165.3212.5186.7 46.0 Melon 98.2158.4 186.2 163.0 Watermelon 97.3151.4 177.3 158.6 S-Pepper 132.4150.6 186.2 181.7 Irrigation Requirement (mm) Chongn-San Isidro, 50% irrigation efficiency Month Jan Feb MarchAprilMayJuneJuly Aug Sept Oct Nov Dec Avocado 94.8 111.0126.4117.9112.3106.1112.8109.4 Asparagus 95.2119.3136.5 91.7 Plantain 76.3 76.2 74.4 85.9151.1139.5146.0145.8 23.6 Citrus 106.8 106.7104.1 99.8126.5 90.5 88.8 89.6 93.2 97.3 100.1 87.2Grapes-T 61.0 60.9 59.5 57.0 74.5 83.0111.1116.5121.9 127.3 123.4 68.6Mango 136.8 136.9133.9133.2183.0143.2146.0150.8155.6 156.6 149.3118.2Onion 111.3116.9139.4138.4 58.1 Pineapple 73.7 60.5 44.6 42.8 28.7 Potato 86.2 133.9171.1150.8 37.4 Melon 83.2134.4 157.5 137.3 Watermelon 82.4103.0 150.0 133.6 S-Pepper 112.1127.8 157.5 153.1 B-Pepper 93.9 115.6153.6147.8111.3116.9139.4136.6

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168 Table F-3. Chongn-El Azcar, 50% efficiency Irrigation Requirement (mm) Chongn-El Azcar, 70% irrigation efficiency Month Jan Feb MarchAprilMayJuneJuly Aug Sept Oct Nov Dec Avocado 96.1 113.7131.2122.9116.7108.9114.1108.9 Asparagus 96.4119.0134.5 92.3 Plantain 77.3 78.0 77.2 89.6157.0143.2147.6145.2 23.3 Citrus 108.2 109.2108.0104.1131.4 92.9 89.8 89.2 91.9 95.5 98.5 86.5Grapes-T 61.8 62.4 61.7 59.5 77.5 85.2112.2116.0120.2 124.7 121.5 67.8Mango 139.1 140.3138.8138.9190.2147.0147.6150.4153.3 153.7 146.9117.3Onion 115.6120.0140.9137.8 57.4 Pineapple 74.6 61.9 46.3 44.6 14.4 Potato 87.5 137.3178.2157.3 38.9 Melon 82.9132.3 154.6 135.2 Watermelon 82.1101.0 147.2 131.6 S-Pepper 111.8125.8 154.6 153.2 B-Pepper 95.2 118.4159.4154.1115.6120.0140.9136.1 Table F-4. Chongn San Isidro, 70% efficiency Irrigation Requirement (mm) Chongn-San Isidro, 70% irrigation efficiency Month Jan Feb MarchAprilMay June July Aug Sept Oct Nov Dec Avocado 82.2 96.2 109.6102.2 97.4 92.0 97.8 94.8 Asparagus 82.5103.4118.3 79.5 Plantain 66.1 66.0 64.4 74.5131.0120.9126.5126.3 20.4 Citrus 92.6 92.4 90.2 86.5109.6 78.4 77.0 77.680.8 84.3 86.7 75.6Grapes-T 52.9 52.8 51.6 49.4 64.6 71.9 96.2101.0105.7 110.3 107.0 59.5Mango 118.5 118.7 116.0115.4158.6124.1126.5130.7134.8 135.7 129.4102.4Onion 96.4101.4120.8119.9 50.3 Pineapple 63.8 52.5 38.7 37.1 24.9 Potato 74.7 116.0 148.3130.7 32.4 Melon 72.1116.5 136.5 119.0 Watermelon 71.4 89.2 130.0 115.8 S-Pepper 97.2110.8 136.5 132.7 B-Pepper 81.4 100.2 133.1128.196.4 101.4120.8118.4

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169 Table F-5. San Isidro-Playas, 70% efficiency Irrigation Requirement (mm) San Isidro-Playas, 70% irrigation efficiency Month Jan Feb MarchAprilMay June July Aug Sept Oct Nov Dec Avocado 101.0 119.0 136.1126.5101.6110.7116.2111.9 Asparagus 98.2122.0139.4 93.9 Plantain 81.3 81.6 80.1 92.1160.1145.5150.4149.1 24.1 Citrus 113.8 114.3 112.1107.1134.0 94.3 91.5 91.6 76.5 99.6 103.0 90.0Grapes-T 65.0 65.3 64.1 61.2 79.0 86.5114.4119.2124.5 130.3 127.0 70.8Mango 145.8 146.8 144.1142.9193.9149.4150.4154.2160.6 160.3 155.1121.9Onion 117.9121.9143.6141.5 59.3 Pineapple 78.5 64.8 48.0 45.9 27.6 Potato 91.9 143.3 184.2161.8 39.8 Melon 85.1137.3 161.4 141.3 Watermelon 84.3131.2 153.7 137.5 S-Pepper 114.8130.5 161.4 157.5 B-Pepper 100.1 123.9 165.4158.5117.9121.9143.6139.8 Table F-6. Chongn-El Azcar, 70% efficiency Irrigation Requirement (mm) Chongn-El Azcar, 70% irrigation efficiency Month Jan Feb MarchAprilMay June July Aug Sept Oct Nov Dec Avocado 83.3 98.5 113.7106.5101.2 94.4 98.8 94.4 Asparagus 83.6103.1116.5 80.0 Plantain 67.0 67.6 66.9 77.6136.1124.1127.9125.8 20.2 Citrus 93.8 94.6 93.6 90.2113.9 80.5 77.8 77.3 79.6 82.7 85.4 75.0Grapes-T 53.6 54.1 53.5 51.5 67.1 73.8 97.3100.6104.1 108.0 105.3 58.8Mango 120.6 121.6 120.3120.4164.8127.4127.9130.3132.8 133.2 127.3101.6Onion 100.2104.0122.1119.4 49.7 Pineapple 64.7 53.6 40.1 38.6 12.5 Potato 75.8 119.0 154.4136.3 33.7 Melon 71.9114.7 134.0 117.2 Watermelon 71.2 87.5 127.6 114.0 S-Pepper 96.9109.1 134.0 132.7 B-Pepper 82.5 102.6 138.1133.5100.2104.0122.1117.9

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170 Table F-7. Chongn-San Isidro, 90% efficiency Irrigation Requirement (mm) Chongn-San Isidro, 90% irrigation efficiency Month Jan Feb MarchAprilMay June July Aug Sept Oct Nov Dec Avocado 69.5 81.4 92.7 86.5 82.4 77.8 82.7 80.2 Asparagus 69.8 87.5100.1 67.3 Plantain 56.0 55.9 54.5 63.0110.8102.3107.1106.9 17.3 Citrus 78.3 78.2 76.4 73.2 92.8 66.3 65.1 65.7 68.4 71.3 73.463.9Grapes-T 44.8 44.7 43.6 41.8 54.7 60.9 81.4 85.4 89.4 93.4 90.550.3Mango 100.3 100.4 98.2 97.7134.2105.0107.1110.6114.1 114.8 109.586.6Onion 81.6 85.8102.2101.5 42.6 Pineapple 54.0 44.4 32.7 31.4 21.1 Potato 63.2 98.2 125.4110.6 27.4 Melon 61.0 98.5 115.5 100.7 Watermelon 60.4 75.5 110.0 98.0 S-Pepper 82.2 93.7 115.5 112.2 B-Pepper 68.9 84.8 112.7108.4 81.6 85.8102.2100.2 Table F-8. San Isidro-Playas, 90% efficiency Irrigation Requirement (mm) San Isidro-Playas, 90% irrigation efficiency Month Jan Feb MarchAprilMay June July Aug Sept Oct Nov Dec Avocado 85.5 100.7 115.2107.0 86.0 93.6 98.4 94.7 Asparagus 83.1103.3118.0 79.5 Plantain 68.8 69.1 67.7 78.0135.5123.1127.3126.2 20.4 Citrus 96.3 96.7 94.9 90.6113.4 79.8 77.4 77.5 64.7 84.3 87.1 76.1Grapes-T 55.0 55.3 54.2 51.8 66.8 73.2 96.8100.8105.4 110.3 107.4 59.9Mango 123.4 124.2 122.0120.9164.1126.4127.3130.5135.9 135.7 131.2103.2Onion 99.8103.1121.5119.7 50.2 Pineapple 66.4 54.8 40.6 38.8 23.4 Potato 77.8 121.2 155.8136.9 33.7 Melon 72.0116.1 136.5 119.5 Watermelon 71.3111.0 130.0 116.3 S-Pepper 97.1110.5 136.5 133.3 B-Pepper 84.7 104.9 139.9134.199.8 103.1121.5118.3

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171 Table F-9. Chongn-El Azcar, 90% efficiency Irrigation Requirement (mm) Chongn-El Azcar, 90% irrigation efficiency Month Jan Feb MarchAprilMay June July Aug Sept Oct Nov DecAvocado 70.5 83.4 96.2 90.2 85.6 79.9 83.6 79.9 Asparagus 70.7 87.2 98.6 67.7 Plantain 56.7 57.2 56.6 65.7115.1105.0108.2106.5 17.1 Citrus 79.4 80.0 79.2 76.3 96.4 68.1 65.8 65.4 67.4 70.0 72.363.5Grapes-T 45.4 45.7 45.3 43.6 56.8 62.4 82.3 85.1 88.1 91.4 89.149.7Mango 102.0 102.9 101.8101.8139.5107.8108.2110.3112.4 112.7 107.886.0Onion 84.8 88.0103.3101.1 42.1 Pineapple 54.7 45.4 33.9 32.7 10.6 Potato 64.1 100.7 130.7115.3 28.5 Melon 60.8 97.0 113.4 99.2 Watermelon 60.2 74.1 108.0 96.5 S-Pepper 82.0 92.3 113.4 112.3 B-Pepper 69.8 86.9 116.9113.0 84.8 88.0103.3 99.8

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APPENDIX G PROGRAM TO CALCULATE CROP IRRIGATION REQUIREMENT This Excel program uses the crop water requirements calculated using CROPWAT (FAO/UN) plus an application (irrigation) efficiency factor, to determine crop irrigation requirement. The program can calculate values for three zones: El Azcar-Chongn, Chongn-San Isidro, and San Isidro-Playas. In Figure G-1, the inputs for the program are entered into Table 1. The user inputs 1 (one) for crops that are considered in the calculations, and 0 (zero) for crops that are not planted. In this scenario asparagus, citrus, onion, melon, and watermelon. In the example presented here 70 % application efficiency was entered. The outputs for the Chongn-San Isidro zone are presented in the graph (Figure G-1) and in Table 2 (Figure G-2). These values are expressed in mm of water per month, and represent supplementary irrigation that has to be applied to the crop in each month. 172

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173 Figure G-1. Program to calcula te crop irrigation requireme nt, input table and graph Table 1 Chongn San Isidro Enter estimated in-field efficiency (percentage): 70 Select Crops Avocado 0 Asparagus 1 Plantain 0 Citrus 1 Grapes Table 0 Mango 0 Onion 1 Pineapple 0 Potato 0 Melon 1 Watermelon 1 Sweet Pepper 0 Black Pepper 0

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174 Table 2 Crop Irrigation Requirement (mm/period) at the given in-field irrigation efficiency Month Jan Feb March April May June July Aug Sept Oct Nov Dec Avocado 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Asparagus 0.0 0.0 0.0 0.0 0.0 0.0 98.2 122.0 139.4 93.9 0.0 0.0 Plantain 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Citrus 113.8 114.3 112.1 107.1 134.0 94.3 91.5 91.6 76.5 99.6 103.0 90.0 Grapes-T 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Mango 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Onion 0.0 0.0 0.0 0.0 117.9 121.9 143.6 141.5 59.3 0.0 0.0 0.0 Pineapple 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Potato 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Melon 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85.1 137.3 161.4 141.3 0.0 Watermelon 0.0 0.0 0.0 0.0 0.0 0.0 0.0 84.3 131.2 153.7 137.5 0.0 S-Pepper 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 B-Pepper 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Tota l 113.8 114.3 112.1 107.1 251.9 216.2 333.3 524.6 543.7 508.6 381.7 90.0 Figure G-2. Program to calculate crop irrigation requirement, results table

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LIST OF REFERENCES Aitchison, J. 1986. The Statistical Analysis of Compositional Data. Monographs on statistics and applied probability. Chapman and Hall. NY. Allen R.G., Pereira L.S., Raes D. & Smith M. 1998. Crop evapotranspiration: Guidelines for computing crop requirements. Irrigation and Drainage paper No. 56 FAO, Rome. Ashrat, M. Loftis, J.C. and Hubbard, K.G. 1995. Application of geostatistics to evaluate partial weather station networks. University of Nebraska Lincoln, NE. Atherton, P. 1997. The Essential Aloe Vera, The Actions And The Evidence 2nd Edition. Bandaragoda, D.J. 1999. Institutional Change and Shared Management of Water Resources in Large Canal Systems: Results of an Action Research Program in 175 Ecuador. Quito, Ecuador. Pakistan, Research Report 36. International Water Management Institute. Colombo, Pakistan. Boyham, G.E. 2001. Commercial Watermelon Production. USDAUniversity of Georgia. GA, http://www.ces.uga.edu/pubcd/B996-w.htm#Culture. October 2002. Breslin, P. 1999. Getting to Know ArcView GIS. ESRI. CA. Brouwer, C. and Prins, K. 1989. Irrigation Water Management: Irrigation Scheduling. FAO. Rome. Brouwer,C. 1988. Irrigation Water Management: Irrigation Methods. Training manual No. 5, FAO. Rome. Brouwer,C. and Heibloem, M. 1985. Irrigation Water Management, Training manual No. 3, FAO. Rome. Brouwer,C., Hoevenaars, J.P. van Bosch, B.E. 1992. Irrigation Water Management: Training Manual No. 6 Scheme Irrigation Water Needs and Supply. FAO. Rome. Burton, M.A., Kivumbi, D. and El-Askari, K. 1999. Opportunities and constraints to improving irrigation water management: Foci for research. Institute of Irrigation and Development Studies, University of Southampton, Southampton, UK. Caadas, L. 1983. El Mapa Bioclimtico y Ecolgico del Ecuador. Banco Central del

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176 Cantaluppi, C.J. and Precheur, R. J. 1993. Asparagus Production Management and Marketing. Bulletin 826. Ohio State University Extension. OH. CEDEGE. 2001. Comisin de Estudios pare el Desarrollo de la Cuenca del Ro Guayas. http://www.fgdc.gov/publications/documents/metadata/workbook_0501_bmk.pdf. Guayaquil, Ecuador. CEDEX. 1984. Plan Hidrulico Acueducto Santa Elena. Centro de Estudios Hidrogrficos. Centro de Estudio y Experimentacion de Obras Publicas. Madrid. CESUR. 1995. Plan de Desarrollo Regional Peninsula de Santa Elena. Desarrollo Rural Regional Integrado. Rehovot, Israel. Chahar B.R. 2000. Optimal design of channel sections considering seepage and evaporation losses. University of Roorkee. India. Chrisman, N.G. and McGranaghan, M. 2000. Accuracy of Spatial Databases. University of Washington-University of Hawaii. HI. Clarke, D., et al. 1998. CropWat for Windows : User Guide. University of SouthamptonUniversity of Southampton-FAO. Rome. CLIMWAT. 1995. A Climatic Database for CROPWAT. FAO Irrigation and Drainage Paper 49. FAO Water Service. Rome. Cornish G. 1998. Modern Irrigation Technologies for Smallholders in Developing Countries, SRP Exeter, www.farmlandinfo.org/cae/caepubs/delaney.html December 2002. Doorenbos, J., Pruitt, W.O., 1977. Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper 24. Rome. El Comercio. 2001. Santa Elena no rinde lo previsto. Year 96. No 35/98. Quito, Ecuador. ESPOL, CEDEGE. 2001. Investigacin de oportunidades agroindustriales en la Pennsula de Santa Elena con ventajas competitivas en los mercados internacionales y de los recursos necesarios para su implantacin. Guayaquil, Ecuador. FAO, 1993. AGROSTAT. PC, Computerized Information Series, FAO Publications Division. FAO, 1994. Water for Life. World Food Day 1994, Rome. FAO. 1996. World Food Summit, 13 to 17 November 1996. FAO. Rome. Federal Geographic Data Committee (FGDC). 2000. Content Standard for Digital Spatial Metadata. Retrieved March 12, 2003 from:

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177 Fruit Facts. 2001. California Rare Fruit Growers, Inc. CA, http://www.crfg.org/pubs/frtfacts.html October 2002. Gandin, L.S. 1970. The planning of meteorological station networks. WMO Tech Note, 1. NM. 111. Hall, A.W. 1999. Priorities for irrigated agriculture. Agricultural Water Management 40, 25-29. UK. Hashmi, M.A., Garcia, L.A., Fontane, D.G. 1994. Spatial estimation of regional evapotranspiration. Trans. Am. Soc. Agric. Engr. 38, 1345-1351. Hillel, D. 1997. Small-scale irrigation for arid zones, Principles and Options. FAO, Rome. Hochmuth, G.J. 2001. Tomato Production in Florida. EDIS. University of Florida. FL, http://edis.ifas.ufl.edu/BODY_CV137. September 2002. Hoevenaars, J., Brouwer, C., and Hatcho, N. 1992. Irrigation Water Management: Canals. FAO. Rome. Hudson, N. W. 1987. Soil and water conservation in semi-arid areas. FAO. Rome. Izuno F.T. and Haman, Z. D. 1987. Basic Irrigation Terminology. Document AE-66 EDIS. Cooperative Extension Service. University of Florida. FL. http://edis.ifas.ufl.edu. September 2002. Jensen, M.E. 1993. Consumptive use of water and irrigation water requirements. Irrig. Drainage Div. American Society of Civil Engineers. NY. Jensen, M.E., Burman, R.D. and Allen, R.G. 1990. Evapotranspiration and irrigation water requirements. Irrig. Drainage Div. American Society of Civil Engineers. NY. Johnston, K., Ver Hoef, L.M., Krivoruchjo, K., and Lucas, N. 2001. Using ArcGIS Geostatistical Analyst. ESRI. CA. Jones, W.I. 1995. The World Bank and Irrigation. A World Bank Operations Evaluation Study, World Bank, Washington, DC. Keller, A., Keller, J., and Seckler, D. 1996. Integrated water resource systems: theory and policy implications. Research Report 3. International Water Management Institute. Colombo, Sri Lanka. Korte, G. 2000. The GIS Book, fifth edition. OnWord Press. NY. Marcey L. Abate, Kathleen V., and Diegert, A. 1998. Hierarchical Approach to Improving Data Quality. Sandia National Laboratories. Data Quality J. Vol 4, Num

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178 Mayberry, K.S. 1996. Mixed Melon Production in California. University of California Cooperative Service. CA. McCoy, J., and Johnston, K. 2001. Using ArcGIS Spatial Analyst. ESRI. CA. Mitchel, A. 1999. The ESRI guide to GIS Analysis, Volume I: Geographic Patterns and Relationships. ESRI. CA. Monteith, J.L. 1965. Evaporation and environment. Symp. Soc. Exp. Biol. 19, 205-234. Mullen R. 1998. Asparagus Production in California. University of California Extension Service. CA. National Department of Agriculture. 2000. Cultivation of avocados. Institute for Tropical and Subtropical Crops. Pretoria, South Africa, www.nda.agric.za/publications. September 2002. Ormsby, T., et al. 2001. Getting to know ArcGIS Desktop: Basics of ArcView, ArcEditor, and ArcInfo. ESRI. CA. Orzolek, M. 2002. Cantaloupes. Pennsylvania Vegetable Growers Association. PA, http://agalternatives.aers.psu.edu/crops/cantaloupe/ October 2002. Penman, H.L. 1948. Natural evaporation from open water, bare soil, and grass. Proc. R. Soc. Ser. A 193, 108-120. Pereira L.S., Perrier A. & Allen R.G. 1999. Evapotranspiration: concepts and future trends. J.Irrigation and Drain. Engrg., ASCE 125, 45-51. Perry, C.J. 1995. Determinants of function and dysfunction in irrigation performance, and implications for performance improvement. Int. J. Water Resource Dev. 11, 25-38. Phillips, R. 1990. Commercial Production of Asparagus in New Mexico. NMSU. NM, http://www.cahe.nmsu.edu/pubs/_h/h-227.html. September 2002. Ray, S.S., and Dadhwal, V.K. 2001. Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS. ISRO. Ahmedabad, India. Rieger, M. 1990. Fruit Crops Encyclopedia. http://www.uga.edu/fruit/index.html. August 2002. Riofrio M., and Wittmeyer, E.C. 2000. Onions Fact Sheet. OSU Extension. OH. Roberts, W. 2002. Watermelon Production. OSU Extension facts. Oklahoma State University. OK, http://www.okstate.edu/OSU_Ag/agedcm4h/pearl/hort/vegetble/f-6236.pdf. October 2002. Sarmiento F. 1986. Desde la Selva hasta el Mar: Antologa Ecolgica del Ecuador. Casa de la Cultura Ecuatoriana. Quito, Ecuador.

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179 Schrader, W.L. 2002. Cucumber Production in California. University of California, Agriculture and Natural Resources Communication Services. CA. http://anrcatalog.ucdavis.edu/pdf/8050.pdf. October 2002. Smith, M. 2000. The application of climatic data for planning and management of sustainable rainfed and irrigated crop production. Land and Water Development Division. FAO. Rome. Smith, M., 1992. CROPWAT a computer program for irrigation planning and management. FAO Irrigation and Drainage Paper 26, Rome. Smith, M., Allen, R., Pereira, L., 1996. Revised FAO Methodologies for Crop Water requirements, In: Proceedings of the International Conference on Evapotranspiration and Irrigation Scheduling, November 1996, San Antonio, TX, ASCE, pp. 116. Stanhill, G. 2002. Is the Class A evaporation pan still the most practical and accurate meteorological method for determining irrigation water requirements? Agric. & Forst. Meteor. 112, 233-236. Stroosnijder L. 1987. Soil evapotranspiration: test of a practical approach under semi-arid conditions. Netherlands J. of Agricultural Science, 35: 417-426. Tomato Production Guide for Florida. 2002. EDIS. University of Florida. FL. http://edis.ifas.ufl.edu/MENU_CV:TOMATOGUIDE. August 2002. van der Goot, E. 1997. Technical description of interpolation and processing of meteorological data in CGMS. Netherlands. Vermillion, D.L. 1997. Impacts of Irrigation Management Transfer: A Review of the Evidence, Research Report No. 11. International Water Management Institute. Colombo. WCD. World Commission on Dams. 2002. http://www.dams.org. Wichelns, D. 2001. An economic perspective on the potential gains from improvements in irrigation water management. University of Rhode Island. RI. World Bank OED (Operations Evaluation Department). 1990. Annual Review of Evaluation Results: 1989. World Bank. Washington DC. World Bank OED (Operations Evaluation Department). 1996. The World Banks Experience with Large Dams: A Preliminary Review of Impacts. World Bank. Washington DC. Zalidis G., Dimitriadis X., Antonopoulos A. & Gerakis A. 1997. Estimation of a network irrigation efficiency to cope with reduced water supply. Aristotle University of Thessaloniki. Greece.

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BIOGRAPHICAL SKETCH Camilo Cornejo Dvila born in July 11, 1978, in Quito, Ecuador. He attended high school at Colegio Salesiano Sanchz y Cifuentes in Ibarra, Ecuador, graduating in 1996. He attended the Escuela Agricola Panamericana El Zamorano in Honduras, and later received his Agronomo degree in December 1999. He continued further studies at the University of Florida, College of Agriculture and Life Sciences, obtaining the Bachelor of Science degree in May 2001; and the Master of Science degree in agricultural and biological engineering in May 2003. 180



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4/16/2003 1:53 PM C:\Documents and Settings\kallay\ Desktop\Temp70-PDF\User Manual_ABEUFprog.doc Page 1 of 2 User Manual Note: This program was created based on the climatic information provided by CEDEGE from the weather stations lo cated in the Santa Elena Peninsula. The Evapotranspiration (ETo) valu es were calculated using the CROPWAT software from FAO-UN. This basic program is intended for a quick reference to calculate water requirements for the Santa Elena Penins ula (SEP). However, if available, real-time weather data should be used to calculate crop water requirement (CWR). To learn more about other me thods please check the Internet sites listed in the ‘Literature’ section of this manual. Instructions: Choose the ‘Enable Macros’ option when the program opens. There are only two inputs require d by this program (Table 1): o The first is to select the crops; to do this the number one (1) has to be entered in the column “Select Crops”. Zero (0) should be entered for the crops that are not being cu ltivated (de-select). o The other one is the in-field applicati on efficiency value, also entered as a percentage. Warning! – It is important to notice that the (i rrigation) application efficiency cannot exceed 100%. How to read the results: The output of the program is expressed in millimeters (mm) of water that must be applied to fulfill the ET demand of the crops. In ‘Table 2’ the monthly Crop Water Requi rement values for the entire year are presented for each crop at the assumed in-fie ld efficiency (depends in the input). These values were also calculated based on the areas entered in the input column. Graph, it shows the crop water requirement (Table 2) in a graphical format for the months of April and August. These mont hs are the ones with the highest CWR. However, for a specific combination of crops and areas other month can give the highest crop water requirement (CWR). Basic Definitions: Crop Area, the proportion of the area planted with the specified crop in the current cropping pattern.

PAGE 2

4/16/2003 1:53 PM C:\Documents and Settings\kallay\ Desktop\Temp70-PDF\User Manual_ABEUFprog.doc Page 2 of 2 Evapotranspiration (ETo) stands for reference crop evapotranspiration in millimeters per time step. Crop Water Requirement (CWR), calculated as ETo*CropKc. Irrigation efficiency refers to th e ‘in-field’ application efficiency. Literature: CROPWAT. FAO-UN. http://www.fao.org/landandwater/aglw/cropwat.stm Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. http://www.fao.org/docrep/X0490E/X0490E00.htm


Permanent Link: http://ufdc.ufl.edu/UFE0000667/00001

Material Information

Title: Use of an evapotranspiration model and a Geographic Information System (GIS) to estimate the trasvase system in the Santa Elena Peninsula, Guayas, Ecuador
Physical Description: xvi, 182 p.
Language: English
Creator: Cornejo, Camilo ( Dissertant )
Haman, Dorota Z. ( Thesis advisor )
Jordan, Jonathan D ( Thesis advisor )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2003
Copyright Date: 2003

Subjects

Subjects / Keywords: Agricultural and Biological Engineering thesis,M.S   ( local )
Dissertations, Academic -- UF -- Agricultural and Biological Engineering   ( local )

Notes

Abstract: Irrigated agriculture produces more than 40 percent of the world food supply, using 20 percent of the agricultural land in developing countries. Food production is important, especially in developing countries like Ecuador. The TRASVASE irrigation system was constructed to provide water for irrigation to the Santa Elena Peninsula in Ecuador. However, this project performs below expectations. One of the limitations is that the total area that this irrigation system could irrigate has not been determined. Available geographic, climatic, and soils and land use data were summarized for the Santa Elena Peninsula using a Geographic Information System. The total area that can be irrigated was calculated based on the evapotranspiration concept used by CROPWAT software from UN/FAO. Evapotranspiration is a sum of the water evaporation from the soil and plant surfaces, and transpiration from the plant leaves. Calculation of evapotranspiration uses weather parameters like air temperature, relative humidity, solar radiation, and wind speed. Taking into consideration the water used by the potable water treatment plants and the water loss thru evaporation and seepage from the canals and dams, total water available for irrigation can be calculated. This total available water divided by the crop water requirement gives the total area that the TRASVASE system could irrigate. To cover a wide range of possible variations in irrigation technology and crops planted in the area, nine scenarios were tested. The variables were three levels of in-field water application efficiency (50 percent, 70 percent, and 90 percent); and three levels of the crop water requirement (high, low, and a mixture of high and low). Results of this project show that with an in-field application efficiency of 90 percent and low-water-requirement crops, 15,506 hectares could be irrigated. However, with 50 percent application efficiency and high-water-requirement crops, the area is reduced to 7,700 hectares. It is obvious that very efficient irrigation technologies must be used in the Santa Elena Peninsula to optimize the use of water. Good management and maintenance of those irrigation systems are also needed. Agricultural production has to be planned to minimize water use and to increase the total area to be irrigated.
Subject: Ecuador, Elena, evapotranspiratio, GIS, irrigation, peninsula, Santa
General Note: Title from title page of source document.
General Note: Includes vita.
Thesis: Thesis (M.S.)--University of Florida, 2003.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0000667:00001

Permanent Link: http://ufdc.ufl.edu/UFE0000667/00001

Material Information

Title: Use of an evapotranspiration model and a Geographic Information System (GIS) to estimate the trasvase system in the Santa Elena Peninsula, Guayas, Ecuador
Physical Description: xvi, 182 p.
Language: English
Creator: Cornejo, Camilo ( Dissertant )
Haman, Dorota Z. ( Thesis advisor )
Jordan, Jonathan D ( Thesis advisor )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2003
Copyright Date: 2003

Subjects

Subjects / Keywords: Agricultural and Biological Engineering thesis,M.S   ( local )
Dissertations, Academic -- UF -- Agricultural and Biological Engineering   ( local )

Notes

Abstract: Irrigated agriculture produces more than 40 percent of the world food supply, using 20 percent of the agricultural land in developing countries. Food production is important, especially in developing countries like Ecuador. The TRASVASE irrigation system was constructed to provide water for irrigation to the Santa Elena Peninsula in Ecuador. However, this project performs below expectations. One of the limitations is that the total area that this irrigation system could irrigate has not been determined. Available geographic, climatic, and soils and land use data were summarized for the Santa Elena Peninsula using a Geographic Information System. The total area that can be irrigated was calculated based on the evapotranspiration concept used by CROPWAT software from UN/FAO. Evapotranspiration is a sum of the water evaporation from the soil and plant surfaces, and transpiration from the plant leaves. Calculation of evapotranspiration uses weather parameters like air temperature, relative humidity, solar radiation, and wind speed. Taking into consideration the water used by the potable water treatment plants and the water loss thru evaporation and seepage from the canals and dams, total water available for irrigation can be calculated. This total available water divided by the crop water requirement gives the total area that the TRASVASE system could irrigate. To cover a wide range of possible variations in irrigation technology and crops planted in the area, nine scenarios were tested. The variables were three levels of in-field water application efficiency (50 percent, 70 percent, and 90 percent); and three levels of the crop water requirement (high, low, and a mixture of high and low). Results of this project show that with an in-field application efficiency of 90 percent and low-water-requirement crops, 15,506 hectares could be irrigated. However, with 50 percent application efficiency and high-water-requirement crops, the area is reduced to 7,700 hectares. It is obvious that very efficient irrigation technologies must be used in the Santa Elena Peninsula to optimize the use of water. Good management and maintenance of those irrigation systems are also needed. Agricultural production has to be planned to minimize water use and to increase the total area to be irrigated.
Subject: Ecuador, Elena, evapotranspiratio, GIS, irrigation, peninsula, Santa
General Note: Title from title page of source document.
General Note: Includes vita.
Thesis: Thesis (M.S.)--University of Florida, 2003.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0000667:00001


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USE OF AN EVAPOTRANSPIRATION MODEL AND A GEOGRAPHIC
INFORMATION SYSTEM (GIS) TO ESTIMATE THE
IRRIGATION POTENTIAL OF THE TRASVASE SYSTEM
IN THE SANTA ELENA PENINSULA,
GUAYAS, ECUADOR















By

CAMILO CORNEJO


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2003


































To my Family















ACKNOWLEDGMENTS

This thesis work would not have been completed without the help of several people

whom I wish to thank. First, I thank my advisor Dr. Dorota Z. Haman for all her help and

support, interest, knowledge, problem solving and advice. Thanks go to my supervisory

committee, whose comments and edits contribute substantially to my research and to this

document. I would also like to thank to thank to all the people from ESPOL and

CEDEGE in Guayaquil, Ecuador for helping me whenever I needed information for my

research. Special thanks go to my friends, who always help me when needed. Finally, I

would like to thank the very special people in my life, my girlfriend and my family, for

their support.















TABLE OF CONTENTS


A C K N O W L E D G M E N T S ................................................................................................. iii

LIST OF TABLES .................. .................. ................. ............ .............. .. vii

LIST OF FIGURES ............................... ... ...... ... ................. .x

LIST OF OBJECTS .............. ............................................... ....... xiii

LIST OF A BBREV IA TION S ....................................................................... xiv

A B S T R A C T .......................................... ..................................................x v

CHAPTER

1 L ITER A TU R E R E V IEW .............................................................. .. ....... ..............

Significance of Irrigation in A agriculture ................................................. ..................
R reference Evapotranspiration................................................. .......................... 3
Use of FAO Penman-Monteith to Estimate Reference Evapotranspiration...............4
Actual Crop Evapotranspiration ...........................................................................5
Computerized Crop Water Use Simulations ........................ .........................5
Irrigation E efficiency ......... ................................................................ ......... .... .7
Irrigation T echniqu es .............. ... .............. ................................ .................10
Application of GIS to Irrigation Management........... .......................11
G IS D ata Q u ality A naly sis ........................................... ........................................ 12
Perceptions about Irrigation........................................ .................... ............... 15

2 INTRODUCTION AND PROJECT AREA REVIEW............................................17

In tro d u ctio n ........................................................................................................... 1 7
Irrig ate d A re a ........................................................................................................ 1 7
A agriculture and Irrigation ........................................................................... 19
O n-F arm T technologies .................................................................... .....................20
P o licy .............. ...... ..................................................... ..... 2 0
A ctual Situation and Projections ........................................... ......................... 21
Characteristics of the Santa Elena Peninsula...........................................................23
M meteorological D ata .................................. .. ......... .... ...............27
Clim atic Classifications .................. .............................. .. ..... .. ........ .... 33
S o ils .................................................................................3 6









3 WEATHER DATA ANALYSIS FOR CROPWAT MODEL .................................39

W weather Stations D distribution .......................................................... .....................40
Estim ating M missing Clim atic Data ........... ....... .. .................................. ... .......... 41
Estimating Weather Data Sets for the Santa Elena Peninsula............................... 45

4 GEOGRAPHIC INFORMATION SISTEM ................................... .................52

In tro d u ctio n ........................................................................................................... 5 2
M ap p in g Sy stem s........... ..... ......................................................................... .. ....... .. 54
ArcGIS ......... ......... ........................................... ...... ...... ............... 55
Original M aps ................................................. ... ................56
Data Quality Problems with the Santa Elena Peninsula Data Set ...........................59
GIS Layers Created or Edited for the Project from the Original Maps .......... ......65
Creation of Evapotranspiration Surface M aps................................. ............... 71

5 WATER AVAILABILITY AND ITS USE IN THE SANTA ELENA
P E N IN S U L A ........................................................................................................ 7 4

Infrastructure ......... ...... ......................................................................74
TR A SV A SE Santa Elena .................. ............. ........... ........ .... ....................... 74
Water Loss from the Canals and Dams to Evaporation................. ...............79
Irrigation Technology used in the Santa Elena Peninsula .......................................82
W ater Consumption .......................... ............................ .. 83
Reference Evapotranspiration Surface Maps............................................................84
Agricultural Production in the Santa Elena Peninsula....................................91

6 M E T H O D O L O G Y ............................................................................ ................... 94

Evapotranspiration .................. ................................. ......... ............... 94
O pen W ater Evaporation .................................................. .............................. 95
Crop W ater R equirem ent........................................................................... 96
C rop Irrigation R equirem ent............................................................ .....................99
S c e n a rio s ....................................................... ................ 9 9

7 RESULTS AND DISCU SSION ......................................... ..........................103

Conclusion ..................................... .................................. ......... 113
Suggestions for Future W ork .............................................................................. 114

APPENDIX

A M A P S ..........................................................................................1 1 6

B AVERAGE W EATHER DATA.................................................... ....... ........ 124

C CROPWAT REFERENCE EVAPOTRANSPIRATION TABLES.......................130



v









D ECOCROP SELECTION CRITERIA TABLES..........................................137

E T R O P IC A L C R O P S ...................................................................... ..................... 14 8

F IRRIGA TION REQU IREM EN TS ................................................ .....................167

G PROGRAM TO CALCULATE CROP IRRIGATION REQUIREMENT..............172

L IST O F R E F E R E N C E S ......... .................................... .............................................. 175

BIOGRAPH ICAL SKETCH .............. ......................... ................... ............... 180
















LIST OF TABLES

Table page

1-1 C onveyance efficiency (Ec) ............................................... ............................ 8

1-2 Field application efficiency (E a) ..................................................................... .... 8

2-1 M inor w atersheds ................. ......... ........................ ...... ........... .. ............ ..26

2-2 Basins that start in the Coastal Mountain Range.................... ........................... 26

2 -3 C lim ate ty p es ....................................................................... 34

2-4 Koppen climate classification for the SEP .......... .................. ..... ...........35

2 -5 S o ils ............................................................................ .3 6

3-1 Distances among stations (m) and elevation (mmsl) ............................................40

3-2 Regression analysis m ethod .............................................................................. 48

3-3 C creating new values ........................... .......................... .. ........ .... ...... ...... 49

4-1 Comparison of interpolation methods....................................... ......................... 69

5-1 Main dams ......... ......... ....... ............................77

5-2 Approximated surface areas of the canals..................................... ............... 80

5-3 Approximated surface areas of the dams ...................................... ............... 80

5-4 C anal description ....... .................................................................... ............... 8 1

5-5 Chong6n-Daular-Cerecita pressurized system, Zone I (2001) .................................82

5-6 Chong6n-Cerecita-Playas canal, Zone I (2001) .....................................................82

5-7 El Azucar-Rio Verde canal, Zone II (2001).........................................................83

5-8 C rop grow ing period ........................................................................ .................. 85

5-9 C rop coefficients ............................................ ... .... ........ ......... 89









5-10 Crops planted and projected increase in the Santa Elena Peninsula........................93

6-1 Chong6n-San Isidro, Zone I, crop water requirements (CWR) ...........................97

6-2 San Isidro-Playas, Zone I, crop water requirements (CWR)..............................97

6-3 Chong6n-El Azucar, Zone II, crop water requirements (CWR) ...........................98

7-1 Scenario A, Zone II ................ ............ ....... .... ........ ............ 104

7-2 Scenario A Zone I ................................................... .............. 105

7-3 Total area that can be irrigated under different scenarios .....................................106

7-4 Areas that could be irrigated during dry season in the SEP, Scenario A..............09

7-5 Areas covered by different buffers of the canals in the SEP...............................111

7-6 Comparison of areas that could be irrigated according different sources .............111

B-l Available w weather data sets ....................... .. .............................. ............... 124

B-2 Chong6n weather station................................ ........ ................... 125

B-3 Playas w weather station .................. ......................... ...............126

B -4 E l A zucar w weather station ........................................................... .....................127

B -5 San Isidro w weather station .............................................. ............................ 128

B -6 Suspiro w weather station ................................................ .............................. 129

C-l Reference evapotranspiration Chong6n ...................................... ............... 130

C-2 Reference evapotranspiration El Azucar................................................. 131

C-3 Reference evapotranspiration Playas ........................................ ............... 132

C-4 Reference evapotranspiration San Isidro..................................... ............... 133

C-5 Reference evapotranspiration Suspiro......................................... ............... 134

C-6 Open water evaporation values per canal............ ...... ...... ....... .........136

C-7 Open water evaporation from dam s ............................................ ............... 136

F-l Chong6n-San Isidro, 50% efficiency ........................................ ............... 167

F-2 San Isidro-Playas, 50% efficiency .............................................. ............... 167









F-3 Chong6n-El Az6car, 50% efficiency ........................................ ............... 168

F-4 Chong6n San Isidro, 70% efficiency ....................................... ............... 168

F-5 San Isidro-Playas, 70% efficiency .............................................. ............... 169

F-6 Chong6n-El Az6car, 70% efficiency ........................................ ............... 169

F-7 Chong6n-San Isidro, 90% efficiency ........................................ ............... 170

F-8 San Isidro-Playas, 90% efficiency .............................................. ............... 170

F-9 Chong6n-El Az6car, 90% efficiency ........................................ ............... 171
















LIST OF FIGURES

Figure page

1-1 D ual crop coefficient curve ...................................................................... 6

2-1 Canal in construction, TRASVASE Santa Elena..................................................22

2-2 Landscape of the Santa Elena Peninsula............................................................... 24

2-3 Location of the Santa Elena Peninsula ...................................... ...............25

2-4 Javita River, an interim ittent river at SEP............................................................ 25

2-5 Historical average precipitation in the Santa Elena Peninsula..............................31

2-7 Papadakis climate classification.................... ....... ............................ 34

3-1 W weather stations ..................................... .. .. .. ...... .. ............41

3-2 Chong6n vs. El A zucar...................... ....................................... ............... 51

3-3 C hong6n vs. E l Suspiro ......................................... .. .. .................. ............... 51

3-4 C hong6n vs. Playas ........................................ ................. .... .. .....51

4-1 Soil types on Santa Elena Peninsula, original map ...............................................57

4-2 Koppen climate classification of Santa Elena Peninsula ......................................57

4-3 Dams location on Santa Elena Peninsula............................................................58

4-4 C anals and other features .............................................................. .....................59

4-5 Errors in the hydrology m aps of the SEP.............................................................. 61

4 -6 O v erlap error ...................................................... ................ 6 3

4-7 Main soil types layer created for the Santa Elena Peninsula............... ................ 65

4 -8 E ecological zon es............ ... ......................................................................... .. ...... .. 66

4 -9 C a n a ls ..................................................................



x









4-10 A actual farm locations ....................................................................... ..................68

4-11 Surface maps of weather data for January..................................... ............... 72

5-1 D aule-Peripa D am ............................... ......... ...... .. .. ................ 74

5-2 Hydroelectric plant, 'Proyecto de Prop6sito Multiple Jaime Rold6s Aguilera' ......75

5-3 Chong6n D am ......................... ........ .. .. ........ .. ............. 76

5-4 D aule pum ping station ..................................................................... ..................76

5-5 Z one II potabilization plant......................................................................... ... ... 78

5 -6 C a n a l ................................ .......................................... ................ 7 8

5-7 Canal San Rafael, TRASVASE project ....................... ...................................79

5-8 Trapezoidal canal ......................... ......... .... .. ..... ........... ... 81

5-9.1 Average reference evapotranspiration for the SEP I...........................................86

5-9.2 Average reference evapotranspiration for the SEP II..........................................87

5-10 Agricultural Production in the Santa Elena Peninsula .........................................92

6-1 Evaporation from canals of the TRASVASE system ..........................................96

6-2 Irrigation zones in the Santa Elena Peninsula..................................................... 100

7-1 Buffers from the canals in the Santa Elena Peninsula...................................110

A-i Maximum annual precipitation isohyets ............................. ...................116

A-2 Minimum annual precipitation isohyets...................................... ............ 117

A-3 Average annual precipitation isohyets ........................................ ...... ............. 118

A-4 Complete map of soils in the Santa Elena Peninsula ....................................119

A -5 Santa Elena farm s ........ ........ ...... .. ......... ......... ...... .. .. ........ .... 120

A -6 C hong6n farm s ............................................ .. .. .... ................. 121

A-7 Cerecita farms ................................. ............... .. ............122

A -8 A zucar-Rio V erde farm s ............................................... ............................. 123

C-1 Surface maps used to create a reference evapotranspiration map ..........................135









E-1 Aloe plantation, Santa Elena Peninsula....... ............ ....... ...... .................. 148

E-2 Plantain in the Santa Elena Peninsula .................. .. ........... .... .............. 152

E-3 G rapes ............................................ ... ........ ................. 154

E -4 M an g o s ............... .................................. .......................................15 5

E-5 Onion plantation and onions, Santa Elena Peninsula.......................................157

E-6 Papaya plantation in the Santa Elena Peninsula .................... .........................158

G-1 Program to calculate crop irrigation requirement, input table and graph..............173

G-2 Program to calculate crop irrigation requirement, results table ...........................174
















LIST OF OBJECTS


Objects

1. Program to calculate water requirement in the Santa Elena Peninsula

2. PDF version of the user manual for the water requirement program

3. Microsoft Word 2000 version of the user manual for the water requirement
program















LIST OF ABBREVIATIONS

CEDEGE Commission for the Development of the Guayas River Basin, Ecuador

http://www.cedege.gov.ec

CWR Crop Water Requirement

CIR Crop Irrigation Requirement

ESPOL Polytechnic School of the Littoral, Ecuador http://www.espol.edu.ec

ETo Reference evapotranspiration

ETc Actual evapotranspiration

FAO Food and Agricultural Organization http://www.fao.org

FGDC Federal Geographic Data Committee http://www.fgdc.gov

GIS Geographic Information System

GPS Global Positioning System

IDW Inverse Distance Weighted

IGM Geographic Military Institute, Ecuador http://www.igm.gov.ec

ISO International Organization for Standardization

Kc Crop coefficient

SEP Santa Elena Peninsula

USDA United States Department of Agriculture http://www.usda.gov

WCD World Commission on Dams http://www.dams.org/
















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

USE OF AN EVAPOTRANSPIRATION MODEL AND A GEOGRAPHIC
INFORMATION SYSTEM (GIS) TO ESTIMATE THE
IRRIGATION POTENTIAL OF THE TRASVASE SYSTEM
IN THE SANTA ELENA PENINSULA,
GUAYAS, ECUADOR

By

Camilo Corejo

May 2003

Chair: Dorota Z. Haman
Cochair: Jonathan D. Jordan
Major Department: Agricultural and Biological Engineering

Irrigated agriculture produces more than 40 % of the world food supply, using

20 % of the agricultural land in developing countries. Food production is important,

especially in developing countries like Ecuador. The TRASVASE irrigation system was

constructed to provide water for irrigation to the Santa Elena Peninsula in Ecuador.

However, this project performs below expectations. One of the limitations is that the total

area that this irrigation system could irrigate has not been determined. Available

geographic, climatic, and soils and land use data were summarized for the Santa Elena

Peninsula using a Geographic Information System. The total area that can be irrigated

was calculated based on the evapotranspiration concept used by CROPWAT software

from UN/FAO. Evapotranspiration is a sum of the water evaporation from the soil and

plant surfaces, and transpiration from the plant leaves.









Calculation of evapotranspiration uses weather parameters like air temperature,

relative humidity, solar radiation, and wind speed. Taking into consideration the water

used by the potable water treatment plants and the water loss thru evaporation and

seepage from the canals and dams, total water available for irrigation can be calculated.

This total available water divided by the crop water requirement gives the total area that

the TRASVASE system could irrigate. To cover a wide range of possible variations in

irrigation technology and crops planted in the area, nine scenarios were tested. The

variables were three levels of in-field water application efficiency (50 %, 70 %, and

90 %); and three levels of the crop water requirement (high, low, and a mixture of high

and low).

Results of this project show that with an in-field application efficiency of 90 % and

low-water-requirement crops, 15,506 hectares could be irrigated. However, with 50 %

application efficiency and high-water-requirement crops, the area is reduced to 7,700

hectares.

It is obvious that very efficient irrigation technologies must be used in the Santa

Elena Peninsula to optimize the use of water. Good management and maintenance of

those irrigation systems are also needed. Agricultural production has to be planned to

minimize water use and to increase the total area to be irrigated.














CHAPTER 1
LITERATURE REVIEW

Significance of Irrigation in Agriculture

Irrigation is a process that uses more than two-thirds of the Earth's renewable water

resources and feeds one-third of the Earth's population (Stanhill 2002). Some 2.4 billion

people depend directly on irrigated agriculture for food and employment. Irrigated

agriculture thus plays an essential role in meeting the basic needs of billions of people in

developing countries (FAO 1996). Although water resources are still ample on a global

scale, serious water shortages are developing in the arid and semi-arid regions (Hall

1999).

There is a need to focus attention on the growing problem of water scarcity in

relation to food production. The World Food Summit of November 1996, drew attention

to the importance of water as a vital resource for future development (FAO 1996). A

major part of the developed global water resources is used for food production. The

estimated minimum water requirement per capital is 1,200 m3 annually (50m3 for

domestic use and 1,150 m3 for food production) (FAO 1996).

Sustainable food production depends on judicious use of water resources as fresh

water for human consumption and agriculture become increasingly scarce. To meet future

food demands and growing competition for clean water, a more effective use of water in

both irrigated and rainfed agriculture will be essential (Smith 2000). Options to increase

water-use efficiency include harvesting rainfall, reducing irrigation water losses, and

adopting cultural practices that increase production per unit of water.









Irrigation is an obvious option to increase and stabilize crop production. Major

investments have been made in irrigation over the past 30 years by diverting surface

water and extracting groundwater. The irrigated areas in the world have, over a period of

30 years, increased by 25 % (mainly during a period of accelerated growth in the 1970s

and early 1980s) (FAO 1993).

A major constraint to the understanding of the use of water is the difficulty

associated with its measurement and quantification. Measurement and data collection of

discharge in canals is difficult and fraught with potential errors.

Necessary conditions for the optimal performance of regional water delivery

systems include well-defined water rights; infrastructure capable of providing the service

embodied in the water rights, and assigned responsibilities for all aspects of system

operation (Perry 1995). One or more of those conditions may be missing in some regional

systems at the start of irrigation deliveries. In other systems problems may develop over

time with changes in land ownership, cropping patterns, and the volume of water

available for delivery in the system. Problems with cost recovery and inadequate

maintenance also can reduce the efficiency of regional water-delivery systems.

Water use for crop production is depending on the interaction of climatic

parameters that determine crop evapotranspiration and water supply from rain (Smith

2000). Compilation, processing, and analysis of meteorological information for crop

water use and crop production are therefore key elements in developing strategies to

optimize the use of water for crop production and to introduce effective

water-management practices. Estimating crop water use from climatic data is essential to,

better water-use efficiency.









Because most of the Earth's irrigated land is in the underdeveloped world (where

food, water, and skilled manpower are in short supply), it is important to use the simplest,

cheapest, and most practical meteorological method to improve crop water-use efficiency

in irrigation. Stanhill (2002) says that in these regions use of standard, correctly sited and

maintained evaporation pans operating within a national network can provide the basis

for a scheduling method in which the use of empirical crop coefficients is accepted.

These coefficients reflect the local economic as well as agronomic, climatologic and

hydrological (water quality) situation (Stanhill 2002). However, the literature often

contradicts. Hillel (1997) said: "the use of 'evaporation pans' has several shortcomings."

Smith (2000) stated that agro-meteorology would play a key role in the looming

global water crisis. Appropriate strategies and policies need to be defined, including

strengthening of national use of climatic data for planning and managing of sustainable

agriculture and for drought mitigation.

The limitations of currently available methods for measuring rates of evaporation

from natural and agricultural surfaces are well known; as is the resulting lack of

information (local and global) on this major element in the hydrological cycle. A

practical method (suitable for routine use in meteorological station networks) is to use

calculations based on other meteorological measurements, like those used by the

Penman-Monteith method (Stanhill 2002).

Reference Evapotranspiration

Several definitions of reference evapotranspiration ETo have been formulated.

Jensen (1993) defined ETo as the rate at which water, if available, would be removed

from the soil and plant surface. Pereira et al. (1999) stated that Duke simplified the

definition of ETo to "the water used by a well-watered reference crop, such alfalfa, which









fully covers the soil surface." The modified Penman combination equation is used to

compute ETo, as it is considered to be a satisfactory estimation equation when daily

estimates of ETo are desired (Jensen et al. 1990).

Use of FAO Penman-Monteith to Estimate Reference Evapotranspiration

This approach was introduced by Penman in 1948 to estimate open-water

evaporation (Penman 1948); and extended by Monteith in 1965 to directly estimate

evaporation from vegetation-covered surfaces (Monteith 1965). It is now the

recommended method by the FAO to calculate reference crop evapotranspiration (Allen

et al. 1998).

Studies showed the superior performance of the Penman-Monteith approach, in

both arid and humid climates, and convincingly confirmed the sound underlying concepts

of the method. Based on these findings, the method was recommended by the FAO Panel

of Experts (convened in 1990) for adoption as a new standard for reference crop

evapotranspiration estimates (Hall 1999).

The use of the Penman-Monteith equation in irrigation practice requires empirical

coefficients to modify-in general to reduce but sometimes to increase-the estimates of

reference crop evapotranspiration (Stanhill 2002).

Use of FAO Penman-Monteith with limited climatic data. The limited

availability of the full range of climatic data (particularly data on sunshine, humidity and

wind) has often prevented the use of the combination methods and resulted in the use of

empirical methods (which require only temperature, pan, or radiation data). This has

contributed to the confusing use of different ETo methods and conflicting

evapotranspiration values. To overcome this constraint and to further use of a single

method, additional studies have been undertaken to provide recommendations on the









using FAO Penman-Monteith when no humidity, radiation or wind data are available. As

a result, procedures are presented to estimate humidity and radiation from

maximum/minimum temperature data and to adopt global estimates for wind speed. The

availability of worldwide climatic databases further facilitates the adoption of values

from nearby stations. Such procedures have proven to perform better than any of the

alternative empirical formulas; and will largely improve transparency of calculated

evapotranspiration values (Smith et al. 1996).

Actual Crop Evapotranspiration

Procedures for estimating crop evapotranspiration have been well established by

Doorenbos and Pruitt (1977), using a series of recommended crop coefficient values (K,)

to determine ETcrop (ETc) from reference evapotranspiration (ETo), as follows:

ETc = KcETo (1-1)

This formula represents the single crop coefficient. Crop evapotranspiration (ETc)

refers to evapotranspiration of a disease-free crop, grown in very large fields, not short of

water and fertilizer. Estimation of ETc is essential for computing the soil water balance

and irrigation scheduling. ETc is governed by weather and crop condition (Smith, 2000).

The specific wetting (irrigation) events are taken into account (spikes in Figure 1-1).

Computerized Crop Water Use Simulations

Practical procedures and criteria need to be defined to enhance the introduction and

application of effective water use practices for crop production. The introduction of

computerized procedures linked to digital databases and geographic information systems

(GIS) will greatly enhance the use of appropriate planning and management techniques

for water use in irrigated and rainfed agriculture. Computerized procedures greatly

facilitate the estimation of crop water requirements from climatic data and allow









the development of standardized information and criteria for planning and management

of rainfed and irrigated agriculture.



OA K id





0.6
S 1Kc ini
z *,Sc end
0.2

Stime days)
i I
Initial -4 crop development anid-seabsn ile season

Figure 1-1. Dual crop coefficient curve

Figure 1-1 shows the crop coefficient divided in different stages according to crop

development.

The FAO-CROPWAT program (Smith 1992) incorporates procedures for reference

crop evapotranspiration and crop water requirements and allows the simulation of crop

water use under various climate (CLIMWAT 1994), crop and soil conditions.

As a decision support system CROPWAT's main functions include: (1) the

calculation of reference evapotranspiration according to the FAO Penman-Monteith

method; (2) crop water requirements using revised crop coefficients (FAO Paper 56,

compared to the data from FAO Paper 49) and crop growth periods; (3) effective rainfall

and irrigation requirements; (4) scheme irrigation water supply for a given cropping

pattern; (5) daily water balance computations (Smith 1992).









Irrigation Efficiency

Classical overall irrigation system efficiency (Eo) is defined as the volume of water

used beneficially (net crop evapotranspiration) divided by the volume of water diverted

(Keller et al. 1996)

Keller et al. (1996) defines effective efficiency (EE) as the ratio of net crop

evapotranspiration divided by the net volume of water delivered to a field (Vs). The

volume of water that becomes usable surface runoff or deep percolation is subtracted

from the total volume delivered when calculating the denominator ratio.

Irrigation efficiency has a tremendous impact on agricultural water demands.

Understanding how irrigation efficiency fits into estimation of water requirements is

essential. Zadalis, et al (1997) consider the effective rainfall in their definition of

efficiency. The mean irrigation efficiency for each system is defined by the ratio of the

net volume actually used by the crops and the volume released at the head of the main

canal:

EE = (ET,- Re)/Vs (1-2)

where ET, is the estimated water used by crops, Re is the effective rainfall, and Vs,

is the volume of water delivered to each network or canal (Zalidis et al. 1997).

The most common way to express the efficiency of irrigation systems is to

subdivide it into conveyance and application efficiencies.

* The conveyance efficiency (Ec), which represents the efficiency of water transport
in canals or pipes in the field.

* The field application efficiency (Ea), which represents the efficiency of water
application in the field.

The conveyance efficiency (Ec) mainly depends on the length of the canals, the soil

type or permeability of the canal banks and the condition of the canals (Brouwer & Prins









1989). In large irrigation schemes more water is lost than in small schemes, due to a

longer canal system. When water is conveyed in pipes, Ec mainly depends on pipe

leakage and is usually close to 100 % for new systems.

Table 1-1 provides some indicative values of the conveyance efficiency (Ec),

considering the length of the canals and the soil type in which the canals are dug. The

level of maintenance is not taken into consideration: bad maintenance may lower the

values of Table 1-1, by as much as 50 % (Brouwer & Prins 1989).

Table 1-1. Conveyance efficiency (Ec)
Percent Efficiency (%) of conveyance
(canal length in meters) Earthen canals Lined canals
Sand Loam Clay
Long (> 2000) 60 70 80 95
Medium (200-2000) 70 75 85 95
Short (< 200) 80 85 90 95


The field application efficiency (Ea) mainly depends on the irrigation method and

the level of farmer discipline. Some indicative values of the average field application

efficiency (Ea) are given in Table 1-2. Lack of discipline may lower the values found in

Table 1-2 (Brouwer & Prins 1989).

Table 1-2. Field application efficiency (Ea)
Irrigation methods Application efficiency (%)
Surface irrigation (border, furrow, basin) 50-60
Sprinkler irrigation 60-80
Drip irrigation 80-up


Once the conveyance and field application efficiency have been determined, the

scheme irrigation efficiency (E) can be calculated, using the following formula (Brouwer

& Prins 1989):

Ec x Ea
E = (1-3)
100









with
E = scheme irrigation efficiency (%)
Ec = conveyance efficiency (%)
Ea = field application efficiency (%)

According to FAO a scheme irrigation efficiency of 50-60 % is good; 40 % is

reasonable, while a scheme Irrigation efficiency of 20-30 % is poor. It should be kept in

mind that the values mentioned above are only indicative values (Brouwer & Prins 1989).

Water productivity increases with improvements in agronomic practices and in

water supply and management, both regionally and at the farm level. Water supply

reliability also is important, as optimal investments in seeds, fertilizer, and land

preparation are less likely to be made when the timing of farm level water deliveries is

uncertain (Brouwer 1988).

Improving agricultural water efficiency is particularly important for improving the

productivity of large irrigation schemes. The recent promotion of participatory irrigation

management or turnover needs to be supported by other measures such as technological

innovations, for example, the development of effective water metering of canal systems

to enable cost recovery measures to be introduced (Brouwer 1988).

Under irrigated conditions, priorities need to be set for reducing losses of irrigation

water and for increasing effectiveness of irrigation management. Considerable amounts

of water diverted for irrigation are not effectively used for crop production. It is estimated

that, on average, only 45 % is used by the crop, with an estimated 15 % lost in the water

conveyance system, 15 % in the field channels and at least 25 % in inefficient field

applications (FAO 1994). This number depends on the type of irrigation system. For

example, in Arizona, farmers have increased irrigation efficiency from 50-60 % in the

1980s to 95 % in 1995 by adopting sub-surface drip methods. This change in technology









results in other benefits such as reduced power consumption, reduced fertilizer and

herbicide use and higher yields (Wichelns 2001).

Irrigation Techniques

An adequate water supply is important for plant growth. When rainfall is not

sufficient, the plants must receive additional water from irrigation. Various methods can

be used to supply irrigation water to the plants. Whatever irrigation method is being

chosen, its purpose is always to attain a better crop and a higher yield.

Surface irrigation. Surface irrigation is the application of water by gravity flow to

the surface of the field. Either the entire field is flooded (basin irrigation) or the water is

fed into small channels (furrows) or strips of land (borders).

Sprinkler irrigation. Sprinkler irrigation is similar to natural rainfall. Water is

pumped through a pipe system and then sprayed onto the crops through rotating sprinkler

heads (Izuno & Haman 1987).

Micro irrigation. Consists of drip irrigation and micro-sprinkler systems.

Drip irrigation. With drip irrigation, water is conveyed under pressure through a

pipe system to the fields, where it drips slowly onto the soil through emitters or drippers

that are located close to the plants. Only the immediate root zone of each plant is wetted.

Therefore this can be a very efficient method of irrigation. Drip irrigation is sometimes

called trickle irrigation (Izuno and Haman 1987).

Microsprinkler. Also known as micro-spray, is an irrigation method that falls into

the trickle category, characterized by the application of water to the soil surface as a

small spray or mist. Discharge rates are generally less than 30 gal/hr (Izuno and Haman

1987).









Application of GIS to Irrigation Management

GIS have potentially considerable application to irrigation water management,

especially in regions where there are poorly defined procedures for irrigation water

management data collection, processing and analysis. The possibility of using GIS to

identify crop areas, plan irrigation schedules and quantify performance offer exciting

possibilities for research (Ray and Dadhwal 2001).

The tools necessary to create a good GIS in irrigation are the availability of weather

data and how it is spatially distributed over the study area. Also important are the

techniques to be used to interpolate the climatic data, evapotranspiration, and other

calculated variables.

The availability of weather data of acceptable spatial resolution for large-scale

irrigation scheduling is an important factor to consider in planning the development and

management of irrigation information systems throughout the world (Hashmi et al. 1994).

The spatial distribution of the available weather data is important. It is of special

concern in developing countries where the availability of weather stations is limited. The

recommended maximum distance between points (weather stations) for least dense

networks is 150-200 km, for the intermediate network, 50-60 km for the densest

network, 30km (Gandin 1970). Once the data is collected and analyzed using statistics, a

surface map can be created using GIS.

There are many interpolation methods; however, inverse-square-distance

interpolation technique appears to be the most accurate method of interpolation

irrespective of number of data points. Hashmi et al. (1994) has also used the inverse-

square-distance approach to interpolate ET values.









GIS Data Quality Analysis

An important step when working with GIS is data quality analysis. The

International Organization for Standardization supplies an acceptable definition of data

quality using accepted terminology from the quality field. The International Organization

for Standardization (ISO) is a federation of national standards bodies. ISO's working

groups from most of the world's nations forge international agreements, which are

published as International Standards. These standards are documented agreements

containing technical specifications or other precise criteria to be used consistently as

rules, guidelines, or definitions of characteristics, to ensure that materials, products,

processes and services are fit for their purpose. Like other ISO standards, ISO quality

standards are frequently updated to reflect advances in quality methodology.

Among the many ISO standards is ISO 8402: Quality Management and Quality

Assurance Vocabulary. ISO 8402 provides a formal definition of quality as: "The totality

of characteristics of an entity that bear on its ability to satisfy stated and implied needs"

(Marcey, et al. 1998). Thus, data can be defined to be of the required quality if it satisfies

the requirements stated in a particular specification and the specification reflects the

implied needs of the user. Therefore, an acceptable level of quality has been achieved if

the data conforms to a defined specification and the specification correctly reflects the

intended use.

Structured analysis of these characteristics, together with careful planning, should

provide a data quality assessment that reveals key data quality problems, root causes for

the problems, and solutions for improving both conformance and utility.









Data Quality Attributes

A set of characteristics, or data quality attributes, is required for the objective and

measurable assessment of data quality. Commonly used attributes to measure data quality

include accuracy, completeness, consistency, reliability, timeliness, uniqueness, and

validity (Chrisman, & McGranaghan 2000).

* Among other technical issues in GIS, accuracy is perhaps the most important, it
covers concerns for data quality, error, uncertainty, scale, resolution and precision
in spatial data and affects the ways in which it can be used and interpreted

* All spatial data is inaccurate to some degree but it is generally represented in the
computer to high precision

Data Quality Components

Recently a National Standard for Digital Cartographic Data (http://www.fgdc.gov/)

was developed by a coordinated national effort in the U.S. (Chrisman, & McGranaghan

2000).

* This is a standard model to be used for describing digital data accuracy
* Similar standards are being adopted in other countries

This standard identifies several components of data quality:

* Positional accuracy
* Attribute accuracy
* Logical consistency
* Completeness
* Lineage

Accuracy

Defined as the closeness of results, computations or estimates to true values (or

values accepted to be true). Since spatial data is usually a generalization of the real world,

it is often difficult to identify a true value, and we work instead with values that are

accepted to be true, e.g., in measuring the accuracy of a contour in a digital database, we

compare to the contour as drawn on the source map, since the contour does not exist as a









real line on the surface of the earth.

The accuracy of the database may have little relationship to the accuracy of

products computed from the database, e.g. the accuracy of a slope, aspect or watershed

computed from a Digital Elevation Model (DEM) is not easily related to the accuracy of

the elevations in the DEM itself.

Attribute Accuracy, defined as the closeness of attribute values to their true value,

has to be noted that while location does not change with time, attributes often do.

Attribute accuracy must be analyzed in different ways depending on the nature of the data

Positional Accuracy

Defined as the closeness of locational information (usually coordinates) to the true

position. Conventionally, maps are accurate to roughly one line width or 0.5 mm,

equivalent to 12 m on 1:24,000, or 125 m on 1:250,000 maps. To test positional accuracy

one of the following options can be used as an independent source of higher accuracy: a

larger scale map, the Global Positioning System (GPS), raw survey data, internal

evidence.

GIS Data Entry

GIS data typically are created from hard-copy source data. The process often is

called "digitizing," because the source data are converted to a computerized (digital)

format. Human digitizers can compound errors in source data as well as introduce new

errors (Korte 2000). Although manual digitizing is used less often today, it was the

predominant digitizing method in the 1980s. In this process maps are affixed to digitizing

tables, registered to a GIS coordinate system and "traced" into a GIS (Korte 2000). Here

also are many opportunities for error, because the process is subject to visual and mental

mistakes, fatigue, distraction and involuntary muscle movements.









In addition, the "set up" of a map on a digitizing table or a scanned raster image can

produce errors.

Source Data

Only recently has it become commonplace to collect GIS data directly in the field.

Data collection can be done using field survey instruments that download data directly

into GIS's or via GPS receivers that directly interface with GIS software on portable

PC's. These techniques can eliminate the need for GIS source data (Korte 2000).

During the last 20 years, GIS data most often have been digitized from several

sources, including hard-copy maps, rectified aerial photography and satellite imagery.

Hard-copy maps (e.g., paper, vellum and plastic film) may contain unintended production

errors as well as unavoidable or even intended errors in presentation (Korte 2000).

Controlling GIS Errors

GIS data errors are almost inevitable, but their negative effects can be kept to a

minimum. Many errors can be avoided through proper selection and "scrubbing" of

source data before they are digitized. Data scrubbing includes organizing, reviewing and

preparing the source materials to be digitized. The data should be clean, legible and free

of ambiguity. "Owners" of source data should be consulted as needed to clear up

questions that arise (Marcey et al. 1998).

Perceptions about Irrigation

Irrigation is perceived by some to be costly, and thus financially and economically

questionable due to low world prices for the grain crops most commonly found on

irrigated land. It has also been criticized as environmentally unfriendly due to water

logging, soil salinization and unsatisfactory resettlement programs. To some extent

irrigation suffers from excessive expectations. For example, a review of World Bank









experience (Jones 1995) shows that irrigation projects yielded overall positive economic

rates of returns with an average of 15 %, higher than the opportunity cost of capital and

greater than the average for other non-irrigated agricultural projects. The actual

achievements were however, lower than the rates of return predicted at appraisal.

The need to manage water holistically has become a familiar message to all

working in water resources. This has helped to focus on the cross-cutting nature of the

resource and the need to optimize allocation between different users that depend on water

for irrigation, drinking water supply, industry, power and between users and the

environment.














CHAPTER 2
INTRODUCTION AND PROJECT AREA REVIEW

Introduction

Large dams and irrigation projects such as the TRASVASE in the Santa Elena

Peninsula, Ecuador consists of a nested set of sub-systems involving a dam as source of

supply, an irrigation system (including canals and on-farm irrigation application

technology), an agricultural system (including crop production processes), and a wider

rural socio-economic system and agricultural market (WCD 2002). The performance

indicators for large dam irrigation projects include:

* Physical performance on water delivery, area irrigated and cropping intensity;
* Copping patterns and yields, as well as the value of production

Irrigated Area

Large dam projects usually fall short of area actually irrigated, and to a lesser

extent the intensity with which areas are actually irrigated. Poor performance is most

noticeable during the earlier periods of project life, as the average achievement of

irrigated area targets compared with what was planned for each period increases over

time from around 70 % in year five to approximately 100 % by year 30 (WCD 2002).

The under achievement of targets for irrigated area development for large dams has

a number of causes. Institutional failures have often been the primary causes, including

inadequate distribution channels, overly centralized systems of canal administration,

divided institutional responsibility for main system and tertiary level system, and

inadequate allocation of financing for tertiary canal development. Technical causes









include delays in construction, inadequate surveys and hydrological assumptions,

inadequate attention to drainage, and over-optimistic projections of cropping patterns,

yields and irrigation efficiencies. Under achievements includes also the late realization

that some areas were not economically viable. In addition, a mismatch between the static

assumptions of the planning agency and the dynamic nature of the incentives that govern

actual farmer behavior has meant that projections quickly become outdated (WCD 2002).

Lower yields are often observed for crops specified in planning documents that

emphasize food grain production for growing populations than for the crops actually

selected by farmers. This occurs as farmers respond to the market incentives offered by

higher-value crops such as seasonal or longer-term orchard based crops and allocate

available resources to these crops. This implies higher-than-expected gross value of

production per unit of area, with the caution that such increases have varied with the

long-term real price trend of the relevant agricultural commodities (Vermillion 1997).

But when changes in cropping patterns are combined with shortfalls in area

developed and cropping intensity, the end result is often a shortfall in agricultural

production from the scheme as a whole. Gross value of production is higher where the

shift to higher-value crops offsets the shortfall in area or intensity targets.

Lower than expected crop yields have been caused by agronomic factors, including

cultivation practices, poor seed quality, pest attack and adverse weather conditions, and

by lack of labor or financial resources. Physical factors such as poor drainage, uneven or

unsuitable land, inefficient and unreliable irrigation application, and salinity also hinder

agricultural production. The efficiency of water use affects not only production but also

demand and supply of irrigated water (WCD 2002).









A general pattern of shortfalls and variability in agricultural production from

irrigation projects in developing countries is also revealed by other sources. In the 1990

World Bank OED study on irrigation cited earlier, 15 of 21 projects had lower than

planned agricultural production at completion. Evaluations of 192 irrigation projects

approved between 1961 and 1984 by the World Bank indicated that only 67 % performed

satisfactorily against their targets.

Agriculture and Irrigation

Efforts to promote sustainable water management practices have necessarily

focused on the agricultural sector as the largest consumer of freshwater. Governments

have several objectives in deciding the nature and extent of inputs in agriculture. These

include achieving food security, generating employment, alleviating poverty and

producing export crops to earn foreign exchange. Irrigation represents one of the inputs to

enhance livelihoods and achieve economic objectives in the agricultural sector with

subsequent effects for rural development (Vermillion 1997).

Irrigated agriculture has contributed to growth in agricultural production

worldwide, although inefficient use of water, inadequate maintenance of physical systems

and institutional and other problems have often led to poor performance. Emphasis on

large-scale irrigation facilitated consolidation of land and in some cases brought

prosperity for farmers with access to irrigation and markets (World Bank 1990).

In the absence of good quality control and effective maintenance the canal linings

often have not achieved the predicted improvements in water savings and reliability of

supply. In most irrigation systems, particularly those with long conveyance lengths, a

disproportionate amount of water is lost as seepage in canals and never reaches the

farmlands.









Inadequate maintenance is a feature of a number of irrigation systems in developing

countries. An impact evaluation of 21 irrigation projects by the World Bank concluded

that a common source of poor performance was premature deterioration of water control

structures. Often poor maintenance reduces irrigation potential and affects the

performance of systems (World Bank 1990).

On-Farm Technologies

A number of technologies exist for improving water use efficiency and, hence, the

productivity of water in irrigation systems. Sprinkler system and micro-irrigation

methods, such as micro-sprinkler and drip systems, provide an opportunity to obtain

higher efficiencies than those available in surface irrigation. For these pressurized

systems, field application efficiencies are typically in the range of 70-90 % (Cornish

1998). The output produced with a given amount of water is increased by allowing for

more frequent and smaller irrigation inputs, improved uniformity of watering and reduced

water losses.

Policy

Policy and management initiatives are fundamental to raising productivity per unit

of land and water and increasing returns to labor. They are often interlinked and require

political commitment and institutional co-ordination. Agricultural support programs tend

to be developed and implemented in relative isolation from irrigation systems. Typically

there is weak co-ordination between agencies responsible for agricultural activities (such

as extension services, land consolidation, credit and marketing) and those responsible for

irrigation development. Price incentives are also inadequate to raise productivity and the

outcome is a significant gap between potential and actual yields. In the absence of better

opportunities from agriculture, many farmers seek off-farm employment. Incentives to









enhance production are necessary and can result from a more integrated set of

agricultural support measures and the involvement of joint ventures that provide capital

resources and market access to smallholder farmers. Appropriate arrangements need to be

introduced for such joint ventures to ensure an equitable share of benefits (WCD 2002).

One of the major contributors to poor performance of large irrigation systems is the

centralized and bureaucratic nature of system management, characterized by low levels of

accountability and lack of active user participation. The structure of farmer involvement

varies from transfer of assets to a range of joint-management models. As yet, there is no

general evidence to suggest that irrigation performance has improved as a result of

transfer alone, although there are promising examples indicating that decentralization

may be a required, but not sufficient measure to improve performance (Vermillion 1997).

Experience has shown that in order to be effective, a strong policy framework is required,

providing clear powers and responsibilities for the farmers' organizations (Bandaragoda

1999). Water rights and trading are highly contentious issues. Win-win situations occur

for farmers when they trade a part of their water to replace lost income while at the same

time being able to finance water use efficiency gains from their remaining water

allocation. The formulation of national policies and strengthening of the national

capacities to implement effectively such national policies in better water use is essential

(Smith 2000).

Actual Situation and Projections

The construction of the TRASVASE irrigation system in the Santa Elena Peninsula

(SEP) was designed to intensify the agricultural use of the land in this Ecuadorian region,

but after several years of functioning the improvement is not as significant and viable as

expected.









The construction of the TRASVASE was started in 1989 by CEDEGE1. After being

operative for nine years, the project has not come close to the expected land use. Less

than 25 % or 6,512 hectares are being under agricultural production in the Santa Elena

Peninsula. According to CEDEGE projections the total land capacity of the TRASVASE

irrigation system is 23,066 hectares (CEDEGE 2001), but producer's organizations do

not think that the theoretical number given by CEDEGE is the real area that can be

irrigated with available water. These organizations of producers say that 16,000 ha to

17,000 ha will be the maximum area that the TRASVASE project could irrigate at any

time (El Comercio 2001). The analysis of the irrigation capacity is one of the main points

of this research.










Figure 2-1. Canal in construction, TRASVASE Santa Elena

Some factors can be cited to try to identify the problems that have caused slow

progress of the TRASVASE project. The climate variability, soil composition, land

tenure, and commercialization problems are among the principal constrains.

The Peninsula has surprisingly reduced solar radiation. It is estimated that the area

has less than 600 hours per year of total solar radiation. The relatively frequent and strong

El Nifio effect, with the last one in 1997, and the next one expected in Ecuador by the


1 CEDEGE, Comision de Estudios para el Desarrollo de la Cuenca del Rio Guayas, in English, Commission
for the Development of the Guayas River Basin.









rainy season of 2002-2003, has also a strong impact on agricultural production. Other

problem associated with the climate in the peninsula is the high relative humidity (RH)

that results in the spread of fungi and bacteria that attack the crops in the Peninsula.

Soils in the Santa Elena Peninsula are of marine origin, mostly semiarid and of low

natural fertility, with high clay content. All those factors require especial soil

management techniques that are not always followed by the agricultural producers in the

SEP due to their high cost. There are other lesser soil problems related to contents of

lithium, sodium, and potassium (CESUR 1995).

Characteristics of the Santa Elena Peninsula

Administrative Jurisdiction

Administratively, the Santa Elena Peninsula is included within the Guayas

province. The area under this administration is 6,050 km2, and represents about 30.5 % of

the Guayas province (19,841 km2) and 2.13 % of the total area of the Republic of

Ecuador (CEDEX 1984).

Geography

Ecuador is traditionally divided into four natural regions, a scheme that is followed

in this document:

The Pacific coastal region (in Ecuador called the costa) includes the lower, western

slopes of the Andes (below 1000 m elevation).

The Andes Mountains above 1,000 m, which occupy the central portion of the

country, know as the "Sierra". Amazon lowlands east of the Andes, are referred to as the

"Oriente", including the lower, eastern slopes of the Andes up to 1,000 m. The Galapagos

Islands, is the last region, is a volcanic archipelago in the Pacific Ocean 1,000 km west of

the mainland (CESUR 1995).









The coastal region of Ecuador is about 150 km wide from the base of the Andes to

the Pacific coastline. A relatively low coastal range of mountains extends parallel to and

just inland from the coast, from the city of Esmeraldas in the north to Guayaquil in the

south, a distance of about 350 km. The summits of the coastal mountains are mostly

between 400 and 600 m elevation, but a few isolated peaks are above 800 m. The coastal

range is fairly continuous throughout its length, but is known by different local names:

from north to south Mache, Chindul, Jama, Colonche, and Chong6n (CESUR 1995).

Between the coastal range and the Andes, south of equator, is the broad, nearly

level Guayas River basin. At the mouth of the Guayas River lies Guayaquil, Ecuador's

largest city and principal port. The estuary of the Guayas River empties into the Gulf of

Guayaquil, the largest embayment of the Pacific Ocean on the South American coast. The

Santa Elena Peninsula extends west and south of Guayaquil (CESUR 1995).










Figure 2-2. Landscape of the Santa Elena Peninsula

The Santa Elena Peninsula (SEP) is located at the southwest of the Guayas

hydrographic basin, in the Ecuadorian Coast, west of Guayaquil. The main coordinates of

the SEP are Latitude 2 12' South, Longitude 790 53' West (Figure 2-3). Its boundary is

to the north the Manabi province, to the south and west is the Pacific Ocean and to the

east is the Guayas River basin, which is separated by the Chong6n-Colonche mountain

range (CEDEX 1984).



































Figure 2-3. Location of the Santa Elena Peninsula

Hydrology

Most of the description of the Santa Elena Peninsula was made by the Center for

Study and Experimentation in Public Works, in Spanish, 'Centro de Estudio y

Experimentacion de Obras Publicas' (CEDEX), with base in Spain.










Figure 2-4. Javita River, an intermittent river at SEP.

The Chong6n-Colonche mountain range divides the hydrologic system of the Santa

Elena Peninsula (SEP) from the Guayas River basin, specifically from the Daule River









sub-basin. The minor watersheds created by the Chong6n-Colonche mountain range are

indicated in the Table 2-1 (CEDEX 1984).

Table 2-1. Minor watersheds
Minor Watersheds in the Santa Elena Peninsula
Basin Area (km2) Area (%) SEP (%) Regime
Ol6n 53.29 1.4 0.9 Permanent
Manglaralto 65.98 1.7 1.1 Permanent
Atravezado 81.88 2.1 1.4 Permanent
Valdivia 137.52 3.5 2.3 Permanent
Grande 161.29 4.1 2.7 Intermittent
Javita 800.00 20.6 13.3 Intermittent
Zapotal 1,050.80 27.1 17.4 Intermittent
Grande 631.42 16.2 10.4 Intermittent
Chong6n 588.00 16.1 9.7 Intermittent
# 20 517.61 8.2 5.2 Permanent
Total 3,887.79 100 64



Table 2-2. Basins that start in the Coastal Mountain Range
Coastal Mountain Range Basins
Basin Area (km2) Area (%) SEP (%) Regime
La Mata 80.24 3.7 1.3 Ephemeral
Asagmanes 166.40 7.7 2.8 Ephemeral
Salado 310.71 14.4 5.2 Ephemeral
Engabao 140.45 6.5 2.3 Ephemeral
Zona Engunga 362.70 16.7 6.1 Ephemeral
El Mate 319.80 14.8 5.5 Ephemeral
San Miguel 295.21 13.6 4.9 Ephemeral
Arenas 152.32 7.1 2.5 Ephemeral
# 18 179.06 8.3 3.0 Ephemeral
# 19 154.82 7.2 2.6 Ephemeral
Total 3,887.79 100 64


Climate

A large variety and range of climatic regimes are found in Ecuador, and this variety

has a major effect on the extent of the diverse flora of the country. The climatic regimes

found in Ecuador are influenced by its geographical position astride the equator, the

general circulation of the atmosphere, the position and movements of the ocean currents,









and by orographic effects produced by the abrupt topography of the Andes as well as the

smaller coastal ranges.

The climatic characteristics in the Santa Elena Peninsula (SEP) are very specific.

This is especially true for conditions in the adjacent Guayas River area, especially

regarding the precipitation. The main factors affecting the climate conditions in the SEP

are two currents of the Pacific Ocean: the Humboldt "cold current" and El Nifio "warm

current" and the displacements of water and air at the inter-tropical convergence zone.

Between the months of January and April, the warm current of El Nifio moves from

Panama to the South along the Pacific coast and close to the SEP encounters the cold

waters of the Humboldt Current. This encounter results in rapid cooling of the air,

releasing the moisture when colliding with the mountains (Cafiadas 1983). The

Ecuadorian Andes create a bigger barrier increasing the effects of the inter-tropical

convergence zone. The temperatures on the Peninsula are characteristically very constant

all year around. The winds come mostly from the South.

Meteorological Data

The location of the weather stations in the Peninsula is presented in the Chapter 3

(Figure 3-1). The registered parameters are: precipitation, temperature, relative humidity,

cloud coverage, evaporation (A Pan), and wind speed. However, not all this data is

complete for all the stations.

Temperature

Due to Ecuador's position on the equator, the day length changes very little

throughout the year every day has about 12 hours of sunlight, varying no more than about

30 minutes at any point in the country. On the equator, the total amount of solar radiation

reaches a maximum at the equinoxes; this is only 13 % higher than the minimum amount









of radiation intercepted at the solstices. A consequence of this relative annual constancy

in solar radiation is the low seasonal variation in mean air temperature at the equatorial

latitudes. From month to month, the mean temperatures at all sites in Ecuador are

relatively constant; monthly means do not vary more than 3 C at any site, and at many

sites vary less than 1 C. In contrast, the daily fluctuations in temperature over 24-hour

periods are much more pronounced; the circadian cycle of temperature change is

therefore much more important than the annual change in mean temperature. Daily

temperature fluctuation at mid-to upper elevations in the Andes is often 20 C or more. In

the lowlands, the daily fluctuation in temperature is generally much less, closer to about

10 C. The daily maximum and minimum do have significant annual variation at some

sites, for example, at high elevations freezing temperatures are more prevalent during the

dry season due to clear skies (Sarmiento 1986).

Temperature in Ecuador varies rather predictably with altitude. At sea level in

coastal Ecuador, the mean annual temperature is about 25 C. On moist tropical

mountains, following the adiabatic lapse rate, temperature decreases at about 0.5 C for

each increase of 100 m in altitude. The lapse rate, as determined from climatic records at

various elevations, is slightly different for the western slopes versus the eastern slopes of

the Andes (Cafiadas 1983).

The average annual temperature is between 23.1 C for Salinas and 25.7 C for El

Azucar where the coastal influence is smaller. From the available historical data, the

maximum value recorded was 36 C in Playas (February) and a minimum of 15.6 C in

the same station (October). It is appropriate to note that the rainy season is from January

to April and this is also the time of the highest temperatures. Here also, daily variations in









temperature are more significant than the monthly variations. However, daily variation on

average is no larger than 5 C. More detailed information of data from the weather

stations can be found in Chapter 3, Weather Data.

Precipitation

In contrast to the constancy of temperature regimes in Ecuador, rainfall regimes

vary enormously from place to place, in both the annual amount of precipitation and in

the patterns of seasonal distribution of rainfall. Different patterns of rainfall are found in

the Coastal, Andean, and Amazonian regions of continental Ecuador, and in the

Galapagos Islands; variation also occurs from north to south in each main geographical

region, and on a local scale according to topography and other factors.

The Inter Tropical Convergence Zone (ITCZ) shifts from a position at about 10 N

latitude at the June solstice, to about 5 S latitude at the December solstice. Therefore, the

ITCZ passes over Ecuador twice during the year on its northward and southward

oscillations. The shifts in the ITCZ produce a bimodal distribution of rainfall at Andean

localities in Ecuador, with two rainy periods and two drier periods during the year. In the

coastal region of Ecuador, annual rainfall patterns are under the influence of the two

principal ocean currents in the Pacific, near the shore of northwestern South America

(Cafiadas 1983). These include the cold Humboldt Current, which flows northward along

the coast of Chile, Peru, and southern Ecuador, and turns eastward at about the equator

and flows past the Galapagos Islands. The second is the warm equatorial current that

flows southward from the Gulf of Panama, along the Pacific coast of Colombia, and

meets the Humboldt Current near the equator along the north-central coast of Ecuador

(Cafiadas 1983).









The Humboldt Current brings arid conditions to the adjacent coast, as the cool

oceanic air passes over the relatively warmer landmass. Another effect of the Humboldt

Current is the overcast skies-the low clouds, known locally as "garua" (Figure 2-6)-

that form a layer about 600 m above sea level and cover most of western Ecuador

throughout the day during the dry season (Sarmiento 1986).

The warm equatorial current that bathes the northwest coast of Ecuador brings with

it moist air and rainfall. During most years, the warm equatorial current pushes farther to

the south of the equator for a few months, December to April (Figure 2-5) generally,

bringing rainfall and warm, moist air to the areas of the central and southern Ecuadorian

coast that are under the influence of the dry, cool Humboldt Current the remainder of the

year. This phenomenon is known locally as ElNifo (the Christ Child) because the annual

rains usually begin in mid- to late December, around Christmas (Cafiadas 1983).

Due to the annual southward incursion of the warm equatorial current, most of

coastal Ecuador, as well as the Galapagos Islands, have a unimodal pattern of

precipitation, with one rainy season extending from December to April, and a long dry

season from May to December. The length and intensity of the dry season vary at

different sites in the coastal region (Sarmiento 1986).

The most arid region within the Santa Elena Peninsula is the zone of Santa Elena,

where the city Salinas shows an annual average precipitation of only 112 mm, 96 % of

which is concentrated in the period from January through April. The topography around

Salinas constitutes of valleys and small hills, no higher than 100 m (CEDEX 1984).

The North section of the SEP is mountainous, with a medium elevation of 600 m.

The effect of the mountain range makes the precipitation increase considerably. The









effect of the humid winds coming from the ocean results in the more uniform distribution

of the rains throughout the year. In El Suspiro, at 456 m of elevation, the average amount

of rainfall for the January-April period is approximately 60 % of the annual total

(CEDEX 1984).

In the region from Nuevo River to the Chong6n River, where the

Chong6n-Colonche mountain range has altitudes from 200 to 500 m, the weather stations

have registered precipitation of approximately 550 mm. The presence of the mountain

range in this zone adds for higher rainfall. The rainfall in the January April period

represents 85 % of the annual total (CEDEX 1984).


Historical Precipitation by Weather Station in the Santa Elena Peninsula

350
300
E 250
c 200
2 150 -
S100 -
50
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Chongon U Playas 0 San Isidro O Suspiro U El Azucar


Figure 2-5. Historical average precipitation in the Santa Elena Peninsula

According to the precipitation pattern (Figure 2-5) for five weather stations in the

Santa Elena Peninsula the months that require supplementary irrigation are from May

thru November. The period for which weather is available for each location is presented

in Appendix B.









In the Cerecita's savanna the effect of the orography is less accentuated, however

the precipitation is greater than in the areas located to the South and Southwest of this

savanna. The average rainfall over Cerecita is 463 mm, with 91 % captured in the period

from January to April (CEDEX 1984).

The semi-arid zone from Colonche to Progreso is a valley; this is not affected by

the orographic precipitations. In Figures A-i, A-2, and A-3 (Appendix A), the isohyets

(isohyet is a line drawn on a map connecting points that receive equal amounts of

rainfall) for the region are presented.

At irregular intervals, but averaging about every seven years, the El Nifio

phenomenon is much stronger than normal along the Pacific coast of South America.

During "El Niho" years, the warm equatorial waters push much farther south into coastal

Peruvian waters, displacing the cold Humboldt Current, bringing heavy rains to the

Peruvian desert as well as coastal Ecuador. The warm water conditions may last for more

than a year before the Humboldt Current again brings dry weather to the coast. The heavy

rains associated with El Nifio cause flooding in coastal Ecuador and destroy roads,

bridges, houses, and crops. The last two major El Nifio events were during 1982-1983

and 1997-1998.

Relative Humidity

The weather in the Santa Elena Peninsula is highly modified by the relative

humidity (Figure 2-6). This relative humidity is presented in the form of fog and low

clouds that cover the skies over the Peninsula most of the year (rainy and dry seasons).

Winds

The highest winds speeds were registered in Salinas, where the monthly average for

each year surpass the 300 km/day, except in February and April.










The dominant winds came from the Southwest, with a frequency of 50 %, followed

by the winds coming from the West. The winds from the North are least frequent.


Historical Relative Humidity by Weather Station in the Santa Elena
Peninsula

95
990
85
E 80
I 75
2 70
6 65
60
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

EChongon Playas O San Isidro OSuspiro El Azucar

Figure 2-6. Historical average relative humidity in the Santa Elena Peninsula

Sunshine

This term refers to the number of hours of effective sunshine impacting the Earth

surface, also described as the impact of the rays from the sun. Sunshine varies respect to

the Latitude of the location. At the Equator the average sunshine should be 12 hours per

day all year around. However, cloudiness in the Santa Elena Peninsula affects negatively

the amount of hours of light impacting the soil, reducing evaporation and photosynthesis.

Climatic Classifications

Papadakis Climatic Classification

The Papadakis method considers characteristics of the winter and summer, the

temperature regime and the humidity balance to classify the climates (Figure 2-3.).

In Table 2-3, the classification is listed, showing that the Santa Elena Peninsula

(SEP) is divided in to three climatic zones: desert-monsoon in Salinas, going through the

semi-arid-monsoon of Playas, Anc6n and Azucar; into the dry-monsoon of Manglaralto.









Table 2-3. Climate types
Weather Station Climate Type
Playas Tropical, equatorial, semiarid, (Eq, mo) with 9 dry months.
Azucar Tropical, equatorial, semiarid, (Eq, mo) with 10 dry months.
El Suspiro Tropical, equatorial, semiarid, (Eq, mo) with 10 dry months
Chong6n Tropical, equatorial, warm, (Eq, mo) with 8 dry months
San Isidro Tropical, equatorial, semiarid, (Eq, mo) with 10 dry months
(CEDEX 1984)


Papadakis Climate Classification
STropb Drll(Eq8 d)
T WopbA Eq iJmIfAAji;Eq,MW


Figure 2-7. Papadakis climate classification

Koppen Climatic Classification

Dr. Vladimir Koppen of Austria devised a climate classification system in 1918

based on the average annual temperature and total precipitation data for areas around the

world. It was the most widely used and recognized climate classification system for many

decades. Most revised climate classification systems are based on Dr. Koppen's initial

system (CEDEX 1984). A shorthand version was produced using letters to designate 5

broad climatic groups, with further subdividing in subgroups distinguished by seasonal

characteristics of temperature and precipitation.


4









Table 2-4. K6ppen climate classification for the SEP
Avg. Avg.
Weather Avg. Temp. Temp. Precip K/2 C
Weather & 1 l Precip. Climatic
Station annual coldest warmest =1/2(20tm Formula
Station (P)(mm) Formula
Temp TC month month ) ) +280)
(t1) (tl2)
Playas 24.3 22.0 26.5 362.3 383 BWh'ai-desert
Chong6n 25.3 23.9 26.6 1118.9 393 AW tropical-
savannah
Azucar 25.7 24.3 27.5 278.0 397 BWh'ai-desert
El Suspiro 23.4 21.4 26.1 530.3 374 AW tropical-
savannah
San Isidro 23.1 20.8 26.0 245.9 371 BWh'ai-desert

A = tropical rainy climate, average temp > 18 C no rainy season, large annual
rainfall ppt > evap
B = dry climate, evap > ppt no surplus water = no perennial streams
AW= prairie
BW= desert
TS= savannah
h'= medium annual temperature > 18 C (dry and warm climate)
a = medium temperature of the warmest month > 22 C
i = annual temperature variation (t12-tl) < 5 C\

To determine the boundaries of each of the climate types, Koppen uses temperature

and precipitation points. Average temperatures of the coldest (tl) and warmest (tl2)

months are needed to define the limits among the different climates. Koppen's "climatic

formula" is a brief description of the climate, especially air temperature and precipitation,

including seasonal tendencies of these variables (CEDEX 1984).

From the BW climate (desert), with very scarce precipitation year-round, gradually

moves to the BS (steppe) with a rainy season that allows fast vegetation growing. From

the steppe it goes to the A W or prairie where the rainfall concentrates during the summer

but few are scattered year around; resulting in a tropical savannah.









Soils

Marine sediments are the parental material of the Santa Elena Peninsula (SEP)

soils. Where this parental material stayed "in situ" it created "residual" soils in hills; but

if it was deposited in lower lands, it created "alluvial" soil in the valleys.

Based on the United States Department of Agriculture (USDA) soil classification

the following soil orders were obtained for the SEP.

Table 2-5. Soils
Units Area (ha)
Individual Units
Entisols 149,615
Inceptisols 99,695
Aridisols 99,075
Vertisols 12,442
Alfisols 4,905
Mollisols 1,070
Associated units
Aridisols/Aridisols 67,288
Inceptisols/Inceptisols 55,230
Inceptisols/Vertisols 13,910
Vertisols/Aridisols 9,630
Aridisols/Vertisols 10,500
Inceptisols/Mollisols 7,425
Aridisols/Entisols 13,065
Inceptisols/Entisols/Aridisols 3,180
Total 546,970

The Entisols, Inceptisols and Aridisols orders account for approximately 95 % of

the area of the SEP.

It can be said that in general the soils in the Santa Elena Peninsula have a great

variability in texture, ranging from clay to silt, and they are more superficial in the hills

than in the valleys. In the hills and mountains the parental material can be found just a

few centimeters below the surface. The erosion is very high in the hills and mountains;

this is especially due to the deforestation and destruction of the vegetation cover.









Following is a more detailed description of the soils units in the Santa Elena

Peninsula:

Residual Soils

Residual soils were developed "in situ" from marine clays, they are the oldest soils

in the Peninsula and can be found in hills and mountains. The residual soils are less thick

when the slope increases. These soils have been classified in the four following units

(CEDEX 1984):

* Soils with Cambic horizons, normally found in locations with high slopes (> 20 %).
They have a superficial horizon A from 10 to 20 cm with silt and clay contents,
sometimes it consists totally of clay. The next horizon is cambic B with a thickness
from 20 to 30 cm its texture has high variability. The C layer is similar to the
parental material.

* All these soils correspond to the Typic Ustropepts and Typic Cambortid, however,
if the clay content is greater than 35 % in the first 50 cm they are classified as
Vertic. Very eroded soils can be classified as Paralithic and Lihic depending if is
the horizon C or the parental material the one that shows in the first 50 cm.

* Soils with Argilic horizons, located in small hills with slopes less than 10 %. The
A2 superficial horizon is very eroded with 1-5cm; it contains silt and clay. The next
is the Bt layer from 15 to 25 cm; in some cases with gravel and rocks (10 cm
diameter). They correspond to the Vertic Paleargid and Vertic Paleustalf.

* Soils with high content of clay can be found in low hills and some times in high
mountains for which the parental material is marine clay. They show cracks up to
80 cm deep and 2 cm wide, and their horizons are considered clay up to 70 cm with
black spots coming from carbonates. The C-horizon also has a high content of clay.
These soils are classified as Pellusters, Chromusters and Torrerts.

* Soils without defined horizons are located in degraded, rocky zones and also
regions with strong slopes, and are grouped in the Ustorthens and Torriorthents
units.

Aluvial Soils

* Sandy non-saline soils, located close to the sea and in some riverbeds. Classified as
Ustipsamment and Torripsamment.

* Non-saline soils with contents of silt and clay (40 %) correspond to the Ustifluvents
and Torrifluvents units.






38


* Saline soils located in the mangroves close to the sea and in some rivers. Soils with
more that 15 % of interchangeable sodium, Entisols.














CHAPTER 3
WEATHER DATA ANALYSIS FOR CROPWAT MODEL

Air temperature and soil temperature, along with water availability and soil, and

relative humidity are key factors for agriculture and forestry systems. Under many

situations temperature is the factor that determines which crops or trees can be grown in a

given area, seed germination, growth rates, rates of maturation or ripening, and yield.

At a global scale, the major pattern of vegetation is defined by latitudinal gradients.

At continental and regional scales, elevation modifies the latitudinal gradients according

to adiabatic lapse rates. Because of the large area and coarse spatial resolution of these

scales, temperature regimes appear smooth and simple interpolation can be adequate for

characterizing patterns.

Most simulation models assume homogeneous conditions over the space they are

representing. In fact, general conditions often do not exceed more than a few kilometers

square. The weather data across the SEP was difficult to interpolate over several square

kilometers between weather stations (Ashrat et al. 1995).

Several interpolation procedures are available, ranging from simple linear

interpolation techniques and triangulated networks, to more sophisticated distance

weighing or kriging techniques. It must be remembered that the only information used by

any interpolation procedure is the location of known values (van der Goot 1997). Most of

the weather data information that is used in this project was provided by CEDEGE. For

this project the weather data available comes from 5 different weather stations, the

parameters recorded are (Appendix B, Table B-1):









* Maximum and minimum air Temperatures, in Celsius degrees (oC)
* Maximum and minimum Relative Humidity (RH), in percentage (%)
* Wind speed (m/s)
* Sunshine (hours/day)


The periods for which the data are available are listed in Appendix B (Table B-l)

In addition to limited years of data, all the stations have some missing data for different

periods of time. Because of these missing data several methods to fill those gaps were

analyzed. Solar radiation and wind speed was also provided from some weather stations

but they had too many missing data points to be useful.

Weather Stations Distribution

The maximum distance between points for least dense networks of weather stations

is 150-200 km, for intermediate networks is 50-60 km and for most dense networks is

around 30 km (Gandin 1970). In the Santa Elena Peninsula (SEP) the distances between

the stations are shown in Table 3-1. Considering the above recommendations, the

distribution of weather stations in the SEP can be considered an intermediate network.

Table 3-1. Distances among stations (m) and elevation (mmsl)
Station name El Azucar Playas Chong6n San Isidro El Suspiro mmsl
El Azucar 27,000 4,560 35,500 56,000 35
Playas 72,200 57,000 70,350 24
Chong6n 32,500 54,500 41
San Isidro 25,000 35
El Suspiro -- 35

The spatial location of the weather stations is shown in Figure 3-1. Spatial

distribution is not very good, but at least, there are weather stations in each of the

ecologic zones of the Santa Elena Peninsula. The small differences in elevation over the

main sea level among stations permits the use of more simple interpolation methods

(Table 3-1). In flat areas such as the Santa Elena Peninsula the effect of elevation will be










a small factor when interpolating the weather data. However, since this is a coastal area

the effect of the wind and ocean is accounted for in the CROPWAT software to calculate

evapotranspiration.


Figure 3-1. Weather stations

Estimating Missing Climatic Data

The calculation of the reference evapotranspiration (ETo) with the Penman-

Monteith method requires mean daily, ten-day or monthly maximum and minimum air

temperature (Tmax and Tmin), actual vapor pressure (ea), net radiation, and wind speed

measured at 2 m. If some of the required weather data are missing or cannot be

calculated, it is strongly recommended that the user estimates the missing climatic data

with one of the following procedures and use the FAO Penman-Monteith method for the

calculation of ETo.


A Weathr stations


20.000
I IMeaters









Estimating Missing Humidity Data

Where humidity data are lacking or are of questionable quality, an estimate of

actual vapor pressure, ea, can be obtained by assuming that dew point temperature (Tdew)

is near the daily minimum temperature (Tmin). This statement implicitly assumes that at

sunrise, when the air temperature is close to Tmin, that the air is nearly saturated with

water vapor and the relative humidity is nearly 100%. If Tmin is used to represent Tdew

then (Allen, et al. 1998):



e, = e(Tm) = 0.611 exp 1727Tmi
a T iin + 237.3 (3-1)


The relationship Tdew > Tmin holds for locations where the cover crop of the station

is well watered. However, particularly for arid regions, the air might not be saturated

when its temperature is at its minimum. Hence, Tmin might be greater than Tdew and a

further calibration may be required to estimate dew point temperatures. After sunrise,

evaporation of the dew will once again humidify the air and will increase the value

measured for Tdew during the daytime (Allen, et al. 1998).

Estimating Missing Radiation Data

Net radiation measuring devices, requiring professional control, have not been used

in the agro meteorological stations managed by CEDEGE. In the absence of a direct

measurement, long wave and net radiation can be derived from more commonly observed

weather parameters, i.e., solar radiation or sunshine hours, air temperature and vapor

pressure.

Solar radiation data can be derived from air temperature differences, the difference

between the maximum and minimum air temperature is related to the degree of cloud









cover in a location. Therefore, the difference between the maximum and minimum air

temperature (Tmax Tmin) can be used as an indicator of the fraction of extraterrestrial

radiation that reaches the earth's surface, principle that has been used by Hargreaves and

Samani to develop estimates of ETo using only air temperature data (Allen, et al., 1998).

The Hargreaves' radiation formula, adjusted and validated at several weather

stations in a variety of climate conditions, becomes:


PR = k~ (Tmax Tmin) R (3-2)

where
Ra = extraterrestrial radiation (MJ m2/d),
Tmax = maximum air temperature (C),
Tmin = minimum air temperature (C),
kRs = adjustment coefficient (0.16-0.19) (oC-o5).

The square root of the temperature difference is closely related to the existing daily

solar radiation in a given location. The adjustment coefficient kRs is empirical and differs

for "interior" or "coastal" regions:

* For 'interior' locations, where land mass dominates and air masses are not strongly
influenced by a large water body, kRs = 0.16;
* For 'coastal' locations, situated on or adjacent to the coast of a large land mass and
where air masses are influenced by a nearby water body, kRs = 0.19.

The fraction of extraterrestrial radiation that reaches the earth's surface, Rs/Ra,

ranges from about 0.25 on a day with dense cloud cover to about 0.75 on a cloudless day

with clear sky. CROPWAT uses the location (coordinates) of each weather station to find

the best coefficient for each station.

The temperature difference method is recommended for locations where it is not

appropriate to import radiation data from a regional station, either because homogeneous

climate conditions do not occur, or because data for the region are lacking.









Missing Wind Speed Data

Importing wind speed data from a nearby station, as for radiation data, relies on the

fact that the airflow above a 'homogeneous' region may have relatively large variations

through the course of a day but small variations when referring to longer periods or the

total for the day. Data from a nearby station may be imported where air masses are of the

same origin or where the same fronts govern airflows in the region and where the relief is

similar.

When importing wind speed data from another station, the regional climate, and

trends in variation of other meteorological parameters and relief should be compared.

Strong winds are often associated with low relative humidity, and light winds are

common with high relative humidity. Thus, trends in variation of daily maximum and

minimum relative humidity should be similar in both locations. Imported wind speed data

can be used when making monthly estimates of evapotranspiration.

In the case of the Santa Elena Peninsula (SEP) a correlation comparison was made

among all the weather station available in the area, but none of them show a good

correlation coefficient (R2 > 0.70), and because of that no data were used from one station

to another.

As the variation in wind speed average over monthly periods is relatively small and

fluctuates around average values, monthly values of wind speed may be estimated. The

'average' wind speed estimates may be selected from information available for the

regional climate, but should take seasonal changes into account.

Where no wind data are available within the region, a value of 2 m/s can be used as

a temporary estimate. This value is the average over 2000 weather stations around the

globe.









For this project the wind data came from historical data from the available weather

stations. This data had some gaps, however, once the wind speed data were used in

CROPWAT using both the FAO world average and the available data the latest produced

values closer to the reality of the zone.

Minimum Data Requirements

Many of the above procedures rely upon maximum and minimum air temperature

measurements. Unfortunately, there is no dependable way to estimate air temperature

when it is missing. Therefore it is assumed that maximum and minimum daily air

temperature data are the minimum data requirements necessary to apply the FAO

Penman-Monteith method.

Estimating Weather Data Sets for the Santa Elena Peninsula

To find a way to complete the missing meteorological data for the "Chong6n"

(used as an example) weather station, two methods were used. The first one is Regression

Analysis, and the second one is Compositional Data. The results were tested against each

other to find which one fits better to this situation.

One month (March) with 31 observations of weather data from each one of the five

(5) stations was used to test the available methods to estimate missing weather data. The

variables to be used are Maximum Temperature (Tmax) and Minimum Temperature

(Tmin), and Maximum Humidity (Hmax) and Minimum Humidity (Hmin). In this

document the Maximum Temperature is used as an example of what was done with all

datasets.

Regression Analysis

Quite often data sets containing a weather variable Yi observed at a given station

are incomplete due to short interruptions in observations. When data are missing, it may









be appropriate to complete these data sets from observations Xi from another nearby and

reliable station. However, to use portions of data set Xi to replace data set Yi, both data

sets Xi and Yi must be homogeneous. The procedure of completing data sets is applied

after the test for homogeneity and needed correction for no homogeneity has been

performed (Allen et al. 1998). The substitution procedure proposed herein consists of

using an appropriate regression analysis.

Procedure:

1. Select a nearby weather station for which the data set length covers all periods

for which data are missing (in this case, data from three stations were tested to find if the

data of one of those have any relationship with the Chong6n weather station data).

2. Characterize the data sets from the nearby station (Azucar, Suspiro, Playas), Xi,

and of the station having missing data (Chong6n), Yi, by computing the mean 7 and the

standard deviation Sx for the data set Xi:


n
x=Vxi/n
i-1 (3-3)



S -( ) (3-4)

and the mean Y and standard deviation Sy for data set Yi:


n
y=vyi/n
i-1 (3-5)


S (i- 7)2/(n 1 (3-6)
\- ) (3-6)









for the periods when the data in both data sets are present (in this case March 2001),

where xi and yi are individual observations from data sets Xi and Yi, and n is the number

of observations in each set.

3. Perform a regression of y (Play_Tmax, Azu_Tmax, and Sus_Tmax) on x

(Cho_Tmax) for the periods when the data in both data sets are present (March, 2001):


i = a + bxi (3-7)

with


E (,i ?)(yi -
b ov- "

i-I

a=y-bx


where a and b are empirical regression constants, and covxy is the covariance

between Xi and Yi. Plot all points xi and yi and the regression line for the range of

observed values. If deviations from the regression line increase as y increases then

substitution is not recommended because this indicates that the two sites have a different

behavior relative to the particular weather variable, and they may not be homogeneous

(Allen et al, 1998). Another nearby station should be selected.

4. Compute the correlation coefficient r:


n
COV(x ) -
xy i-I
Sr S n n 112
Xi- i )2
S- )i-1 ) (3-8)









Both a high r2 (r2 > 0.7) and a value for b that is within the range (0.7 < b < 1.3)

indicate good conditions and perhaps sufficient homogeneity for replacing missing data

in the incomplete data series (Allen et al, 1998). These parameters r2 and b can be used as

criteria for selecting the best nearby station.

5. Compute the data for the missing periods k = n+1, n+2..., m using the regression

equation characterized by the parameters a and b, thus


k= a + b (3-9)

6. The complete data set with dimension m will now be


Yj = yi (j = i = 1,...,n)

Yk (j =k=n+1, n+2,...,m)

Results: After running the regression analysis of Chong6n versus each one of the

other three stations the output in Table 3-2, was obtain.

Table 3-2. Regression analysis method
Station r2 F
Azucar (Figure 3-2) 0.0718 2.2443
Suspiro (Figure 3-3) 0.0145 0.4262
Playas (Figure 3-4) 0.0801 2.5257

The result can also be look at in the scatter plots created for each of the

comparisons among the weather stations (Figures 3-2 to 3-4).

Since at least an r2 of 0.7 is needed, the data from any of these stations cannot be

used to complete the missing data from Chong6n weather station. It was concluded that

there is no relation between Chong6n and any other existing weather station in the area.









Compositional Data

Any vector x with non-negative elements xi, .., xD representing proportions of

some whole is subject to the obvious constraint

xi+...+ XD=

Compositional data, consisting of such vectors of proportions, play an important

role in many disciplines and often display appreciable variability from vector to vector.

This concept can be used to estimate missing weather data (Aitchison, 1986).

Procedure:

1. Find the daily average for a given variable (Maximum Temperature, Tmax) from

many years of a given month (this month will be May).

2. For each daily value calculate the percentage value compared to "one" (one

equals the sum of the daily values for a given month).

3. Once these values are calculated new values can be created for May 2001, by

multiplying the number for the day with the missing data (obtained in step 2) by the sum

of the daily data of May 2001 (sum = 846).

Table 3-3. Creating new values
Cho_avgmay % Chomay Chocrt may
31.4 0.031126 26.3
Formulas: = (31.4*1.0)/1008.8 Missing daily value = 0.031126*846


4. Now a regression analysis is used to test how these new values fit to the weather

data.

Results: The compositional data method gives an r2 of 0.46 and a F value of 25.13

for the Chong6n station for May 2001 with 3 days of "created" data compared with the










daily historic values for this month. A better r2 value was obtained, but it did not meet the

r2 = 0.7 mark.

Using Data from Other Years to Replace Missing Meteorological Data

Special means were employed to maintain serially complete files of weather data

when long segments of missing meteorological data were found. The majority of these

situations occurred at stations that were not operated during the evening or on weekends,

but in some instances a station would be shut down for several weeks or even longer.

When these situations occurred, the gaps in the data were filled with data from other

years, for the same days of the year. Averaged data from other years for the same time

periods were selected to fill the gap.

Conclusion

Since none of the statistical methods tried to complete the missing weather data for

the weather stations in the Santa Elena Peninsula were successful, the only option

available was to use the data from other years from the same station to complete the

missing data, and make all computation just with the currently available data. When

possible and available, better weather data should be used to calculate crop water use.


Chongon vs. El Azucar





IJ 25
410
30
.) 25
E 20

E < 15
10o
E
*X 5
S 0-
0 5 10 15 20 25 30 35 40
Maximum Temperature Chongon







51


Figure 3-2. Chong6n vs. El Azucar


Chongon vs. B Suspiro


30
S 25
CL--
|E 20
E 15
E 10
2 5


10 20 30
Maximum Temperature Chongon


Figure 3-3. Chong6n vs. El Suspiro


Chongon vs. Playas


40
> 35
30 3-
5 25
0. 20
E
I 15-
E
- 10
E
1' 5
0
0 5 10 15 20 25 30
Maximum Temperature Chongon


Figure 3-4. Chong6n vs. Playas


35 40


, ~


$*cllw














CHAPTER 4
GEOGRAPHIC INFORMATION SYSTEM

Introduction

Geographic Information System (GIS) technology is about 30 years old. However

for the most part, it is still often used just to make maps. However, GIS can do much

more. Using GIS databases, more up- to-date information can be obtained or information

that was unavailable before can be estimated and complex analyses performed. This

information can result in a better understanding of a place, can help make the best

choices, or prepare for future events and conditions (Mitchel 1999).

Many countries and organizations are still building their GIS databases, as in the

case of Ecuador and more specifically CEDEGE. This process of creating GIS databases

has been difficult and cumbersome. Now, new easy to use software employing graphic

interfaces is removing that obstacle.

The most common geographic analyses that can be done with a GIS are (McCoy &

Johnston 2001):

* Mapping where things are
* Mapping the maximum and minimum values
* Mapping density
* Finding what is inside (intersection analysis)
* Finding what is nearby (proximity analysis)
* Mapping change (overlay analysis)

The steps for a good geographic information system analysis are (McCoy &

Johnston 2001):









Stating the problem. Stating the problem defines what information is needed, and

it is often in the form of a question. Being as specific as possible about this statement will

help when trying to decide how to approach the analysis, which method to use, and how

to present the results. Other factors that influence the analysis are how it will be used and

who will use it.

Understanding the data. The type or data and features to be used in the project

will help determine the specific method to be used.

Choosing a method. There are almost always two or three ways of getting the

information that is needed. Often one method is quicker and gives more general

information. Others may require more detailed data and more processing time and effort,

but provide more precise results. To decide which method to use the level of precision to

answer the problem has to be again evaluated.

Processing the data. Once the method has been selected, the necessary steps in the

GIS have to be performed.

Reviewing the results. The results of the analysis can be displayed as a map,

values in a table, or a chart. It has to be decided which information to include in the

maps, and how to group the values to best present the information. Looking at the results

will help in the decision making process, deciding what information is valid or useful, or

whether the analysis should be rerun using different parameters or even a different

method. GIS makes it relatively easy to make these changes and create new output. The

results using different methods can be compared to decide which one presents the most

needed information and produces it in an efficient way.









Mapping Systems

The mapping systems today range from display only systems like electronic

atlases to full featured geographic information systems. The dividing lines between one

type of system and the next are not sharply defined. The systems do differ in a number of

important ways: how they link geographic locations with information about those

locations (topology and relational database management), the accuracy with which they

specify geographic locations (positional accuracy), the level of analysis they perform, and

the way they present information as graphic drawings (Mitchel 1999).

Electronic atlases, for instance, allow displaying pictures of geographic areas on

the computer screen. They provide limited information about the geographic areas, and

limited ability to alter the graphics. Without any tools for analyzing the information,

these systems are most useful for providing graphics that can be used in presentations and

reports. They can also be used as reference tools (Mitchel 1999).

Unlike electronic atlases, thematic mapping systems have the capacity to create

graphic displays using information stored in spreadsheet or database. These systems are

especially useful for creating graphic presentations. Each map produced is based on a

theme, such as population or income, and uses color, patterns, shading, and symbols of

various sizes to show the relative value of the information stored for that theme, at each

geographic location.

Street-based mapping systems are more sophisticated than electronic atlases and

thematic mappers. They relationally link information to geographic locations.

Street-based mapping systems can display address locations on street maps as points, and

can plan travel routes via topological information.









More sophisticated mapping systems can import database or spreadsheet files or

provide direct access to outside information sources. Some mapping systems let the user

create and manage tabular information, use tabular information to create charts and

graphs, and even analyze information statistically.

ArcGIS

ArcGIS (Environmental Systems Research Institute, ESRI) desktop is a group of

tools to develop and edit digital maps, and also allow some modeling (Breslin 1999). The

tools used in this project are described in this section (Ormsby 2001).

ArcMap. It is an application for displaying maps and investigating them, for

analyzing, maps to answer geographic question and producing maps that make analysis

persuasive. In ArcMap, maps can be made from layers of spatial data, colors and

symbols, query attributes can be selected, analyze spatial relationships analyzed, and map

layouts designed. The ArcMap interface contains a list of the layers in the map, a display

area for viewing the map, and menus and tools for working with the map.

ArcCatalog. This tool is used to browse spatial files on the computer's hard drive,

on a network, or on the Internet. The program can be used to search the spatial data,

preview it, and add it to ArcMap. ArcCatalog also has tools for creating and viewing

metadata (information about spatial data, such as who created it and when, its intended

use, its accuracy, etc).

Spatial analyst. It is an application used to create raster (cell-based) surfaces,

query them, and do overlay analysis on them. It can also be used to derive new surfaces

from other raster or vector layers. For example, a slope surface can be derived from an

elevation surface or a population density surface from population points.









Geostatistical analyst. It is a tool that can create continuous surfaces from a small

number of points by predicting the values of unsampled locations.

ArcPress. This is an application that improves map printing speed and renders

high-quality maps without requiring additional memory or hardware.

Original Maps

The Instituto Geografico Militar (IGM) publishes the official maps of Ecuador.

Topographic maps of the country at 1:1,000,000 and 1:500,000 scales are available, and a

series of topographic sheets at 1:50,000 scale, published gradually during the past 20

years, now covers most of the country, except remote areas of the Amazon basin and

parts of the Andean slopes. The IGM has also published thematic maps at 1:1,000,000

scale, including geologic, soils, bioclimatic, and life zones maps. A branch of the IGM,

the "Centro de Levantamientos Integrados de Recursos Naturales por Sensores Remotos"

(CLIRSEN), that operates a Landsat and SPOT satellite image receiving station near the

Cotopaxi volcano, carries out geographic and natural resources studies using remote

sensing data, and sells the satellite imagery to other users.

Soils

The original layer containing the different types of soils in the Santa Elena

Peninsula (SEP) contain a large number of various soils that was too complex to use in a

model using CROPWAT. This original map is presented in Figure 4-1, a complete view

of this map is shown in Figure A-4.

CROPWAT model from FAO/UN (Food and Agricultural Organization of the

United Nations) uses only three soil texture groups of clay, silt, and sand to calculate

irrigation schedules.




























Figure 4-1. Soil types on Santa Elena Peninsula, original map

Ecological Zones


Koppen Classification
Santa Elena Peninsula


\



Classification
I I Savannah (AW)
I Esteppe (BS)
Desert (BW)


pI
I


Kilometers
5 10 30 40
9j


Figure 4-2. Koppen climate classification of Santa Elena Peninsula


I









The Peninsula was subdivided into three zones (Figure 4-2) based on the climate

data presented in Chapter 2.

According to the Koppen method the Santa Elena Peninsula has the following

ecological zones: Desert, Steppe, Savannah.

Dams


Figure 4-3. Dams location on Santa Elena Peninsula

The geographic location and area of the main dams that constitute the

TRASVASE irrigation system is presented on the layer in Figure 4-3. The information in

this layer was used to calculate the capacity of the system, evaporation and their storage

efficiencies.


Dams of the TRASVASE Project


0 5 10 20 30 40









The three dams that are presented in Figure 4-3 that are part of this project are:

Chong6n, El Azucar, and De Cola. Also, the projected areas for El Azucar and Velasco

Ibarra dams are shown in this layer.

Canals

A layer containing geographic locations of the canals, length and materials used

in their construction is also available and is presented in Figure 4-5. This layer also

contains roads and drainage information that will not be used in this project.


Figure 4-4. Canals and other features

Data Quality Problems with the Santa Elena Peninsula Data Set

Data quality problems in the data set available from the Santa Elena Peninsula

during the creation of the geographic information system (GIS) for this area. Lineage,









unclosed polygons, lines that overshoot or undershoot junctions, labeling problems,

missing metadata, and maps in different projection systems.

Lineage

It is important to create a record of the data sources and of the operations that

created the database:

* How was it digitized, from what documents?
* When was the data collected?
* What agency collected the data?
* What steps were used to process the data?


The matching of final results, after calculation, can be a good indicator of the initial

data accuracy. In the case of the data for the Santa Elena Peninsula even when one

institution digitized the maps, the original "hard copy" maps came from multiple

agencies and in different projections and scales. This made the process of interpolation

very difficult and introduced an imbedded error. Some of the agencies that collected and

digitized the data for the SEP are: ESPOL, CEDEGE, and IGM. This resulted in a set of

data with different projections, scales, and different information in the metadata.

In addition the weather and agricultural databases (CEDEGE, ESPOL) have certain

problems. One major problem is that these databases do not describe the time when the

information was gathered and how the data was collected. The most serious errors

resulted from the missing data, especially weather data.

Accuracy

Overshoots and undershoots may be used as a measure of positional accuracy.

These are presented in few layers for this project, but most of the errors occur in the









elevation contours, and the hydrology maps (Figure 4-5) where logical consistency errors

are also presented.

Logical consistency refers to the internal consistency of the data structure,

particularly it applies to topological consistency related to the polygon closure and

polygon labeling.


Figure 4-5. Errors in the hydrology maps of the SEP

Many of the polygons in the maps that were available for the Santa Elena Peninsula

have more than one label. One example is the ecological zones map, where up to three

labels can be found. Some labels are not accurate or longer than required. Large









descriptions of each attribute in the table make it difficult to read and interpret the labels,

and are difficult to place in the map itself. All of that required significant changes during

data preparation. To correct these errors the databases related to each of the maps

presenting this type of error (Koppen, Papadakis, ecological zones soils, canals) were

edited, adding or subtracting data fields as needed to create layer suitable for use in a

GIS.

Map scale is the other source of error. Cartographers and photogrammetrists work

to accepted levels of accuracy for a given map scale. Locations of map features may

disagree with actual ground locations, although the error likely will fall within specified

tolerances. Scales from 1:5,000 to 1:50,000 are found for the maps available for the SEP

and the combination of different scales added error to the final maps produced for this

project. Although it is not possible to eliminate this error it should be recorded in the

metadata for future reference, this was done for all the maps produced, using ArcCatalog.

Precision

The accuracy and precision errors can be located in the Santa Elena Peninsula data,

overlapping the soils map, and the ecological zones (Koppen) layer (red line) shows the

difference between the two maps.

A shift to the northwest in the ecological zones layer can be identified.

Georeferencing between this vector layers was used to correct the ecological zones layer.

The soil layer was used as a reference layer because all its metadata were known and

correct.

Completeness

Completeness is the degree to which the data exhausts the universe of possible

items: are all possible objects included within the database? In the case of the data









available for this project this aspect relates to a "logical consistency". Basically, it does

not matter if there is a lot of information in a database if that information cannot be used

in an efficient manner.


Figure 4-6. Overlap error


Metadata Errors

Coordinate transformation introduces error, particularly if the projection of the

original document is unknown, or if the source map has poor horizontal control. The

digital maps of the SEP were that the maps were in different projections or did not have

any stated projection; however this is not something difficult to correct. ArcGIS contains

a feature that allows setting the desired coordinate system for a map. The coordinate

system selected for the maps of the SEP was the Universal Transverse Mercator (UTM)

Zone 17 South. It is important to note that bad metadata can be a source of spatial error,









especially when trying to overlay different layers. The most accepted standard for

metadata is giving by the Federal Geographic Committee (2000) in the Content Standard

for Digital Spatial Metadata.

Manual Digitizing

Manual digitizing was the method predominantly used to create the digital maps of

the different attributes for the Santa Elena Peninsula (SEP). This digitizing method can

introduce more errors in the final product, especially when done without proper trained

people and without supervision and quality control (Figure 4-5). The digitizing process

was made in ESPOL (Polytechnic School of the Littoral) Guayaquil, Ecuador, in most of

the cases by students and people with little practice in digitizing.

Source Data for the Santa Elena Peninsula

The main problem with the SEP maps is related to many different data sources.

Starting with maps created by various national and international agencies, and digitized

in different manners. Several databases (climatic, agricultural production, water use,

water consumption, etc.) the collected with unknown methods by different institutions. In

addition, there was difficulty to compare this data against other sources because of the

few studies completed in the area of the Peninsula.

Controlling GIS Errors

Data entry procedures should be thoroughly planned, organized and managed to

produce consistent, repeatable results. Nonetheless, a thorough, disciplined quality

review and revision process also is needed to catch and eliminate data entry errors. All

production and quality control procedures should be documented, and all personnel

should be trained in these procedures. Moreover, the work itself should be documented,

including a record of what was done, who did it, when was it done, who checked it, what









errors were found, and how they were corrected. GIS data should not be provided without

metadata indicating the source, accuracy and specifics of how the data were entered.

GIS Layers Created or Edited for the Project from the Original Maps

Soils

The new soil map divides all the soils into three groups (silt, sand and clay). The

distribution of these soils within the Peninsula is presented in Figure 4-7. The "merge"

tool in the "Geoprocessing" wizard from Arc View 3.2 was used to simplify this layer.

The original soil map is presented in Figure 4-1.



Soils
Santa Elena Peninsula


0 5 10 20 30 40

Figure 4-7. Main soil types layer created for the Santa Elena Peninsula

Ecological Zones

This layer shows the ecological division in the Santa Elena Peninsula. According to

the Koppen method the Peninsula is divided in desert, steppe and savannah.









The canals of the TRASVASE irrigation system cross the different ecological

zones. The "clipping" geoprocessing tool available in Arc View 3.2 was used to clip the

canals to the ecological zones (Figure 4-8).


Koppen Classification
Santa Elena Peninsula


~ 4 I
~r



j~i...


Classification

SI Savannah (AW)
[I ] Esteppe (BS)
SDesert (BW)


S Kilometers
30 40


Figure 4-8. Ecological zones

Canals

This figure represents several layers; ecological zones, canals, and the dams in the

area of interest.

The roads, and drainage lines were deleted to create this layer. The layers were also

clipped using the Koppen Ecological Zones. The different materials of the canals where

maintained but join in one canal per ecological zone, this was done to facilitate the

evaporation calculation in the later phase on in the project.


re.


-


i


\


5 10



































Figure 4-9. Canals

Existing Farm Locations

A layer that shows the geographic locations of most of the farms close to the canals

(Figure 4-9) was also created. More detailed maps of the farms locations can be seen in

Appendix A (Figures A-5 to A-8). The farms are grouped in five regions: Santa Elena,

Chong6n, Cerecita, and El Azucar Rio Verde. This layer shows the total areas of the

farms, however that is not the actual area under agricultural production. As information

about crops produced in each farm becomes available, these layers will be used to create

maps that will show the total area that can be irrigated in each zone according to the

weather parameters and crop water requirements. These concepts will be explained in the

following Chapters.



































Figure 4-10. Actual farm locations

Weather Stations Location

Known coordinates of each one of the weather stations were used to create a

Microsoft Access database. An "ID and Station Name" fields were added for better

identification of the stations and to be used to link or join this table with others in Arc

View 3.2. With the Access file a shape file was created in Arc View 3.2 so it can be

displayed as a layer in the GIS (Figure 2-5 in Chapter 2).

Weather Data

There are a number of commonly used interpolation techniques described in the

literature, such as simple average, Thiessen polygon, classical polynomial interpolation,

inverse distance, multi quadratic interpolation, optimal interpolation, kriging and others.

In this study the inverse distance interpolation technique was chosen for its simplicity.









Researchers have used diverse statistical and geostatistical models to generate

temperature surfaces from point sampling locations. The simplest technique uses the

nearest measurements. Trend surfaces, inverse distance weighted interpolation (IDW),

and thin plate spline, all have been used to interpolate temperature measurements over

global, continental, and broad regional scales. These models, assume the underlying

surface is smooth as it is the topography found in the Santa Elena Peninsula.

Inverse Distance Interpolation. As is obvious for the name of the interpolation

technique, the weighting factor is inversely proportional to the distance. The weights of

this interpolation technique are solely a function of the distance between the point of

interest and the sampling points.

Table 4-1. Comparison of interpolation methods
Method Advantages Disadvantages
Bi-linear interpolation Simple, conservative Smoothing
Polynomial trend surface Designed degree of smoothing Unstable near edges

Inverse square distance Preserves high frequencies Outliers
weighting
Kriging (variogram) Uses variance of data Directional effects
Spline interpolation Optimal fit Strong edge effects
Laplacian fitting Good fitting, smooth decay at Smoothing
edges


The inverse distance technique does not take advantage of spatial correlation

structure explicitly. However, for climate data these correlation structures tend to be

linear and it is a good guess to assume that the inverse distance weighting would work

fairly well.

The Inverse Distance Weighted interpolation method was used to create the surface

maps (Figure 4-11) with the Geostatistical Analysis Tool from ArcGIS for the weather









variables: maximum, minimum and medium temperature, and maximum, minimum and

medium relative humidity. Examples are the surface maps for the month of January

(Figure 4-11).

Inverse Distance Weighted (IDW) interpolation explicitly implements the

assumption that places that are close to one another are more alike than those that are

farther apart. To predict a value for any unmeasured location, IDW will use the measured

values surrounding the prediction location, with values closest to the prediction location

having more influence on the predicted value than those farther away (Johnston, et al.

2001). IDW assumes that each measured point has a local influence that diminishes with

distance, and weights the point closer to the prediction location greater than those farther

away (Johnston, et al. 2001).


z(So)= i ,z s,) (4-1)

Z(So) value to predict for location So
N number of measured points
X weight, these weights will increase with distance
Z(S) observed value at the location Si

The formula to determine the weights is (Johnston, et al., 2001):

-P

N
1, =do d o (4-2)


Z= 1

As the distance becomes larger, the weight is reduced by a factor ofp. The d,o value

is the distance between So and S,.

The Inverse Distance Weighted method includes a power (p) parameter. This p

parameter influences the weighting of the measured location's value on the prediction









location's value; that is, as the distance increases between the measured sample locations

and the prediction location, the weight that the measured point will have on the prediction

will decrease exponentially (Johnston, et al. 2001). The optimal p value is determined by

minimizing the root-mean-square prediction error (RMSPE). The RMSPE is a summary

statistic quantifying the error of the prediction surface (Johnston, et al. 2001).

After one map for each weather variable was created for each month of the year

they were classified into 5 classes because the variation in the data was small. To

reclassify those surface maps the Equal Interval method was used. In the Equal Interval

method the range of possible values is divided into equal-sized intervals. Because there

are usually fewer endpoints at the extremes, the numbers of values are less in the extreme

classes. This method is used in data ranges as percentages (relative humidity or

temperature) (McCoy, and Johnston 2001).

Creation of Evapotranspiration Surface Maps

For this study, the climatic information used to make interpolation is based on

inverse distance weighted method of 5 stations in the Santa Elena Peninsula (SEP). While

interpolation of a value at reach cell in the study area using 5 meteorological stations is

technically easy, some important questions can be raised. Are the stations representative

of the areas around them? How large or small an area do they represent? How does the

spatial feature in question change over space? Is it continuous or discontinuous and

abrupt?









72










January


NA
N *


Maximum' ..
Temperature (C)
279 -29.4
29.4-304
30.4-31.9
31,9-34,2
M 34.2- 37.9


Maximum
RH(%)



90.2 934
93.4- 95,6
95.6 98,8


Minimum. 6
Temperature (C). ,

1,79 1 91,

19,9-21.4
214 235
23.5-26.3


Minimum
RH(%)
58B1 2
626-65.
65-5-70
700- 77,
77.0-88,


,-

.., .. .

Average
Temperature (CL
21i5 229

24.3-25.7
25.7 27.1
27.1 -28.5


Average
RH (%)
69.0-72
72.4 -74
74.5-77.
77.9 83
83.5 92,


6
5



















5 y.-
9
9
5
9


Figure 4-11. Surface maps of weather data for January


U;

K









Characterization of climate data for a study area typically relies upon a series of

measurements at discrete locations. Spatial interpolation of these discrete data into a

continuous surface is generally the first step for use with other GIS data layers.

These surface maps layers were used to determine relationships between stations and to

identify the agricultural production zones affected by each of the weather stations.

Weather parameters affect evapotranspiration and as a result they modify irrigation

requirements. Later in this document (Chapter 5, Figures 5-9.1 and 5-9.2) reference

evapotranspiration surface maps are presented to show the monthly variations within the

Santa Elena Peninsula.














CHAPTER 5
WATER AVAILABILITY AND ITS USE IN THE SANTA ELENA PENINSULA

Infrastructure

In the TRASVASE irrigation system the water is used for irrigation and also for

human consumption, and as in any irrigation systems there are losses. To calculate the

total available water for irrigation the amount used for potable water and the losses of the

system have to be calculated.

TRASVASE Santa Elena

Daule Peripa Dam

This dam (6,000 million cubic meter) works to control floods, regulate water flow,

control salinity levels, and produce hydroelectric power. Because of that its name is

'Proyecto de Prop6sito Multiple Jaime Rold6s Aguilera', in English, Multi-purpose

Project 'Jaime Rold6s Aguilera' (Figures 5-1 and 5-2).

















Figure 5-1. Daule-Peripa Dam
























Figure 5-2. Hydroelectric plant, 'Proyecto de Prop6sito Multiple Jaime Rold6s Aguilera'.

The area directly influenced by this project is 50,000 ha in the Daule Valley.

Indirectly 42,000 ha are projected to be irrigated in the Santa Elena Peninsula. Another

50,000 ha (500 m3/year) are projected to be irrigated from the same Dam in the Manabi

province (CEDEGE 2001).

History of the Project

The Santa Elena Peninsula (SEP) has suffered a water crisis for more than 100

years. Most people agree that deforestation is the main cause, converting what was once

tropical forest to a near desert. To mitigate the drought conditions, and to use this land for

agricultural production the TRASVASE project was built. In 1992 the first hydraulic

structures were put together to start the TRASVASE Daule-Santa Elena.

Following that, the Chong6n Dam (Figure 5-3), the Chong6n irrigation canal, the

Chong6n Cerecita irrigation canal and the irrigation infrastructure in the Chong6n,

Daular and Cerecita irrigation zones were built; total project covering approximately

5,000 ha (CEDEGE 2001).









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Figure 5-3. Chong6n Dam

In order to promote the use of most efficient irrigation techniques, and demonstrate

the agricultural potential in the SEP, pressurized irrigation systems were installed, at

Chong6n and Cerecita, consisting of pumping stations, subterranean conveyance pipes,

and portable sprinkler systems.










Figure 5-4. Daule pumping station

At the end of 1995 the construction of the Daule-Chong6n canal was completed

assuring constant water supply to the Chong6n Dam, the Chong6n-Cerecita-Playas canal

and the Daule Pumping Station. Sixty-one kilometers of canals were lined with concrete,









and the 6.5 km Cerro Azul tunnel was finished. It was estimated that this system will

allow the irrigation of 15,000 ha of land (CEDEGE 2001).

In 1998 the first part of the project was finished. In that part the water from the

Chong6n Dam was conducted to El Azucar Dam and from here to the Rio Verde

irrigation zone. To accomplish this the following structures were build: the Chong6n

Pumping Station, a 3 km pressurized conveyance pipeline, and 40 km of canals lined with

high-density polyethylene. The El Azucar Dam was refurbished, and the Cola dam in San

Juan (Playas) was built to regulate the water flow.

Table 5-1. Main dams
Dams Volume (106 m3) Surface (km2) Water level (m)
Max Min Max Min Max Min
Chong6n 273.6 148.5 25.7 16.9 51 45
El Azucar 53.8 25 14 8.5 45 42
De Cola 2.44 1.4 0.4 0.5 26.5 24.8

In the last part of this project the Sube y Baja Dam, the Sube y Baja-Javita,

Afaye-Atahualpa, and Villangota and Azucar-Zapotal canals will be built.

Other important and complementary projects were constructions of two

potabilization plants and wastewater treatment plants in 2000.

Potabilization Plants

The water from the TRASVASE is now also used to supply two water

potabilization plants for two of the larger towns in the Peninsula. One of those is the

Santa Elena plant (Irrigation Zone I), which supplies water for the cities of Santa Elena

and Salinas. The second one provides water to Playas (Irrigation Zone II). The flow water

required by the plants are 1.6 m3/sec and 0.55 m3/sec respectively (CEDEGE 2001).















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Figure 5-5. Zone II potabilization plant

The construction of sanitary canals in the main cities and towns of the Peninsula is

included in the CEDEGE plans for the SEP. Also planned are controls for prevention of

surface and groundwater contamination.

Transmission Canals

The TRASVASE irrigation system uses canals and pipes to deliver the water to its

users. The seepage loss from the canals constitutes a substantial percentage of the usable

water. Irrigation canals lose water through seepage and evaporation (Chahar 2000). The

seepage loss from canals is governed by hydraulic conductivity of the subsoil, canal

geometry, location of water table relative to the canal, and several other factors (Burton et

al. 1999).











Figure 5-6. Canal

Transmission canals are used in the TRASVASE system to convey water from the

source to a distribution canal. Often the area to be irrigated lies very far from the source,

and hence requires long transmission canals.


















Figure 5-7. Canal San Rafael, TRASVASE project

Normally, there should be no withdrawal from a transmission canal. In the case of

the TRASVASE this occurs because the secondary (delivery) canals have not been

completed and water from the transmission canals is used to supply irrigation water to the

adjacent land.

The canals of the TRASVASE system are lined with various materials, from

reinforced concrete, to polyethylene, to the original material of the riverbed. To calculate

the seepage losses of each section of the canal we need to know the characteristics of

each of these materials. However, in this study the average number estimated by

CEDEGE (7 %) was used for calculation of seepage losses.

Water Loss from the Canals and Dams to Evaporation

The main causes of loss of stored water are seepage through a leaking basin or dam

wall, and evaporation from the surface. Many methods have been developed for

controlling both, but few are economically attractive (Hudson 1987).

Evaporation from open water can easily reach 7 mm per day in arid or semi-arid

countries. This equals 5 cm per week and 20 cm per month (Brouwer, et al. 1992). The

amount of water lost by evaporation can be considerable, particularly in reservoirs, which

are large and shallow. Therefore, irrigation from shallow lakes and reservoirs should be

started as soon as possible after the rainy season.









Evaporation from the dams and canals (Appendix C, Tables C-6 and C-7) has to be

calculated based on the available weather data and the surface area of the dams and

canals (Tables 5-2 and 5-3) that are part of the TRASVASE irrigation system.

Table 5-2. Approximated surface areas of the canals
Canal Length (m) Width (m) Surface Area (m2)
Chong6n-Cerecita 37,520 8.6 322,672
Cerecita-Playas 18,309 7.7 140,979
Chong6n-Sube y Baja 19,600 8.3 162,680
Azucar-Rio Verde 19,863 12.5 248,288


Table 5-3. Approximated surface areas of the dams
Surface (m2104)
Dams Max Min Average
Chong6n 25.7 16.8 21.25
El Azucar 14 8.5 11.25
De Cola 0.3 0.5 0.40

A well-designed and constructed canal system transports water from the source to

the farmers' fields with a minimum amount of water loss. However, water losses will

occur and can seriously reduce the efficiency of water delivery. Water may be lost by

seepage, leakage, or both (Hoevenaars, et al. 1992).

Seepage

Water that seeps through the bed and sides of a canal will be lost for irrigation. This

so-called "seepage loss" can be significant where a canal is constructed from material

which has a high permeability: water seeps quickly through a sandy soil and slowly

through a clay soil, and so canals constructed in sandy soils will have more seepage

losses than canals in clay soils. The results of seepage through the sides of a canal can

sometimes be very obvious, such as when fields adjacent to a canal become very wet, and

even have standing water. Seepage loss through the canal bed is difficult to detect









because water goes down and does not appear on the nearby ground surface (Hoevenaars

et al., 1992).




'.I /V"- V -- "- --







Figure 5-8. Trapezoidal canal

Leakage

Water may also be lost by leakage. This water does not seep, but flows through

larger openings in the canal bed or sides (Hoevenaars, et al. 1992).

* Seepage around structures, leading to severe leakages
* Gates which are not tightly sealed
* Cracked concrete canal linings, or joints that are not tightly sealed
* Tom asphalt or plastic lining


Leakage often starts on a small scale, but the moment that water has found a way

through a canal embankment a hole will develop through which water will leak. If the

leakage is not stopped in time, the tunnel becomes larger and the canal bank may be

washed away at a certain moment. In the case of a lined canal, the canal foundation may

be undermined after some time and the canal will collapse.

Table 5-4. Canal description__cnlnsojtttetile
Canal Length (m) Q (m3/sec) Lining
Daule-Chong6n (tunnel) 32,723 44 Concrete
Chong6n-Cerecita 24,494 12 Concrete
Cerecita-Playas 31,741 9 Concrete
Chong6n-Sube y Baja 19,600 9.2 Polyurethane
Azicar-Rio Verde 19,863 5.5 Polyurethane











Irrigation Efficiency

The application efficiencies considered for this project were within those defined

by FAO (Table 1-2). The in-field irrigation efficiency values considered were 50 %, 70 %

and 90 %. Using an ample range of efficiencies assures the inclusion of a wide range of

conditions where irrigation is applied to the field.

Irrigation Technology used in the Santa Elena Peninsula

In the period 2000-2001 CEDEGE conducted a survey to the agricultural producers

in the SEP. The results from this survey show the adoption of the latest irrigation

technology by large farmers that have the capital to implement the technology (Tables

5-5 to 5-7).

Table 5-5. Chong6n-Daular-Cerecita pressurized system, Zone I (2001)
System Area %
Drip 19.17
Sprinkler 18.63
Microsprinkler 9.89
Water hose 11.41
Other 2.28
Not using irrigation 38.02

In this zone consisting of 2,780 ha cultivated (Table 5-5) the percentage of farmers

not using any type of irrigation system is highest compared to the other regions. This

corresponds to the area owned by small farmers.

Table 5-6. Chong6n-Cerecita-Playas canal, Zone I (2001)
System Area %
Drip 27.78
Sprinkler 8.89
Microsprinkler 21.11
Water hose 11.11
Not using irrigation 31.11









More than half (57 %) of the area in this zone of 3,175 ha under agricultural

production (Table 5-6) is covered by irrigation systems that theoretically will have a field

application efficiency of 70 % of higher.

Table 5-7. El Azucar-Rio Verde canal, Zone II (2001)
System Area %
Drip 35.4
Sprinkler 7.96
Microsprinkler 3.54
Surface 13.27
Not using irrigation 39.82

The conditions in this area (565 ha cultivated) (Zone II) differ from the previous

two areas (Zone I) because in Zone II the water is scarcer and the irrigation systems used

should be more efficient to overcome that deficit. This can explain the higher use of drip

systems over sprinklers.

Water Consumption

CROPWAT is meant as a practical tool to help agro-meteorologists, agronomists

and irrigation engineers to carry out standard calculations for evapotranspiration and crop

water use studies and more specifically, the design and management of irrigation

schemes. It allows the development of recommendations for improved irrigation

practices, the planning of irrigation schedules under varying water supply conditions, and

the assessment of production under rain fed conditions or deficit irrigation.

Calculations of crop water requirements (CWR) and irrigation requirements are

carried out with inputs of climatic and crop data. Standard crop data are included in the

program and climatic data can be obtained for 144 countries through the CLIMWAT

database. Ecuador is included in this database, however, the data for the Santa Elena

Peninsula is for weather stations that do not represent accurately the weather in this area.









The development of irrigation schedules and evaluation of rain fed and irrigation

practices are based on a daily soil-water balance using various options for water supply

and irrigation management conditions. Scheme water supply is calculated according to

the cropping pattern provided for the model.

Reference Evapotranspiration Surface Maps

The first step to calculate CWR is to calculate reference evapotranspiration (ETo).

The process starts by entering historical average weather data for the Santa Elena

Peninsula into CROPWAT (FAO/UN). This is a process that requires careful quality

control since each value has to be entered manually into the system, and errors are easily

made. All weather data (maximum and minimum temperatures and relative humidity,

available wind speed data, and sunshine) for each station was introduced to the

CROPWAT database. Missing parameters like wind speed data (for some months and

stations) and solar radiation were calculated using the same program. Once all the data

was complete, the reference evapotranspiration was calculated using the Penman-

Monteith method.

To create the ETo surface maps, the output from CROPWAT had to be entered to

an ArcMap (ESRI) database to georeference the ETo data set. Inverse distance weighting

(IDW) interpolation method (Chapter 4) was selected to interpolate the reference

evapotranspiration data between the weather stations.

The average monthly surface maps of ETo for the Santa Elena Peninsula (Figures 5-

9.1 and 5-9.2) present the variation in reference evapotranspiration in the months of June

to November considered to be the dry season, and December to April (or May)

considered to be the wet season.