Analysis of urban water demand for southeast Florida

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Analysis of urban water demand for southeast Florida
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Florida Water Resources Research Center Publication Number 78
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Gibney, Richard D. III
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Notes

Abstract:
As an aid to drought management, an analysis of the rainfall-pumpage relationship, an estimation of per-capita consumption, and a model to predict pumpage based on antecedent conditions are presented. Daily pumpage, rainfall, and evaporation data from twelve selected utilities are analyzed to determine the governing interactions. A methodology for converting pan evaporation to evapotranspiration is shown. The literature related to consumption is reviewed yielding per-capita, indoor, and outdoor consumption figures. Additionally, the effect of conservation on consumption is discussed. A model relating pumpage decline to rainfall is formulated that will allow planners to forecast pumpage based on the anticipated rainfall patterns. The accuracy of this model is evaluated using the actual pumpage and rainfall data of one of the selected utilities.

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Publication No. 78










AN ANALYSIS OF URBAN WATER DEMAND


FOR


SOUTHEAST FLORIDA








by


Richard D. Gibney III


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AN ANALYSIS OF URBAN WATER DEMAND FOR SOUTHEAST FLORIDA


BY



RICHARD D. GIBNEY III


















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



UNIVERSITY OF FLORIDA


1983














ACKNOWLEDGEMENTS


I would like to express my appreciation to the following people

who have offered their help and encouragement throughout this study:

Dr. James P. Heaney, my committee chairman for his guidance and sup-

port, Dr. Wayne C. Huber for his guidance on the use of the various

analysis techniques, Robert Dickinson, whose computer expertise has

been invaluable, and to all of the others in the Enviornmental Re-

source Management section of the University of Florida Department of

Environmental Engineering for their friendship and support. Finally,

I would like to thank my wife, Susan, for her love and understanding

throughout this work.



This work was sponsored by the South Florida Water Management

District. Their financial support and aid in data acquisition are

gratefully acknowledged. Special appreciation is extended to Dr.

Carl Woehlcke, Director of the Water Use Planning Division of the

Resource Planning Department, and his staff for their time and atten-

tion.



I would also like to thank all of the people who were so help-

ful in providing the necessary utility information: Mr. Dale Holin-

back, City of Deerfield Beach, Mr. Robert Douglas, City of Lake Worth,

Mr. Lawton McCall, Palm Beach County, Mr. Perry Cessna, City of Boynton











Beach, Ms. Barbara Carlton, Miami-Dade WASA, Mr. Richard Mills, City

of Pompano Beach, Mr. Walton Gerrard, City of Sunrise, and Mr. Ronald

Collins, City of Hollywood. Special thanks go to Ms. Cindy Martin,

City of Boca Raton for her help with the outdoor use information.








TABLE OF CONTENTS


PAGE

ACKNOWLEDGEMENTS . . . .... ... .. .ii

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

LIST OF FIGURES . . . . .. .. ix

ABSTRACT . . . . ... . . x

CHAPTER I INTRODUCTION . . . . 1

CHAPTER II PREVIOUS STUDIES OF URBAN WATER USE .. 6

Forecasting Methods and Techniques . . 6
Factors Influencing Water Use . . . 9
Impact of Conservation Practices . . .. 10
Outdoor Water Use . . . . ... .12
Summary . . . . ... .... .15

CHAPTER III DESCRIPTION OF STUDY AREA . ... .17

Required Data . . . . ... .17
Pumpage . . . . ... .17
Population . . . ... .18
Evaporation . . . . ... .22
Rainfall . . . . . 31
General Characteristics . . . . 36

CHAPTER IV METHODOLOGY . . . .... .42

Analysis Procedures . . . . 42
Rainfall-pumpage Relationship . . .. 42
Consumption And Conservation . .... .49
Data Preparation . . . . 51
Evaporation . . . . 51
Pumpage . . . . . 51
Water Consumption Models . . . .. 54
Irrigation Model. ... . . . 54
Simulator Techniques .. . .. . 59
The Precipitation-pumpage Model .. .. 60
Summary of Models . . . .... .60

CHAPTER V RESULTS . . ........ .61

Rainfall-Pumpage Relationship . . ... .61
Visual Analysis . . . ... .61
Quantification. . . . .. 64









TABLE OF CONTENTS -- CONTINUED


Page


Consumption-Conservation Literature Review
Indoor Consumption . . .
Water Use . . . .
Model Results . .
Evaporation Data Preparation .
Pumpage Data Preparation . .
Irrigation Model . . .
Rainfall-pumpage Simulator ..


Results


CHAPTER VI


SUMMARY AND CONCLUSIONS .


Objectives . . . .
Methodology . . . .
Study Area . . . .
Evaluation . . . .
Method . . . .
Findings . .
Suggestions For Additional Investigation


APPENDIX A


APPENDIX B


APPENDIX C


APPENDIX D


APPENDIX E


APPENDIX F



APPENDIX G


APPENDIX H


DAILY WATER PUMPAGE DATA IN MGD
DEERFIELD BEACH, FLORIDA 1976


- 1981


RAINFALL DATA IN INCHES AT TREATMENT PLANT
DEERFIELD BEACH, FLORIDA 1976 1981 .

PLOTS OF PUMPAGE AND RAINFALL VERSUS TIME
DEERFIELD BEACH, FLORIDA 1976 1978 .

HISARS EVAPORATION DATA USED IN DEERFIELD
BEACH ANALYSIS 1953 1979 . .

EVAPORATION DATA FOR FT. LAUDERDALE EXPERIMENT
STATION 1976 1978 . . .

MEAN AND STANDARD DEVIATION OF EVAPORATION
DATA FOR THE FORT LAUDERDALE EXPERIMENT
STATION . . . . .

EVAPORATION DATA SUMMARY FORT LAUDERDALE
EXPERIMENT STATION BROWARD COUNTY, FLORIDA

LISTING FOR IRRIGATION MODEL . .


. 113


123


133


151


178



184


190

197










TABLE OF CONTENTS -- CONTINUED


PAGE


APPENDIX I LISTING FOR RAINFALL-PUMPAGE SIMULATION .

REFERENCES . . . . . . .

BIOGRAPHICAL SKETCH . . . . . .









LIST OF TABLES


Table Page

1 Summary of Past Drought Management Measures 13

2 Service Area and Period of Record For the
Twelve Selected Utilities in Southeast Florida 19

3 Monthly Pumpage For Twelve Utilities in South-
east Florida 21

4 Past and Projected Population of Broward, Dade,
and Palm Beach Counties in Florida 23

5 Bureau of Business and Economic Research, Uni-
versity of Florida Population Estimates 24

6 Estimated Permanent Population and Population
Served by Thirteen Utilities in Southeast
Florida 26

7 Mean Monthly, Seasonal, and Annual Class A Pan
Evaporation (Inches) For Stations With 10 Years
or More of Record For Best Month 30

8 Clewiston U.S. Eng. Station Summary 32

9 Hydrologic Information Storage and Retrieval
System Evaporation Station Listing For South
Florida 33

10 Summary Statistics on Pumpage For the Twelve
Utilities 1978 Through 1981 37

11 Daily Variability in Pumpage For Deerfield

Beach, Florida 1976 Through 1981 39

12 Long Term Summary Statistics For Pan Evapor-
ation Fort Lauderdale Experiment Station;
1953-1979 40

13 Per Capita Use: 1978 Through 1981 For Selected
Utilities 41

14 Typical Range of Available Soil Moisture By
Soil Textural Class 55











LIST OF TABLES -- CONTINUED


Table Page

15 Pumpage Decline Due to Rainfall 65

16 Pumpage-Recovery For Selected Water Utilities in
South Florida 75

17 Summary of Water Demand Studies 81

18 Annual Water Use Patterns in Twelve Water Utilities
in Southeast Florida 1978 Through 1981 83

19 Product of Evapotranspiration Conversions 93

20 Coefficients Used in Conversion of Pumpage From
Millions of Gallons (MG) to Inches 95

21 Results of Calculations to Determine Pervious Area
Services 9_6

22 Results of Irrigation Simulator 99

23 Recovery Coefficients Used in the Rainfall-Pumpage
Model 105


viii












LIST OF FIGURES


Figure Page

1 Map of Florida Showing the South Florida Water
Management District's Area of Responsibility 2

2 Map of South Florida Water Management Service
Areas 5

3 Approximate Service Area of Twelve Utilities in
Southeast Florida 20

4 Iso-Pan Evaporation Map for Southeast Florida 35

5 Idealized View of Pumpage (left ordinate) and
Rainfall (right ordinate) Versus Time 45

6 The Inverted Hydrograph, Inverted Idealized
View of Pumpage Versus Time 47

7 Idealized Schematization of Rectangular Method
of Volume Determination 57

8 Configuration of Upper Zone Storage Reservoir 63

9 Visual Depiction of Pumpage Decline Due to
Rainfall For Deerfield Beach, Florida 72

10 Effective Storage Capacity of the Soil Layer
For Deerfield Beach, Florida 73

11 Correlation Between Pumpage Decline and Rain-
fall For Deerfield Beach, Florida 103

12 Results of Irrigation Model Simulation Using
Deerfield Beach as Test Case 106

13 Rainfall-Pumpage Model Results For a Selected
Period 107















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

AN ANALYSIS OF URBAN WATER DEMAND FOR SOUTHEAST FLORIDA

By

Richard D. Gibney III

December, 1983

Chairman: James P. Heaney
Major Department: Environmental Engineering

As an aid to drought management, an analysis of the rainfall-

pumpage relationship, an estimation of per-capita consumption, and a

model to predict pumpage based on antecedent conditions are presented.

Daily pumpage, rainfall, and evaporation data from twelve selected

utilities are analyzed to determine the governing interactions. A

methodology for converting pan evaporation to evapotranspiration is

shown. The literature related to consumption is reviewed yielding per-

capita, indoor, and outdoor consumption figures. Additionally, the

effect of conservation on consumption is discussed. A model relating

pumpage decline to rainfall is formulated that will allow planners to

forecast pumpage based on the anticipated rainfall patterns. The ac-

curacy of this model is evaluated using the actual pumpage and rainfall

data of one of the selected utilities.



Chairman















CHAPTER I

INTRODUCTION



The South Florida Water Management District (SFWMD) boundaries

encompass most of the State of Florida south of Lake Okeechobee as well

as the drainage from the Kissimmee River basin into the lake (see Figure

1). This area includes the heavily urbanized lower east coast area, the

large and diverse agricultural areas, and the ecologically sensitive

Everglades area. The agricultural areas range from the large acreage

used by citrus and sugar cane farms surrounding the lake to small

vegetable truck farms which dot the region. Urban water use averages

between 800 and 900 million gallons per day (MGD) while supplemental

consumptive use for agriculture is about 1200 MGD. Consumptive use

figures for the Everglades area are not presently available (Leach,

1983). The SFWMD is responsible for monitoring and regulating water

consumption in the South Florida area. The District acts as a whole-

saler to its licensed dealers. Therefore, they need to know how much

water is pumped out of their supplies.

Rainfall patterns for the South Florida area are extremely vari-

able. Periods of extreme high and low precipitation levels have

occurred throughout history. The severe drought that South Florida

experienced in 1980-1981 may be contrasted to the overabundance of pre-

cipitation in 1982 and early 1983. While a six or seven year cycle is













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Tallahassee


0
Jacksor















Tampa







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Gulf of Mexico
















Approximate Scale
1 inch = 72 miles


e



N


I/I,


I,


Atlantic
Ocean


West Palm Beach
e/ f0 /
Deerfield Beach
/' /01


Figure 1. Map of Florida Showing
Management District's


the South Florida Water
Area of Responsibility









thought to regulate the rainfall patterns over the southeast part of

the state, the severity and duration of future droughts cannot be

accurately forecasted.

The 1980-1981 drought was unusual due to its severity and duration.

Precipitation during this drought was less than half of the normal

annual average. This caused the main surface reservoir, Lake Okee-

chobee, to reach its lowest recorded levels. The South Florida Water

Management District (SFWMD), which has the ultimate responsibility for

insuring adequate water supplies for the area, was forced to take

unprecedented action and call for a 10 percent cutback in water use.

The drought was relieved by higher than normal precipitation levels in

Spring 1982 and early 1983. These high levels have forced the SFWMD to

draw down Lake Okeechobee from its resultant near record levels in

anticipation of the 1983 hurricane season.

This thesis focuses on urban water consumption. Therefore, no

attempt will be made to account for water use by other consumers. This

non-urban use accounts for approximately two-thirds of the total con-

sumptive use in the South Florida area.

The recent drought caused planners to ask some very difficult

questions regarding urban water use:

1) What is the effect of rainfall on demand? Specifically,

how does the lack of rainfall during the drought event

increase the water use rate?

2) How much of the daily per capital urban use can be attri-

buted to indoor and outdoor use?

3) How closely does irrigation (outdoor) use correlate to the

evapotranspiration needs of grasses?










The goals of this thesis are to ascertain how rainfall affects urban

water use, to determine where the water is being used, and finally to

produce models which will use climatic indicators such as evaporation

and rainfall to predict urban water use.

Urban use in the District's Lower East Coast Service Areas one,

two, and three (see Figure 2) will be analyzed. These service areas

include Dade, Broward, and most of Palm Beach County. Some 74 different

utilities operate in these areas with served populations ranging from

hundreds for a small trailer park, to over a million for the Miami-Dade

Water and Sewer Authority.

A three-fold approach will be taken. First, rainfall and pumpage

data will be analyzed. Second, an extensive review of the existing

literature as well as some other methods will be employed to determine

per capital indoor and outdoor consumption patterns. Additionally, the

effect of conservation on per capital consumption will also be studied.

Finally, a model utilizing pan evaporation as converted to evapotran-

spiration, and rainfall as effective precipitation will be developed to

predict demand on a short term basis.

The literature review and a description of the study area are

contained in Chapter II. Chapter III provides an enumeration of the

data required in this thesis. Chapter IV contains the methodology for

the rainfall-pumpage analysis, a discussion of data preparation and

analysis, and the model methodology. Chapter V presents the results of

the analysis of the rainfall-pumpage relationship, the literature

review of water consumption patterns, and the simulation models.

Chapter VI discusses the results and gives conclusions.


















---2700








ce






26000


Approximate Scale
1 inch = 35 miles


Figure 2. Map of South Florida Water Management Service Areas















CHAPTER II

PREVIOUS STUDIES OF URBAN WATER USE



Severe droughts have occurred throughout man's history. As pop-

ulation and water demand increase, the finite nature of water as a

natural resource becomes more evident. Droughts such as those in

England in 1972, California in 1975, and in Florida in 1980-1981 have

only heightened man's awareness of his vulnerability to the uncertainty

of natural supplies.

In order to prepare for and react to a drought the water use agency

needs to understand the demands that will have to be met during a

critical situation. Several elements must be analyzed to aid in this

understanding. These elements include a knowledge of forecasting tech-

niques and models, factors which influence water use, effects of con-

servation, and outdoor water use factors. This literature review sur-

veys these elements.



Forecasting Methods and Techniques

Boland et al. (1981) analyzed existing water demand forecasting

approaches. These approaches include: simple time extrapolation,

single coefficient regression methods, multiple coefficient regression

methods, and probabilistic analysis. These researchers conclude that

the first three of these methods are routinely used in forecasting.

They further suggest that the reason for nonuse of probabilistic anal-

ysis is the lack of information and documentation.
6







7

Heaney et al. (1981) reviewed 18 water demand models. These

models rely on one or more of the 14 socio-economic variables estab-

lished by the researchers. These variables include: income, property

value, price of water, cultural factors, water consumption behavior,

precipitation, evaporation, temperature, population, technology, irri-

gated area, land use, number of dwelling units, and lot size. Clearly

other variables may affect water use, but these are the ones which have

been used in existing water demand models. All of the models reviewed

were deterministic models; each will predict the same outcome in re-

sponse to an identical forcing function.

Hittman Associates, Inc. (1969) MAIN II Model, the only well known

water demand model that is widely available, utilizes the multiple

coefficient method to obtain municipal water use. It sums the level of

activity to arrive at an equivalent overall water use activity. The

main problem encountered with the use of this model is its large data

requirement.

Heaney et al. (1981) discuss the WRE/SCS Demand Model. This model

is a combination of Water Resources Engineers' urban water use model and

the Soil Conservation Service Model (TR21). It estimates monthly water

use for each month of a one year period. The composite model is rela-

tively simple to use with complexities occurring only in the manipu-

lation of the data.

Morgan and Smolen (1976) studied regression variables associated

with climatic conditions. They found that models using either temper-

ature and precipitation or evapotranspiration and precipitation per-

formed best.









Since water use data are a time series, it would seem reasonable

that good forecasting results could be obtained by the use of formal

time-series analysis techniques. Salas-LaCruz and Yevjevich (1972) have

performed a comprehensive study of time series analysis of water use.

They observed that the annual cycle of water use is primarily related to

temperature, but also to rainfall. However, they did not discuss cause-

effect relationships using factors such as population as explanatory

variables. No forecasting of water use was done to demonstrate their

findings in the form of a model.

Maidment and Parzen (1981) also used time series analysis for ana-

lyzing water demand. Their study focused on six Texas cities exhibiting

two distinct water use patterns. The study employed a so called

"Cascade Model" whereby the data are transformed at each step of the

process; these transformed data are then used as the input for the next

step of the model. The model involves four steps: detrending, desea-

sonalizing, autoregressive filtering, and multiple regression. It was

found that the process left a residual error which accounted for between

13 and 20 percent of the variance in water use. Population was found to

be the most significant variable. Their examination of the relationship

between water use and rainfall explained between one and eight percent

of the variance.

Franklin (1982) used the Maidment and Parzen Cascade Model to

perform a time series analysis of Deerfield Beach, Florida. Using

monthly and weekly data she was able to explain 61 and 72 percent of

the variation in water use. Correlating rainfall to usage accounted for

21 and 12 percent of the variation when using weekly and monthly data,

respectively.









In a recent presentation Maidment (1983) stated that to analyze

time series data adequately, it would be necessary to go to at least a

daily time step. Walker (1982) also concluded that monthly data were

inadequate when trying to model urban water demand. In her study of the

City of Gainesville, Florida using a time series approach she found

results similar to those of Franklin (1982).

Wong (1972), Yamauchi and Huang (1977), and Sterling and Antcliffe

(1974) have also used time series analysis with varying success in

their efforts to model water use.

Perhaps the best compilation of forecasting techniques for water

use can be found in "An Annotated Bibliography on Techniques of Fore-

casting Demand for Water", by Boland et al. (1981). This report lists

the recent publications in the water forecasting field and gives a cross

reference table of forecasting methods.



Factors Influencing Water Use

Maidment (1979) gives a bibliography of water demand factors by

reviewing the works of over 100 authors from 15 countries. He describes

the dependence of water use forecasts on the accurate knowledge of

demands and the factors which determine them.

In "Trends in Water Use" (1963) Bogue makes several general com-

ments on municipal water use. He further identifies ten factors which

affect water use: 1. size and type of community, 2. location, 3. water

quality, 4. pressure in the water system, 5. sewered or non-sewered

community, 6. metering, 7. age of the community, 8. lawn sprinkling, 9.

cost of water, and 10. air-conditioning. The author, however, did not

show the interrelationship of these factors or their relative impor-

tance.









Boland (1978) describes some of the various approaches used in

forecasting urban water demand and the criteria that should be employed

in the selection of a model. His primary conclusion is that the number

of customer connections is better correlated with use than is popula-

tion. This differs with the findings of Maidment and Parzen (1981).

They found that population is a much better regressive parameter than

the number of connections, especially in areas that exhibit volatile

population fluctuations. The South Florida urban area appears to

exhibit these types of fluctuations.

Bachelor (1975) discusses the proliferation of water using durable

goods. The five water-using appliances studied that were found to have

an effect on water use were washing machines, automobiles, dishwashers,

showers, and garden sprinklers. His results show that these variables

were the most important in estimating demand.

Clouser and Miller (1980) studied the shifts in household water use

due to technological shifts and implementation of conservation prac-

tices. Their study of two Indiana communities focused on the creation

of a water demand model. Statistically significant variables were

washing machines, dishwashers, swimming pools, and lawn watering. They

conclude that water saving devices when used in conjunction with the

afore-mentioned devices can be useful. They further state that the

conservation technique has merit any time construction of new facil-

ities can be avoided.



Impact of Conservation Practices

Several conservation methods are usually examined or implemented

when a drought situation is encountered. Boland et al. (1981) ranked









water conservation measures according to the amount of water saved,

cost, and acceptance as follows: 1. building codes requiring water-

conservation devices, 2. sewage reuse for irrigation and industry, 3.

educational campaigns, 4. individual installation of water-conservation

devices, 5. government intervention during a drought event, 6. lawn

watering devices, 7. pricing, and 8. control of urban growth. There-

fore, the researchers conclude that building code changes are the least

objectionable alternative for water conservation.

Feldman (1977) reviewed 34 types of conservation devices used in

reducing residential water use. Although no recommendations were made,

a complete and detailed description of the devices, their estimated sav-

ings, and a list of their manufacturers was given.

Bailey et al. (1969) found that shower head and faucet flow re-

duction devices could save 24 and 2 gpcd, respectively. They also found

that low flow toilets could save up to 30 gal/day over the present 5

gal/flush toilets.

The U.S.G.S. (1980) has determined that the following water con-

servation fixtures are cost effective when installed by the home owner;

toilet dams, plastic bottles for toilet tank displacement reduction,

dual flush equipment, shallow trap toilets, shower flow restriction

devices, and faucet aerators. Additionally, it was found that pricing

incentives would not be effective in reducing average daily demands on a

short term basis. They conclude that a public education campaign could

be the most cost-effective method of reducing residential water demand.

The above methods as well as outside water use bans and rationing

will reduce water use. Marin County, California was able to reduce









water demand by as much as 63 percent during the 1976 California drought.

Once the public has been educated as to the nature and consequences of

the water demand problem, conservation measures implemented during the

emergency situation remained. This is evidenced by Marin County's water

use still being 25 percent below pre-drought levels (Bollman and Merritt,

1977).

Baumann (1979) presented a summary of past drought management

measures. He lists the location of the event, the year of occurrence,

the type of restriction imposed, and the resulting decrease in water

use. Table 1 gives the resulting values of reduction. It is evident

that a ban on outside water use is the most prevalent method of reducing

water demand.

Blackwelder and Carlson (1982) surveyed water conservation programs

currently being used in the United States. It is an extremely com-

prehensive review of what each state has done in the way of water

conservation program implementation. Further, they give a model water

conservation program for the nation.


Outdoor Water Use

Evapotranspiration is the combined loss of moisture to the atmo-

sphere by both evaporation and transpiration (Eagleson, 1970). Evap-

oration is the direct vaporization of liquid from a free water surface

of a saturated layer. Transpiration is the flow of water vapor from a

plant to the atmosphere through a plant. In the long term global

hydrologic cycle, precipitation equals evapotranspiration. However, in









Table 1. Summary of Past Drought Management Measures


Investigator
(Location)


Groopman
(N.Y. City)

Anderson, R.W.
(Pawtucket)

Abbott, et al.
(17 Eastern
utilities)

Jezler
(San Paulo,
Brazil)

E.A.I.
(Washington
Suburban Sanitary
Commission)

Bol Iman
(Marin Co., Ca.)

National Water
Council (Great
Britain)

Larkin, D.G.
(Oakland, Ca.)

ililler
(Denver, Co.)

Griffith
(Los Angeles, Ca.)


Robie


Restriction
Imposed


Year


1968


1967


1972



1975



1977




1977


1976



1978


1978


1978


Ban outside use
and appeals.

Ban outside use
and appeals.

Voluntary and compulsory
bans on outside use and
appeals.

Ban outside use.
Limit on household use.


Ban outside use, appeals
to specific acts.



Ban outside use.
Rationing with fines.

Ban outside use.
Rotate cut-offs and
ban outside use.

Rationing with fines.


Limit outside use to 3
hours every third day.

Appeals and limited in-
dustry cutbacks with some
mandatory control.

Voluntary Restriction.
Rationing.


1978


Source: Baumann, D.D. The Role of Conservation in Water Supply
Planning. IWR Contract Report 79-2, Fort Belvoir,
Virginia, 1979.


Resulting
Decrease


10-22%


16-18%


18-50%



26%



40%




25%
63%

25%
40-50%


38%


21%


10-20%


up to 20%
up to 50%






14

Florida, precipitation exceeds evapotranspiration with the extra mois-

ture coming from evaporation from the surface of the sea (Jones et al.

1983).

A number of sources for evaporation data are available. Most of

them rely on the National Weather Service (NWS) and its cooperating

services. The Hydrologic Information Storage and Retrieval System,

HISARS (Portier, 1981), used in this thesis is a computer-assisted data

base using information supplied by NWS.

Unfortunately, pan evaporation is not equal to actual evapotrans-

piration (ET) in all but the rarest of cases. Therefore, a method of

converting pan evaporation to ET must be found. This will allow a

prediction of ET, leading to a prediction of crop water demand.

Several methods of determining potential ET in Florida are dis-

cussed by Jones et al. (1983). These methods include Penman, pan

evaporation, Thornthwaite, Blaney-Criddle, and the modified Blaney-

Criddle. This report compares the prediction of potential ET by all of

these methods. The Penman method was found to be the most accurate,

with the modified Blaney-Criddle and pan methods acceptable. Although

the Penman method is the most accurate, it is also the most complex with

a great deal of climatological data required. The pan evaporation

method produced a pan coefficient of 0.70. That is, potential ET is

found by multiplying the pan evaporation by the pan coefficient.

Khanal (1980) in his presentation to a Water Use Workshop stated

that a good approximation for the pan coefficient for turfgrass in South

Florida is 0.60 between October and March and 0.70 between April and

September. This is consistent with the results in the Institute of

Food and Agricultural Sciences, IFAS, ET report (Jones et al. 1983).









Allen et al. (1978) conducted a comprehensive investigation of ET

requirements of turfgrass. They found that if water table depths were

kept at 36 inches or less, the actual ET was 0.62 times pan evaporation.

Their experimentation was conducted at the Fort Lauderdale Experiment

Station in Broward County, Florida. This finding is similar to Jones et

al. (1983) where the total coefficient needed to convert pan evaporation

to ET was found to be 0.65.

Danielson et al. (1981) also studied turfgrass and the effect of

the irrigation scheme on ET. They used various treatments on grasses

such as cutting and fertilizing. They found that grasses cut to a five

cm height used 15 percent more water than grasses cut to 2 cm. Further,

they found that grasses that received adequate nitrogen used 10 percent

more water. They also found that the 5 cm grass and the nitrogen-

enriched grass were more resistant to moisture stress.

Barnes et al. (1979) found that lawn watering application rates

were between 125 and 175 percent of the average seasonal ET in two

Wyoming cities. They also noted that an aesthetically pleasing lawn can

be maintained with an average water application rate equal to or less

than the average seasonal ET. In a similar study, Cotter and Chavez

(1979) found that actual lawn water application rates exceeded the

estimated need by up to 42 percent.



Summary

This literature review has shown that there are four primary

methods used in water demand forecasting. These methods include single

coefficient regression, multiple coefficient regression, simple time










extrapolation, and probabilistic analysis. All of these methods except

probabilistic analysis are currently being used extensively. It was

further shown that the use of time series analysis requires the use of

the smallest time step possible (e.g. use of daily or hourly data).

Several factors were identified that can be used to estimate water

demand. Population was found to be the most significant regression

parameter in areas of volatile population fluctuations such as South

Florida. Precipitation and evaporation were found to be important

climatic factors.

It was found that public education is the most cost effective means

of attaining conservation of water. Once this conservation has begun,

it can remain long after the drought situation has ended. Flow re-

striction devices for the shower head, faucet, and toilet offer a

potential savings of up to 27% over the standard fixtures.

Finally, it was found that evapotranspiration is the primary

method of water removal from the soil layer. Determining this evapo-

transpiration from pan evaporation requires the use of two conversion

factors. The first converts pan evaporation to potential ET; the second

converts potential ET to actual ET. For the South Florida area the

product of these coefficients was found to be 0.62.















CHAPTER III

DESCRIPTION OF STUDY AREA



This chapter gives a description of the study area and defines the

required data. These data include pumpage, population, evaporation, and

precipitation. The general characteristics exhibited by the data are

also examined.



Required Data

The required data include: daily pumpage from the utilities,

estimates of the population served by these utilities, and daily evapo-

ration and rainfall over the service area. Pumpage, the dependent

variable, will be correlated to the following independent variables:

population served, rainfall, and evaporation. These variables were

chosen due to their significance in determining pumpage, as was reported

in the literature review (Morgan and Smolen, 1976 and Maidment and

Parzen, 1981). The daily time step requirement is used in response to

the findings of Maidment and Parzen (1981), Franklin (1982), and Walker

(1982) which were discussed previously; it was found that the smallest

time step possible should be used in the analysis of water use during

drought.



Pumpage. All of the utilities in the South Florida area report

daily pumpage to the Florida Department of Environmental Regulation









(DER). Therefore, each utility normally has on file several years of

daily pumpage data. DER keeps the daily data on file locally for up to

two years. It was not feasible to obtain daily pumpage data from all 74

utilities which currently serve the South Florida area. Hence, only the

12 utilities with daily pumpage of ten MGD or greater were utilized.

The selected utilities are listed in Table 2. The Sunrise and Palm

Beach County listings are aggregates of the four and six treatment

plants serving the areas, respectively. Figure 3 shows the approximate

area served by these 12 utilities. Table 2 lists the approximate total

area serviced by these utilities. Each utility was contacted by phone,

written correspondence, and when possible, by personal visitation. The

utilities were very helpful in providing the necessary data. Table 2

also gives the period of record of daily pumpage that was supplied by

the participating utilities. A list of the daily pumpage for the City

of Deerfield Beach, Florida is provided in Appendix A. Daily data for

all of the utilities used in this study proved too voluminous for

inclusion; however, the data may be obtained from the author. Monthly

data for the selected utilities, as collected by Dr. Woehlcke of the

South Florida Water Management District, are provided in Table 3.



Population. Once the pumpage for the utilities has been obtained

it is necessary that some idea of the population using the water be

determined. The United States Census Bureau population data has the

most credibility and validity over the long term. However, due to the

ten-year time frame between census counts, the volatile nature of

population changes are not always reflected. Municipal estimates, while

having a yearly or even shorter time frame, are not consistent from one











Table 2. Service Area and Period of Record For the Twelve Selected
Utilities in Southeast Florida
Period of Record

Utility Pumpage Rainfall Area in Acres (1978)

Boca Raton 1/81 12/81 (2) 21,632

Boynton Beach 1/77 12/81 1/77 12/81 18,099

Deerfield Beach 1/76 12/81 1/76 12/81 5,920

Delray Beach (2) (2) 10,508

Fort Lauderdale 6/81 6/81 6/81 6/82 27,315

Hollywood 1/80 12/81 1/80 12/81 14,086

Lake Worth 1/77 12/81 (2) 5,414

Miami-Dade WASA 1/77 12/81 (1) 276,732

North Miami Beach 1/80 12/81 (2) 18,958

Palm Beach County 1/77 12/81 (2) 186,750

Pompano Beach 1/80 12/81 1/80 12 81 9,485

Sunrise 1/77 3/82 12/80 3/82 25,401
620,300

(1) Rainfall Data Provided as Reported at Miami

(2) Data Not Provided by Utility








































Tamiami Canal


Palm Beach County

Lake Uorth

Boynton Beach
Delray Beach
__ Boca Raton
Deerfield Beach
Pompano Beach


Fort Lauderdale

_Hollywood

North Miami Beach








Miami-Dade WASA









Approximate Scale
1 inch = 20 miles


Figure 3. Approximate Service Area of Twelve Utilities in
Southeast Florida


Lake
Okeechobee

















Table 3. Monthly Pumpage For Twelve Utilities in Southeast Florida, MGD


Deerfield B


Pompano Bea


Boca Raton



Boynton Bea



Delray Beac


Lake Worth



Palm Beach


Fort Lauder



Sunrise


Miami-Dade



North Miami


Hollywood


YEAR


79
each 73
8-1
78
ich 79
GO
S 81
78
79
80




778
ch 60
78
:h ?9

78
79
80
7s



County 71
81
78
dale 79



78
80





---Jaj
79
60

78







BO
79

79


R t
78
79
so
R I


JA.t FE3 MAI A? R nIAY


195. 90
172 61
239.31
284.91
314 17
337.95
647.32
722. &6
541.60
609. 10
721-60
798.83
198 16
174. 4-
229. 91
183.09
275 15
232.09
371. 80
409.30
169. 10
172.44
223.57
223.44
105. 81
140.96
221. 40
224. 05
1314. 54
1326. 00
140 26
1510.73
184.09
211. 45
267. 29
310.23
6381.21
6549.397
7464.99
8301.49
727.76
770. 19
664. UB
829.47
504. 17
547-49
616.95
627 80


IC. 33
21,1 04
34. 213
235 65
490 00
372. 9
600.00
590 96
322 so
67. 30
662 40
633 70
106. 027
17. 65
209. 31
216 50
25. 05
332. 4
342 0G0
345 20
167. 23
194. 1I
186.8 1
194.30
107.43
136 65
204 71
1U7.45
1244.62
1333 74
I13i5. 68
1278 63
215. 71
226.65
243. 19
25. 36
5055.29
6361.29
6370 78
7339. 53
667. 70
743. 86
761. 55
694.79
497.45
534. 95
57. 14
545. 14


229. 25
2.233 11
3I:. 66
320.75
587. 34
716. 56
747.62
723. ,3
605.60
830.00
824.20
201. 8o
27.853
214. 76
237. 74
25A3 11
317. 72
396. 2
427. 38
424 65
197. 37
231.97
222 43
242. 79
123 94
163. 24
233 37
236 34
1433. 42
1734 90
1715. 61
153-. 72
207. 86
276. 02
279. 8
300 43
6637.389
7455 35
7925. 32
9313 59
o00.04.
901.74
877. 99
893. 44
554. 11
639. C
652. 80
50O. 05


217. 47
251.03
240. 05
33. 93
623.38
741. 43
615 65
8)32. 70
685 10
829.00
674.60
073 70
353. 59
203 53
310 55
303.65
334 61
133 17
367. 92
455.60
218. 60
239.01
13. 33
200 85
141. 15
175.52
136. 89
216. 53
1196. 58
1669. 40
1459. 95
1735.05
220. 53
Z90. 47
250. 86
354. 47
6552.91
7642.897
7220. 81
C751.96
60S.35
'.3 27
705. 73
533. 82
533. 43
619.30
592. 54
13 1. 96


320 31
I .3 -1.3
2-<. 01
2'0. 47
55J 96
237. 13
743 55
555 35
644. 00
616 70
784 00
671 90
257 04
12 33
26; 611
233. 76
351. 10
311.15
411. 12
337. 11
197 94
190. 93
211. 50
217.93
135.62
13d.30
205. 37
200 17
1403 01
12 7. 4.0
1517.23
13 4. 37
221. 25
214. 21
294.05
269. 73
6574 47
6735. 52
7003. 11
7903 60
7 O.*1
744.97
52. 37
031.79
533. 81
522. 78
623 C0
347. 50


JUN

204 43
2115. 33
235.67
224.56

73 U 20
64-14.
53'9. 6
343. 40
01 7. 50
637. 0
6'5 970
17 0.3 0

252. 71
234. 31
255. 19
406. 63
375. 3
329. 66
1-3. 83
2133. 7
230.47
Id9. 71
232.01
113.37
171. 6,
106. 57
105 24
1413. 10
1519.46
1469. 71
12 3. 74
215. 21
2 0. 77
29. 05
241. 1 1
6129.37
6 57. 42
7451.70
76b44.0
750. 16
827. 97
770. 79
736.25
508, 43
53. 72
595. 50
457. 31


JUL


320 .99
2 0.94
2[63 51

780 45
670. 76
641. 29
636.70
927. 90
717. 10
E01 70
208 07

240 02
254. 67
306. 04
44 62
374.E 0
374. 30
200.00
2679. 45
152 53
227. 06
120. 03
1 4. 31
165 90
210. 23
1405. 79
1690. 75
1501. .3
1477,!0
215. 54
251. 60
246. 53
284. 47
6516.20
7343. 72
7623 61
90/7.62
7/7. 4
02. '2
814. 27
47. 37
505. 71
624.02
61 1. 65
331. '6


AUC Sf:: CCT NOV DEC TOTAL


200 51
2.6 22
=12 03
223 01
578 16

785 46
530 34
669. 70
532 00
765 30
616 70
225 04
257. 32
251.65
201 77
32 991
443 07
421 00
325. 30
203 32
23?- ?
197 47
137. 82
130 02
163. 70
I73. 1
101 96
13d3 GO
137. 97
1337. 53
1203-1 0
S1t3 55
255. 72
259. 13
232. 83
6731.00
7227. 32
7JA32 21
76U0 46
7u3J 78
G4 7.26
016. 54
735. 05
530. 33
5 .7 9
615 74
503. 70


197. 67
1ot. 27
04. 62
210 50
505 56
502 92
646 64
46 45
626 30
519 00
63. 50
831 40
217. 62
165 33
223. 73
179. 9
327 31
307 81
370. 74
2=0 33
204. 13
177. 82
156. 72
190 50
121. 29
134. a
167. 52
131. 56
14CO 3d
1127. 54
1412 5O
1I!a Id
1 1 6 125
14. 77
219. 05
252. 53
276 6-1
6343 22
6473. 10
7503 G5
7554. 65
706.32
703. 05
764.09
632. 14
507. 32
521. 3
630. 2
514. ,11


10 12
207. 56
3J7. 14
233.62

7 ;61
3555
LOC3 90
631. CO
045 60
6J4. 00
2023 1
104. 73
2 5. 57
232. 92
304 37
355 64
443 74
323 S6
16 29
196 03
208. 25
213. 77
121. 80
154.60
211.94
206 91
1351. 12
1255 23
13jO 66
1IJ5 30
10U. 49
233. 73
2705 70
313 05
6375.84
;J 17.8 3
7720. 5.
902. 13
192. 76
755. 76
703. 94
601. 50
503 71
551. 93
615- 1 1
301.20


173 21
193 77
';4 7199
-*33 25
536 80
530 19
607. 51
593 97
575 60
585 60
655 30
603 O0
105. 37
107. 83
211. 6 1
23. 40
277 54
314. EO
35.977
325. 63
171.30
137. 45
190. 71
13. 67
11 63
152 61
197. 70
216. 74
1320. 27
1327.04
1305. 23
13'4 62
200 40
3322 52
261. 13
322. 73
6296. 71
6725. 43
733 27
7/d7. I1
723.07
765 40
777 63
691. 27
516 30D
516. 30
570. 19
233. 4;I


171 01
229. 14
257 ce
ZEJ 71
971 21
'603 66
6z3 G3
633 43
623. 70
672 20
757. 70
U17. 50
174. 4
212. 29
230 07
264 63
301. 71
315 12
340 90
420 01
172 415
204 56
209. 33
213 34
143 23
134 64
212 13
2416 0-3
1442 10
1346 19
1402 30
1517 2S
219 2'?

2.-9 33
347. 63
6614.42
7030 84
7'511 03
93'?7. HO3
')0 01
8 0. 15
81. 17
733 41
551.74
59 337
57/. 33
51/. 50


2449.8
2/';3.
3216 a
3162 9
6035 2
7670 a
0019.1
73-3 6
72J3. 4
55 2. I
0755 2
0525. 6
2565.0
2477.
2 .3 3. 2
25089 .2
2d39.8
3537. 9
375. 4
47 2 3
4.1322 0
2255. 6
25 7. 0
2337 4
2525.3
1437. 3
1905.3
2411. 3
2536. 9
16 57. 4
1/196 4
17655 3
16910 2
2451. 5
2934. 0
3 11. 7
3555 8
77239. 5
3203. 6
9; 16. 3
?6737. 2
9. : 4 I

97 7. 6
9337. 3
6 12. 6
7272. 5
6537. 7


Source: Woehlcke, C., Director of the Water Use Planning Division of

Department, South Florida Water Management District, Personal


the Resource Planning
Correspondence, 1982.









city to the next. That is, every city has their own way of estimating

population. Therefore, it is necessary to find population estimates for

a common time-frame, developed with a consistent set of assumptions.

An estimate meeting the criteria is available from the University

of Florida's Bureau of Business and Economic Research. The Bureau has

been doing yearly population estimates since 1972. These estimates use

several economic indicators including: census data (as base data),

electrical hook-ups, utility connections, and building occupancy cer-

tificates. The Bureau's estimates closely parallel both U.S. Census and

municipal population estimates.

The population data used in this study are a combination of census

data (for long-term trends) and Bureau of Business and Economic Research

data (for short-term trends). Table 4 shows the U.S. Census data for

the three counties of interest. This table includes historical data as

well as base-line projections made by the Bureau. Table 5 is the Bureau

of Business and Economic Research population estimates for the counties

and cities that will be analyzed in this thesis.

Unfortunately, city boundaries rarely correspond to the utility

service area boundaries. Therefore, an estimate must be made of the

population served by the utilities. Communication with the SFWMD, DER,

and the utilities provided input into these estimates. The selected

estimate of the population served for each of the utilities of interest

is given in Table 6.


Evaporation. The evaporation data were obtained from the Hydro-

logic Information Storage and Retrieval System (HISARS) at the University

of Florida (Portier, 1981). Table 7 is an excerpt from NOAA Technical











Table 4. Past and Projected Population of Broward,
Dade, and Palm Beach Counties.in Florida

County


Date


Broward


Dade


Palm Beach


267,739

495,048

935,047

1,267,792

1,616,000

1,866,700

2,246,100

2,700,400

3,215,900


79,989

114,688

228,106

348,753

440,800

542,800

653,100

785,100

935,000


387,522

693,669

1,497,099

2,236,645

2,291,600

3,437,400

4,136,100

4,972,500

5,921,800


Source:a United States Census Bureau (1980)
b Bureau of Business and Economic Research (1980)


Total


1940 a

1950 a

1960 a

1970 a

1980 a

1990 b

2000 b

2010 b

2020 b


39,794

83,933

333,946

620,100

834,800

,027,900

,236,900

,487,000

,770,900









Table 5. Bureau of Business and Economic Research, University of Florida
Population Estimates


A. Broward


Year


1973

1974

1975

1976

1977

1978

1979

1980

1981


County

Fort
Lauderdale


151,546

153,911

154,616

152,791

153,374

154,365

155,650

153,279

153,814


B. Dade County


North Miami
Beach


34,463

35,690

36,491

35,736

35,971

35,654

36,130

36,481

36,652


Miami
WASA


347,618

350,499

350,742

343,977

344,109

344,393

344,755

346,931

346,865


Total


1,373,609

1,413,102

1,437,998

1,449,300

1,468,270

1,494,276

1,519,247

1,625,979

1,718,516


Hollywood


118,334

121,138

121,400

118,872

117,777

118,095

120,172

121,323

121,955


Pompano
Beach


50,135

53,836

54,458

54,063

54,708

55,398

56,207

52,618

55,911


Sunrise


16,149

20,727

25,912

28,529

29,241

34,442

34,428

39,681

42,706


Total


769,419

828,169

876,296

884,872

902,542

929,584

966,083

,014,043

,047,313


Year


North
Miami


42,970

44,356

44,473

43,544

43,371

42,887

42,746

42,566

42,986


1973

1974

1975

1976

1977

1978

1979

1980

1981









Table 5. Continued


C. Palm Beach County


Year


1973

1974

1975

1976

1977

1978

1979

1980

1981


Boynton
Beach


26,507

30,462

32,275

32,473

33,036

34,204

36, 265

35,624

36,489


Lake
Worth


25,934

26,628

27,615

27,009

27,340

27,927

28,634

27,048

27,111


Boca
Raton


38,884

41,518

43,511

44,549

46,624

48,563

49,744

49,505

50,408


Delray
Beach


25,046

27,050

28,305

28,065

29,456

32,782

35,701

34,328

36,476


Total


427,983

459,167

477,751

488,044

505,605

534,551

564,447

573,125

576,863


Source: Bureau of Economic and Business Research, University of
Florida, Florida Estimates of Population 1972-1981, An-
nual Report, University of Florida, Gainesville, Florida,
1973 through 1982.









Table 6. Estimated Permanent Population and Population Served
of Thirteen Utilities in Southeast Florida


BROWARD COUNTY

Deerfield Beach


Date


Estimated
Permanent
Population


Estimated
Population
Serviced


Fort Lauderdale








Hollywood








Pompano Beach


1977
1978
1979
1980
1981


1977
1978
1979
1980
1981


1977
1978
1979
1980
1981


1977
1978
1979
1980
1981


30,649
35,079
40,243
39,143
40,138


153,374
154,365
155,650
153,279
153,814


117,777
118,095
120,172
121,323
121,955


54,708
55,398
56,207
52,618
55,911


31,000
35,480
40,703
39,591
40,598


226,430
227,892
230,273
232,654
238,625


100,000
100,212
102,033
102,180
103,547


63,000
63,794
64,726
60,593
64,385










Table 6. Continued


BROWARD COUNTY

Sunrise


Date


1977

1978

1979

1980

1981


Estimated
Permanent
PoDulation


Estimated
Population
Serviced


29,241

32,422

34,428

39,681

42,400


42,333

46,333

49,842

57,448

61,393


PALM BEACH COUNTY

Boynton Beach











Lake Worth


33,036

34,204

36,365

35,642

36,489


36,800

37,308

39,491

43,000

43,982


1977

1978

1979

1980

1981



1977

1978

1979

1980

1981


27,340

27,927

28,634

27,048

27,111


34,200

34,789

34,690

34,150

34,414


I










Table 6. Continued


PALM BEACH COUNTY

Boca Raton


Date


1977

1978

1979

1980

1981


Estimated
Permanent
Population


46,624

48,563

49,744

49,505

50,408


Estimated
Population
Serviced


46,200

48,792

54,821

58,490

60,129


Delray Beach











Unincorporated
(County)


29,456

32,782

35,701

34,325

36,476


31,000

34,395

37,458

36,014

38,271


1977

1978

1979

1980

1981



1977

1978

1979

1980

1981


61,566

61,997

62,834

62,530

63,996


76,212

76,746

77,782

77,406

79,250









Table 6. Continued


DADE COUNTY

North Miami


Date


1977

1978

1979

1980

1981


Estimated
Permanent
Population


Estimated
Population
Serviced


43,371

42,887

42,746

42,566

42,982


65,000

64,275

64,063

63,793

64,417


North Miami Beach











Miami-Dade WASA


1977

1978

1979

1980

1981


35,917

35,659

36,130

36,481

36,653


165,000

163,814

165,978

167,591

168,381


1977

1978

1979

1980

1981


344,189

344,396

344,745

346,931

387,357


,210,000

,210,728

,219,540

,213,600

,361,757




















Table 7. Mean Monthly, Seasonal, and Annual Class A Pan Evaporation (Inches)

For Stations With 10 Years or More of Record For Best Month









St.lion aL- 1 Oth.r Secord L.at e
Slr.4 Ino, ot Ap S. on Annual I-.. I D t.
o. 'o.-' J.n feb 5 K pr Apr Har Jun J.l AUK ,ep Ort No. Te .. ** ***' *** Mo/Tr Ho/Tr


Belle GIdr Ixp Statlon
26' 0, 80. 18'


rlialnrC X $
25' 09'. 0B" 55'


Ft. Lt.iArd.rle tr St.
26' 05', 80o' 1-




RI( lesh
25" 50', 80- I7'



Moore Bvn Locl4 5o. I
256 50', 81' 05"


a 0611 3.13 1.99 5.70 6.45 7.07 6.21 6.31 6.15 5.30n .73 3.66 1.14 35.87 76.2
39 35 40 40 40 40 40 40 40 to 40 o0


97 5 9 10 1 0 1 9 9 o


a 3171 3. 1 4.1) 6.24 7.54 7.01 5.97 7.13 6.97 5.94 5.52 5.31 3.51 tn 11 ]0.0
5 23 5 23 2 15 ) I 20 I2 23 24 24
1] R 9 4 11 11 10 7 17 9 y7 5 4



g 390 3.81. 4.42 5.17 7.26 7.0O 7.12 7.1& 7.27 5.91 5 51 4.79 3.1 0 41.77 I0.70
35 37 37 36 39 35 38 38 37 3] 39 35
10 9 7 5 7 11 & 7 1I4 6 II 5 4


8 5S95 4.05 4.30 6.47 7.87 8.50 7.64 7.10 7.17 6.56 5.91 &.46 31.0 41.13 30.75
31 31 II 11 1l 30 31 31 1I 31 30
II 9 10 9 II II 10 I7 7 II 7


T.=a..l Trill (to MI1 a-.4) 8570 3.36 1.85 5.41 6.31 6.53 6.15 6.7 6.57 5.36 5.53 3.1 1.20O 37.31 75.94
I2" 65'. o' 50o' 29 30 10 2 31 17 77 5Z 1 3I1 I 29
? 9 7 1 13 4 I 9 140 7 3


Ver'o r. h V7
27* 38". BOO 21"


S 7219 7?.O 3.60 5.44 6.64 7.07 6.65 6.64 5.3G 5.03 4.1I 1.31 7.65 31.51 24.43
14 14 1I I4 I4 15 15 15 15 15 I5 15
7I 13 15 II II 9 17 14 7 7I 77 II 1I


62.16 3/40 12/79


5/63 9/75


70.39 11/13 5/75

4








74.07 1/49 12/79

6

63.25 /41l 11/19

3

).?96 5/65 12/19

12I


Ylri lIn of data In the itblr for .crh rt4.lo, 4I ,..n vror lon In Inchel; s-ro.n line I th. nu,-r of 0 y-or of r.rcor r-r -t"h; *no d thlr4 line li the' ro.fflrlc n
of variltlon I. prrrent (co I.-.,l ^ only .i rre Ih te re !0 y-7ar or wnre of r.rnrd A 4rlng 197 -1970).
"* Cll_.to! lrl. nt V (N;Al4 EDIS)

** o. Inu llrern c drls b-r~~5n 1956-70 to "-;7.le Ih4 ror;l lr l of v r rli on.







Source: Farnsworth, R.K., and Thompson, E.S. "Mean Monthly, Seasonal, and

Annual Pan Evaporation For the United States.", NOAA Technical

Report NWS 34, December 1982.









Report NWS 34 (Farnsworth and Thompson, 1982). It contains pan evapo-

ration data summaries for seven of the eight stations in the study area.

The summaries consist of the monthly, seasonal, and annual means, the

years of record, and the coefficient of variation (C.V.). A note of

caution on the validity of the C.V. is given here with a discussion to

follow. The Clewiston Engineer Station is not included in the NOAA

report.

HISARS has current data of sufficient length for the Clewiston

Engineer station. Table 8 lists the mean monthly, and annual evapo-

ration data for Clewiston. A complete list of the evaporation stations

that have data available through HISARS is given in Table 9. As shown,

HISARS contains all of the NOAA stations except the Flamingo Ranger

station. The Loxahatchee station was not used because the last month of

record is December 1962, too early for the purposes of this study.

The eight pan evaporation stations used in this thesis, with the

exception of Vero Beach 4W, have been located in Figure 4. The iso-

evaporation lines are also shown on this figure. Care should be taken

in the interpretation of the accuracy of the iso-evaporation lines in

that only eight stations were used in their construction. Additionally,

all of the evaporation stations in the study area are located in the

eastern half of the area with none located on the west coast.



Rainfall. Daily rainfall data are available through the National

Weather Service and also through HISARS. Hourly rainfall for most

stations can also be obtained from these sources; ,however, for the

purpose of this report only daily data will be analyzed. In addition,











Table 8. Clewiston U.S. Eng. Station Summary




Month Mean Evaporation
(inches)



January 2.91

February 3.54

March 4.88

April 5.62

May 6.33

June 5.57

July 5.62

August 5.09

September 4.69

October 4.42

November 3.50

December 2.75


Total 54.56






Source: National Oceanographic and Atmospheric Administration. NOAA
Climatological Data for Florida 1979. National Climatic Cen-
ter, Vol. 83, Asheville, North Carolina, 1979.






33

Table 9. Hydrologic Information Storage and Retrieval System
Evaporation Station Listing For South Florida

BELLE GLADE EXPERIMENT STATION
LATITUDE 26-40-00 LONGITUDE 80-38-00
ELEVATION 16 FT MSL REGION
PERIOD OF RECORDS LENGTH, MONTHS
08/1941 08/1941 1
07/1948 09/1948 3
12/1948 01/1949 2
03/1949 03/1949 1
05/1949 07/1949 3
10/1949 12/1949 3
02/1950 03/1950 2
05/1950 06/1951 14
08/1951 08/1951 1
10/1951 02/1952 5
04/1952 06/1952 3
09/1952 07/1953 11
09/1953 04/1954 8
07/1954 07/1954 1
09/1954 09/1954 1
11/1954 11/1954 1
01/1955 06/1955 6
08/1955 02/1959 43
04/1959 01/1978 226
03/1978 12/1979 22


CLEWISTON U S ENG
LATITUDE 26-45-00
ELEVATION 20
PERIOD OF
08/1948 -
10/1948 -
08/1955 -
05/1957 -
10/1963 -


LONGITUDE 80-55-00
FT MSL REGION
RECORDS LENGTH, MONTHS
08/1948 1
06/1955 81
03/1957 20
08/1963 76
12/1979 195


FORT LAUDERDALE EXPERIMENT STATION
LATITUDE 26-05-00 LONGITUDE 80-15-00
ELEVATION 6 FT MSL REGION
PERIOD OF RECORDS LENGTH, MONTHS
11/1953 12/1954 14
02/1955 07/1955 6
09/1955 07/1970 179
10/1971 06/1974 33
01/1975 07/1975 7
09/1975 06/1979 46


LATITUDE 25-50-00
ELEVATION 12
PERIOD OF
07/1948 -
04/1950 -
06/1952 -
08/1953 -
12/1954 -
06/1955 -
10/1955 -
02/1957 -
02/1965 -
11/1969 -
04/1970 -
03/1972 -
10/1972 -
09/1973 -
05/1974 -
10/1974 -


LONGITUDE 80-17-00
FT MSL REGION
RECORDS LENGTH, MONTHS
02/1950 20
04/1952 25
06/1953 13
10/1954 15
04/1955 5
08/1955 3
12/1956 15
12/1964 95
09/1969 56
02/1970 4
11/1971 20
08/1972 6
07/1973 10
01/1974 5
08/1974 4
12/1979 63


HIALEAH









Table 9. Continued


LOXAHATCHEE
LATITUDE 26-41-00
ELEVATION 14
PERIOD OF
03/1948 -
12/1962 -

MOORE HAVEN LOCK 1
LATITUDE 26-50-00
ELEVATION 35
PERIOD OF
08/1948 -
01/1977 -
TAMIAMI TRAIL 40 MI BEND
LATITUDE 25-45-00
ELEVATION 15
PERIOD OF
07/1748 -
11/1950 -
03/1752 -
08/1952 -
05/1953 -
09/1953 -
09/1957 -
05/1970 -
08/1970 -
VERO BEACH 4 W
LATITUDE 27-38-00
ELEVATION 20
PERIOD OF
05/1965 -
10/1979 -


LONGITUDE 80-16-00
FT MSL REGION
RECORDS LENGTH, MONTHS
01/1960 138
12/1962 1


LONGITUDE 81-05-00
FT MSL REGION
RECORDS LENGTH, MONTHS
11/1976 310
12/1979 36

LONGITUDE 80-50-00
FT MSL REGION
RECORDS LENGTH, MONTHS
09/1950 27
12/1951 14
06/1952 4
03/1953 8
07/1953 3
07/1957 47
02/1970 150
06/1970 2
12/1979 113

LONGITUDE 80-27-00
FT MSL REGION
RECORDS LENGTH, MONTHS
08/1979 172
12/1979 3


Source:"Hydrologic Information Storage and Retrieval
System" Data Base, UF/NERDC System
University of Florida


























































































KISSIMMEE EVEkGLADES AREA
FLO-IDA
I D( l' tl. i> Of LAICwLlui[
SOIL COMLh^AoI.N 51 vilC


I-- lj l.6-- -


Figure 4: Iso-Pan Evaporation For
Southeast Florida







36

most of the water treatment plants that were contacted for information,

keep a record of rainfall at the plant.

The NWS data are the most reliable but the stations are not always

located near the service areas of some of the utilities. This can lead

to errors because rainfall patterns are highly variable in South Florida.

The utility's precipitation data seem to be the most accurate of those

located within the service area. However, errors may arise because

treatment plant personnel may be inadequately trained in operating rain

gauges.

For the purposes of this thesis rainfall at the water treatment

plants will be used when the amounts recorded correspond to the nearby

weather station gauge. Otherwise, data from the nearest weather station

will be used. Appendix B contains the daily rainfall data as recorded

at the Deerfield Beach Water Treatment Plant. The values recorded are

in inches of rainfall over a 24 hour period. As with the pumpage data,

rainfall data for all of the utilities of interest proved too voluminous

to include herein. These data are available by contacting the author.



General Characteristics

This section will give some of the general characteristics of the

pumpage, population, and evaporation data. The pumpage data are char-

acterized in Tables 10 and 11. Table 10 gives the summary statistics of

pumpage for the 12 selected utilities. The average monthly pumpage was

analyzed to determine if monthly seasonality has a significant impact.

It was found that April has the highest average pumpage at 456.28 MGD.

September was found to have the lowest monthly average pumpage at 398.84

MGD. The data exhibit little variability with the percent of total

pumpage ranging from a high of 9.04 to a low of 7.90.









Table 10. Summary Statistics on Pumpage For the Twelve Utilities
1978 Through 1981


Month Average Pumpage MGD Percent of Total


January 406.62 8.06

February 417.91 8.28

March 444.88 8.81

April 456.28 9.04

May 411.58 8.15

June 416.76 8.26

July 437.86 8.68

August 419.40 8.31

September 398.84 7.90

October 406.55 8.06

November 406.07 8.05

December 424.34 8.40

Total 5047.09 100.00










The daily variability of pumpage for Deerfield Beach, Florida was

also examined to discern if the day of the week was significant in

weekly water use patterns. Table 11 gives the variability in daily

pumpage as a percent of the total weekly pumpage. The values range from

a high of 14.81 percent for Wednesday to a low of 13.62 percent for

Sunday, indicating little daily variability.

The pan evaporation data for the Fort Lauderdale Experiment

Station were analyzed to determine if any long term trends were ex-

hibited. Table 12 shows the variabiltiy in pan evaporation as a per-

centage of the yearly total. As is expected, the cool season (October

through March) shows a lower pan evaporation average than the hot

season.

Per-capita pumpage was determined by using the average population

served and the average pumpage in MGD. The results for each of the 12

selected utilities and the totals are given in Table 13. This yields

an approximate per-capita consumption rate of 186 gallons.

In summary, the pumpage data exhibits little variability between

the monthly or daily values. Pan evaporation exhibits a great deal

of variability between the warm and cool months. The average per

capital consumption for the 12 selected utilities was found to be

186 gallons/day.









Table 11. Daily Variability in Pumpage For Deerfield
Beach, Florida 1976 Through 1981



Day Percentage of Total


Monday 14.66

Tuesday 14.06

Wednesday 14.81

Thursday 14.15

Friday 14.57

Saturday 14.13

Sunday 13.62

Total 100.00










Table 12. Long Term Summary Statistics For Pan Evaporation
Fort Lauderdale Experiment Station: 1953-1979


Average Pan Evaporation (in/day)


Percent of
Total


January

February

March

April

May

June

July

August

September

October

November

December

Total


Month


0.1216

0.1514

0.2035

0.2397

0.2439

0.2247

0.2345

0.2261

0.1950

0.1829

0.1427

0.1229

2.2889


5.31

6.61

8.89

10.47

10.66

9.82

10.25

9.88

8.52

7.99

6.23

5.37

100.00









Table 13. Per Capita Use 1978 Through 1981
For Selected Utilities


Average Population
Utility Served


Average Pumpage
MGD


Per Capita
Pumpage gpcd


Boca Raton

Boynton Beach

Deerfield Beach

Delray Beach

Fort Lauderdale

Hollywood

Lake Worth

Miami-Dade WASA
(include North Miami)

North Miami Beach

Palm Beach (units 1-6)

Pompano Beach

Sunrise (units 1-4)

Total


55,558

40,945

39,093

36,535

232,361

101,993

34,511

1,315,543


166,441

77,7,96

63,374

53,754

2,217,903


22.77

7.34

7.96

11.79

46.91

18.47

6.68

244.98


25.91

5.71

20.49

8.33

420.64


409.76

179.18

203.73

322.82

201.87

181.09

193.61

181.14


155.66

73.35

323.25

154.98

189.72















CHAPTER IV

METHODOLOGY


As was stated in the introduction, a three-fold approach will be

taken in this study. First, rainfall and pumpage records will be ana-

lyzed to determine the relationships that govern their interactions.

Second, an extensive review of the existing literature as well as two

other methods will be employed to determine per capital indoor and out-

door consumption. Additionally, the effect of conservation measures on

these consumption patterns will be reviewed. Finally, two models will

be established whereby the pumpage-rainfall and short-term demand re-

lationships can be simulated.

This chapter will discuss the methodology to be utilized. The

first section gives the methodology used to analyze the pumpage-rainfall

relationships. The second section details the methodology for consump-

tion and conservation analysis. The final section details the data

preparation and methodology of the consumption models established for

this report.



Analysis Procedures

Rainfall-pumpage relationship. The effect of a drought on water

use can be marked. Outdoor use in response to the lower rainfall can

rise dramatically. To determine the relationship between the two









constituents, the change in pumpage in response to a rainfall event will

be examined. A decrease in water production would be expected following

a precipitation event with a subsequent rise over time to pre-event

levels.

Maidment (1983) emphasized that for the six Texas cities he was

researching, a rainfall event caused a significant and drastic reduction

in subsequent water pumpage. Further, he found that five to seven days

elapsed before the pumpage returned to its pre-event level. Addition-

ally, he observed that the decrease in pumpage appeared to be inde-

pendent of the amount of precipitation.

For the purpose of this thesis the daily rainfall and pumpage data

for the period of record will be analyzed. The goal of this section

will be to answer the following three questions:

1. Does rainfall cause a significant reduction in water

production levels?

2. Is the quantity of rainfall correlated with the amount of

pumpage reduction? Or, as Maidment found, does simply the

fact that it rains at all cause the reduction in pumpage?

3. What is the time (days) until completion of the recovery

of the pumpage to pre-rainfall event levels? Specifically,

what is the short term memory of the system?

To answer the first question, a plot of rainfall and pumpage versus

time will be made. A visual analysis will be used to ascertain if there

is a decrease in water production due to a rainfall event. This de-

creased water use would be shown by a dip on the graph of pumpage versus

time immediately after a precipitation event.









The answers to the next two questions require a more in-depth

analysis of the data. A listing of the daily pumpage and rainfall data

must be made. This listing will be analyzed by recording the decrease

in pumpage, in million gallons, corresponding to a rainfall event. The

correlation can most easily be seen when there is an extended period of

dry weather proceeding the rainfall. Therefore, only single events will

be used in this analysis. By single events it is meant that there is a

dry period proceeding the rainfall event and that only one rainfall

event occurs. A list will also be made of the pumpage values one day

prior to the rainfall event through recovery to the pre-event levels.

The amount of the rainfall event will be recorded for comparative pur-

poses. Recovery will be defined as the period of time from the peak

decrease in water pumpage until pumpage has returned to within a certain

percentage of the pre-event pumpage levels.

A method similar to the rainfall-runoff hydrograph method used in

hydrology (Chow, 1964, p. 134) will be employed to analyze the rainfall-

pumpage relationship. Figure 5 shows an idealized view of the actual

rainfall-pumpage relationship. By contrast, Figure 6 shows the inverse

of the pumpage curve. The similarity between this curve and the typical

runoff hydrograph can be easily seen. The common characteristics of the

runoff hydrograph peak, volume, and duration are also shown in Figure 6.

The peak is a measure of the amount of decline in pumpage rate due

to a rainfall event. It is calculated using the equation:


Z = Pt) P(t-l)















1

2 ^
C)

3


4-
1--

5


6

7


1 2 3 4 5 6 7 8 9 10 11


Time (Days)
Idealized View of Pumpage
Rainfall (right ordinate)


(left ordinate)
Versus Time


Time (Days)


The Inverted Hydrograph, Inverted
View of Pumpage Versus Time


Idealized


Figure 5.


r)
3
r=



ro
4 -

c-
5 "

6


7


Figure 6.










where Z = peak pumpage rate decline, MGD,

P(t-1) = pumpage one time step prior to
the rainfall event, MGD, and

P(t) = pumpage rate at time step equal to
that of the rainfall event, MGD.

Hence, the pumpage recorded for the day that the rainfall event occurs

is subtracted from the pumpage of the previous day.

The volume of the pumpage corresponds to the integrated total

decline in pumpage due to the rainfall event. It is calculated by

computing the area of the shaded portion of Figure 6. The general

equation used is:


t2
Volume = f f(t) dt (2)
t1


where f(t) = equation of the curve representing pumpage,

t = time one time step prior to pumpage
decline, days, and

t2= time of pumpage return to pre-rainfall
event levels, days.

For the purpose of this thesis the volume is found by equating the

area under the curve to a number of rectangles. Figure 7 illustrates

this method of area calculation. The horizontal axis corresponds to the

day of the rainfall event. That is, zero is equal to the day of pumpage

corresponding to the rainfall event. Negative days are the days prior

to the rainfall event whereas positive days correspond to days after

the rainfall event. Thus, the area under the curve is equal to the sum

of the areas of the rectangles.



















I I I I I I11


Rainfall = 0.80 inches











Reduction in Pumpage = 0.30 inches




I- CD CD
C)o )


M C O CICDCC) ) C)
i CD I CD CD C\i --

L C
I o o I I I

-3 -2 -1 0 1 2 3 4 5 6


7 8 9 10


Days From Beginning of Rainfall Event


Figure 7. Idealized Schematization of Rectangular Method of Volume Determination


0. I


0.0(



0.01



0.0;


0.0(


-0.0


-0.5


-1.0


-1.5


-2.0


-2.5
U
ar

-3.0

4-
-3.5 .=









The duration of the event is the time from the beginning of the

rainfall caused pumpage drop until the pumpage returns to its pre-event

level. This time corresponds to the short-term memory of the water

user. Specifically, it is the interval of time where the water user's

perceived effect of the rainfall event is still present. For the pur-

pose of this thesis the pumpage rate will be considered returned to pre-

event levels when the pumpage following the event is at least 98 percent

of the pre-rainfall levels.

The volume of the hydrograph will be compared to the volume of

rainfall to ascertain if there is a true correlation. Maidment (1983)

did not find any correlation between the volume of the two constituents.

However, it must be noted that two-thirds of the water use in his study

was for outdoor use. This is not the case with the South Florida area,

as will be shown in the next section.

One method of analysis of the recovery phase of the hydrograph is

to equate the recovery leg to an exponential function. This method will

be employed in this research. A plot of the natural log (In) of pumpage

(Q) versus time for each single event will be made. This plot will

allow the slope and intercept of the line of best fit for each event to

be found. The line of best fit will be determined by using least

squares regression techniques. It is projected that a linear semi-log

relationship comparable to the typical rainfall-runoff recovery rela-

tionship will be displayed. Thus, the slope of this linear relationship

will be used to establish a recovery function. The exponential function

will be of the form










P = a ebt (3)



where a = the intercept of the line of best fit,

b = the slope of the line of best fit,

t = the time since the last rainfall event,
days, and

P = the amount of pumpage, inches.

This method of analysis has been used reliably in rainfall-runoff

studies. It has not yet been proven to simulate the perceived rainfall-

pumpage relationship of the consumer adequately.

In summary, visual inspection of the plots of rainfall and pumpage

versus time suggests the relationship between these two variables. The

pumpage analysis allows a quantification of this relationship by de-

fining quantity, peak, and duration. The use of the exponential func-

tion allows the simulation of the recovery leg of the hydrograph. If

the exponential function for the recovery leg does not provide usable

results a trial-and-error method will be employed in the models to

simulate the recovery leg.



Consumption and conservation. Where does the water go? The answer

to this question would seem trivial at first inspection. However, for

South Florida the answer is unknown except for some broad and sweeping

generalities. Researchers who have addressed this question vary on the

answer. This section seeks to determine the water use characteristics

of the South Florida urban area. Specifically, the present research

seeks estimates of total per capital use, indoor use, outdoor use, and









the effects of conservation. To accomplish this task a review of the

related literature has been performed. In addition, two numerical

methods of determining indoor and outdoor use will be discussed. These

two methods will provide a check on the findings of the literature

review.

The literature review will focus primarily on the South Florida

area. Unfortunately, the literature relating specifically to this area

is limited in that most studies deal with regional water use patterns.

Additionally, the segregation of indoor and outdoor water use is not

defined in most of the studies.

The results of this literature review will be presented in the

following chapter. A listing will be made of the results of the most

pertinent studies, including this one, for both the total per capital use

and the indoor versus outdoor division.

Two methods were employed to delineate indoor and outdoor use:

1) Assume that per capital indoor water use will remain constant

throughout a region; an average person from the same

region will consume approximately the same amount of water

for indoor use.

2) Use a base flow argument where, if there is a relationship

between outdoor use and rainfall, the pumpage immediately

following a lengthy rainfall event will be solely for

indoor use.

The literature study in conjunction with the two numerical methods

of consumption analysis will provide reasonable values for per capital

consumption as well as for the percentages of indoor and outdoor use.









Data Preparation

Evaporation. The pan evaporation data from the HISARS system are

given as inches/day over the entire area. A correction is needed to

reflect the fact that impervious areas do not contribute to evapo-

ration. Additionally, pan evaporation must be converted to evapotrans-

piration.

To make the conversion from pan evaporation to evapotranspiration

the following equation is used



ET = K1 K2 K3 E (4)



where K1 = coefficient to convert pan evaporation to
potential evapotranspiration,

K2 = coefficient to convert potential evapotranspiration
to actual ET,

K3 = coefficient to account for the percent
perviousness of the area, and

E = actual pan evaporation, inches.

The values for K1 and K2 are the ones found by Jones et al. (1983) and

Khanal (1980) as presented in the literature. They are 0.70 and 0.89

respectively. The value for K3 is found in the pumpage data preparation

section of this report.



Pumpage. The pumpage figures reported by the utility are in mil-

lions of gallons per day. These values must be converted to like units

(inches) for further use in the modeling section of this thesis. Addi-

tionally, since any outdoor portion of this pumpage will be used only

for the pervious area the values must be converted to reflect only

pervious area use.









The following equation was developed to convert the pumpage values

to inches over the service area:


PI = 36.83 PG (5)


where PI = pumpage, inches/acre, and

PG = pumpage, million gallons/acre.
This PI represents the pumpage in inches over the entire serviced area.

The units for pumpage that are required by the models are inches

over just the pervious area. Therefore, some method of determining the

pervious area must be established. Heaney et al. (1977) present equa-

tions that can be used to estimate the percent imperviousness of a

catchment. The first of these equations is (Stankowsky, 1977):


I = 9.6 Dd(0.573-0.0391 log10PDd) (6)


where I = impervious, percent, and

PDd = population density in developed portion of the
urbanized area, persons/acre.

A second equation is used to define PDd. This equation is:


PD e0.17PD
PD = eD_ (7)
0.17PD 1


where PD = average gross (developed and undeveloped)
population density, persons/acre











PD is calculated by using equation


PD = P/A


where P = population, and

A = service area, acres.

Once the percent impervious is known it is necessary to determine

the percent pervious. This is accomplished using;


Per = 100 I


where Per = pervious area, percent, and

I = impervious area, percent.

The area in acres is then multiplied by Per to get pervious acreage

using:


SPer A
P 100


where Ap = pervious area, acres

Per = pervious percent, and

A = service area, acres.

The pumpage in inches is then determined by:


P
Q -er 100
Per


(10)


(11)









where Q = pumpage, inches,

PI = pumpage, inches/acre, and

Ap = pervious area, acres.

This value of Q can then be input into the simulator that will be

developed in the next section.



Water Consumption Models

This thesis has previously examined the rainfall-pumpage rela-

tionship and the question of where the water goes. The third topic is

to determine if pan evaporation can be an effective tool in the deter-

mination of irrigation needs. To make this determination two models

were developed. The first model will utilize pan evaporation and

rainfall in an effort to establish irrigation needs. The second model

will utilize rainfall and consumption to simulate pumpage patterns. The

ultimate goal for these two models is to forecast short-term water

demand.



Irrigation model. The irrigation model uses the storage reservoir

principle for the upper zone of soil. The upper zone in this case

corresponds to the root zone. Typical values of available soil moisture

by soil textural class are shown in Table 14. Similar values for soil

moisture capacity are used in a soon to be released model for estimating

quantities of sanitary landfill leachate (Walski et al. 1983).

It is generally accepted that Florida's typically sandy soils hold

approximately one inch of water in the top 12 inches of soil (Augustin,








Typical Range of Available Soil Moisture
By Soil Textural Class


Soil Textural Class


Available Soil Moisture Storage'


Range in./ft.


Very coarse textured sands and
fine sands

Coarse textured loamy sands and
loamy fine sands

Moderately coarse textured sandy
loams and fine sandy loams

Medium textured very fine sandy
loams, loams, and silt loams

Moderately fine textured sandy
clay loams and silty clay loams

Fine textured sandy clays, silty
clays, and clays


0.50-1.00


0.75-1.25


1.25-1.75


1.50-2.30


1.75-2.50


1.60-2.50


aStorage between field capacity (1/10 to 1/3 ATM) and wilting
point ('15 ATM).



Source: Metcalf & Eddy, Inc., Process Design Manual for Land
Treatment of Municipal Wastewater, EPA, Army Corps of
Engineers and USDA, EPA-625/1-75-008, 1977.


Table 14.


Average


0.75


1.00


1.50


2.00


2.20


2.30










undated). This assumes that the majority of roots will be in the top

12 inches of soil. This is usually the case, especially with the turf-

grass in Florida. Bermuda, Saint Augustine, and Bahia grass all exhibit

this characteristic. Thus, the root zone can be modeled as a one inch

reservoir.

Once the amount of upper zone storage has been determined, it is

necessary to estimate outflow of this reservoir. Some crucial assump-

tions had to be made in order for a workable model to be established.

The first assumption is that rainfall on the area in question will be

added directly to the upper zone of the reservoir. Any rainfall in

excess of the amount required to raise the storage (rootzone) volume

above the one inch level is discarded. This relieves the model of the

arduous task of modeling surface runoff and groundwater zones. This is

not to say that there is no flow through the upper zone to the lower

zone, only that the upper zone will fill first; once filled, no further

precipitation is of interest. Figure 8 shows this inflow configuration.

In conjunction with the first assumption, the second assumption is

that the only drawdown of the reservoir level in the storage basin is by

evapotranspiration. This has been shown to be a reasonable assumption

when estimating the effects of rainfall on irrigation water requirements

(Quackenbush, undated).

The final assumption has been stated previously; only the rainfall

over the pervious area enters into the soil layers. This can be a

reasonable assumption if it is assumed that the impervious areas of the

catchment are sewered so that the rainfall that does occur is directed

away from the storage areas.

























Precipitation


Overflow (to lower zone or runoff)


Figure 8. Configuration of Upper Zone Storage Reservoir








Simulator techniques. The simulator utilizes the prepared data as

input for the modeling process. The model acts as a simple moisture

accounting device; it keeps track of the inflows (precipitation and

irrigation) and the outflow evapotranspirationn) of the soil moisture

storage system.

User supplied inputs to the model include the coefficients neces-

sary to convert pan evaporation to evapotranspiration, the coefficient

to convert pumpage from millions of gallons to inches, the beginning,

minimum, and total storage capacities of the root zone. Also input to

the model are two parameters which initialize the irrigation procedures.

Three different scenarios for irrigation initiation can be simulated.

The first uses the number of days since the last rainfall event to

determine the start of irrigation. The equation used is


If t > tmax' then irrigate (12)



where t = time since the last rainfall, days, and

tmax = user specified number of dry days prior to
the start of irrigation.

The second irrigation initiator is the volume of the storage reser-

voir. In this case the minimum storage value that is input by the user

is compared to the actual storage in the reservoir. If the storage

level is less than the minimum storage level irrigation is initiated.

The equation used is


If V < Vmin, then irrigate


(13)










where V = storage volume in the root zone, inches, and

Vmin = user supplied value of minimum storage level
prior to irrigation.

The final scenario utilizes a combination of the first two methods;

irrigation will commence when a certain number of dry days have occurred

and/or the value of the level of storage in the reservoir (root zone)

has reached a user specified minimum.

The simulator examines each day of the simulation period. It first

determines if there is sufficient storage volume available for irri-

gation. It then determines if rainfall has taken place. If it has

rained, the rainfall amount is added to the storage volume with any

excess of the total storage capacity of the reservoir being discarded.

If it has not rained the model determines the irrigation method to be

used and the amount of irrigation that will take place. Evapotranspi-

ration is subtracted from the storage volume and this volume is the

beginning volume for the next day of simulation.

For the purposes of this thesis the value of the storage volume for

simulation will be either the commonly used one inch value or the value

of effective storage that is found in the pumpage-rainfall relationship

analysis. Both of these reservoir volumes should be adequate for the

modeling process but the effective storage will give a better simulation

of the real world situation. The value for this effective storage

volume will be computed in the rainfall-pumpage relationship section

and the results will be given in Chapter V.










The precipitation-pumpage model. This second model seeks to

simulate the rainfall-pumpage relationship as quantified in the first

section of this chapter using the methodology provided. This is accom-

plished through the use of either a reference daily pumpage or by com-

puting the estimated daily pumpage using the per capital consumption

rates. Once this has been accomplished the simulator strives to follow

as closely as possible the actual rainfall-pumpage values.

The model utilizes the coefficients established by using the

methodology as shown in the first section of this chapter to simulate

the amount that pumpage drops off due to rainfall. It further uses the

recovery function to establish the amount of recovery that occurs after

the rainfall event.



Summary of models. The two models proposed herein will give some

indication of the effect of evapotranspiration on water use and the

relationship of rainfall to pumpage. The second model is envisioned as

a method of estimating water use during a drought situation if the

rainfall events are also estimated. The results of the output from the

models as well as the other methods of analysis will be given in the

following chapter.















CHAPTER V

RESULTS



The results of this thesis are presented this chapter. The first

section gives the results of the analysis of the relationship between

rainfall and pumpage. The second section describes the findings of the

review of the water consumption literature to determine total per cap-

ita, indoor, and outdoor use. The results of the two numerical methods

of determining indoor and outdoor water use will also be presented in

this section. The final section will review the results of the two

water use models; the first based on the relationship of evapotrans-

piration to water use and the second dealing with the rainfall-water

pumpage relationship.



Rainfall-Pumpage Relationship

The results of the analysis of the relationship between rainfall

and water pumpage are presented in this section. The findings for the

visual analysis will be presented first, followed by the results of the

pumpage analysis.

Visual analysis. A plot of pumpage and rainfall versus time for

the City of Deerfield Beach, Florida is presented in Appendix C. The

horizontal axis of these graphs corresponds to the time in days since

the beginning of the record that is being analyzed. In this case,









time one corresponds to January 1, 1976; time two corresponds to January

2, 1976, etc. The values of pumpage are given on the left hand vertical

axis in inches over the pervious area. The conversion to inches/day

from MGD was performed using Equations 5 through 11 found in the meth-

odology chapter. The plot of the pumpage data is represented by the

small squares and interconnected by the short dashed lines.

The rainfall data plotted along the top of the graphs are repre-

sented by asterisks connected by long dashed lines. The right hand

vertical axis corresponds to the actual rainfall in inches. It should

be noted that the two vertical axes represent different scales but with

the same units.

Each time a rainfall event occurs there is a corresponding decline

in the water pumpage. However, there are times when this cause-effect

relationship is most striking. Some examples of the often striking

nature of pumpage drop due to rainfall are found in Figure 9 and are

listed below

1) Time 120-121: In response to a 0.4 inch rain a drop of

0.05 inch in pumpage is noted.

2) Time 186-187: In response to a three day rain event totaling

1.8 inches a 0.10 drop in pumpage occurs.

3) Time 305-306: In response to rainfall after a period of

little or no rain a 2.1 inch rain produces a pumpage

decline of 0.06 inches.

4) Time 448-449: In response to a trace of rain following

a long dry spell a 0.06 inch pumpage drop is found.

















0.0




1.0




2.0



(-J Gj

3.0 .5





4.0


SI I
CO CD \l It T) CO0
C!'l CM Ci CM C\J C'


,I I I
d cc Cc 0 CM ~- cc
cc 0 cc Cc oo c" Ok


Day of Event


Visual Depiction of Pumpage Decl
Deerfield Beach, Florida


ine Due to Rainfall For


0.14-
0.12-
0.10
0.08
0.06


Figure 9,


-I I


t^- 0 in in in


-I--I--T----I--l
000 -i-- i--
C mCm CD









5) Time 560-561: The most graphic illustration of the cause-

effect relationship; the rainfall plot drops all the way

down and touches the plot of pumpage, a rainfall of 4.2

inches produces a decline of 0.10 inches in pumpage.

These few examples give a clear indication that there is a cause and

effect relationship between rainfall and pumpage decline. The dips

and peaks do show that there is some relationship. However, the quan-

tity of pumpage decline does not appear to be significant.

Similar rainfall-pumpage relationships were found for the other

test cities. However, the larger the utility, the smaller the response

is to rainfall as reflected in the pumpage. This can be explained by

the extreme variability of the rainfall patterns in South Florida.

Rainfall at the water treatment plant does not necessarily mean that it

has rained throughout the service area.

Quantification. The quantification of the relationship between

rainfall and pumpage decline was accomplished by comparing the quantity

of decline to the quantity of rainfall that caused the decline. This

comparison will enable a check of the quantities to see if they are

consistent. By consistent it is meant that a like rainfall will produce

a similar pumpage decline. Table 15 gives the rainfall quantities

causing the resulting pumpage quantity decline for some selected util-

ities. The rainfall values are in inches, whereas the pumpage is in

million gallons. As is evident, only a few of the originally selected

utilities were analyzed in this fashion. This is because the rainfall

was either not recorded at the treatment plant or the rainfall that was

recorded at the treatment plant often did not compare to the values

recorded at the closest NWS station.











Table 15. Pumpage Decline Due to Rainfall


A. Hollywood, Florida


Pumpage Decline (in)
Day After Event

Rain
Date (in) 0 1 2 3 Total Slope


2-01-80 .007 .0078 .0024 .0016 .0117 -0.802

3-11-80 .007 .0082 .0013 .0001 .0096 -2.120

4-26-80 .094 .0089 .0060 .0046 .0229 -0.377

5-09-80 .127 .0098 .0053 .0043 .0041 .0236 -0.281

5-14-80 .007 .0053 .0132 -0.396

9-20-80 .013 .0029 .0029 ----

11-23-80 .174 .0016 .0023 .0039 0.339

12-12-80 .670 .0055 .0041 .0095 -0.297

12-29-80 .040 .0033 .0034 .0167 0.031

1-23-81 .469 .0099 .0062 .0161 -0.479

3-07-81 .067 .0034 .0023 .0057 -0.377











Table 15. Continued


B. Pompano Beach, Florida


Pumpage Decline (in)
Rain Day After Event
Date (in) 0 1 2 3 4 5 Total Slope

1-04-80 .054 .0143 .0150 .0290 -0.027
1-23-80 .121 .0156 .0263 .0100 .0519 -0.220
1-26-80 .977 .0318 .0177 .0189 .0095 .0057 .0836 -0.405
2-10-80 .020 .0235 .0028 .0261 -2.102
3-01-80 1.233 .0357 .0470 .0276 .0230 .0139 .0037 .1510 -0.433
4-14-80 .559 .0031 .0079 .0110 0.924
4-19-80 .162 .0184 .0280 .0056 .0524 -0.211
5-09-80 .061 .0200 .0291 .0172 .0664 -0.076
12-12-80 .020 .0027 .0113 .0140 1.450
1-16-81 .061 .0244 .0273 .0041 .0023 .0581 -1.230
3-13-81 .047 .0236 .0239 .0159 .0082 .0715 -0.358
3-22-81 .409 .0228 .0259 .0381 .0093 .0961 -0.231
5-07-81 1.644 .0499 .0380 .0327 .0294 .0022 .1522 -0.650












Table 15. Continued


C. Boynton Beach, Florida


Pumpage Decline
Day After Event


.0002
.0024
.0033
.0034
.0013


.0027
.0036
.0003
.0060
.0004
.0005
.0021


(in)


.0014
.0009
.0028



.0007



.0034 .0028 .0012 .0004



.0000


Total


.0015
.0075
.0073
.0930
.0064
.0014
.0064
.0077
.0022
.0173
.0009
.0019
.0040


Slope


-1.980
-0.466
-0.591
-0.467
-1.330


-0.878
-0.777
-1.190
-0.458
-0.060
-1.137
-2.287


Date


1-03-77
1-29-77
2-28-77
3-25-77
4-14-77
7-25-77
8-17-77
3-09-78
4-14-78
6-09-78
2-12-79
3-17-79
5-28-79


Rain
(in)


.363
.044
.058
.131
.007
.073
.044
.777
.022
2.439
.004
.004
.327


.0041
.0037
.0031
.0070
.0051
.0014
.0032
.0034
.0019
.0036
.0005
.0015
.0027











Table 15. Continued


Boynton Beach, Florida (Continued)

Pumpage Decline (in)
Day After Event


.0013
.0000
.0029
.0006
.0003
.0003
.0013


.0022
.0013


.0000


.0021 .0016 .0008


Rain
(in)

.051
1.960
.312
.131
.007
.726
.102
.109
.363
.399


.0001


Date


7-14-79
8-07-79
2-10-80
3-14-80
4-26-80
11-02-80
1-05-81
1-16-81
1-23-81
5-20-81


.0003
.0014


.0043
.0044
.0043
.0043
.0046
.0046
.0028
.0031
.0040
.0048


Total

.0056
.0044
.0118
.0049
.0049
.0049
.0041
.0031
.0067
.0076


Slope

-2.512
-5.069
-0.393
-2.054
-2.809
-2.809
-0.818


-1.289
-1.056











Table 15. Continued


D. Deerfield Beach, Florida


Pumpage Decline (in)
Rain Day After Event
Date (in) 0 1 2 3 Total Slope

1-11-76 .101 .0081 .0030 .0122 -0.981
1-21-76 .047 .0162 .0142 .0001 .0307 -0.253
1-26-76 .013 .0041 .0092 .0031 .0153 -0.144
2-01-76 .241 .0214 .0153 .0122 .0490 -0.280
3-10-76 .174 .0143 .0041 .0184 -1.250
3-22-76 .007 .0204 .0071 .0001 .0276 -2.649
4-01-76 .054 .0143 .0184 .0071 .0122 .0520 -0.140
4-14-76 .007 .0092 .0031 .0122 -1.099
7-01-76 .094 .0112 .0031 .0143 -1.299
7-13-76 .007 .0051 .0051 -----
10-18-76 .603 .0173 .0133 .0071 .0001 .0378 -1.600
11-28-76 .013 .0031 .0031 -----











Table 15. Continued


Deerfield Beach, Florida (Continued)


Pumpage Decline (in)
Rain Day After Event
Date (in) 0 1 2 3 Total Slope

2-24-77 .013 .0041 .0041
3-25-77 .027 .0265 .0092 .0061 .0020 .0439 -0.810
4-06-77 .080 .0214 .0316 .0530 0.389
5-21-77 .838 .0133 .0398 .0255 .0092 .0877 -0.155
8-17-77 .067 .0112 .0041 .0153 -1.012
10-30-77 .147 .0031 .0153 .0020 .0204 -0.203
11-07-77 .087 .0122 .0081 .0204 -0.405
11-30-77 .060 .0143 .0071 .0071 .0326 -0.151
3-09-78 1.032 .0204 .0245 .0143 .0102 .0694 -0.261
4-14-78 1.246 .0428 .0398 .0316 .0173 .1316 -0.294






71

A plot of total pumpage decline versus rainfall for Deerfield Beach

is given in Figure 10. From this plot the effective storage capacity of

the soil layer was determined. This effective capacity of the soil

storage layer.can subsequently be input into the evapotranspiration-

irrigation model that will be discussed later. If a true one-to-one

relationship exists between effective rainfall and pumpage decline then

a plotted line with a slope of one would have been found. This line

would have represented the relationship between incremental rainfall and

corresponding incremental pumpage decline. However, this is not the

case. The plotted line does not show this one-to-one relationship.

Rather, it shows that for a small pumpage decline the slope is much

greater than one, but for a larger pumpage decline the slope decreases

greatly.

Another plot of rainfall versus pumpage decline for Deerfield Beach

is shown in Figure 11. Two lines of correlation are shown. Specif-

ically, the values corresponding to a rainfall of greater than 0.4

inches seem to be correlated to each other, whereas the values corre-

sponding to a rainfall of less than 0.4 inches seem to be uncorrelated.

It should be noted that there are only four values of rainfall greater

than 0.4 inches; thus, these lines of correlation may not reflect the

actual relationship. Using the least squares method of analysis of all

22 of the values, a slope of 0.072, an intercept of 0.0181, and a corre-

lation coefficient of 0.84 were found. Similar analysis for the 18

values corresponding to a rainfall of less than 0.4 inches produced a

value for slope of 0.0838, intercept of 0.0181, and correlation coef-

ficient of 0.33. Conversely, the same analysis of the four values


















0.20 -


0.18 -


0.16 -

0.14-


0.12

0.10 -

0.08 -

0.06 -


0.8


1.0 I
1.0


Rainfall (in)


Figure 10.


Effective Storage Capacity of the
Deerfield Beach, Florida


Soil Layer For


3




0

0.2


0.04


0.02


0.4


0.6


1.2 I
1.2


1.4


--
~





















---- 4 points
----18 points
----22 points


0.2 0.4 0.6 0.8 1.0
Rainfall (in)
Figure 11. Correlation Between Pumpage Decline and
Deerfield Beach, Florida


1.2 1.4


Rainfall For


0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02









corresponding to rainfall greater than 0.4 inches yielded values of

0.125, -0.035, and 0.88 for the three statistical parameters of interest.

The small sample size precluded the making of any strong gener-

alizations. Thus, for the limited number of singular events reviewed

very little correlation was found between the amount of rainfall and the

amount of pumpage decline. A review of the other utilities shown in

Table 15 yield similar conclusions. It would then seem that these

utilities exhibit the same phenomenon that Maidment (1983) found in

Texas; the amount of rain is not as important as the fact that it has

rained.

The short term memory of the system was then examined. Table 16

shows that the recovery time varies from just one day to three or four

days depending on the storm. Recovery begins immediately following the

storm and in most cases the recovery is complete within four days.

As stated in the methodology section dealing with the recovery

phase of the hydrograph, for pumpage decline an exponential function

should fit the recovery leg of the hydrograph. The exponential function

would then be used to simulate the natural streamflow processes. How-

ever, simulating the need for irrigation as perceived by the consumer

does not lend itself to this analysis. As shown in Table 15 the slope

of the line found when plotting the natural log of pumpage recovery

versus time for Deerfield Beach, depends on the storm. The slope as

determined by least squares linear regression is equal to


S (14)
t









Table 16. Pumpage-Recovery For Selected Water Utilities
in South Florida

A. Hollywood, Florida

Pumpage (MG)
Rain Day of Event

(in) -1 0 1 2 3 4

0.01 20.38 18.39 19.77 19.98 20.37

0.01 21.17 19.08 20.84 21.14

0.14 19.89 17.52 18.32 18.67 19.14

0.19 21.08 18.57 19.71 19.98 20.02 22.23

0.01 22.23 20.21 20.87 22.70

0.02 20.43 19.69 20.61

0.26 19.26 18.84 18.67 19.96 20.15

1.00 19.40 18.00 18.36 19.55

0.06 19.23 18.49 18.46 19.60

0.70 19.94 17.39 18.36 20.35

0.10 18.15 17.29 17.56 19.12










Table 16. Continued



B. Pompano Beach, Florida

Pumpage (MG)

Rain Day of Event


-1 0 1


20.14

22.97

21.19

22.45

23.14

18.06

20.26

26.60

22.32

24.43

24.44

25.00

22.05


17.67

20.28

15.70

18.44

16.97

17.52

17.09

23.14

21.86

20.22

20.37

21.06

13.43


17.60

18.42

18.14

21.96

15.02

17.70

15.42

21.57

20.37

19.72

20.32

20.52

15.48


2 3 4 5


23.50

21.24

18.92

23.09

18.37

19.20

19.13

23.63

23.43

23.72

21.69

18.42

16.41


19.55 20.20



19.16 20.74 22.50



22.67

29.33



24.03

23.03

23.40

16.97 21.67 22.25


(in)


0.08

0.18

1.45

0.01

1.83

0.83

0.24

0.09

0.03

0.09

0.07

0.60

2.44









Table 16. Continued



C. Deerfield Beach, Florida

Pumpage (MG)
Rain Day of Event


-1 0 1 2 3 4


(in)

0.15

0.07

0.02

0.30

0.26

0.01

0.08

0.01

0.14

0.90

0.02

0.02

0.04

0.12

1.25

0.10

0.22

0.13

0.09

1.54

1.86


6.1

6.5

6.8

6.8

6.2

8.7

9.0

7.1

6.7

6.6

7.6

8.0

8.6

9.3

9.2

7.7

8.0

6.8

7.3

7.8

9.8


7.0

7.0





7.8 3.9





6.6





8.4



8.3



8.9


5.3

4.9

6.4

4.7

4.8

6.7

7.6

6.2

5.6

4.9

7.3

7.6

6.0

7.2

7.9

6.6

7.7

5.6

5.9

5.8

5.6


5.8

5.1

5.9

5.3

5.8

8.0

7.2

6.8

6.4

5.3

8.1

8,5

7.7

6.2

5.3

7.3

6.5

6.0

6.9

5.4

5.9


6.3

6.5

6.5

5.6

6.6

8.7

8.3

8.4

7.4

5.9





8.0

9.5

6.7

8.7

7.8

6.9

6.6

6.4

6.7


7.5

8.0


6.6

6.8

8.1









Table 16. Continued



D. Boynton Beach, Florida

Pumpage (MG)
Rain Day of Event


-1 0 1 2 3 4 5


6.21
7.11
6.69
7.83
7.51


7.71
8.34
7.91


5.82
7.84
9.22
8.42
8.90


7.34
9.28






8.43


(in)

0.50
0.06
0.08
0.18
0.01
0.10
0.06
0.87
1.07
0.03
3.36
0.06
0.06
0.45
0.07
0.27
0.43
0.18
0.01
1.00
0.14
0.15
0.50
0.16
0.55
0.18
0.56


5.82
7.63
7.03
8.82
7.29
7.75
7.91
7.91
8.17
9.63
7.04
7.71
8.99
8.37
8.41
10.49
8.79
10.04
9.06
7.90
7.88
9.33
9.30
7.24
7.34
8.26
8.14


5.31
6.31
5.92
6.30
5.47
7.25
6.75
7.17
6.94
8.95
5.76
7.54
8.46
7.40
6.88
8.90
7.27
8.48
7.34
6.24
6.86
8.21
7.88
6.87
5.63
7.74
6.64


5.75
6.78
5.85
7.60
6.81
7.90
6.95
7.28
6.88
9.52
4.88
7.55
8.82
7.60
7.95
10.48
7.74
9.84
8.89
7.80
7.43
9.36
8.56
7.14
6.87
8.58
6.48


6.93 7.48


6.05 6.60


8.04 8.28 8.50


9.15
8.30
8.03


6.90


9.16
7.21
6.85


9.38


7.29










where In P = natural log of the change in the pumpage, and

t = change in time.

An average slope may be used in the exponential equations but the

results of the simulations are not very accurate. The pumpage recovery

values and the slope of the semi-log plots for some of the other util-

ities can also be found in Table 15.

In summary, the following characteristics of the rainfall-pumpage

relationship were found. First, there is a definite relationship be-

tween the two variables as was shown by the visual inspection of the

Deerfield Beach and other utility data. Secondly, there does not appear

to be a one-to-one relationship between the quantities of the two vari-

ables; it would be difficult to predict the exact amount of pumpage

decline due to a certain size storm. Finally, the maximum memory of the

consumer in relationship to his/her irrigation needs seems to be about

four days. The values of the recovery function have been found using

the data supplied by the utilities to be 40, 75, 90, and 100 percent for

the each of the four days respectively. Also, an exponential function

does not reflect the recovery leg function; although the semi-log plot

does produce a straight line, the slopes of each storm are different and

inconsistent.



Consumption-Conservation Literature Review Results

There have been countless numbers of studies trying to determine

per capital consumption. Unfortunately, there are as many resultant

values as there are studies. Even within agencies, reports on the same

areas within the same time frame can produce different results.









Average national daily consumption on a per capital basis was found to be

157 gallons and 166 gallons for 1965 and 1970 respectively (Todd, 1970).

The United States Water Resource Council (1970) estimates that the

average daily per capital consumption is approximately 163 gallons.

This can be contrasted to the south Atlantic states average of 187 gpcd

(USGS, 1972).

Clearly, using a national or even a regional consumption figure

would not meet the needs of this thesis. Therefore, a comprehensive

review of existing water use literature was made. Table 17 lists the

values determined either by the author or by the use of the authors'

data for some of the more reliable reports. The table lists the reports

by date published, author, specified area, and date of values reported.

As is shown, the values range from 157 gpcd to 189 gpcd for the SFWMD

service area.

Table 18 lists the per capital consumption rates found in this

thesis. The table gives year, estimated service population, pumpage,

and per capital consumption for each of the 12 selected utilities as well

as the area as a whole. The average per capital consumption rates for

the utilities vary from a low of 73 GPCD for Palm Beach County to a high

of 409 GPCD for Boca Raton. The average consumption for all of the

utilities together was found to be approximately 186 GPCD. This period

encompasses the drought of 1980-81, so the per capital use may be higher

than normal. The yearly values are 177, 189, 202, and 191 GPCD for

1978, 1979, 1980, and 1981 respectively. It would seem reasonable that

the per capital consumption during a non-drought period would range

between 185 and 190 GPCD.












Table 17. Summary of Water Demand Studies


Area of
Estimate


Per Capita
Consumption (Gal)


Todd, David K. Editor "Water
Encyclopedia" Water Information
Center, Port Washington, New York

U.S. Geological Survey, Circular
Number 567

U.S.G.S. "Annual Summary of
Public Water Supplies in
Florida

Kreitman, A., R.H. Walker, and
J.A. Beck Central and Southern
Florida Flood Control District
(CSFFCD) Technical Pub. 74-3

Khanal, N. SFWMD Technical
Publication 75-2 (DATA)

Leach, S.D., & H.G. Healy,
"Estimated Water Use in Florida
1977" U.S.G.S. Water Resources
Investigations 79-112

SFWMD, Water Use and Supply De-
velopment Plan, April 1977


United States



South Atlantic States
Florida


Florida


South Florida


SFWMD


Florida

SFWMD


SFWMD


157 (1965)
166 (1970)


187 (1972)
163 (1972)

157 (1972)


197 (1974)


194 (1974)


168 (1975)
171 (1977)
178 (1977)


189 (1970)
159 (1975)


Date of
Report


Source


1970


1972


1972


1974


1976


1977


1977












Table 17. Continued


Area of
Estimate


Per Capita
Consumption (Gal)


Toomey, J., & C. Woehlcke
SFWMD Technical Publication
79-3

Franklin, S. Master's Thesis

Walker, B.J. Senior Honor's
Project, University of Florida

Present Study


SFWMD


179 (1979)


Deerfield Beach

Gainesville,
Florida

SFWMD, Urban Area


169 (1982)

157 (1982)


186 (1982)


Date of
Report


Source


1979


1982

1982


1982










Annual Water Use Patterns in Twelve Water Utilities
in Southeast Florida 1978 Through 1981


Population Served


Pumpage (MGD)


Boca Raton


Boynton Beach


Deerfield Beach


Delray Beach


36,534 11.79


Table 18.


Util i ty


Year


GPCD


1978
79
80
81
Average


1978
79
80
81


Average


1978
79
80
81


Average


1978
79
80
81


48,792
54,821
58,490
60,129
55,558


37,308
39,491
43,000
43,982
40,945


35,480
40,703
39,541
40,598
39,094


34,395
37,458
36,014
38,271


19.95
23.46
24.01
23.63
22.76


6.95
6.79
7.91
7.70
7.34


6.71
7.67
8.81
8.67
7.96


10.11
11.99
12.97
12.12


408
427
410
392
409


186
172
184
175
179


189
188
222
213
203


294
320
360
317


Average












Table 18. Continued


Utility


Fort Lauderdale


Hollywood


Lake Worth


Miami-Dade WASA


Year


Population Served


1978
79
80
81
Average


1978
79
80
81
Average


1978
79
80
81


Average


1978
79
80
81


227,892
230,273
232,654
238,625
232,361


100,212
102,033
102,180
103,547
101,993


34,789
34,690
34,150
34,414
34,511


1,275,003
1,283,603
1,277,393
1,426,174
1,350,054


Pumpage (MGD)


GPCD


200
205
208
195
202


171
185
196
173


45.64
47.11
48.37
46.49
46.91


17.12
18.87
19.98
17.91
18.47


6.18
6.95
6.40
7.19
6.68


211.78
228.03
248.35
265.04


178
200
188
209
194


166
178
194
186


244.98 181


Average











Table 18. Continued


Population Served


Pupmage (MGD)


North Miami Beach


Palm Beach County


Pompano Beach






Sunrise


Utility


Year


GPCD


151
160
159
153
156


1978
79
80
81
Average


1978
79
80
81


Average


1978
79
80
81
Average


1978
79
80
81
Average


163,814
165,978
167,591
168,381
166,441


76,746
77,782
77,406
79,250
77,796


63,794
64,726
60,593
64,385
63,375


46,333
49,842
57,448
61,393
53,754


24.72
26.56
26.62
25.72
25.91


4.07
5.22
6.61
6.92
5.71


18.73
21.07
21.97
20.17
20.49


6.74
8.04
8.80
9.74
8.33


85
87
73


293
325
363
313
324


146
161
153
159
155















Population Served


2,144,558
2,181,400
2,186,510
2,359,149
2,217,904


Pumpage (MGD)


378.70
411.76
440.80
451.30
420.64


Continued


Table 18.


Utility


Total


Year


1978
79
80
81


Average


GPCD


177
189
202
191








Indoor consumption. In conjunction with the determination of per

capital consumption it is necessary that some effort be made to determine

indoor and outdoor use. Kreitman et al. (1974) obtained values of

between 40 and 50 percent for outdoor use. Franklin (1982), in her time

series analysis of Deerfield Beach, Florida obtained values ranging from

30 to 40 percent for outdoor use. She recommended the use of 36 percent

for outdoor use. Robert Douglas of Lake Worth Utilities estimated that

at times his service area used as little as 18 percent of the total

water pumped for outdoor use.

By contrast Cindy Martin (1982), the Water Conservation Analyst for

Boca Raton, Florida, found that outside water use in her city was be-

tween 63 and 78 percent of the total. These high percentages are ex-

plained, in part, by the affluent population that resides in the city

and the city's own beautification standards.

The area of concern for this report encompasses the entire eastern

coastline from West Palm Beach southward to the tip of the state. Some

of the communities are newly developed while others are older and well

established. The outdoor use is thus affected in that many of the new

homes and condominiums use alternative methods for irrigation. Basing

outdoor use on any one community would be inviting error.

Therefore, two methods of estimating outside water use were em-

ployed in this report. The first is based on the assumption that an

average person, regardless of the area in which they live, uses the same

amount of water indoors. The second method utilizes the low pumpage

flows immediately following a storm event to yield a baseflow. This

baseflow equates to indoor use.









For the first method, the indoor use for Gainesville, Florida was

used to represent indoor water use in south Florida. This number was

chosen due to its availability and known reliability. Walker (1980)

analyzed the extensive and complete records of the City of Gainesville

Regional Utilities and found that indoor use was approximately 120 gpcd.

If this indoor use is subtracted from the total per capital consumption

previously found, a value of about 70 gpcd for outdoor use is found.

This equates to about 37 percent of the total use being for outdoor

purposes. Using sewage flows from some of the selected utilities a

similar value of 39 percent was found for the outdoor portion of total

usage.

The second method required an analysis of the data for all of the

cities of interest in the study area. The results found by using the

minimum flow after a heavy rain storm produced a low pumpage quantity of

between 96 and 122 gpcd depending on the city being analyzed. Taking

the average, about 104 gpcd is obtained for indoor use. Subtracting

this from the previously found average per capital consumption yields 43

percent as outdoor use. Therefore, for the purpose of this study a

value of 40 percent of the total water use was found to be for outdoor

use.



Water use. This report has previously examined the per capital

consumption as well as the indoor and outdoor use patterns. Next, the

components of indoor and outdoor use are examined and savings that can

be expected from conservation techniques are determined.









It is generally accepted that indoor use can be segregated as

follows (Blackwelder and Carlson, 1982):

1) Toilet flushing 40%

2) Bathing 35%

3) Dish washing and laundry 20%

4) Personal consumption 5%

From this, it may be shown that significant savings can be achieved by

the use of certain indoor conservation devices. Boland (1978) found

that the use of flow restriction devices in showers, aeration devices in

faucets, and flush capacity modifications could result in up to a 27

percent reduction in water use. This equates to a savings of 30 gpcd.

Once these devices have been installed, there will not be as much room

for conservation when future drought situations arise. Therefore, a

utility should take these conservation devices into account when issuing

further reduction quotas.

Outdoor use does not easily break down into many constituents.

Most of the water used outdoors, especially in Florida, is for irri-

gation. Only a very small percentage is for other outdoor activities

such as automobile washing. However, even if the water is used outdoors

for uses other than irrigation, it usually finds its way into the soil

surface. Therefore, for the purpose of this thesis all of the water

used outdoors, approximately 40 percent, is assumed to be for irri-

gation.

One of the problems encountered in this thesis was the determina-

tion of the amount of irrigation water that is supplied by private




Full Text

PAGE 1

WATER IiRESOURCES researc center Publication No. 78 AN ANALYSIS OF URBAN WATER DEMAND FOR SOUTHEAST FLORIDA by Richard D. Gibney III UNIVERSITY OF FLORIDA

PAGE 2

AN ANALYSIS OF URBAN WATER DEMAND FOR SOUTHEAST FLORIDA BY RICHARD D. GIBNEY III A THESIS PRESENTED TO THE GRADUATE COUNCIL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENGINEERING UNIVERSITY OF FLORIDA 1983

PAGE 3

ACKNOWLEDGEMENTS I would like to express my appreciation to the following people who have offered their help and encouragement throughout this study: Dr. James P. Heaney, my committee chairman for his guidance and support, Dr. Wayne C. Huber for his guidance on the use of the various analysis techniques, Robert Dickinson, whose computer expertise has been invaluable, and to all of the others in the Enviornmental Resource fVlanagement secti on of the Uni versi ty of Flori da Department of Environmental Engineering for their friendship and support. Finally, I would like to thank my wife, Susan, for her love and understanding throughout this work. This work was sponsored by the South Florida Water Management District. Their financial support and aid in data acquisition are gratefully acknowl edged. Speci a 1 appreci ati on is extended to Dr. Carl Woehlcke, Director of the Water Use Planning Division of the Resource Planning Department, and his staff for their time and attention. I would also like to thank all of the people who were so helpful in providing the necessary utility information: Mr. Dale Holin back, Ci ty of Deerfi e 1 d Beach, Robert Dougl as, Ci ty of Lake Worth, Mr. Lawton McCall, Palm Beach County, Mr. Perry Cessna, City of Boynton i i

PAGE 4

Beach, Ms. Barbara Carlton, Miami-Dade Mr. Richard Mills, City of Pompano Beach, Mr. Walton Gerrard, City of Sunrise, and Mr. Ronald Collins, City of Hollywood. Special thanks go to Ms. Cindy Martin, City of Boca Raton for her help with the outdoor use information. iii

PAGE 5

TABLE OF CONTENTS ACKNOWLEDGEMENTS LIST OF TABLES LIST OF FIGURES ABSTRACT CHAPTER I CHAPTER II I NTRODUCTI ON PREVIOUS STUDIES OF URBAN WATER USE Forecasting Methods and Techniques Factors Influencing Water Use ... Impact of Conservation Practices Outdoor Water Use . . Summa ry ............ CHAPTER III DESCRIPTION OF STUDY AREA Required Data .. Pumpage .. Population .. Evaporation Ra i nfa 11 .. General Characteristics CHAPTER IV METHODOLOGY Analysis Procedures ....... Rainfall-pumpage Relationship Consumption And Conservation Data Preparation . . Evaporation .... Pumpage . Water Consumption Models Irrigation Model .. CHAPTER V Simulator Techniques ..... The Prec;pitation-pumpage Model Summary of Models RESULTS ..... Rainfall-Pumpage Relationship Vi sua 1 Ana lysi s . Quantification ..... iv PAGE i i vii ix x 1 6 6 9 10 12 15 17 17 17 18 22 31 36 42 42 42 49 51 51 51 54 54 59 60 60 61 61 61 64

PAGE 6

TABLE OF CONTENTS -CONTINUED Page Consumption-Conservation Li terature Revi ew Resul ts 79 Indoor Consumption . 87 Water Use .. . 88 Model Results ........... 90 Evaporation Data Preparation 91 Pumpage Data Preparation 94 Irrigation Model ....... 97 Rainfall-pumpage Simulator 102 CHAPTER VI SUMMARY AND CONCLUSIONS Objecti ves Methodology Study Area Evaluation. 107 107 107 108 108 108 109 110 Method Findings ..... Suggestions For Additional Investigation APPENDIX A DAILY WATER PUMPAGE DATA IN MGD DEERFIELD BEACH, FLORIDA 1976 -1981 113 APPENDIX B RAINFALL DATA IN INCHES AT TREATMENT PLANT DEERFIELD BEACH, FLORIDA 1976 -1981. 123 APPENDIX C PLOTS OF PUMPAGE AND RAINFALL VERSUS TIME DEERFIELD BEACH, FLORIDA 1976 -1978. 133 APPENDIX D HISARS EVAPORATION DATA USED IN DEERFIELD BEACH ANALYSIS 1953 -1979 . 151 APPENDIX E EVAPORATION DATA FOR FT. LAUDERDALE EXPERIMENT STATION 1976 -1978 . . .. 178 APPENDIX F MEAN AND STANDARD DEVIATION OF EVAPORATION DATA FOR THE FORT LAUDERDALE EXPERIMENT STATION ................ 184 APPEND! X G EVAPORATION DATA SUMMARY FORT LAUDERDALE EXPERIMENT STATION BROWARD COUNTY, FLORIDA 190 APPENDIX H LISTING FOR IRRIGATION MODEL . .. 197 v

PAGE 7

TABLE OF CONTENTS -CONTINUED APPENDIX I LISTING FOR RAINFALL-PUMPAGE SIMULATION REFERENCES BIOGRAPHICAL SKETCH vi PAGE 202 207 212

PAGE 8

Table 1 2 3 4 5 6 7 8 9 10 LIST OF TABLES Summary of Past Drought Management Measures Service Area and Period of Record For the Twelve Selected Utilities in Southeast Florida Monthly Pumpage For Twelve Utilities in Southeast F1 ori da Past and Projected Population of Broward, Dade, and Palm Beach Counties in Florida Bureau of Business and Economic Research, Uni versity of Florida Population Estimates Estimated Permanent Population and Population Served by Thirteen Utilities in Southeast Florida Mean Monthly, Seasonal, and Annual Class A Pan Evaporation (Inches) For Stations With 10 Years or More of Record For Best Month Clewiston U.S. Eng. Station Summary Hydrologic Information Storage and Retrieval System Evaporation Station Listing For South Florida Summary Statistics on Pumpage For the Twelve Utilities 1978 Through 1981 11 Daily Variability in Pumpage For Deerfield 12 13 14 Beach, Florida 1976 Through 1981 Long Term Summary Statistics For Pan Evapor ati on Fort Lauderdale Experiment Station; 1953-1979 Per Capita Use: 1978 Through 1981 For Selected Uti1 i ti es Typical Range of Available Soil Moisture By S01l Textural Class vi 1 Page 13 19 21 23 24 26 30 32 33 37 39 40 41 55

PAGE 9

Table lS 16 17 18 19 20 21 22 23 LIST OF TABLES -CONTINUED Pumpage Decline Due to Rainfall Pumpage-Recovery For Selected Water Utilities in South Florida Summary of Water Demand Studies Annual Water Use Patterns in Twelve Water Utilities in Southeast Florida 1978 Through 1981 Product of Evapotranspiration Conversions Coefficients Used in Conversion of Pumpage From Millions of Gallons (MG) to Inches Results of Calculations to Determine Pervious Area Services Results of Irrigation Simulator Recovery Coefficients Used in the Rainfall-Pumpage Model viii Page 6.5 ]S 81 83 93 9.5 lOS

PAGE 10

LIST OF FIGURES Figure Page 2 3 4 5 6 7 8 9 10 11 12 13 Map ofFlorida Showing the South Florida Water Management District's Area of Responsibility 2 Map of South Florida Water Management Service Areas Approximate Service Area of Twelve Utilities in Southeast Florida Iso-Pan Evaporation Map for Southeast Florida Idealized View of Pumpage (left ordinate) and Rainfall (right ordinate) Versus Time The Inverted Hydrograph, Inverted Idealized View of Pumpage Versus Time Idealized Schematization of Rectangular Method of Volume Determination Configuration of Upper Zone Storage Reservoir Visual Depiction of Pumpage Decline Due to Rainfall For Deerfield Beach, Florida Effective Storage Capacity of the Soil Layer For Deerfield Beach, Florida Correlation Between Pumpage Decline and Rainfall For Deerfield Beach, Florida Results of Irrigation Model Simulation Using Deerfield Beach as Test Case Rainfall-Pumpage Model Results For a Selected Period i:x 5 20 35 45 47 57 63 72 73 103 106 107

PAGE 11

Abstract of Thesi.s Presented to the Graduate Council of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Engineering AN ANALYSIS OF URBAN WATER DEMAND FOR SOUTHEAST FLORIDA By Richard D. Gibney III December, 1983 Chairman: James P. Heaney Major Department: Environmental Engineering As an aid to drought management, an analysis of the rainfall-pumpage relationship, an estimation of per-capita consumption, and a model to predict pumpage based on antecedent conditions are presented. Daily pumpage, rainfall, and evaporation data from twelve selected utilities are analyzed to determine the governing interactions. A methodology for converting pan evaporation to evapotranspiration is shown. The literature related to consumption is reviewed yielding percapita, indoor, and outdoor consumption figures. Additionally, the effect of conservation on consumption is discussed. A model relating pumpage decline to rainfall is formulated that will allow planners to forecast pumpage based on the anticipated rainfall patterns. The ac curacy of this model is evaluated using the actual pumpage and rainfall data of one of the selected utilities. Chairman x

PAGE 12

CHAPTER I INTRODUCTION The South Florida Water Management District (SFWMD) boundaries encompass most of the State of Florida south of Lake Okeechobee as well as the drainage from the Kissimmee River basin into the lake (see Figure 1). This area includes the heavily urbanized lower east coast area, the large and diverse agricultural areas, and the ecologically sensitive Everglades area. The agricultural areas range from the large acreage used by citrus and sugar cane farms surrounding the lake to small vegetable truck farms which dot the region. Urban water use averages between 800 and 900 million gallons per day (MGD) while supplemental consumptive use for agriculture is about 1200 MGD. Consumptive use figures for the Everglades area are not presently available (Leach, 1983). The SFWMD is responsible for monitoring and regulating water consumption in the South Florida area. The District acts as a wholesaler to its licensed dealers. Therefore, they need to know how much water is pumped out of their supplies. Rainfall patterns for the South Florida area are extremely variable. Periods of extreme high and low precipitation levels have occurred throughout history. The severe drought that South Florida experienced in 1980-1981 may be contrasted to the overabundance of precipitation in 1982 and early 1983. While a six or seven year cycle is

PAGE 13

I i 2 \ ------------o Tallahassee Gulf of Mexico Approximate Scale 1 inch = 72 miles N 1'\ Atl anti c L 1\ Ocean { / 1\ V '\ I ,v, I II ,/ / / rLake I I ,( / I '/ / I I / / I 1 / /1 / / Beach 1'1 /1/; I / 6eerfi e 1 d Beach 11/' I i / / /0 I / I / .I ,: / I / / /. /L ;' / / / / ,"11 a I//)'J J /' ,/ Figure 1. Map of Florida Showing the South Florida Water t"lanagement District's Area of Responsibility

PAGE 14

3 thought to regulate the rainfall patterns over the southeast part of the state, the severity and duration of future droughts cannot be accurately forecasted. The 1980-1981 drought was unusual due to its severity and duration. Precipitation during this drought was less than half of the normal annual average. This caused the main surface reservoir, Lake Okee chobee, to reach its lowest recorded levels. The South Florida Water Management District (SFWMD), which has the ultimate responsibility for insuring adequate water supplies for the area, was forced to take unprecedented action and call for a 10 percent cutback in water use. The drought was relieved by higher than normal precipitation levels in Spring 1982 and early 1983. These high levels have forced the SFWMD to draw down Lake Okeechobee from its resultant near record levels in anticipation of the 1983 hurricane season. This thesis focuses on urban water consumption. Therefore, no attempt will be made to account for water use by other consumers. This non-urban use accounts for approximately two-thirds of the total con sumptive use in the South Florida area. The recent drought caused planners to ask some very difficult questions regarding urban water use: 1) What is the effect of rainfall on demand? Specifically, how does the lack of rainfall during the drought event increase the water use rate? 2) How much of the daily per capita urban use can be attributed to indoor and outdoor use? 3) How closely does irrigation (outdoor) use correlate to the evapotranspiration needs of grasses?

PAGE 15

4 The goals of this thesis are to ascertain how rainfall affects urban water use, to determine where the water is being used, and finally to produce models which will use climatic indicators such as evaporation and rainfall to predict urban water use. Urban use in the District's Lower East Coast Service Areas one, two, and three (see Figure 2) will be analyzed. These service areas include Dade, Broward, and most of Palm Beach County. Some 74 different utilities operate in these areas with served populations ranging from hundreds for a small trailer park, to over a million for the Miami-Dade Water and Sewer Authority. A three-fold approach will be taken. First, rainfall and pumpage data will be analyzed. Second, an extensive review of the existing literature as well as some other methods will be employed to determine per capita indoor and outdoor consumption patterns. Additionally, the effect of conservation on per capita consumption will also be studied. Finally, a model utilizing pan evaporation as converted to evapotranspiration, and rainfall as effective precipitation will be developed to predict demand on a short term basis. The literature review and a description of the study area are contained in Chapter II. Chapter III provides an enumeration of the data required in this thesis. Chapter IV contains the methodology for the rainfall-pumpage analysis, a discussion of data preparation and analysis, and the model methodology. Chapter V presents the results of the analysis of the rainfall-pumpage relationship, the literature review of water consumption patterns, and the simulation models. Chapter VI discusses the results and gives conclusions.

PAGE 16

5 82 /27000\L. __ Sub-Area 4 N Approximate Scale 1 inch = 35 miles \ \ \ \ \ I I ic:3J '. \. ''''. \ \ Sub-Area 2 \ Sub-Area '\ 1 Serv'ce Area 1 --, L_""r-\, ,/ v Figure 2. Map of South Florida Water Management Service Areas

PAGE 17

CHAPTER II PREVIOUS STUDIES OF URBAN WATER USE Severe droughts have occurred throughout man's history. As pop-ulation and water demand increase, the finite nature of water as a natural resource becomes more evident. Droughts such as those in England in 1972, California in 1975, and in Florida in 1980-1981 have only heightened man's awareness of his vulnerability to the uncertainty of natural supplies. In order to prepare for and react to a drought the water use agency needs to understand the demands that will have to be met during a critical situation. Several elements must be analyzed to aid in this understanding. These elements include a knowledge of forecasting techniques and models, factors which influence water use, effects of con-servation, and outdoor water use factors. This literature review sur-veys these elements. Forecasti ng Methods and Techni gues I Boland et al. (1981) analyzed existing water demand forecasting approaches. These approaches include: simple time extrapolation, single coefficient regression methods, multiple coefficient regression methods, and probabilistic analysis. These researchers conclude that the first three of these methods are routinely used in forecasting. They further suggest that the reason for nonuse of probabilistic anal-ysis is the lack of information and documentation. 6

PAGE 18

7 Heaney et al. (1981) reviewed 18 water demand models. These models rely on one or more of the 14 socia-economic variables established by the researchers. These variables include: income, property value, price of water, cultural factors, water consumption behavior, precipitation, evaporation, temperature, population, technology, irrigated area, land use, number of dwelling units, and lot size. Clearly other variables may affect water use, but these are the ones which have been used in existing water demand models. All of the models reviewed were deterministic models; each will predict the same outcome in response to an identical forcing fUnction. Hittman Associates, Inc. (1969) MAIN II Model, the only well known water demand model that is widely available, utilizes the multiple coefficient method to obtain municipal water use. It sums the level of activity to arrive at an equivalent overall water use activity. The main problem encountered with the use of this model is its large data requi rement. Heaney et al. (1981) discuss the WRE/SCS Demand Model. This model is a combination of Water Resources Engineers' urban water use model and the Soil Conservation Service Model (TR21). It estimates monthly water use for each month of a one year period. The composite model is relatively simple to use with complexities occurring only in the manipulation of the data. Morgan and Smolen (1976) studied regression variables associated with climatic conditions. They found that models using either temperature and precipitation or evapotranspiration and precipitation performed best.

PAGE 19

8 Since water use data are a time series, it would seem reasonable that good forecasting results could be obtained by the use of formal time-series analysis techniques. Salas-LaCruz and Yevjevich (J972) have performed a comprehensive study of time series analysis of water use. They observed that the annual cycle of water use is primarily related to temperature, but also to rainfall. However, they did not discuss causeeffect relationships using factors such as population as explanatory variables. No forecasting of water use was done to demonstrate their findings in the form of a model. Maidment and Parzen (1981) also used time series analysis for ana lyzing water demand. Their study focused on six Texas cities exhibiting two distinct water use patterns .. The study employed a so called "Cascade Modell! whereby the data are transformed at each step of the process; these transformed data are then used as the input for the next step of the model. The model involves four steps: detrending, deseasonalizing, autoregressive filtering, and multiple regression. It was found that the process left a residual error which accounted for between 13 and 20 percent of the variance in water use. Population was found to be the most significant variable. Their examination of the relationship between water use and rainfall explained between one and eight percent of the variance. Franklin (1982) used the Maidment and Parzen Cascade Model to perform a time series analysis of Deerfield Beach, Florida. Using monthly and weekly data she was able to explain 61 and 72 percent of the variation in water use. Correlating rainfall to usage accounted for 21 and 12 percent of the variation when using weekly and monthly data, respectively.

PAGE 20

9 In a recent presentation Maidment (1983) stated that to analyze time series data adequately, it wou.ld be necessary to go to at least a daily time step. Walker (1982) also concluded that monthly data were inadequate when trying to model urban water demand. In her study of the City of Gainesville, Florida using a time series approach she found results similar to those of Franklin (1982). Wong (1972), Yamauchi and Huang (1977), and Sterling and Antcliffe (1974) have also used time series analysis with varying success in their efforts to model water use. Perhaps the best compila ti on of forecasting techni ques for water use can be found in "An Annotated Bibliography on Techniques of Fore casting Demand for Water", by Boland et al. (1981). This report lists the recent publications in the water forecasting field and gives a cross reference table of forecasting methods. Factors Infl uenci n9 Water Use Maidment (1979) gives a bibliography of water demand factors by reviewing the works of over 100 authors from 15 countries. He describes the dependence of water use forecasts on the accurate knowledge of demands and the factors which determine them. In "Trends in Water Use II (1963) Bogue makes several general comments on municipal water use. He further identifies ten factors which affect water use: 1. size and type of community, 2. location, 3. water quality, 4. pressure in the water system, 5. sewered or non-sewered community, 6. metering, 7. age of the community, 8. lawn sprinkling, 9. cost of water, and 10. air-conditioning. The author, however, did not show the interrelationship of these factors or their relative impor tance.

PAGE 21

10 Boland (1978) describes some of the various approaches used in forecasting urban water demand and the criteria that should be employed in the selection of a model. His primary conclusion is that the number of customer connections is better correlated with use than is population. This differs with the findings of Maidment and Parzen (1981). They found that population is a much better regressive parameter than the number of connections, especially in areas that exhibit volatile population fluctuations. The South Florida urban area appears to exhibit these types of fluctuations. Bachelor (1975) discusses the proliferation of water using durable goods. The five water-using appliances studied that were found to have an effect on water use were washing machines, automobiles, dishwashers, showers, and garden sprinklers. His results show that these variables were the most important in estimating demand. Clouser and Miller (1980) stUdied the shifts in household water use due to technological shifts and implementation of conservation practices. Their study of two Indiana communities focused on the creation of a water demand model. Statistically significant variables were washing machines, dishwashers, swimming pools, and lawn watering. They conclude that water saving devices when used in conjunction with the afore-mentioned devices can be useful. They further state that the conservation technique has merit any time construction of new facilities can be avoided. Impact of Conservation Practices Several conservation methods are usually examined or implemented when a drought situation is encountered. Boland et al. (1981) ranked

PAGE 22

11 water conservation measures according to the amount of water saved, cost, and acceptance as follows: 1. building codes requiring water conservation devices, 2. sewage reuse for irrigation and industry, 3. educational campaigns, 4. individual installation of water-conservation devices, 5. government intervention during a drought event, 6. lawn watering devices, 7. pricing-, and 8. control of urban growth. There fore, the researchers conclude that building code changes are the least objectionable alternative for water conservation. Feldman (1977) reviewed 34 types of conservation devices used in reducing residential water use. Although no recommendations were made, a complete and detailed description of the devices, their estimated sav ings, and a list of their manufacturers was given. Bailey et ale (1969) found that shower head and faucet flow reduction devices could save 24 and 2 gpcd, respectively. They also found that low flow toilets could save up to 30 gal/day over the present 5 gal/flush toilets. The U.S.G.S. (1980) has determined that the following water con servation fixtures are cost effective when installed by the home owner; toilet dams, plastic bottles for toilet tank displacement reduction, dual flush equipment, shallow trap toilets, shower flow restriction devices, and faucet aerators. Additionally, it was found that pricing incentives would not be effective in reducing average daily demands on a short term basis. They conclude that a public education campaign could be the most cost-effective method of reducing residential water demand. The above methods as well as outside water use bans and rationing will reduce water use. Marin County, California was able to.reduce

PAGE 23

12 water demand by as much as 63 percent during the 1976 California drought. Once the public has been educated as to the nature and consequences of the water demand problem, conservation measures implemented during the emergency situation remained. This is evidenced by Marin County's water use still being 25 percent below pre-drought levels (Bollman and Merritt, 1977) Baumann (1979) presented a summary of past drought management measures. He lists the location of the event, the year of occurrence, the type of restriction imposed, and the resulting decrease in water use. Table 1 gives the resulting values of reduction. It is evident that a ban on outside water use is the most prevalent method of reducing water demand. Blackwelder and Carlson (1982) surveyed water conservation programs currently being used in the United States. It is an extremely comprehensive review of what each state has done in the way of water conservation program implementation. Further, they give a model water conservation program for the nation. Outdoor Water Use Evapotranspiration is the combined loss of moisture to the atmosphere by both evaporation and transpiration (Eagleson, 1970). Evaporation is the direct vaporization of liquid from a free water surface ofa saturated layer. Transpiration is the flow of water vapor from a plant to the atmosphere through a plant. In the long term global hydrologic cycle, precipitation equals evapotranspiration. However, in

PAGE 24

13 Tabl e 1. Summary of Past Drought r,1anagement Measures Investigator (Location) Year Groopman 1968 (rJ. Y. City) Anderson, R.W. 1967 (Pawtucket) Abbott, et al. 1972 (17 Eastern utilities) Jezler 1975 (San Paulo, Brazil ) E.A.I. 1977 n'Jashi ngton Suburban Sanitary Commission) Bollman 1977 (t4a ri n Co., Ca.) fJational 1976 Counci 1 (Great Britain) Larkin, D.G. 1978 (Oakland, Ca.) Hiller 1978 (Denver, Co.) Griffith 1978 (Los Angeles, Ca.) Robie 1978 Restriction Imposed Ban outside use and appeals. Ban outside use and appeals. Voluntary and compulsory bani on outside use and appeals. Ban outside use. Limit on household use. Ban outside use, appeals to specific acts. Ban outside use. Rationing with fines. Ban outside use. Rotate cut-offs and ban outside use. Rationing with fines. Limit outside use to 3 hours every thi rd day.' Appeals and limited industry cutbacks wi th some mandatory control. Voluntary Restriction. Rationing. Resulting Decrease 10-22% 16-18% 18-50% 26% 40% 25% 63% 25% 40-50% 38% 21% 10-20% up to 20% up to 50% Source: Baumann, D.O. The Role of Conservation in Water Supply Planning. IWR Contract Report 79-2, Fort Belvoir, Virginia, 1979.

PAGE 25

14 Florida, precipitation exceeds evapotranspiration with the extra moisture coming from evaporation from the surface of the sea (Jones et ala 1983) A number of sources for evaporation data are available. Most of them rely on the National Weather Service (NWS) and its cooperating services. The Hydrologic Information Storage and Retrieval System, HISARS (Portier, 1981), used in this thesis ;s a computer-assisted data base using information supplied by NWS. Unfortunately, pan evaporation is not equal to actual evapotrans piration (ET) in all but the rarest of cases. Therefore, a method of converting pan evaporation to ET must be found. This will allow a prediction of ET, leading to a prediction of crop water demand. Several methods of determining potential ET in Florida are dis cussed by Jones et ala (1983). These methods include Penman, pan evaporation, Thornthwaite, Blaney-Criddle, and the modified Blaney Criddle. This report compares the prediction of potential ETby all of these methods. The Penman method was found to be the most accurate, with the modified Blaney-Criddle and pan methods acceptable. Although the Penman method is the most accurate, it is also the most complex with a great deal of climatological data required. The pan evaporation method produced a pan coefficient of 0.70. That is, potential ET is found by multiplying the pan evaporation by the pan coefficient. Khana1 (1980) in his presentation to a Water Use Workshop stated that a good approximation for the pan coefficient for turfgrass in South Florida is 0.60 between October and March and 0.70 between April and September. This is consistent with the results in the Institute of Food and Agricultural Sciences, IFAS, ET report (Jones et al. 1983).

PAGE 26

15 Allen et ala ()978) conducted a comprehensive investigation of ET requirements of turfgrass. They found that if water table depths were kept at 36 inches or less, the actual ET was 0.62 times pan evaporation. Their experimentation was conducted at the Fort Lauderdale Experiment Station in Broward County, Florida. This finding is similar to Jones et al. (1983) where the total coefficient needed to convert pan evaporation to ET was found to be 0.65. Danielson et ala (1981) also studied turfgrass and the effect of the irrigation scheme on ET. They used various treatments on grasses such as cutting and fertilizing. They found that grasses cut to a five cm height used 15 percent more water than grasses cut to 2 cm. Further, they found that grasses that received adequate nitrogen used 10 percent more water. They also found that the 5 cm grass and the nitrogen enriched grass were more resistant to moisture stress. Barnes et al. (1979) found that lawn watering application rates were between 125 and 175 percent of the average seasonal ET in two Wyoming cities. They also noted that an esthetically pleasing lawn can be maintained with an average water application rate equal to or less than the average .seasonal ET. In a similar study, Cotter and Chavez (1979) found that actual lawn water application rates exceeded the estimated need by up to 42 percent. Summary This literature review has shown that there are four primary methods used in water demand forecasting. These methods include single coefficient regression, multiple coefficient regression, simple time

PAGE 27

16 extrapolation, and probabilistic analysis. All of these methods except probabilistic analysis are currently being used extensively. It was further shown that the use of time series analysis requires the use of the smallest time step possible (e.g. use of daily or hourly data). Several factors were identified that can be used to estimate water demand. Population was found to be the most significant regression parameter in areas of volatile population fluctuations such as South Florida. Precipitation and evaporation were found to be important climatic factors. It was found that public education is the most cost effective means of attaining conservation of water. Once this conservation has begun, it can remain long after the drought situation has ended. Flow restriction devices for the shower head, faucet, and toilet offer a potential savings of up to 27% over the standard fixtures. Finally, it was found that evapotranspiration is the primary method of water removal from the soil layer. Determining this evapotranspiration from pan evaporation requires the use of two conversion factors. The first converts pan evaporation to potential ET; the second converts potential ET to actual ET. For the South Florida area the product of these coefficients was found to be 0.62.

PAGE 28

CHAPTER III DESCRIPTION OF STUDY AREA This chapter gives a description of the study area and defines the required data. These data include pumpage, population, evaporation, and precipitation. The general characteristics exhibited by the data are also examined. Required Data The required data include: daily pumpage from the utilities, estimates of the population served by these utilities, and daily evaporation and rainfall over the service area. Pumpage, the dependent variable, will be correlated to the following independent variables: population served, rainfall, and evaporation. These variables were chosen due to their significance in determining pumpage, as was reported in the literature review (Morgan and Smolen, 1976 and Maidment and Parzen, 1981). The daily time step requirement is used in response to the findings of Maidment and Parzen (1981), Franklin (1982), and Walker (1982) which were discussed previously; it was found that the smallest time step possible should be used in the analysis of water use during drought. Pumpage. All of the utilities in the South Florida area report daily pumpage to the Florida Department of Environmental Regulation 17

PAGE 29

18 (DER). Therefore, each utility normally has on file several years of daily pumpage data. DER keeps the daily data on file locally for up to two years. It was not feasible to obtain daily pumpage data from all 74 utilities which currently serve the South Florida area. Hence, only the 12 utilities with daily pumpage of ten MGD or greater were utilized. The selected utilities are listed in Table 2. The Sunrise and Palm Beach County listings are aggregates of the four and six treatment plants serving the areas, respectively. Figure 3 shows the approximate' area served by these 12 utilities. Table 2 lists the approximate total a rea servi ced by these uti 1 i ti es. Each util i ty was contacted by phone, written correspondence, and when possible, by personal visitation. The utilities were very helpful in providing the necessary data. Table 2 also gives the period of record of daily pumpage that was supplied by the participating utilities. A list of the daily pumpage for the City of Deerfield Beach, Florida is provided in Appendix A. Daily data for all of the utilities used in this study proved too voluminous for inclusion; however, the data may be obtained from the author. Monthly data for the selected utilities, as collected by Dr. Woehlcke of the South Florida Water Management District, are provided in Table 3. Population. Once the pumpage for the utilities has been obtained it is necessary that some idea of the population using the water be determined. The United States Census Bureau population data has the most credibility and validity over the long term. However, due to the ten-year time frame between census counts, the volatile nature of population changes are not always reflected. Municipal estimates, while having a yearly or even shorter time frame, are not consistent from one

PAGE 30

Table 2. Service Area and Period of Record For the Twelve Selected Utilities in Southeast Florida Period of Record Util ity Pumpage Boca Raton 1/81 -12/81 Boynton Beach 1/77 -12/81 Deerfield Beach 1/76 12/81 Delray Beach (2) Fort Lauderdale 6/81 -6/81 Hollywood 1/80 12/81 Lake Worth 1/77 12/81 Miami-Dade WASA 1/77 12/81 North Beach 1/80 12/81 Palm Beach County 1/77 12/81 Pompano Beach 1/80 12/81 Sunrise 1/77 3/82 (1) Rainfall Data Provided as Reported at Miami (2) Data Not Provided by Utility Rainfall (2) 1/77 12/81 1/76 12/81 (2) 6/81 6/82 1/80 -12/81 (2) (1) (2) (2) 1/80 -12 81 12/80 3/82 Area in Acres (1978) ------21 ,632 18,099 5,920 10,508 --' UJ 27,315 14,086 5,414 276,732 18,958 186,750 9,485 25,401 620,300

PAGE 31

20 --11----11--....;Pa 1 m Beach County ..,..I-I_-.:Lake Horth Boynton Beach -t-1 ray Beach Boca Raton -+---' Deerfield Beach i-==T--__ Pompano Beach Fort Lauderdale -+-Tamiami Canal North Miami Bea6h Miami-Dade HASA -+-:"-:::-----' Approximate Scale 1 inch = 20 Figure 3. Approximate Service Area of Twelve Utilities in Southeast Florida

PAGE 32

Table 3. Monthly Pumpage For Twelve Utilities in Southeast Florida, MGD YEAR FE:! II?:! M""( "UN .JL"L AUG !:f:t' cc r NOV DEC TO r ,\L IS i'7::;.90 lCIJ.23 229.2::; 2.7.47 :?::'O 31 204.43 197.67 100.12 173.21 171.01 2';-178 Deerfleld Beach 79 172.61 21".0'; :W"].H 251.0J 1[;3 2,]5.33 32:).'/:j 1!Jl.2! 207.56 l'7Cl 77 22'-1.14 21';3'8 80 :11:.66 2.0::; '2n.ol 255.69 2'72.03 ;:-:'-1.62 ::.:l7.14 ce ::1216:8 _81 28't.91 23::J.65 320. 7::; 93 2:'0.47 22-1.:S6 :lGu 51 22-:1. 01 21:) 50 62 2.':. 71 31.':.2 9 78 17 4'10.00 5a7.:H 623.38 5:S':;. 76 74 620. '10 16 'jIJ. YJ6 1:30 21 6()35' 2 Pompano Beach 7'1 ::131.96 S72.89 716. S6 7";1. 43 527.13 73'.1.20 780.4:5 '17'7.26 582 92 iJJ3.17 5::JiJ. 19 66 8 GO 647.32 600. Oil 747.62 6t::;.6::; 743.55 644. l:! 670. '16 7"]5. -16 646. 6-1 607.::;1 !..:::J 53 0')1',1' 1 _81 722.6:; 590.9t> 723. S8 8<12.70 556 36 5:l'I.b'{ 641. 2'-1 5.JO :Jt -l6t3. 'l:> :.... 593. 97 6JiJ 6 B R 'e S4 L 60 :122.50 606.60 686. 10 644.00 S4:1.40 636.70 669. 70 626 30 <..0:] 90 576.60 62:::1. 70 72:.13. 'l oca aton 79 609. to 610.30 830.8,) 82'7.00 616 70 017.50 '127.90 852 00 519.00 621. co 5a5.60 672.20 S5!:.2 1 80 721.60 662. 40 674.60 784. 00 7i7. 10 3D 60 655 30 75? 70 79B.8 87:::1.70 67'1.'70 802.70 61t>.70 :>5140 6::14.00 6GJ SO !:l17.S0 0"266 J'9 198.16 186.82 253.59 257.04 17u.8:J 20807 22::.04217.1:2202.11 IU6.37 174.'l? 251!:. Boynton Beach ?9 4-1 187.65 214.76 203 5::1 1'32.:'1<1 2:JG. 2!:.6. 9'1 165.2J 104.,73 107.83 212.2'7 2417' 6 00 229.61 20'1.31 237.74 <'10.55 26:; 61 252.71 2 .. 0 0::;: 65 22:::1.7:.1 2':15.57 21 L [;'1 230 07 2'3']a'-, _8J 183.0'1216.502,>3.1' 303.65 2:::1.J.76 234.:::1125-1.61201.77179.09 2J2.'12 236.40 264.1:328::;'7'8 78 27::;.1525-1.05317.923:>4.61351.1025'5.1'1306.04329.91 327.:J1 304.312/7.54 30!.71 Delray Beach 79 232.0'1 :::IJ2.40 396.82 :Jl1.15 406.63 446.62 -143.07 307.81 :.JY;.64 314.EO 345.12 IJ75'4 80 :l71.S0 427.38 367. '12 411.12 395.82 39-1. ep 421.00 370.'14 44:3.74 355. 71 '10 -172:>' 3 ---61 '109.:]0 345.20 '124.65 455.60 :J37.11 329.66 374.30 326.:30 2:;0 33 32::1 86 325. 6::1 01 .H2; 0 N 78 16'7.10 167.2:::1 197.37 218.60 '197.94 185.83 200.00 20:3:::12 204.1':1 166.29 171.30 172.46 225;'6 ....... Lake Horth 79 172.4-1 1'14.11 2:31.97239.01 190.'13 2:30 .... 7 2:;9.09 177.e:z 196 OJ la7.-15 204.5!:. 2:):37'0 80 22:1.57 tSt..St 222.43 IG3.33 211.50 109.71 152.5:3 197.47 15.':>.72 1"0.71 ;;:0'1.3,) 233/'-1 _8...1 194.30 2-12.79 200.8.':> 217.93 232.01 227.8.':> 139.82 190.::0 21:::1.77 193.67 2.':>25'3 78 105.el 107.43 1-l1.15 135.62 113.:17 120.03 130.02 121.2-l 121.80 lid 63 1437'3 Palm Beach County 79 HO.96 130.65 16a.::!'; 175.52 13d.::lI3 171.6b HH.Jl 163.70 1:J-1.CJJ 154.61] 152.61 1 lU7. -15 236.3-1 216. 200.17 I[)S.24 210.23 10!. '16 181. ':10!.0 :206,91 :?l6.74 2 tJl ;;:5?!J' 9 70 J 3 1 5; 1 24';. 62 1 :J 3. -1:2 1; 96. 58 1 4 Q 3. 0 1 1 .; 1 3. I 8 1 .. 0 5. 79 1 3 t3 3. G 'oJ I,' COg d 1 35 1. I 2 I 320. 2 7 1-t 4 2. lUI 6 '> 5'7' .; Fort Lauderdale 79 132.':>.80 1734.90 40 12.':>9.40 1519.46 1690.75 1037.97 1127. 1256.23 1227.0-1 19 17176' 80 1408.26 138:>.68 1715.61 1-16'7.95 15/7.:::?:::J 1469.71 1501. 3') I':1J'7.:):J 1412. Y) 13diJ 66 1305.2lJ 1';02.30 17.':>5'> 3 1::110.73 127H 63 15J-1.72 1735.85136 .... 27129:::1.74 147l.Btl 0:> It) 1:.J25.30 13'-14 6215'19.2.'> 1.':>'llO J'B 184.09 215.71 207.8." 220.53 221.25 215.21 21::;.54 18:.1 5", lU/.4'1 20040 21'1.2" 2.1.':>15 Sunrise 79 211.4522.':>.65 276.02 214.212,]0.77 25l.60 25lJ.12 23:3.73 2::?2.52 80 267.29 243.19 2'7'1.88 :;:50.86 294. 05 a:> 2-16.53 259.13 252.5:::1 27il.7lJ 261. 13 2:;9.3:::1 3211' 7 __ -u8 .... \ 310.28 251. 36 3')0. ::'54. 269.7::1 2\1.11 284. 49 83 2/6 fJ'I 313. 0.'> 322.73 3-17.63 3555' 8 78 6381. 21 58.':>:> 29 663'1.39 66.':>2.9t 6574.47 6129.37 6616. 67:31. 0',) 63'15.84 62'16.71 66\ -1.42 772'1'1 5 Mlami-Dade 79 6:;'9.39 6J61:29 7 .. 55 35 89 6735.52 63:i7. 72 7227.32 6478. 10 6725,43 7030 a-1 832JO'6 80 74.':>4.99 6t17(). 78 7925.:32 7220.81 7iJ03. 11 7451. 70 762:3.1.,1 7EJ:J2. 21 uS 7720.::::il 13:n.27 ;S=H. 0:1 3 --8301. 4!:j 7])'1.53 831lJ. 69 [;7:51. 96 790:3 60 76,\-1.03 '7017.62 76Utl. 46 7554.6!:. 8002.13 7/cJ7. Ie 83'>7. h'') 1!:./J7 2 ,0 72'1.76 667.70 001.0\ EJ06.35 780.:!l 750.16 717.'H 7ljJ,78 72:1.01 8')001 't.l:!4 N th t B h 79 770.1874::1.86 901.'H '.32.27 744.'-17 827.97 802.'12 0 .. 7.26 703.0-:; 755.'16765.48820.15 7::"l6 or lar.n eac 80 761.55 8/7.'19 :"06.70 8:i2.37 770.79 814.27 016.54764.0'1 7U:3.'14 771.63 3.17 9711:6 __ -<:;Aul 829.41 694.78 893.44 US:>.8Z O'JI. 79 7:::16. 947.:37 785.0::; 632.14 601. 50 691. 27 7:)3.';1 "ITJ7.:J 78 504. 17 45 554.11 ::>33.45 :)33.81 :50d.';<1 SO,.11 530.:3CJ 501.32 j03.71 516. :),) 55/. 7 6 Hollywood 79 5i7.49 534:9S 639, Oil 619.30 522.78 553. 72 597. JO 97 6 80 616.9::; 662.80 5<;2.::;4 62306 5'15.50611.6661::;.'74 6:JO.2:> 615.11 S7U.I'1 57/.33729-:>5 81 629.80 4:17.51 ,:)31.96 50:>.9tJ 514.l :)U1.28 :l:!J.';', 5bl.:J',) 65J7:7 Source: Woehlcke, C., Director of the Water Use Planning Division of the Resource Planning Department, South Florida Water Management District, Personal Correspondence, 1982.

PAGE 33

--22 city to the next. That is, every city has their own way of estimating population. Therefore, it is necessary to find popu1ati9n estimates for a common time-frame, developed with a consistent set of assumptions. An estimate meeting the criteria is available from the University of Florida's Bureau of Business and Economic Research. The Bureau has been doing yearly population estimates since 1972. These estimates use several economic indicators including: census data (as base data), electrical hook-ups, utility connections, and building occupancy certificates. The Bureau's estimates closely parallel both U.s. Census and municipal population estimates. The population data used in this study are a combination of census data (for long-term trends) and Bureau of Business and Economic Research data (for short-term trends). Table 4 shows the U.s. Census data for the three counties of interest. This table includes historical data as well as base-line projections made by the Bureau. Table 5 is the Bureau of Business and Economic Research population estimates for the counties and cities that will be analyzed in this thesis. Unfortunately, city boundaries rarely correspond to the utility service area boundaries. Therefore, an estimate must be made of the population served by the utilities. Communication with the SFWMD, DER, and the utilities provided input into these estimates. The selected estimate of the population served for each of the uti 1 iti es of interest is given in Table 6. Evaporation. The evaporation data were obtained from the Hydrologic Information Storage and Retrieval System (HISARS) at the University of Florida (Portier, 1981). Table 7 is an excerpt from NOAA Technical

PAGE 34

23 Past and Projected Population of Broward, Dade, and Palm Beach Counties. in Florida County Date Broward Dade 1940 a 39,794 267,739 1950 a 83,933 495,048 1960 a 333,946 935,047 1970 a 620,100 1,267,792 1980 a 834,800 1,616,000 1990 b 1,027,900 1,866,700 2000 b 1,236,900 2,246,100 2010 b 1,487,000 ,700,400 2020 b 1,770,900 3,215,900 Source: a United States Census Bureau (1980) Pa 1m Beach 79,989 114,688 228,106 348,753 440,800 542,800 653,100 785,100 935,000 b Bureau of Business and Economfc Research (1980) Total 387,522 693,669 1,497,099 2,236,645 2,291,600 3,437,400 4, 136, 1 00 4,972,500 5,921,800

PAGE 35

24 Table 5. Bureau of Business and Economic Research, University of Florida Population Estimates A. Broward County Fort Pompano Year Lauderdale Hollywood Beach Sunrise Total 1973 151,546 118,334 50,135 16,149 769,419 1974 153,911 121 ,138 53,836 20,727 828,169 1975 154,616 121,400 54,458 25,912 876,296 1976 152,791 118,872 54,063 28,529 884,872 1977 153,374 117,777 54,708 29,241 902,542 1978 154,365 118,095 55,398 34,442 929,584 1979 155,650 120,172 56,207 34,428 966,083 1980 153,279 121 ,323 52,618 39,681 1,014,043 1981 153,814 121,955 55,911 42,706 1 ,047,313 B. Dade County Year North North Miami Miami ami Beach WASA Total 1973 42,970 34,463 347,618 1 ,373,609 1974 44,356 35,690 350,499 1 ,413 ,1 02 1975 44,473 36,491 350,742 1 ,437,998 1976 43,544 35,,736 343,977 1,449,300 1977 43,371 35,971 344,109 1,468,270 1978 42,887 35,654 344,393 1 ,494,276 1979 42,746 36,130 344,755 1,519,247 1980 42,566 36,481 346,931 1,625,979 1981 42,986 36,652 346,865 1,718,516

PAGE 36

Table 5. Continued C. Palm Beach County Year 1973 1974 1975 1976 1977 1978 1979 1980 1981 Boynton Beach 26,507 30,462 32,275 32,473 33,036 34,204 36, 265 35,624 36,489 25 Lake Worth 25,934 26,628 27,615 27,009 27,340 27,927 28,634 27,048 27 ,111 Boca Raton 38,884 41 ,518 43,511 44,549 46,624 48,563 49,744 49,505 50,408 Delray Beach 25,046 27,050 28,305 28,065 29,456 32,782 35,701 34,328 36,476 Total 427,983 459,167 477,751 488,044 505,605 534,551 564,447 573,125 576,863 Source: Bureau of Economic and Business Research, University of Florida, Florida Estimates of Population 1972-1981, Annual Rport, University of Florida, Gainesville, Florida, 1973 through 1982.

PAGE 37

26 Table 6. Estimated Permanent Population and Population Served of Thirteen Utilities in Southeast Florida BROWARD COUNTY Estimated Estimated Permanent Population Deerfield Beach Date Population Serviced 1977 30,649 31 ,000 1978 35,079 35,480 1979 40,243 40,703 1980 39,143 39,591 1981 40,138 40,598 Fort Lauderdale 1977 153,374 226,430 1978 154,365 227;892 1979 155,650 230,273 1980 153,279 232,654 1981 153,814 238,625 Hollywood 1977 117,777 100,000 1978 118,095 100,212 1979 120,172 102,033 1980 121,323 102,180 1981 121,955 103,547 Pompano Beach 1977 54,708 63,000 1978 55,398 63,794 1979 56,207 64,726 1980 52,618 60,593 1981 55,911 64,385

PAGE 38

Table 6. Continued BROWARD COUNTY Sunrise PALM BEACH COUNTY Boynton Beach Lake Worth Date 1977 1978 1979 1980 1981 1977 1978 1979 1980 1981 1977 1978 1979 1980 1981 27 Estimated Permanent Population 29,241 32,422 34,428 39,681 42,400 33,036 34,204 36,365 35,642 36,489 27,340 27,927 28,634 27,048 27,111 Estimated Population Serviced 42,333 49,842 57,448 61,393 36,800 37,308 39,491 43,000 43,982 34,200 34,789 34,690 34,150 34,414

PAGE 39

Table 6. Continued PALM BEACH COUNTY Boca Raton De 1 ray Beach unincoryorated (County Date 1977 1978 1979 1980 1981 1977 1978 1979 1980 1981 1977 1978 1979 1980 1981 28 Estimated Permanent Population 46,624 48,563 49,744 49,505 50,408 29,456 32,782 35,701 34,325 36,476 61,566 61,997 62,834 62,530 63;996 Estimated Population Serviced 46,200 48,792 54,821 58,490 60,129 31 ,000 34,395 37,458 36,014 38,271 76,212 76,746 77,782 77 ,406 79,250

PAGE 40

Table 6. Continued DADE COUNTY North Miami Date 1977 1978 1979 1980 1981 North Miami Beach 1977 Miami-Dade WASA 1978 1979 1980 1981 1977 1978 1979 1980 1981 29 Estimated Permanent Population 43,371 42,887 42,746 42,566 42,982 35,917 35,659 36,130 36,481 36,653 344,189 344,396 344,745 346,931 387,357 Estimated Population Serviced 65,000 64,275 64,063 63,793 64,417 165,000 163,814 165,978 167,591 168,381 1 ,210,000 1,210,728 1,219,540 1,213,600 1,361,757

PAGE 41

Table 7. Mean Monthly, Seasonal, and Annual Class A Pan Evaporation (Inches) For Stations With 10 Years or More of Record For Best Month Source: St_t Jon ""r-Ot .... .,. 'e"CDrtl L..t'll!_t SI_teo l"lit's 0.. Apr Anl'\".1 B .. O.t. No. p:". J.n '.b I<.or Ap. 1<.0, Jun Ju\ Au. 0" nOY p.. Ho/fr Ho/Tr hlh GI .. ,Jf! r: .. p Shtloft 0611 J.n J.9' i.l0 '-'I 7.07 6.11 6.15 J. )0 '.71 1.t)6 1.14 lL1;.Z' 61.1' J/I" U/1. 2,-&0'. BO )8' n J8 '0 '0 '0 '0 '0 '" '0 '0 '" <0 8 8 6 1 1 7 10 8 rh .. ln&1J s )010 8.U 1.11 4.JII ., 1l lIn "1) 2,' 09". !JO' SS' 10 10 10 'to t.0I ..... rd.1e r.., St. JI1. 1.Sl l.J] 5.2" 1 .. '" 7.51 6.91 1.11 6.91 I.U Ln 'oll 1.81 '".11 )0.0' 10.n I1IH "" 2ft' as ... 80 U' n n n n 11 n l\ 70 U U 1l J) 8 J) II 10 7 Il 1 HI.lut. ]90' 1.81 '_'2 <.Il 7.26 7. AD >.Il 1. J6 '.It 5.91 ').'" '.7' 'J.U JO.l" 11.41 II"I IU" H' "0' 11' J8 )7 J7 l' J9 )8 J8 18 )7 18 J9 )8 10 7 I 7 II 7 .. 6 8 II Hoore loe\: No. I 55H '.OS 4.10 6 .. U ".50 7 1.10 J .11 6 .. 56 '-'I ." 1.60 "). Jl JO.l) ".07 II" 12/1, ,.' SO'. 8.-OS' JI JI 1I 1I 1I )0 ]\ )1 1I 1I JI JO II 10 11 \I 10 Il 7 11 Tull (.0 HI &-n.) 8'''0 J. )6 '.S' S.'I 5.)1 6.81 6.n 5.81 6.51 5.16 l.n '.51 1.10 17.11 'J.U 1I41 III" l'S loS'. "0" 19 JO 10 29 1I 21 11 1l 1I JI 11 29 t Il J] S I' 10 1I .. _c" ,,, 9119 7 _so J_60 S.U fi.6' 7.07 6.65 6.U 6.1l 1.0] '.81 '.lI 1.6l J'_H 7'.0\) 1161 1711' 17 )8'. !l0 17' .. .. Il Il 15 15 15 15 n n u u 7] IJ n \I II II n 71 21 21 \I I' Il ._-----rt"tt' Ilnl!' or d.lt. I,. the tabt .. r ... r ".ch .'.l[lon I. __ n ",,,er-:H t1o,, In '"che": s",",on
PAGE 42

31 Report NWS 34 (Farnsworth and Thompson, 1982). It contains pan evapo ration data summaries for seven of the eight stations in the study area. The summaries consist of the monthly, seasonal, and annual means, the years of record, and the coefficient of variation (C.V.). A note of caution on the validity of the C.V. ;s given here with a discussion to follow. The Clewiston Engineer Station is not included in the NOAA report. HISARS has current data of sufficient length for the Clewiston Engineer station. Table 8 lists the mean monthly, and annual evapo ration data for Clewiston. A complete iist of the evaporation stations that have data available through HISARS is given in Table 9. As shown, HISARS contains all of the NOAA stations except the Flamingo Ranger station. The Loxahatchee station was not used because the last month of record is December 1962, too early for the purposes of this study. The eight pan evaporation stations used in this thesis, with the exception of Vero Beach 4W, have been located in Figure 4. The i50evaporation lines are also shown on this figure. Care should be taken in the interpretation of the accuracy of the iso-evaporation lines in that only eight stations were used in their construction. Additionally, all of the evaporation stations in the study area are located in the eastern half of the area with none located on the west coast. Rainfall. Daily rainfall data are available through the National Weather Service and also through HISARS. Hourly rainfall for most stations can also be obtained from these sources; for the purpose of this report only daily data will be analyzed: In addition,

PAGE 43

32 Table 8. Clewiston u.s. Eng. Station Summary Month January February March Apri 1 May June July August September October November December Total Mean Evaporation (inches) 2.91 3.54 4.88 5.62 6.33 5.57 5.62 5.09 4.69 4.42 3.50 2.75 54.56 Source: National Oceanographic and Atmospheric Administration. NOAA Climatological Data for Florida 1979. National Climatic Center, Vol. 83, Asheville, North Carolina, 1979.

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33 Table 9. Hydrologic Information Storage and Retrieval System Evaporation Station Listing For South Florida BELLE GLADE EXPERIMENT STATION LATITUDE 26-40-00 LONGITUDE 80-38-00 ELEVATION 16 FT MSL REGION PERIOD OF RECORDS LENGTH, MONTHS 08/1941 -08/1941 1 07/1948 -09/1948 3 12/1948 -01/1949 2 03/1949 -03/1949 1 05/1949 -07/1949 3 10/1949 -12/1949 3 02/1950 03/1950 2 05/1950 -06/1951 14 08/1951 -08/1951 1 10/1951 0211952 5 04/1952 -06/1952 3 09/1952 -07/1953 11 09/1953 -04/1954 8 07/1954 -07/1954 1 09/1954 -09/1954 1 11/1954 -11/1954 1 01/1955 -06/1955 6 08/1955 -02/1959 43 04/1959 -01/1978 226 03/1978 -12/1979 22 CLEW1STON U S ENG LATITUDE 26-45-00 LONGITUDE 80-55-00 ELEVATION 20 FT MSL REGION PERIOD OF RECORDS LENGTH, MONTHS 08/1948 -08/1948 1 10/1948 06/1955 81 08/1955 -03/1957 20 05/1957 -08/1963 76 10/1963 -12/1979 195 LAUDERDALE EXPERIMENT STATION LATITUDE 26-05-00 LONGITUDE SO-15-00 ELEVATION 6 FT MSL REGION PERIOD OF RECORDS LENGTH, MONTHS 11/1953 -12/1954 14 FORT 02/1955 -07/1955 6 09/1955 07/1970 179 10/1971 -06/1974 33 01/1975 -07/1975 7 09/1975 -06/1979 46 HIALEAH LATITUDE 25-50-00 LONGITUDE 80-17-00 ELEVATION 12 FT MSL REGION PERIOD OF RECORDS LENGTH, MONTHS 07/1948 02/1950 20 04/1950 -04/1952 25 06/1952 -06/1953 13 08/1953 -10/1954 15 12/1954 -04/1955 5 06/1955 -08/1955 3 10/1955 12/1956 15 02/1957 12/1964 95 02/1965 -09/1969 56 11/1969 02/1970 4 04/1970 11/1971 20 03/1972 -08/1972 6 10/1972 07/1973 10 09/1973 -01/1974 5 05/1974 -08/1974 4 10/1974 12/1979 63

PAGE 45

34 Table 9. Continued LOXAHATCHEE MOORE LATITUDE 26-41-00 ELEVATION 14 PERIOD OF 08/1948 -12/1962 -HAVEN LOCK 1 LATITUDE 26-50-00 ELEVATION 35 PERIOD OF 08/1948 -01/1977 LONGITUDE 80-16-00 FT MSL REGION RECORDS LENGTH, MONTHS 01/1960 138 12/1962 1 LONGITUDE 81-05-00 FT MSL REGION RECORDS LENGTH, MONTHS 11/1976 3110 12/1979 36 TAMIAMI TRAIL 40 MI BEND LATITUDE 25-45-00 LONGITUDE eO-50-00 ELEVATION 15 FT MSL REGION PERIOD OF RECORDS LENGTH, MONTHS 07/1948 09/1950 27 11/1950 -12/1951 14 03/1952 -06/1952 4 08/1952 -03/1953 8 05/1953 -07/1953 3 09/1953 -07/1957 47 09/1957 02/1970 150 05/1970 -06/1970 2 08/1970 -12/1979 113 VERO BEACH 4 W LATITUDE 27-38-00 LONGITUDE 80-27-00 ELEVATION 20 FT MSL REGION PERIOD OF RECORDS LENGTH, MONTHS 05/1965 -08/1979 172 10/1979 -12/1979 3 Source:"Hydrologic Information Storage and Retrieval System" Data Base, UF/NERDC System University of Florida

PAGE 46

35 .' I ..... MH = Moore Haven C = Cl ev/i s ton = Belle Glade FL = Fort Lauderdale H = Hialeah TT = Tamiami Trail FR = Flamingo Ranger Q Figure 4: Iso-Pan Evaporation For Southeast Florida .\ I tJ I I KI55IMMEE'EVEkGLADE5 AREA FLORID.A. u I or, .. .. ,_, 0' .l.GaIC ... LI""U iOIL, COh)LMW"".ON HUlce .,.1.' ... _-_ .... --------

PAGE 47

36 most of-the water treatment plants that were contacted for information, keep a record of rainfall at the plant. The NWS data are the most reliable but the stations are not always located near the service areas of some of the utilities. This can lead to errors because rainfall patterns are highly variable in South Florida. The utiJity's precipitation data seem to be the most accurate of those located within the service area. However, errors may arise because treatment plant personnel may be inadequately trained in operating rain gauges. For the purposes of this thesis rainfall at the water treatment plants will. be used when the amounts recorded correspond to the nearby weather station gauge. Otherwise, data from the nearest weather station will be used. Appendix B contains the daily rainfall data as recorded at the Deerfield Beach Water Treatment Plant. The values recorded are in inches of rainfall over a 24 hour period. As with the pumpage data, rainfall data for all of the utilities of interest proved too voluminous to include herein. These data are available by contacting the author. General Characteristics This section will give some of the general characteristics of the pumpage, population, and evaporation data. The pumpage data are char acterized in Tables 10 and 11. Table 10 gives the summary statistics of pumpage for the 12 selected utilities. The average monthly pumpage was analyzed to determine if monthly seasonality has a significant impact. It was found that April has the highest average pumpage at 456.28 MGD. September was found to have the lowest monthly average pumpage at 398.84 MGD. The data exhibit little variability with the percent of total pumpage ranging from a high of 9.04 to a low of 7.90.

PAGE 48

37 Table 10. Summary Statistics on Pumpage For the Twelve Utilities 1978 Through 1981 Month Average Pumpage MGD Percent of Total January 406.62 8.06 February 417.91 8.28 444.88 8.81 April 456.28 9.04 411 .58 8.15 June 416.76 8.26 July 437.86 8.68 August 419.40 8.31 September 398.84 7.90 October 406.55 8.06 November 406.07 8.05 December 424.34 8.40 Total 5047.09 100.00

PAGE 49

38 The daily variability of pumpage for Deerfield Beach, Florida was also examined to discern if the day of the week was significant in weekly water use patterns. Table 11 gives the variability in daily pumpage as a percent of the total weekly pumpage. The values range from a high of 14.81 percent for Wednesday to a low of 13.62 percent for Sunday, indicating little daily variability. The pan evaporation data for the Fort Lauderdale Experiment Station were analyzed to determine if any long term trends were ex hibited. Table 12 shows the variabiltiy in pan evaporation as a per centage of the yearly total. As is expected, the cool season (October through March) shows a lower pan evaporation average than the hot season. Per-capita pumpage was determined by using the average population served and the average pumpage in MGD. The results for each of the 12 selected utilities and the totals are given in Table 13. This yields an approximate per-capita consumption rate of 186 gallons. In summary, the pumpage data exhibits little variability between the monthly or daily values. Pan evaporation exhibits a great deal of variability between the warm and cool months. The average per capita consumption for the 12 selected utilities was found to be 1 86 ga 11 onsl day.

PAGE 50

39 Table 11. Daily Variability in Pumpage For Deerfield Beach, Florida 1976 Through 1981 Day Percentage of Total Monday 14.66 Tuesday 14.06 Wednesday 14.81 Thursday 14.15 Friday 14.57 Saturday 14.13 Sunday 13.62 Total 100.00

PAGE 51

40 Table 12. Long Term Summary Statistics For Pan Evaporation Fort Lauderdale Experiment Station: 1953-1979 Percent of Average Pan Evaporation (in/day) Total January O. 1216 5.31 February 0.1514 6.61 March 0.2035 8.89 April 0.2397 10.47 May 0.2439 10.66 June 0.2247 9.82 July 0.2345 10.25 August 0.2261 9.88 September 0.1950 8.52 October 0.1829 7.99 November 0.1427 6.23 December 0.1229 5.37 Total 2.2889 100.00

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41 Table 13. Per Capita Use 1978 Through 1981 For Selected Utilities Average Population Util ity Served Boca Raton 55,558 Boynton Beach 40,945 Deerfield Beach 39,093 Delray Beach 36,535 Fort Lauderdale 232,361 Ho 11 ywood 101 ,993 Lake Worth 34,511 Miami-Dade WASA 1,315,543 (include North Miami) North Miami Beach 166,441 Palm Beach (units 1-6) 77,796 Pompano Beach 63,374 Sunrise (units 1-4) 53,754 Total 2,217,903 Average Pumpage 22.77 7.34 7.96 11 .79 46.91 18.47 6.68 244.98 25.91 5.71 20.49 8.33 420.64 Per Capita Pumpage gpcd 409.76 179.18 203.73 322.82 201.87 181.09 193.61 181. 14 155.66 73.35 323.25 154.98 189.72

PAGE 53

CHAPTER IV METHODOLOGY As was stated in the introduction, a three-fold approach will be taken in this study. First, rainfall and pumpage records will be ana lyzed to determine the relationships that govern their interactions. Second, an extensive review of the exi1ting literature as well as two other meth.ods will be employed to determine per capita indoor and outdoor consumption. Additionally, the effect of conservation measures on these consumption patterns will be reviewed. Finally, two models will be established whereby the pumpage-rainfall and short-term demand relationships can be simulated. This chapter will discuss the methodology to be utilized. The first section gives the methodology used to analyze the pumpage-rainfall relationships. The second section details the methodology for consurnp and conservation analysis. The final section details the data preparation and methodology of the consumption models established for this report. Analysis Procedures Rainfall-pumpage relationship. The effect of a drought on water use can be marked. Outdoor use in response to the lower rainfall can rise dramatically. To determine the relationship between the two 42

PAGE 54

43 constituents, the change in pumpage in response to a rainfall event will be examined. A decrease in water production would be expected following a precipitation event with a subsequent rise over time to pre-event 1 evel s. Maidment (1983) emphasized that for the six Texas cities he was researching, a rainfall event caused a significant and drastic reduction in subsequent water pumpage. Further, he found that five to seven days elapsed before the pumpage returned to its pre-event level. Additionally, he observed that the decrease in pumpage appeared to be inde pendent of the amount of precipitation. For the purpose of this thesis the daily rainfall and pumpage data for the peri od of record wi 11 be analyzed. The goal of thi s secti on will be to answer the following three questions: 1. Does rainfall cause a significant reduction in water production levels? 2. Is the quantity of rainfall correlated with the amount of pumpage reduction? Or, as Maidment found, does simply the fact that it rains at all cause the reduction in pumpage? 3. What is the time (days) until completion of the recovery of the pumpage to pre-rainfall event levels? Specifically, what is the short term memory of the system? To answer the first question, a plot of rainfall and pumpage versus time will be made. A visual analysis will be used to ascertain if there is a decrease in water production due to a rainfall event. This de creased water use would be shown by a dip on the graph of pumpage versus time immediately after a precipitation event.

PAGE 55

44 The answers to the next two questions require a more in-depth analysis of the data. A listing of the daily pumpage and rainfall data must be made. This listing will be analyzed by recording the decrease in pumpage, in million gallons, corresponding to a rainfall event. The correlation can most easily be seen when there is an extended period of \ dry weather preceeding the rainfall. Therefore, only single events will be used in this analysis. By single events it is meant that there is a dry period preceeding the rainfall event and that only one rainfall event occurs. A list will also be made of the pumpage values one day prior to the rainfall event through recovery to the pre-event levels. The amount of the rainfall event will be recorded for comparative pur poses. Recovery will be defined as the period of time from the peak decrease in water pumpage until pumpage has returned to within a certain percentage of the pre-event pumpage levels. A method similar to the rainfall-runoff hydrograph method used in hydrology (Chow, 1964, p. 134) will be employed to analyze the rainfallpumpage relationship. Figure 5 shows an idealized view of the actual rainfall-pumpage relationship. By contrast, Figure 6 shows the inverse of the pumpage curve. The similarity between this curve and the typical. runoff hydrograph can be easily seen. The common characteristics of the runoff hydrograph peak, volume, and duration are also shown in Figure 6. The peak is a measure of the amount of decline in pumpage rate due to a rainfall event. It is calculated using the equation: (1)

PAGE 56

0 c..!J z OJ +-' ro n::: OJ CJ) co 0.. E :::l Cl.. OJ c: u OJ o OJ +-' co cr:; CJ CJ) ro 0.. 8 7 6 5 4 3 .t. 45 1-1 I I [: -l 1 2 3 4 5 6 7 8 9 10 11 Time (Days) Figure 5. Idealized View of Pumpage (left ordinate) and Rainfall (right ordinate) Versus Time 4 5 6 7 1 2 3 4 5 6 7 2 r3 fi L4 Peak I r-S --1 I -:;:--------........._-__ 6 I Duratlon 7 1 2 3 4 5 6 7 8 9 10 11 Time (Days) Figure 6. The Inverted Hydrograph, Inverted Idealized View of Pumpage Versus Time U'l OJ u c: r-co 4C ..... u c r r-co 4-c ro 0::

PAGE 57

where 46 Z = peak pumpage rate decline, MGD, = pumpage one time step prior to the rainfall event, MGD, and p(t) = pumpage rate at time step equal to that of the rainfall event, MGD. Hence, the pumpage recorded for the day that the rainfall event occurs is subtracted from the pumpage of the previous day. The volume of the pumpage corresponds to the integrated total decline in pumpage due to the rainfall event. It is calculated by computing the area of the shaded portion of Figure 6. The general equation used is: t Volume = f 2 f(t) dt (2) where tl f(t) = equation of the curve representing pumpage, tl = time one time step prior to pumpage decline, days, and t2 = time of pumpage return to pre-rainfall event levels, days. For the purpose of this thesis the volume is found by equating the area under the curve to a number of rectangles. Figure 7 illustrates this method of area calculation. The horizontal axis corresponds to the day of the rainfall event. That is, zero is equal to the day of pumpage corresponding to the rainfall event. Negative days are the days prior to the rainfall event whereas positive days correspond to days after the rainfall event. Thus, the area under the curve ;s equal to the sum of the areas of the rectangl es.

PAGE 58

I I' I Rainfall = 0.80 inches 0.14-, LO.O 0.5 0.16.-1.0 I I >, O.lOl rU -0 0.08-1 "Vl 0.06J 0) ...c: U C "r-I; I I I ,"l I I Reduction in Pumpage = 0.30 inches 1.5 [ 2.0 2.5 .j:::> Vl 0) ,.... u C 0) 0.0 +-l r\l r 3.0 LO LO N "r-rU 0:: 0) 0.02 0) rU r-a a f-a 0 a r-::l r-a 0 a 1-3.5 a I 1 I I I a I a I 1-=, 1-, r-rU 4C "r-rU c.. E :::l 0.00 D... l I L .. ---r ,-,-, I I 0:: -3 -2 -1 0 1 ') 3 4 5 6 7 8 9 10 L. Days From Beginning of Rainfall Event Figure 7. Idealized Schematization of Rectangular of Volume Determination

PAGE 59

48 The duration of the event is the time from the beginning of the rainfall caused pumpage drop until the pumpage returns to its pre-event level. This time corresponds to the short-term memory of the water user. Specifically, it is the interval of time where the water user's perceived effect of the rainfall event is still present. For the purpose of this thesis the pumpage rate will be considered returned to pre event levels when the pumpage following the event is at least 98 percent of the pre-rainfall levels. The volume of the hydrograph will be compared to the volume of rainfall to ascertain if there is a true correlation. Maidment (1983) did not find any correlation between the volume of the two constituents. However, it must be noted that two-thirds of the water use in his study was for outdoor use. This is not the case with the South Florida area, as will be shown in the next section. One method of analysis of the recovery phase of the hydrograph is to equate the recovery leg to an exponential function. This method will be employed in this research. A plot of the natural log (In) of pumpage (Q) versus time for each single event will be made. This plot will allow the slope and intercept of the line of best fit for each event to be found. The line of best fit will be determined by using least squares regression techniques. It is projected that a linear semi-log relationship comparable to the typical rainfall-runoff recovery relationship will be displayed. Thus, the slope of this linear relationship will be used to establish a recovery function. The exponential function will be of the form

PAGE 60

where 49 P = a ebt (3) a = the intercept of the line of best fit, b = the slope of the line of best fit, t = the time since the last rainfall event, days, and P = the amount of pumpage, inches. This method of analysis has been used reliably in rainfall-runoff studies. It has not yet been proven to simulate the perceived rainfall-pumpage relationship of the consumer adequately. In summary, visual inspection of the plots of rainfall and pumpage versus time suggests the relationship between these two variables. The pumpage analysis allows a quantification of this relationship by de fining quantity, peak, and duration. The use of the exponential func tion allows the simulation of the recovery leg of the hydrograph. If the exponential function for the recovery leg does not provide usable results a trial-and-error method will be employed in the models to simulate the recovery leg. Consumption and conservation. Where does the water go? The answer to this question would seem trivial at first inspection. However, for South Florida the answer is unknown except for some broad and sweeping generalities. Researchers who have addressed this question vary on the answer. This section seeks to determine the water use characteristics of the South Florida urban area. Specifically, the present research seeks estimates of total per capita use, indoor use, outdoor use, and

PAGE 61

50 the effects of conservation. To accomplish this task a review of the related literature has been performed. In addition, two numerical methods of determining indoor and outdoor use will be discussed. These two methods will provide a check on the findings of the literature review. The literature review will focus primarily on the South Florida area. Unfortunately, the literature relating specifically to this area is limited in that most studies deal with regional water use patterns. Additionally, the segregation of indoor and outdoor water use is not defined in most of the studies. The results of this literature review will be presented in the following chapter. A listing will be made of the results of the most pertinent studies, including this one, for both the total per capita use and the indoor versus outdoor division. Two methods were employed to delineate indoor and outdoor use: 1) Assume that per capita indoor water use will remain constant throughout a region; an average person from the same region will consume approximately the same amount of water for indoor use. 2) Use a base flow argument where, if there is a relationship between outdoor use and rainfall, the pumpage immediately following a lengthy rainfall event will be solely for indoor use. The literature study in conjunction with the two numerical methods of consumption analysis will provide reasonable values for per capita consumption as well as for the percentages of indoor and outdoor use.

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51 Data Preparation Evaporation. The pan evaporation data from the HISARS system are given as inches/day over the entire area. A correction is needed to reflect the fact that impervious areas do not contribute to evapo ration. Additionally, pan evaporation must be converted to evapotrans-piration. To make the conversion from pan evaporation to evapotranspiration the following equation is used where Kl = coefficient to convert pan evaporation to potential evapotranspiration, (4) K2 = coefficient to convert potential evapotranspiration to actual ET, K3 = coefficient to account for the percent perviousness of the area, and E = actual pan evaporation, inches. The values for Kl and are. the ones found by Jones et al. (1983) and Khanal (1980) as presented in the literature. They are 0.70 and 0.89 respectively. The value for K3 is found in the pumpage data preparation section of this report. Pumpage. The pumpage figures reported by the utility are in mil lions of gallons per day. These values must be converted to like units (inches) for further use in the modeling section of this thesis. Additionally, since any outdoor portion of this pumpage will be used only for the pervious area the values must be converted to reflect only pervious area use.

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52 The following equation was developed to convert the pumpage values to inches over the service area: ( 5) where PI = pumpage, inches/acre, and P G = pumpage, million gallons/acre. This PI represents the pumpage in inches over the entire serviced area. The units for pumpage that are required by the models are inches over just the pervious area. Therefore, some method of determining the pervious area must be established. Heaney et al. (1977) present equa-tions that can be used to estimate the percent imperviousness of a catchment. The first of these equations is (Stankowsky, 1977): where (6) I = impervious, percent, and = population density in developed portion of the urbanized area, persons/acre. A second equation is used to define PDd' This equation is: where PO eO.17PO (7) PO O.17PO e -1 PO = average gross (developed and undeveloped) popul ation dens i ty, persons/acre

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53 PO is calculated by using equation PO = PIA (8) where P = population, and A = service area, acres. Once the percent impervious is known it is nec.essary to determine the percent pervious. This is accomplished using; Per = 100 -I (9) where Per = pervious area, percent, and I = impervious area, percent. The area in acres is then multiplied by Per to get pervious acreage using: where A = Per A P 100 Ap = pervious area, acres Per = pervious percent, and A = service area, acres. The pumpage in inches is then determined by: PI Q = Per 100 (10) ( 11)

PAGE 65

where 54 Q = pumpage, inches, PI = pumpage, inches/acre, and Ap = pervious area, acres. This value of Q can then be input into the simulator that will be developed in the next section. Water Consumption Models This thesis has previously examined the rainfall-pumpage relationship and the question of where the water goes. The third topic is to determine if pan evaporation can be an effective tool in the determination of irrigation needs. To make this determination two models were developed. The first model will utilize pan evaporation and rainfall in an effort to establish irrigation needs. The second model will utilize rainfall and consumption to simulate pumpage patterns. The ultimate goal for these two models is to forecast short-term water demand. Irrigation model. The irrigation model uses the storage reservoir principle for the upper zone of soil. The upper zone in this case corresponds to the root zone. Typical values of available soil moisture by soil textural class are shown in Table 14. Similar values for soil moisture capacity are used in a soon to be releas.ed model for estimating quantities of sanitary landfill leachate (Walski et al. 1983). It is generally accepted that Florida1s typically sandy soils hold approximately one inch of water in the top 12 inches of soil (Augustin,

PAGE 66

55 Table 14. Typical Range of Available Soil Hoisture By Soil Textural Class Soil Textural Class Available Soil Moisture Storage a Range in./ft. Average Very coarse textured sands and fine sands Coarse textured loamy sands and loamy fine sands Moderately coarse textured sandy loarns and fine sandy loams Medium textured very fine sandy loams, and silt loams Moderately fine textured sandy clay loams and silty clay loams Fine textured sandy clays, silty clays, and clays ---0.50-1.00 0.75-1.25 1.25-1.75 1.50-2.30 1.75-2.50 1.60-2.50 aStorage between field capacity (1/10 to 113 and wilting poi nt (-15 ATr1). Source: Metcalf & Eddy, Inc., Process Design Manual for Land Treatment of Municipal Wastewater, EPA, Army Corps of Engineers and USDA, EPA-625/1-75-008, 1977. 0.75 1.00 1.50 2.00 2.20 2.30

PAGE 67

56 undated). This assumes that the majority of roots will be in the top 12 inches of soil. This is usually the case, especially with the turfgrass in Florida. Bermuda, Saint Augustine, and Bahia grass all exhibit this characteristic. Thus, the root zone can be modeled as a one inch reservoir. Once the amount of upper zone storage has been determined, it is necessary to estimate outflow of this reservoir. Some crucial assumptions had to be made in order for a workable model to be established. The first assumption is that rainfall on the area in question will be added directly to the upper zone of the reservoir. Any rainfall in excess of the amount required to raise the storage (rootzone) volume above the one inch level is discarded. This relieves the model of the arduous task of modeling surface runoff and groundwater zones. This is not to say that there is no flow through the upper zone to the lower zone, only that the upper zone will fill first; once filled, no further precipitation is of interest. Figure 8 shows this inflow configuration. In conjuntion with the first assumption, the second assumption is that the only drawdown of the reservoir level in the storage basin is by evapotranspiration. This has been shown to be a reasonable assumption when estimating the effects of rainfall on irrigation water requirements (Quackenbush, undated). The final assumption has been stated previously; only the rainfall over the pervious area enters into the soil layers. This can be a reasonable assumption if it is assumed that the impervious areas of the catchment are sewered so that the rainfall that does occur is directed away from the storage areas.

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57 Prec-i pi tati on Irrigation Evapotranspiration Upper Zone Overflow (to lower zone or runoff) Figure 8. Configuration of Upper Zone Storage Reservoir

PAGE 69

58 Simulator techniques. The simulator utilizes the prepared data as input for the modeling process. The model acts as a simple moisture accounting device; it keeps track of the inflows (precipitation and irrigation) and the outflow (evapotranspiration) of the soil moisture storage system. User supplied inputs to the model include the coefficients necessary to convert pan evaporation to evapotranspiration, the coefficient to convert pumpage from millions of gallons to inches, the beginning, minimum, and total storage capacities of the root zone. Also input to the model are two parameters which initialize the irrigation procedures Three different scenarios for irrigation initiation can be simulated. The first uses the number of days since the last rainfall event to determine the start of irrigation. The equation used is where If t > tmax' then irrigate (12) t = time since the last rainfall, days, and tmax = user specified number of dry days prior to the start of irrigation. The second irrigation initiator is the volume of the storage reservoir. In this case the minimum storage value that i"s input by the user is compared to the actual storage in the reservoir. If the storage level is less than the minimum storage level irrigation is initiated. The equation used is If V < Vmin, then irrigate ( 13)

PAGE 70

where 59 v = storage volume in the root zone, inches, and Vmin = user supplied value of minimum storage level prior to irrigation. The final scenario utilizes a combination of the first two methods; irrigation will commence when a certain number of dry days have occurred and/or the value of the level of storage in the reservoir (root zone) has reached a user specified minimum. The simulator examines each day of the simulation period. It first determines if there is sufficient storage volume available for irri-gation. It then determines if rainfall has taken place. If it has rained, the rainfall amount is added to the storage volume with any excess of the total storage capacity of the reservoir being discarded .. If it has not rained the model determines the irrigation method to be used and the amount of irrigation that will take place. Evapotranspi ration is subtracted from the storage volume and this volume is the beginning volume for the next day of simulation. For the purposes of this thesis the value of the storage volume for simulation will be either the commonly used one inch value or the value of effective storage that is found in the pumpage-rainfall relationship analysis. Both of these reservoir volumes should be adequate for the modeling process but the effective storage will give a better simulation of the real world situation. The value for this effective storage volume will be computed in the rainfall-pumpage relationship section and the results will be given in Chapter V.

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60 The precipitation-pumpage model. This second model seeks to simulate the rainfall-pumpage relationship as quantified in the first section of this chapter using the methodology provided. This is accomplished through the use of either a reference daily pumpage or by computing the estimated daily pumpage using the per capita consumption rates. Once this has been accomplished the simulator strives to follow as closely as possible the actual rainfall-pumpage values. The model utilizes the coefficients established by using the methodology as shown in the first section of this chapter to simulate the amount that pumpage drops off due to rainfall. It further uses the recovery function to establ ish the amount of recovery that occurs after the rainfall event. Summary of models. The two models proposed herein will give some indication of the effect of evapotranspiration on water use and the relationship of rainfall to pumpage. The second model is envisioned as a method of estimating water use during a drought situation if the rainfall events are also estimated. The results of the output from the models as well as the other methods of analysis will be given in the following chapter.

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CHAPTER V RESULTS The results of this thesis are presented this chapter. The first section gives the results of the analysis of the relationship between rainfall and pumpage. The second section describes the findings of the review of the water consumption literature to determine total per capita, indoor, and outdoor use. The results of the two numerical methods of determining indoor and outdoor water use will also be presented in this section. The final section will review the results of the two water use models; the first based on the relationship of evapotranspiration to water use and the second dealing with the rainfall-water pumpage relationship. Rainfall-Pumpage Relationship The results of the analysis of the relationship between rainfall and water pumpage are presented in this section. The findings for the visual analysis will be presented first, followed by the results of the pumpage analysis. Visual analysis. A plot of pumpage and rainfall versus time for the City of Deerfield Beach, Florida is presented in Appendix C. The horizontal axis of these graphs corresponds to the time in days since the beginning of the record that is being analyzed. In this case, 61

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62 time one corresponds to January 1, 1976; time two corresponds to January 2, 1976, etc. The values of pumpage are given on the left hand vertical axis in inches over the pervious area. The conversion to inches/day from MGD was performed using Equations 5 through 11 found in the methodology chapter. The plot of the pumpage data is represented by the small squares and interconnected by the short dashed lines. The rainfall data plotted along the top of the graphs are repre sented by asterisks connected by long dashed lines. The right hand vertical axis corresponds to the actual rainfall in inches. It should be noted that the two vertical axes represent different scales but with the same units. Each time a rainfall event occurs there is' a corresponding decline in the water pumpage. However, there are times when this cause-effect relationship is most striking. Some examples of the often striking nature of pumpage drop due to rainfall are found in Figure 9 and are listed below 1) Time 120-121: In response to a 0.4 inch rain a drop of 0.05 inch in pumpage is noted. 2) Time 186-187: In response to a three day rain event totaling 1.8 inches a 0.10 drop in pumpage occurs. 3) Time 305-306: In response to rainfall after a period of little or no rain a 2.1 inch rain produces a pumpage decline of 0.06 inches. 4) Time 448-449: In response to a trace of rain following a long dry spell a 0.06 inch pumpage drop is found.

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(l) e:: u (l) 0 (l) O"l 10 D.. E 0.. VI (l) ..t:: u e:: 0.0 co ON<;;t "I "I (' .... ,.... ,.... ,.... 0.0 co 0 "I "1M I i I J I I I <;;t\O coo N<;;t\O co co co 0'1 0'1 0'1 0'1 ,.... ,....,.... I I I o N<;;t \0 r-r--r-r--M MM M Day of Event Figure 9. Visual Depiction of pumpage Decline Due to Rainfall For Deerfield Beach, Florida LO \0 \0 \0 \0 0.0 r--. LO LO LO LO LO LO LO 0.0 1.0 2.0 3.0 4.0 en w ,.... 1/1 10 (l) If-..t:: e:: u 'r-e:: 10 .r-0::: '-"

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64 5) Time 560-561: The most graphic illustration of the causeeffect relationship; the rainfall plot drops all the way down and touches the plot of pumpage, a rainfall of 4.2 inches produces a decline of 0.10 inches in pumpage. These few examples give a clear indication that there is a cause and effect relationship between rainfall and pumpage decline. The dips and peaks do show that there is some relationship. However, the quantity of pumpage decline does not appear to be significant. Similar rainfall-pumpage relationships were found for the other test cities. However, the larger the utility, the smaller the response is to rainfall as reflected in the pumpage. This can be explained by the extreme variability of the rainfall patterns in South Florida. Rainfall at the water treatment plant does not necessarily mean that it has rained throughout the service area. Quantification. The quantification of the relationship between rainfall and pumpage decline was accomplished by comparing the quantity of decline to the quantity of rainfall that caused the decline. This comparison will enable a check of the quantities to see if they are consistent. By consistent it is meant that a like rainfall will produce a similar pumpage decline. Table 15 gives the rainfall quantities causing the resulting pumpage quantity decline for some selected util-i ti es The ra infa 11 values are in inches, whereas the pumpage is in million gallons. As is evident, only a few of the originally selected utilities were analyzed in this fashion. This is because the rainfall was either not recorded at the treatment plant or the rainfall that was recorded at the treatment plant often did not compare to the values recorded at the closest NWS station.

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Table 15. Pumpage Decline Due to Rainfall A. Hollywood, Florida Pumpage Decline (in) Day After Event Rain Date ( in) 0 2 3 Total Slope 2-01-80 .007 .0078 .0024 .0016 .0117 -0.802 (j) ()l 3-11-80 .007 .0082 .0013 .0001 .0096 -2.120 4-26-80 .094 .0089 .0060 0046 .0229 -0.377 5-09-80 127 .0098 .0053 .0043 .0041 .0236 -0.281 5-14-80 .007 .0053 .0132 -0.396 9-20-80 .013 .0029 .0029 -----11-23-80 174 .0016 .0023 .0039 0.339 12-12-80 .670 .0055 .0041 .0095 -0.297 12-29-80 .040 .0033 .0034 .0167 0.031 1-23-81 .469 .0099 .0062 .0161 -0.479 3-07-81 .067 .0034 .0023 .0057 -0.377

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Table 15. Continued B. Pompano Beach, Florida Pumpage Decline (in) Rain Day After Event Date (i n) 0 1 2 3 4 5 Total Slope 1-04-80 .054 .0143 .0150 .0290 -0.027 1-23-80 121 .0156 .0263 .0100 .0519 -0.220 1-26-80 .977 .0318 .0177 .0189 .0095 .0057 .0836 -0.405 2-10-80 .020 .0235 .0028 .0261 -2.102 (j) (j) 3-01-80 1.233 .0357 .0470 .0276 .0230 .0139 .0037 .1510 -0.433 4-14-80 .559 .0031 .0079 .0110 0.924 4-19-80 .162 .0184 .0280 .0056 .0524 -0.211 5-09-80 .061 .0200 .0291 .0172 .0664 -0.076 12-12-80 .020 .0027 .0113 .0140 1.450 1-16-81 .061. .0244 .0273 .0041 .0023 .0581 -1.230 3-13-81 .047 .0236 .0239 .0159 .0082 .0715 -0.358 3-22-81 .409 .0228 .0259 .0381 .0093 .0961 -0.231 5-07-81 1.644 .0499 .0380 .0327 .0294 .0022 .l522 -0.650

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Table 15. Continued C. Boynton Beach, Florida Pumpage Decline (i n) Rain Day After Event Date ( i n) 0 1 2 3 4 5 Total Slope 1-03-77 .363 .0041 .0002 .0015 -1.980 1-29-77 .044 .0037 .0024 .0014 .0075 -0.466 2-28-77 .058 .0031 .0033 .0009 .0073 -0.591 0'\ N 3-25-77 .131 .0070 .0034 .0028 .0930 -0.467 4-14-77 .007 .0051 .0013 .0064 -1.330 7-25-77 .073 .0014 .0014 8-17-77 .044 .0032 .0027 .0007 .0064 -0.878 3-09-78 .777 .0034 .0036 .0077 -0.777 4-14-78 .022 .0019 .0003 .0022 -1.190 6-09-78 2.439 .0036 .0060 .0034 .0028 .0012 .0004 .0173 -0.458 2-12-79 .004 .0005 .0004 .0009 -0.060 3-17-79 .004 .0015 .0005 .0019 -1 .137 5-28-79 .327 .0027 .0021 .0000 .0040 -2.287

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Table 15. Continued Boynton Beach, Florida (Continued) Pumpage Decline (in) Rain Day After Event Date (i n) 0 1 2 3 4 5 Total Slope 7-14-79 .051 .0043 .0013 .0056 -2.512 8-07-79 1.960 .0044 .0000 .0044 -5.069 2-10-80 .312 .0043 .0029 .0021 .0016 .0008 .0118 -0.393 3-14-80 131 .0043 .0006 .0049 -2.054 (])
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Table 15. Continued D. Deerfield Beach, Florida Pumpage Decline (in) Rain Day After Event Date (i n) 0 1 2 3 Total Slope 1-11-76 .101 .0081 .0030 .0122 -0.981 1-21-76 .047 .. 0162 .0142 .0001 .0307 -0.253 1-26-76 .013 .0041 .0092 .0031 .0153 -0.144 2-01-76 .241 .0214 .0153 .0122 .0490 -0.280 0'1 to 3-10-76 .174 .0143 .0041 .0184 -1.250 3-22-76 .007 .0204 .0071 .0001 .0276 -2.649 4-01-76 .054 .0143 .0184 .0071 .0122 .0520 -0.140 4-14-76 .007 .0092 .0031 .0122 -1.099 7-01-76 .094 .0112 .0031 .0143 -1.299 7-13-76 .007 .0051 .0051 10-18-76 .603 .0173 .0133 .0071 .0001 .0378 -1.600 11-28-76 .013 .0031 .0031

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Table 15. Continued Deerfield Beach, Florida (Continued) Pumpage Decline (in) Rain Day After Event Date ( i n) 0 1 2 3 Total Slope 2-24-77 .013 .0041 .0041 3-25-77 .027 .0265 .0092 .0061 .0020 .0439 -0.810 4-06-77 .080 .0214 .0316 .0530 0.389 5-21-77 .838 .0133 .0398 .0255 .0092 .0877 -0.155 ........ 0 8-17-77 .067 .0112 .0041 .0153 -1.012 10-30-77 .147 .0031 .0153 .0020 .. 0204 -0.203 11-07-77 .087 .0122 .0081 .0204 -0.405 11-30-77 .060 .0143 .0071 .0071 .. 0326 -0.151 3-09-78 1.032 .0204 .0245 .0143 .0102 .0694 -0.261 4-14-78 1.246 .0428 .0398 .0316 .0173 .1316 -0.294

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71 A plot of total pumpage decline versus rainfall for Deerfield Beach is given in Figure 10. From this plot the effective storage capacity of the soil layer was determined. This effective capacity of the soil storage layer.can subsequently be input into the evapotranspirationirrigation model that will be discussed later. If a true one-to-one relationship exists between effective rainfall and pumpage decline then a plotted line with a slope of one would have been found. This line would have represented the relationship between incremental rainfall and corresponding incremental pumpage decline. However, this is not the case. The plotted line does not show this one-to-one relationship. Rather, it shows that for a small pumpage decline the slope is much greater than one, but for a larger pumpage decline the slope decreases greatly. Another plot of rainfall versus pumpage decline for Deerfield Beach is shown in Figure 11. Two lines of correlation are shown. Specifically, the values corresponding to a rainfall of greater than 0.4 inches seem to be correlated to each other, whereas the values corre sponding to a rainfall of less than 0.4 inches seem to be uncorrelated. It should be noted that there are only four values of rainfall greater than 0.4 inches; thus, these lines of correlation may not reflect the actual relationship. Using the least squares method of analysis of all 22 of the values, a slope of 0.072, an intercept of O.OlBl, and a correlation coefficient of 0.B4 were found. Similar analysis for the lB values corresponding to a rainfall of less than 0.4 inches produced a value for slope of 0.OB3B, intercept of O.OlBl, and correlation coefficient of 0.33. Conversely, the same analysis of the four values

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..--. c ..... '--" OJ C ..... r-u OJ Cl OJ en n:1 0... E ::::l c.. r-n:1 +J 0 I-0.20 1 0.18 0.16 0.14 0.12 /",,----// 0.10 0.08 ,/ 0.06 0.04 // 0.02 I --I' 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Rainfall (in) Figure 10. Effective Storage Capacity of the Soil Layer For Deerfield Beach, Florida -....,J N

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c .r-......, OJ c .r-....... U OJ Cl OJ Ol co 0.. E :::s 0-....... co +-> 0 l-0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.041 ... 0.02 0.2 0.4 0.6 0.8 Ralnfall (in) 'II 1.0 J 1.2 Figure 11. Correlation Between Pumpage Decline and Rainfall For Deerfield Beach, Florida -----4 points ------18 points -----22 points 1.4 "-.J W

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74 corresponding to rainfall greater than 0.4 inches yielded values of 0.125, -0.035, and 0.88 for the three statistical parameters of interest. The small sample size precluded the making of any stronggeneralizations. Thus, for the limited number of singular events reviewed very little correlation was found between the amount of rainfall and the amount of pumpage decline. A review of the other utilities shown in Table 15 yield similar conclusions. It would then seem that these utilities exhibit the same phenomenon that Maidment (1983) found in Texas; the amount of rain is not as important as the fact that it has rained. The short term memory of the system was then examined. Table 16 shows that the recovery time varies from just one day to three or four days depending on the storm. Recovery begins immediately following the storm and in most cases the recovery is complete within four days. As stated in the methodology section dealing with the recovery phase of the hydrograph, for pumpage decline an exponential function should fit the recovery leg of the hydrograph. The exponential function would then be used to simulate the natural streamflow processes. How-ever, simulating the need for irrigation as perceived by the consumer does not lend itself to this analysis. As shown in Table 15 the slope of the line found when plotting the natural log of pumpage recovery versus time for Deerfield Beach, depends on the storm. The slope as determined by least squares linear regression is equal to s = .l!L.E t (14 )

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75 Table 16. Pumpage-Recovery For Selected Hater Utilities in South Florida A. Hollywood, Florida PumQage (MG) Rain Day of Event (in) -1 0 1 2 3 4 0.01 20.38 18.:19 19.77 19.98 20.37 0.-01 21. 17 19.08 20.84 21.14 0.14 19.89 17.52 18.32 18.67 19.14 0.19 21.08 18.57 19.71 19.98 20.02 22.23 0.01 22.23 20.21 20.87 22.70 0.02 20.43 19.69 20.61 0.26 19.26 18.84 18.67 19.96 20.15 1.00 19.40 18.00 18.36 19.55 0.06 19.23 .49 18.46 19.60 0.70 19.94 17.39 18.36 20.35 0.10 18.15 17.29 17.56 19.12

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76 Table 16. Continued B. Pompano Beach, Florida {MG} Rain Day of Event (in) -1 0 1 2 3 4 5 0.08 20.14 17.67 17.60 23.50 0.18 22.97 20.28 18.42 21.24 1.45 21. 19 15.70 18.14 18.92 19.55 20.20 0.01 22.45 18.44 21.96 23.09 1.83 23.14 16.97 15.02 18.37 19. 16 20.74 22.50 0.83 18.06 17.52 17.70 19.20 0.24 20.26 17.09 15.42 19. 13 22.67 0.09 26.60 23.14 21.57 23.63 29.33 0.03 22.32 21.86 20.37 23.43 0.09 24.43 20.22 19.72 23.72 24.03 0.07 24.44 20.37 20.32 21.69 23.03 0.60 25.00 21.06 20.52 18.42 23.40 2.44 22.05 13.43 15.48 16.41 16.97 21.67 22.25

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77 Table 16. Continued C. Deerfield Beach, Florida PumQage Rain Day of Event (in) -1 0 1 2 3 4 0.15 6. 1 5.3 5.8 6.3 0.07 6.5 4.9 5.1 6.5 0.02 6.8 6.4 5.9 6.5 7.0 0.30 6.8 4.7 5.3 5.6 7.0 0.26 6.2 4.8 5.8 6.6 0.01 8.7 6.7 8.0 8.7 0.08 9.0 7.6 7.2 8.3 7.8 3.9 0.01 7. 1 6.2 6.8 8.4 0.14 6.7 5.6 6.4 7.4 0.90 6.6 4.9 5.3 5.9 6.6 0.02 7.6 7.3 8.1 0.02 8.0 7.6 0.04 8.6 6.0 7.7 8.0 8.4 0.12 9.3 7.2 6.2 9.5 1.25 9.2 7.9 5.3 6.7 8.3 0.10 7.7 6.6 7.3 8.7 0.22 8.0 7.7 6.5 7.8 8.9 0.13 6.8 5.6 6.0 6.9 0.09 7.3 5.9 6.9 6.6 6.6 7.5 1.54 7.8 5.8 5.4 6.4 6.8 8.0 1.86 9.8 5.6 5.9 6.7 8.1

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78 Table 16. Continued D. Boynton Beach, Florida PumQage (MG) Rain Day of Event (i n) -1 0 1 2 3 4 5 0.50 5.82 5.31 5.75 6.21 0.06 7.63 6.31 6.78 7.11 0.08 7.03 5.92 5.85 6.69 7.34 0.18 8.82 6.30 7.60 7.83 9.28 0.01 7.29 5.47 6.81 7.51 0.10 7.75 7.25 7.90 0.06 7.91 6.75 6.95 7.71 0.87 7.91 7.17 7.28 8.34 1.07 8.17 6.94 6.88 7.91 8.43 0.03 9.63 8.95 9.52 3.36 7.04 5.76 4.88 5.82 6.05 f).60 6.90 0.06 7.71 7.54 7.55 7.84 0.06 8.99 8.46 8.82 9.22 0.45 8.37 7.40 7.60 8.42 0.07 8.41 6.88 7.95 8.90 0.27 10.49 8.90 10.48 0.43 8.79 7.27 7.74 8.04 8.28 8.50 0.18 10.04 8.48 9.84 0.01 9.06 7.34 8.89 9.15 1.00 7.90 6.24 7.80 8.30 0.14 7.88 6.86 7.43 8.03 0.15 9.33 8.21 9.36 0.50 9 30 7.88 8.56 9.16 9.38 0.16 7.24 6.87 7.14 7.21 0.55 7.34 5.63 6.87 6.85 7.29 0.18 8.26 7.74 8.58 0.56 8.14 6.64 6.48 6.93 7.48

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where 79 ln P = natural log of the change in the pumpage, and t = change in time. An average slope may be used in the exponential equations but the results of the simulations are not very accurate. The pumpage recovery values and the slope of the semi-log plots for some of the other utilities can also be found in Table 15. In summary, the following characteristics of the rainfall-pumpage relationship were found. First, there is a definite relationship between the two variables as was shown by the visual inspection of the Deerfield Beach and other utility data. Secondly, there does not appear to be a one-to-one relationship between the quantities of the two variables; it would be difficult to predict the exact amount of pumpage decline due to a certain size storm. Finally, the maximum memory of the consumer in relationship to his/her irrigation needs seems to be about four days. The values of the recovery function have been found using the data supplied by the utilities to be 40, 75, 90, and 100 percent for the each of the four days respectively. Also, an exponential function does not reflect the recovery leg function; although the semi-log plot does produce a straight line, the slopes of each storm are different and inconsistent. Consumption-Conservation Literature Review Results There have been countless numbers of studies trying to determine per capita consumption. Unfortunately, there are as many resultant values as there are studies. Even within agencies, reports on the same areas within the same time frame can produce different results.

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80 Average national daily consumption on a per capita basis was found to be 157 gallons and 166 gallons for 1965 and 1970 respectively 1970). The United States Water Resource Council (1970) estimates that the average daily per capita consumption is approximately 163 gallons. This can be contrasted to the south Atlantic states average of 187 gpcd (USGS, 1972). Clearly, using a national or even a regional consumption figure would not meet the needs of this thesis. Therefore, a comprehensive review of existing water use literature was made. Table 17 lists the values determined either by the author or by the use of the authors' data for some of the more reliable reports. The table lists the reports by date published, author, specified area, and date of values reported. As is shown, the values range from 157 gpcd to 189 gpcd for the SFWMD service area. Table 18 lists the per capita consumption rates found in this thesis. The table gives year, estimated service population, pumpage, and per capita consumption for each of the 12 selected utilities as well as the area as a whole. The average per capita consumption rates for the utilities vary from a low of 73 GPCD for Palm Beach County to a high of 409 GPCD for Boca Raton. The average consumption for all of the utilities together was found to be approximately 186 GPCD. This period encompasses the drought of 1980-81, so the per capita use may be higher than normal. The yearly values are 177, 189, 202, and 191 GPCD for 1978, 1979, 1980, and 1981 respectively. It would seem reasonable that the per capita consumption during a non-drought period would range between 185 and 190 GPCD.

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Table 17. Summary of Water Demand Studies Date of Report 1970 1972 1972 1974 1976 1977 1977 Source Todd, David K. Editor IIWater Encyclopediall Water Information Center, Port Washington, New York u.s. Geological Survey, Circular Number 567 U.S.G.S. IIAnnual Summary of Public Water Supplies in Florida Kreitman, A., R.H. Walker, and J.A. Beck Central and Southern Florida Flood Control District (CSFFCD) Technical Pub. 74-3 Khanal, N. SFWMD Technical Publication 75-2 (DATA) Leach, S.D., & H.G. Healy, IIEstimated Water Use in Fl ori da 197711 U. S. G. S. Water Resources Investigations 79-112 SFWMD, Water Use and Supply Development Plan, April 1977 Area of Estimate United States South Atlantic States Florida Florida South Fl ori da SFWMD Florida SFWMD Per Capita Consumption (Gal) 157 (1965) 166 (1970) 187 (1972) 163 (1972) 157 (1972) 197 (1974) 194 (1974) 168 (1975) 171 (1977) 178 (1977) 189 (1970) 159 (1975) 00 --'

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Table 17. Continued Date of Area of Per Capita Report Source Estimate Consumption (Gal) 1979 Toomey, J., & C. Woehlcke 179 (1979) SFWMD Technical Publication 79-3 1982 Franklin, S. Masterls Thesis Deerfield Beach 169 (1982) 1982 Walker, B.J. Senior Honoris Gainesville, 157 (1982 ) Project, University of Florida Florida 00 N 1982 Present Study SFWMD, Urban Area 186 (1982 )

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Table 18. Annual Water Use Patterns in Twelve Water Utilities in Southeast Florida 1978 Through 1981 Util ity Year Population Served Pumpage GPCD Boca Raton 1978 48,792 19.95 408 79 54,821 23.46 427 80 58,490 24.01 410 81 60,129 23.63 392 Average 55,558 22.76 409 Boynton Beach 1978 37,308 6.95 186 79 39,491 6.79 172 80 43,000 7.91 184 co w 81 43,982 7.70 175 Average 40,945 7.34 179 Deerfield Beach 1978 35,480 6.71 189 79 40,703 7.67 188 80 39,541 8.81 222 81 40,598 8.67 213 Average 39,094 7.96 203 Delray Beach 1978 34,395 10.11 29479 37,458 11 .99 320 80 36,014 12.97 360 81 38,271 12.12 317 Average 36,534 11 .79 323

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Table 18. Continued Uti 1 ity Year Population Served Pumpage GPCD Fort Lauderdale 1978 227,892 45.64 200 79 230,273 47.11 205 80 232,654 48.37 208 81 238,625 46.49 195 Average 232,361 46.91 202 Hollywood 1978 100,212 17. 12 171 79 102,033 18.87 185 OJ 80 102,180 19.98 196 .j:::> 81 103,547 17.91 173 Average 101 ,993 18.47 181 Lake Worth 1978 34,789 6.18 178 79 34,690 6.95 200 80 34,150 6.40 188 81 34,414 7.19 209 --Average 34,511 6.68 194 Miami-Dade WASA 1978 1,275,003 211 .78 166 79 1,283,603 228.03 178 80 1 ,277 ,393 248.35 194 81 1,426,174 265.04 186 Average 1,350,054 244.98 181

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Table 18. Continued Utility Year Population Served Pupmage (MGD) GPCD North Miami Beach 1978 163,814 24.72 151 79 165,978 26.56 160 80 167,591 26.62 159 81 168,381 25.72 153 Average 166,441 25.91 156 Palm Beach County 1978 76,746 4.07 53 79 77 ,782 5.22 67 co 80 77 ,406 6.61 85 Ul 81 79,250 6.92 87 --Average 77 ,796 5.71 73 Pompano Beach 1978 63,794 18.73 293 79 64,726 21.07 325 80 60,593 21.97 363 81 64,385 20.17 313 Average 63,375 20.49 324 Sunrise 1978 46,333 6.74 146 79 49,842 8.04 161 80 57,448 8.80 153 81 61 ,393 9.74 159 --Average 53,754 8.33 155

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Table 18. Continued Util ity Year Population Served Total 1978 2,144,558 79 2,181 ,400 80 2,186,510 81 2,359,149 Average 2,217,904 Pumpage (MGD) 378.70 411.76 440.80 451.30 420.64 GPCD 177 189 202 191 190 co CJ)

PAGE 98

87 Indoor consumpti on. In conjuncti on with the determi nati on of per capita consumption it is necessary that some effort be made to determine indoor and outdoor use. Kreitman et al. (1974) obtained values of between 40 and 50 percent for outdoor use. Franklin (1982), in her time series analysis of Deerfield Beach, Florida obtained values ranging from 30 to 40 percent for outdoor use. She recommended the use of 36 percent for outdoor use. Robert Douglas of Lake Worth Utilities estimated that at times his service area used as little as 18 percent of the total water pumped for outdoor use. By contrast Cindy Martin (1982), the Water Conservation Analyst for Boca Raton, Florida, found that outside water use in her city was between 63 and 78 percent of the total. These high percentages are ex plained, in part, by the affluent population that resides in the city and the citys own beautification standards. The area of concern for this report encompasses the entire eastern coastline from West Palm Beach southward to the tip of the state. Some of the communities are newly developed while others are older and well established. The outdoor use is thus affected in that many of the new homes and condominiums use alternative methods for irrigation. Basing outdoor use on anyone community would be inviting error. Therefore, two methods of estimating outside water use were employed in this report. The is based on the assumption that an average person, regardless of the area in which they live, uses the same amount of water indoors. The second method utilizes the low pumpage flows immediately following a storm event to yield a baseflow. This baseflow equates to indoor use.

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88 For the first method, the indoor use for Gainesville, Florida was used to represent indoor water use in south Florida. This number was chosen due to its availability and known reliability. Walker (1980) analyzed the extensive and complete records of the City of Gainesville Regional Utilities and found that indoor use was approximately 120 gpcd. If this indoor use is subtracted from the total per capita consumption previously found, a value of about 70 gpcd for outdoor use is found. This equates to about 37 percent of the total use being for outdoor purposes. Using sewage flows from some of the selected utilities a similar value of 39 percent was found for the outdoor portion of total usage. The second method required an analysis of the data for all of the cities of interest in the study area. The results found by using the minimum flow after a heavy rain storm produced a low pumpage quantity of between 96 and 122 gpcd depending on the city being analyzed. Taking the average, about 104 gpcd is obtained for indoor use. Subtracting this from the previously found average per capita consumption yields 43 percent as outdoor use. Therefore, for the purpose of this study a value of 40 percent of the total water use was found to be for outdoor use. Water use. This report has previously examined the per capita consumption as well as the indoor and outdoor use patterns. Next, the components of indoor and outdoor use are examined and savings that can be expected from conservation techniques are determined.

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89 It is generally accepted that indoor use can be segregated as follows (Blackwelder and Carlson, 1982): 1) Toilet flushing 40% 2) Bathing 35% 3) Dish washing and laundry 20% 4) Personal consumption 5% From this, it may be shown that significant savings can be achieved by the use of certain indoor conservation devices. Boland (1978) found that the use of flow restriction devices in showers, aeration devices in faucets, and flush capacity modifications could result in up to a 27 percent reduction in water use. This equates to a savings of 30 gpcd. Once these devices have been installed, there will not be as much room for conservation when future drought situations arise. Therefore, a utility should take these conservation devices into account when issuing further reduction quotas. Outdoor use does not easily break down into many constituents. Most of the water used outdoors, especially in Florida, is for irrigation. Only a very small percentage is for other outdoor activities such as automobile washing. However, even if the water is used outdoors for uses other than irrigation, it usually finds its way into the soil surface. Therefore, for the purpose of this thesis all of the water used outdoors, approximately 40 percent, is assumed to be for irrigation. One of the problems encountered in this thesis was the determina tion of the amount of irrigation water that is supplied by private

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90 wells. Efforts were made to determine how many consumers had and used private irrigation wells. The SFWMD and the utilities have been in terested in determining this quantity for some time, but as yet have no good understanding. The one organization that was found to have information on the percentage of homes having these wells is the county realty board. However, this information is considered proprietary and was impossible to obtain. Estimates of the number of households employing private irrigation wells have been as high as 60 percent. Since no acceptable quantification of private irrigationwells is available the previously determined 40 percent value will be used for outdoor use in this report. In summary, it was found that per capita daily water consumption for the South Florida area is approximately 186 gpcd. Of this 186 gpcd approximately 40 percent is used outdoors. This means that 112 gpcd is used indoors and 74 gpcd is used outside. It was also found that up to 27 percent of the indoor use can be saved using water conservation devices as opposed to a home where no conservation devices are installed. It was additionally found that savings of up to 63 percent can be achieved through the use of rationing and fines (Baumann, 1979) as shown in Table 1. Mode 1 Resul ts The following section details the results of the data preparation and the two models. The evaporation and pumpage data must be adjusted to reflect perviousness and size of the catchment. Further, the data must be converted into the correct units so that it can be used in the models.

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91 Evaporation data preparation. To convert pan evaporation data to the useful form of evapotranspiration it is necessary that three con versions be completed. The first of these utilizes a coefficient (COEFA in the model) to convert pan evaporation to potential evapotranspiration (PET). As discussed in the literature review, Jones et al. (1983) and Allen et al. (1978) determined that this oefficient should be approx imately 0.70 for the South Florida area. A second coefficient (COEFB in the model) is needed to convert this potential evapotranspiration to actual evapotranspiration. Khanal (1980) found the value of this coefficient to be 0.89 for south Florida grasses. Using the values specified for the two coefficients, 0.70 and 0.89 a totar coefficient of 0.62 is obtained. Multiplying this total coefby the annual average pan evaporatation, 70 inches, a total ET value of about 44 inches per year is obtained. This means that the grasses and shrubs usually associated with the urban areas consume 44 inches of water as provi ded by precipi tation and i rri gati.on throughout the year. If all of the precipitation water was caught and held for plant use there would be no need to irrigate. The annual average rainfall of 55 inches is greater than the ET. However, the spatial variability of rainfall is such that_runoff and infiltration to the lower zones remove a significant portion. Using the 0.62 coefficient found previously a value of 0.12 inches is found for the average daily ET. DeerHeld Beach's pumpage of 7.96 MGD annual average corresponds to only 0.11 inches of water over the pervious area. This means that if there is a severe drought all of the water that is pumped by Deerfield Beach would be required

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92 to meet irrigation demand. This obvously does not happen. Therefore, the total coefficient needed to convert pan evaporation to ET needs to be smaller. After many simulations with the irrigation simulator a total coefficient value of 0.42 was used. The final conversion requires a third coefficient. This coefficient converts the total service area to just the pervious service area. It is calculated by using Equation 10 described in the pumpage preparation section. The overall values for the product of these three coefficients are given in Table 19. For Deerfield Beach the value is 0.29. For the area serviced by the 12 selected utilities the value was found to be 0.29 also. The proposed irrigation model requires evaporation as an input. To provide these needed data for future model applications the long-term evaporation recor.ds of the Fort Lauderdale Experiment Station have been analyzed. This station was chosen due to its central location within the study area and its location on the 70 inch iso-pan evaporation line as shown in Figure 4. The mean pan evaporation value for each day of the year was determined and is provided in appendix F. Additionally, the values of the standard deviations are also provided. Appendix G provides additional monthly summaries of the Fort Lauderdale Experiment Station pan evaporation data. For each month, the yearly number of observations, mean, coefficient of variation, minimum, and maximum values are given. Additionally, the average of the number of observations, means, and the coefficient of variation are

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93 Table 19. Product of Evapotranspiration Conversions Product of ET Utility Conversion Coefficients Boca Raton 0.306 Boynton Beach 0.307 Deerfield Beach 0.286 Delray Beach 0.301 Fort Lauderdale 0.278 Hollywood 0.283 Lake Worth 0.286 Miami-Dade WASA 0.296 North Miami Beach 0.276 Palm Beach County 0.315 Pompano Beach 0.285 Sunrise 0.308

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94 given. Comparison of the values in Appendix G with the NWS report values shown in Table 9 shows that the means are similar. The coefficients of variation are not in agreement. This is because the NWS values of C.V. are based on the monthly means, whereas the C.V. values in Appendix G are based on daily values. Pumpage data preparation. The pumpage data reported by the utilities is in millions of gallons. These values must be adjusted to reflect the pervious area and converted to inches. This can be accomplished by using Equations 5 through 11 as stated in the methodology. For Deerfield Beach this conversion yields a value of 0.0102. This value will be multiplied by the daily pumpage in millions of gallons to obtain inches. For the entire area serviced by the 12 utilities a value of 0.0000834 is found. The values computed for all of the utilities are given in Table 20. The manipulation required to convert the pumpage from inches over the entire area to inches over just the pervious area was presented in the methodology section (see Equations 6 through 11). The results of this manipulation are found in Table 21. This table lists the values computed for population (1978), area (1978), population density (PD), developed population density (POd)' impervious area percentage, and pervious area percentage for all of the utilities that have been selected. Population values range from a low of 34,395 for Delray Beach to a high of 1,210,728 for the Miami-Dade Water and Sewer Authority (MDWASA). Service area ranges from 5,414 acres in Lake Worth to 276,732 acres for MDWASA. All of the values are based on 1978 data.

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95 Table 20. Used in Conversion of Pumpage From Millions of Gallons (MG) to Inches Ci ty Boca Raton Boynton Beach Deerfield Beach Delray Beach Fort Lauderdale Hollywood Lake Worth t,1i ami -Dade 'vJASA North Miami Beach Palm Beach ebunty Pompano Beach Sunrise Total Area Serviced Coefficient 0.00236 0.00278 0.01020 0.00494 0.00204 0.00390 0.01000 0.00019 0.00299 0.00027 0.00579 0.00198 0.00008

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Table 21. Results of Calculations to Determine Pervious Area Serviced Population City 1978 Service Density, persons/acre Impervious Pervious Population Area Overall Developed % % Served (Acres) Area Boca Raton 48,792 21 ,632 2.26 7.08 27.61 72.39 Boynton Beach 37,308 18,099 2.06 6.97 27 .40 72.60 Deerfield Beach 38,000 5,920 6.42 9.66 32.27 67.73 1.0 OJ Delray Beach 34,395 10,508 3.27 7.67 28.75 71 .25 Fort Lauderdale 227,892 27,315 8.34 11.00 34.42 65.58 Hollywood 100,212 14,086 7.11 10.13 33.05 66.95 Lake Worth 34,789 5,414 6.43 9.67 32.28 67.72 ti ami -Dade WASA 1,210,728 276,732 4.38 8.34 30.00 70.00 North Miami Beach 163,814 18,958 8.64 11 .02 34.75 65.25 Palm Beach County 76,746 186,750 0.41 6.09 25.57 74.43 Pompano Beach 63,794 9,485 6.73 9.87 32.62 67.38 Sunrise 46,333 25,401 1.82 6.84 27.14 72.86 2,082,803 620,300 3.36 7.72 28.85 71.15

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97 For Deerfield Beach, Florida the population served was approxi mately 38,000 with a gross area of service of 5920 acres. This results in values of 6.42 for PO as found using Equation 8, 9.66 for POd by using Equation 7, and aipercent impervious of 32 using Equation 6. This gives a value of 68 percent for the fraction of the total area that is pervious. For the entire service area, the total served population is 2,080,888 and the approximate area served is 620,300 acres, as of 1978. Utilizing the same equations as in the Deerfield Beach calculations values of 3.35,7.72, and 29 were found for PO, POd' and I, respectively. This means that for the 12 utilities studied in this thesis, the per vious area is about 71 percent of the total or about 440,000 acres (see Table 21). Irrigation model. The irrigation model was used to simulate the outdoor water use patterns for the City of Deerfield Beach, Florida. The simulator uses the computed evapotranspiration as the outflow of the storage reservoir (root zone). The tnf10'ws to the reservoir are precipitation and any irrigation that is applied. The model tracks the reported daily pumpage of the utility to determine the quantity of water necessary to meet outside water demand. This outside water demand ;s determined by the irrigation initiation scheme specified by the user and the percentage of the total storage volume available that is to be filled during the irrigation procedure.

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98 For the purpose of this study, each of the three irrigation schemes will be simulated using various values for the user specified coefficients. The pan evaporation data from the Fort Lauderdale Experiment Station, and the pumpage and rainfall recorded at the Deerfield Beach plant were input into the simulator. The simulator then tracked the water depths in the root zone reservoir using the recorded rainfall and irrigation as inputs. The outflow was the computed ET. The simulator was run until coefficients were obtained that give good results. Good results are obtained when the reservoir does not go dry, the pumpage is in the acceptable range for outdoor use, and the indoor requirements are still met satisfactorily. The results of some of these simulations can be found in Table 22. The simulation using minimum storage as the irrigation initiator with the one inch reservoir gives unacceptable results. The quantity of water needed to meet the irrigation .needs at times exceeds the amount of total pumpage available. This could be a result of the simulator's use of irrigation and precipitation only over the pervious areas. The value of the effective storage as found in the rainfall-pumpage analysis section was then employed as the total storage volume. Using this storage volume and various values for the coefficients of minimum storage, total storage, and beginning storage produced a very good simulation of the pumpage for outside irrigation. The optimum combination of coefficients for this simulation technique are 0.2 inches for total storage volume, 0.125 for minimum storage before irrigation,

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99 Table 22. Results of Irrigation Simulator Total 1.0 1.0 1 .0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 Irrigation Initiation By Minimum Storage STORAGE Minimum 0.50 0.50 0.50 0.50 0.75 0.75 0.75 0.75 0.90 0.90 0.90 0.90 0.100 0.100 0.100 0.100 0.150 0.150 0.150 0.150 0.125 0.125 0.125 0.125 Beginning 0.50 0.50 0.50 0.50 0.75 0.75 0.75 0.75 0.90 0.90 0.90 0.90 0.100 0.100 0.100 0.100 0.150 0.150 0.150 0.150 0.125 0.125 0.125 0.125 Available Storage Percentage Filled 25 50 75 100 25 50 75 100 25 50 75 100 25 50 75 100 25 50 75 100 25 50 75 100 Comments Pumpage for irrigagation exceeds total pumpage Reservoir dries Sati sfactory Irrigation pumpage too high Negative Storage Negative Storage Pumpage for irrigaation exceeds total pumpage Negative storage Negative storage Satisfactory Pumpage for irrigation too high

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100 Table 22. Continued Simulation of Irrigation Pumpage Models Useing the Number of Dry Days as the Irrigation Initiator Number of Dry Days Storage Reservoir Depth 3 1 2 1 1 o 1 3 .2 2 .2 12 0 .2 Comments In each case, reservoir went dry regardless of percent In each case, reservoir went dry regardless of percent In each case, reservoir went dry regardless of percent Works well with a per cent available storage of 50 Reservoir goes dry Reservoir goes dry Reservoir goes dry or pumpage for irrigation is too high Reservoir goes dry with a percentage to be filled of less than 40

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101 0.15 inches for beginning storage, and 0.50 as the percentage of available storage to be filled. The maximum amount of pumpage that was used for the irrigation needs of Deerfield Beach was approximately 58 percent of the total water usage. The second method of irrigation initiation was then simulated. This method uses the number of days since the last rainfall as the method of irrigation initiation. Various values of the other coefficients were simulated in order to determine the optimum set (the set that best reflects the actual pumpage data). For the first set of trials the value for total storage was again set equal to one inch. This resulted in similar findings to the minimum storage method of irrigation initiation. That is, the quantity of water needed to meet the irrigation needs of the root zone was greater than the total daily pumpage. The value of total storage was then set equal to 0.2 inches, the effective storage found in the previous analysis. The optimum value for the number of days before irrigation was determined to be zero. This is the case when the coefficient for the amount of available storage (COEFD) that will be filled on irrigation was below 0.50. Using values of less than 0.50 for the percentage to be filled yielded negative values for storage volume. The final scheme was a combination of the first two whereby both a minimum storage level and a specific number of dry days had to be attained prior to irrigation. The results show that whichever of the tvw factors is the limiting one controls the irrigation process. Therefore, it is no more than a replication of the previous simulation effort.

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102 In summary, no one method of irrigation initiation proved to be the most accurate in following the daily pumpage quantities; rather a combination of coefficients has the best effect. The irrigation initiation scheme is less important than the coefficients used, especially the coefficient for the percentage of available storage to be filled by each irrigation. A graphical depiction of the results of the simulation of best fit is shown in Figure 12. A listing of the final model is given in Appendix H. Rainfall-pumpage simulator. This model uses either the actual pumpage or the pumpage as calculated by the per capita consumption and pan evaporation to track the pumpage changes due to a rainfall event. This allows the water management agency to predict the demand that will have to be met based on the expected rainfall patterns. Again, the City of Deerfield Beach, Florida was used as the test city. Actual pumpage and rainfall data were input into the model. The amount of pumpage decline due to rainfall events was then simulated. Once this had been done sucessfully, that is, the pumpage decline due to rainfall in the simulator1s output paralleled the actual pumpage decline, the recovery function was simulated. It was found previously that the recovery leg of the pumpage decline hydrograph did not follow an exponential function. Therefore, it was necessary that a trial and error approach be used to simulate this recovery. It was found that there is no one set of equations that govern recovery in every case. Referring to the pumpage-rainfall analysis a four day effect of the rainfall event was used. Additionally, the

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0.10Pumpage 0.08 V1 Q) .c 0.061 u 1\ --' c: 'r-0 W Q) O.J I \ Irrigation tJ) I
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104 recovery equations are governed by the number of dry days preceeding the event; that is, different equations govern the recovery leg of the pumpage decline hydrograph depending on whether one, two, three, four, or more days of dry weather preceed the rainfall event. The model that gave the best fit for the input data is shown in Appendix I. The recovery leg coefficients that gave the best fit are given in Table 23. These recovery leg coefficients are used to simulate the recovery function of the pumpage decline to pre-rainfall event levels. For example, for only one dry day prior to the rainfall event the first day recovery coefficient is 1.12. This means that the value of pumpage after the rainfall event is multiplied by 1.12 to get a value of pumpage for the first recovery day. If it continues not to rain the recovery coefficients will be used as multipliers for each successive day. The same method is used to for the other events. A graphical depiction of the best fit simulator is given in Figure 13. As can be seen, the predicted pumpage is quite close to the actual pumpage in most cases. The one period where a bad fit shown is where the actual pumpage reacts to a rainfall event but there was no rainfall recorded at the treatment plant, hence, the simulator did not react to it. In summary, both of the models proved useful in trying to predict urban water use. The irrigation simulator gives a good indication of just how much of the average daily pumpage is used outdoors. The rainfall-pumpage simulator will allow the user to predict water demand for the days and weeks forthcoming.

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105 Table 23. Recovery Coefficients Used in the Rainfall-Pumpage Model Number of Dry Days Prior to Rainfall Event 1 2 3 >4 Recovery Day 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 Coefficient 1.120 1.140 1.060 1.008 1.060 1.100 1.030 1.005 1.050 1.060 1.005 1.001 1.040 1.050 1 .015 1 .001

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--.. 0 u:; :E.: OJ +J ro ex: OJ Vl ro o. E ::I 08 Legend Simulated 7 ---Actual 1 6 \ \ 5 \ -"-... :.J \ \ I 'V 'j""', \\ IY Vi \ \ \ \ /\ (/\ \ \ I \ I \\ I ,/ ( I \ /\ I v \ I /\ \ J Day of Simulation Figure 13. Rainfall-Pumpage Nadel Results For a Selected Period --' o 0'1

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Objectives CHAPTER VI SUMMARY AND CONCLUSIONS The regional drought of 1980-81 had a deleterious effect on South Florida. The drought was of such severity that the South Florida Water Management District was forced to take the unprecedented step of calling for a mandatory ten percent cutback of water use and caused the District to analyze its drought response strategies. Of particular interest was knowledge of water consumption patterns exhibited by the urban areas, specifically, the relationship between meteorological phenomena and urban water use. To attain this knowledge the relationship between rainfall and pumpage and urban water use consumption patterns was studied. These studies culminated in the formulation of irrigationpumpage and rainfall-pumpage simulators. Methodology The rainfall-pumpage relationship was analyzed using the rainfallrunoff hydrograph method. This method assigns values of peak, duration, and volume of pumpage decline as a result of an individual rainfall event. Per capita consumption patterns were investigated by reviewing the related literature. In conjunction with the literature review two numerical methods of determining the percentage of indoor and outdoor 107

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108 use were employed. The first of these methods used indoor use as determined in a similar locale to determine outdoor use in South Florida. The second method used a base flow argument whereby the amount of water being pumped immediately following a rainfall event was considered to reflect solely indoor use. The irrigation-pumpage simulator uses a storage reservoir principle where all of the inflows and outflows to the reservoir (root zone) are simulated. The rainfall-pumpage simulator employs the previously discussed relationships to model the pumpage quantities in response to the rainfall event. Study Area The study area for this thesis consists of the urban areas in the southeastern portion of the South Florida Water Management the urban areas in the District which receive water from service areas one, two, and three (Figure 2). Twelve of the largest utilities operating in the Gold Coast area were contacted for data. These twelve utilities serve approximately two million people with a combined service area of over 620,000 acres. Eval uation Method. The analysis of the rainfall-pumpage relationship gave a good indication of the peak, volume, and duration of the decline in pumpage due to a rainfall event. The use of the exponential equation (Equation 3) to simulate the recovery leg of the curve, which is customary when analyzing rainfall-runoff hydrographs proved to be inconclusive. The method provided good results for peak decline and duration, but not for the recovery leg of the decline function.

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109 The use of the hydrograph method which is normally used to simulate natural phenomena to simulate the perceived effect of rainfall by the consumers was unsuccessful. Instead, the storage reservoir principle used in the irrigation model has been employed in many cases for soil moisture storage simulation, and it performed more satisfactorily. Findings. The rainfall-pumpage relationship was analyzed to determine peak, volume, and duration of the decline in the pumpage rate following a storm. These values vary depending of the storm, but the duration is between one and five days. In most cases full recovery has been achieved by the fourth day following the rainfall event. Generally speaking, the peak pumpage decline is about forty percent of the dry day pumpage. The recovery leg, while not following an exponential function, does exhibit some regularity. The values of recovery for the four day cycle are 40, 75, 90, and 100 percent of the pre-storm pumpage, respectively. The consumption patterns for the South Florida urban area were analyzed to determine per capita consumption, indoor versus outdoor use, and the effects of conservation. The per capita consumption value is approximately 186 This equates to about 67,000 gals per capita per year. It was found that about 40 percent of the total water pumped in the South Florida area is used for outdoor use. It varied from a low of 18 percent in Lake Worth to a high of 63 percent in Boca Raton. Conservation water savings can vary depending on the type of conser vation program implemented. Simple retrofit water conservation

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110 devices can save up to 27 percent, whereas stringent measures of con servation and rationing can achieve up to a 63 percent reduction. The final run of the irrigation model was able to simulate the total water use and hence the outdoor portion to within plus or minus 10 percent. This model will allow the user to vary the coefficients to simulate the pumpage-irrigation response of cities other than Deerfield Beach. The pumpage-rainfall model is able to simulate the behavior of the pumpage due to a rainfall event to some extent. This level is determined by the fact that the model was able to track the actual pumpage for the City of Deerfield Beach, Florida within .limits. The daily pumpage was off by as much as 28 percent, but the monthly and yearly average values were much closer registering a 16 and 10 percent error respectively. Suggestions For Additional Investigation This study is by no means exhaustive. There are many areas for futher investigation such as a determination of whether the time of year affects the rainfall-pumpage relationship, the effects of private irriwells, further determination of the effects of conservation activities, and the aggregation of urban use to smaller units. Whether the time of year affects the rainfall-pumpage relationships can be determined by examining the differences in the behavior of pumpage due to rainfall with the changes in the seasons. Seasonality of not only meteorologic origin but also of population should be studied. In areas like South Florida where there is a very high dependence on the

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111 tourist trade, seasonal fluctuation due to this segment of the population can have a marked effect on the water use patterns. Outdoor water use is an area of increasing concern for the water managers. Some estimates of the number of households irrigating using private shallow wells have ranged as high as 60 percent. If this is the case it will greatly affect the amount of water conservation the utility can expect when outdoor water use is eliminated. Therefore, some effort should be made to determine the number of households using this alternative method of irrigation. Conservation efforts have been an ongoing goal of the utilities for several years. However, utilities need more information regarding how many of their customers use conservation methods. Some consumers have already done their utmost to conserve water, and further conservation by them would be a severe hardship, while others have done virtually nothing, and great savings can be achieved by them. A final area for further investigation is the effects of the different types of consumers on urban water use. This study lumps all of the urban consumers into one group. Perhaps a better understanding of the actual use patterns can be obtained by dissecting the urban user into residential, commercial, industrial, hotels and motels, apartments, etc .. While the results of this thesis should prove useful to the water managers, improvements can be made that would allow the managers to understand the actual water consumption which must be supplied during a drought event better.

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APPENDIX A DAILY WATER PUMPAGE DATA IN DEERFIELD BEACH, FLORIDA 1976 -1981

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....... ....... w Week 085 19761 2 3 :I 6 7 B 9 10 11 12 13 1:1 16 17 IB 19 20 21 22 23 24 2:1 26 27 2B 29 30 31 32 33 34 35 36 37 :m lHURSDAY 6. 4 :I. 1 5. I :I. 1 6.:1 7.1 7.9 B. I 4.9 :1.0 6.B B.7 7.6 :1.:1 6.B B.7 B.9 6.7 :I. :I 4.4 6.B 4.3 3.B :I.B 3.9 :1.6 4.6 9.2 7.6 10. :I B.4 7.9 4.7 5.2 7. 7 B. 4 4.9 FR lOAY 7.2 :I 3 4.9 6.5 7.0 7.2 B. I 7.9 4.B :1.7 5.B 9.0 9.B 7.2 6.6 B. 4 9.2 B.6 6.3 6. :I 4. I 5. I 4.7 3.9 4.9 4.0 6.4 7 9.2 9. 7 10.2 7.3 7.6 6. 5 5 4 7. :/ 7.3 :; 0 SATURDAY 7.0 6. I 4.5 6.6 6.9 6.0 7.3 7.:1 4. 7 6. 5 6.6 7.B 9.3 9.3 7.3 B.O 9.6 4.7 7.2 :1.0 4. I 4.2 4.7 4. I 4.6 4.7 7.4 5.5 9.9 6.9 9.0 7.4 B.O :1.7 I.. 5 6.B 6 3 6 2 SUNDAY 6.6 5.3 :1.0 6.6 4.7 :1.6 7.0 5.B 4.:1 6.0 6.2 7.2 9.0 7.B 6.9 7.5 7.6 3." 6.0 4.2 3.7 3." 'I. 7 4.0 4.3 4.:1 7. I 5.6 9.9 7.9 B.O B.O B.2 :I. 1 6.9 6.2 4.B :; 5 MONDAY 4.B :I.B :1.6 6.9 :1.3 6.B 9.2 6.:1 :I. :I :I. 5 7.6 B.7 9.0 9.3 B.B B.6 9.3 4.5 5.7 4.9 4.3 4.4 :1.7 4.4 :1.7 5.7 9.2 7.2 9.0 9.6 7.0 9.2 7.5 5. :I 7.5 B.2 5.3 6. 7 TUESDAY 4. 7 6.3 6.5 6.4 5.6 6.9 B.O 5.3 5. I :1.2 6.B 6.7 B.4 4.B 7. I B.3 B.6 4.5 5.2 4.6 4.9 4.B 4.2 5 6. I :I. B 7.5 6. 7 9.4 B. 4 7.2 B.3 5. 5 5.2 6. 5 7.5 5 5 7 D IJEONSOAY :I. :; 6. 5 4.9 :1.9 7.0 B.3 B.3 :1.0 I.. 7 6.2 6.7 B.O 9.0 I.. I 6.2 9.2 B.B 6.4 4.9 :1.9 6.0 :1.0 4.6 4.9 4.2 6.7 4.9 7.9 9. 7 9.7 9. 3 ".6 5.9 5. 4 7. 7 B I 5 0 6 :;

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Week aBS 37 41 4:1 46 47 4B 47 :10 :51 :52 1977 :53 :54 :5:1 :16 :17 :lB 59 60 61 62 63 64 6:1 66 67 6B 67 70 71 72 73 74 75 76 THURSDAY 7.B B. :5 7.B :5. 1 :5.9 :5.4 4.9 7.9 :5.2 6. :I :5.1 :5.2 :5. I :1.9 6.4 :5.9 6.2 :5.0 7.6 7. 4 :1.2 :5.4 7.3 7.4 B.O B.4 B.6 9.4 6.2 :5. 4 6. 4 B. I 4.7 4.7 B. 7 4.2 4.6 4. 7 FRI[lAY 6, :5. 2 7.B 6.3 6.6 6. :1.8 7.9 B.O 7.1 :I. 7 :1.6 :5.B 4.B 6. 1 6.3 :1.7 6.7 7.B 7.6 6.2 6.7 B. 1 7.6 7.6 B.B 6.0 9.9 9.:1 7.1 7.7 9. 1 5.1 5.1 7.2 :I 4 '1.5 'I. SA1URDAY 5. 7 6 6 6.'1 6. 4 6. 4 B.l :1.:5 7. 1 6.1 7.4 6. 1 :1.2 6.2 4.6 5.0 6. 1 '.1 6." 6.0 6.7 6.3 6.4 7.6 9.9 B. :I 7.3 7.7 '1.6 ".2 6.6 7.9 7.0 5.3 6.1 7.7 '1.7 4.4 4. sut.fOI\Y 5.0 7.3 '.4 5. 7 6.6 7. :I 6.2 7.3 :1.6 6.:1 6.0 :I.B :I.B 4.7 :1.4 :1.7 7.0 6. 4 7.0 :1.6 6.4 6.9 7.6 B. :I B.3 B.O 9.3 7.9 6.7 B. :I 6.2 7.0 5.3 4.7 4.B :5.1 MONDAY 6.7 B 3 '.0 6.6 8.0 7.6 6.B B.2 :1.3 7.7 :1.0 :I." 7. 1 :1.4 4." 6. 1 :1.2 7.2 6.6. 7.6 :I. 7 6.B 6. 1 B.O B.O B.7 B. 9.2 6.2 9.0 6.0 B.7 :1.6 7.3 6.7 :1.0 :1.1 6. 1 TUE SDAY B.3 7.4 3.B 6.6 6.6 7.6 B.O 6.3 7.5 :I. a :I.B :I. I :I. 7 4.9 7.8 :I. :I :1.0 4.B 7.4 :1.6 6.1 B.3 7.2 9.3 9.3 7.0 7.:1 7.3 B.7 B.O B.3 5. 7 5. 7.1 '-'EDNSDAY 7.7 7.7 :5.:1 ::1.3 B.6 4.6 B.7 :I. 7 7.0 :1.3 5. a :1.3 :I.B :I. :I :1.5 6.7 4.6 B.O 6.0 :1.2 :1.0 7.6 7.6 6.B B.7 9.2 9.5 7.2 6.6 6. 4 B.4 :I.B 5.3 B.3 B. :; 7 B.O ...... ...... +>-

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Heek ODS 77 7B 7'1 BO BI B2 B3 84 85 86 87 8B 8'1 '10 '11 '12 '13 '14 '15 '16 '17 '1B '1'1 100 101 102 103 104 197810:; 106 107 lOB 10'1 ltD III lt2 113 114 THURSDAY B.O B. I '1.2 '1.B II. 0 6.7 '1.3 '1.2 7.2 7.3 6.B 4.3 6.7 B.3 5.0 6.2 7. I B.3 '1. 5 7. I B.4 7. I 7. I 5.2 6.'1 6.2 5.'1 6.B 6.B 5.:i 6.3 5. 4 :;.'1 7.B 5. 3 B.O 6. 7. 3 rRI[JAY '1.0 B.B 6.3 10.4 10. I '1.3 '1.3 B.B 5.6 B.7 6.'1 4.6 6.B 7. I 5.2 7.5 7.7 B.3 '1. I B.7 B.5 7.4 7.'1 5. 6 7. 7 7. I 6.3 7.5 B. I 6.2 6.6 5.4 6.'1 B. I 6. 6. 5 7.3 6. 4 SATURDAY B.'1 '1.0 6.4 '1.B :;.'1 6.5 B.4 6.B 5.2 B.4 5.2 4.7 7.0 7.'1 4.'1 7.7 B. I B. 4 6.6 B.O 6.4 6.'1 6.'1 5. 4 7.2 5.5 5. I 6.7 7.5 6.6 5. 5 5.5 6.6 5. B 6.3 6.2 7. I 5. B SUNDAY B.2 B. 4 '1. I '1.B 5.'1 5.5 '1.4 6.B 6.1 7.3 5.1 4.4 7.4 7.0 5.5 7.B B.O B. 1 5.2 7.7 7. I 7.3 7.3 :I. B 7.4 :1.:1 5.3 6.3 7. 1 6. I 6. I 5.'1 6.6 5.2 6. 5 5. 4 7. 6. I HONDAY 7.7 '1.1 B. 7 10.3 6.0 7.4 B.B B.:; 7.7 6.2 4.'1 5. I B.O B.O 5.5 7.5 B.B '1.2 5.B 6.5 B.4 B.3 B.2 6.B 7.6 6. 1 5.B 6.0 6 ... 5.6 6.'1 6.B 7.5 6. I 7.'1 5. '1 B.4 7.0 TUESDAY 7.6 II. I B.6 10.3 :I.B 7:2 '1.6 '7.4 7.7 5.3 4.B 5.2 B.5 B.O 5.2 6. I '1.4 B.B 6.0 7.B B.2 B.2 6.4 5.6 5.4 5.B 6.5 6. I 5.5 5.3 6.0 6. 7 7. 7 6.2 7.B 5. 7.B 7.2 WEDNSDAY 5.B 10.2 '1.6 10.7 7. I 7.4 B.B '1. 5 6.6 5.6 4. B 5.B '1.0 6.3 5.7 7.7 '1. I '1.6 7.7 B.'1 B.B 7.3 7.5 6.0 6.0 6. I 6.2 7.5 5.B 7.6 6. 7 7.3 7.5 6.7 B. I :1.'1 B.3 7. B --' --' U1

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Week O"S 115 116 117 lIB 117 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 13B 137 140 141 142 143 144 146 147 14B 147 151 1:)2' THURSDAY 5. B 7. 8. 7 7. 7.6 7. B 6. 2 7.0 6.7 7 :I. 6 7.8 7. :I 7. 2 7. 5 6. :I 8.0 8. 10.0 :I.B 5. B :I. 7 :I. 2 7.6 :I. :I 8. 7 6.7 :I. 6 5. B 6.7 6.6 :1.6 :1.7 5. 3 5.0 4. 7 5.0 6 I rRIOAY 5 5 7. I 7.6 6. I 9.4 :1.6 7. 4 B.B 8 7.6 :1.3 6.3 7.7 8.2 7. 4 :1.3 8. 6 8. 8.6 6.9 7. 2 :I. 0 :1.8 7. 7 7.0 7.9 7.7 6.2 :I. 2 7.3 7. I :I. I 7.6 5. 7 5. 5 4.6 4.7 6. 2 5ATVnOt\Y I, 4 7. 3 8.8 7. I 9.0 :I. 9 7.1, 8.2 7.0 1,.7 6.3 :I. 4 8.0 B.O 7. 1 :1.3 8.0 7.7 5. 8 7.0 6. :I .... :I 4.7 6.1, 7.6 7.7 8.4 6. 7 1,.0 8.1 1,.0 :I. 2 7. I 5. I I, 3 4. 4 4. 8 6. 4 SUNDAY I, 8 7.2 8.0 7.8 8.7 1,.7 7. I 7.8 7.2 6. 4 7.0 4. :I b. 8. 1 b.4 :1.0 7.3 8. 1 b. b.b 4.8 4.4 5.5 7.0 7.5 7.0 7.8 6.8 5.9 5. 9 1,.2 4.4 4. 8 5. 6 6 3 4. 5.0 b 2 HOtJ[)I\Y 8 0 7.0 7.6 8.9 9.3 8. 1 5.3 7. :; 8. 7 8. 5 8.6 4.8 :1.7 7. 5 5.7 5. 4 8 4 9.0 b.b 7.8 5. 7 5.3 6. b 8.2 8.9 7.0 b.8 7.8 b. 1 b. 5 7.8 4.9 4. :5 6.0 7.3 4.9 b 2 7 3 TUtSDAY 7.9 B.b 7. I 7. I 7. 7.7 6. I 6.9 9.7 B. B 8.9 5. 7 b.3 7.3 6. 5 5. 7 7. 4 9.3 5.3 9.2 4.8 4.9 7.2 6.3 8.6 :I. 5 5.B 5.3 5.7 6.2 6. 4 :I. 4.4 2 5. 3 5. 3 6. 6 7. WEDNSDAY B 2 7 0 6. 3 9 .. 2 9.9 B.4 6.2 8.6 6.9 8. 7 9.6 6.7 6. 6.7 6. I b.9 7.8 9.2 5.2 8. 7 4. :; 6.7 7.:1 :I .... 9. 1 6.0 5.0 7.3 7.2 6.6 7.7 6. 5 4.2 4. 7 4.8 4. I 7. 0 7. 7 0)

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197') Heek 085 1::13 1::14 1::15 1::16 1::17 IS8 1::19 160 161 162 163 16"\ 16::1 166 167 168 169 170 171 172 173 174 17::1 176 177 178 179 180 IBI 182 183 184 18:! 186 187 188 189 190 6.7 6.4 :3.9 7.2 :3.2 4.9 5.3 7.0 :'-'3 6. 1 6.7 B.9 B.O B. 1 7.0 9.0 10.2 9.7 B.9 10.6 10.6 ::I. 7 ::1.7 6.4 4.9 6.4 7.9 10.1 9.3 B.8 9.::1 10. 7 10.B 11.8 11. 6 10. 7 B.B 7.9 6. 6 6.9 6.9 7.0 5.3 6.0 ::1.4 6.8 7.3 B.::1 7.3 9.0 7.9 9.2 9.4 9.1 9.1 9.4 7.4 9.3 8.9 6.4 6.9 7. 2 ::1.3 ::I. 7 7.6 9.8 10.6 9. 1 9.6 11. 6 10. 4 11. 1 12.3 11. 4 10.6 10.7 5ATUflOAY 6.6 7. 1 6.6 6.::1 4.7 ::1.9 ::1.::1 4.B ::1.9 7.1 6.7 8.9 8. 4 8. ::I 9.3 B.3 B.2 9.::1 7. 1 10.4 B.9 6.6 7.2 7. 4 ::1.6 6.9 9.3 B. 4 B.2 9.::1 8.4 9.9 10.3 9.1 10.3 10. 4 9.9 9. 8 SUtJ!)r'\Y 7.2 6.::1 6.0 6.0 4.B ::I.B ::1.6 4.7 4.8 6.8 6.9 B.B 6. ::I B.2 7.9 9. 1 8.4 10.3 6.7 8.6 B.3 ::1.9 ::1.3 6. :J ::1.4 6.6 8.8 7.:J 8.0 9.4 B.4 10.6 9.1 7.6 9.9 B.2 7. [J. 4 Mu:m.\Y 6.2 ::I. 4 4.5 ::1.2 4.::1 4.9 6. 1 ::1.6 ::1.3 7.6 7.8 B.l :J.O 9.1 8.9 10.::1 10.0 10.::1 6.9 8.9 B.7 ::1.3 4.9 4.9 6.9 6.7 9.3 11. 3 9.9 10.7 10. 1 11. 4 10.6 B.B 11. S 7.0 11. 7.2 TUESDAY :l. 8 ::1.3 6.6 ::1.2 4.6 4.B .6.1 ::1.2 6.0 7. ::I 7.6 B. ::I 6.3 B.4 B.4 9.6 10.2 11. ::I 9.2 8.6 ::1.3 4.B ::1.2 4.8 7.3 4.4 9.3 11. 1 .9.7 11. 4 10.4 10.7 10.6 7.1 11. 9 8.8 11. 1 7. 4 7.0 ::I. 7 7.3 ::1.8 4. S ::1.2 7.6 ::1.0 ::I. 7 6.::1 B.S B. 7 B.4 6. 1 10.0 11. 4 10.2 9.B 10.7 9.1 :J.7 ::1.2 ::I. 4 ::1.2 7.::1 7. 8 10.4 10.3 10.3 10.3 10.6 11. 3 10.8 10. 7 10.6 9.3 10.0 8. 0 ...... ...... '-J

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Week oos THURSDAY 191 7.3 1'12 6 3 193 7.3 :1,2 195 6.4 196 :1.7 197 :1.4 IYB 6.3 199 :1.:1 200 7. I 201 6.4 202 6.3 203 4.7 204 7.9 205 :1.6 206 7.:1 207 .. 7.3 20B 7.4 1980209 2'0 211 212 213 214 215 216 217 21B 219 220 221 222 223 224 225 226 227 228 B.4 B.7 7.5 B.:I 6. 5 B.2 B.:I 9.7 7.7 10.9 B.6 10.4 10.7 11.9 7.:1 6.2 7. 5 9.4 10.7 11.5 rRIOAY 6 5 6 6.B :I. B 6.0 :I. 5 :I. B 10.3 :1.7 6.6 6.0 7.2 7. :I 7.7 6.B 6.3 7.7 B.O 7.3 B. 9. I 10.2 9.4 B. I 9. :I 6.3 9. 5 B.B 9.B 10.7 11. B 12. I 7.7 6. I B.O 10. I B.5 9.1 5ATUFHJI\V 6.2 6.4 7.9 :I. :I 5.:1 :I. 3 6.:1 6.2 6.7 7.B 6.3 7.0 6.6 7.4 6.3 :1.9 6. 4 7.B 10.0 7.3 7.B 9.2 B.5 7.4 9.3 B.O B.B 6. I 9.5 9.2 II. 0 II. 2 7. :I 7.3 6.9 \0. :I \0.0 B.B SUNDAY 6 2 7.:1 :1.9 :1.4 :I. P 6.3 :1.4 7.0 B.9 5.2 7.6 7. I 6.7 7. I :1.6 6.2 7.6 9.6 9.2 B.3 B. 4 6.6 6.3 7.7 6.2 9.:1 6.3 9.9 9.2 10. I 9.9 7.3 7.2 5.9 7.3 9.9 \0 B MUtH1AY 6. :; 0 6 6 3 5. 4 5.1 7. 7 5. I B.3 B.9 6.0 7. :I 7.6 6.2 7.5 .6 6.B B.B B.2 9.2 B. I 11.0 6.9 7.B 9.4 6.B 9.4 6. 7 10.4 \0.4 11. :I \2.:1 7.3 7. B. 4 9.7 II. I 11. I TU[ SOAY 6 6 6 I 6. 3 6. 6.2 :1.1 7.B :1.3 6.2 7.3 6.0 :1:5 7.B 6. 7 7.6 6.0 7. I 6.6 7.9 9.6 7.0 9.3 7.2 B.O 7.B B.6 B.3 B. 4 9.4 9.9 11.3 9. 5 6. 7 6.6 9.3 9.3 10.6 12.3 \-I( 7.3 6 2 6. :I. 7 6.2 6. B. 7 5.4 7.1 6.0 :I. 4 :1.9 B.O 6.0 9.5 7.4 7.4 B. 4 B.6 9. 4 B.2 7.B 7.6 B.B 9.6 B.B 9. 1 B.6 10.6 11.6 12.B 7.3 B.3 7. I 9. 5 11: :I 12.6 12.B --' --' co

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OflS THUh'SflAY ;'29 10.7 230 7.0 231 b. b 23;:0 11. 3 233 7.2 234 7 '4 235 7 236 6.7. 237 11. 7 238 8.2 239 7.3 2-40 9.3 2H 9.2 242 7. B 243 b. 244 10.b 245 b.4 24b 247 248 249 251 252 253 254 255 25b 257 258 259 2bO 19812b1 262 263 264 265 266 B.B 9.0 11. 1 10. 7.B 7.2 B. 10.0 10.2 7. 5 7.b B. 5 B. ::I 10.B 7.B b.9 10.3 7. 4 10. b 9. I 9.9 f J f)1\Y B 2 7. 4 8 1 10 2 B 4 7. 0 7. b b 8 12.5. 7.2 9.2 10.9 12.' 10.8 7.2 8.b b.b 10. 1 10.0 10.9 10. b B 9.4 7. 2 9. 5 10.8 8.8 9.::1 b. 7 9. 5 8.3 9. 9 7. 7 7. 9 10 b 9. 3 9. 3 10. 3 SATUHOAY 11. 2 7:.2 7. 8 11. 9.9 b. 1 7.9 b. ::I 11.9 b. 8.2 10.2 II. 8 9.7 7.2 9.::1 7. I B.O 9.2 11. 3 B ::I 7. 7 9.b 7.9 9.3 9.9 b.b 7.B b. 9 8. 7 8. 7 9. 8.4 10 3 8. 8.0 7. 7 9. ::I 10 5 5.9 7.9 11. 2 10.4 b.2 b.b 9. 4 10.9 b. I 7.5 B.3 9.2 10.2 b. b B.B 7.9 8. 10. 11. 8 10.b 9.3 10.3 7.9 B.2 B.3 b.9 7. 1 b. 9 9. 1 B.9 B. 7 7. 1 8 1 B. 4 B.2 7.4 8.4 NorWAY 12 b. I 9. I 10.'9 II. 7 b. 7 b. b 10 2 12.0 7.4 B.8 12.2 11. 5 10.b 7.3 b.9 B.B B. I 11.0 12.3 10.3 9. :I II. :I 9. 10.4 7.5 b.9 7.B 7.b 10.0 10.0 9.B 7.4 8. ::I 9. 0 9.2 10. 7 8 I 11 b b.O 8.3 8.4 II. 9 b.B S. 7 9.3 9. b b.b 7.B 9.::1 9.5 9.3 8.0 b.3 7.3 b. 9.4 B. 7 10.2 B.9 10.4 9. 9.4 b.B b. B.4 7.2 B.3 B.3 8.b 8. 4 8.B 10.2 9.B 9. ::I B. 1 "-'F ['NSD.I\ B 5 b 3 '9.4 b.7 10. 7 7. b. ::I 11. 0 11.2 b.B 7.B 9.B B.O 10.4 10.3 b.2 B.2 B. 11. b 10.0 10.:1 7.:1 11.3 10.0 10.3 b.b B. B.3 B.5 10. :2 7.2 7.::1 B.b 9.b 10.3 B. 7 9.2 8 2 --' --' IJ.:>

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ons ;]67 2f.B 269 270 271 272 273 274 27:) 276 277 27B 279 2BO 2BI 2B2 2B3 2B4 285 2B6 287 2BB 2B9 290 291 292 293 294 295 296 297 29B 299 300 301 30;Z 303 304 lHlmSnAY B 7 6 :) 7. 4 10.0 9. :I 11 6 9. 4 9. 1 10.3 10.1. 11. 11. :I 10.9 7.:1 7.9 1..7 7.0 B 0 6.2 10.2 B.O 7.4 10.9 12.1 B.3 9. 7 9. 7 7.7 6.2 :1.9 7.0 7. '" 6.1. 7. I 6. 7.6 6. B. 9 FR][lAY B 2 7. I 7.3 10 4 12.3 9.1. 10.3 10. II. 2 II. 6 B. 3 13.4 II. 5 6.B 6. 7 6.0 5. 7 b. 6. 7 9.3 B. 4 :1.9 9.9 12.2 7.B 9.3 9. 3 6.0 6.3 6.9 B.O 6.B 6. 9 6. 5 7. 7 7. I 7.3 7. 7 SA1UfU11\Y 9. 7 7.B B 0 10. 10.9 9. 4 11.7 9.9 11. I II. I 9.8 11. :I B.9 7.6 8. 8.8 5. 1 7.3 9.0 8. 4 8.5 6.0 9. 1 10.9 7.2 9.3 B.9 7.2 6.4 6.B 7.B 7. 1. 6.'" 6. :I 8. 7 B. 1 7. B 7.9 SUtJOAY B. 1 7 .., B. 7 9.B 10.6 B.9 10.0 10. 11. .., 11. 10.7 11.4 8 8 7. 7. 5 7. 4 5. 9 7. 1 7.6 6.8 7.4 7.2 10. 9.9 6. 7 7.3 8.6 6.0 7. 1 7.4 8 0 7. :1.6 6. :I 6. 7 6.9 B. 4 B 3 8 ;> B 3 9. I 10 9 10.4 lO.!) B 4 11. 2 10.6 13.2 12.8 12.0 12.2 9.0 9. B. 9 7.0 :I. 7 9.5 :l. 9 7. 1 9.2 10.B 10.7 6.9 6. 3 B.O 5.6 8.3 6.7 6. 9 7.9 6.0 7.0 8.9 7. 8 5 8. 4 TUI S(JAY 8 3 8 4 9.1 10 1 10 3 11 4 9. 4 II. 4 10 3 12.2 13. II. I 11.3 6.8 7. 4 7.3 6.6 4.7 8.3 :).7 7. 5 8.3 11. 4 10.2 B. 6 3 7.2 5. B 6.9 :I. 7 6.6 6. 9 6.0 7.9 6. 7 6 S 7. 2 B I &...I[ B. I 7 B 10 S 10 :) II. 7 11 3 10.2 12. 1 10 4 129 13 2 13.9 11. 4 11. '" B 2 6.6 B. 5.4 11. 6.9 10.6 9.B 12.0 10 9.B 7.6 7.9 6.0 7.2 7.4 9.0 B.4 5. B 7. 7 7. 3 6 9 9.B 9 1 N o

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ODS THURSDAY FRJDAY SATURDAY SUNDAY MONDAY TUESDAY 30S 9.0 7.3 1>.9 7.4 6.3 7.S 6.1 301> 6.3 6.B 7.2 7. 1 6 ... 7.0 B.I> 307 B.S 7.2 10.0 B 7 B. B 9. 7 9.B 308 9. 6 9. 4 9.7 B.8 9.6 10.0 10.2 3D9 9.3 10. 10.0 9. B 7. 4 9.9 9.9 310 9. 6 9.4 10. 9.0 10.0 9.1> 9.B 311 B.3 9.2 9.7 B.B 10.3 7.4 9.2 312 9.2 B.9 7.6 B.B 9. 7 9.B 10. 1982 313 9.0 9.1 9.0 9.B 10.0 10.2 B.3 N .......

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APPENDIX B RAINFALL DATA IN INCHES AT TREATMENT PLANT DEERFIELD BEACH, FLORIDA 1976 -1981

PAGE 134

o 0 c c ... ci ci 0 0 Q : egg g g g g gog g : g g g g 2 ci 6 ci ci 6 ci 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0 it! 0 g g g g g g g !:? ci :i 0 ci ci ... ... ... ... ... ci C ceo o g g g 6 g g g c c e c 0 coo 0 c: c g g g g c 0-0 C .... C 0 g g g g g .... g g g .... g egg ... ........ # .... C e c c c c .... c ... .... co: C .... .... .... ... c .... .... g g g g g g g g g 5 g g g g g g g g g g g g g g 2 c = = = 0 ceo ci ci ci 6 ci 6 ci 6 ci ci 0 ci ci ci ci ci 0 ci 6 ci ci ci c ... 6 = 6 .... g ... ... g g g g g g g g g ... g ... 8 g .... g ... .... ... ... .... ... '. .... .... g .... ... 5 g g g g g g g ... g g g .... g g ... .... 6 6 c 6 6 ci ci 0 ci 0 0 ci ci ci ci ci ci ci ci ci 0 ci ci ci ci ci 0 ci 0 0 C ci 0 6 c c >'" g g g g g g g g g g g g g g g g N 0 g ; g g g g g ; 500 0 ci 0 000 ci ci ci 0 ci ci 6 ci 0 ci ci 0 ci ci ci ci ci ci ci ci ci ci ci ci ci ci ci ci :;: -n.r M ..c til ['II rJ C'J ttJ til 123

PAGE 135

1977 Ijeek 009 39 40 41 42 43 44 45 410 47 48 4'7 :;0 51 52 53 54 55 510 57 58 59 100 101 102 103 M 105 lob 67 108 69 70 71 72 73 74 75 76 lHURSDAY O. 07 o 05 0.02 0.00 o. 00 0.01 0.00 0.00 o. 00 0.00 0.00 0.02 0.00 0.510 0.22 0.00 0.00 O. 00 0.00 0.00 0.25 0.00 0.00 0.00 0.00 o. 00 0.04 0.00 O. 00 0.00 0.00 0.00 O. 01 0.00 0.00 O. 55 O. J2 0.19 rRIOAY o (In o ('0 O(M 0.00 0.00 0.00 000 0.00 0.00 0.00 0.00 O. 10 0.00 0.00 0.00 0.00 2. 40 0.00 0.0? 0.00 D.OO 0, 00 0.00 o.uo 0.00 0.00 o 00 0.00 o. 00 0.17 O. 00 0.00 O. (10 o (I() J. o 00 O. 00 O. (I[) G,\""JH",\y (1 (10 n. (lO 0. JI (l. (lO O. (10 0.00 O. (10 D.OO O. 00 0.02 0.00 0.00 0.35 D.OJ 0.(10 O. 17 0.(10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 O. (10 0.00 O. J4 O. 00 D.Il;! (} (K. 000 on (I (m :-'HJ"'OAY 0.00 n.oo :J6 O. 'lO O. (10 0.00 0.00 0.00 o 00 0.(10 1. 2:::; J. Ii! 0.00 0.00 0.72 0.00 0.00 0.00 O. JO 0.00 0.32 O. ('0 O. 00 0.00 0.00 0.00 0.00 0.00 0.00 o.o(] O. flO O. 00 l. 31 O. 00 0.00 n.O(} 0.00 O. (1(1 tl(lN'MY (1,00 0, (10 ('.07 (t. (I. (10 (I .,D 0.00 (l.U;;! (1.(10 (1.00 O. (10 O. ;'2 O.roo 0.00 0.00 0.00 0.00 0.00 0.00 O. J2 (l.oa 0.00 0.(10 f),OO 0.00 0.00 (I. no 0.01 n.oo 0.00 0.06 (I, J:1 (I. (10 (I, (10 O. (10 O. el7 o 0(1 HII (I. (10 (I. :J5 0.010 0.00 0.0(1 0.00 0.4'7 0.00 0.32 0.00 0.01 0.00 0.010 0.00 0.00 0.00 0.00 0.00 O. 58 0.00 0.00 0.00 0.00 0.00 0.00 0.00 O. 12 I. 02 0.01 0.00 J. 76 (1.09 0.00 0;' O. O. Jb (l.OO WFUNSO,\Y 0.03 0.00 O.OU 0.00 0.0'1 0.02 0.00 0.00 0.00 0.0:1 0.00 0.00 0.3U 0.00 O.OU 0.05 0.00 0.00 0.00 0.00 O.UO 0.02 0.00 0.02 0.00 0.00 0.00 0.00 0.104 O. II 0.00 1.00 0.(14 0.00 O. 16 I. 01 3.13 D. (10 --' N -I'>

PAGE 136

flIt;, 77 78 77 UO BI B2 B3 B1 B5 Bb B7 BB B7 70 71 72 73 74 75 710 77 7B 77 100 101 102 103 104 1978 105 lOb 107 lOB 107 110 III JJ2 113 lH HlunSD;\) 0.00 o. 00 o 62 000 0.11 0.00 0.00 O. 10 0.04 0.00 o. 13 1. 10 0.00 O. 12 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 1. 6 .. 0.31 0.00 0.00 0.00 0.16 0.13 0.00 0.04 0.00 0.02 0.00 1. 21 Po 111,,," (' (10 o uo o u:) a (I(J o. :1.'. o. 11 0.00 O. '1/ 0.0" 0.00 o.r'n o fi6 0.00 0.00 0.(10 0.00 0.00 0.00 0.07 0.00 0.12 0.00 0.00 0.00 0.00 0.(12 0.31 0.00 0.00 0.0(; D.no 0,00 o (I!) o 6:1 0.00 o. :-'.'. 0.00 o. (1(1 !',YIII'!II(\Y n.o(l o (I(l O.(I(J 0, ('(l O. O.Or! 0.00 o. {IO 0.00 o. II 0.37 O. 17 0.00 0.04 0.02 0.00 0.00 0.00 4.38 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.32 0.0'1 o. :'l-' QOO QOO QOO Qlb OW o (100 ntlNU,\Y o.();? 0.(10 () (lO O. 0.10 O. (10 0.06 O. ti3 O. (10 1. '16 1.11 0.01 0.00 0.02 0.;"0 0.02 0.00 0.00 0.04 0.02 0.00 0.00 O. 01 0.00 0.05 0.00 O. 00 0.33 0.00 0.00 0.00 O. (to 0.00 0.00 o 00 0.16 1l.IIO o 110 tIlINn,\y (I. III (I (10 (I (I(l (I.(J(J (1,;'(1 0.00 0.00 0.00 0.00 0.00 1. 33 0.00 0.00 0.00 O. 10 0.36 0.00 0.00 0.00 0.00 0.00 0.04 0.02 0.13 0.46 0.00 0.00 0.00 0.11 0.00 O.Ofl (1,00 n.oo 0.(10 o.no (I :lC' (1,(10 (I (10 IUt,HUI\Y n.ou (1.00 (1.00 0.0(1 (1.00 0.31 0.44 0.01 O. 10 J. 83 0.20 0.00 0.00 0.40 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.03 0.04 0.00 0.06 0.00 a.o;:? 0.00 0.0(1 0.9':' 0.00 (I, (,0 (1,0(1 (t.(J(J 0.00 0, (.:; 0.00 0.00 0.20 0.00 0.04 0.11 0.00 0.00 2."16 0.00 0.00 2. 54 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 6. 10 0.00 0.00 0.02 0.00 0.00 O. 52 0.00 1. 06 0.1l4 0.00 O. 13 0.00 O. (10 0.01 1. :;"'1 --' N U1

PAGE 137

Ueek UDS 115 fib 117 liB 119 120 121 '22 J23 124 125 126 J27 J28 J29 130 131 132 13:1 131 J35 13b 137 130 139 140 I'll l'I3 114 145 I1b 147 1'10 J II'" 15(1 .. BjJ THUnSDAY 0.00 O. 05 0.00 0.10 0.00 I. Db 0.00 0.00 0.00 0.00 O. 14 O. 17 0.00 0.00 O.OB O. 51 0.00 0.00 1. 3B 0.00 0.04 1. 37 0.00 0.01 0.00 0.02 0.00 0.00 0.13 0.05 O. 00 0.00 0.00 0.12 0.00 0.03 0.00 0.00 rn III,W o. ('0 o. (.n 0.(111 0.00 O. ('0 o.()o 0.110 o. ('0 0.011 o. 17 0.00 o. :;3 0.117 0.110 0.110 I. 3" O.Ob O. Hi O. J;:! n. ()2 J. 77 o. '13 0.01 0.110 0.01 o liD O. (II! 0.1'1 O. (II O. (10 O. :,., O. ('0 O. .. t! 0.00 O. '", 0.110 o ('I! nr.l,f!Ultn 0.0(1 (1 (1(' 0.00 o.on 0.00 n.O() 0.10 o. ('0 0.01! 0.110 0.00 :!.03 J. 05 0.00 0.110 O.Ob 0.115 0.00 0.00 O. n2 0.(10 o. :'!5 0.00 0.00 0.00 0.01 0.00 O. ('0 O. ('9 0.0::; o. ('0 o. O. (,0 0.00 ('.00 O. ('" O.O(J nONOAY o. ('0 o.no O.fll! o ()() 0.00 0.00 O.O;! 0.00 0.00 0.00 O.ti6 0.01 0.15 0.30 0.03 0.00 0.01 O.;tO 0.00 ?.04 0.10 0.00 0.00 0.00 o. Ib O.C'3 0.;:'3 o. Ib 0.14 0.00 0.01 3.00 0.110 0.00 o. ('0 o. ('0 0.(10 t.IINVI\Y U,()() ('.00 (1."7 (I,UO 0.00 o. ('0 0. "0 (1.00 0.07 0.00 0.00 0.00 O. II 0.01 O. 10 0.01 0.00 0.00 J. 12 o. ('0 o. 00 1.35 0.00 O.OB 0.03 0.00 11.00 (I (JU O. nc) ". :11 n. (If, ('.('" ('.11" lUI!:i))I\Y o. "0 0.110 0.00 0.110 0.011 O. Oil 0.00 O.B4 0.04 0.00 0.00 0.03 0.b4 0.05 0.00 0.00 0.00 0.00 0.00 O. 10 0.00 0.03 0.30 0.00 0.00 0.19 0.00 0.00 0.0;! 0.00 0.011 0.00 II. 10 hi ".0:; 0.00 O.I!O WEDW;oI\Y O. ('0 o. ('0 0.00 0.110 .0.00 0.2:; 0.00 O. 10 0.00 O. 13 0.1>1 0.00 0.00 0.00 0.37 0.00 0.02 0.00 O. 12 0.50 0.00 O. 01 0.00 0.00 0.00 0.54 O. ID o.cm 0.110 O. '7 0.11:; 0.00 0.04 ('.04 o. :!O o liD o. ('0

PAGE 138

1979 I'leek UlH1 157 150 159 1"0 161 162 163 J61 .65 ,,,f167 IbO Ib9 170 171 173 174 175 176 177 170 J79 100 101 103 JB1 IB:J J(16 1B7 JOII 10'7 J90 THUnSOl\Y 0.00 o.no o. III 0.01 O. J2 0.00 0.15 0.05 0.00 0.00 0.00 0.00 0.1l3 0.00 0.00 0.00 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00 O. 44 0.01 0.00 0.04 0.00 0.16 0.04 0.1l0 0.01 0.01 0.00 0.00 0.00 0.00 FP. II);\\' (I,(lC) n. (I(J U (IJ o. (t(l 0.00 O. (HI 0.(10 O. (I] 0:':.1 O. (10 0.1'0 0.00 O. lID 0.00 0.00 O. (10 O. (I:J 0.00 0.00 0.(10 0.00 O. (10 H 0.00 0.(10 a.oo 0.00 o r'U Il. '10 O.CJ(J O. ('0 O. ('7 1l.(l1l O,Uf) O. (,0 O.(lJ u.on (I,PU 81\ 1 \II(U;'1' (I. (In {I (leJ O. 0(1 0.(19 O. :'0 O. (10 O. III 0.12 0.00 0.00 0.00 0.45 0.(10 0.00 0.00 0.00 0.00 0.(10 o.uo O. (10 0.(1'1 0.00 O. 12 0.(10 0.00 0.110 0 .. (10 o.no 0.(10 0.110 O. (10 O. (1(1 0. (Ul O. (In (l,UJ 0(1(1 O. Cfl Iri ().(lO 0.01 0.01 0.11 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 O. 10 0.00 O.tH J. 06 0.00 0.00 0.00 0.00 0.01 0.110 0.(10 0.110 II 01 n. (10 0.(1(1 O. 10 0.(10 (J.I'I tUtNOi)V 0,'00 (t,O() (I. Of) (1.00 o.lIn o.on 0.00 0.(10 0.00 0.00 0.00 n.;:!'1 0.00 0.00 0.0(1 (1.00 0.00 0.00 J. 35 0.'16 O.'IB O. l'I 0.00 O. (1.00 (I. (to (I. (10 '1.1. (10 (1.(111 U. (If) (I,Cltl (I. J1 n,on (1.00 (I (lJ (,.('" (I. (10 (I,no O. (10 O. :11 O. (1/ n.no O. (10 0.1'1 0.110 0.00 0.00 0.40 0.00 0.00 0.00 0.(10 0.00 0.00 0.00 J2.32 0.00 0.10 o.no 0.(10 o.no 0.1l0 0.0(1 O. ('0 0.00 O. ('0 (I.(IU 0.01l 0.(10 O. 11.(10 O. (11 0.(1(1 0.11 0.07 0.00 0.00 0.(10 0.1l0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.(10 0.00 0.(10 0.00 0.(10 0.(10 0.00 0.00 0.(10 0.00 0.03 0.51 O. II 0.00 0.01 0.01 0.07 0.(10 O. J6 O. (10 O. (10 0.(10 11.01 II. (Ill 0.'10

PAGE 139

oou TI-fURSDAY 191 I. 00 192 0.05 19.3 O. 1(1 1'74 I. 10 19:; 0.29 196 0.22 197 0.1'0 190 0.00 199 000 200 0.00 ;!OJ 0, 12 :!02 0.00 20:1 O. ('0 20-4 D.{10 20fl 0.00 206 1. U4 207 0.00 200 1980 209 210 211 212 2J3 214 215 216 L!JB 21'1 220 223 227 0.00 0.00 0.01 O. "0 (1.00 0.00 0.03 0.00 0.:17 O. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 0.00 0.00 rR1J1I'Y o (tU 0, J:j 0, {In o 0", O.:JIl D.;!'" 0.0(1 I. (10 0.1'0 0.(10 0.{l0 O.{I{I 0.01 0.00 o. :.!O O. ('0 0.00 0.{l0 O.{lO O. ('0 0.{l0 O. ( .. O. ('0 O. 0.(10 0.(10 D,OO 0.(10 O. (,(J O. (l;..! o (III O. "" O. ('0 0(1(1 0.110 OtYfUUIl,\Y o.rl(. II I;) II ;'1 (I.{I(I O. I;) I. 55 (I.{I(I I. 01 0.(10 0.00 2. 16 0.{l0 O. ('0 0.::14 0.00 {I. 16 {I. 00 0.01 0.00 0.00 0.00 O. ('0 O. 12 0.(10 0.42 0.00 0.00 0.110 o.on 0.1111 O. II{I O. '1:. II. "0 (1.(10 O. ('(1 {H'No,\\" 0, PI I.U:! 0.01 O. ('0 O.ClP O. ('0 1.25 0.00 0.00 0.00 2.r.o 0.00 0.03 0.00 O. ('(1 0.00 0.00 0.01 0.{l0 0.(10 0.00 O. (10 0.01 0.01 O. :"3 O. 00 0.00 0.00 0.0(1 0.011 O. '10 0.00 0.1111 0.(10 0.('" 0.00 O.U(I tIlINU,\V 11.('1 (1.0(1 o J'T II. (1(1 V,Clt) ('.00 (I. t,JI O. 16 O. 13 0.00 0.6B O. ('0 0.06 0.(10 (1.1111 (1.0(1 0.(12 (1.00 (1.(10 0.1'0 (1.00 (1.(10 11.(10 11.110 11.25 ('. (10 (1,00 (1.('1 {1.00 fl, ('{) ('.('1 J.M (1,('(1 ('.(111 (1.(111 (1,(1(1 Ct. (1{1 0.5(1 0.011 (1.:11 0.:12 0.::14 0.00 0.01 O. 17 0.00 0.5B O. 1:1 0.72 11.00 O. 14 0.00 0.00 O. 14 0.00 11.00 0.21 0.(10 0.20 11.00 0.00 0.00 0.25 (1.(10 0.011 0.00 0.011 ('.011 0.53 O. 54 ('.011 (1.0(1 0,00 (1.0(' Ct. 0(1 WEON5D,W 1.6:1 0.00 0.64 0.02 0.69 0.65 0.00 0.02 0.01 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.110 0.00 0.00 0.00 0.21 0.00 0.00 11.110 0.00 0.110 0.00 O.ClO 0.00 0.110 0.(10 D.no O. ('7 O.ClO --' N CO

PAGE 140

Heek IJItH 235 236 237 238 23'1 240 2-41 243 ;:!49 251 252 253 ;-!5B ::'60 1981 ::'61 266 HllInSOAY o. OJ J. OB 0.12 0.00 o 00 1. 51 0.00 O. II 0.00 0.'13 0.02 0.03 0.02 0.00 0.9J O.OJ 0.00 0.00 0.00 0.00 0.00 0.00 O.OJ 0.00 0.00 0.00 3.9B 0.00 0.97 0.00 0.00 0.00 0.00 o 00 o 00 0.00 0.'10 0.00 f'n IIJ,\Y 0.(10 I. "., 0.00 O. (10 0.00 o JlI O. (IU O. CHi O. :'0 O.OIl O. e'o O. J;;! O.U!i 0.00 u. :,'4 0.00 O. (I{) O. ('J 0.00 D.OO 0.00 0.00 0.00 0.00 O. (.n 0,(10 O. ('0 () (10 O. (10 O.O() 0, (10 () O(J O. (10 O. <'0 0.(" J.Ml O. 0;' 0.00 O. 00 O. (13 O. '10 0.00 0.17 O. J2 0.03 O.oZ' 0.01 0.00 0.00 0.110 0.00 O. 10 0.01 D ... 0.00 0.00 0.(10 0.07 0.00 0.01 O. KI 0.00 o (II 0.01 o (10 0.(10 (J (I() O. (IJ'I 0.00 11.00 (1,00 m'r"Jl,\y n. <'. 0."" 0.01 0.3" 0.00 0.13 O. 00 0, (10 O. 01 O. "" 0.00 O.OB O. 1'1 0.00 .0.00 0.30 O. II O.O!) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 O. 17 0.00 0.;-0 0.00 0.00 o 7';.1 n bO O.lIO o 00 0.00 0.00 (J.(IO o. I'll '''''U\Y ('. Tl 0,00 U.(I() 0.J3 0.:'0 (1.17 0.07 ('.IJ 0.00 0.0:; 0.00 (1.00 O.PO J. 00 0.00 0."3 0.00 0.00 (1.00 0.00 0.00 O.OJ (1.00 0.00 (1.(111 0.00 O. ('0 (I. (10 (I. e)(J (l.('O (I,(JO O. (I" (J,(J(. (),UO O. ('0 (1,(1(1 (1.00 (t.(J(f (',C,'i' (l.Ot, 0.00 O.OJ O. 7:! 0.06 0.00 0.00 2. JO 0.00 0.00 0.00 0.00 J!.30 0.00 0.00 O.vo 0.00 0.00 0.00 0.30 0.00 0.07 (1.00 0.00 0.00 0.00 (I, (10 0.00 0.00 0.00 (1.00 n.on 1>.0(1 0.00 (1.00 (1.00 (1,00 (I, J', o.uu 0.00 0.00 O. 73 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.00 0.00 O.OJ O. :iO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.1>0 0.00 0.00 0.30 O.ltO O.ltO O.I:J D.OI O.OD 0,(10

PAGE 141

I'leek oon 270 ;'7. 272 273 274 275 276 277 270 277 ;-!f'2 '-03 200 :'01 :'0;" :103 TfIUm-JDAY o 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 O. 00 0.00 0.00 0.00 0.00 o 01 0.00 0.05 O. 57 0.00 0.00 0.00 O.;!7 o. 00 0.00 0.00 0.00 0.0.0 0.26 0.30 o.bo 0.14 o. :;3 2.07 0.00 0.02 r H I p,w o ):, O.(lO O.OCJ O. (10 0.00 O. :'0 o. (10 0.00 O.CJO O. {IO 0.(10 O.OU 0.00 O. {IO 0.00 O. {'O O .. !iO O.BO I. :<3 O. J2 O. {'O 0.00 O. 10 0.46 O.fJO O. flO J .. 0.116 O. (10 O. ('0 'I. O. (10 0.(10 0.(10 O. (10 fil""'HI"y 0. (1(1 () (10 O. (10 (J (10 o 00 O. <'0 0.00 0.00 0.07 0.00 O. 00 0.00 O. ('0 O. (10 O. {IO O. (10 0.00 0.00 0.00 O. (10 0.03 0.00 O. {'O 0.(10 O. ;-'0 0.(13 O. 10 J.75 O. flO 0.16 .0.;'0 (1.00 O. (l::i (l,CIO n.O(' (I.U" {I.(IU () (10 1,\JNJiIIV O. (10 (I,(IJ 0.(10 O. (1{) 0.00 (I.{IO 0.00 0.(10 0.00 0.00 0.00 0.00 0.(10 0.00 0.00 0.00 O. (10 I. 10 0.00 0.00 0.00 .00 (1.(10 0.(10 0.00 0.35 1.03 0.:17 0.0(1 0.00 0.00 o. :13 (t, fl. O.b3 O. O.CJ(I u.O() (I, 'Ii, (1,'1 0.(1(1 (1,(1(1 (1,(le) (J.elo (1.00 CI, (10 n.n7 {I. 00 o.no o.nn (I. no O.(JO (J.O:i 0.:1-1 0.03 {I. Uti O.CI(I O. ('0 (1.00 n.(lo o. (10 O.4;? O. 00 0.00 fl,02 (1.00 (1.7(1 (',00 fl.H (J.(II) (1,(lU (I,U;' (I. II (I, :JIJ (I. (10 (I, (1(1 lllH:;JJAY {l.Ot, 1.10 O. (l(1 (t.01. (1.011 o.no 0.00 o.on (I. on 0.00 0.00 0.00 I. 'iJ3 0.60 O. 15 0.32 O. 13 0.00 o.on o. 16 1.00 0.00 0.00 1.67 0.07 0.00 0.23 O. 13 (1. {I. 00 I. 6(1 (1.0(1 (1.0(' O. 11 (1.17 (1.07 ('.0(1 {I. (1(1 (I.O? 0.00 0.00 0.00 0.35 0.00 0.25 0.00 0.00 0.00 0.30 0.00 0.00 0.00 1. 29 0.31 0.(10 0.(10 0.(10 0.13 0.00 0.00 0.35 O. 00 0.00 0.05 0.00 0.00 0.(10 o. 00 O.ClO (1.1'1 O.CJO 0.(10 0.03 0, 1!i 0.00 ...... W o

PAGE 142

Heel.: ous HllIllSDAY I R JII,W U"",,JlfU,\V !aJrmAY tllINIlAY 1\', W'I\V ... 'EON!)[I,\y 305 o. I 0 ()(J o. J;' 0 :f(l :1. 1f1 0. 3;-0 301> 0.01 O. UO 0 II 0.11 (I.{lO O. 0(1 0 307 0.00 o.on o. UO O. (10 (I. U(I 0.00 0 30B 0.00 o.o() o. 110 O.O() (I. (Itt O. OIl 0 30'1 O. 00 O. (10 0.00 0.00 (I. ('0 (10(1 0 310 o. 00 0.(10 (1.11(1 0.(10 (J.<'() (1.00 0 311 o. 00 o. (10 o. 0.01 (I. 00 0.03 0 1982 312 0.00 o. 0(1 O. (1(1 0.00 (I. 00 0.00 0 --' W --'

PAGE 143

APPENDIX C PLOTS OF PUMPAGE AND RAINFALL VERSUS TIME DEERFIELD BEACH, FLORIDA 1976-1981

PAGE 144

DEERFIELD BEACH PRECIPITATION (1976-1978) 0.50 0.35 D.3C C:.25 USING RIGHT HRND RXIS PREC : LONG DRSHED L 133 ,"It", Fff. o. 01 I I tiD' 9 II I /I o.6l1 II I 1\ I'f' I 0.9 I' I II I I I L. I I "1 I I i I I L. 5 I I I I R \ I A L. Bj"i I I I \ I. hi 1 2. S 3. 3. gJ J :: 91 u.21 u.

PAGE 145

.. fJ C u I, E S 134 DEERFIELD BEACH PRECIPITATION (1976-1978) USING RIGHT RXIS LONG ORSHED L[NE 0.75 0.7D 0.65 0.80 .-, r-:-U.::;:iO 0.50 D.t!S 0.35 1 0.25 R A I hJ

PAGE 146

/1.1 C H E. S 135 DEERFIELD BEACH PRECIPITATION (1976-1978) PLcrTTED TSP USING RIGHT : LSHG L[NE 0.50 [I.US O.l.oO..:j 0.30 0.25 ..... I I I \ I I I I I ",,":II; ...... '* .... ""* ;:"':IM!< / If:'" :jo"'j I', ,*I /' I I I \ I \ I I I \} \,1 \ I \ I \ I I I I, I I '. I I' \1 II / + I / \ \ I : i 'I I I I I ,I \1 ,I ,I I \ I I t : I I I I, 'I \, \, r J \ ,(: 'I' I I I / \ I \ / 'I II t A A I I I C H E S

PAGE 147

.' I hi C H S 136 DEERFIELD BEACH PRECIPITATION (1976-1978) TSP USING RIGHT HRND HXIS 0.50 O.US O. 35 0.30 0.26 U1NG DASHED UNE I t>J C H E S 0-:0 0.3 O.S 0.8 L.2 L.S L.8 2.1 2.1.1 2.7-3.0-3.33.6

PAGE 148

I C H E ,.. .::l 137 DEERFIELD BEACH PRECIPITATION (1976-1978) FROi'l mp USING RIGHT HE=lND RXIS I LflNG DASHED LCNE 0.75 0.70 0.85 0.60 O.SS 0.50 (J .lJ.S O. UO...; 0.35 0.30 0.25 G.2!] r til I til C H ,.. c. S J.O-: 0.3-: o. Si 0.8.3 L.s3 4 4 L.B1 2. 2. Ld 1 j 2.?-j -i 3. =1 3. 3. 3. sJ 1 J

PAGE 149

I C H E s 138 DEERFIELD BEACH PRECIPITATIOl\r USING RIGHT HRND RXIS 1 DRSHED L[NE 0.30.6-0.9-L. 2L.5-A A L a-I III 2.1I III 2.U": I III 0.30 C 2.7": H E 0.25 S 3.0':

PAGE 150

I,J C H E s 139 DEERFIELD BEACH PRECIPITATION (1976-1978) PREC[PITRTH1N PU:lTTEO FROt<1 mp USING RIGHT Hf::tr'JO RXIS : DRSHED L[NE 0.60 5'" u. 01 o. '51 R A I t.J I t.J I t.J C H E S 0.0-: o.s..: 0.8": L. S": L. B-: 3.0": 3. S": 3.9": u.s..: Februarv 1977 Harch 1977 DEED1=i1IELD TIT.1., i'H "IF"'TPR (1 or;p)_ .l. \rL ..D w.fi .L.. ... .i.....,;.,! \ .... V .;.. J.. 1. "'_" ... ..!.. U I .t;, .l.. \..... .-: ESTIMRTED EVRFCTARN5FCRRTICN : SOLID LINE CRSHED LINE

PAGE 151

.. fJ C H c::. S 140 DEERFIELD BEACH PRECIPITATION (1976-1978) FUITTED FA ElI'l HlP USING RIGHT HF=lNO IiXIS PREC[PITIiTH1H : LONG DRSHEO L(NE 0.70 0.85 o.ao o r-r. .,., 0.50 O.us 0 ... 0"; oj 0.30 0.25 (' / I I \ I I I \ I 1/ 1/ II o. of I I : I I \ I '/ .). A A I 11/ I 11/ I 11/ C H E 0.3-: 0.8'; 0.9'; -L.2-:

PAGE 152

141 DEERFIELD BEACH PRECIPITATION (1976-1978) FRDM TOP USING RIGHT HRND RXIS LONG DRSHED L[NE 0.50 o. R A I hJ I hJ I hJ C H E s 0.0 0.3 O.S 0.9 L.2 L.5L. B-2.1-2.7": 3.3-: 3. S-= 3.

PAGE 153

I C H c: r :J 142 DEERFIELD BEACH PRECIPITATION (1976-1978) PREC (PITHHiH HIP USING RIGHT HI=lND RXIS PREc(PIHlTHIH I LSNG DASHED UrJE O. ,.. ,...... 1 '1', I ,. o 70 I \ /' I f I "I 11 "I '\' /1 /' I "I o "I II' I "I o SO I \ 'I II I \, I f II \ I I o 1=,1'" \, ,I 'It I I \1 II I \I ,I I o 50 II \1 I II ,I II II ,I 1/ O. US-:j : i :: j II< 1/ o.l.I.O..:/ 0.35 0.30 0.:25 1/ I, I I '" I I I I I I / I 1/ I' I' I' I' I' \' 1/ I' I' I' I' I I/< A A I 11/ 3.3 3.8 3.91 u.21 lJ.S{

PAGE 154

::. 143 BEACH PRECIPITATION (1976-1978) PREC CPIHlTIrm FAON HIP USnJG RIGHT Hf!:lNO i LCNE I t..I C ft 0.35 E s 0.30 A A I III I I C H E o. [ 0.3 0.6 0.9 L.2 L.5L.82.1--S 3.0-: 0.25

PAGE 155

I N C H ", .:J 144 LaNG DRSHEO L[NE .-. ""5 j u. "' O.51j O. O. 35 D.30 ,-, ':I':: .*"'_.-+-:jr. \ I I I \ I II R A I f.J I f.J I f.J C H E S 0.0 0.3 O.B-0.9L 2L 5L 82.12.1.1-3.0-

PAGE 156

I h! C H t 5 145 DEERFIELD BEACH PRECIPITATION (1976-1978) USrNG RIGHT HRND PREC [P : L(lNG DASHED UNE 0.60 0.55 0.50 [I.U5 O.UO' 0.35 0.80 0.25

PAGE 157

I C H Eo S 146 DEERFIELD BEACH PRECIPITATION (1976-1978) PREC CPITRTII!W nlP USING RIGHT HF=lND RXIS LSNG DASHED LCNE u.75 I .... +* ... r"', ..... '1/4 .r"''!' ,. ...... '" /f '\'1..+ ,.,..,.....o. 0 0.70 0.65 0.60 0.56 0.50 0.1J.5 I I I l'f +-I I \ r I I / \ I \ I ,J \ I \ / I I I I I '" I y; I I I 1 \ / \ (I / \ I \I I + I I II II< I '1/ II< I I I I 1/ \ \/ I, II Ii< \ \1 I ,.. I I I / I I \ I 1/ II II 1/ II 1/ I I II I I Y I I + II II II l R A I t>J 0.3 o. s o. sl.2-l. 5-l. BO. LJ.0..:j / :Ie 2.1I 0.35 0.30 0.25 t>J I t>J. 2.U': C H E s \ \' \ 88688--888888888888888B8886B88868888-8886668868888888888898988988 3.3-: "I 3 U..21 3 1978 -June 1978 DEERFIE .bEACH WATER CONStMPTIONS (1976-1978) E5TII-IRTED ::YRF!:JTFlRN5F CFlRTWN : !...IN:: ILED F'Ul'lF'RG=: : =HOflT LI HE

PAGE 158

I t.! C H E S 147 DEEHFIELD BEACH PRECIPITATION (1976-1978) TOP USING RIGHT HRND : LSNG DRSHED L[NE 0."(5 0.70 0.85 0.8 1..1. 0.50 O.!!5 O.IJ.O 0.35 0.30 0.25 0.00 i-m-""""'mT'l,""T\-;T1 n,,.,., (.,." ,TT' ,"'"''(''''',''''I(T'T, ,T7' ,""l",.,.".rr,n-, ,.,.." """"'.-, [ .( i l' I' l i \ It. (. (I (. \ i\ 1 \ F(l \ I t I (1 \' t' R A I hJ I hJ I hJ C H E S July 1978 ugust 1978 DEEHFIELD BEACH lVATER C NSUMPTIONS (1976-1t:178) E:5TUIATED [AATrON : :.sOLID LIHE' HOFl!'lRL ILED f'U!'lFP.CE : :5:-lr.JR"T IJFl5M=!J LI HE

PAGE 159

fJ C 148 DEERFIELD .BEACH PF:ECIPITATION (1976-1978) Tap USING RIGHT RXIS : LONG L[NE 0.50 O.I.lS I H 0.35 2. E S 0.30 0.25 I hJ C H E L 1111 L 1111 L l111L 1111 L 1111 L 1111 L 1111L 1111 5G?8S0123U5G?8S0123USG78S0122U5G78S0122U5S?8S0123US67e80123U:S78S 2.7-3 3.0":: U.5"; September 1978 October 1978 November 1978 DEERFIELD BEACH -WATER CONSUMPTIONS (1976-1978) ESTIMRTED EY?FCTARNSFCARTIVN : LINE NOAMRLIZED FUMFRS= : DR5HED LINE

PAGE 160

149 DEERFIELD BEACH PRECIPITATION (1976-1978) FRCl/Ol USING RIGHT HRND t:lXIS : LONG DRSHED L[NE D.50 O.t2.5 >Ioo!: +-*-..+ rojl" ... """ ... ,... ... """' ... J"""'\= I r I I I I I \ I I I I I \ II I / If \ I II I I I I I I II II II II II II L 11 iLL 1111L 1111.L 1 illL i 111L 1111L ll1lL 1111L I11H ll1lL 111 iL 1111 L 1111 R A I hJ I hJ I hJ C H E S

PAGE 161

APPENDIX D HISARS EVAPORATION DATA USED IN DEERFIELD ANALYSIS 1953 -1979

PAGE 162

FORT LAUDEllDflLC EXPFRHIENT STATION BRQ\.IARD 00-3171 DAILY EVAPORATION IN INCHES AND WIND MOVEMENT IN MILES 1954 FEBRUARY MARCH APRIL HAY ,JUNE ,JULY AUGUST SEPTEtlDER OCTOBER NOVEMBER DECEMBER 1 O. 10 91 O. 14 50 0.15 108 0.26 58 0.22 56 O. 10 31 0.26 56 O. 14 29 O. 14 00:' 0.24 119 0.20 2 O. 13 50 0.16 62 0.21 91 0.22 40 0.22 66 45 0.29 53 0.24 33 0.03 :m 89 61 O.OB 117 3 0.07 32 O. 14 35 0.09 43 0.24 38 0.26 42 O. 16 72 0.29 58 0.25 24 O. 19 3:> O. 18 69 O. 09 41 4 0.11 31 0.17 57 O. 19 89 0.16 35 0.24 42 0.07 36 0.20 50 0.25 15 0.16 24 O. 16 35 O. 12 93 O. 16 35 0.21 40 O. 18 5 0.00 26 0.17 67 0.20 131 0.10 92 0.25 49 O. 16 50 O. 27 33 0.23 27 O. 30 60 0.24 64 O. 11 25 0.15 59 6 0.11 55 O. 15 37 0.09 33 0.29 133 0.06 49 O. 18 40 0.29 33 0.30 -45 0.30 129 0.19 56 0.09 21 7 0.14 52 O. 13 38 0.20 152 0.21 78 0.32 32 0.23 34 0.24 30 0.27 32 0.26 147 O. 16 42 O. 12 47 0.11 29 O. 13 1 8 0.14 1 0.09 4B 0.20 102 0.33 83 0.26 40 0.25 37 0.03 13 0.41 21 0.27117 0.03 45 o. 12 81 0.00 lOB 9 O. 12 42 0.15 99 0.12 59 0.09 27 0.26 37 0.27 41 O. 17 25 0.17 24 0.28 96 0.16 78 0.11 131 0.13 104 10 0.08 41 O. 10 50 0.02 41 0.17 44 0.29 3B 0.25 36 o. 17 27 O. 17 20 0.21 51 0.25 72 O. 10 70 0.15 54 11 0.05 50 0.14 57 o. 1:1 44 0.33 81 0.28 37 0.27 30 O. 16 39 O. 18 27 0.20 36 O. 12 :;7 0.19 110 0.00' 20 12 O. 16 106 0.14 44 0.10 44 0.20 31 0.25 38 0.24 31 O. 28 37 0.20 19 o. 17 41 0.22 71 0.12 38 13 0.12 107 O. 14 100 0.13 74 0.23 39 0.00 52 0.22 30 O. 12 20 O. 18 50 0.23 36 O. 17 96 0.09 52 0.21 103 111 14 0.12 107 0.25 174 0.15 82 0.10 37 O. U 73 0.25 42 0.27 33 0.28 37 50 0.23 121 O. 10 54 15 0.25 91 O. 10 92 0.20 116 O. 14 43 0.23 78 0.10 26 36 0.20 42 O. 12 45 74 0.05 103 16 0.07 46 0.20 114 0.36 109 0.30 66 0.23 34 0.00 34 O. 12 30 0.25 38 0.25 53 O. 16 7 0.07 51 O. 14 36 17 O. 16 3 O. 16 60 0.24 129 0.25 64 0.00 21 O. 12 19 O. 15 44 O. 13 31 0.20 68 0.23 147 0.11 20 0.10 30 18 O. 13 34 O. 12 25 0.18 83 0.24 100 0.26 49 56 0.26 54 0.25 25 0.27 37 0.27 119 0.13 24 0.09 41 19 0.03 66 O. 10 0 O. 16 83 O. 17 79 0.20 22 45 O. 30 37 0.25 46 0.04 41 0.20 107 0.11 23 O. 10 41 0.22 116 0.13 14 --' 20 O. 11 4 0.15 51 142 0.22 86 0.27 45 O. 18 120 0.25 35 0.15 14 0.20 32 O. 18 95 O. 10 31 21 0.12 81 0.20 71 0.20 69 0.19 70 0.25 61 O. 18 45 0.24 22 0.26 73 0.24 O. 14 42 0.07 52 Ul 31 0.22 70 0.15 22 0.11 81 0.11 54 0.21 55 0.06 76 0.25 62 0.22 28 0.28 32 0.43 95 O. 19 40 0.11 57 --' 28 O. 11 78 O. 10 18 23 0.'13 2 0.22 82 0.20 67 0.05 83 0.32 133 0.09 24 0.28 44 0.04 63 0.11 32 O. 14 126 0.11 .115 24 O. 1.11 78 0.18 48 0.21 61 O. 19 53 0.38 132 0.08 22 0.28 58 0.28 102 O. 12 36 O. 12 29 0.09 16 25 0.11 52 0.16 124 0.21 57 0.25 83 0.28 95 O. 15 35 22 0.31 65 O. 16 62 0.27 134 0.12 26 0.07 19 26 O. 10 6 O. 16 38 0.22 60 0.17 30 0.30 0 o. 12 14 O. 12 24 0.24 49 O. 13 0.21 99 0.12 36 0.07 23 42 0.21 92 0.13 44 27 O. 14 48 O. 10 31 0.20 57 O. 19 62 0.23 77 O. 14 21 O. 13 22 0.23 50 0.11 17 0.17 73 O. 12 42 28 0.09 37 0.15 59 0.'23 61 0.19 37 0.00 46 O. 10 24 O. 16 19 0.30 29 O. 10 O. 12 32 0.08 35 20 0.00 ,21 0.11 37 29 0.10 8 0.21 41 O. 14 29 O. 12 31 O. 19 36 O. 10 16 0.28 47 0.11 13 0.07 40 0.00 0 30 0.13 34 0.23 40 0.22 44 O. 13 48 0.28 30 o. 17 27 O. 16 40 O. IB 34 O. 17 92 O. 12 45 0.14 61 31 O. 14 46 O. 19 43 O. 10 30 0.27 36 0.30 52 0.19 72 0.20 1 O. 12 36 O. 10 21 TOTAL 3. 51 4. 18 5.45 5.90 6.57 4.60 6.15 7.33 5.26 5.38 3.75 TOTAL 1508 1767 2366 1821 1615 ,JJ34 1065 1263 1473 2467 2.94 1471 1439

PAGE 163

FORT LAUDERDALE EXPERIMENT STATION OU 1955 DAILY EVAPORATION IN INCHES AND WIND HDIJEM!=NT IN MILES JANUARY FEORUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMUErI oeTOUER NOI/EMOER DECEMBER 1 O. 12 37 O. 16 0.20 46 2 20 0.17 82 0.26 60 0.32 44 0.24 22 0.23 70 0.21 49 O. 09 37 3 O. 15 36 O. 14 33 0.20 47 0.33 101 9. 10 49 O. 161 35 0.21 24 O. 21 29 0.18 55 O. 15 87 4 O. 11 19 0.11 23 0.22 ::; O. 10 0 0.32 118 0.31 68 0.10 27 0.21 73 O. 15 38 O. 06 80 5 0.12 30 O. 20 50 0.31 6'1 0.46 59 0.31 44 0.29 73 J7 O. J4 28 O. 11 23 O. 06 50 6 O. 10 228 O. J7 43 0.22 2:i 0.00 90 0.30 52 0.26 53 0.20 47 0.19 40 O. 16 36 0.16 37 7 O. J5 J28 0.18 40 0.24 51 0.20 30 0.27 86 0.11 45 0 .. 21 40 0.10 39 O. 12 21 0.09 24 8 0.16 117 O. 1.9 42 0.22 60 0.22 40 0.27 2 0.07 41 O. 37 0.24 77 0.12 23 '0.06 23 9 0.15 0 O. 18 50 0.23 53 O. 18 37 0.28 47 O. 18 37 O. J7 36 0.20 49 0.13 20 0.11 51 10 O. J4 44 O. 24 83 0.15 86 0.25 57 0.31 63 0.25 36 0.0'1 33 0.21 56 O. 14 29 0.06 33 11 O. J7 135 O. 20 46 O. 12 44 0.24 27 0.30 62 0.25 38 0.15 3J 0.21 57 0.01 27 0.16 43 12 O. 14 42 O. 20 32 0.20 100 0.26 40 0.26 50 0.25 41 0.18 40 O. 16 53 0.10 62 0.04 19 13 O. 20 143 O. 20 40 0.00 2 0.27 41 0.01 28 0.15 39 0.25 21 0.12 53 O. 15 31 O. 07 13 14 0.21 171 O. 22 36 O. 16 86 0.20 :iO 0.20 43 0.22 28 0.24 32 0.05 14 0.14 47 0.02 34 15 0.16 160 0.2J 32 0.17 102 0.30 :H o. 10 22 O. 04 22 O. 23 79 0.15 20 O. JO 51 O. 06 69 16 O. 11 61 O. 21 38 0.21 51 0.28 41 0.24 21 0.26 37 0.25 62 O. 33 45 O. 16 39 O. 01 49 17 O. 16 70 0.23 45 0.22 56 0.26 44 O. 18 24 0.26 30 0.30 65 O. 15 45 O. 13 34 O. 15 50 18 O. 14 64 0.23 46 O. 19 38 0.20 36 O. 10 37 0.20 30 0.27 71 O. 18 26 0.10 30 0.13 47 19 0.07 52 O. 19 28 0.25 45 O. 10 39 0.13 44 0.22 26 0.25 33 0.20 68 O. 15 34 0.08 21 20 O. 13 37 0.20 46 0.27 89 0.12 19 49 0.15 16 O. 15 42 0.11 25 O. 15 35 0.09 18 21 0.00 J04 0.22 51 0.22 51 0.15 27 0.17 42 O. 13 16 0.20 35 0.20 52 0.10 20 O. 12 43 22 O. 33 100 0.23 50 0.23 32 0.25 35 0.21 37 O. 17 27 0.09 21 O. 23 120 0.20 ::i4 0.05 40 23 O. 20 90 0.23 4 0.24 5B 0.2B 61 0.12 18 O. 13 12 0.23 28 0.27 128 0.10 26 0.09 27 24 O. 10 41 O. 30 87 0.28 6B 0.20 123 0.16 30 0.24 31 0.21 33 0.22 166 0.13 24 0.07 15 25 0.21 44 O. 20 58 0.20 0.31 0 0.26 34 0.25 14 0.20 34 8.21 126 0.13 21 0.07 16 26 O. 14 63 0.15 3 0.26 67 0.29 54 O. 18 27 O. 14 46 O. is 57 18 60 O. 15 42 O. 07 15 27 0.24 109 0.15 52 0.24 91 0.31 47 O. 15 31 0.2B 26 0.e3 60 0.16 50 0.15 20 O. 12 10 28 O. 18 93 O. 22 60 0.32 44 O. 30 38 0.15 11 0.20 29 0.123 100 0.10 25 O. 05 25 O. 06 16 29 O. 19 35 0.15 110 0.37 45 0.29 36 0.19 37 0.23 25 0.24 77 0.17 25 0.13 30 O. 15 24 30 O. 17 124 0.41 59 0.28 45 0.14 28 0.20 26 O. 24 75 O. 14 34 0.13 41 0.07 123 31 O. J8 91 0.20 44 0.18 32 O. 19 37 0.19 45 O. 25 82 0.12 32 O. 20 98 0.22 76 0.15 6 O. 12 29 0.23 41 0.21 68 O. 11 25 Ul lTAL 4. 28 6.01 6.22 5. 60 3.90 2. 85 N JTAL 6. 72 7.19 14.9:1 6.13 2253 1469 1644 1389 1217 1109 1361 1753 1084 1215

PAGE 164

FORT LAUDERDALE EXPERIMENT STATION DRmlARD 08 <07J DAILY EVAPORATION IN lNCHES AND !'UND MOVEMENT lN MILIOS 1956 JANUARY FEDRUARY MARCH APRIL HAY JUNE .JULY AUGUST SEPTEMDER OCTODF.R NOVEMDER DECEMDER 1 O. JO 40 O. J3 4B O. JO 0 0.24 65 0.06 54 O. 30 25 O. 30 40 0.30 20 0.11 20 0.24 32 O. J9 2 O. JJ 22 0.24 J22 O. J9 7J 0.31 65 0.3J 0 O.OB 33 O. 31 31 0.32 49 B8 0.13 33 0.21 30 0.11 24 0.10 102 3 0.11 20 O.IB 94 O. J9 36 0.25 2 O. 28 80 0.26 29 0.22 J6 0.35 44 0.23 30 0.21 19 0.24 112 O. J3 33 4 O. JO 21 0.09 B4 0.21 30 0.25 51 O. 3B 66 O. 26 29 0.09 15 O. 31 44 0.15 21 O. JO 42 0.29 J42 O. J2 33 5 O. J6 71 O. 10 54 O. J9 33 0.25 71 0.3J 2 O. 18 26 O. 00 33 0.29 32 0.08 24 0.29 3J 0.30 101 O. 11 21 6 0.13 41 0.10 5B O. J9 36 0.20 0 0.31 42 0.21 26 0.11 17 0.30 15 O. 10 17 0.15 35 O. 16 O. JO 30 7 0.00 20 O. 12 51 0.20 67 0.26 59 0.24 124 0.28 35 0.20 50 0.25 25 0.20 40 0.20 J8 0.15 52 0.11 27 B O. J3 7:5 O. J4 94 0.21 43 0.24 42 0.04 40 0.24 31 0.20 0.32 28 O. 18 41 0.20 22 28 0.12 60 9 O. 12 62 0.11 46 O. 17 69 0.20 50 O. 40 26 0.26 23 O. 36 39 0.28 29 0.19 34 O. J2 35 O. 24 0.22 33 O. J5 10 O. J3 54 0.17 35 O. J4 2 0.05 23 O. 16 B2 0.26 20 0.22 14 0.26 22 O. 14 22 0.17 24 0.13 35 19 O. 20. Jl 0.13 59 0.14 67 0.22 40 0.19 112 0.24 0 0.14 40 O. 10 20 0.31 31 0.22 107 0.26 lIO 6B O. 12 39 12 O. 12 54 0.07 49 0.21 35 0.32 120 0.29 52 0.20 45 0.21 17 0.32 3J 0.10 J 0.20 J53 O. 16 50 O. J4 28 J3 0.00 50 O. 11 32 0.11 30 0.27 75 0.29 50 0.23 44 O. 03 52 O. 32 31 0.25 0.16 41 0.10 J8 99 124 O. J5 30 14 O. 10 55 O. J4 50 0.33 50 0.23 36 0.20 0 0.26 47 O. 30 23 0.30 53 0.26 97 O. 15 164 0.09 43 15 0.09 23 0.14 55 O. 19 65 0.22 5 0.29 24 0.29 54 O. 30 0 0.15 2J O. 25 41 0.10 J40 0.14 26 0.12 42 16 0.00 20 0.15 49 0.26 61 0.22 67 0.28 25 0.29 66 0.27 :5 0.213 20 0.22 24 0.07 0.10 50 O. 15 34 17 0.13 59 O. 16 35 0.28 94 0.29 97 0.30 19 0.16 67 O. 10 21 O. 24 17 0.22 67 O. 20 57 0.11) 25 49 0.21 78 O. JB 45 18 0.07 17 O. 10 50 0.21 43 0.20 46 O. 30 26 0.30 63 O. 12 J5 0.13 J9 0.20 3J 0.21 O. 10 20 34 0.17 BO 0.15 J9 O. J2 20 0.20 29 0.22 44 0.25 34 0.27 37 0.27 49 0.20 16 0.22 13 0.21 9 0.16 25 9 O. 16 80 O. 14 20 0.12 64 0.13 31 O. 23 :55 0.23 36 0.20 43 0.30 37 O. 10 22 0.23 14 O. 19 20 O. 15 27 28 21 0.11 23 0.15 23 0.26 75 O. 10 27 0.20 43 0.30 J7 O. 23 15 0.2B 20 O. 10 20 O. J5 20 0.12 111 0.10 41 22 0.06 0 0.22 115 0.2J 3 0.26 40 0.27 5B 0.27 34 0.33 77 0.21 23 O.OB IB 0.15 0.15 92 0.15 24 23 0.06 44 O. 17 63 0.22 23 0.25 41 0.00 52 0.20 27 0.20 5 0.2B 27 0.09 3,1 O. 1:5 42 O. 1"4 26 19 0.15 19 24 O. 14 86 0.16 41 0.19 54 0.20 43 0.29 31 0.27 16 0.2B 73 0.15 23 0.23 20 O. JO 2J O. 10 42 0.14 20 25 0.00 42 O. 18 57 0.15 69 0.26 43 O. 35 37 O. 18 15 0.32 41 10 0.26 43 0.15 27 0.14 67 0.14 27 26 0.09 35 0.18 40 O. 13 69 O. 14 43 0.31 0 0.27 22 O. 20 20 O.OB 18 0.27 65 0.07 35 0.19 22 0.19 43 27 O. J4 69 O. 10 40 0.19 34 O. 10 10 0.30 103 0.19 19 0.29 81 0.40 18 0.21 O. 05 22 0.10 60 24 0.25 42 O. 07 49 0.11 ..... 28 0.10 0 O. 19 59 O. 10 20 0.23 44 0.31 57 0.22 21 O. 29 13 O. 17 11 O. 19 47 0.17 57 37 tTl 29 0.08 B3 0.27 B5 0.25 34 0.30 96 O. 10 30 0.00 'IB O. 24 0 O. 15 B O. 19 29 0.16 47 O. J2 38 O. 14 24 W 30 O. J9 67 0.24 35 O. 10 0 O. 36 64 0.2B 32 0.32 44 0.11 6 0.23 26 0.12 O. JO J6 O. 00 51 32 0.10 19 0.14 37 31 O. 13 40 O. 13 45 0.31 63 0.24 16 0.10 12 0.17 47 O. 11 24 TOTAL 3.07 4.34 6. 12 6.60 7.94 6.94 b.68 7.41 5.53 5.04 4. 76 3. 73 TOTAL 1335 1656 1365 1443 1329 1010 2B31 734 1060 1559 1730 1023

PAGE 165

FORT LAUDERDALE EkPERIMENT STATION DROI.JARD 08-3171 DAILY EVAPORATION IN INCHES AND \.lIND MOVEMENT IN MILFS 1957 .JANUARY FEBRUARY MARCH APRIL HAY .JUNE .JULY AUGUST SEPTEMDER OCTODER NOVEMDER DECEMDER 1 0.22 10 0.15 59 0.24 62 O. 28 106 O. 12 25 O. 12 25 O. 17 27 0.2'1 30 0.03 0 0.18 33 2 0.11 9 0.16 28 0.10 30 O. 27 89 0.07 25 0.31 35 O. 26 18 0.25 26 o. 16 18 0.18 26 0.28 54 3 0.17 57 0.10 20 0.18 31 0.15 34 0.15 23 0.31 35 0.20 20 0.15 21 0.05 13 O. 13 22 0.13 63 'I 0.12 40 0.18 39 o. 18 57 O. 22 38 0.14 34 O. 13 15 O. 26 18 0.16 11 O. 15 15 6 0.13 19 O. 10 2 5 0.13 15 o. 19 48 0.21 32 0.15 63 O. 10 60 0.1'1 16 0.26 34 0.15 11 0.21 33 O. 16 O. 15 3'1 6 O. 14 19 0.22 60 O. 26 58 0.25 59 0.17 41 O. 17 23 0.20 31 33 0.08 16 0.22 12 0.06 10 O. 12 51 13 O. 14 15 7 0.12 13 0.10 50 0.17 53 0.24 49 o. 17 0 0.20 17 0.22 33 0.24 25 O. 10 23 0.21 37 0.20 8 O. 11 15 0.18 38 O. 00 30 O. 22 37 0.26 120 0.22 66 O. 29 20 O. 12 20 O. 19 15 O. 14 0.12 24 O. 11 6r; 9 O. 16 30 0.16 27 0.22 61 O. 18 '12 0.20 78 0.20 62 0.27 4 0.20 32 o. 17 54 0.18 32 O. 14 22 0.12 10 O. 12 13 O. 10 26 0.22 30 O. 19 4 0.00 0 0.19 56 0.24 12 0.25 32 0.20 30 10 0.17 30 0.05 20 O. 10 23 0.18 15 11 0.12 11 O. 16 22 0.10 33 O. 15 37 0.20 46 0.20 42 o. 18 27 O. 15 14 0.25 61 0.15 35 O. 17 95 0.10 12 o. 10 10 0.19 41 0.17 27 47 O. 15 28 0.24 '14 O. 15 10 o. 17 13 0.29 56 0.15 20 0.25 250 0.16 31 13 O. 12 10 0.21 40 0.17 27 0.22 22 0.22 37 0.31 51 O. 17 17 o. 18 15 0.00 50 O. 10 10 0.15 134 0.00 100 14 0.14 16 0.15 40 0.20 33 0.17 28 O. 13 21 0.24 25 17 O. 12 13 0.24 54 O. 17 19 O. 15 76 O. 15 94 15 0.09 10 0.20 16 0.20 31 O. 27 43 0.21 30 0.28 29 10 0.22 31 O. 18 74 0.26 85 0.07 44 0.15 55 16 0.12 13 0.15 24 O. 16 17 O. 29 124 0.18 33 0.07 36 0.26 23 0.22 38 O. 10 15 0.23 0.12 75 83 0.14 31 17 O. 13 24 O. 12 15 0.22 20 0.39 111 0.23 31 0.39 56 0.26 30 0.20 20 O. 18 15 0.24 37 0.11 76 18 0.20 76 O. 18 47 O. 18 21 O. 12 70 O. 16 13 0.26 4 0.12 22 O. 16 12 O. 10 31 O. 13 0.14 32 0.20 80 19 O. 19 32 0.08 19 0.13 103 0.24 7 0.17 16 31 0.00 20 0.28 44 o. 18 23 O. 16 20 0.23 7 0.16 33 0 0.09 46 0.?7 20 0.17 121 o. 18 32 0.23 51 0.22 116 0.31 38 0.25 28 o. 14 35 0.20 34 0.13 2 0.16 34 21 0.07 113 0.12 35 O. 18 25 O. 22 56 0.23 22 0.21 25 0.13 10 O. 13 44 0.26 15 32 o. 17 21 o. 1 20 0.21 125 0.00 22 0.08 77 O. 10 0 0.21 55 O. 23 67 0.21 20 0.22 31 0.14 13 O. 17 12 0.20 20 0.20 130 60 O. 14 40 23 0.13 43 0.25 102 0.17 58 O. 27 60 0.25 52 0.28 35 o. 18 12 O. 10 31 0.22 49 0.19 106 0.00 76 0.11 67 2'1 O. 13 26 0.19 96 O. IB 40 0.20 53 0.25 48 0.20 33 0.21 12 0.11 14 O. 18 27 0.21 0.19 39 0.01 72 22 O. 11 10 0.20 70 25 0.00 10 0.19 0 0.22 67 0.23 60 0.24 33 0.21 30 o. 28 34 o. 16 16 O. 18 24 O. 12 0.09 26 0.15 23 0.26 63 0.26 71 0.23 89 0.22 22 O. 19 36 0.29 21 o. 10 10 0.11 26 20 0.18 58 27 o. 16 44 0.28 8 0.05 23 O. 19 91 0.23 25 0.11 23 0.20 20 0.21 33 0.08 0.16 58 0.09 61 28 0.12 43 0.00 21 0.22 22 0.20 100 0.20 33 26 0.27 48 0.23 45 0.20 24 O. 13 39 0.02 6 -J 29 0.16 35 o. 10 20 0.19 36 O. 25 24 O. 14 17 0.32 27 20 0.22 2B 0.04 29 0.15 61 Ul 30 0.16 34 O. 19 55 0.05 24 O. 12 16 O. 10 23 O. 04 11 0.26 42 0.22 26 0.09 80 39 -J:::> 31 0.15 25 0.29 79 0.20 25 o. 13 14 0.21 53 0.11 70 0.12 6 0.06 51 TOTAL 4.03 4.71 5.65 5.99 5.57 I.. 17 6.02 5.22 4.91 4.18 3. 38 3. 59 TOTAL 108B 1003 1235 1786 1023 98B 653 771 B46 959 1377 1569

PAGE 166

FORT LAUDERDALE EXPERIMENT STATION BROI-JARD 08-3171 DAILY EVAPORATION IN INCHES AND MOVEMENT IN MILE'S JANUARY FEBRUARY MARCH APRIL HAY JUNE JULY AUGUST SEPTHIDER OCTOBER NOVEMDER DECEMBER 1 0.15 51 0.10 103 o. 19 :56 0.18 14 0.29 42 0.26 50 O. 17 37 0.27 30 O. 12 28 O. 1:) 14 O. 11 .., 0.07 42 79 o. 09 42 O. 18 26 O. 27. 49 0.17 :50 O. 23 38 O. 32 ;!7 O. 20 23 O. 11 70 L 3'1 0.22 26 3 201 0.13 30 0.17 59 0.16 15 0.25 :50 O. 30 45 o. 26 44 0.28 33 0.2:5 O. 19 50 O. 20 '1J O. 16 24 O. 14 25 40 4 0.05 146 O. 16 76 0.12 23 o. 09 37 0.2B 54 0.25 74 O. 15 61 o. 31 :59 O. 09 '1'1 O. 21 22 0.16 84 5 o. 14 101 0.12 42 o 1:5 74 0.20 40 0.23 34 O. 30 74 o. 26 :55 O. 22 2 0.22 0.19 26 O. 21 30 O. 08 48 6 0.19 160 0.10 B 0.13 31 0.21 56 o. 03 28 0.22 61 o. 28 60 O. 32 9 O. 17 O. 10 19 0.15 15 26 0.12 30 O. 12 7 O. 04 125 o. 11 50 O. OB 83 O. 20 78 o. 30 67 0.25 20 o. 30 62 0.22 10 0.20 14 0.09 26 0.09 15 9 o. 07 69 o. 07 15 O. 13 37 0.16 84 0.28 43 0.20 31 o. 28 50 0.09 29 0.19 57 O. 18 33 0.08 18 22 0.21 9 0.12 55 o. 14 71 0.18 28 0.21 34 O. 25 25 0.24 39 O. 31 62 0.14 23 0.13 61 o. 11 :.27 O. 08 27 18 0.16 29 0.10 10 o. 11 35 0.15 66 O. 22 78 O. 12 63 0.25 23 0.25 41 O. 32 70 0.01 24 O. 16 12 o. 16 26 37 O. 15 22 11 0.04 46 0.11 18 0.23 44 O. 22 58 0.26 25 0.21 38 o. 22 42 O. 13 9 0.2:> 7 O. 27 0.17 35 0.12 14 12 0.00 40 0.10 70 O. 09 31 O. 12 39 0.22 75 0.21 21 o. 31 28 o. J8 18 0.20 55 O. 17 35 0.04 30 19 O. 16 30 O. 16 13 O. 11 46 0.12 35 0.16 32 0.20 59 o. 14 20 0.12 30 O. 20 31 O. 23 26 0.24 30 O. 13 71 22 0.25 124 0.17 14 O. 06 37 O. 17 61 o. 22 64 0.28 61 o. 23 35 0.23 26 0.19 49 0.23 75 25 0.30 164 63 0.10 46 15 O. 13 18 o. 13 46 O. 20 41 0.19 69 o. 14 85 0.29 30 O. 25 48 0.41 30 0.2:; 27 0.19 51 O. 07 35 16 O. 11 49 0.12 102 0.25 45 0.29 130 0.27 97 0.30 30 O. 26 33 0.12 17 O. 26 O. 31 120 O. 16 69 0.0:;' 45 62 O. 18 162 O. 13 17 O. 16 65 0.27 64 0.20 32 0.29 110 0.21 53 0.29 40 O. 31 33 O. 24 17 0.23 65 55 0.09 88 0.19 30 O. 17 50 18 0.00 34 0.06 42 o. 12 44 0.17 30 O. 29 65 0.28 16 O. 28 31 O. 24 25 0.21 23 0.13 23 0.19 65 92 19 O. 11 32 O. J2 46 0.10 49 o 25 11 o. 28 78 0.30 25 O. 28 11 O. 29 23 0.24 O. 07 55 23 O. 08 29 O. 09 44 20 0.11 31 o. J 1 41 0.12 80 O. 24 79 0.21 15 o. 31 33 O. 00 26 0.35 23 0.23 20 O. 13 78 o. 16 O. 14 57 21 0.10 38 0.23 59 0.25 65 O. 20 78 0.19 75 0.25 31 0.14 14 0.17 31 0.15 36 o. 15 43 --' 23 o. 17 24 22 0.01 117 O. 09 70 0.23 50 O. 11 22 0.25 32 0.20 40 O. 27 24 0.20 10 0.26 O. 08 9 O. 07 20 (J1 J9 O. 18 23 0.12 31 23 O. 11 19 0.12 16 O. 15 55 0.19 34 0.21 65 0.20 40 O. 22 26 O. 41 18 o. 18 O. 10 26 (J1 10 0.13 14 O. 07 24 O. 05 72 0.15 20 O. 24 97 O. 20 21 30 O. 28 58 O. 28 20 O. 23 23 O. 18 30 o. 13 26 33 0.18 10 0.18 25 0.07 65 0.10 17 O. 11 50 O. 26 19 34 O. 15 35 0.19 16 O. 21 19 0.28 35 49 O. 10 20 0.18 25 0.05 26 O. 09 67 0.17 77 38 0.29 56 O. 23 32 O. 10 12 0.21 16 O. 29 17 0.10 12 O. 15 20 33 O. 13 53 27 0.17 40 0.06 108 0.19 50 O. 24 40 0.22 28 0.30 51 0.28 27 O. 21 45 O. 23 26 O. J2 20 0.08 70 O. 19 20 O. 12 18 28 O. 12 38 0.13 74 o. 21 31 0.29 53 0.21 46 0,31 24 O. 23 25 O. 13 51 0.23 20 0.17 64 22 0.21 40 29 0.12 47 O. 15 27 0.29 41 18 O. 06 15 O. 26 9 O. 27 29 0.22 2'1 O. 16 25 O. J 1 o. 08 21 30 0.08 68 O. 20 62 0.26 41 0.12 33 o. 14 10 O. 24 17 o. 24 15 O. 14 36 0.13 70 O. 10 23 26 0.19 150 31 0.16 34 O. 08 87 O. 12 54 0.27 31 0.11 15 o. 01 29 0.06 21 o. 13 61 TOTAL 2 84 3. 69 4.96 6.29 6.23 6. 97 7,45 7.07 5.81 5.32 4, 27 TOTAL 1986 1505 1585 1498 1410 1090 J098 782 fl25 1368 3. 04 1253 1320

PAGE 167

FORT LAUDERDALE EXPERIMENT STATION BrWI-IAfW 08-3171 DAILY EVAPOHATION IN INCHFS AND I.nND HOVE ME NT IN HILES 1959 JANUARY FEllRlJARY MARCH APRIL MAY .JUNE .JULY AUGUST SEPTEHDEn OCTOBER NDVEl1tlER DECEHDER 1 0.14 68 0.13 15 0.20 46 0.17 26 0.19 45 0.25 26 O. 20 15 0.29 26 32 O. 17 9 2. 0.03 49 O. 10 17 0.03 54 O. 21 47 0.22 40 0.11 39 0.21 18 O. 18 26 O. 18 33 0.16 0.14 25 O. 10 57 3 0.10 28 0.13 43 0.15 33 0.22 77 0.29 40 0.30 38 0.16 16 0.22 29 0.25 5 0.16 29 0.11 9 15 0.19 7 0.13 40 0.17 '20 O. J2 26 0.20 9, O. 29 52 0.38 32 O. 18 1::1 0.23 12 0.30 O. 10 15 0.02 4 5 0.06 63 0.22 85 O. 14 100 0.22 27 0.27 24 0.23 20 0.11 34 0.04 11 O. 10 J.4 O. 17 13 O. 16 17 0.16 23 O. 16 J.4 42 6 0.22 72 0.01 45 O. 14 100 0.20 40 0.28 55 0.26 38 0.21 37 O. 10 22 O. 13 0.15 19 0.10 31 15 0.11 16 O. 19 7 0.07 69 O. 17 116 O. 19 75 0.26 34 0.32 99 0.28 6 O. 23 39 O. 16 15 0.18 13 O. 10 23 40 0.08 26 8 0.08 29 0.14 59 0.07 72 0.25 52 0.30 50 O. 14 13 O. 20 27 0.25 34 O. 13 17 O. 19 30 0.25 29 O. 11 32 9 O. JJ 59 0.16 49 0.00 24 0.29 64 O. 18 25 0.11 11 0.14 10 0.20 41 0.;10 22 O. 16 33 O. 12 46 O. 12 12 10 O.IB 96 0.13 23 O. 12 28 0.27 54 0.32 71 0.26 41 0.23 19 0.30 34 0.06 40 0.11 J.4 9 O. 21 23 0.07 25 11 0.17 27 O. 20 27 0.15 14 0.23 86 0.33 65 0.25 30 O. 25 3B O. 13 13 O. 18 30 0.18 26 0.22 119 0.11 30 12 O. 14 75 O. 18 52 0.16 60 0.29 63 0.31 81 O. 19 16 O. 32 48 0.2::1 26 18 0.23 22 O. 18 69 0.12 39 13 0.08 25 0.21 83 0.22 112 0.28 51 0.16 49 0.22 25 O. 12 8 0.26 34 0.2"'1 15 0.14 16 0.11 73 O. 07 28 J4 0.14 23 O. 18 "'II 0.15 45 O. 17 51 O. 25 "'17 0.27 20 O. 16 24 0.25 25 28 0.09 13 0.12 41 0.16 15 15 0.07 25 0.15 "'10 0.11 67 0.39 119 0.33 41 19 O. 22 16 0.1"'1 10 0.15 7 O. 18 25 0.16 13 0.12 37 16 O. 11 25 0.06 35 0.22 72 O. 18 62 O. 13 16 19 0.21 39 B 0.15 15 O. 10 24 0.08 35 17 0.16 67 O. 12 15 0.22 53 0.07 28 0.07 13 O. 16 29 0.05 38 O. 19 27 O. 12 0.17 50 0.15 35 34 0.17 25 O. 14 60 18 O. 14 30 O. 18 66 O. 12 70 O. 15 82 O. 12 13 72 O. 21 2 0.09 38 O. 1'1 11 0.22 55 O. 12 37 19 0.08 65 0.20 74 O. 12 J.48 0.23 59 0.06 25 46 0.20 2 0.22 33 0.22 15 0.00 78 56 0,00 28 20 0.10 23 0.15 39 66 O. 13 49 0.29 72 O. 14 53 1 0.24 45 :i5 109 O. 14 23 0.04 42 0.13 128 21 0.04 59 O. 17 52 0.20 63 0.19 30 0.15 59 0.13 17 0.22 23 0.26 37 0.23 64 0.06 15 O. 16 42 O. 15 25 22 O. J I 60 0.20 34 0.15 84 0.22 41 0.15 45 0.12 32 39 0.15 27 O. 17 71 0.11 19 0.10 37 23 0.15 73 O. 10 38 0.25 22 0.26 39 0.24 47 0.19 27 0.02 12 0.30 15 0.19 107 0.20 33 O. 12 35 O. 09 45 24 0.07 21 O. 14 77 O. 15 14 0.2::1 54 O. 16 31 0.20 12 O. 24 35 O. 19 26 0.22 58 50 0."15 122 25 0.02 77 O. 14 6 0.21 74 0.27 62 0.30 39 0.35 38 O. 17 18 O. 17 23 0.23 0.13 53 O. 14 10 0.16 36 53 O. 10 53 0.14 42 26 0.17 158 0.19 ::5::1 0.20 4::1 0.27 60 0.23 49 0.26 24 O. 26 30 0.20 27 0.11 37 O. 16 15 0.19 47 .1.51 27 O. 12 1 O. 18 45 0.22 38 0.26 64 0.28 98 O. 18 30 O. 17 15 0.23 22 0.21 40 O. 18 16 O. lB 79 28 0.13 21 O. 19 53 0.19 41 0.22 29 0.01 91 0.27 12 0.16 22 0.25 15 0.20 0.09 17 0.17 75 46 0.00 J6 0.09 35 --' 29 0.14 1 0.14 74 O. 19 35 O. 15 70 0.27 29 O. 22 40 0.24 19 0.11 24 O. 14 110 U1 30 O. 10 28 0.09 60 0.17 25 0.27 50 O. 18 18 0.28 38 0.21 18 O. J4 O. 10 17 0.25 105 0.08 37 11 0.21 27 O. 13 86 O'l 31 0.15 34 O. 16 60 0.23 43 0.23 27 O. 10 29 O. 18 16 O. 13 17 O. 13 11 TOTAL 3.51 4.30 4.59 6.71 6.88 5.70 5. 58 6.04 4.54 -4.40 3.88 TOTAL 1491 1304 1840 1519 1545 832 745 767 932 779 3.46 J.472 1279

PAGE 168

03-3171 FORT LAUDERDALE EXPERIMENT STATION 1960 DAILY EVAPORATION tN INCHES N.D UINO IN MILES JANUARY FEDRUARY MARCH APRIL MAY O. 07 20 .1lJNE JU.-y AUGUST SEPTEMllEfI OCT OIlER DECEMDER 2 0.08 22 O. 24 47 O. 12 13 O. 26 3 0.09 18 O. 05 25 0.22 41 4:5 O. 1:5 27 0.10 3t 0.25 O. 17 33 0.20 :::!.5 0.10 20 0.06 36 O. 12 40 4 0.12 0.06 15 0.20 71 0.20 60 0.34 32 0.21 27 0.24 ;>-0 0.;1.7 27 0.19 2'1 '0.17 I:! 19 O. 14 84 5 0.09 22 0.10 14 O. 22 0.23 60 O. 13 2S 0.19 19 O. 19 17 0.28 27 0.17 12 0.20 15 O. 12 31 0.15 71 6 7 O. 10 76 56 0.30 64 0.46 2S O. 13 2? 0.23 26 0.24 24 0.0-'> 13 0.23 51 0.16 30 O. 14 80 0.09 31 O. 22 63 7 O. 14 25 O. 11 38 O. 18 35 0.24 90 0.11 29 29 0.15 1-'1 O. HI 18 0.24 12 0.00 4'1 0.17 'dl 0.21 54 8 O. 23 48 0.29 41 0.25 46 0.45 2:J 0.20 13 0.21 26 0.05 IS 0.33 28 O. 14 60 0.16 48 9 O. 14 27 0.09 42 O. 15 55 0.26 '16 0.25 30 0.22 28 0.17 12 0.22 25 0.11 10 0.08 13 0.16 4 0.08 22 O. 13 58 O. 10 21 10 0.15 O. 15 29 O. 20 54 0.22 22 0.21 32 O. 19 20 0.30 O. 16 25 0.25 '10 0.10 12 0.17 53 0.08 19 11 25 O. 14 O. 19 37 0.21 31 0.09 11 0.17 30 0.23 21 O. 18 10'1 0.15 25 0.18 53 0.05 2 0.10 20 O. 14 33 12 34 O. 15 89 0.22 42 0.20 24 0.31 49 0.44 0.24 21 71 O. 13 10 0.12 24 0.10 50 13 O. 14 11 0.19 48 O. 16 17 0.17 40 0.23 28 0.20 138 0.20 12 0.28 19 270 O. 20 10 0.07 16 O. 12 50 14 O. 11 25 0.17 39 O. 16 50 0.30 118 0.25 38 0.43 62 0.12 19 0.22 0.28 87 0.11 10 0.07 16 0.15 22 0.12 8 O. 13 '15 15 O. 11 9 0.14 120 O. 14 39 0.32 89 0.32 52 0.29 69 :1:3 0.29 20 O. 16 31 0.07 5 0.19 36 O. 16 27 16 O. 23 50 0.28 76 0.25 20 0.26 20 0.09 12 0.31 19 0.21 40 O. 17 7 O. 14 28 0.10 45 17 0.11 20 O. 13 23 O. 18 36 0.28 69 0.23 25 0.23 9 17 0.02 19 O. 10 10 13 0.17 66 O. 11 18 18 O. 12 22 O. 16 19 O. 19 75 0.22 26 0.28 29 O. 12 16 0.22 13 0.23 44 O. 17 45 0.25 15 O. 15 44 0.13 76 19 0.08 22 O. 13 41 0.25 52 0.23 30 0.22 26 O. 1'1 31 0.22 22 O. 16 40 0.24 25 O. 1'1 18 0.15 21 0.09 31 0.19 0.24 106 20 O. 16 52 O. 12 55 O. 09 54 0.25 31 0.29 20 0.23 43 22 0.23 34 0.32 30 0.21 22 O. 10 22 O.OB 26 21 39 0.17 40 O. 17 14 0.26 24 0.14 13 0.17 23 O. 18 33 0.27 20 0.09 17 O. 09 34 O. 11 27 O. 12 26 0.20 33 22 O. 13 O. 14 40 O. 24 30 0.14 52 0.26 19 0.20 18 0.213 B 0.23 10 0.05 10 0.16 23 O. 23 0.12 13 23 55 0.11 4"" 0.22 53 0.29 24 0.06 17 0.27 8 0.07 8 O. 16 20 0.13 27 0.12 22 0.08 6 O. 15 33 O. 21 46 24 0.11 21 O. 23 56 O. 19 24 0.2:} 44 0.27 20 O.lB 16 0.30 21 O. 13 10 ::;0 O. 16 18 0.15 36 0.14 37 25 O. 13 30 O. 14 34 O. 20 34 0.17 49 0.27 29 0.21 15 0.27 24 O. IB 17 40 O. 15 30 0.12 38 0.14 72 26 O. 14 43 O. 10 61 O. 20 21 0.22 77 0.26 22 0.30 23 0.27 24 0.22 16 25 0.15 29 0.14 22 0.16 50 27 O. 18 62 O. 18 66 O. 21 25 0.11 10 0.22 25 O. 16 21 0.27 21 0.13 20 55 0.06 20 0.10 22 O. 05 60 28 0.15 28 O. 10 28 O. 20 25 O. 15 IB 0.24 22 O. 19 32 0.19 14 0.21 25 O. 19 40 O. 19 50 0.12 23 0.11 32 29 0.16 19 O. 17 3B O. 24 34 0.05 22 O. 16 27 0.21 11 O.OB B 0.17 24 0.17 10 0.16 21 0.08 19 0.12 18 -' 30 0.15 21 O. 04 53 0.20 45 O. 16 31 O.OB 12 0.27 23 0.04 8 0.25 28 0.09 10 O. 10 28 0.08 24 0.03 29 (.Jl 31 O. 17 31 O. 27 67 0.23 27 O. 1'1 21 0.23 23 O. 19 49 0.25 28 0.14 40 0.15 17 0.0'1 17 O. 15 47 --..J O. 15 69 0.34 40 0.05 22 0.22 22 O. 19 31 0.26 29 0.11 20 0.16 15 0.11 7 O. 11 20 :TAL 3. 93 0.03 23 0.09 32 O. 16 27 0.11 12 0.08 11 'TAL 866 4.07 5.80 1296 1399 6.66 6.91 6.21 6.01 6.38 4.11 4. 41 3. 67 3. 57 1423 829 889 621 739 2206 637 873 1187

PAGE 169

FORT LAUDERDALE EXPERIMENT STATION BROI-lARD 08-3171 DAILY EVAPORATION IN INCHES AND HOVEHENT IN HILES 1961 JANUARY FEBRUARY MARCH APRIL HAY JUNE JULY AUGUST SEPTEtlUER OCTOOER NovnlUER DECEMOE 0.10 40 O. 10 15 0.23 37 0.37 88 0.23 39 0.14 6 0.16 14 0.:;:>8 36 O. O. 16 2 0.0' 10 O. 13 23 O. 07 32 O. 13 12 O. 21 70 O. 1'1 50 O. :;:>1 1'1 0.2'1 29 0.31 32 0.12 50 0.18 30 O. 14 l 3 O. 10 33 O. 09 29 0.2'1 43 O. 28 11 O. 05 12 0.25 26 0.19 21 O. 31 8 O. 19 '10 0.18 6 O. J6 26 0.17 4 O. 1'1 39 0.16 48 O. 16 45 0.24 '16 O. 35 17 30 O. 10 7 0.25 11 0.21 3J O. 25 23 0.19 12 O. 15 :> O. 11 18 0.09 '0 O. 16 30 O. 30 49 O. 21 18 O. 23 18 O. 21 35 0.18 36 0.33 27 0.19 19 O. 20 33 O. 19 6 0.08 12 O. 14 62 O. 18 30 0.15 21 0.26 39 O. 2'1 27 O. 23 12 0.27 21 0.:20 IB O. 13 48 0.14 7 O. 09 20 0.13 68 0.28 54 0.23 48 O. 29 19 0.23 24 O. 10 38 0.30 40 3'1 0.16 50 0.10 12 O. 05 0.27 36 0.21 8 O. 1'1 2 0.16 62 0.23 32 0.11 60 0.29 92 O. 14 19 O. 33 23 O. 26 36 0.20 50 0.05 29 O. 00 ::;0 O. 10 48 9 0.07 14 O. 20 32 O. 20 52 O. 16 50 O. 21 36 0.09 19 O. 1'1 11 O. 32 29 O. 19 32 O. 25 0.13 10 0.08 10 0.06 89 0.11 28 0.30 61 O. 20 59 0.19 26 0.07 43 0.11 18 0.04 22 O. 19 50 0.17 18 O. 15 38 O. 29 11 0.14 106 O. 14 18 0.20 36 O. 26 40 0.29 37 0.03 20 O. 27 7 0.26 33 0.25 69 0.16 15 O. 15 '10 O. 22 61 0.14 12 0.07 84 O. 08 20 O. 16 35 0.17 38 O. 14 45 0.18 10 O. 29 41 0.31 33 0.21 18 0.13 13 0.05 52 0.12 20 0.20 42 O. 37 98 0.23 30 O. 19 21 O. 29 36 0.23 IB 17 O. 24 1'1 0.16 23 0.20 0.23 31 O. 06 14 0.06 19 0.16 21 0.13 60 O. 22 24 O. 2'1 25 O. 23 32 0.26 '13 0.2'1 32 O. 31 14 0.10. 19 0.12 31 O. 21 24 O. 15 i 15 0.16 40 0.12 20 O. 22 25 0.15 30 O. 26 34 0.14 27 O. 33 41 0.37 21 O. 18 29 0.18 37 0.07 16 0.18 42 0.14 23 0.21 21 O. 24 53 0.19 34 O. 34 30 O. 28 30 0.27 34 O. 10 22 O. 16 48 O. 13 1 10 0.24 lOB 17 0.16 15 O. 14 36 0.20 15 O. 38 68 O. 23 28 0.22 21 O. 24 27 0.25 15 0.21 O. 09 22 0.18 1 32 0.33 134 O. 17 18 0.05 20 O. 24 65 0.19 47 0.22 3'1 O. 28 13 0.22 21 0.17 43 0.10 15 O. 18 30 21 O. 15 19 O. 11 15 0.12 45 O. 18 45 O. 20 33 0.29 28 O. 22 20 0.15 25 19 0.21 28 O. 19 134 O. 07 9 O. 09 t 20 0.10 56 O. 15 76 O. 22 40 O. 24 23 O. 22 35 0.31 15 O. 25 29 36 0.15 50 O. 15 68 0.14 5 0.13 -. 0.21 22 L 21 O. 17 51 0.16 56 0.21 35 O. 13 3'1 O. 25 23 0.14 35 O. 25 30 21 O. 16 0.12 6 O. 10 J 22 O. 26 42 O. 08 22 0.04 6 0.17 53 O. 17 38 0.42 140 O. 27 38 O. 37 55 O. 26 46 17 0.28 20 O. 09 10 O. 14 30 O. 13 23 0.14 21 0.17 65 0.18 5 0.18 27 O. 19 28 0.21 15 O. 29 26 3 O. 18 64 O. 06 1 24 0.09 8 0.16 50 0.21 14 O. 23 2' O. 34 32 0.19 22 O. 34 39 0.11 6 19 0.17 11 0.15 53 O. 06 -, 0.25 41 O. 11 9 O. 15 25 0.12 21 O. 22 45 O. 2'1 32 O. 26 17 O. 25 34 O. 26 26 O. 31 59 0.29 25 0.22 40 0.16 61 44 O. 06 ;C 26 O. 10 2' 0.21 60 0.19 30 0.28 22 0.18 11 0.29 42 O. 37 24 0.12 19 0.25 36 O. l.7 O. 14 48 O. 15 ..; 27 0.12 37 0.19 29 0.18 6 O. 25 47 59 0.23 38 O. 20 31 0.26 20 0.27 10 0.18 52 O. 14 ..; 30 O. 20 49 28 0.06 28 0.12 29 0.19 26 O. 21 33 O. 23 24 O. 27 25 O. 20 21 0.20 22 0.21 32 O. 26 O. 11 19 O. DB 29 0.11 20 O. 22 '12 O. 25 29 0.14 15 0.22 8 0.28 24 0.25 33 0.19 50 0.11 28 O. OB -, 40 O. 12 204 0.13 c 30 0.04 24 0.20 30 O. 26 21 0.08 9 O. 1'1 4 O. 13 45 0.22 23 O. 10 1 O. 18 c 14 0.18 76 0.12 31 0.12 41 0.23 32 O. 31 123 O. 30 9 0.23 '12 0.17 80 30 O. 10 J 0.14 :: TOTAL 3.13 4.12 6.18 7.09 6.90 6.15 7.24 6.16 6.48 5. 88 Ul TOTAL 1008 11'18 1072 1280 1073 749 869 754 934 1606 4.07 3.66 CO 000 1080

PAGE 170

FORT LAUDERDALE EXPERIMENT STATION urWWARD 08-3171 DAILY EVAPORATION IN INCHES AND WIND MOVEMENT IN MILES J962 JANUARY FEBRUARY MARCH APRIL HAY JUNE JULY AUGUST SEPTEI'lDER OCTOBER NOVEMDER DECEMBER J o 13 48 O. 10 19 0.37 6 0.19 35 O. 25 28 0.31 36 O. 13 30 o. 25 20 0.28 44 o. 24 20 2 O. 05 0 0.13 17 0.15 22 0.16 44 o. 22 17 0.18 30 0.24 1 O. 33 19 0.20 O. 11 21 O. 10 8 17 0.04 13 3 o. 08 20 O. 12 9 0.24 52 0.12 46 0.23 26 O. 18 28 o. 23 23 0.17 24 O. 1'1 33 0.24 O. 1 '1 17 o. 06 22 -4 O. 09 14 0.12 24 o. 24 36 0.26 84 O. 21 16 o. 10 20 O. 20 22 0.17 30 0.33 16 o. 11 9 0.07 14 2:; O. 15 22 ::; O. 05 10 0.14 11 0.07 10 0.26 110 0.11 8 0.29 37 o. 21 15 O. 27 28 0.25 10 O. 14 6 O. 1 J 18 O. 07 10 6 0.09 82 0.13 38 0.11 0 0.18 68 O. 20 57 o. 19 25 O. 18 27 0.22 35 O. 19 21 0.18 0.14 22 O. 08 5 17 O. 16 7 0.17 68 o. 10 51 0.21 68 0.20 87 O. 20 48 0.20 31 0.15 24 0.31 14 0.24 36 O. 20 16 0.19 76 8 0.05 22 0.13 1 1 0.14 34 0.10 4 o. 30 58 0.37 74 0.29 19 0.22 21 36 0.13 18 O. 05 30 0.23 31 O. 25 ;>0 O. III 9 0.18 34 0.11 18 O. 14 18 0.21 44' 0.25 33 0.18 7 0.22 26 0.27 33 0.20 1:1 0.19 5 39 0.12 13 JO O. 07 23 0.24 72 0.12 31 0.44 43 0.28 16 O. J8 11 o. 10 20 0.05 45 o. 29 30 0.29 0.16 75 O. 02 9 11 O. 06 27 O. 17 78 O. 19 19 O. 28 53 0.26 28 0.18 7 O. 14 16 0.19 59 0.23 14 0.10 23 O. 04 46 40 O. 15 47 o. 13 28 12 O. 09 31 o. 18 72 0.10 60 0.21 58 0.19 33 O. 20 33 O. 26 14 0.20 12 O. 15 21 0.26 78 O. 14 o. 12 24 13 O. 09 50 O. 20 20 0.23 61 0.23 49 0.26 46 O. 13 18 o. 00 25 0.08 3 0.19 30 0.17 40 0.13 .7 0.19 10 14 O. CO 1 o. 09 8 0.23 53 O. 10 44 o. 27 46 O. 09 13 o. 18 n 0.35 17 31 O. 20 106 0.05 38 O. 23 50 0.14 49 15 o. 06 50 0.11 40 0.13 6 0.26 30 O. 31 97 0.12 27 0.19 15 0.11 18 0.26 5 0.25 50 0.10 49 16 0.13 6 0.10 3 o. 27 70 0.16 26 0.29 68 0.03 25 o. 10 16 0.13 11 0.41 27 O. 22 O. 12 37 0.13 24 50 O. 14 30 17 O. 12 24 0.13 19 0.12 62 0.30 2 0.18 86 O. 10 25 o. 20 8 0.22 17 0.02 20 O. 26 70 0.13 10 O. 00 13 18 0.11 38 o. 13 20 O. 27 94 0.20 50 0.25 66 O. 09 28 O. 21 20 0.26 22 0.24 21 O. 21 0.10 24 19 0.05 32 o. 15 41 0.20 54 0.19 12 0.35 63 O. 18 17 o. 20 20 0.15 3 0.42 53 0.09 35 o. 11 6 21 O. 16 37 0.00 6 20 O. 15 10 0.18 28 o. 19 29 0.20 30 O. 20 36 O. 22 23 O. 13 18 O. 32 48 0.44 25 0.20 20 O. 06 15 21 o. 11 41 0.13 28 o. 10 34 0.19 5 O. 27 23 0.15 36 O. 19 11 0.26 53 0.40 :; O. 21 0.09 9 O. 08 11 22 0.16 114 0.17 40 O. 27 86 O. 30 108 0.22 40 25 O. 20 31 0.20 4'1 20 O. 14 26 0.12 39 O. 11 38 0.19 10 O. 17 2 23 0.13 35 o. 27 18 O. 27 71 0.24 61 O. 22 26 O. 17 16 o. 28 22 0.24 15 0.25 37 O. 23 50 0.1315 0.10 1 I 24 O. 13 33 O. 11 57 O. 14 0 O. 21 39 0.25 21 0.25 28 O. 20 24 0.28 46 O. 12 33 0.12 20 o. 11 29 25 0.15 17 0.17 49 0.2'1 70 0.28 30 0.30 30 0.28 15 o. 23 30 O. 12 29 0.19 51 0.23 50 O. 20 136 0.13 19 26 0.15 16 0.17 56 0.13 74 0.20 61 0.27 40 0.18 20 O. 32 29 0.22 20 0.23 6 O. 21 0.19 14 o. 12 6 50 O. 22 36 27 O. 15 20 0.18 38 0.32 66 0.24 21 O. 32 26 O. 30 32 0.29 34 O. 10 42 0.25 7 O. 19 0.12 38 28 0.12 54 0.21 61 0.06 30 0.21 18 0.18 33 0.25 23 o. 20 20 O. 18 15 O. 24 60 O. 23 104 0.14 37 38 0.27 120 29 O. 19 48 O. 18 50 O. 25 36 0.21 42 0.14 10 o. 28 33 O. 51 27 0.21 26 0.26 117 0.11 13 0.12 0 30 O. 15 43 0.10 5 0.24 34 0.36 26 O. 18 12 o. 25 32 o. 10 23 0.27 18 O. 32 0.04 14 o. 07 13 --J 52 o. 04 24 31 0.16 29 0.24 90 0.27 67 o. 10 10 O. 50 18 O. 06 22 O. 06 37 tn O. 00 55 1.0 TOTAL 3.42 4. 17 5. 77 6. 61 7. 68 5. 42 6. 10 6.98 7.03 6. 32 3.96 TOTAL 1040 9'16 1361 1332 1250 732 646 811 769 1235 2. 98 884 799

PAGE 171

FORT LAUDERDALE EXPERIHENT STATION 00-3171 DI\ILY EVAPOIlATION IN INCHES AND I.JIt
PAGE 172

FORT LAUDERDALE EXPERIMENT STATION IlRmJARD 08-3171 DAILY EVAPORATION IN INCIlf=S AND HOVEHENT IN HILES 1964 JANUARY FEDRUARY MflRCH APRIL HIIY JUNE JULY AUGUST SEPTEI1IlER OCTOilER NOVEMDER DECEMilER 1 O. 08 33 O. 1 27 0.20 94 O. 21 38 0.20 43 0.24 25 O. 31 36 O. 29 43 o ;?4 3!i O. 20 40 O. 09 33 0.14 54 2 0.12 12 0.15 43 0.12 31 O. 25 55 38 0.14 17 O. 24 31 O. 28 4;> 0.22 26 0.16 43 O. 11 16 0.10 46 3 0.10 12 O. 14 34 0.17 66 0.24 80 59 O. 10 14 O. 31 39 0.18 35 0.22 26 0.14 45 O. 08 17 0.12 65 4 0.08 15 0.12 67 O. IB 44 0.15 56 O. 32 lOB 0.07 9 O. 27 32 O. 22 33 0.26 35 0.21 42 0.11 35 0.11 67 5 0.12 16 O. 13 39 0.16 71 0.24 54 0.22 73 O. 08 24 O. 22 28 0.21 35 O. 17 36 O. 23 53 0.19 93 0.07 30 6 0.09 39 0.19 109 0.29 57 O. 26 87 O. 25 87 0.11 29 0.21 36 0.19 28 0.18 24 0.11 30 O. 19 49 0.08 41 7 0.07 22 O. 13 38 0.20 72 0.26 86 O. 30 65 0.24 52 0.14 21 0.24 28 O. 13 80 0.19 43 O. 16 27 O. 11 57 B O. 08 22 0.17 110 0.21 58 O. 28 62 0.28 42 0.15 47 O. 23 27 0.26 41 0.32 50 0.13 17 O. 17 32 O. 06 58 9 0.11 41 O.IB 50 0.22 69 O. 25 62 0.25 24 0.12 17 0.33 39 0.17 35 0.28 78 0.13 11 O. 11 18 O. 06 31 10 0.16 20 O. 16 39 O. 14 88 0.21 37 O. 28 33 0.25 26 O. 18 33 0.34 51 0.30 0.16 34 0.16 27 0.12 95 II O. 23 25 O. 10 41 0.29 76 O. 16 39 0.27 13 O. 20 20 0.27 38 0.17 50 0.28 107 0.19 90 0.12 26 0.17118 12 O. 06 30 O. 20 66 0.15 28 0.20 107 0.27 29 0.23 25 O. 28 36 O. 23 47 0.23 33 o. O. 14 24 0.08 18 13 0.09 17 0.14 47 0.23 49 0.25 73 O. 22 34 0.27 42 O. 29 44 0.21 30 0.17 81 0.04 102 0.13 24 O. 20 71 14 0.15 15 0.15 35 O. 18 64 O. 22 74 0.14 34 0.25 26 O. 29 55 0.22 30 0.19 83 0.13 44 0.13 29 O. 12 35 15 O. 09 14 0.11 34 0.19 64 O. 36 46 24 O. 27 29 O. 36 107 0.15 18 0.18 37 143 O. 04 90 O. 09 52 16 0.08 3:5 0.17 64 0.23 62 0.17 47 0.04 13 O. 25 28 0.33 105 0.20 20 O. 19 25 O. 27 62 O. 30 85 O. 09 103 J7 0.11 43 O. 21 59 O. 17 42 0.32 153 0.18 40 O. 28 32 0.17 68 O. 26 30 0.25 40 0.21 86 0.15 52 O. 17. 77 JB O. 05 20 0.10 63 O. 15 36 0.32 117 O. 32 57 0.31 32 O. 20 6J O. 29 34 0.26 45 0.17 26 0.13 18 0.11 42 19 0.04 25 O. 20 91 0.25 52 O. 27 80 0.33 68 0.31 37 O. 29 71 0.30 32 0.24 64 O. 17 12 O. J2 12 O. 13 52 20 0.07 17 0.16 4:5 0.21 78 0.31 72 0.23 70 0.31 25 0.26 4:5 0.29 26 0.19 63 O. 13 14 0.12 33 0.16 94 21 O. 15 41 O. 16 36 0.00 75 0.25 8J 15 0.33 67 O. 29 39 O. 20 27 0.18 38 0.24 86 O. 12 35 0.13 24 22 0.12 12 O. 10 50 0.06 44 0.25 61 O. 37 78 0.32 48 O. 26 27 0.14 23 O. 23 64 O.IB 60 O. 13 42 0.08 23 O. 11 19 O. 06 65 0.33 56 0.29 69 O. 25 95 0.25 31 0.24 31 0.21 31 0.22 47 O. 18 59 123 0.12 38 24 O. 12 17 O. 17 40 O. IB 60 52 O. 26 63 0.19 34 O. 17 18 0.21 35 0.1::; 29 0.19 76 0.13 60 O. 10 23 25 O. 15 15 O. 10 24 0.21 75 0.24 53 O. 28 52 0.07 24 O. 22 33 O. 33 79 0.20 30 0.22 88 0.13 21 O. 11 9 26 O. 11 14 0.17 88 0.18 58 0.17 35 O. 24 35 0.17 28 O. 22 35 O. 31 B6 0.31 39 0.17 44 O. 01 20 O. 07 29 27 0.10 8 O. 15 21 0.24 65 0.26 51 0.27 41 0.19 32 0.26 36 0-0.09 61 0.21 133 0.12 24 O. 11 36 --' 28 0.10 13 0.17 93 0.13 39 O. 26 7:1 O. J7 28 0.29 35 O. 30 36 321 0.22 112 0.17 J48 O. 08 49 0.17 41 O'l 29 0.19 :5 0.26 131 O. 20 40 O. 40 89 0.12 29 0.31 33 O. 26 40 0.23 83 0.22 84 63 O. 12 24 O. J 1 J2 30 0.21 "2 O. 17 54 68 0.43 15 0.30 32 O. 22 37 O. JB 17 0.23 54 O. 21 48 O. 11 12 O. 20 43 31 O. 13 1 0.30 58 0.26 28 O. 30 37 O. 24 48 0.10 47 O. 15 44 TOTAL 3.47 4.40 5.94 7. 33 6. 75 6. 60 7.92 6. 75 6. 55 4. 84 3. 70 3. 64 TOTAL 620 1649 J825 2059 1433 920 1321 1438 1683 1789 1150. 1537

PAGE 173

FORT LAUDERDALE STATION IHlmJARD OB-3171 DAILY EVAPORATION IN INCItE-S AND \.lIND HOVEHl::NT IN MlLES 196:5 JANUARY FFDRUARY MARCH APRIL JUNE JULY AUGUST SEPTEMBEH OCTOBER NOVEMBER DECEMBER 1 o. O. 18 42 O. 17 39 0.21 47 O. 24 94 0.25 84 O. 29 54 0.20 28 0.2'1 33 O. 20 38 2 158 O. (18 30 O. 17 76 o. 42 O. 25 IS 0.46 98 O. 19 42 0.26 24 O. 15 56 O. 19 109 29 O. 20 33 0.18 3 0.22 17 O. 19 91 O. 14 70 0.26 59 O. 24 85 0.31 62 O. 25 38 O. 22 42 O. 18 30 O. 20 56 O. 14 7"2 4 O. 11 41 O. 14 63 O. 14 60 0.15 38 0.28 23 0.29 41 O. 29 30 0.24 32 0.27 25 O. 31 168 O. 14 71 116 O. 15 23 0.28 143 5 0.21 101 0.08 55 O. IS 65 O. 23 33 O. 30 75 0.28 48 O. 28 38 0.25 36 O. 10 26 0.16 31 0.10 3E 6 0.11 52 O. 08 86 0.18 61 0.25 29 0.25 70 0.34 116 O. 30 35 0.28 43 0.23 60 0.24 37 0.23 107 O. 09 3< 7 O. 14 15 O. 08 116 0.20 65 0.25 33 0.28 53 0.13 16 O. 27 37 0.29 41 0.22 131 0.21 O. 17 95 O. 09 2 8 O. 04 29 0.18 0.17 60 0.23 29 0.24 7 0.15 22 O. 30 42 0.25 38 O. 19 36 0.12 41 O. 13 21 27 O. 10 26 0.14 9 0.12 31 O. IS 28 0.20 44 O. 29 32 O. 30 96 0.00 28 O. 29 40 0.22 31 0.20 24 O. 16 0.16 28 0.12 21 10 0.11 41 0.18 43 0.15 26 0.27 73 0.25 65 0.31 44 O. 28 39 0.19 29 O. 10 75 0.20 21 0.11 11 0.16 20 0.16 46 0.18 22 0.19 13 O. 31 56 0.16 72 O. 18 55 0.16 27 0.28 ::;4 0.21 0.13 39 0.10 4(: 24 O. 12 12 0.12 21 0.17 38 O. 17 28 0.25 3:5 0.29 47 0.18 53 O. 12 19 0.06 23 0.24 19 0.20 28 0.11 4:; 30 O. 11 14 13 0.08 19 O. 15 41 0.18 62 O. 26 44 O. 24 41 0.26 41 0.16 28 0.25 40 0.25 98 0.19 31 O. 08 2:, 14 0.11 13 0.14 48 0.22 33 0.29 84 O. 22 37 O. 08 24 0.26 29 0.31 43 0.26 54 0.11 0.13 20 O. 08 23 44 0.04 15 0.11 20 0.16 50 0.22 39 O. 26 50 O. 30 54 0.21 52 0.23 32 0.2b 14 0.27 53 21 O. 09 28 36 O. 09 26 16 0.11 35 0.10 75 0.12 42 0.28 38 O. 30 89 0.29 67 O. 23 31 0.23 10 O.Ob 15 0.10 0.08 11 27 0.12 49 17 0.14 96 0.17 43 0.19 39 0.14 62 O. 28 58 0.22 39 0.22 27 o. Ib 9 O. 13 :;3 0.17 36 O. 09 26 18 O. 11 32 O. 20 40 0.21 85 O. 28 '50 O. 29 38 0.21 32 O. 21 32 0.13 14 0.15 96 0.18 O. 10 21 0.10 30 34 0.13 24 19 0.07 18 O. 15 25 0.19 53 O. 28 39 O. 29 3b 0.22 30 O. 08 25 0.18 10 0.18 117 0.12 O. 11' 28 27 0.11 .18 0.09 16 20 O. 10 34 O. 13 2:5 O. 11 32 0.23 30 0.24 33 0.23 42 0.22 13 O. 18 17 0.37 93 0.13 10 0.10 16 0.19 100 21 0.09 16 0.13 22 0.35 110 0.16 3b 0.27 50 0.21 63 O. 19 16 0.22 19 0.27 76 0.14 20 0.10 26 O. 12 22 0.10 33 O. 02 18 O. J2 104 O. 20 45 O. 30 87 0.25 49 O. 06 17 0.18 31 0.25 8;:> 0.13 33 33 0.09 17 o. 09 26 23 O. 09 68 O. 24 44 O. J4 96 0.23 72 0.27 74 0.21 41 0.14 17 0.18 46 0.35 60 0.12 40 O. 15 72 O. 11 30 24 O. 09 53 O. 23 44 0.19 42 0.26 53 o. 23 57 O. 27 40 O. 21 28 0.27 36 0.18 43 0.13 28 0.12 33 O. 12 6b 25 0.16 60 0.21 108 0.22 40 O. 21 47 O. 25 62 O. 24 32 0.17 33 0.2b 31 0.12 19 0.16 43 0.13 40 0.11 26 O. 1:1 21 O. 26 8b 0.23 64 0.27 74 0.25 55 0.24 42 O. 20 19 0.25 28 0.17 37 O. 15 39 45 O. 05 22 0.10 33 27 0.13 26 0.17 49 0.20 46 0.24 52 O. 30 45 0.2b 32 O. 23 32 0.27 29 0.00 18 0.18 72 0.12 25 0.14 37 28 0.19 67 O. 14 42 0.24 43 O. 20 40 O. 27 :l6 0.18 106 o. 32 45 0.33 44 0.09 30 0.18 35 0.11 17 0.12 47 29 O. 19 70 0.21 38 O. 24 41 O. 28 60 0.33 95 O. 21 49 0.15 11 0.13 33 0.11 25 O. 08 13 0.16 97 30 0.09 44 0.25 34 0.30 77 O. 31 56 0.29 70 O. 23 50 0.24 22 0.14 27 O. 28 128 O. 13 47 0.07 101 31 O. 15 76 0.20 32 0.20 45 0.14 26 0.24 43 81 --' 0.17 91 0"1 TOTAL 3. 60 4.27 5.81 7.16 8. 32 7.0b 6. 7:1 6.91 5.87 4. 81 3.96 3. 58 N TOTAL 1327 1425 1650 1397 1719 1581 1018 891 1527 1147 1299 1483

PAGE 174

FORT LAUDEnDALE EXPERIMENT STATION BROWARD 08-3171 DAILY EVAPORATION IN INCHES AND WIND MOVEMENT IN MILES 1966 JANUARY FEDRUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMDER OCTOBER t'OVE MBER DECEMBER 1 0.10 63 0.07 45 0.15 79 0.24 41 0.18 8:3 0.18 35 0.02 76 31 26 47 0 0 13 2 0.12 30 0.06 39 0.2:3 64 0.26 7:3 0.28 72 0.10 26 0.24 63 0.11 20 026 6 O' 1:3 0.12 16 3 0.13 31 0.18 50 0.10.,1 0.21 61 0.24 .,6 0.19 ll., 0.17 13 0.17 32 0: 29 32 O'l? g. n 616 0.09 15 4 0.08 57 0.17 50 0.16 .,4 0.24 79 0.28 54 0.26 133 0.21 28 0.23 16 0.01 9 0'13 17 .... 0'15 ",2 0.11 26 5 0.07 64 0.16 120 0.16 52 0.25 106 0.29 85 .,0 0.22 24 0.26 30 0.14 31 O' 29 187 O' 17 0.11 90 6 O.ll 300.12 180.1944 0.04 39 0.23 94 O.OB 41 0.21 30 0.1734 0.21 12 0'1" .,5' 0.1865 7 0.13 53 0.10 17 O.IB 46 0.17 4., 0.20 68 O.IB 56 0.04 13 0.29 39 0.05 .., o 27 41 g'?6 0 0 .19 B9 8 0.06 28 0.10 19 0.17 50 0.19 31 0.12 49 0.32 90 0.1" 26 0.24 22 0.04 1., O' 05 17 0'''3 6., OB 2B 9 0.11 100 0.19 88 0.18 66 0.24 35 0.27 88 181 0.21 30 0.11 30 0.1" 39 O' 15 33 0.13.,6 10 O. J7 44 0.20 123 0.19 68 0.40 22 0.21 85 0.25 82 0.19 40 0.22 34 0.14 39 0: 15 J6 g?r 00' 1169 77 11 0.01 31 0.23 117 0.19 70 0.25 92 0.21 35 0.30 80 0.11 55 0.38 51 0.10 23 0 16 .... 2 O' 63 12 0.00 22 0.22 108 0.17 81 0.17 27 0.29 111 0.24 49 0.25 47 0.11 71 0.10 J7 0: 18 41 1$. g.?4 32 13 0.15 80 0.11 105 0.17 81 0.26 59 0.26 56 0.19 20 0.21 9 0.34 5 0.16 23 0.17 48 O' 11 0'05 26 14 O. 14 45 O. 16 61 0.10 69 0.22 38 0.20 23 0.2" 31 O. 13 4 O. 15 22 0.12 20 0 22 54 O' 09 97 o 0 6 57 15 0.10 26 0.10 19 0.11 30 0.20 28 0.24 41 0.20 40 0.27 42 0.37 31 0.03 27 O' 07 O' .... 8 13 16 0.09 54 0.14 36 0.23 55 0.20 3B 0.26 35 0.2:3 42 0.23 23 0.33 28 0.21 50 0: 14 33 o 10 g'b3 9 79 17 0.14 40 0.17 34 0.18 57 0.25 62 0.24 45 0.27 25 0.2:3 40 0.15 21 0.10 29 0.23 24 0'''9 133 O' 11 9 18 0.11 42 14 28 91 30 100 0.18 32 32 0.29 24 23 28 11 5 0 18 "9 0;.... .:33 19 0.11 27 0.18 90 0.25 76 0.27 97 0.25 29 0.22 11 0.26 57 0.20 31 32 0: 16 17 0'071g1 g'?1 i37 20 0.13 42 0.14 66 0.18 58 0.26 83 0.26 39 0.33 18 0.22 87 0.26 47 0.21 38 0.21 69 O 23 29 O' 14 10 21 0.02 22 0.19 73 0.22 32 0.28 95 0.30 48 22 0.18 20 0.24 26 0.24 23 0.18 93 O I' 85 0'08 19 22 0.05 36 0.10 69 0.23 41 0.30 117 0.22 41 0.18 28 0.27 3 0.25 16 0.17 26 53 O 26 0.12 51 110 0.;7 50 0.15 74 0.;2 33 0.07 20 0.05 80 0.10 14 0.09 0.04 16 0: 12 ggb ;4 0.10 46 0.09 51 O.cO 37 O. 146 0.,0 30 0.11 23 11 0.16 17 O. 16 0.09 24 0.20 159 O' 11 32 ,5 0.06 27 0.14 3:3 0.15 .,0 O. 51 0.28 26 0.20 56 0.2c 22 0.14 19 0.04 20 0.20 10 0 17 7' O' 11 10 26 0.05 61 0.14 38 O. J6 48 0.20 26 0.29 28 0.18 45 0.27 41 0.35 23 0.08 27 0.13 18 O 02 26 0'07 42 27 60 0.16 37 0.14 63 0.28 83 27 0.15 45 17 24 19 33 13 16 O. J2 23 0'23 1 28 0.15 57 0.15 103 0.21 45 0.24 72 0.18 38 0.19 62 0.12 22 0.21 30 0.19 24 0.29 33 0: 12 I g. --' 29 0.08 37 0.19 38 O. 82 0.11 47 0.10 47 0.26 19 0.26 51 0.20 44 0.03 52 0.08 7 o 09 31 30 0.12 87 0.07 42 O. ,2 94 0.29 38 78 0.22 33 0.23 44 0.21 33 0 21 48 0 12 43 O' 13 31 uv 31 0.12 41 0.16 40 37 18 48 28' 0:08 32 TOTAL 3.00 3.91 5.45 7.13 6.89 4.98 6.02 7.07 4.34 4 86 4 27 TOTAL 1434 17.,9 1698 1997 1572 1024 939 765 1532 1750

PAGE 175

FORT LAUDERDALE EXPERIHENT STATION BR[Jl..JARD OU-3171 1967 DAILY EVAPORATION IN INCHES AND \.lIND tlOVEHENT IN HILES .JANUARY FEBRUARY MARCH APRIL HAY .JUNE .JULY AUC;UST SEPTEtlDER OCTOBER NOVEHBER DECEMBER 1 0.14 12 0.11 25 0.16 33 0.26 220 0.25 33 O. 17 46 0.02 22 0.26 28 O. 19 44 O. J1 32 0.15 34 O. 12 2 0.01 40 O. 13 30 0.12 37 0.27 65 0.27 76 0.25 23 0.32 50 0.24 24 0.26 5 32 24 0.15 21 0.14 55 3 0.10 25 0.05 62 0.11 41 O. 16 93 0.25 56 O. 18 22 0.21 7 0.28 32 O. 17 23 0.21 93 0.05 16 4 0.12 62 0.13 10 0.09 32 O. 16 3 0.27 48 0.26 27 0.18 14 O. 17 24 0.21 35 0.19 o. 14 35 35 0.11 23 O. 12 5 0.13 60 0. 10 O. 14 37 0.25 64 0.26 58 O. 19 13 O. 31 23 0.21 21 0.11 30 0.24 69 58 o. 15 26 0.17 55 6 0.12 35 o. 12 23 o. 19 54 0.23 32 0.28 42 0.24 32 0.27 42 0.15 20 O. 16 7 0.13 21 0.18 7 0.14 71 O. 13 67 O. 19 55 0.21 30 0.28 41 o. 17 27 0.26 53 O. 19 25 O. 17 18 O. 10 64 0.14 60 17 o. 17 76 0.14 36 B 0.13 108 0.15 62 0.22 48 0.22 75 0.27 90 0.22 20 0.22 42 0.16 19 0.28 31 0.15 16 o. 15 68 0.11 21 9 O. 10 48 0.09 28 0.07 26 0.22 45 0.27 103 0.26 25 0.28 53 O. 10 16 0.24 39 0.14 14 10 O. 13 31 0.15 68 0.19 32 0.09 23 0.33 77 0.23 20 0.30 99 0.21 22 0.15 34 0.21 13 0.09 112 0.07 33 J1 O. 12 29 O. 13 28 0.23 35 0.21 35 O. 33 55 0.24 43 0.28 6 20 0.20 14 O. 16 0.27 96 0.07' 20 29 0.22 143 0.11 70 12 O. 11 42 0.08 47 0.21 29 0.23 28 0.27 46 0.23 47 0.28 26 O. IB 25 0.26 !i3 0.17 17 0.26 89 O. 13 79 13 0.07 29 0.11 78 0.19 33 0.29 7' 0.29 48 60 O. 27 22 0.23 34 O. 18 26 O. 17 45 0.26 53 O. OS 17 14 O. 12 38 0.18 7' 0.20 25 0.24 105 o. 25 25 29 O. 33 30 0.26 27 O. 13 4 O. 13 34 0.13 41 0.08 25 15 0.06 30 0.22 87 0.19 30 0.28 56 0.26 38 24 o. 29 32 0.23 15 0.22 187 0.17 47 O. 13 31 0.11 32 16 0.15 43 0.20 53 0.24 38 0.26 38 O. 31 49 14 O. 37 31 O. 17 21 0.28 25 0.18 59 0.12 11 0.10. 9 17 0.08 56 0.22 52 O. 20 109 0.21 28 0.25 36 0.27 49 o. 28 27 O. 24 32 O. 17 29 O. 14 22 0.12 56 O. 09 20 IB 0.09 17 0.15 39 0.09 12 0.23 36 O. 17 45 0.08 36 0.26 35 0.20 32 0.35 21 0.17 25 O. 10 31 0.07 9 19 0.08 12 O. 16 20 0.29 94 0.24 58 0.43 90 O. 12 38 0.31 28 0.24 35 O. 18 19 O. 17 36 0.14 28 0.12 24 20 0.09 45 0.13 37 0.15 85 0.27 41 O. 34 68 O. 16 20 O. 31 41 O. 19 25 0.21 29 0.21 58 0.12 33 21 0.17 98 0.26 36 0.22 62 0.31 96 O. 28 71 0.23 28 O. 27 45 0.21 32 0.21 27 0.19 0.13 20 83 0.12 28 0.17 43 -' 22 0.11 75 0.06 6 O. 20 34 0.26 47 0.30 69 O. 13 16 0.23 34 0.26 40 0.22 30 0.13 I'll O. 10 14 0.14 46 O'l 23 0.16 63 O. 14 100 O. 19 31 0.26 21 O. 32 69 0.20 24 0.25 29 0.27 33 0.22 19 O. 12 62 0.09 16 24 0.12 101 O. 18 34 0.22 52 0.25 60 O. 33 104 0.28 25 0.22 23 0.26 37 0.25 37 O. 10 55 0.13 0.12 41 .j::>. 28 0.22 115 25 0.03 38 0.21 57 O. 28 103 0.22 35 0.29 58 0.20 15 O. 20 20 0.25 52 O. 16 22 0.25 29 0.12 33 26 0.11 44 0.21 78 O. 25 56 0.24 44 0.29 57 0.25 31 O. 26 29 0.24 17 O. 12 14 O. 13 23 0.12 0.05 30 30 0.03 19 27 O. 10 35 0.11 62 0.23 31 O. 28 52 0.28 35 0.29 39 0.18 25 0.25 24 0.40 17 O. 12 15 0.12 26 2B 0.20 B2 0.11 36 0.23 80 0.27 53 0.28 34 0.27 10 O. 30 33 0.34 27 0.16 29 0.15 J4 O. 10 0.10 24 3 0.12 54 29 0.13 37 0.07 34 0.35 79 0.25 40 O. 12 36 O. 32 33 0.2B 37 0.11 ::;9 O. 18 24 O. 13 39 30 O. J2 JB 0.02 47 0.27 65 0.27 37 0.33 25 O. 30 31 0.25 20 0.12 29 0.18 85 0.05 44 0.14 43 3J O. 10 22 0.23 125 0.22 37 O. 19 22 0.22 34 0.15 64 0.09 31 0.09 24 TOTAL 3.44 4.03 5.61 7.24 B. 74 5. 57 B.07 6.74 6.09 4.85 4. 15 3.4B TOTAL 1446 1310 1540 1702 1735 864 1007 B50 955 1290 1313 1191

PAGE 176

FORT LAUDERDALE EXPERIMENT SlATION nflOI-iARD 00-317 J DAILY EVAPORATION IN INCHES AND \-lIND MOVEMENT IN HILES 1'16B JANUARY FEBRUARY MAflCH APRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER 1 0.09 20 O. 13 21 0.21 119 0.21 32 O. 22 B6 0.09 16 0.16 24 0.29 39 0.41 0.29 72 O. 1 B 115 2 0.11 16 0.13 27 0.19 43 O. 16 20 0.2B 50 0.14 105 0.29 56 0.28 39 0.32 10 0.06 26 0.20 26 0.15 24 3 0.11 20 o. 10 35 0.15 36 O. 28 69 O. 29 39 0.08 20 0.17 34 0.14 3J 0.25 11 O. 16 21 O. 17 47 0.12 19 4 0.11 27 0.25 29 O. 13 42 0.19 56 0.25 58 0.06 124 0.21 33 0.24 70 0.21 33 0.13 22 O. 11 29 0.19 19 5 0.11 19 0.01 56 0.20 22 O. 25 67 0.32 73 0.21 179 O. 19 25 0.18 14 0.27 33 O. 19 37 0.13 23 0.14 30 6 O. 12 5B 0.17 38 O. 16 29 0.22 56 O. 17 86 0.22 152 0; 15 21 0.30 53 0.23 30 0.21 0.13 46 25 0.13 21 7 0.12 27 0.18 51 0.21 45 O. 19 30 O. 25 86 0.20 76 O. 20 12 0.30 66 0.27 29 0.19 20 O. 12 59 O. 11 22 B 0.19 57 O. 17 71 0.22 72-O. 26 41 O. 24 127 0.19 29 0.20 23 0.32 39 0.27 21 0.15 O. 11 21 21 0.11 8 O. 11 9 0.18 135 0.14 45 O. 16 38 0.23 72 0.18 135 0.07 18 0.09 16 0.31 42 0.22 33 O. 14 30 0.13 23 10 0.14 73 0.11 30 0.14 37 0.25 28 87 0.15 64 O. 22 30 0.24 85 0.23 28 0.28 :.J O. 14 44 21 0.21 64 O. 11. 44 11 0.06 34 0.11 27 Q.18 54 0.30 41 O. 19 79 O. 25 24 O. 26 32 0.20 44 0.20 25 O. 20 42 0.15 31 0.10 37 12 0.11 19 0.05 19 0.22 43 0.27 85 O. 09 35 0.11 7 O. 39 4 0.11 7 0.17 3 0.27 J3 O. 10 44 O.OB 18 0.24 138 0.31 103 O. 24 46 0.33 29 O. 21 22 0.11 7 O. J3 85 0.21 11 O. 15 55 32 0.'27 122 0.16 B6 O. 18 73 14 0.04 22 O. 16 28 0.18 55 0.21 39 0.26 40 0.30 47 O. 32 33 0.31 30 0.19 52 0.25 84 O. 12 27 0.14 31 15 0.13 40 0.15 31 0.27 114 O. 19 28 O. 24 29 0.20 23 O. 20 24 O. 27 33 0.20 24 0.12 0.10 46 0.20 66 16 0.09 20 O. 15 27 O. J9 84 0.23 53 O. 24 3J 0.14 26 O. 24 26 0.25 29 '0.24 30 O. 18 36 O. 12 23 0.15 45 17 0.10 15 0.16 27 0.13 87 0.26 40 O. 25 36 O. 14 38 O. 28 23 0.29 31 0.25 24 99 0.11 24 0.08 21 IB 0.10 B 0.15 33 O. 14 37 0.26 37 O. 28 41 16 O. 27 15 0.2:1 27 0.21 83 0.12 90 O. 12 22 19 O. 13 134 0.21 27 0.22 46 0.17 23 O. 28 39 O. 10 31 O. 20 17 0.27 25 O. 18 21 0.15 70 0.09 24 20 O. 22 92 O. 10 47 0.18 33 0.26 52 O. 25 38 O. J.4 55 O. 23 23 0.27 25 O. 12 4 0.22 0.18 57 0.09 29 69 0.19 44 0.08 21 0.15 40 0.11 32 O. 23 33 0.29 30 0.20 18 O. 17 35 O. 30 20 0.29 34 O. 12 5 0.21 20 27 0.14 59 0.12 22 0.09 27 0.12 75 0.24 65 O. 26 40 O. 15 31 0.25 30 O. 26 24 0.27 36 0.26 66 0.15 ";'6 15 23 0.10 7 0.13 6 0.24 60 0.23 26 49 0.25 32 O. 29 29 0.25 39 0.22 60 0.14 O. 12 9 0.12 44 17 0.13 24 O. 14 44 0.12 19 O. 19 62 0.26 47 0.09 18 0.23 26 0_ 29 33 0.32 42 0.24 40 0.06 24 79 O. 07 28 0.10 --.. 25 0.17 64 O. 18 79 0.20 59 0.30 49 O. 04 22 0.18 26 O. 33 40 0.24 27 O. 16 2::' 0.04 40 0.23 87 16 O. 13 23 O. 14 42 en 26 0.11 35 0.13 38 o. 13 42 0.20 53 O. 12 32 0.16 21 O. 30 39 0.09 8 0.09 15 0.13 40 0.16 .119 0.10 24 01 27 0.11 47 0.13 29 Q.15 73 0.22 49 26 0.20 14 O. 32 61 O. 19 7 25 0.18 53 2B 0.10 65 0.21 50 O. 17 89 0.22 34 O. 10 28 O. 10 33 O. 32 21 0.15 16 0.11 26 0.14 0.16 64 O. 07 26 .115 0.11 40 0.10 67 29 0.18 93 0.15 39 0.27 69 0.23 24 0.27 72 0.17 26 O. 32 31 0.25 22 O. 14 22 0.11 11 0.16 60 O. 14 69 30 O. 18 72 0.22 30 0.26 18 O. 07 34 0.22 17 O. 29 29 0.23 24 O. IB 26 0.18 24 O. 12 31 O. 19 41 0.21 33 0.23 21 0.27 35 0.24 26 O. 14 12 39 0.15 26 0.10 43 TOTAL 3.88 4.02 5.97 7. 16 5.79 5.05 7. 76 7.43 6.09 5.06 .11. 29 3.90 TOTAL 1415 1799 1342 1591 1339 855 1015 B87 1316 1225 1171

PAGE 177

FORT LAUDERDALE EXPERIMENT STATION DR()WARD 08-3171 DAILY EVAPORATION IN INCHES AND MOVEMENT IN MILES 1969 JANUARY FEDRUARY MARCH APRIL HAY JUNE JULY AUGUST SEPTEr1DER OCTOllER tJO\)EMllER DECEMDER 1 0.12 40 0.14 34 0.13 32 O. 1 B 102 o. 27 2B 0.30 41 O. 24 39 0.27 12 0.24 2B 0.20 31 44 2 o. 18 '57 o. 07 17 0.19 '57 0.16 101 o. 21 49 O. 35 49 o. IB 20 0.26 17 0.07 27 O. 19 23 o. 15 o. 2'5 8 3 0.13 '50 o. 10 22 O. 23 26 0.14 44 O. 19 7'5 0.29 35 0.16 22 0.27 50 0.23 37 O. 15 27 O. 00 6 66 0.2B 7 o. 14 4 0.06 '5'5 O. 23 73 0.07 '55 0.21 63 0.17 15 o. 30 36 O. 23 '5 O. 17 21 0.29 29 O. 24 32 O. 04 4 5 O.OB B4 O.IB 7B 0.17 29 0.26 46 0.17 B4 0.24 27 O. 26 84 0.24 34 0.30 33 O. 21 13 0.10 39 6 O. 02 O. 11 0.18 0.13 44 0.25 34 O. 30 30 O. 31 7 0.21 18 O. 13 1 O. 20 5B 0.13 61 46 30 51 20 0.17 47 O. IB 7 0.09 33 o. 13 37 o. 14 84 0.25 37 O. 34 39 O. 06 21 O. 33 8 0.20 5 0.06 9 0.27 34 O. 14 17 81 0.17 22 O. 13 81 8 0.12 23 O. 11 29 0.19 37 0.13 102 0.20 77 O. 1 '5 J9 O. 32 33 0.23 6 0.26 63 0.25 54 9 0.08 28 0.15 55 O. JJ 75 0.27 86 O. 28 58 0.13 20 O. 21 25 0.21 28 0.21 49 0.25 0.08 11 O. 08 40 49 0.13 15 O. 09 40 10 0.07 12 o. 20 61 0.25 60 0.14 90 O. 28 55 O. 24 32 O. 31 23 0.27 19 0.32 32 0.20 39 o. 09 17 0.10 48 11 0.05 17 0.15 37 0.18 42 O. 10 37 0.27 46 0.2'5 20 O. 22 13 0.22 14 0.04 26 o. 20 29 0.19 3 O. 09 35 12 0.10 46 O. 12 30 0.19 56 o. 16 27 O. 19 57 0.18 15 O. 2'5 31 O. 10 13 0.29 12 O. 21 48 0.05 20 0.13 31 13 0.12 54 o. 12 21 0.14 29 0.32 112 o. 15 25 0.13 18 O. 35 17 O. 13 11 0.13 10 O. 25 29 0.14 22 14 0.05 31 O. 20 44 0.07 12 0.21 77 o. 18 55 0.16 33 O. 30 25 0.11 11 0.06 0.24 7 o. 14 o. 15 40 47 35 15 0.12 39 O. 18 95 o. 19 8 o. 20 62 50 o. 19 36 o. 20 37 o. 17 21 o. 09 33 o. 12 12 0.22 BS 16 0.10 46 0.11 76 0.20 123 o. 20 67 0.25 29 O. 14 38 o. 12 10 0.23 37 0.16 16 0.19 25 O. 15 50 5 17 0.17 54 0.18 27 O. 09 77 O. 21 41 0.27 46 0.16 53 O. 30 38 0.28 49 O. 16 :15 0.14 23 O. 16 108 24 18 0.11 71 0.14 69 O. OB 7 O. 22 49 0.23 134 O. 22 32 O. 25 2B 0.29 50 O. 16 28 0.11 24 O. 14 80 16 19 0.15 82 O. 14 46 0.27 100 O. 25 5B 0.21 71 0.26 30 0.18 21 0.28 81 0.14 51 0.13 50 o. 22 O. 09 23 68 O. 12 33 20 o. 16 65 o. 15 37 O. 23 65 O. 35 B4 O. 12 35 0.13 13 O. 12 23 o. 29 5 O. 17 72 O. 15 22 0.16 21 0.17 44 21 0.11 52 0.15 33 O. 15 35 O. 27 30 20 o. 16 25 0.31 16 0.26 8 0.24 48 O. 2B 52 0.17 59 0.17 46 22 0.11 19 0.13 34 0.14 28 0.19 32 0.23 44 0.19 J4 O. 20 10 0.27 26 0.23 30 O. 06 8 0.17 60 0.09 lJ2 23 O. 06 17 0.11 23 0.19 30 o. 22 22 o. 30 61 0.25 16 0.07 21 0.31 14 0.25 22 O. 14 48 0.27 124 O. 24 9B 24 O. 11 24 O.IB '50 0.18 120 0.27 44 0.2'5 68 0.09 16 O. 28 '5 0.30 29 O. 12 10 0.05 32 O. 00 63 O. 12 32 25 O. 13 24 0.16 37 0.19 24 o. 30 79 O. 25 46 0.25 29 0.27 17 O. 32 19 o. 13 8 0.09 144 O. 09 28 O. 11 :;0 26 0.15 28 O. 14 30 0.19 78 0.27 90 O. 24 35 0.32 23 o. 27 22 0.02 31 o. 16 16 0.20 52 0.13 38 0.12 80 27 0.13 41 0.17 :o!5 0.08 26 0.24 101 0.27 45 0.26 30 O. 21 16 0.11 15 O.OB 6 0.14 48 0.11 56 0.24" 145 29 0.15 67 0: 16 18 O. 1 '5 41 o. 26 97 O. 22 44 0.32 56 o. 24 20 0.29 38 0.20 23 0.14 41 O. 05 35 O. 16 33 29 0.25 135 0.14 93 O. 04 34 O. 19 43 0.3B 33 o. 12 9 o. 16 22 O. 14 26 O. 14 49 0.12 72 O. 12 39 30 O. 21 55 o. 14 91 o. 24 39 0.29 48 0.15 39 O. 20 11 0.22 26 0.15 16 0.18 137 0.09 --' 31 0.13 100 O. 15 68 O. 23 26 O. 17 33 O. 19 12 I'll 20 o. 19 130 0'1 O. 11 65 0'1 TOTAL 3. 62 4.11 5.00 6. 39 6.70 6. 55 7.18 6.88 5.21 5. 29 4.09 3. 58 TOTAL 1495 1168 1649 1897 1542 879 689 742 840 1454 J310 1460

PAGE 178

FORT LAUDERDALE EXPERIMENT STATION BR[)I-JARD 03-31"/1 1970 DAILY EVAPORATION IN AND MOVEMENT IN MILES FFIlRUARY "lARCH APRIL tlAY JUNE JULY AUGUST SEPTHIDER OCTOBER NOVEMDER DECEMBER 1 0.13 48 O. 26 135 0.10 180 0.23 135 0.28 162 O. 39 204 O. 08 15 2 0.13 60 O. 34 245 0.34 215 0.15 52 O. 34 208 0.32 215 0.28 41 3 O. 10 80 0.15 245 O. 23 148 O. 28 146 O. 23 92 0.38 132 0.11 59 4 0.13 130 O. 42 230 0.16 137 0.21 81 O. 30 42 0.38 120 0.10 41 5 O. 11 130 0.18 170 0.09 115 0.09 41 O. 30 78 0.18 80 O. 10 69 6 0.15 188 O. 04 93 0.17 120 O. 12 96 0.29 97 50 O. 22 10 7 0.19 197 0.02 102 0.20 43 0.22 65 0.37 115 O. 27 39 0.27 40 8 0.12 171 0.08 110 162 0.34 125 0.40 136 0.32 58 49 9 0.22 157 O. 11 48 240 0.14 126 0.25 200 0.28 43 51 10 0.16 13:5 0.23 110 0.23 113 O. 23 58 O. 50 202 O. 32 50 40 11 0.09 74 O. 22 97 O. 22 97 O. 23 55 0.41 95 0.26 78 35 12 0.09 34 0.10 39 O. J2 J85 O. 21 85 O. 37 216 O. J8 62 43 13 O. 22 88 0.04 24 109 O. I3 70 0.30 177 0.19 48 57 14 0.12 82 0.10 42 O. 09 104 O. 12 60 O. 33 142 0.23 32 75 15 O. 23 109 O. 10 90 0.26 112 0.12 40 O. 35 82 0.28 60 61 16 O. 30 45 0.14 102 0.20 65 O. 14 52 O. 30 93 O. 31 90 84 17 0.19 42 0.18 128 O. 22 165 0.33 96 O. 36 100 0.41 70 77 18 O. 11 88 O. 11 87 0.25 190 0.24 68 0.30 87 0.22 57 43 19 0.17 80 0.09 25 0.17 85 O. 30 65 O. 35 lOB 0.29 70 50 20 0.13 75 0.08 50 O. 14 120 O. 35 123 O. 28 180 0.24 63 45 21 O. 15 BO 0.16 150 0.12 70 0.29 BB 0.33 215 0.40 72 20 22 0.18 87 O. 19 166 0.21 87 O. 19 76 0.41210 0.38 64 70 --' 23 0.10 58 O. 12 67 0.23 190 O. 15 159 O. 37 205 O. 28 52 90 en 24 0.19 82 0.14 55 0.18 68 0.25 175 0.24 210 0.17 37 0.34110 -....J 25 0.18 58 0.16 80 O. 17 68 0.12 108 O. 35 120 0.30 45 0.36 180 26 O. 15 42 0.15 180 O. 23 142 0.10 70 O. 18 70 0.21 40 O. 31 105 27 O. 14 28 0.11 82 0.02 98 O. 26 72 O. 29 108 0.28 70 0.10 40 28 0.14 33 0.09 88 O. 10 105 O. 22 83 92 0.38 75 0.20 35 29 0.21 121 0.11 87 0.33 100 90 0.26 54 O. 35 98 30 O. 15 75 O. 22 62 0.34 43 128 0.22 46 0.16 100 31 O. 31 71 O. 23 93 0.20 151 0.26 112 TOTAL 4. 99 4.11 5.01 6.43 8.98 8.33 3 .. 24 TOTAL 2748 3040 3775 2613 4211 2176 194:5

PAGE 179

FORT LAUDERDALE EXPERIMENT STATION DROWIRD 00 J DAILV EVAPORATION IN INCHES AND MOVEMENT IN MILES 1'171 .JIINUARY FEBRUARY HARCH APRIL HAY .JUNE .JULY AUGUST SEPTENDER OC.TODER DECEMDER O. J'1 25 27 0.09 20 2 0.20 10 O. J3 44 O. 22 132 3 0.22 23 O. II 32 0.27 166 -4 O. 28 25 O. J ... 20 0.35 81 5 O. 12 17 49 O. 13 120 6 0.26 46 62 O. 12 13 7 0.24 30 O. J2 98 (l. 1 J 31 B 0.06 'I O. 2J 40 0.15 34 '1 0.30 76 0.21 J22 0.14 21 JO O. 15 30 0.15 57 0.05 37 11 0.25 52 0.21 86 O. 21 15 J2 0.05 14 0.17 63 0.16 59 J3 O. J3 23 O. J6 30 O. 12 36 14 O. 13 33 O. J7 55 0.19 .77 15 O. J5 2J 0.22 120 0.17 75 J6 O. 12 20 O. ;:>4 J40 O. 18 74 17 0.15 21 J41 0.15 44 IB 0.20 72 O. 33 143 0.2J 7 19 O. 18 23 O. 21 97 O. 14 162 20 0.18 -4 O. 11 43 0.07 89 21 0.05 9 0.19 34 0.23 109 22 O. J5 15 O. J2 20 O. 12 22 23 O. J5 11 0.18 57 0.35 140 24 0.09 16 O.OB 90 0.33267 25 O. 17 48 O. 55 0.21 124 26 O. 18 20 0.14 51 0.28 139 27 0.22 29 0.16 18 O. 16 46 ....... 28 0.15 6 0.12 11 0.14 28 (j) 29 O. 12 23 O. 10 20 O. 20 4B CO 3(1 0.22 34 0.0-4 35 0.12 69 3J 0.01 33 0.2B 34 TOTAL 5.07 .11.66 5. 65 TOTAL BI3 1060 2319

PAGE 180

FORT LAUDERDALE EXPERIMENT STATION BROWArW Ofl-317J DAILY EVAPORATION IN ItlCHf-S AND WIND MOVEMENT IN HILES 1972 JANUARY FEBRUARY MARCH APRIL HAY JUNE JULY AUGUST SEPTEHDEIl OCTODER NOVEMDER DECEMBER 1 0.00" 45 O. 35 0.24 59 130 0.17 33 0.20 50 O. 31 32 0.24 55 0.20 lJI o. 23 5 0.21 2 0.00" 12 0.13 125 0.22 60 0.17118 0.25 55 O. 09 36 0.22 14 0.26 30 0.11 14 O. J9 26 70 0.13 46 3 O. 25 21 O. 10 78 0.09 92 0.20 47 0.30 29 0.20 14 0.24 12 0.28 3:> 0.24 18 0.21 29 0.21 63 0.43 M 4 0.15 6 0.25 117 0.17 83 0.24 45 0.31 58 0.16 10 0.25 38 0.18 28 0.28 27 0.09 0.14 46 0.13 :; O. 16 35 0.25 170 0.28 23 0.24 43 0.22 20 0.39 40 0.25 29 0.23 19 0.18 40 7 O. 14 11 0.08 2: 6 0.12 35 0.04 45 65 O. 18 40 0.27 90 20 O. 19 8 0.35 26 0.06 0.10 9 0.07 7 0.07 23 O. 16 28 7 O. 16 30 0.04 33 0.10 80 0.31 50 0.19 145 0.31 54 0.25 28 0.21 22 0.25 24 0.27 39 0.12 18 0.14 3 8 0.25 130 0.19 67 O. 13 66 0.32 56 O. 23 84 0.28 14 0.22 39 0.21 24 0.20 19 O. 41 20 O. 13 49 0.07 2!9 0.22 125 0.14 16 0.18 54 0.33 47 0.34 92 0.30 47 O. 18 19 O. 17 21 O. 13 15 O. 16 O. 04 17 O. 11 I' 10 O. 13 52 O. 13 84 0.20 96 O. 17 171 0.35 50 0.27 41 0.23 32 0.32 25 O. 13 O. 17 36 0.17 6: 16 0.09 51 0.09 16 11 0.17 93 O. 14 108 0.29 118 0.30 123 0.31 44 0.26 26 0.23 10 0.20 53 0.24 24 0.41 28 0.17 12 0.19 59 0.16 45 0.38 192 0.22 18 0.22 43 O. 17 59 O. 17 24 0.25 28 0.20 29 O. II] 24 0.18 61 13 0.14 :57 O. 24 82 0.25 86 0.27 14 0.31 111 0.28 83 0.00* 7 0.27 28 0.37 36 0.29 129 0.14 19 O. 20 7: 14 0.09 70 0.20 56 0.21 49 0.29 24 0.36 91 0.33 75 0.24 11 0.29 24 0.21 21 0.22 100 O. 13 18 0.16 8f. O. 32 24 O. 18 68 15 0.21 69 0.16 23 0.21 36 0.28 69 0.21 39 0.25 126 O. 24 37 O. 13 14 0.22 15 0.14 23 O. 06 23 0.16 6;: 16 0.22 136 O. 12 58 O. 13 38 0.29 38 0.26 43 0.22 118 0.30 41 O. 17 19 0.24 23 O. 18 15 O. 04 53 0.17 53 17 0.21 139 O. 15 90 0.27 46 0.28 26 0.33 178 0.29 25 0.26 24 0.22 11 0.21 18 O. 13 159 18 0.19 105 0.20 62 0.39 36 O. 52 38 O. 28 234 O. J8 47 0.2'1 33 0.26 O. 12 0.17 23 0.24 145 3 0.14 37 19 0.05 55 0.32 86 0.30 2 32 0.30 141 49 0.26 28 0.28 27 O. 25 35 O. 14 105 20 0.09 19 0.18 182 0.26 49 0.09 32 0.17 155 32 0.2'1 27 0.25 '1 0.24 0.11 28 0.14 63 21 O. 23 53 0.22 68 0.28 31 0.23 25 0.44 146 58 0.26 22 O. 19 23 0.11 29 0.05 34 32 0.17 85 22 O. 01 52 0.04 59 0.33 39 O. 30 58 0.35 115 0.29 61 O. J5 22 0.11 7 O. 20 0.20 69 0.06 47 23 0.18 50 O. 14 35 38 0.38 48 0.18 47 0.45 97 0.31 46 O. 15 19 O. 16 73 0.14 39 0.34 155 20 0.23 t68 O. 15 19 24 0.06 10 0.09 :H 0.24 44 O. 36 44 0.30 83 0.35 93 0.25 17 O. 16 24 0.22 15 O. 32 18 0.09 54 25 0.04 27 0.08 101 0.26 63 0.25 42 O. 31 33 0.26 71 O. 13 36 0.27 15 0.20 17 0.23 O. 10 :i!i O. 08 22 26 0.27 0.44 0.27 58 0.26 48 0,19 44 0.29 72 0.22 38 0.23 25 0.11 21 0.11 77 0.07 2J 22 6 24 0.09 11 O. 10 94 27 O. 27 17 0.08 24 0.27 43 O. 39 102 0.37 48 0.31 69 0.21 61 O. 15 11 0.27 34 0.11 38 0.10 0.07 50 28 0.08 92 0.33 24 0.22 '13 O. 38 92 0.25 25 0.36 32 O. 28 51 0.23 23 0.25 40 O. 17 '17 0.18 3S 29 O. J8 31 0.19 34 0.37 53 0.32 HI 0.23 23 O. 33 33 0.35 83 0.25 7 0.23 25 42 0.13 15 0.17 47 0.05 33 0.11 28 30 O. 17 17 0.18 54 0.33 147 0.24 33 0.31 32 0.3'1 79 o. 17 25 0.22 20 O. 52 37 O. 03 17 --' 31 0.17 44 0.26 155 0.29 38 0.33 69 0.20 23 0.18 24 0.05 25 0.31 108 0"1 0.07 40 \.0 TOTAL 4.66 5.00 S. IS 8.31 8.08 B. 24 6. 70 7.08 6.25 6. 56 3. "17 4. 54 TOTAL 1708 1964 1748 1900 1612 2280 1133 779 661 1184 1J23 1824

PAGE 181

FORT LAUDEnDALE EXPERIHENT STATION HROWARD 08-3J71 DAILY EVAPORATION IN INCHES AND \.lIND MOVEHEONT IN HILES 1973 .JANUARY FEIlRUARY MARCH APRIL tlAY .JUNE .JULY AUGUST SEPTEMBER OCTOBER NOVnmER DECEHIlER 1 O. 07 41 0.18 116 0.23 61 0.23 94 0.20 62 0.3B 18B 23 0.20 35 13 0:14 J7 O. 10 2 0.04 40 0.09 229 0.29 24 O. 12 17 0.22 81 0.19 112 0.31 33 0.16 36 0:; 0.00. 13 0.14 40 3 O. J3 9 0.18 57 0.19 34 0.06 '91 0.33 85 0.30 120 37 O. 14 43 O. 12 35 0.63 0 .. 0.16 EJ O. 15 44 4 0.05 47 0.17 20 0.20 13 0.38 120 0.34 36 0.40 105 O. 20 27 0-0.20 :;1 32 0.06 3 0.12 40 :; 0.15 10 0.07 4 0.21 6 0.35 95 0.25 4 0.44 75 O. 22 28 0-0.23 46 JA 0.09 14 0.05 7': 0.04 2 O. 12 6 0.17 23 O. 13 :; 0.23 12 0.23 31 16 0.15 40 0.20 22 14 0.18 :;4 0.17 25 8 0.12 9( 7 O. 13 9 0.07 2 0.20 18 0.23 90 26 0.40 185 O. 19 11 0.22 18 0.39 33 0.20 5 18 O. 16 'I' 8 0.06 q 0.21 2 0.22 21 0.26 153 lJ2 0.47 61 0.20 35 0.15 10 0.0'1 34 O. 10 0.09 33 0.15 1 0.19 37 0.08 q 0.07 42 0.17 41 0.22 40 O. 16 51 111 0.48 16 O. 17 13 O. 18 13 0.32 6 O. 10 3 O. 12 19 3, 10 0.19 66 0.38 107 O. 16 38 0.33 54 54 102 0.27 J7 13 0.31 :2 0.11 0.06 6 0.14 7 0.10 11 0.06 26 0.22 53 0.26 99 0.29 73 56 0.07 96 0.20 18 O. 18 16 0.23 9 30 0.29 104 2( 12 0.02 17 0.03 65 0.21 33 0.19 46 44 0.34 59 0.03 38 0.15 10 0.21 21 0.25 78 O. 18 0.11 3: 13 0.15 125 O. 17 28 O. 19 40 0.28 30 41 O. 14 22 0.22 47 0.17 145 0.26 6 0.23 104 58 0.11 5;: 14 O. 10 47 O. 18 22 0.23 52 0.27 62 0.25 28 O. 12 30 O. 18 67 0.22 18 O. 35 91 79 0.08 2: 15 0.06 72 0.15 73 0.30 83 0.26 109 50 0.47 41 0.09 12 0.22 39 O. 19 :3 0.11 o. 13 55 0.08 J1 0.18 16 0.07 42 O. 21 43 0.26 108 O. 52 59 O. 14 31 0.08 26 0.11 16 0.24 14 0.16 14 O. 19 40 O. 16 33 28 0.15 .18 0.08 26 17 0.08 77 0.28 82 0.21 151 0.34 89 0.42 71 0.27 o. O. 12 12 0.0.26 5 0.18 J1 O. 16 25 0.10 56 18 0.15 70 O. 13 39 0.16 49 0.35 90 0.29 57 0.23 48 0.24 29 0-0.37 12 0.21 25 O. 14 14 O. 10 34 19 0.15 30 0.03 19 0.19 41 0.29 66 0.23 36 0.31 31 O. 19 21 0-O. 18 15 0.11 57 20 0.17 18 0.03 53 0.25 26 0.25 59 0.20 51 0.20 30 O. 17 19 0 0.27 9 0.01 14 O. OB 27 59 12 0.10 102 21 0.17 39 O. 15 49 0.22 85 0.44 125 O. 22 60 0.30 33 0.48 32 0-O. 13 7 O. 13 84 22 0.10 48 O. 12 51 0.26 112 0.38 148 0.23 41 O. 17 32 O. 10 50 220 0* 0.37 O. 18 67 0.27 79 65 79 O. 13 16 23 0.11 42 0.27 29 O. 10 73 0.34 77 0.24 44 O. 15 35 0.07 43 0.21 18 O 0.05 84 24 O. 18 39 0.05 20 O. 26 74 0.27 49 O. 19 55 O. 50 32 0.31 58 O. 26 36 44 0.27 69 O. 0.00* 25 O. 12 27 0.05 11 0.26 135 0.23 52 0.35 42 0.20 42 O. 30 54 O. 18 18 O. 12 3 0.27 54 64 0.00. Ot 26 O. 14 40 0.03 1 O. 51 176 0.09 73 0.33 62 0.23 43 O. 26 36 0.22 43 0.11 17 O. 10 28 0.00. O' 0.22 34 0.09 2 O. 52 346 27 O. 13 33 0.22 9 0.35 62 0.46 103 0.22 58 0.15 o. 0.22 17 0.23 39 0.24 37 15 0.15 77 0.08 65 28 O. 10 16 O. 16 18 0.26 43 0.37 109 0.23 29 o. 20 0.24 29 79 O. 16 19 0.07 39 0.14 3:> 29 0.23 67 0.22 94 O. 30 33 0.20 126 107 21 0.28 46 O. 17 34 0.18 38 0.10 46 0.07 14 -" 30 0.15 18 O. 16 74 O. 19 35 0.30 82 0.31 33 21 0.15 41 0.34 13 0.21 43 0.21 139 O. 12 30 -.....J 31 0.11 12 0.28 124 0.33 85 0.27 34 0.33 30 0.15 14 O. 13 54 a TOTAL 3.61 4. 13 7.29 B.46 5.46 7. 58 5.26 4.29 5.25 5. 1'1 3.28 3. 59 TOTAL 1201 1248 2001 2259 1770 1742 B74 993 710 1122 1120 1501

PAGE 182

FORT LAUDERDALE EXPERIMENT STATION 00-3171 1974 DAILY EVAPORATION IN INCHES AND MOVEMENT IN MILES FEBRUARY WInCH APRIL HflY JUNE JULY AUGUST SEPTEMBER OCTOBER DECEMBER 1 0.06 9 22 0.15 41 0.40 53 0.34 2 o. 10 25 0.09 33 o. 0.27 41 0.29 3 0.11 95 0.14 15 0 .. 0.28 81 0.38 0.06 4 0.08 79 0.05 20 103 0.16 101 O. 29 0.24 21 5 O. 17 49 0.22 83 0.24 77 0.27 0.32 0.28 12 6 0.11 33 0.15 86 0.24 49 0.28 O. 36 0.06 8 7 o. 10 24 o. 14 95 o. 13 40 0.31 0.21 0.28 7 8 0.11 0.17 49 O. 17 47 0.24 0.19 40 9 0.11 13 90 o. 20 38 0.25 0.23 0.29 48 10 O. 1EJ 56 68 o. 21 40 0.31 0.25 0.24 40 11 O. 17 62 0.25 61 o. 18 22 0.21 O. 18 31 12 0.07 11 0.11 28 o. 16 31 0.26 27 13 0.13 36 O. 14 22 69 0.34 0.4'1 16 21 14 O. 10 11 o. 16 17 0.21 38 O. 54 0.35 62 0.25 24 15 8 o. 13 17 93 0.05 0.24 58 0.22 33 16 '12 73 0.33 130 0.35 0.14 48 0.43 12 17 o. 13 22 0.01 30 0.28 43 0.31 J17 O. 14 1 18 o. 10 8 o. 17 35 0.15 34 0.40 173 0.3'1 5 19 0.08 18 0.17 42 0.25 35 0.30 0.20 99 0.32 8 20 0.11 34 0.15 78 0.22 48 0.31 0.32 106 0.30 21 0.09 25 0.19 46 0.17 38 o. 38 0.32 147 0.20 22 O. J:i 20 0.22 110 0.26 78 0.28 0.22 105 O. JJ 23 O. J4 19 97 o. 00" o. 0.25 o. 30 67 o. 10 211 0.12 37 0.05 11 0.'14 86 0.21 0.37 49 0.07 25 0.18 84 o. 14 15 0.17 48 o. 30 O. 26 41 O. 18 26 0.18 27 o. 24 82 0.28 53 0.31 0.21 50 0.25 27 0.16 140 0.18 132 0.23 44 0.43 0.33 23 0.28 28 0.16 55 o. 10 55 0.35 53 0.31 0.33 61 0.33 --' 29 0.16 48 0.25 40 O. 14 0.40 24 O. 17 ...... 30 0.10 41 0.38 89 0.27 0.25 21 0.09 --' 31 0.30 20 10TAL 3.46 3. 37 5.65 8.01 8.36 5.60 TOTAL 1146 1512 1507 276 1287 339

PAGE 183

FORT EXPERIMENT STATION BRm)t,RD 08-3171 EVAPOnATION IN INCHES AND MOVEMENT IN MILES 1975 JANUARY FEBRUARY MARCH APRIL JUNE JULY AUGUST SEPTEMBer? OCTOBER NOVEMBER DECEMllER 1 O. 09 O. 30 0.15 O. 20 O. 40 0.10 O. 0.07 0.42 2 O. J3 O. 23 O. 16 O. 35 O. 37 O. 41 0.21 0.17 0.16 3 0.17 O. 03 O. 20 O. 33 O. 40 O. 14 O. 06 0.07 0.20 0.06 4 O. 03 O. 20 0.29 O. 40 O. 34 O. 19 O. 14 0.2J O. 17 5 0.03 0.10 0.18 O. 51 0.28 0.38 O. 15 0.24 0.15 0.00. 0.18 6 O. 03 O. 16 0.20 0.03 0.37 O. 12 0.27 O. 25 0.20 0.21 7 0.12 0.15 O. J 5 O. 29 O. 43 0.45 0.25 0.19 0.17 0.22 0.22 9 O. 14 0.00 O. 00. 0.29 O. 14 0.25 0.29 O. 25 O. 26 0.28 9 O. 17 0.27 0.51 0.28 O. 12 0.23 0.23 O. 18 0.20 0.12 10 O. ')4 O. 19 0.17 O. 34 0.28 O. 18 0.19 0.19 O. 17 O. 14 11 O. 23 0.09 0.24 O. 32 0.34 0.17 O. 13 0.09 0.14 0.10 O. 20 12 0.19 O. 14 0.25 0.35 0.22 0.27 O. 18 0.21 0.04 0.16 13 0.07 0.23 0.32 0.23 0.28 0.28 0.40 O.OB O. 27 0.10 14 0.25 0.22 0.25 0.25 0.16 0.29 O. 33 O. 08 O. 14 0.19 O. 31 15 0.16 0.29 0.00. O. 33 O. 23 0.29 0.35 0.03 0.23 16 0.18 0.48 0.27 0.27 0.24 O. 36 0.25 0.19 O. 15 17 O. 07 O. 11 0.25 O. 38 O. 13 O. 26 O. 43 O. 22 0, 10 O. 11 O. 05 0.06 18 O. 00 O. 20 O. 27 0.28 0.11 O. 21 0.22 0.20 O. 15 0.13 19 O. 16 0.23 0.33 O. 32 O. 02 0.20 O. 20 O. 07 0.20 20 O. 15 0.15 0.29 0.41 O. 32 0.18 O. 14 0.21 0.03 0.22 0.01 21 0.19 0.15 0.23 O. 27 O. 32 0.20 0.29 0.25 0.34 O. 43 0.24 22 0.04 0.23 0.28 0.34 O. 26 0.17 O. 20 0.11 O. 20 O. 11 23 O. 15 0.41 0.27 0.20 0.27 O. 15 O. 26 0.31 O. 14 0.12. 24 0.06 0.03 O. 23 O. 33 O. 38 0.1'7 O. 30 0.24 0.10 25 0.22 O. 27 0.31 O. 30 O. 10 O. 31 O. 10 O. 12 0.10 O. 11 26 0.12 0.15 0.28 O. 31 0.25 0.37 0.15 0.20 O. 23 0.12 0.23 --' 0.13 0.10 '-l 27 0.11 O. 17 O. 33 O. 32 0.27 0.32 O. 2B 0.12 0.17 O. 05 O. 13 N 28 0.14 O. 14 O. 1'7 O. 34 0.13 0.26 O. 31 O. 13 0.17 O. 11 0.15 29 0.20 0.33 O. 35 O. 1'7 O. 10 O. 18 O. 19 0.17 30 0.19 0.25 0.37 0.20 O. 22 0.21 0.11 O. 12 31 0.13 0.28 0.39 0.26 0.20 0.09 0.20 TOTAL 4. 14 4.83 7.67 9.29 6.99 6.01 6.31 5.1:12 4. 43 4.37 4.71 TOTAL 0 0 0 0 0 0 0 0 0 0 0

PAGE 184

FORT LAUDERDALE EXPERIMENT SlATION IJr/(lI-JARV 08 -3171 DAILY EVAPOfUITION IN HICHf-S AND MOVEMENT IN MILES 1976 JANUARY FEBRUARY MARCH APRIL tlAY JUNE JULY AUGUST SEPTEMBER OCTODF.R NOVEMBER DECEMBER O. 32 0.12 D. J8 0.26 O. 39 0.06 D. 23 O. 20 2 O. 11 0.18 0.16 0.40 O. 12 0.31 D. 18 0.17 0.13 0.28 0.21 0.13 3 0.16 O. 17 O. 28 0.82 0.07 0.17 0.113 0.11 0.07 0.26 0.09 4 0.09 O. II O. ;--3 0.03 O. 04 0.17 O. 15 O. 29 0.31 0.05 O. J3 0.13 5 O. 13 D. 12 O. 28 0.04 O. 39 0.30 O. 21 0.15 0.45 0.14 0.13 D. 09 6 0.05 0.16 0.26 0.26 O. 30 0.00 O. 30 0.26 0.26 0.08 7 0.01 0.25 0.19 0.14 0.33 0.09 0.26 0.25 O. 28 O. 30 0.17 O. 09 8 O. 13 0.07 0.16 0.28 0.38 0.29 O. 34 0.24 0.3-1 0.20 0.1-1 0.19 9 0.22 O. 07 0.22 0.27 0.40 0.3-1 0.11 0.23 O. 16 O. 15 0.10 10 O. J 4 0.15 0.26 0.48 O. 38 0.63 0.17 O. 57 0.35 O. 21 O. J9 0.J 11 O. J 5 O. 12 0.15 0.34 O. 19 0.12 0.27 0.29 0.31 0.16 0.19 0.06 12 O. J 3 0.15 O. 25 0.0'1 O. 33 O. 51 O. 30 0.31 0.25 0.19 13 O. J2 O. 13 0.26 0.24 0.29 0.18 0.27 0.25 O. 12 O. 19 O. J 4 0.02 14 O. J4 O. 43 0.20 0.30 0.12 0.13 0.22 0.43 0.21 0.27 O. JO 0.19 15 0.07 0.20 0.17 0.46 O. 18 0.32 O. 16 0.21 O. 13 O. J2 0.15 J6 0.04 O. 13 0.23 0.36 O. 27 0.39 0.41 O. 04 0.29 O. J 5 O. 03 O. 01 17 O. 26 0.19 0.32 D. 50 O. 25 0.35 O. 32 0.15 O. 10 0.17 0.'27 O. 10 18 O. J6 0.19 0.26 0.26 0.26 0.29 0.15 0.29 O. 20 O. 01 O. 12 J9 0.13 0.19 0.04 O. 31 0.41 0.20 0.31 0. 24 O. 1-1 20 0.09 O. 23 0.27 0.37 O. 23 0.2:5 O. 25 0.09 0.10 0.17 2J 0.13 O. 29 O. 24 O. 24 0.27 0.22 O. 32 0.23 0.05 O. 11 0.06 22 O. 04 O. 1-1 O. JJ 0.32 O. J9 0.28 O. 38 0.10 0.33 0.35 O. 03 O. 31 --' 23 O. 14 D. 19 0.25 0.21 D. JJ 0.17 0.16 0.28 0.07 0.29 0.12 O. 14 -.J 24 O. 21 O. 25 0.32 0.32 O. 34 0.26 0.1';' 0.30 0.25 O. 17 O. 09 W 25 O. 19 D. 00. 0.36 0.40 0.35 0.07 0.21 0.05 0.10 0.17 O. 03 26 O. 05 O. 07 0.15 0.20 O. 23 0.-11 0.11 0.32 O. 08 O. J 5 O. 29 27 O. 14 O. 2J1 0.32 0.27 O. 32 0.17 O. 26 0.15 0.28 O. 2-1 O. JJ 28 O. 17 0.32 0.31 O. 36 0.28 O. 31 0.22 O. 05 O. 03 0.03 29 0.16 0.23 0.26 0.-18 0.18 0.31 O. 18 0.21 0.12 O. 22 30 O. 15 0.27 0.37 0.29 0.24 0.24 0.20 0.32 O. 26 O. 15 31 0.17 0.31 O. 20 O. 13 0.19 0.05 0.03 O. 07 TOTAL 4.20 4. 15 7.43 8. 55 8.65 6.94 5.93 6.64 5.63 5.90 4.05 4.00 TOTAL 0 0 0 0 0 0 0 0 0 0 0 0

PAGE 185

FORT LAUDERDALE EXPERIMENT SlATION 08'-3171 DMLY EVAPOHlIl ION IN INCIII'S AND \.JIND MOVEMENT IN HILES .977 JANUARY FEDRUARY APRIL tillY JUNE JULY AUGUST SEPTHIIJEI1 OCTOBER DECEMBER 1 0.29 O. 16 O. 16 0.26 0.24 O. 35 0.3'1 0.41 O. 17 0.08 ;:! 0.05 0.11 0.31 O. 30 0.15 0.17 0.28 O. 18 0.16 3 0.17 0.20 0.26 0.25 0.31 0.23 0.26 0.40 0.09 4 O. 17 0.30 0.07 O. 09 0.2B 0.04 0.21 0.17 0.33 5 0.12 O. 21 0.20 0.30 O. 14 O. 30 0.33 0.17 0.24 O. J8 0.04 6 O.OB 0.09 0.17 0.44 0.01 0.03 O. 0!4 0.25 O. IB 0.36 0.27 0.08 7 O. Jl O. 09 0.32 0.08 0.62 O. 16 0.29 0.14 O. 14 0.05 0.05 0.15 B O. 14 O. 13 0.30 O. 40 O. OB 0.34 O. 37 0.28 O. 1:; 0.23 O. J 5 0.18 O. J6 9 0.06 0.07 0.07 O. 37 O. 59 0.23 O. 26 0.09 O. 14 0.16 O. 12 10 0.09 O. JO 0.20 0.37 0.14 0.26 0.26 0.23 0.33 0.04 J1 0.20 O. 10 0.10 0.21 0.20 O. 18 0.20 0.03 O. 17 0.10 0.19 0.22 0.05 0.22 O. J7 0.34 0.15 0.31 O. 33 0.23 0.22 0.2B 0.22 0.09 13 0.12 0.16 O. 22 0.27 0.15 0.31 O. 35 0.27 0.15 0.20 0.24 14 0.14 O.OB 0.05 0.32 0.19 0.33 O. 14 O. 13 0.2B 0.17 O. 16 15 0.02 O. 09 O. 30 0.14 0.23 0.27 O. IB 0.2:; O. 22 O. 12 16 O. 12 O. 05 0.11 0.23 0.35 O. 17 0.09 0.33 O. 16 0.32 0.15 O. 13 17 O. 10 O. 24 0.29 0.45 0.33 0.39 0.30 O. 32 O. 12 O. 10 O. J5 O. J2 IB 0.10 0.14 0.19 0.24 0.26 0.47 O. 13 O. 10 0.22 0.27 0.14 0.17 19 O. 10 0.16 0.26 0.34 0.36 0.08 O. 24 0.26 0.09 0.20 0.14 0.13 20 0.07 0.02 O. 32 0.32 0.37 0.26 O. 21 0.29 O. IB O. 19 0.15 0.12 21 0.16 0.36 0.25 0.2B O. 37 0.17 0.33 0.15 0; IB O. lB 0.12 22 0.20 O. lB 0.24 O. 37 0.27 0.22 0.25 0.24 O.OB O. 15 0.21 0.11 23 0.12 O. 12 0.30 O. 17 0.24 0.24 O. 19 O. 19 O.OB O. J7 0.20 24 0.05 O. 10 0.33 O. 38 0.30 0.34 O. 22 0.21 O. 13 0.17 0.14 O. 16 O. 05 --...J 25 O. 10 0.27 0.24 0.24 0.32 0.40 O. 19 0.14 0.29 O. 13 0.09 O. 10 -J::> 26 0.19 0.19 0.21 0.26' 0.44 0.24 0.27 0.26 0.2J 0.15 0.15 O. 10 27 0.11 O. 12 0.26 0.37 0.09 0.37 0.28 0.25 O. 12 0.15 2B 0.11 0.27 0.35 0.22 0.49 0.30 O. 38 0.31 O. 17 0.31 0.29 0.25 29 O. 26 0.30 0.40 0.21 0.28 0.30 0.24 0.22 0.20 0.02 0.05 30 0.16 O. 35 0.66 0.29 0.29 0.43 O.OB 0.20 0.03 0.11 0.05 31 0.11 0.23 0.31 0.14 0.3B 0.09 0.05 O. 10 0.20 TOTAL 3. 53 4.19 7. 17 9.13 7.B4 6.1.7 B.05 7.ll 4.62 6.04 4.86 4.13 TOTAL 0 0 0 0 0 0 0 0 0 0 0 0

PAGE 186

FORT EXPERIMENT STATION DRmJARD DAILY EVAPOHATION IN INCHES AND MOVEMENT IN MILl'S 1978 APRIL JANUARY FEBRUARY NAr"lCH MAY JUNE JULY AUGUST SEPTEMDEIl OCTODER NO'JEMDEr! DECEMDER 1 0.01 0.15 O. J7 0.12 0.35 0.3J O. 25 0.24 0.41 0.09 0.17 0.14 2 O. J 5 0.02 0.20 0.26 0.11 0.28 0.17 0.23 0.63 0.25 O. J IJ O. 20 3 O. 06 0.17 o. 13 0.25 0.25 0.28 O. 22 o. 12 0.21 0.22 4 O. 09 O. 07 O.OB 0.28 O. 38 o. 37 o. 27 O. 48 O. 10 O. 30 O. 113 O. 15 5 0.09 O. 07 0.29 O. 31 0.26 0.27 0.24 O. OJ O. 19 O.2J O. 09 6 0.14 O. 09 O. 19 O. 27 0.06 O. 55 0.47 O.lEl O. 14 0.18 0.19 7 0.04 0.14 O. J7 0.27 0.27 O. 14 O. OB O. 10 O. 50 O. 18 0.16 8 0.12 o. 13 o. J9 0.21 0.37 0.24 O. 20 0.23 O. 06 O. 05 0.21 9 0.19 0.09 0.28 0.'27 O. 37 0.39 0.17 O. 21 0.16 O. 06 10 0.19 0.17 0.08 0.29 O. 30 0.32 O. 32 0.30 0.:?7 O. 12 O. 20 11 0.14 0.12 0.30 0.35 0.41 0.29 0.29 O. 20 O. 13 0.15 12 O. 11 0.17 0.19 0.34 0.29 0.36 O. 30 O. 44 0.2b 0.11 O. 22 J3 0.10 0.13 0.14 0.32 O. 30 0.27 O. 31 0.30 0.23 0.13 0.14 O. 11 14 O. 33 0.19 O. 22 O. 31 O. 48 0.26 O. 32 0.16 0.36 O. 15 O. 21 0.13 15 0.18 0.16 0.27 0.47 0.43 0.28 0.20 0.43 O. 14 o 06 O. J5 0.12 16 0.10 0.16 0.21 0.29 O. 33 0.27 O. 24 0.36 0.22 O. 04 0.18 0.17 17 0.11 0.19 0.24 O. 29 O. 24 0.42 O. 36 0.29 0.14 O. ;>4 0.18 18 0.14 0.11 0.32 0.25 O. 40 0.31 O. 28 O. 32 O. 32 O. 28 0.09 19 0.14 O. 30 0.24 O. 25 0.08 0.27 0.22 0.2<1 O. 26 0.07 O. 20 20 0.15 0.15 O. 25 0.17 0.22 0.19 O. 00. 0.27 O. 09 0.14 21 0.16 O. 06 O. 18 O. 35 0.22 0.16 0.28 0.34 0.24 O. 22 O. 19 0.12 -....j 0.11 0.06 0.15 0.26 0.28 0.15 0.27 0.21 0.28 0.14 01 22 O. 37 0.17 0.12 23 0.17 0.17 O. 17 0.36 O. 'II 0.20 0.36 0.22 0.24 0.07 O. 11 24 0.05 O. 19 0.35 0.31 0.20 0.33 0.15 O. 15 O. 16 O. 08 0.19 0.14 25 0.10 030 o. 36 0.03 O. 27 O. 39 O. 13 0.20 O. 17 0.16 O. 13 O. 25 26 0.23 0.02 0.15 0.23 0.25 0.44 0.33 O. 26 O. 19 0.25 O. 14 0.07 27 0.17 O. 12 0.24 0.38 O. 30 0.02 O. 35 O. 23 0.20 0.13 0.17 0.16 28 O. 20 0.27 0.28 O. 32 0.29 0.35 0.17 0.28 O. 15 0.45 0.15 0.09 29 O. 20 0.02 0.33 0.15 0.32 O. 26 0.26 O. 19 0.07 O. 10 0.16 30 O. 15 0.15 O. 25 O. 08 0.28 O. 21 0.29 O. 17 0.10 O. 23 O. 26 31 0.18 0.26 0.39 O. 18 O.OB O. 18 0.21 TOTAL 4. 15 3.97 6.37 8.47 8.69 B. 35 8.05 5.70 5.87 5. 81 5. 10 4. 76 TOTAL 0 0 0 .0 0 0 0 0 0 0 0 0

PAGE 187

FORT LAUVERlI,\LI: EXPERHIF.NT STATION BRmJI\RD 03 :JllI DI'IILY EVAPORATION IN JNCHr-S AND WIND MOVEMENT 11'1 MII.FS 1979 .JANUARY FEBRUARY MARCH APRIL tillY .JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMDER DECEMBER 1 0.23 O. 19 O. 16 0.39 0.09 0.26 2 0.18 O. 30 0.20 0.30 O. 17 0.32 :J O. 10 0.23 0.26 0.35 O. 31 4 0.09 O. I:::! O. 33 0.28 0.26 :; O. 18 O. 16 0.15 0.21 0.27 0.33 b 0.20 0.07 0.24 0.29 0.29 0.21 7 0.22 0.2::; 0.11 0.35 O. 12 0.37 B O. 16 0.24 0.33 0.43 O. 05 0.37 9 0.09 0.11 0.27 0.15 0.26 0.38 10 0.09 0.22 0.20 0.38 0.25 0.49 11 O. 18 0.16 0.23 0.41 0.24 O. 18 12 0.13 0.16 O. 16 0.22 0.28 0.34 13 0.22 0.12 0.22 O. 36 O. 32 0.35 14 0.07 O. J7 0.21 0.31 0.37 15 0.13 0.17 0.25 0.2B 0.30 16 0.19 0.13 0.22 0.'13 17 O. 13 O. J7 0.47 0.61 0.32 0.06 IB O. J4 0.21 0.28 0.33 0.16 0.22 19 0.11 O. 15 0.16 O. 34 O. 35 0.27 20 O. 12 O. 19 0.23 0.22 0.4'1 0.34 21 0.24 0.23 0.25 0.35 0.33 0.36 22 0.04 0.28 O. 21 0.37 0.27 0.23 23 0.09 0.15 0.17 0.32 0.30 0.23 24 O. 10 0.25 0.31 0.39 O. 29 0.33 25 0.26 O. 16 0.26 O. 13 0.39 26 0.17 0.28 0.28 0.39 0.29 27 0.13 O. 34 0.22 0.36 0.45 0.25 --' 28 0.08 O. 16 O. 30 0.2B O. 17 O. 14 29 0.22 0.32 0.31 O. 14 0.31 0"1 30 0.15 0.33 0.35 O. 13 0.21 31 O. 12 O. 50 0.35 TOTAL 4. 64 5.37 7.83 8.35 7. 72 8.33 TOTAL 0 0 0 0 0 0

PAGE 188

APPENDIX E EVAPORATION DATA FOR FT. LAUDERDALE EXPERIMENT STATION 1976-1978

PAGE 189

'-I co 1976 \Ieek OIlS ;:> 3 5 6 7 B 9 10 II I;:> 13 H 15 16 17 IB 19 ;:>0 ;:>1 22 23 24 25 26 27 28 29 30 31 3;:> 33 34 35 36 37 38 THURSDAY O. 32 0.13 o 07 0.04 0.16 0.12 0.15 0.17 0.07 0.23 0.15 0.26 O. 36 0.;:>6 0.;:>8 O. 17 0.::12 0.26 O. 30 O. 2'1 O. 23 0.32 O. 17 0.63 0.35 0, 26 0.27 0.34 0.32 0.38 0.27 0.15 0.30 0.23 O. II 0.13 0.16 0.29 rR fP,\Y O.IJ 0'-' o 04 0.14 0.15 o 16 0.13 o ;>:1 O.z::!4 0.::'0 0.25 0.17 0.15 0.40 O.;!7 0.36 0.21 0.37 0.33 0.12 D.:;!7 0.36 O. 17 0.12 0.07 O. III O. II 0.41 0.16 Q.:-!l 0,26 O.2:i 0.0'7 o.;-tlJ 0,(17 0.10 Sl\lUHTI"y 0.16 0.14 0.;:>6 O. ?I 0.17 0.;J5 0.43 O. 17 0.26 0.26 0.27 0.32 0.82 0.48 O. 50 0.32 0.:19 O. 38 O. 46 O. 1'7 O. 48 0.:10 0.25 0.41 0.41 0.43 0.17 0,32 0.27 0.27 O. 43 0.2;] 0.31 0. :1J 0.31 O. not-lLlAY 0.(19 0.15 O. 16 O. 1'7 0.12 0.07 O. 17 0.14 0."0 O. 1'7 0.20 O. 32 0.03 0.34 0.26 0.40 O. I::! O. 40 O. 27 O. II 0.2'1 0.25 O. 18 0,;.'5 0.17 O. 15 0.15 0.27 0.23 0.24 0.16 0.10 O. 31 O. 0.31 O. 26 tltll'H>I\Y {l J3 o I:J 0.13 O. OJ O.IB 0.07 0.13 O. II' O. 18 O. 16 0.20 O. II 0.23 0.04 0.0'7 0.04 0.20 0.07 0.38 O. O. 34 0.20 0.0'1 0.13 0,22 O. 28 0.21 O. :;1 o.;:!o 0.27 o. 17 o. (1.011 O. ;'0 U. 211 O. O. J::! O. :'b lUL'SlIfW 0.05 O. I" 0.0'1 0.1.11 0.17 0.15 0.1'7 0.25 0.16 0.22 0.23 0.25 0.27 0.26 0.24 0.37 0.27 0.04 O. 1'7 O.:lb 0.35 0.06 0.2'1 O. 18 0.2B O.IB 0.30 0.27 0.25 0.27 o. II O. 57 0.15 0.17 0.13 0.26 0.21 0.05 l-JErJN!';OAY O. 01 o 14 O. 13 0.17 O. II O. 12 O. J9 O. J7 0.28 0.26 0.32 0.32 0.3J O. J4 0.30 0.24 0.3J 0.39 O. 33 0.31 0.23 0.31 0.34 0.39 O. 17 O. 24 0.26 0.22 0.32 0.27 0.29 0.27 0.27 o. ::!J 0.34 0.33

PAGE 190

(.Jeek 37 42 46 47 4B 49 50 51 52 1977 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 THt)HS[l"y O. 07 0.20 0.17 0.27 0.15 0.05 o. 13 0.19 0.24 0.15 0.09 0.21 0.10 0.09 O. 03 O.OB O. 12 0.07 o. II 0.17 O. 10 0.24 O. 10 O. 20 O. 20 0.29 0.33 0.23 0.08 0.28 O. 22 0.28 0.15 o 36 O. 44 0.25 0.23 Ttl rn.\y o :m 02:1 o. o 13 0.:-5 O. o. 13 0.14 0.10 D.;;!" o 13 0.06 0.12 0.03 0.07 O. II 0.14 0.16 0.11 0.17 0.10 0.14 0.27 0.20 0.10 O. 1'1 O. 0.r'6 0.:10 0.37 o. 0.01 o. 15 o. 31 0.0'1 o. o. o tY:, o ;'8 o 15 O. 15 0.;'7 0.32 O. 10 O. II 0.03 0.09 o. 19 O. 1'1 0.::"'9 O. 2'i' 0.14 0.02 0.:.'6 0.21 0.22 o. 16 0.19 0.20 0.17 0.='6 O.;!! 0.30 0.37 0.17 o. (of. O.b::! 0.37 O. 47 0.07 o. "0 !JONDAY o 3:' o 26 O. ;:'1 0.17 o.;?!; o. I? 0.09 O. 12 0.03 O. 12 O.OB 0.02 O. 17 O. II o. 0.06 O. J;! O. 1.2 o. 16 0.09 O. 16 0,02 O. 12' o. 17 O. 22 0.32 0.:16 0.26 o. 37 0.45 o. 30 0.2-1 O. 00 0.14 0.'27 0.21 o 14 o. 31 tl{ltW,'y o. I:; o. 0.16 (l.;tO 0.10 0. 20 0.14 0.03 O. J2 0.26 0.30 o. 19 0.06 0.03 o. 1'2 O. 09 O. JO 0.05 O. II 0.09 O. 08 0.36 O. 27 0.32 o.os O. :15 0.30 O.:!I 0.24 O. 15 O. 0, 0.;'4 0.03 0. 3J TtISPI\Y o 14 0.25 0.31 0.00 0.21 O. 19 0.27 O. 17 0.05 o. 19 O. 15 0.31 0.22 o. 1'2 O. 20 O. 10 O. 10 0.16 O. 13 0.09 O. 18 o. 16 0.30 0.27 0.24 0.30 0.30 0.34 o. 0.26 0.25 0.14 o. 33 0.30 0.31 0.16 0.17 t..'EDNSn"Y O. Hl O. 26 0.17 0.15 0.28 0.15 0.15 0.04 0.17 0.13 0.10 0.01 O. 14 0.15 o. 12 0.05 O. 10 O. 19 O. Jl 0.07 0.05 O. 12 0.31 0.07 O. II 0.30 0.35 0.44 0.27 0.32 0.37 0.20 0.2B 0.26 O. 32 0.;'4 0.34 O. ........ <..0

PAGE 191

Heek ons 77 7B 79 no BI B2 B3 B5 B6 B7 BB B9 90 91 92 93 94 95 96 97 9B 99 100 101 102 103 104 1978 105 106 107 lOB 109 110 III 112 113 114 TI H.m S Df\Y 0.17 o ;:::'4 O.;!9 O. ;.:'9 O. 33 0.33 0.38 0.28 0.2B O. 10 0.14 0.41 O. 15 O.:!5 O.OB 0.22 0.36 O. 15 O. 19 O. 15 0.17 O. 10 O. 14 0.16 o. 16 O. 12 O. 13 0.05 O. 09 0.11 0.14 0.23 0.02 0.09 0.16 o 17 0.20 rRJniW o ;17 o. ;1" O. O. 37 D.':!.7 O. ;'!j O. :10 0.3:1 O. :2(, 0,26 O. J() 0.14 0.16 0.10 0.::'0 0.05 o. ;?O O. Itl 0.31 O.I[J O.::!;? 0.14 0.0'1 0.09 O. J;:! 0.14 0.10 O. 14 0.10 o 14 O. 17 0.17 O. J" O. J? O. J'I 0.1:1 S/ .... lUHHi\Y O. 117 (1 IU 0.17 0.:--" O. 0'1 0.19 0.'4::1 0.?9 O. IS O. O.IB 0.17 O.::!3 0.2;-0.15 0.20 0.27 0.22 o. 15 0.15 O. :l.."l O. 1'1 O. 17 0.05 0.;;>0 O.OlS O.:t:I O. Jb O. ;"0 0.07 O. 1;0 O. I I O. :1(. O.(lH rHJNDAY o.oa o ;:t., O. ;:11 0.::'6 O. O.:?=' o. 14 O. H 0.14 0.1::; 0.31 0.04 0.03 O.2,;t O.;:t9 O.IB 0, 32 O. OB 0.03 O. 05 0.20 O.IB O "1 0.0'1 0.09 O. 13 0.10 0.01 O. J::! O. IB O.H 0.::'0 0.07 ()_ J7 0.:10 o. O. ;!7 tlllNOAY o. ;!{, (I, :17 0.0" O.IB 0.13 O. 19 0.39 O.;!8 O. J" 0.24 0.17 0.09 0.26 0.33 O. 10 0.13 0.09 o. 15 O. 17 0.21 (l.0::-O. OB 0.24 O. 12 O. 10 0.15 O. 19 0. 10 (I, 17 O. 15 (l.09 n. J3' (-(l. J:l 0. 19 lur !-l'!lI'Y 0.17 0.30 0.30 0.33 {I,211 0.27 0 .28 0.09 0.33 O. 19 O.OB O. IB 0.:27 O.IB 0.12 O.:!J 0.17 0.27 O. 13 O.OB O. 16 0.15 O. 17 O. II 0.15 O. 16 O. 12 O.:!5 0.06 O. 19 o. II {l. 05 O. IB {l. 14 0.19 0.06 !l. 27 0. 17 \.JEDNSDI\Y D.:?.:? O. ::!4 0.35 0.21 O.:::!B O,;!6 0.32 O.:U 0.3B O. 1'1 0.13 0.20 O. J7 O.:!4 0.29 0.20 0.15 0.40 o. 16 O. 15 o. J7 0.05 O. IB O. 12 O. J1 O. 05 0.09 0. 14 0.14 O. 10 0.15 0.13 o. 16 o 06 O. 17 O. 19 00 o

PAGE 192

Week nns II:! II" 117 liB 119 120 J2J 124 J:!5 127 I;;'B 129 130 131 132 133 135 136 137 13B 139 HO '41 142 143 144 145 146 1'17 HB ",9 150 151 152 THURSDAY O.;;'B 0.17 0.15 0.27 0.::12 0,25 0.3B 0.38 0.40 0.27 0.31 0.24 0.2B I 0.15 0.32 0.31 O. 19 0.35 O.::!7 0.27 0.29 0.15 o OB O. 10 0.36 0.24 0.15 O. 19 O. II 0.26 o 25 O. IB O. 16 O.IB O. II rR rn"y O. (JrJ O.;:t4 o :J!i o 0.31 O. :.:1:; 0.(10 O.:::!O 0.:19 0.0'7 o. O.O!) O. O.::?O 0.17 O.'ID O. 13 O. 'II 0.::'3 O. 1'1 O."!) O. ''1 0.14 0.13 O. (t? 0.13 O. ;tj't O. () J'1 Sl\lUHlll\Y (I.:I() 0.:16 0.12 0.0'1 0.'17 0.26 0.:13 0.n6 O. :)0 0.17 O. :10 0.::'8 0.32 0.<42 0.33 0.25 0.::'0 0.20 0.'27 0.26 0."7 o. 4'1 0.:-'7 0.26 0.63 0.27 0.24 O. 17 O. 50 0.15 0.11:, O. J[j 0.1:1 (I. O. I? 0.::'11 O. J!j o ::'6 0.27 o.;!? 0.36 0.'18 0.29 o.:n 0.27 0.31 0.;.-0'1 0.17 0.17 0.2.11 0.36 0.27 0.30 0.27 0.23 O. J:! 0.30 0.27 o. 16 0.07 0.06 0.06 0.:17 0.07 O. ;'1 O.2? 0.07 0.1'1 tlflNnAV (I. '" 0. H; o.:Jn 0.37 0.'13 O.:"B O. 15 0 .27 0.36 0.27 0.'1'1 0.22 O. :.J:! 0.36 0. 15 O. 10 0.:>7 O. Ib 0.:14 0.28 O. 10 0.27 0.20 0.21 (t. n'l O. ('7 n. In 0. IB O. J4 U, .7 lll'SHAY n.2;> O.IB O.:?U 0.2"(1 0.35 0.25 0.03 0. II 0.37 0.33 O. 'II O.OB 0.55 0.27 0.22 0.02 0.27 0.29 0.28 0.39 0.24 0.27 0.43 0.21 0.26 0.01 0.26 O.:!'l 0: 19 O.rl) (1.27 (I ..... O.OB O.IB O. IB (1.:>1 O. I? O. 15 WEON5DAY' 0.::'7 O. IS 0.31 0.34 O. ;15 0.:1::1 O. :?5 O.:1D 0.20 0.39 O. 14 0 .26 O. 16 0.35 0.30 0.22 0.33 O.::!l 0.27 0.36 0.22 0 .29 O. 10 0.23 0.27 0.20 0.30 0.32 O. 17 O. 17 0.05 0.15 D. 17 o. /0

PAGE 193

Week ODS lHlIflSDAY rRJllAY ShIHUIJ"Y rH/I,rOI\Y tlftNDI\Y lUI SHAY W[DNSDI\Y 153 0.23 0.11 O. ;'0 O. In (I, (19 o. I? o. 16 154 D.:;!1 D.OlJ o. ;.() O. o. :.';::! o. II o. 13 155 O. J2 0.17 o. III 0.07 o. ;'() o. 14 O. 12 156 0.14 0.12 0.1'1 0.13 (I. ;!:i 0.07 o. 16 1979 IS7 o.o? o. J6 0.26 O. ;"1 0,;'3 o. IB o. lB ISB O.O? o. HI 0.;-0 o. 16 0.0? 0.0? IS? o. lB 0.13 O.::r2 0.07 0.13 O. IV o. 13 160 0.14 o. II O. J:! 0.;--11 0.0'1 0.0? o. 10 161 0.26 0.17 o. 13 O.OB 0.;:';'-O. 15 o. 12 162 o. IV 0.30 0.:13 0,1:-' 0.16 0.07 O. 25 163 0.24 0.11 o. 16 o. 16 o. 12 0.17 164 0.17 o. 13 o. 17 0.21 0.15 o. IV 0.23 165 0.28 O. 0.25 o. 16 0.34 o. 16 166 o. 16 O.::!O 0.33 0.15 0.24 o. II 167 0.33 0.27 O.C'3 o. 16 0.22 0.:!1 16B 0.25 D.::!2 0.47 0.28 o. 16 0.23 0.25 --' 0.22 0.30 ()) 16V 0.21 0.'17 0.31 O.;t6 0.28 N

PAGE 194

APPENDIX F MEAN AND STANDARD DEVIATION OF EVAPORATION DATA FOR THE FORT LAUDERDALE EXPERIMENT STATION

PAGE 195

aBS DAY MEAN STDDEV 1 1 0.135 O. 078 2 2 0.097 0.046 3 3 O. 128 0.048 4 4 0.100 O. 033 5 5 0.117 0.044 6 6 O. 119 0.050 7 7 O. 114 0.049 8 8 O. 119 O. 047 9 9 0.132 0.049 10 10 O. 129 O. 039 11 11 O. 120 0.063 12 12 0.106 0.044 13 13 0.117 0.042 14 14 O. 121 0.063 15 15 0.120 0.059 16 16 0.125 0.059 17 17 O. 134 0.047 18 18 O. 115 O. 038 .19 19 O. 109 0.042 20 20 O. 123 0.037 21 21 O. 131 O. 053 22 22 0.095 0.051 23 23 0.123. O. 031 24 24 O. 112 O. 044 25 25 O. 129 0.061 26 26 O. 135 0.052 27 27 O. 138 0.039 28 28 O. 131 O. 040 29 29 O. 169 0.049 30 30 O. 143 0.041 31 31 O. 147 O. 044 32 1 O. 155 0.057 33 2 O. 144 0.077 34 3 O. 131 0.047 35 4 O. 163 0.074 36 5 O. 135 O. 057 37 6 0.115 0.050 38 7 O. 132 0.056 39 8 O. 142 0.045 40 9 O. 140 0.044 41 10 0.166 0.061 42 11 O. 150 0.044 43 12 O. 142 0.053 44 13 0.148 0.048 45 14 0.172 O. 066 46 15 0.149 0.049 47 16 O. 138 O. 039 48 17 O. 162 O. 062 49 18 O. 142 0.053 50 19 0.165 0.067 51 20 O. 0.050 52 21 O. 184 0.069 53 22 O. 141 0.065 54 23 0.177 0.077 55 24 O. 146 O. 060 56 25 O. 167 0.061 57 26 0.180 0.085 58 27 O. 166 0.070 59 28 0.173 0.057 60 1 O. 178 O. 056 61 2 0.177 0.075 62 3 0.175 O. 055 63 4 0.179 O. 062 64 5 0.184 0.053 65 6 0.190 0.047 66 7 O. 186 0.056 67 8 0.186 O. 059 68 9 0.200 0.087 69 10 O. 174 0.061 70 11 0.202 0.052 71 12 0.183 O. 059 72 13 O. 196 O. 050 73 14 0.184 0.062 74 15 0.208 O. 049 75 16 0.231 O. 077 76 17 0.226 0, 075 184

PAGE 196

185 DBS DAY MEAN STDDEV 77 18 0.198 0.064 78 '19 0.206 O. 058 79 20 O. 210 O. 054 80 21 O. 220 O. 058 81 22 0.205 0.061 82 23 0.216 0.059 83 24 O. 230 O. 070 84 25 0.225 O. 062 85 26 0.215 0.080 86 27 0.210 O. 081 87 28 0.213 O. 074 88 29 0.201 O. 082 89 30 0.205 O. 085 90 31 O. 229 0.084 91 1 0.230 O. 063 92 2 0.228 0.070 93 3 0.247 0.137 94 4 0.224 O. 084 95 5 0.240 0.091 96 6 0.216 0.084 97 7 0.229 O. 052 98 8 0.237 O. 072 99 9 0.227 O. 065 100 10 0.248 O. 111 101 11 0.240 O. 065 102 12 0:233 O. 071 103 13 0.264 0.055 104 14 0.238 O. 091 105 15 0.247 O. 093 106 16 0.257 0.079 107 17 0.286 O. 097 108 18 0.248 O. 061 109 19 0.243 O. 065 110 20 0.263 O. 067 111 21 0.251 O. 076 112 22 0.262 0.081 113 23 0.234 O. 073 114 24 0.261 0.055 115 25 0.242 O. 073 116 26 0.221 0.061 117 27 0.264 O. 100 118 28 0.268 0.072 119 29 0.274 0.091 120 30 0.266 O. 116 121 1 O. 238 0.080 122 2 0.238 O. 079 123 3 0.238 0.090 124 4 0.290 0.096 125 5 0.249 0.074 126 6 0.215 O. 096 127 7 0.290 O. 095 128 8 O. 250 O. 091 129 9 0.275 0.097 130 10 0.275 -0.079 131 11 0.279 O. 062 132 12 0.242 0.069 133 13 0.253 0.069 134 14 0.246 O. 088 135 15 0.268 O. 078 136 16 0.243 0.072 137 17 0.249 0.074 138 18 0.272 0.092 139 19 0.246 0.103 140 20 0.241 0.068 141 21 O. 258 O. 052 142 22 O. 249 0.069 143 23 O. 250 0.072 144 24 O. 262 O. 081 145 25 O. 266 O. 076 146 26 O. 251 O. 063 147 27 0.270 O. 062 148 28 O. 226 0.103 149 29 0.215 0.094 150 30 O. 223 O. 110 151 31 O. 241 O. 090 152 1 0.220 O. 097

PAGE 197

186 aBS DAY MEAN STDDEV 153 2 0.216 O. 104 154 3c 0.226 O. 085 155 4 0.220 O. 107 156 5 0.245 0.089 157 6 0.232 0.125 158 7 0.227 0.087 159 8 0.235 O. 086 160 9 0.227 O. 106 161 10 0.257 O. 103 162 11 0.211 0.076 163 12 0.224 O. 086 164 13 0.225 0.063 165 14 0.211 0.071 166 15 0.233 O. 076 167 16 0.222 0.106 168 17 0.244 O. 100 169 18 0.243 O. 093 170 19 0.242 0.082 171 20 0.225 0.061 172 21 0.223 0.089 173 22 0.224 0.081 174 23 0.208 O. 079 175 24 O. 238 O. 098 176 25 0.218 O. 082 177 26 0.240 0.085 178 27 0.223 0.082 179 28 0.255 0.080 180 29 0.209 0.089 181 30 0.227 O. 071 182 1 0.207 O. 094 183 2 0.244 O. 061 184 3 0.226 0.079 185 4 O. 198 O. 065 186 5 0.224 O. 056 187 6 0.227 O. 079 188 7 0.208 O. 079 189 8 0.245 0.078 190 9 0.221 O. 073 191 10 0.237 O. 076 192 11 0.212 O. 058 193 12 0.245 O. 107 194 13 0.221 0.084 195 14 0.232 0.087 196 15 0.253 0.063 197 16 .0.239 0.089 198 17 0.242 0.088 199 18 0.217 0.056 200 19 0.215 0.060 201 20 0.207 0.068 202 21 0.254 0.078 203 22 0.235 0.077 204 23 0.210 0.092 205 24 O. 256 0.051 206 25 0.251 0.073 207 26 0.254 0.060 208 27 0.226 0.072 209 28 0.239 0.080 210 29 0.249 0.069 211 30 0.234 0.082 212 31 0.214 0.074 213 1 0.256 0.055 214 2 0.248 0.057 215 3 0.228 0.069 216 4 0.243 0.074 217 5 0.220 0.070 218 6 0.243 0.066 219 7 0.241 0.049 220 8 0.236 0.078 221 9 0.205 0.069 222 10 0.233 O. 126 11 0.221 0.069 224 12 0.215 0.091 225 13 0.225 0.070 226 14 0.241 0.084 227 15 0.219 O. 106 228 16 O. 226 0.077

PAGE 198

187 aBS DAY MEAN STDDEV ... 229 17 0.224 0.055 230 18 0.211 O. 076 231 19 0.224 O. 053 232 20 0.232 O. 076 233 21 0.225 0.065 234 22 0.203 0.079 235 23 O. 212 0.090 236 24 0.212 0.062 237 25 0.210 O. 072 238 26 0.203 0.089 239 27 0.216 0.066 240 28 0.243 0.061 241 29 O. 258 0.076 242 30 0.207 0.062 243 31 0.226 O. 104 244 1 0.229 O. 103 245 2 0.221 O. 121 246 3 O. 169 0.076 247 4 O. 197 0.093 248 5 O. 205 0.096 249 6 O. 174 0.064 250 7 O. 194 O. 090 251 8 0.210 0.077 252 9 0.186 0.059 253 10 0.207 0.082 254 11 O. 197 O. 077 255 12 0.215 0.058 256 13 0.224 0.073 257 14 0.204 0.080 258 15 O. 195 0.071 259 16 0.205 0.081 260 17 O. 188 0.065 261 18 0.217 0.076 262 19 0.203 0.081 263 20 0.212 0.087 264 21 O. 196 0.077 265 22 O. 191 0.079 266 23 O. 194 0.075 267 24 0.188 0.059 268 25 O. 170 0.067 269 26 0.176 0.075 270 27 O. 184 0.081 271 28 O. 180 0.051 272 29 O. 180 0.042 273 30 O. 196 0.061 274 1 O. 187 0.057 275 2 O. 169 0.060 276 3 0.214 O. 102 277 4 O. 184 O. 066 278 5 O. 189 0.064 279 6 O. 194 0.076 280 7 O. 196 O. 093 281 8 O. 166 0.088 282 9 O. 186 0.051 283 10 O. 195 0.066 284 11 O. 199 0.061 285 12 O. 194 0.066 286 13 O. 159 0.070 287 14 0.211 0.075 288 15 O. 163 0.082 289 16 0.177 0.066 290 17 O. 198 0.058 291 18 O. 187 0.049 292 19 O. 158 0.062 293 20 O. 160 0.069 294 21 O. 187 0.067 295 22 O. 189 O. 083 296 23 O. 153 0.061 297 24 O. 153 0.074 298 25 O. 161 0.060 299 26 O. 157 0.056 300 27 O. 177 0.049 301 28 O. 192 0.087 302 29 O. 137 O. 056 303 30 0.204 O. 100 304 31 O. 135 0.061

PAGE 199

188 aBS 'DAY MEAN STDDEV 305 1 O. 165 0, 072 306 2 0.167 0, 065 307 3 O. 155 O. 067 308 4 O. 159 O. 057 309 5 0.160 O. 059 310 6 0.150 O. 049 311 7 O. 137 0.042 312 8 O. 153 0.056 313 9 0.144 0.041 314 10 O. 130 O. 055 315 11 O. 153 O. 061 316 12 0.163 O. 060 317 13 O. 138 0.045 318 14 O. 140 O. 043 319 15 O. 140 O. 059 320 16 O. 155 O. 057 321 17 0.145 O. 056 322 18 0.163 O. 064 323 19 O. 134 O. 045 324 20 O. 147 O. 045 325 21 0.157 0.077 326 22 O. 136 0.035 327 23 O. 141 0.042 328 24 O. 138 0.034 329 25 O. 131 O. 039 330 26 O. 128 O. 056 331 27 O. 127 0.060 332 28 O. 106 0.038 333 29 O. 122 0.050 334 30 O. 124 O. 060 335 1 O. 137 0.047 336 2 O. 143 0.075 337 3 O. 142 0.064 338 4 O. 122 0.062 339 5 O. 124 0.045 340 6 O. 140 0.054 341 7 O. 124 O. 054 342 8 O. 105 O. 027 343 9 0.114 0.052 344 10 O. 119 O. 044 345 11 O. 122 0.050 346 12 O. 125 O. 052 347 13 O. 125 O. 048 348 14 O. 120 0.055 349 15 O. 123 O. 053 350 16 O. 121 O. 032 351 17 O. 121 0.044 352 18 O. 108 O. 045 353 19 O. 117 0.033 354 20 O. 108 O. 041 355 21 O. 136 0.067 356 22 O. 119 0.057 357 23 0.116 0.069 358 24 O. 130 O. 068 359 25 O. 133 0.066 360 26 O. 127 O. 096 361 27 O. 120 0.058 362 28 O. 122 0.043 363 29 O. 113 O. 045 364 30 O. 129 0.066 365 31 O. 128 0.055

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APPENDIX G EVAPORATION DATA SUMMARY FORT LAUDERDALE EXPERIMENT STATION BROWARD COUNTY, FLORIDA

PAGE 201

JANUARY VARIABLE N MEAN C. V. DAILY MINIMUM MAXIMUM (in/day) % VALUE VALUE (in/dar) (in/da:5? Y1954 30 0.117 33.454 0.0 0 0.2 0 Y1956 27 0."114 25.484 0.060 0.190 Y1957 30 O. 134 24.077 0.070 0.220 Y1958 28 0.101 42. 572 0.010 O. 180 Y1959' 31 0.113 41. 008 0.020 0.220 Y1960 31 .0.127 23.872 0.070 0.190 Y1961 31 O. 101 39.728 0.040 O. 180 Y1962 30 0.114 37.401 O. 050 O. 190 Y1963 29 O. 116 37.750 0.040 0.240 Y1964 31 0.112 39.310 O. 040 0.230 Y1965 29 O. 124 33.666 O. 040 0.220 Y1966 30 O. 100 38. 596 0.010 0.170 Y1967 31 O. 111 34.225 0.010 0.200 Y1968 31 0, 125 32,479 0.040 0.220 Y1969 31 0.117 41.448 0.020 0.250 Y1970 31 O. 161 34.078 0.090 0.310 Y1972 29 O. 161 43.782 0.010 0.270 Y1973 31 0.116 43.082 0.020 0.230 Y1974 28 O. 124 28.903 0.060 0.1"80 Y1975 31 O. 134 45.611 0.030 0.250 Y1976 31 O. 135 48.206 0.010 0.320 Y1977 29 O. 122 50.407 0.020 0.290 Y1978 30 O. 138 45.762 0.010 0.330 Y1979 31 O. 150 37. 576 O. 040 0.260 AVERAGE 30.1 0.124 37.603 Min= 0,010 Max= 0.330 DAILY VARIABLE' N MEAN C. V. MINIMUM MAXIMUM (in/day) % VALUE VALUE (in/dwd (in/da5'6 Y1954 28 O. 149 25.454 O. 0 0.2 Y1955 27 O. 159 32.998 0.070 0.330 Y1956 29 0.150 31. 060 O. 070 0.270 -Y1957 27 0.174 28.456 O. 100 0.280 Y1958 28 O. 132 38. 418 0.060 O. 270 Y1959 28 O. 154 30.399 0.010 0.220 Y1960 29 0.140 37.486 0.04 0.240 Y1961 28 O. 147 27.049 O. 080 0.240 Y1962 28 O. 149 29. 768 0.090 0.270 Y1963 28 O. 131 47. 018 O. 010 0.310 Y1964 29 O. 152 27. '007 0.060 0.260 Y1965 28 0.152 35.318 0.020 0.260 Y1966 27 O. 145 30.365 O. 060 0.230 Y1967 28 O. 144 35. 394 0.050 0.260 Y1968 29 O. 139 32. 533 0.040 0.250 Y1969 28 O. 147 24.037 0.070 0.230 Y1970 28 O. 147 59. 682 O. 020 0.420 Y1972 29 0.172 53.398 0.040 0.440 Y1973 28 0.147 58.456 0.030 0.380 Y:!.974 23 O. 147 41. 033 0.010 0.250 Y:975 26 0.186 45. 528 0.030 0.11-10 .' Y:976 24 0.173 46. 780 0.070 O. '1 Y1977 28 0.150 50. 583 0.020 0.360 Y1978 28 0.142 50. iJ O. 020 0.300 Y1979 28 O. 192 32.979 0.070 0.340 AVERAGE 27.6 0.153 38.068 Min= 0.010 Max= 0.440 190

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191 MARCH DAILY VARIABLE N MEAN C. V. MINIMUM MAXIMUM (in/day) % in day) VA7UE (in day) Y1954 30 O. 182 33. 501 O. 020 0.360 Y1955 31 O. 194 19.285 0.110 0.300 .::Y1956 31 O. 197 26. 578 O. 100 0.330 Y1957 30 O. 188 27.292 0.050 O. 290 Y1958 30 O. 165 32. 542 O. 080 O. 250 Y1959 29 O. 158 32.042 O. 030 O. 250 Y1960 31 O. 187 23. 549 O. 090 0.270 Y1961 31 O. 199 21. 121 0.070 O. 300 Y1962 31 O. 186 41. 190 O. 060 O. 370 Y1963 31 0.223 25. 704 O. 110 0.350 Y1964 30 O. 198 28.919 0.060 0.330 Y1965 31 0.187 24.987 O. 110 0.350 Y1966 31 O. 176 24.653 O. 070 0.250 Y1967 :31 O. 181 35.736 0,020 0,290 Y1968 31 O. 193 20. 812 O. 130 O. 270 Y1969 31 O. 161 31.765 O. 070 0.270 Y1970 28 O. 179 38.007 0.020 0.340 Y1972 23 O. 224 32. 977 O. 090 O. 380 Y1973 31 0.235 29. 926 O. 100 0.510 Y1974 24 0.235 33. 140 O. 130 0.440 Y1975 29 0.264 31. 772 O. 150 0.510 Y1976 31 0.240 25.275 O. 110 0.360 Y1977 31 0.231 34. 476 0.050 0.350 Y1978 31 0.205 38.665 0.020 0.360 Y1979 31 0.253 33. 547 O. 110 O. 500 AVERAGE 30.0 0.202 29.899 Min= 0.020 Max= 0.510 APRIL DAILY VARIABLE N MEAN C. V. MINIMUM MAXIMUM VArcHE VALUE .(in/day) % (in ay) (in/day) Y1954 O. 197 36.772 O. 050 0.330 30 0.410 Y1955 29 O. 232 26.902 O. 120 0.320 Y1956 30 0.220 29.482 O. 050 29.381 O. 050 0.390 Y1957 28 0.214 O. 290 Y1958 30 0.210 27.398 O. 090 0.390 Y1959 30 0.224 26. 709 O. 070 Y1960 30 O. 222 28. 768 O. 050 0.340 Y1961 30 0.236 32. 482 O. 110 O. 420 Y1962 30 0.220 30. 117 O. 100 O. 440 24.228 O. 100 O. 440 Y1963 30 0.257 0.400 Y1964 29 0.253 22.403 0.150 0.300 Y1965 30 0.239 17.319 O. 140 O. 040 O. 400 Y1966 30 0.238 26.350 0.350 Y1967 30 0.241 19.998 0.090 0.310 Y1968 30 0.239 16. 124 O. 160 0.350 Y1969 30 0.213 31. 9.11 O. 040 0.350 Y1970 30 0.214 37. 778 O. 090 0.390 Y1972 29 0.287 21. 998 O. 170 O. 520 Y1973 30 O. 282 37.033 0.060 O. Y1974 28 0.286 32. 127 O. 050 0.510 Y1975 30 0.310 26.419 O. 030 O. 820 Y1976 30 0.285 54. 535 O. 030 O. 660 Y1977 29 0.315 33. 579 O. 080 O. 470 Y1978 30 0.282 27. 556 O. 030 AVERAGE 29.7 0.246 29.058 Min= 0.030 Max= 0.820

PAGE 203

192 MAY VARIABLE N MEAN I Daily C. V. MINIMUM MAXIMUM (in/day) % VALUE Y1954 28 0.235 30.896 (in/day) (in day) 0.060 0.380 Y1955 30 0.240 32.477 O. 100 0.460 Y1956 30 0.265 32.225 0.040 0.400 Y1957 29 O. 192 28.462 0.070 0.310 Y1958 28 0.222 28.362 0.030 0.300 Y1959 31 0.222 39. 288 0.010 0.330 Y1960 31 0.223 39.233 0.030 '0.460 Y1961 30 0.230 29. 533 0.050 0.350 Y1962 31 O. 248 21.680 0.110 O. 360 Y1963 30 0.244 26. 716 0.'080, O. 340 Y1964 27 0.250 31. 261 0.040 O. 430 Y1965 31 0.268 10.888 0.200 0.310 Y1966 :::l0 0,230 23, 139 0,110 0,300 Y1967 31 0.282 0.170 0.430 Y1968 28 0.207 36. 476 0.040 0.320 Y1969 29 0.231 21.404 ,0. 120 0.340 Y1970 28 0.321 21.451 O. 180 O. 500 Y1972 30 0.269 29.250 0.090 O. 520 Y1973 21 0.260 26. 564 O. 140 0.420 Y1974 28 0.299 23.696 O. 140 0.440 Y1975 26 0.269 39.016 0.020 O. 430 Y1976 31 0.279 39. 523 0.040 0.480 Y1977 28 0.280 51. 636 0.010 O. 620 Y1978 31 0.280 38.975 0.060 0.480 AVERAGE 29.0 0.252 29.908 Min= 0.010 Max= 0:520 JUNE Daily VARIABLE N MEAN C. V. MINIMUM MAXIMUM (in/day) % VALUE VALUE (in/dab) (in/day) Y1954 26 O. 177 37.484 0.07 0.280 Y1955 28 0.208 38.829 0.010 0.320 Y1956 29 0.239 22.831 0.080 0.300 Y1957 29 0.213 34.605 0.070 0.390 Y1958 30 0.232 28.988 O. 060 0.310 Y1959 26 0.219 32.992 0.110 O. 380 Y1960 29 0.214 40. 549 0.060 0.450 Y1961 30 0.205 36.945 0.030 0.370 Y1962 29 O. 187 40. 525 O. 030 0.370 Y1963 30 0.230 29. 764 O. 100 0.380 Y1964 30 0.220 37.369 0.070 0.330 Y1965 29 0.243 30. 150 O. 080 0.460 Y1966 25 O. 199 35.819 0.070 0.330 Y1967 26 0.214 28.075 0.080 0.330 Y1968 29 0.174 38.933 O. 060 0.330 Y1969 30 0.218 37. 114 0.060 0.380 Y1970 29 0.287 25.086 0.170 0.410 Y1972 29 0.284 28. 708 0.090 0.450 Y1973 27 0.281 45.519 0.070 O. 500 Y1974 26 0.215 45.038 0.060 0.430 Y1975 27 0.223 40.997 O. 100 0.450 Y1976 28 0.248 49.782 0.060 0.630 Y1977 27 0.247 42.304 0.030 0.470 Y1978 29 0.288 35. 118 0.020 O. 550 AVERAGE 28.2 0.228 35.979 Min= 0.010 Max= 0.630

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193 JULY VARIABLE I Daily N MEAN C. V. MINIMUM MAXIMUM (in/day) % VALUE VALUE (in/day) (iIi/ d'6() Y1954 29 0.212 35.047 0.030 0.3 0 Y1955 31 0.201 31. 989 0.040 0.310 Y1956 30 0.223 38. 758 0.030 0.360 Y1957 29 0.208 31. 501 0.040 O. 320 Y1958 30 0.248 19.387 O. 140 0.320 Y1959 29 O. 192 32.742 O. 020 0.320 Y1960 29 0.207 39. 135 0.040 0.440 Y1961 31 0.234 32.052 O. 100 0.370 Y1962 30 O. 203 28.889 O. 100 0.320 Y1963 31 O. 267 15.326 O. 140 O. 340 Y1964 31 0.255 20. 559 O. 140 O. 360 Y1965 31 0.218 30.015 O. 060 0.320 Y1966 31 O. 194 38. 850 O. 020 0.310 Y1967 31 O. 260 24.786 O. 020 O. 370 Y1968 31 0.250 25.941 0.090 O. 390 Y1969 31 0.232 30.327 0.070 0,350 Y1970 15 0.216 47.174 0.080 0.360 Y1972 27 O. 248 22.045 O. 130 0.350 Y1973 26 O. 202 46.312 0.030 O. 480 Y1975 27 0.234 38. 109 O. 060 0.430 Y:!.976 22 0.270 38.341 O. 110 0.510 Y1977 31 0.260 32.486 O. 090 0.430 Y1978 31 O. 260 31. 529 O. 080 O. 470 AVERAGE 28.9 0.230 32.230 Min= 0.020 Max= 0.510 AUGUST VARIABLE N MEAN C. V. MINIMUM Daily MAXIMUM (in/day) % (in day) VALfcE (in day) Y1954 31 0.236 33. 113 0.040 0.430 Y1956 30 0.247 33.337 0.080 O. ij-OO Y1957 29 O. 180 26. 561 O. 100 O. 260 Y1958 31 0.228 39.664 0.010 0.410 Y1959 30 0.201 32.340 O. 040 O. 300 Y1960 31 O. 206 30. 802 O. 020 0.310 Y1961 26 O. 237 31. 648 O. 040 0.370 Y1962 31 0.225 4-7. 459 O. 050 0.510 Y1963 30 0.227 31. 884 0.080 O. 340 Y1964 29 O. 233 23.019 O. 140 O. 340 Y1965 31 0.223 25.462 O. 060 0.330 Y1966 31 O. 228 35.286 O. 100 0.380 Y1967 30 0.225 21. 290 O. 100 O. 340 Y1968 31 0.240 27. 178 O. 080 O. 320 Y1969 31 0.222 32. 584 O. 020 0.320 Y1972 31 0.228 24.434 O. 130 0.350 Y1973 21 0.204 23. 759 O. 140 0.330 Y1976 29 0.229 46.986 0.040 O. 570 Y1977 30 O. 237 34.389 O. 080 O. 390 Y1978 21 0.271 37. 613 O. 080 0.480 AVERAGE 29.2 0.226 31.940 Min= 0.020 Max= 0.570

PAGE 205

194 SEPTEMBER Daily VARIABLE N MEAN C. V. MINIMUM MAXIMUM (in/day) % VALUE VAlUE (in/day) (in day) Y1954 29 0.181 40.346 O. 03"0 O. 300 Y1955 30 0.204 25. 468 0.090 0.300 Y1956 30 O. 18432. 757 0.080 0.290 Y1957 29 O. 169 38. 760 O. 030 0.290 Y1958 29 0.200 25.258 O. 090 O. 280 Y1959 26 0.175 31. 668 O. 060 0.300 Y1960 24 0.171 42. 991 O. 050 0.320 Y1961 30 0.216 25.375 O. 100 0.330 Y1962 30 0.234 42.345 O. 020 O. -1-40 Y1963 26 O. 181 46. 194 O. 020 0.370 Y1964 30 0.218 23.998 0.090 O. 320 Y1965 29 0.202 37.825 0, 060 0,370 Y1966 30 0, 145 49,970 0,010 0,2'10 Y1967 30 0.203 33.314 O. 110 0,400 Y1968 29 0.210 32.786 0.080 0.410 Y1969 30 0.174 45.023 0.040 0.320 Y1972 30 0.208 31. 641 O. 060 0.390 Y1973 24 0.219 39. 700 O. 040 0.390 Y1975 28 0.208 38.048 O. 070 O. 400 Y1976 25 0.225 48. 340 0.050 0.450 Y1977 26 0.178 45.227 O. 030 0.410 Y1978 26 0.226 52. 216 0.010 _0. 630 AVERAGE 28.2. 0.197 37.693 Min= 0.010 Max= 0.630 OCTOBER Daily VARIABLE N MEAN C. V. MINIMUM MAXIMUM VALUE VALUE (in/day) % (in/ dab) (in/d7'e) Y1954 29 O. 186 30.951 O. 03 O. 2 0 Y1955 31 O. 181 31. 182 O. 050 0.330 O. 070 0.290 Y1956 30 O. 168 32. 692 O. 260 Y1957 24 0.174 2-4.705 O. 100 O. 310 Y1958 31 0.172 35.352 O. 010 Y1959 29 O. 152 32. 196 0.0-4 0.230 Y1960 29 O. 152 38.238 0.060 O. 330 30.936 O. 060 O. 330 Y1961 31 O. 190 0.320 Y1962 31 0.204 30.069 0.040 0.330 38.029 O. 020 Y1963 29 O. 199 0.040 0.270 Y1964 28 0.173 27.805 0.280 26.40-4 O. 100 Y1965 29 O. 166 O. 030 0.290 Y1966 30 O. 162 42.222 0.250 Y1967 30 O. 162 23. 823 O. 100 0.290 Y1968 30 O. 169 37. 875 O. 040 0.280 33.879 O. 050 Y1969 30 O. 176 O. 010 O. 300 Y1971 31 O. 164 42.231 O. 520 Y1972 31 0.212 49.444 O. 050 0. 630 Y1973 27 O. 190 64. 376 0.010 0.340 Y1975 26 0.170 -4.078 O. 030 0. 350 Y1976 29 0.203 38.776 O. 050 0. 360 Y1977 31 O. 195 42.300 0.030 O. 500 Y1978 31 0.187 60. 929 O. 040 AVERAGE 29.4 0.179 37.413 Min= 0.010 Max= 0.630

PAGE 206

195 NOVEMBER VARIABLE N MEAN C. V. Daily MINIMUM MAX I Mur1 (in/day) % VALUE VALUE Y1953 30 O. 125 41. 682 (in/dab) (in/day) O. 03 0.260 Y1954 28 O. 134 24.499 0.070 0.200 ;' Yl.955 30 O. 133 30.916 0.010 0.210 Y1956 30 O. 159 34.865 0.050 0.300 Y1957 25 O. 135 34.635 0.040 0.250 Y1958 30 O. 142 30.4' O. 050 O. 210 Y1959 26 0.14 30.380 0.070 O. 250 Y1960 29 O. 127 28.823 O. 060 O. 190 Y1961 30 C. 136 26. 589 0.050 0.200 Y1962 29 O. 137 32. 117 0.040 0.230 Y1963 30 O. 157 31. 655 0.060 0.240 Y1964 29 O. 128 39. 505 O. 010 0.300 Y1965 ::30 0.132 43.241 O. 040 0.310 Y1966 ::30 O. 142 42.664 0.020 0.290 Y1967 30 O. 138 39. 140 0.050 O. 270 Y1968 30 O. 143 22.986 O. 100 0.210 V1969 28 O. 146 41. 343 O. 040 0.280 Y1971 28 0.166 35. 006 O. 040 O. 330 Y1972 30 O. 126 37. 188 O. 040 0.210 Y1973 23 O. 143 36.633' O. 060 O. 290 V1975 25 0.175 58.016 0.040 0.430 V1976 28 O. 145 51. 203 0.030 0.280 V1977 30 O. 162 46. 062 O. 020 0.400 Y1978 30 O. 170 29.391 0.050 O. 280 AVERAGE 28.7 0.144 36.206 Min= 0.010. Max= 0.430' DECEMBER VARIABLE' N MEAN C. V. Daily MINIMUM 1"1 A X IMUM (in/day) % VALUE (in/day) in day) Y1953 30 O. 111 40.769 0.040 0.220 Y1954 28 O. 105 26. 114 0.050 O. 160 Y1955 31 0.092 50.429 0.010 0.220 Y1956 30 O. 124 16.723 0.090 O. 180 Y1957 29 O. 124 45. 921 0.010 0.280 Y1958 29 0.105 35.476 0.040 O. 200 Y1959 29 O. 119 29.372 0.020 O. 180 Y1960 31 O. 115 33.471 0.030 0.210 Y1961 30 O. 122 35.002 0.050 O. 200 Y1962 29 0.103 42.251 O. 020 O. 200 Y1963 29 104 27. 501 0.020 O. 150 Y19M 31 O. 117 32.390 0.060' 0.200 Y1965 31 O. 115 27.821 O. 070 O. 190 V1966 31 O. 108 36. 111 0.04 O. 190 Y1967 31 0.112 35.087 0.030 O. 220 Y1968 31 O. 126 30. 564 0.,070 O. 230 Y1969 26 O. 138 34.758 0.080 0.250 Y1971 31 O. 182 42.729 0.050 0.350 Y1972 31 O. 146 60.422 0.030 O. 430 Y1973 28 O. 128 68.622 O. 050 O. 520 Y1975 31 0.152 42.871 0.010 0.310 Y1976 31 O. 129 62.366 O. 010 0.310 Y1977 31 O. 133 49. 539 O. 040 O. 330 Y1978 31 O. 154 32.630 0.060 0.260 AVERAGE 30.0 0.124 39.122 Min= 0.010 0.520

PAGE 207

APPENDIX H LISTING FOR IRRIGATION MODEL

PAGE 208

0000 DIMENSION EVAPCl095). PRECIP(1095), PUMPCl095),[TCI095), 000 1 + H P LJ r'lF' ( lQ9;i ) J i; Hl I? ( ). 0 '7(, ) 1\ J:(i ( J. () '/ ) f" U /-! PIN ( J. (} ) D IInn G ( 1 095 ) 0002 .SlJMB(:l096) 'J 0 I) .. 1 J. () 0 () r Ll I'd'l ri T ( '2 r ',: 7 F n ;) ) 0004 100] FORMATCI5,2F8.2,F9.1) 0005 21)1)0 FORMATCX5,10FO.2) 0006 F: I: A I.. /'IINSTO 0007 0008 C 0009 C ROUTINE TO READ VARIOUS COEFFICIENTS I VARIABLES OO.!. () C ()Oi.:l C COEFA = FRACTHJN OF I'AN ION TiiAT CONS1 nUTI:S 0012 C THE ACTUAL EVAPGTRAHSPIRHTION. OOU C 0014 C 00 ).3 C OOU, C 0017 C 00 J. iJ C 0019 C O(PO C 002:[ C C 0023 C 0024 C OO?:; C 00'::0. C oon c 00?8 C o 9 C 0':':':; 0 C o(n.!. c 003::: C oo,n c 0034 C 003;; C F'ROCEED cor:rn FF,ACTJON OF THF. (iRr:tl TiiAT IS F'E:RVIOlJS. COEFC CONVF.RSION OF MILLION GALLONS OF WATER PUMP AGE TO I NCHES OF lolA n:r, over, T liE' PERV I OlJS AR EA. COE'F)) 1"[f,CENT OF THE IWOTZONF. VACfiNCY TrJ BE. GATr'Ii. TOTSTO = TOTAL STORAGF. AVAILABLE IN THf ROOT ZONE. ro AVOHl CALCUI..ATION MINSTO MINIMUM SlORAGE IN RESERVOIR. (MAY BE: IN Wiir:::;:r. H,rnGArIlJN aI?OIN8). BEGS TO S10RAGE IN THE ROOT ZONE AT THE BEGINNING OF ION. IRRDAY = DAYS OF NO 1:'lnOR TO rrmIGATION. ITYPE RAINFALL INl TUITI()N GCHF.ME = () If IR 1(111I\ Tr 0 N BY N lJ 11 B E r, OF DRY DAYS 1 Ir-IRrnGATlON BY /lINIMlJM STORAGE H riWICA rHlN BY COl1B r NA T H1N OF N I N I MlJI1 197 AND NUM

PAGE 209

003{' C 0037 0038 C 0040 C 004J. C 00.,2 C 00 <}.] C 0044 C 0045 C 198 READC5,1(00) M nw TO, n r:: G S T 0 L.OOP TO READ VAl.UES OF EVAPORATION, F'\\E"CIPnATlON. F'UMI" A 0( ADD IT I ONAU. y, THE H:IU GA TI ON ARRAY I S IN ITIALIZED, THE EVAPOTRANSPIRATION OVrR lHf AREA IS Cfil..CUI..r-l n::D, AND THE I"UMPAGI:' CL1N')r::RTr.::n Ffn1M MIL.LION GALJ_ONS TO INCHES 0\1[\:': THr:: M
PAGE 210

007i C 007',: C LA S T {II N F A I. L. 199 007:1 24 rr: (F'RF:Crr e ,J) < I:r (). 0) GO TO 25 0074 0 R (.: e J :: 0 E e J .. 1 ) ,. elF' C ,J ) .. ::: T e J ) 007:'; IF (STORE (,J) 8T, TDT!H[J) STORE C J) =TOTSTO 0076 NDAY=O 0077 GO TO 37 OO? C 0079 25 NDAY.NDAY+l OORO IF(NDAY.LT.IRRDAY)OO TO 351 0081 AIRRIG(J'=(TClTSTO-STOREeJ-l"CCOEFD*COEFB 008 2 HJ I( I:: ( J ) :: f ( ,J.' l 1+ IH RIG ( ,J ) ... r:: T ( .J 0083 IF(STOR[(J).GT.TOTSTO)STOREeJ'=TOTSTO o 0 8 4 G () r () :) 6 00G5 C o 0 8 6 :3 0 J r (f; TOR [;" ( J --:l ) G T MIN S TO) r;o T 0 2 0087 11I1(1(x() C.J)::.:( (J J.) ) 0088 ST ORC (,J) (,J --:i. )-to?l I RFn G (,J H F'REC H' (.J' FT (J, 0089 J. F ( S r 0 I,: ( .J) ,or, r () nn () S TO f( r:: (.n .: r [] T S TO 0070 GO TO 36 01)') J. C 32 STDREeJ)=STOR[(Jll+PRECIPeJI-[TeJ) 0093 IFeSTORE(JI 0094 GO TO 37 009(, C 0097 C CClNVEr,fiIDN OF Ir,IUGATION Ff.:OM INCllF.S TD IHI.I. ION GAI.I.ONS 009'7 0100 0101 o 1. C 01 0104 0105 F'G:()Cr.:::D 35 STDRE (J) =STDRE ( .. Joo:l) ( .. J) (.J) IFCSTORE(J).or,TOTSTO'STOREeJ'=TOTSTO GO TO :P 351 STOREeJ)=STORC(JltPRECIPeJ'-CT(J: IF (.J) GT. TO rs ro, (,J' GO TO 37

PAGE 211

:t, 0106 0107 01(>8 0109 0110 01 J. J. 011:' OU.:i 01:i.4 01J.5 OlJ (, 0117 o l.J. ('I 0119 0120 012:l 0122 0123 0124 0125 01.26 0127 0:1. 0129 one) 013:\ o J. 0133 c c 36 IF(J,(G.l)GO TO :is 37 SUMA(Jtl)=GUMA(J)tAIRRIG(J) SUI1B (.1+J.) "SUliB (.J) (J) GO TO 40 JD SUMA(J)=AIRRIGCJ) SUMDCJ)-DIRRIGCJ) 40 PUMF'IN('J)=PUMPC.J)-J.nrHGGC.J) .\1 B C .J+l) C.J) 200 C ROUTINE 10 PRINT RESULTS OF CALCULATIONS C c DO 50 K=:l, 1095 WRITt::({,,:'OOO)K.ET(K) ,PRr:CIP(K) .APUMP(K) ,PUMP(K) I :HRJU G (;0 (K) K) (10, F'UMPIN ( K) 50 CONTINUE BI'ClF' END CH:NTRY I*INCLUDE lPPtDATA I*INCLUDE 1* I NCLUD F. f'!'E.JJAT A2 I Nel .. lJ \)1:: I*fOF

PAGE 212

APPENDIX I LISTING FOR RAINFALL PUMPAGE SIMULATION

PAGE 213

J.I I ME N S ION E \! A F' ( :l 095), 1" 1\ ET H' ( :i, (95), 1" IJ M F' ( 1095 ) F' IJ h F' A ( 10S-:;; ) I Nn"[lr::I:: 999 rORMATC3F8.4) J,OC)() rOJ(Mrll' C 4FO. 4) 0000 0001 0003 0004 0006 0007 OO()8 OOc)'! C OO:l (l C OO.l J, C 001:? (; OOJ,;) C 00:i,4 C JOOl rORMATCI5,2F8.2,r9.1) 2C)0() OOU, C 0017 C 001[; C Oc) J,? C 0020 C l C 002? C 0023 0024 0025 0026 0027 0028 o 0 '.7 0030 C J, C 0032 C 0033 0034 0035 PROCEED N=4 [lSJ.,R=4 TOTPRF.:=O.o Rr::AD IN 1HE DECJ. ,., DEC2 ::: CClErrlCIENTS n::::CUNC IN J:if'CJ..:tNF: :c 1" lJMF'A Cf. I.. r N Ii; IN f"lJ lii"A 81:: READ(5,999) DEC1.DfC2.D[CJ READ IN RECOVEr
PAGE 214

203 0036 IF ( F' R E" C H' ( ,J ) f:" R (l (l ) G [) HJ :l () 0 00::':7 (ill TO () 0 0038 C OO:r7 C 0040 C 004 :l 100 N=Ntl 0042 I) n 1 1(.:: [I S I .. r, + 1 OOLj C ()O'14 C C 004{, IF ( IT.! A Y ('; [ ) GO TO :l:lO 0047 r F ( X D rH J:. :? ) () () TO :110 0048 IF (IT.1AY. E('I.:l) (';0 HJ 'LlO 0049 IF( Xl:IH'( .EC1.()(i() TO 0050 C () () '1 C 00:')2 C 005:? 110 IF(N.[fJ.l)GO TO :l 0054 IF(N.I:.('J.?)GO TO H,O 00:')5 1: F ( H I:: II :'\ ) G () TO l.70 00:,)<'> IF ( 4 ) fi 0 10 :l BO OOS? C 0058 C 00::;" C OOt,O ?00 11" i'F... 4) GO l'n OO{,! J r > GO TO J F En. 2) (';0 '1 () ::'70 0063 IF ())SI .. :'" 1.,[ .I.) C() TO '280 00(,4 C oot,:', c OOM, e 0067 P lJ M F' A ( .J ) = r' lJ M F' A ( .) :i ) :t.).1 r:. c; 1 0068 J J) Y :,; l) ::; L R 006'1 GO TO ;"90 0070 C F' R [) C[ F.: D

PAGE 215

204 007:l 260 F' U M F' A ( ,J ) = F' I.IM F' A ( J-:l ) )/:J.I [C 2 007:? R 0073 GO TO 0074 C 0075 270 F'lJMF'A(J)=F'IJMF'A(') :l ) t[:EC3 0076 IDAY=DSLR 0077 GO TO 290 0078 C oon 280 PlJMPA (,J) =PUMPA (J-':l) 0080 J. )) Il Y "' D L R OOf; 1 GO Tn ::'90 OOW? C 0083 290 0084 N=O 0085 GO TO 20 OOOr, C 008i' C OOHn C 00f)S' 1 F' lJ M P A ( J ) = P IJ M F' A ( .J :l ) :';: C C)f:T 111 0090 GO ro 20 0091 1(,0 PlJMPA ( ,J) =f"lJHF'A (,)'-1) tCOF. F A2 00')2 GO l'O 20 0093 Ii'O F'lJMF'A(J)=PlJMF'AeJ"l,tCoErAJ 0094 (;0 ro :20 0095 :l fj 0 F'lJMPA (J) =J"'lJMF'A (.)-:l ) :(:Cnl':T/14 0096 Gl1 TO 20 0097 C 00')8 C 009'1 310 rr (N. [n. J.) GO TO 0 0100 f. r: (N. GO Tn :\60 010:l IF (N. [;0 TO 370 0102 r r" ( N (1 t:: 'I ) (j () '['0 :-S80 C 0104 C 01(>5 410 :rF(N.ER.l )GO TO 0 PROCEED

PAGE 216

010f, 0107 Ol08 0109 C 0110 C 01U OU? 01 J.3 o 1:i. 4 OU:=; C 011.{' C OlD 0118 0119 0120 012J 0122 o 1 0124 0125 C o 1. C 0127 0128 0129 0130 0131 0132 01:::.3 0134 0135 C (, c 0137 0138 0139 0140 F'ROCF.:ED 205 IF(N.E(L2)[;0 TO 460 IF(N.EG.3)[;0 TO 470 If(N,GC,<\)[lO TO 480 5:l0 JF"(N.EQ.l)GO TO TO 560 JT ( N E (J :1 ) GO TO 570 I F ( N C; r:: 4 If;[) TO 580 350 f' lJMF'A ( oj) =PUMF'A ( J -:l ) *(;Of:" 1 no TO ""3/,0 F'UMF'A (oJ) =r'UMF'A (,J i) *COU[l2 GO TO :.:!o :::'70 F"'lJMF'A (oj) =PUMF'A (,j) :!leOF: f""B3 GO TO 20 300 PUMF'A (oj) =r'UMPA (oJ --1) *COf:TB4 (;0 ro 20 450 F'U M F' A ( oj) = I"UM P A ( J .. :l ) tco F. F r; 1 00 TO 460 F'UMF'A (oJ) =I:'UMF'A (oJ--:l) GO ro 20 470 F'UMF'A(J'=PUMF'A(Jl)*COEFC3 GO TO 20 4f;0 f'lJ M F' A ( J = r-'u M F' A ( .1j ):t. C [J F.. F r; 4 GO TO 20 550 F'UMF'A(J)=PUMF'A(Jll*C()[rb1 00 ro 20 560 PUMF'A (.J) =I:'UMF'A <'J-l ) *efJr:rT.l2 00 TO

PAGE 217

014:[ o 14 0144 OJ. ')5 C 0146 C 01.47 C 014fJ 0149 0150 C 0152 C C 0154 C C 0156 01::-j7 r. C 0160 016l 206 570 F'UMF'A (,J) =F'tJMF'A ) *COF.FT.i3 GO TO 20 (.j) (,J-' J.) *CtKFD4 GO TO 20 ;::00 IF (.J d::O. j) GO TO 20 PUMPA(J)=PUMPAeJ-l) GO TO 20 20 CONTINUE rWUTINE TO F'RJ:NT TB OF' C/o)!. ClJl..ATIONS DO 1<::.:1,1095 W R I T r: ,. :? () () 0) 1<, r V A F' ( K ) t P IU' C :t p e K ) P II M F' e 1<) F' U M F' A e 10 CONTINUE Oil.::! STOP 0163 END 01,.4 C$ENTF,Y 0165 OJ. (,() Oil.7 01{,U 0169 OJ.7C) 017:[ 017'2 017;7, 0.75 1 j .06 1 () 1. 04 1* 1. NC LlJDt:: IUNCL.UT.lE 1* 1.NGI .. lJl:lI:' I*EOF 0.83 0.90 1.1.4 J.,I)() 1.10 1.(13 J. ()6 J. 1.05 PP[T.lATA2 PPED(HA3 J 008 j,,()()5 J..001 :[,()01

PAGE 218

REFERENCES Allen, L., Rogers, J., and Stewart, E. Evapotranspiration as a Benchmark for Turfgrass Irrigation. Proceedings of the TwentY-Sixth Annual Florida Turfgrass Management Conference, 26,85-97, 1978. Augustin, B.J. Water Your Florida Lawn. Department of Ornamental Horticulture Fact Sheet, OH-9, IFAS, University of Florida, Gainesville, Florida, Undated. Bachelor, R.A. Household Technology and Domestic Demand for Water. Land Economics, 208-223, 1975. Bailey, J., Benoit, R., Dodson, J., Robb, J., and Wallman, H. A Study of Flow Reduction and Treatment of Wastewater from Households. Federal Water Quality Program Number 11050 FKE, Contract Number 14-12-428. Summarized in Water and Sewage Works, 85, 57-66, 1969. Barnes, J., Boulli, J., and Pochop, L. Optimum Lawn Watering Rates for Esthetics Conservation. Journal of American Water Works Asso-ci a ti on, 21, 204-209, 1979. --Baumann, D.O. The Role of Conservation jQ Water Supply Planning. IWR Contract Report 79-2, U.S. Army Corps of Engineers, Fort Belvoir, Virginia, 1979. Blackwelder, B., and Carlson, P. Survey of the Water Conservation Programs in the Fifty States: Model Water Conservation Program for the Nation. United States Department of Interior, Contract Number 14-34-001-1437, Washington, D.C., 1982. Blaney, H., & Criddle, W. Determining Water Requirements in Irrigated Areas from Climatological and Irrigation Data. United States Department of Agriculture, Soil Conservation Service, SCS-TP-96, Carbondale, Illinois, p. 48, 1950. Boland, J.J. Forecasting the Demand for Urban Water. In Municipal Water Systems: The Challenre for Urban Resource Management. Holtz, D., & Sebastian, S.Eds.;, University Press, Bloomington, Indiana, 1978. Boland, J., Baumann, D., and Dziegielewski, B. An Annotated Biblio graphy on Techniques of Forecasting Demand for IWR Report 81-C03, United States Army Corps of Engineers, Ft. Belvoir, VA, 1981. 207

PAGE 219

208 Bollman, F., and Merritt, M. Community Response and Change in Resi dential Water Use to Conservation and Rating Measures: A Case Study, Marin Municipal Water District. American Water Works Fall Conference Presentation, San Jose, California, 1977. Bogue, S.H. Trends in Water Use. Journal of American Water Works Association, 55, 548-554, 1963. Bureau of Economic and Business Research, University of Florida, Florida Estimates of Population 1972-1981. Annual Reports, University of Florida, Gainesville, Florida, 1973 through 1982. Chow, V.T. (ed.) Handbook of Applied Hydrology. McGraw-Hill, New York, 1964. Clouser, R., and Miller, W. Household Water Use: Technological Shifts and Conservation Implications. Water Resources Bulletin, 453-458, 1980. Cotter, D., and Chavez, F. Factors Affecting Water Application Rates on Urban Landscapes. Journal of American Society of Horticulture Science, 104, 189-201, 1979. Danielson, R., Fe1dhake, C., and Hart, Urban Lawn Irrigation and Management Practices for !later Saving with Minimum Effect Q!l Lawn Quality. OWRT Project Number A-043-COLO, Report, Fort Collins, Colorado, 1981 Eagleson, P.S. Dynamic Hydrology. McGraw-Hill, Inc., New York, 1970. Farnsworth, R.K., and Thompson, E.S. Mean Monthly, Seasonal, and Annual Pan Evapotranspiration for the United States. NOAA Technical Report NWS 34, Washington, D.C., 1982. Feldman, S.L. A Handbook of Water Conservation Devices. Graduate School of Geography, NSF/RANN Grant APR 76-19369, Clarke University, Worcester, Massachusetts, 1977. Franklin, S.L. A Time Series Analysis of Municipal Water Production and Rainfall Characteristics of Deerfield Beach, Florida. Master's Thesis, University of Florida, Gainesville, Flm"ida, 1982. Heaney, J., Huber, W., Medina, M., Nix, S., and Hansan, S. Nationwide Evaluation of Combined Sewer Overflows and Urban Stormwater 2, Report Number EPA-600/2-77-064, University of Florida, Gainesville, Florida, 1977. Heaney, J., Lynne, G., Khanal, N., Martin, W., Sova, C., and Dickinson, R. Municipal Water Demand Projection Models. Florida Water Resources Research Center Pub1 ication Number 61, University of Florida, Gainesville, Florida, 1981.

PAGE 220

209 Hittman Associates, Inc. Forecasting Municipal Water Requirements. Vol. I, The Main 1l System. Report no. HIT-413. Columbia, Maryland, 1969. Jones, J., Allen, L., Shih, S., Rogers, J., Hammon, L., Smajstrala, A., and Martsolf, J. :Estimated and Measured Evapotranspiration for Florida Conditions and Crops. Report Draft, IFAS, University of Florida, Gainesville, Florida, 1983. Khanal, N. Advanced Water Supply Alternatives for the Upper East Coast Planning Area. Technical Publication Number 80-6, South Florida Water Management District, West Palm Florida, 1980. Khanal, N. Agricultural Water Use Modeling. Unpublished Report, South Florida Water Management District, West Palm Beach, Florida, 1980. Khanal, N. Predictive Water Demand Model for Central and Southern Florida. Technical Publication Number 76-2, South Florida Water Management District, West Palm Beach, Florida, 1976. Kreitman, A., Walker, R., and Beck, J. Water Consumption Trends Within the Central and Southern Florida Flood Control District. Technical Publication Number 74-3, Central and Southern Florida Flood Control District, West Palm Beach, Florida, 1974. Leach, S.D. Source, Use, and Disposition of Water in Florida, 1980. United States Geological Survey Water Resources Investigations 824090, Tallahassee, Florida, 1983. Leach, S., and Healey, H. Estimated Water Use in Florida, 1977. United States Geological Survey Water Resources Investigations 79-112, Tallahassee, Florida, 1980. Lewis, K., and Carriker, R. Non-Market Valuation of Water and Resi dential Uses. Florida Water Resources 'Research Center:iReport Number 5..,.-;university of Florida, Gainesville, Florida, 1981. Maidment, D.R. Annotated Bibliography on Water Demands. Water Supply and Management, 1, 117-129, 1979. Maidment, D.R. Municipal Water Use Modeling ... Texas. ASCE National Specialty Conference, Tampa, Florida, 1983. Maidment, D., and Parzen, E. A Cascade Model of Monthly Municipal Water Use. Texas Engineering Experimental Station, Texas A&M University, 1981. Martin, C. Outdoor Water Consumption Figures for the City of Boca Raton, Florida. Unpublished Report, 1982. Metcalf and Eddy, Inc. Process Design Manual for Land Treatment of Municipal Wastewater. EPA, Army Corps of Engineers and USDA, EPA625/1-77-008, 1977.

PAGE 221

Morgan, D., and Smolen, J. Municipal Water Demand. 1976. 210 Climatic Indications in the Estimation of vJater Resources Bulletin, E, 511-512, National Oceanographic and Administration. NOAA Climato logical data for Florida, 1979. National Climatic Center, Asheville, North Carolina, 1979. Portier, K.M. Hydrologic Information Storage and Retrieval System. University of Florida, Gainesville, Florida, 1981. Quakenbush, T.H. Estimating the Effect of Rainfall on Irrigation Water Requirements in Humid Areas. Soil Conservation Service, U.S.D.A., Washington, 25, D.C., Undated. Salas-LaCruz, J., and Yevjevich, V. Stochastic Structure of Water Use Time Series, Hydrology Paper 52, Colorado State University, Fort Collins, Colorado, p. 71, 1972. South Florida Water Management District. Water Use and Supply Development Plan. Vol. 3-A, West Palm Beach, FL, 1977. Stankowski, S.J. Magnitude and Frequency of Floods in New Jersey with Effects of Urbanization. Special Report 28, U.S. Geological Survey, Water Resources Division, Trenton, N.J., 1974. Sterling, M., and Antcliffe, D. A Technique for the Prediction of Water Demand from Past Consumption Data. Journal of the Institution of Water Engineers, 28, 413-420, 1974. Stewart, E., and Mills, W. Effect of Depth to Water Table and Plant Density on Evapotranspiration Rate .i.!!. Southern Florida. Trans actions of ASAE, lQ, 746-747, 1967. Todd, D.K. (ed.) Water Encyclopedia. Water Information Center, Port Washington, New York, 1970. Toomey, J., and Woehlcke, C. An Analysis of Water Requirements and Water Demand for the South Florida Water District. Technical Publication Number South Florida Water Management District, West Palm Beach, Florida, 1979. U.S. Census Bureau. City and County Data Book. U.S. Department of Commerce, Census Bureau, 1980. U.S. Geological Survey. Annual Summary of Public Water Supplies of Selected Municipalities in Florida .. United States Beological Survey, Circular Number 81, 1972. U.S. Geological Survey. Before the Well Runs Dry: Literature Survey and Analysis of Water Conservation. United States Geological Survey Water Conservation Project, Vol. 1, Washington, D.C., 1980.

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BIOGRAPHICAL SKETCH Richard D. Gibney III was born on March 12, 1951, in Coatesville, Pennsylvania, to Mr. and Mrs. Richard D. Gibney Jr. He attended public schools, graduating from North Miami Senior High in 1969. He was accepted into the University of Florida where he remained until his enlistment into the United States Navy in 1971. He served as both a Special Intelligence Communications Officer and Command Training Petty Officer. In 1976 he returned to school, first attending MiamiDade Community College for a refresher, and later attended the University of Florida where in June 1981 he received his Bachelor of Science degree in environmental engineering with honors. In August 1983 he ob tained his Master of Engineering degree from the University of Florida, specializing in environmental resource management. 212