|UFDC Home||myUFDC Home | Help|
This item has the following downloads:
1 WATER ALLOCATION UNDER CLIMATIC VARIABIL I T Y: STATISTICAL ANAL YSIS OF WATER RESOURCES MODELING AND DROUGHT OPERATION IN THE APALACHICOLA CHATTAHOOCHEE FLINT RIVER BASIN By NATHAN TAYLOR JOHNSON A THESIS PRESENTED TO THE GRADUAT E SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENGINEERING UNIVERSITY OF FLORIDA 2010
2 2010 Nathan Taylor Johnson
3 To my Mom and Dad and Jesus and all those who have loved me thr ough this process
4 ACKNOWLEDGMENTS Not only was this the hardest thing I have ever done, it has been the most confusing and constructive This thesis caused much strife and I would slip into spirals of despair looking at all the data and ideas that I had that were in no way useful Fortunately I was not in it alone even though it felt like it and there were those who gave lecture I want to thank my advisor, Dr Greg Kiker fo r his inspirational talks about being a I am still confused about how you are an engineer with such an amazing ability to inspire through speech Dr. Chris Martinez saved the day several times in terms o f statistics and just being able to give guidance for my research Through him, I was given the technical knowledge to complete much of this I am quite certain I would still be beginning phases of this research if it had not been for Steve Leitman and h is guidance, experience, and ability to explain things clearly I also appreciate very much the hospitality and the fact that you give so freely of knowledge and yourself I will always be grateful for your generosity. I have drawn much energy to complete this thesis from friends that have been so faithful and encouraging in the process that is the beauty of friendship You allow me to take a break from work and enjoy the things and people that God has given me Thanks to my good buddies and all the sisters that have been there to distract me and show me the reason I worked so hard on this thing.
5 My dad is my best frien d and without his encouragement throughout the process I think I would have quit getting things done Thanks for your hard words about life to just keep going and think later when you have more time Likewise e verybody knows that the only reason I know anything though is because of mom endlessly demanding excellence in school Also, my sister Melissa keeps me connected with the world outside of Gainesville and she always lets me know that she cares. Finally, as I was going along with this thesis, I have comes to terms with the reality that most of science and engineering involves simply discovering relationships There are relationships between people and water, water and climate, climate and industry, industry and ethics, ethics and people, etc When it all comes full circle the most important relationship, not only to study but to be a part of, is one with Jesus So with that, I wanted to thank Jesus.
6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 11 AB STRACT ................................ ................................ ................................ ................... 13 CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW ................................ ..................... 15 2 EL NINO/SOUTHERN OSCILLATION AND ITS RELATIONSHIP TO THE APALACHICOLA CHAT TAHOOCHEE FLINT STREAMFLOW CHARACTERISTICS ................................ ................................ .............................. 23 Introduction to Water Resources and Climate Variability ................................ ........ 23 Literature Review ................................ ................................ ................................ .... 25 Climate Variability Indices for Defining ENSO Phases ................................ ..... 26 Recorded and Synthetic Streamflow Datasets in the ACF Basin ..................... 29 Data and Methods ................................ ................................ ................................ .. 30 Nonparametric Testing of ENSO Phases Based on Annual JMA ..................... 31 Parametric Testi ng of Lagged Seasonal ENSO and Streamflow Relationships ................................ ................................ ................................ 32 Results ................................ ................................ ................................ .................... 32 Non parametric Rank Sum Test for JMA Annually ................................ ........... 32 Nonparametric Rank Sum Test for ONI Monthly ................................ .............. 33 Pearson Correlation at Lags up to 12 Months of NINO 3.4 SST ...................... 34 Conclusions ................................ ................................ ................................ ............ 35 3 REVIEW AND TESTING OF THE ACF UNIMPIARED FLOW DATASET WITH CORRELATIVE STATISTICS ................................ ................................ ................. 46 Introducti on ................................ ................................ ................................ ............. 46 Review of Concepts ................................ ................................ ................................ 50 Development and Application of Unimpaired Flow Datasets for Water Resources Analysis ................................ ................................ ....................... 50 Development of the Unimpaired Fl ow Dataset for the Apalachicola Ch attahoochee Flint River Basin ................................ ................................ ... 54 Creation of comprehensive initial flow data ................................ ................ 54 Estimating evaporation/precipitation losses from reaches and reservoirs .. 55 Estimation of flow routing through ACF river reaches ................................ 56 Estimating municipal and industrial water use ................................ ........... 57 Estimating thermal plant water uses ................................ .......................... 58
7 Estimating agricultural water demand ................................ ........................ 58 Estimating leakage from dams and reservoirs ................................ ........... 60 Creating flow adjustments to represent ex pected hydrographs in different ACF reaches ................................ ................................ ............. 61 Mitigating uncertainty within ACF gauging stations ................................ .... 62 Methodology ................................ ................................ ................................ ........... 63 Selection of a Comparative Hydrological Dataset Using the USGS Hydro Climatic Data Network ................................ ................................ ................... 64 Parameteric, Non Parameteric and Cross Correlation Analys is of the ACF Unimpaired Flow Dataset with Selected USGS HCDN Stations ................... 65 Comparison on Pre (1939 1954) and Post (1970 1988) Dam UIF Datasets ................................ ................................ ................................ ........ 68 Exploration of Negative Flow Months within the ACF UIF Dataset ................... 69 Wavelet Analysis on Pre and Post Dam Subsections of the ACF UIF Dataset ................................ ................................ ................................ .......... 70 Results ................................ ................................ ................................ .................... 73 Parametric, Non Parametric and Cross Correlation Tests ............................... 73 Comparison of Pre and Post Dam UIF Subsect ions ................................ ......... 75 Negative Flows and their Potential Influence on Overall System Flows ........... 77 Wavelet Analysis of Pre and Post Dam UIF Subsections ................................ 78 Discussion / Conclusion ................................ ................................ .......................... 80 4 A SYSTEMS DYNAMICS MODEL APPLICATION FOR DROUGHT OPERATIONS IN THE APALACHICOLA/CHATTAHOOCHEE/FLINT RIV ER WATERSHED ................................ ................................ ................................ ....... 103 Introduction ................................ ................................ ................................ ........... 103 Literature Review ................................ ................................ ................................ .. 105 Hydrological and Water Resource Management Modeling in the ACF Basin 106 ACF STELLA Model Development Overview ................................ ........... 107 Design and Construction of the ACF S TELLA Model ................................ ..... 109 Fish and wildlife management ................................ ................................ .. 110 Flood control ................................ ................................ ............................ 111 Hydrop ower ................................ ................................ .............................. 112 Navigation ................................ ................................ ................................ 112 Recreation ................................ ................................ ................................ 113 Water supply ................................ ................................ ............................ 113 Water quality ................................ ................................ ............................ 114 Agriculture ................................ ................................ ................................ 114 Federal dam operation summary ................................ ............................. 116 Revising ACF Operations with the Revised Interim Operating Plan (RIOP) ................................ ................................ ................................ .. 116 Data and Methodology ................................ ................................ .......................... 119 Update of the ACF Stella Model to Incorporate Current Operational Strategies ................................ ................................ ................................ .... 119 Building Confidence in ACF STELLA Model Results ................................ ..... 121 Results ................................ ................................ ................................ .................. 124
8 Conclusions ................................ ................................ ................................ .......... 126 5 CONCLUSION ................................ ................................ ................................ ...... 144 APPENDIX: LAGGED CORRELA TION BETWEEN NINO 3.4 AND HCDN DATASETS ................................ ................................ ................................ ........... 150 LIST OF REFERENCES ................................ ................................ ............................. 156 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 165
9 LIST OF TA BLES Table page 2 1 Significance values of the difference in median streamflow between the El Nino and La Nina phase as classified by the annual JMA ................................ .. 41 2 2 Significance values of the difference in median streamflow between the El Nino and La Nina phase as classified by monthly ONI ................................ ....... 42 2 3 ONI rank sum significance value s compared against HCDN dataset ................. 43 2 4 Oceanic Nino Index El Nino ................................ ................................ ................ 44 2 5 Oceanic Nino Index La Nina ................................ ................................ ............... 45 3 1 Chattahoochee, Flint, and Apalachicola HCDN river stations used for comparison with UIF stat ions ................................ ................................ .............. 94 3 2 Total UI average flows over all years from 1939 2008 and sorted f rom the smallest to the largest ................................ ................................ ......................... 95 3 3 Total HCDN average flows over all years available from 1939 1988 and sorted f rom the smallest to the largest ................................ ................................ 96 3 4 List of dams on the ACF with their corresponding completion year and what river reach it is located on. ................................ ................................ .................. 97 3 5 r than 0.9 for all years ................................ ................................ ................................ ................... 98 3 6 .......................... 100 3 7 Selected differences between pre an d post dam correlations as well as samp le size correlation is based on ................................ ................................ .. 101 3 8 List of UIFs with corresponding instances of negative monthly flows, average negative flows, and cumulative impact o f flows ................................ ................ 102 4 1 Composite action zones of the ACF with corresponding basin inflows and releases. ................................ ................................ ................................ ........... 141 4 2 Number of days in Lanier rese rvoir zone elevations 1939 2001 ....................... 141 4 4 Number of days in Lanier reservoir zone elevations 1999 2001 ....................... 142 4 6 Number of days in Lanier reservoir zone elevations 1949 1952 ....................... 142 4 7 Number of days less than flow thresholds at Jim Woodruff for 1939 2001 ....... 143
10 4 8 Number of d ays less than flow thresholds at Jim Woodruff for 1998 2001 ....... 143 4 9 Number of days less than flow thresholds at Jim Woodruff for 1949 1952 ....... 143 4 1 0 Number of days less than flow thresholds at Jim Woodruff for 1984 1988 ....... 143
11 LIST OF FIGURES Figure page 1 1 Relative location of ACF Basin in the Eastern United States .............................. 21 1 2 Three main sub basins: Chattahoochee, Flint, and Apalachicola ....................... 21 1 3 Three main sub basins with bo th federal and private da ms as well as 4 main reservoirs ................................ ................................ ................................ ............ 22 2 1 Classified ENSO indices on the coast of P eru NWS/ CPC ................................ .. 37 2 2 January significance values of the differ ence in median streamflow between the El Nino and La Ni na phase as classified by ONI. ................................ ......... 38 2 3 March significance values of the difference in median streamflow between the El Nino and La Ni na phase as classified by ONI ................................ .......... 39 2 4 Pearson correlation at lags up to 12 months of ENSO 3.4 SST for a) Sumatra unimpaired flow and b) Chipola (2359000) HCDN ga uge ................................ ... 40 3 1 The Apalachicola Chattahoochee Flint with HCDN and control points for UIFs with control points labeled. Zones are also labeled 1 5 ................................ ...... 86 3 2 Map of HCDN stations with US GS gage stations labeled and div ided into different zones 1 5. ................................ ................................ ............................. 87 3 3 Example of cross correlation ................................ ................................ .............. 88 3 4 Generalized system flow diag ram for developing UIFs ................................ ....... 89 3 5 Histogram of total number of negative flow s by month in the UIF dataset .......... 89 3 6 Instances of negative v a lues by year over all gauges. ................................ ....... 90 3 7 Example of wavelet analsysi using Nio 3.4 ................................ ....................... 91 3 8 Wavelet analysis of (a) Sweetwater Creek (23370 00) (b) Atlanta (ATL_UI), (c) Morgan Falls (MF_UI) ................................ ................................ ................... 92 3 9 Wavelet analysis of (a) Ichawaynochaway Creek (2353500) (b) Jim Woodruff (JW_UI) ................................ ................................ ................................ .............. 93 3 10 Wavelet analysis of (a) Chipola River (2359000) (b) Blountstown (BLO_UI) ...... 93 4 1 User Interface level of the ACF STELLA model ................................ ............... 130 4 2 Systems dynamics level of the ACF STELLA model ................................ ........ 131
12 4 3 Main sector of the ACF STELLA model ................................ ............................ 132 4 4 Lake Lanier reservoir z one elevat ions ................................ .............................. 133 4 5 WF G eorge reservoir zone elevations ................................ .............................. 133 4 6 West Point reservoir zone elevations ................................ ............................... 134 4 7 Composite storage of the ACF system and co rresponding zones during 2008 134 4 8 Description of the RIOP operations at Jim Woodruff RIOP NORAMP .............. 135 4 9 Description of Jim Woodruff preliminary release JW Prelim Release cfsd ....... 136 4 10 Description of the Drought Contingency Operations Switch ............................. 136 4 11 Description of final release with ramping considerations JWRelease cfsd ....... 137 4 12 Lake Lanier Elevations for 1998 2001 drought under 2010 demand dataset ... 138 4 13 Jim Woodruff Outflow for 1998 2001 drought under 2010 demand dataset ..... 138 4 14 Lake Lanier Elevations for 1984 1988 drought under 20 10 demand dataset ... 139 4 15 Jim Woodruff Outflow for 1984 1988 drought under 2010 demand dataset ..... 139 4 16 Lake Lanier Elevations for 19 49 1952 drought under 2010 demand dataset ... 140 4 17 Jim Woodruff Outflow for 1949 1952 drought under 2010 demand dataset ..... 140 A 1 Lagged correlation between Nino 3.4 and HCDN datasets .............................. 155
13 Abstract o f Thesis Presented t o t he Graduate School o f t he University o f Florida i n Partial Fulfillment o f t he Requirements f or t he Degree o f Master o f Engineering WATER ALLOCATION UNDER CLIMATIC VARIABILTIY: STATISTICAL ANALSYSIS OF WATER RESOURCES MODELING AND DROUGHT OPERATION IN the APALACHICOLA CHATTAHOOCHEE FLINT RIVER BASIN By Nathan Taylor Johnson August 2010 Chair: Gregory Kiker Major: Agricultural and Biologi cal Engineering The unimpaired flow data was developed to populate the HEC 5, ACF STELLA, and more recent ly Res Sim models for 23 local inflows at control points throughout the Apalachicola Chattahoochee Flint (ACF) watershed Unimpaired flows are create d when the influence of human regulation and withdrawals are removed from historical streamflow gauge records R egional ly accumulated unimpaired inflows or unimpaired local incremental inflows were developed and routed together through a model in the main channels to form the main channel unimpaired flows The UIFs are used as inputs into several models where they are routed through a series of diversions, consumption, and water control structure releases Simulations of policies on the ACF can then be c onducted for a variety of operations This type of model has been used to analyze the impacts of various governance scenarios on the ACF basin and other basins throughout the nation The unimpaired flows must accurately represent the flows that would hav e occurred historically without the influence of human cons umption and routing throughout the watershed Only when the unimpaired flows resemble natural flows can policies be accurately evaluated Throughout the development of the ACF STELLA model
14 howeve r, there has been some speculation as to the accuracy of the validation and thus sheds uncertainty on the entire modeling process This observation leads to the questionability of the model to accurately model the processes within the basin One method t o build confidence in the underlying dataset is to compare the unimpaired flow to other tributaries that have not been affected substantially by anthropogenic influences The Hydroclimatic Data Network (HCDN) was constructed to give such independent strea mflow datasets for comparison with climate and other streamflow datasets The unimpaired flows were compared with the HCDN datasets and through both correlation and spectral analysis confidence was built for local inflows into the model From this analys is, it was determined that 10 of the 23 local inflows have been given confidence while 4 local flows are considered questionable. Additionally, the ACF STELLA model was updated to represent current operations in the watershed Metrics were created to gaug e the relative success of the operations plan The RIOP operations were the most conservative during the entire time period of modeling as well as during the three selected droughts. Finally, climate variability was studied through a series of statistical tests as well as direct correlation with sea surface temperatures The results indicated that sea surface temperatures have a significant correlation at the southern end of the basin with streamflow
15 CHAPTER 1 INTRODUCTION AND LIT ERATURE REVIEW Wate r conflicts in the Apalachicola Chattahoochee Flint (ACF) River Basin have persisted for over twenty years with ongoing negotiation, discussion, mandated compromise and litigation among Georgia, Alabama and Florida Increasing human demands on water resou rces have put pressure on river systems to provide consistent and sustainable flows for often competing interests Complex water conflicts can persist at low levels for decades and escalate rapidly under drought conditions, providing a challenging environ ment for the systematic analysis and implementation of resolution strategies (Scholz and Stiftel, 2 005; Dellapenna, 2006) This dynamic has been evident in the Apalachicola Chattahoochee Flint (ACF) River Basin, covering three southern states with diverse populations and water resource objectives (Leitman and Hatcher, 2005; Jordan et al., 2006) Water conflicts in the Apalachicola Chattahoochee Flint (ACF) River Basin have persisted for over twen ty years with ongoing negotiation, discussion, mandated compromise and litigation among Georgia, Alabama and Florida (Jordan et al., 2006) T he ACF basin is located in southeastern United States and contains one of the largest rivers in this region (Figure 1 1) The basin covers approximately 50,800 km 2 and drains parts of eastern Alabama, northern Florida, and much of western Georgia (Figure 1 2) south of Atlanta (Figure 1 3) The Apalachicola River begins at the confluence of the Ch attahoochee and Flint Rivers The Apalachicola runs south through the panhandle of Florida and drains into the Gulf of Mexico. The Chattahoochee River is impounded at a
16 number of locations while the Flint River is considered for most purposes unregulated Lake Sydney Lanier is the principle storage reservoir and lies in North Georgia above the city of Atlanta It contains 1,087,600 acre feet (62.5%) of storage capacity within the river system while West Point provides 17.6% followed by WF George at 14.0 % and Jim Woodruff at 5.0%. Lake Seminole forms the reservoir behind Jim Woodruff dam and though it has storage capacity is considered for most purposes a run of the river project (USACOE, 1989) The basin has a diverse stakeholder group as the river spans over the southeast United States To the north, the city of Atlanta is a large municipal and industrial water user of the headwaters of the Chattahooch ee and demands significant water resources The southern part of the basin is mainly used for agriculture. Alabama Power uses ACF water to provide cooling to multiple power plants, including the Farley Nuclear plant The lower ACF in Florida supports a significant seafood industry provides a home to the gulf sturgeon ( Acipenser oxyrinchus desotoi ), fat threeridge mussel ( Amblema neislerii ), and the purple bankclimber mussel ( Elliptoideus sloatianus ) protected under the Endangered Species Act Moreover t here are shipping interests in the Apalachicola River upstream to Columbus, Georgia where the federally maintained channel navigation ends growth and the basin stakeholders have signific ant and differing demands on the water supplied by the ACF (Meruelo, 2006; Jordan et a l., 2006) One phenomenon that may play a role in managing the ACF basin is climate variability associated with sea surface temperatures (SST)s. Interannual variability associated with various SSTs can provide significant predictive information for
17 hydro logic resources around the world Many economies and environments depend on their water resources to provide both groundwater and surface water for sustenance In the southeastern United States El Nino conditions are characterized by above normal annual precipitation patterns while La Nina conditions are characterized by below normal patterns (Ropelewski and Halpert, 1986) While El Nino Southern Oscillati on (ENSO) based studies of precipitation have been conducted in the ACF (Stevens, 2008; Green et al ., 1997) the effects of ENSO on streamflow patterns in the basin have not been explored This correlation will be explored in this research in an effort to understand if climate variability is correlated with streamflow in the ACF Increasingly, water resource managers have turned to model representations of the ACF channel/reservoir system with a specific dependence on the use of Unimpaired Flow (UIF) datasets to provide the baseline flow conditions to model water allocation scenarios (USACOE, 1997) UIF data are described as the historically derived flows that have been systematically adjusted to remove the effects of anthropogenic influences such as w ithdrawals, returns, and the effects of water control structures. Once a UIF dataset has been established, the information is used to drive models and tools that place future demands and water control structure s on the river system. In this way, alternat e demand scenarios can be placed on the UIF data to test certain allocation formulas From the simulations, the outputs can be assessed to determine what would be considered acceptable flows according to various performance measures Many variables exist when determining the proper water control plan including various user demands, duration of low flows, frequency of low flows, risk of flooding, and water control structure infrastructure.
18 While water resources models have been developed to facilitate th ese complex, multi objective negotiations, no permanent basin management plan has been adopted by the three conflicting states The object oriented graphical simulation ACF STELLA (System Thinking Experimental Learning Lab Application) model was created d uring the comprehensive study of the Apalachicola Chattahoochee Flint watershed in the mid It was initially created at a monthly time step as a shared vision model to assess the hydrologic regimes of the river under different forecasted demand datas ets to determine viable allocation strategies for the river network Later the United States Fish and Wildlife Services (USFWS) provided funding to scale up the ACF STELLA model to a daily time step in efforts to construct it to be more comparable to the HEC 5 modeling platform Unlike a more physical based watershed model, this model uses mechanistic deterministic processes on a STELLA platform Reaches have been simplified into the Chattahoochee, Flint, and Apalachicola with local inputs and grouped e xternal inputs The model was then overlain by human constructs and dam release logic under the Water Control Plan (WCP), Interim Operation Plan (IOP) as well as the Revised Interim Operations Plan (RIOP) The synthetic Unimpaired flow (UIF) datasets fro m 1939 2001 produced by the Army Corps of Engineers derived from original USGS datasets force the model A UIF dataset was developed to provide water inputs to the HEC RESSIM/HEC 5 (Klipsch and Hurst, 2007) and ACF STELLA (Ahmad et al., 2004; ISEE, 2009; Goodman et al., 2001) reach/reservoir models for 23 local inflows at control points throughout the ACF watershed. These locations are inflow points that are regionally accumulated inflows in th e main channels by the reach/reservoir models Within the
19 ACF river system models, the flow data is routed through the simulated diversions, consumption, and managed river system Simulations of scenarios on the ACF have been conducted for a variety of w ater resource operations. As a result of data analysis, model simulations and a series of significant droughts within the basin, a new operations plan for the management of federal reservoirs was introduced between 2006 and 2008 These new (and current as of June 2010) operations are called the Revised Interim Operations Plan (RIOP) which replaced the previous Interim Operations Plan (IOP) Even though Jim Woodruff Dam is operated as a run of the river dam, t he RIOP only addresses the Jim Woodruff relea se schedule and contains within it the lower releases when the Drought Contingency Operations have triggered (USACOE, 2008a) On the other hand, flexibilit y in upstream dam releases has been given to the Corps to meet the needs downstream The p roposed action for other than the use of the composite reservoir storage of t he system and releases from the upstream reservoirs as necessary to assure releases from Jim Woodruff Dam support and minimize adverse impacts to endangered or threatened species or critical (USACOE, 2008b) This increased dependence on simulation tools and UIF datasets within a high tension, drought frequent environment highlights an interesting quandary. While much use and critical emp hasis is placed on the development and use of unimpaired datasets and their concomitant water resource models within the ACF, limited information concerning the testing of these datasets is available beyond institutional technical reports. This limitation is evident where the primary technical reference for UIF datasets
20 are provided in a technical appendix (USACOE, 1997) and the primary technica l reference for the ACF STELLA model is (Leitman and Hamlet, 2000; USACOE, 1997) Gi ven the critical role these datasets and models play within water resources allocation and planning decisions, a useful undertaking would be to analyze and test the UIF datasets and water system models to provide greater confidence and knowledge of the inh erent uncertainties contained in the data and the models. Consequently, this research has three primary objectives: 1. Evaluate the potential effects of ENSO phase on streamflows in the ACF basin, 2. Provide further time series and correlation analysis of the UIF datasets developed for the ACF basin, and 3. Explore the effects of introducing modified drought sensitive operations (RIOP) on the ACF STELLA model. This m submissions. The fi rst paper investigates the correlation between ENSO and both measured an independent streamflow dataset (USGS HCDN) as well as the synthetic unimpaired flows dataset developed for ACF model inputs The second paper reviews the development of the unimpaire d flow dataset and through statistical correlations with HCDN dataset confidence is built The third paper performs a review of the ACF STELLA model structure as well as includes updates to current operations The model was then tested to observe flows d uring drought years against previous operations. A short conclusion chapter is included to integrate the results from the three papers and to provide ideas for the way forward in continuing water resources modeling research in the ACF.
21 Figure 1 1 Re lative location of ACF Basin in the Eastern United States Figure 1 2 Three main sub basins: Chattahoochee, Flint, and Apalachicola
22 Figure 1 3 Three main sub basins with both federal and private dams as well as 4 main reservoirs: Lake Sidney Lanier West Point Lake, Walter F. George Reservoir, Lake Seminole
23 CHAPTER 2 EL NINO/SOUTHERN OSC ILLATION AND ITS REL ATIONSHIP TO THE APALACHICOLA CHATTAH OOCHEE FLINT STREAMF LOW CHARACTERISTICS Introduction t o Water Resources a nd Climate Variability Interannua l variability associated with various sea surface temperatures (SST) can provide significant predictive information for hydrologic resources around the world Many economies and environments depend on their water resources to provide both groundwater and surface water for sustenance Studies suggest that precipitation and other climate factors have some relationship to SST conditions in the Atlantic and Pacific Oceans (Tootle and Piechota, 2004; Schmidt et al., 2001; Chiew et al., 1998; Continental U.S. streamflows have shown significant responses to climate indices such as El Nino Southern Oscillation (ENSO) (Dracup and Kahya, 1994; Kahya and Dracup, 1993; Beebee and Manga, 2004; Chiew and McMahon, 2002; Piec hota and Dracup, 1999; Hamlet and Lettenmaier, 1999; Tootle et al., 2005) and the Pacific Decadal Oscillation (PDO) (Beebee and Manga, 2004; Hamlet and Lettenmaier, 1999) and the Atlantic Multidecadal Oscillation (AMO) (Tootle and Piechota, 2006) ENSO describes the periodic inter annual warming and cooling of the eastern equatorial Pacific Ocean along with the atmospheric pressure (Southern Oscillation) pattern across the tropical Pacific The warm phase is referred to as El Nino while the cool phase is termed La Nina with phase cycles of two to seven years (Trenberth, 19 97; Hanley et al., 2003) Specifically in the southeast United States ENSO has been used in studies of agricultural yield and vigor correlation (Martinez et al., 2009; Mennis, 2001; Hansen et al., 1998) water quality (Lipp et al., 2001; Keener et al.) streamflow and precipitation (Sun and F urbish, 1997)
24 In the southeastern United States El Nino conditions are characterized by above normal annual precipitation patterns while La Nina conditions are characterized by below normal patterns (Ropelewski and Halpert, 1986) This characteristic holds for the Apalachicola/Chattahoochee/Flint (ACF) River basin where most of the precipitation comes in the winter and spring. ACF flows are often limited in the spring and fall when the water is most needed for agricultural purposes (Arrocha et al., 2005) ENSO was found to be the dominant cor relation with precipitation in the southern ACF basin while both AMO and PDO were prevalent in the northern part of the basin (Stevens, 2008; Green et al., 1997) While ENSO based studies of precipitation have been conducted in the ACF (Stevens, 2008; Green et al., 1997) the effects of ENSO on streamflow patterns in the basin have not been explored This may be due to a combination of factors including the management of the ACF flows by the US Army Corps of Engineers (USACOE) through a series of dams as well as by extensive agricultural extraction for irrigation throughout the basin that has substantially changed the characteristics of flow (USACOE, 1989) For this study, streamflow patterns will be explored with respect to ENSO phase using synthetic unimpaired flow datasets derived by the USACOE (USACOE, 1997) as well as by using the USGS Hydroclimatic Data Network (HCDN) flow dataset The ACF basin provides a useful exp loration site for ENSO phase as it extends across a large part of the Southeastern United States and encompasses a broad range of precipitation and hydrologic interannual variation Knowledge of the relationship between water resources and regional climat e variation in this area will be valuable for agricultural irrigation efficiency, assisting water managers in making more
25 informed decisions and formulating proper regulation policy (Chiew et al., 2003; Hansen et al., 1998) The overall objective of this study is to statistically examine the regional patterns of associations between sele cted ENSO indices and streamflow variability in the ACF basin Specific objectives of the study include the following: 1. Use nonparametric testing to evaluate the inter annual response of yearly streamflow to the phase of the Japan Meteorological Agency ( JMA) index 2. Utilize a nonparametric rank sum test to evaluate the inter annual response of monthly streamflows to the phase of the Oceanic Nino Index (ONI) index, and 3. Conduct a parametric testing of the lag correlation between ENSO 3.4 and ACF streamflow. Through these statistical tests, we intend to explore how ENSO phases can significantly aid water resource managers in yearly and monthly planning (Schmidt e t al., 2004) Accordingly, this paper is divided into three sections. A Literature Review summarizes the variety of ENSO indices in use by various agencies to describe climate variability along with the hydrological datasets used to describe unimpaired streamflows in the ACF basin. A Data and Methods section highlights the nonparametric and parametric statistical tests used in the analysis. Finally, results and conclusions sections highlight the locations of statistical relevance and the lessons to be drawn from this effort. Literature Review As there are many varying descriptions of ENSO variation, a review of the primary indices and their formulation will allow the reader to gain perspective on the methods and dynamics for defining El Nino, Neutral an d La Nina events Emphasis is given to the review of these phases within the ACF basin. In addition, a review of streamflow
26 datasets is provided to give insight on both actual and synthetic datasets used for streamflow analysis. Climate Variability Indic es for Defining ENSO P hases Throughout climate science there is no single universally accepted definition of ENSO signal (Beebee and Manga, 200 4) This is largely due to statistical correlations of various time series with SST having stronger correlations in different parts of the ocean (Trenberth, 1997) However, consistently highly correlated areas have been classified into indexes Figure 2 1 shows various sea surface areas used for statistical correlations aver age anomalies over the region (5N 5S, 120 170W) defined using a set of improved homogeneous historical SST analyses named ERSST.v2 (Extended Reconstruction of SST version 2) (Smith and Reynolds, 2005) This index is used to classify the highly regarded Oceanic Nino Index (ONI) into categorical phases (El Nino, La Nina, Nuetral) when it meets certain thresholds described below The ONI is distributed by the Climate Prediction Center (CPC) and is considered to be a good indicator of ENSO phase classification in the equatorial Pacific It is used frequently in climate research (Beebee and Manga, 2004) The ONI, available from NOAA at ( http://www.cpc.ncep.noaa.gov/products/precip/CWlink/MJO/enso.shtml ), is d efined as +/ 0.5C a 3 month running mean departure from average SST in the Nino 3.4 region, based on the 1971 2000 base period Moreover, in order to be classified as an ENSO phase there must be a minimum of five consecutive over lapping seasons ONI i s typically defined from 1950 through the present due to limitations in reconstructing sea surface temperatures with ERSST.v2
27 Another popular index is distributed by Center for Ocean Atmospheric Prediction Studies (COAPS) called the Japan Meteorological Agency Index (JMA) ENSO phases have also been classified by year from research from the JMA index based on observed data from 1949 to the present (Trenberth, 1997) The index is defined based on a six month running average of spatially averaged SST anomalies over the region of the tropical Pacific Ocean (4S 4N, 150W 90W) If the running average is 0.5C or greater for six consecutive mon ths for the water year (Oct Sep) and includes (Oct, Nov, Dec) then it is categorized as El Nino Likewise, if the running average is less than 0.5C for six consecutive months and meets the other El Nino conditions it is considered La Nina Otherwise th e phase is called neutral since it is between the two thresholds (Trenberth, 1997) Additionally, this paper will also explore the difference s between yearly JMA and monthly ONI relationships to the ACF UIF and HCDN streamflow datasets The JMA index is a broad index used to develop yearly correlations for streamflow This method is useful when exploring data as well as providing even number of months per phase when the index is distributed monthly or seasonally It can be used at a monthly resolution; however it is not considered a monthly classification A large disadvantage of JMA is that summer months (July, Aug, Sep) are defined by the previous years (Oct, Nov, Dec) The ONI conversely, is used in seasonal and monthly studies and is not applied in yearly applications since it is not on a yearly scale Summer months typically do not correlate well with ENSO due to the high spatial and t emporal variability of precipitation caused by localized convection precipitation
28 Previous research has established strong relationships between ENSO and hydrometerological regimes in the southeastern United States during winter and spring months (Schmidt et al., 2001) A study conducted on drought within the ACF defined three or more years with below average precipitation and at least one ye ar below the 25 th percentile The research showed that there was no obvious return period for droughts in the basin, however an initial relationship between precipitation anomalies and ENSO was discovered (Arrocha et al., 2005) Since La Nina is typically associated with below average precipitation in the southeast, the study showed that La Nina occurred during about 30% of the below normal pre cipitation years Further study (Stevens, 2008) was conducted on the relationship between precipitation and other climate indexes including At lantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and El Nino Southern Oscillation (ENSO) Canonical correlation was used to establish the individual and coupled relationship of climate indices on precipitation and temperature The result confirmed that the lower climatic region of the ACF had precipitation patterns that correlated with ENSO while the northern climatic region better correlated with coupled AMO and PDO (Stevens, 2008) The AMO and PDO were not used in this study because both indexes are Interdecadal and not useful for the interannual scale that is being examined Both t he relationship of streamflow and precipitation to climate indices were investigated in two studies in the state of Florida (Schmidt et al., 200 1) Using autoregressive moving average models (ARMA) and linear transfer function methods on annual Florida streamflows, statistical analysis established that 40% of annual precipitation variation and 30% of river discharge were correlated with the JMA index of
29 ENSO (Sun and Furbish, 1997) El Nino and La Nina events correlate well with higher/lower streamflow in the Gulf of Mexico region whi ch includes Georgia and Florida (Dracup and Kahya, 1994; Kahya and Dracup 1993) Recorded and Synthetic Streamflow Datasets in the ACF Basin Since the waters of the ACF are heavily regulated, it would be difficult to observe the effects of ENSO cycles on the streamflow characteristics as measured within various river reache s Streamflows in the ACF basin are largely influenced by human management of the rivers system The Chattahoochee has 5 federal dams and upwards of 6 private full river dams Conversely the Flint River is relatively untouched by water control structure s having only 2 smaller dams (Leitman and Hatcher, 2005) This being said, the Flint River is influenced by significant groundwater pumping fo r irrigating the farmlands in the area (GDNR, 2006; Zhang, Hawkins, et al., 2005) However, durin g a comprehensive study of the ACF basin (Jordan et al., 2006) a streamflow dataset was developed (USACOE, 1997) that has been adjusted to remove the effects of anthropogenic influences such as withdrawals, returns, and the effects of water control structures Adjustments were made to remove agricultural, municipal, industrial, and thermal power withdrawals and returns and then the dataset was smoothed to provide a more historical characteristic hydrograph (USACOE, 1997) There are 24 locations at which the flow in the main channel can be attained but only 21 of these locations were used since the local inflow contributions are very small at three sites These flows will be called the unimpaired flows (UIF) dataset Another streamflow dataset that was used in this research is called the Hydro Climatic Data Network (HCDN) dataset It was produced by the USGS to help gauge the effects of climate variability and change on wa ter resources throughout the United
30 States (Slack and Landwehr, 1992) Investigating long term changes in rainfall, hydrologic conditions, and other geophy sical data is extremely important to water allocation and land use management The USGS collected records of streamflow that have been considered relatively unaltered by anthropogenic influences such as artificial diversions, storage or other changes in s tream channels that affect hydrologic conditions Inspecting these datasets can reveal patterns in natural flow regime and extreme hydrologic condition frequencies that would have otherwise been covered by anthropogenic influences on impaired waterways Each streamflow was individually selected based on strict conditions of measurement accuracy and natural conditions No values in the HCDN dataset were filled in with empirical algorithms and the minimum length of continuous record was 20 years The dat a was reviewed jointly with data specialists of each USGS District office The total dataset consists of 1,659 gauges throughout the United States concentrated mostly in the Northeast where water control structures are relatively less abundant The datas et starts in the late 19th century and extends through September of 1988 (Slack and Landwehr, 1992) Due to the variable length of time series and the questionable quality of data before 1935, HCDN data before 1939 were removed (USACOE, 1997) The dataset has been used in multiple s tudies to provide validation for hydrologic and climatic models (Leung et al., 2003; Dai et al., 19 98) It has also been used extensively to examine the effects of climate variability on streamflow (Cayan et al., 1999; Barlow et al., 2001; Stone et al., 1999; Piechota et al., 1997) Data and Methods For this research, the ONI was ex tended back in time from 1950 to 1939 to match the streamflow record used in this study All thresholds for the index were met and the
31 months of La Nina increased in instances 49 months from 195 to 244 and El Nino increased just 9 from 176 to 185 Table 2 4 and 2 5 show the running seasonal categories and the corresponding ONI El Nino and La Nina events respectively This is important to note for the analysis because La Nina will have a larger pool in which to sample from in the statistical analyses This section will explain how nonparametric testing is used to evaluate the interannual response of annual and monthly streamflow to the ENSO Parametric testing of lag correlation between the ENSO 3.4 index and streamflow will also be explored to determi ne if there is any predictive relationship Nonparametric Testing of ENSO P hases B ased on A nnual JMA The nonparametric rank sum test (Helsel and Hirsch, 19 93) was performed to determine if there are significant differences between annual streamflow in the El Nino and La Nina phases of ENSO as defined by water years (Oct Sept) The method compares two independent data sets to determine if the median of the two datasets are significantly different statistically larger than the other sample The test assumes that the two data sets are homoscedastic (have the same variance) and follow the same dist ribution Moreover, there is no assumption of normality since streamflow is typically not normally distributed The rank sum test is also useful as it does not assume a linear relationship inherent in correlation analysis A limitation of this method ar ises when there are small sample sizes as may occur with small incidences of ENSO phases One of the strengths of nonparametric tests is that they remove the effects of extremes and outliers on correlation; however this can hinder important extreme values from being represented (Tootle et al., 2005) For this research, the general difference in streamflow is of more
32 importance than any hydrolog ic extremes since the overall correlation is being explored While, other papers explore extreme flood and drought events, this is not in the scope of this work (Cayan et al., 1999) Parametric Testing of L agged S easonal ENSO and S treamflow R elationships Parametric correlation analysis (Helsel and Hirsch, 1993) is used to investigate the strength of the linear relationship between stream flow and Nino 3.4 It can also be used to examine preliminary lagged relationships used in many climate forecast studies; however cross validation and other forecasting analysis would need to be performed For this study three month moving averages of streamflow were compiled (JFM, MAM, MJJ, JAS, SON, NDJ) To remove seasonal characteristics, the annual cycle in both streamflow datasets were remov ed by subtracting the long term three month mean of the entire time series for each three month period respectively This procedure creates streamflow anomalies that can be more directly correlated with lagged Nino 3.4 with minimal seasonal influence Mo reover, three month moving averages of Nino 3.4 were retrieved from (http://climexp.knmi.nl/) deannualized three month averaged stream flow datatsets and ENSO were evaluated up to 12 months (Figure A 1) Resu lts Both parametric and nonparametric tests were used to define the relationship between yearly, seasonal, and monthly streamflows in the ACF. Non parametric R ank S um T est for JMA Annually The JMA did not show yearly significant differences in streamflow b etween El Nino and La Nina with either the HCDN or the UIF datasets except at Chipola (Table 2 1) The Chipola River rank sum test results determined that streamflow during EL Nino
33 years is significantly greater than La Nina years All other flows were n ot significantly different at p<0.05 as seen in Table 2 1 Most of the differences between phases in the HCDN dataset were nowhere near significant Spring Creek being the closest at p>0.10 still did not pass this nonparametric significance test set at p <0.05 The UIF dataset showed interesting results as the gauges are listed from north to south and show a general increase in significance as the gauges proceed further south in the watershed The rank sum test is never passed by the UIFs at a p<0.05 lev el Overall, the yearly JMA index did not show significant differences between streamflow during the El and La Nina phase Nonparametric R ank S um T est for ONI M onthly The monthly ONI method (Table 2 2 and 2 3) produced much more useful results than the JMA as it is more specific to monthly differences While many of the gauges in the HCDN and the UIF dataset were not significant most of the year, winter and spring months for several gauges was the exception The UIFs showed the most instances of signif icance Whitesburg and Norcross are both in the upper section of the basin where El Nino streamflow was significantly greater than La Nina in November It is also interesting to note that the upper basin have values for November and December that met the p<0.10 threshold Albany and Newton are located on the Flint River and testing showed that El Nino is greater than La Nina during March and nearly went over the significance threshold in January and February Finally, in the southern part of the basin d uring winter months, El Nino streamflows were significantly greater than La Nina Rank sum testing on the HCDN dataset showed some unexpected results Three statistical differences occurred where La Nina streamflow was found to be significantly greater than El Nino streamflow in August and September Etowah River, Chestatee
34 River, and the Flint River near Montezuma all had single instances of significance On the other hand, the Chipola River showed significantly greater El Nino streamflows during the winter months January through March Figures 2 2 and 2 3 illustrate the spatial distribution of the two months with most instances of significant differences, January and March for both streamflow datasets The general trend with a few exceptions is that there is no significant difference between streamflows during El Nino and La Nina phase except at the southern end of the basin during winter months (JFM) Pearson C orrelation at L ags up to 12 M onths of NINO 3.4 SST Standard Pearson correlation is usef ul for assessing linear correlation between a numerical climate index and streamflow After deannualized anomalies were constructed for streamflow, lag correlations provide even more information that may be helpful in predictive power of ENSO phases Lag s up to 12 months of NINO 3.4 SST were correlated with streamflow at all gauges Lagged correlation on unimpaired flows proved to have the highest value of 0.36 at Sumatra gauge with a lag of 2 months for JFM (Figure 2 4), however most lags proved to have correlation between 0.1 and 0.2 (Figure A 1) The unimpaired flows seemed to show similar correlation lagged characteristics through the basin They contained a weak correlation in the winter and spring months while exhibiting even less correlation in summer and fall months (Figure A 1) The figures are arranged from North to South in the basin and one can visually notice that correlation increases as the rivers head toward the Gulf Also, the Flint and Apalachicola Rivers seem to correlate better th an the Chattahoochee during winter months at lags up to 3 This may, however, be due to the gauges placed further south of many of the Chattahoochee gauges.
35 The HCDN dataset had several instances of larger correlation (r>0.4) in Chipola River, Whitewater Creek, and Turkey Creek at small lags from 0 4 months Whitewater is in the middle Flint subbassin and the correlation throughout much of the winter months is strong The Chipola River and Turkey Creek showed correlations from 0.3 0.4 in winter months up to 7 month lag All correlation values above 0.3 were found to be significant The overall result of parametric statistical correlation showed that the main rivers (Chattahoochee, Flint, and Apalachicola) increase in correlation with ENSO as they procee d south Conclusions The ACF is a long watershed that proceeds from north Georgia through the panhandle of Florida The results of this study indicate that streamflow in the southern end of the basin exhibited stronger correlation to Nino 3.4 SSTs Thr ough the use of both synthetic unimpaired flows on the main channel and physical HCDN streamflow datasets, a more comprehensive view of the relationship between streamflow and ENSO could be examined in the ACF basin Twenty one HCDN gauges and 21 UIFs wer e used to test this relationship with ENSO JMA is considered a general ENSO indicator since it classifies interannual years instead of months and in this study proved less informative One of main challenges in doing ENSO correlations with most geophys ical data is limited data For this research all streamflows were started in 1939 and the HCDN dataset ended in 1988 It has been noted that for short periods with few instances of ENSO episodes significant differences between El Nino and La Nina can be hard to gauge A reason that might explain some of the significant values found in the summer and fall months of the HCDN dataset could be due to this To address this problem
36 other studies have loosened the threshold of conventional indices such as JMA or ONI to include more episodes (Tootle et al., 2005) Testing of lagged correlation produced results that showed that in the southern part o f the basin at Chipola River, lagged ENSO relationship of up to 4 months shows correlation greater than r > 0.4 in JFM However, this amount of correlation is not seen throughout the basin Generally winter and spring months have larger correlation and l onger correlated lags in the basin Specifically, January through March exhibited statistically larger streamflows during El Nino in the south of the basin (Figures 2 2 and 2 3). Water management in the ACF would benefit only slightly in the southern end of the watershed from using these results It was established that only significant differences between streamflow occurred in the winter and were at the southern end of the watershed The extent of these differences is not investigated in this study, on ly that streamflow during El Nino is significantly greater than La Nina This would be useful for developing navigation windows during El Nino phases if flows in winter If correlation extended further north throughout more of the ba sin it would be much more useful If the trend existed as far north as the city of Atlanta, water managers would be able to manage with much more foresight Since the general trend has been explored in this paper, it would be a great next step to examine correlations between extreme streamflow events (flood, drought) and ENSO This would be of more use to management since a principle purpose of many of the dams is for flood control and drought mitigation.
37 Overall, streamflow in the ACF is correlated at t he very southern end of the basin with ENSO Through the use of both parametric and nonparametric statistical methods the relationship was explored with similar results Both main channel synthetic flows and USGS gauged flows confirmed this outcome Figure 2 1 Classified ENSO indices on the coast of Peru NWS/CPC http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/nino_regions .shtml
38 Figu re 2 2 January significance values of the difference in median streamflow between the El Nino and La Nina phase as classified by ONI Significance values that are stronger are larger circles and small is no significant differences between the medians
39 Figure 2 3 March significance values of the difference in median streamflow between the El Nino and La Nina phase as classified by ONI Significance values that are stronger are larger circles and small is no significant differences between the medi ans
40 Figure 2 4 Pearson correlation at lags up to 12 months of ENSO 3.4 SST for a) Sumatra unimpaired flow and b) Chipola ( 2359000) HCDN gauge These are the most southern gauges of the each dataset respectively
41 Table 2 1 Significance values of the difference in median streamflow between the El Nino and La Nina phase as classified by the annual JMA Reject null hypothesis when (p < 0.05) Gauge Station Significance P<0.05 Unimpaired Flow Station Significance P<0.05 1 2331000 CHATTAHOOCHEE RIVER 1 Norcross 0.5711 1 2331600 CHATTAHOOCHEE RIVER 0.2232 Morgan Falls 0.4501 1 2333500 CHESTATEE RIVER 0.602 Atlanta 0.4501 1 2389000 ETOWAH RIVER 1 Whitesburg 0.4501 2 2335700 BIG CREEK 0.4519 West Point In 0.4731 2 2392500 LITTLE RIVER 0.5133 West Point Out 0.4967 2 2337000 SWEETWATER CREEK 0.7721 Bartlette's Ferry 0.4278 2 2337500 SNAKE CREEK 0.7751 Oliver 0.4278 3 2339500 CHATTAHOOCHEE RIVER 0.817 Goat Rock 0.4278 3 2340500 MOUNTAIN OAK CREEK 0.3413 North Highlands 0.4278 3 2341800 UPATOI CREEK 0.3153 Columbus 0.4278 3 2342500 UCHEE CREEK 0.1489 WF George 0.4731 4 2347500 FLINT RIVER NR CULLODEN GA 0.8167 George Andrews 0.4731 4 2349000 WHITEWATER CR 0.5228 Montezuma 0.3079 4 2349500 FLINT RIVER NR MON 0.8167 Albany 0.1408 4 2349900 TURKEY CREEK 0.5244 Newton 0.1216 5 2353500 Ichawaynochaway Creek 0.2204 Bainbridge 0.1044 5 2356000 FLINT RIVER 0.7728 Jim Woodruff 0.1513 5 2357000 SPRING CREEK 0.1066 Chattahoochee 0.1513 5 2358000 APALACHICOLA RIVER 1 Blountstown 0.1 623 5 2359000 CHIPOLA RIVER 0.0272* Sumatra 0.1127
42 Table 2 2. Significance values of the difference in median streamflow between the El Nino and La Nina phase as classified by monthly ONI Reject null hypothesis when (p < 0.05) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Norcross 0.25 0.33 0.25 0.31 0.60 0.54 0.73 0.24 0.50 0.29 0.023* 0.05 Morgan Falls 0.25 0.39 0.31 0.24 0.60 0.65 0.82 0.40 0.29 0.36 0.08 0.08 Atlanta 0.30 0.39 0.29 0.26 0.57 0.74 0.89 0.45 0.24 0.39 0.10 0.08 Whitesburg 0.24 0.20 0.18 0.22 0.20 0.31 0.56 0.54 0.18 0.30 0.026 0.047 West Point In 0.35 0.32 0.31 0.31 0.57 0.35 0.98 0.45 0.72 0.53 0.07 0.07 West Point Out 0.26 0.27 0.25 0.33 0.27 0.28 0.73 0.49 0.78 0.37 0.06 0.08 Ferry 0.26 0.28 0.29 0.36 0.39 0.31 0.82 0.56 0.70 0.50 0.07 0.06 Oliver 0.26 0.27 0.29 0.36 0.39 0.31 0.79 0.56 0.72 0.47 0.07 0.06 Goat Rock 0.26 0.32 0.27 0.39 0.37 0.35 0.67 0.63 0.70 0.46 0.08 0.06 North Highlands 0.25 0.33 0.27 0.39 0.35 0.37 0.64 0.63 0.74 0.47 0.08 0.06 Col umbus 0.25 0.33 0.27 0.39 0.35 0.37 0.64 0.63 0.76 0.43 0.08 0.06 WF George 0.30 0.20 0.25 0.45 0.57 0.71 0.45 0.22 0.34 0.78 0.10 0.17 George Andrews 0.28 0.24 0.23 0.45 0.52 0.71 0.48 0.21 0.36 0.99 0.11 0.15 Montezuma 0.21 0.25 0.20 0.39 0.69 0.71 0. 76 0.40 0.44 0.69 0.07 0.18 Albany 0.07 0.07 0.041 0.28 0.55 0.49 0.98 0.40 0.47 0.85 0.06 0.08 Newton 0.05 0.06 0.046 0.24 0.57 0.42 0.82 0.47 0.39 0.93 0.15 0.09 Bainbridge 0.046 0.05 0.06 0.18 0.55 0.46 0.79 0.63 0.57 0.99 0.25 0.09 Jim Woodruff 0.0 34 0.08 0.08 0.22 0.49 0.57 0.82 0.34 0.49 0.85 0.13 0.06 Chattahoochee 0.034 0.08 0.08 0.22 0.49 0.57 0.82 0.34 0.49 0.85 0.13 0.06 Blountstown 0.036 0.07 0.07 0.13 0.52 0.51 0.95 0.26 0.38 0.70 0.17 0.06 Sumatra 0.026 0.047 0.06 0.18 0.42 0.35 0.73 0. 56 0.41 0.76 0.21 0.044
43 Table 2 3 ONI rank sum significance values compared against HCDN dataset Reject null hypothesis when (p < 0.05) El Nino and La Nina Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 2331000 Chattahoochee River 0.89 0.44 0.9 3 0.79 0.64 0.69 0.94 0.08 0.64 0.83 0.18 0.75 1 2389000 Etowah River 0.92 0.73 0.73 0.96 0.85 0.30 0.55 0.21 0.48 0.55 0.62 0.88 2 2392500 Little River 0.97 0.25 1.00 0.52 1.00 0.95 0.25 0.045 0.63 0.36 0.94 0.53 3 2340500 Mountain Oak Creek 0.67 0.3 6 1.00 0.27 0.82 0.59 0.53 0.27 0.26 0.57 0.80 0.58 4 2349000 Whitewater Cr 0.87 0.86 0.38 0.92 1.00 0.11 0.70 1.00 0.70 0.65 0.69 0.83 5 2356000 Flint River 0.80 1.00 0.56 0.72 0.37 0.24 n/a 0.38 0.33 0.06 0.88 0.87 1 2331600 Chattahoochee River 0.76 0 .95 0.58 1.00 0.94 0.90 0.51 0.06 0.22 0.69 0.77 0.82 1 2333500 Chestatee River 0.91 0.83 0.83 0.87 0.67 0.59 0.65 0.045 0.20 0.66 0.34 0.46 2 2335700 Big Creek 0.78 0.72 0.78 1.00 0.63 1.00 1.00 0.27 0.29 0.89 0.82 0.97 2 2337000 Sweetwater Creek 0.28 0.50 0.69 0.41 0.85 0.85 0.60 0.35 0.26 0.92 0.20 0.43 2 2337500 Snake Creek 0.56 0.79 1.00 0.32 0.53 0.14 0.36 0.41 0.31 0.65 0.13 0.83 3 2339500 Chattahoochee River 0.38 0.21 0.45 0.24 0.29 0.21 n/a 0.33 0.49 0.15 1.00 0.46 4 2347500 Flint River Nr Cu lloden Ga 0.43 0.89 0.81 0.63 0.85 0.87 1.00 0.08 0.25 0.82 0.14 0.56 3 2341800 Upatoi Creek 0.48 0.41 0.90 1.00 0.52 0.65 1.00 0.40 0.28 0.32 0.85 0.95 3 2342500 Uchee Creek 0.12 0.17 0.07 0.39 0.37 0.26 0.59 0.12 0.17 0.76 0.57 0.63 4 2349500 Flint Ri ver Nr Mon 0.36 1.00 0.63 0.81 1.00 0.92 0.86 0.10 0.043 0.48 0.46 0.56 4 2349900 Turkey Creek 0.15 0.62 0.24 0.92 0.33 0.83 0.61 0.56 0.54 0.89 0.76 0.44 5 2353500 Ichawaynochaway Creek 0.36 0.43 0.10 0.73 0.58 0.96 0.52 0.05 0.15 0.71 0.62 0.22 5 2358 000 Apalachicola River 0.80 0.56 0.56 0.29 0.37 0.24 n/a 0.19 0.33 0.15 1.00 0.66 5 2359000 Chipola River 0.015 0.023 0.004 0.12 0.42 0.16 0.25 0.37 0.41 0.74 0.82 0.10
44 Table 2 4. Oceanic Nino Index El Nino DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ El Nino 1941 1941 1941 1941 1941 1941 1957 1951 1951 1940 1940 1940 1958 1958 1958 1957 1957 1957 1963 1957 1957 1951 1951 1951 1964 1966 1966 1958 1958 1958 1965 1963 1963 1957 1957 1957 1966 1969 1969 1966 1969 1965 1972 1965 1965 1963 1963 1 963 1969 1973 1973 1969 1972 1969 1982 1972 1969 1965 1965 1965 1970 1977 1983 1983 1982 1972 1987 1982 1972 1969 1968 1968 1973 1983 1987 1987 1983 1982 1991 1986 1976 1972 1969 1969 1977 1987 1992 1992 1987 1983 1992 1987 1977 1976 1972 1972 19 78 1988 1995 1998 1991 1987 1994 1991 1982 1977 1976 1976 1983 1992 1998 1992 1991 1997 1994 1986 1982 1977 1977 1987 1995 2003 1994 1992 2002 1997 1987 1986 1982 1982 1988 1998 1997 1994 2004 2002 1991 1987 1986 1986 1992 2003 1998 1997 200 3 1994 1991 1987 1987 1995 2005 2002 2002 2004 1997 1994 1991 1991 1998 2004 2006 2002 1997 1994 1994 2003 2003 2002 1997 1997 2005 2004 2003 2002 2002 2007 2006 2004 2003 2003 2006 2004 2004
45 Table 2 5. Oceanic Nino Index La Nina DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ La Nina 1939 1939 1939 1943 1945 1945 1942 1942 1942 1942 1942 1942 1943 1943 1943 1945 1950 1946 1945 1945 1943 1943 1943 1943 1944 1945 1945 1950 1954 1949 1946 1946 1944 1 944 1944 1944 1945 1950 1950 1954 1955 1950 1948 1948 1946 1946 1946 1948 1950 1951 1951 1955 1956 1954 1949 1949 1948 1948 1948 1949 1951 1955 1955 1956 1964 1955 1950 1950 1949 1949 1949 1950 1955 1956 1956 1964 1971 1956 1954 1954 1950 1950 1950 1954 1956 1968 1968 1968 1973 1964 1955 1955 1954 1954 1954 1955 1957 1971 1971 1971 1974 1971 1956 1956 1955 1955 1955 1956 1963 1974 1974 1974 1975 1973 1964 1964 1956 1956 1956 1962 1965 1975 1975 1975 1985 1974 1970 1970 1962 1962 1962 1964 1968 1976 1976 1976 1988 1975 1971 1971 1964 1964 1964 1967 1971 1985 1985 1985 1989 1985 1973 1973 1970 1970 1970 1970 1972 1989 1989 1989 1999 1988 1974 1974 1971 1971 1971 1971 1974 1996 1996 1999 2000 1999 1975 1975 1973 1973 1973 1973 1975 199 9 1999 2000 2008 2000 1985 1985 1974 1974 1974 1974 1976 2000 2000 2008 1988 1988 1975 1975 1975 1975 1985 2001 2008 1998 1998 1985 1984 1984 1984 1989 2008 1999 1999 1988 1988 1988 1988 1996 2007 1995 1995 1995 1995 1999 19 98 1998 1998 1998 2000 1999 1999 1999 1999 2001 2007 2000 2000 2000 2008 2007 2007 2007
46 CHAPTER 3 REVIEW AND TESTING O F THE ACF UNIMPIARED FLOW DATASET WITH CORRELATIVE STATISTI CS Introduction Increasing human demands on wat er resources has put pressure on river systems to provide consistent and sustainable flows for often competing interests. Within critical water resources negotiations, unimpaired flow (UIF) information on a river system provides a n important baseline towa rds allocation and yield calculations. UIF data are described as the historically derived flows that have been systematically adjusted to remove the effects of anthropogenic influences such as withdrawals, returns, and the effects of water control structu res. The UIF dataset in this chapter is defined as local unimpaired flows flowing into the main channel of the river. These flows are regionally lumped local inflows that take into account regional contributions to the main channel by combining flows from tributaries and overland flow that contribute to the Ap a lachicola Chattahoochee or Flint riv ers. The local unimpaired flows are accumulated into the main stem of the river system to form the main channel flows called the cumulative unimpaired flows that are not analyzed in this study. This method is comparable to a pipe flow model where many small pipes or the local unimpaired flows (UIF) are funneled into the main large pipes (CUIF) Once a UIF dataset has been established, the information is used to drive models and tools that place future demands and water control structures on the river system. In this way, alternate demand scenarios can be placed on the UIF data to test cert ain allocation formulas From the simulations, the outputs can be assessed to determine what would be considered acceptable flows according to various performance measures Many variables exist when determining
47 the proper water control plan including var ious user demands, duration of low flows, frequency of low flows, risk of flooding, and water control structure infrastructure. As a critical water resource under long term conflict amongst water users in three southern states, residents of the Apalachicol a Chattahoochee Flint (ACF) watershed have a need of systematic flow analysis and allocation tools (Jordan et al., 2006) The ACF contains one of the largest rivers in t he southeastern United States The basin covers approximately 50,800 km 2 and drains parts of eastern Alabama, northern Florida, and much of western Georgia Much of the basin area lies in western Georgia with the Chattahoochee confluence of the Chattahoochee and Flint Rivers The Apalachicola runs south through the panhandle of Flori da and drains into the Gulf of Mexico The Chattahoochee River is impounded at a number of locations while the Flint River is considered for most purposes unregulated. Lake Sydney Lanier is the principle storage reservoir and lies in North Georgia above the city of Atlanta It contains 1,087,600 acre feet (62.5%) of storage capacity within the river system while West Point provides 17.6% followed by WF George at 14.0% and Jim Woodruff at 5.0% Lake Seminole forms the reservoir behind Jim Woodruff dam an d though it has storage capacity is considered for most purposes a run of the river project (USACOE, 1989) The basin has a diverse stakeholder group as the river spans over the southeast United States To the north, the city of Atlanta is a large municipal and industrial water user of the headwaters of the Chattahoochee and demands significant water resources The southern part of the basin is mainly used for agriculture Alabama Power uses ACF
48 water to provide cooling to multiple power plants, including the Farley Nuclear plant The lower ACF in Florida supports a significant seafood industry provides a home to the gulf sturgeon ( Acipenser o xyrinchus desotoi ), fat threeridge mussel ( Amblema neislerii ), and the purple bankclimber mussel ( Elliptoideus sloatianus ) protected under the Endangered Species Act Moreover there are shipping interests in the Apalachicola River upstream to Columbus, Ge orgia where the federally maintained channel navigation ends growth and the basin stakeholders have significant and differing demands on the water supplied by the ACF While the Florid a Water Resources Act (Section 371.042, F.S.) mandates that a Minimum Flow and Level (MFL) be set for the Apalachicola River, to date, there has not been any MFL set in the basin as no allocation agreement has been made for the overall ACF watershed (Jordan et al., 2006) Increasingly, water resource managers have turned to model representations of the ACF channel/reservoir system with a specifi c dependence on the use of UIF datasets to provide the baseline flow conditions to mod el water allocation scenarios. An Unimpaired Flow (UIF) dataset was developed to provide water inputs to the HEC RESSIM/HEC 5 (Klipsch and Hurst, 2007) and ACF STELLA (Ahmad et al., 2004; ISEE, 2009; Goodman et al., 2001) reach/reservoir models for 23 local unimpaired inflows at control points throughout the ACF watershed These locations are inflow points that are regionally accumulated inflows in the main channels by the reach/reservoir models Within the models, the flow data is routed through the simulated diversions, consumption, and managed river system Simulatio ns
49 of scenarios on the ACF have been conducted for a variety of water resource operations. This increased dependence on simulation tools and datasets hig hlights an interesting quandary While much use and critical emphasis is placed on the development and use of unimpaired datasets within many regions across the US, limited information concerning the testing of these datasets is available beyond limited, non peer reviewed technical reports This limitation is also the case within the ACF basin where the p rimary technical reference for UIF datasets is provided in a technical appendix (USACOE, 1997) Given the critical role these datasets play wi thin water resources modeling and water allocation, a useful undertaking would be to analyze and test these UIF datasets to provide greater confidence and knowledge of the inherent uncertainties contained in the data The overall objective of this researc h is to examine the synthetic UIF datasets developed for the ACF with statistical tools to explore their relationship with known physical USGS unimpaired gage stations. Accordingly, this research paper has the following specific ob jectives: 1. Review the development of building UIF datasets at a national level, 2. Review the construction of the UIF dataset for the ACF basin, 3. Construct an alternate and comparative hydrological dataset for the ACF using the USGS Hydro Climatic Data Network, 4. Perform basic parametric and non parametric statistical analysis of the sections of the UIF dataset in the ACF, 5. Perform a wavelet analysis on subsections of the UIF dataset in the ACF Following this introduction, this paper is organized into three sectio ns. A Review of Concepts section provides background on the development and use of UIF datasets within other US basins. In addition, a summary of the UIF development methods in the
50 ACF is provided. A Methodology section explains the different statistica l methods used to explore temporal and spatial variations within the ACF UIF dataset A Results section provides details of the statistical testing of the ACF dataset and a Conclusions section discusses the implications of the results and provides a way f orward for increased testing of these critical datasets for water resource managers. Review of Concepts This section provides a review on the development and use of UIF datasets nationally and reviews the construction of the UIF for the ACF basin (Object ives 1 and 2). Development and Application of Unimpaired Flow Datasets for Water Resources Analysis Developing a comprehensive UIF dataset has proven to be a difficult task in both regions where data is sparse and within regions where data is abundant Many problems arise when trying to develop these datasets to determine what the hydrology would be like without human influence One issue that often arises is the limited amount of data that is available for current and historical conditions Problems arise when previous land uses, precipitation data, stream flow data, withdrawals from the system, have not been well documented In order to develop UIF datasets, various techniques have been used One method is to develop a physical daily rainfall runo ff model of the system (Hughes, 2001) Another method would be to find an independent data source such as tree ring data and fit the observed natural strea mflow with tree ring observations and using empirical curve fitting methods fill in the streamflow data (Meko and Graybill, 1995; Timilsena et al., 2007) Lastly, the method that was used for the development of the ACF UIF datasets, was to use measured stream flow and removing
51 all anthropogenic effects with empirical regression methods (USACOE, 1997) This last method will be explored further in this study The United States Bureau of Reclamations developed an unimpaired flow dataset for the Colorado River utilizing measured USGS streamflow gage data, reservoir elevations, municipal, agricultural, and industrial data Much of the data used to create the unimpaired flows were derived from studies completed every five years on water uses a nd losses documented by the U.S Beureau of Reclamation which is typically reported by hydrologic unit or state (USBR, 2004) The streamflow and consumpti ve use data are then inputted into Riverware software developed by the Center for Advanced Decision Support for Water and Environmental Systems (CADWES) to be adjusted for flow routing and reservoir regulation by following guidelines provided in (Lindenmayer, 2006; Prairie and Callejo 2005; Zagona et al., 2001) The output of the Riverware model is unimpaired flows The unimpaired flows were then validated by inputting them into a long term model of the system called the Colorado River Simulation System (CRSS) and were used to comp are different operations and policy over its historic record (Lindenmayer, 2006; Prairie and Callejo, 2005) However, the validation process expressed shows that the natural flows and the model are internally consistent and reflect the opposite operations of one another UIF datasets have been developed for Cali (DWR, 2007; Technical Service Center, Denver, CO, 2005) They are name rather than the term UIF datasets as used in this study Most of these flows are updated monthly or even daily by the USACOE Models have been developed to utilize these flows to consider construction of reservoir storage through a GIS platform to
52 minimize effects on salmonids and water supply for growers (Merenlender et al., 2008) Salinity models in the San Francisco estuary have b een established to provide scenario analysis based on the amount of impairment from further upstream Through this model, researchers were able to further understand the effects of freshwater inflows of natural variability in upstream management and its e ffects on salinity in the estuary (Knowles, 2002) In climate studies, the synthetic UIF datasets have been used to analyze trends in climatic periodicity and trends (Freeman, 2002) Furthermore habitat restoration alternatives have been simulated using the UIF datasets and water temperature models (Null et al., 2009; Null, 2008) The United States Bureau of Reclamation in California uses these UIF data sets to develop scenarios based modeling for the central valley and much of the rest of the state (Technical Service Center, Denver, CO, 2005) The unimpaired flows were developed to investigate the natural flow regimes as well as the impact that current water management has on the flows The ultimate goal of this study was to examine natural flow conditions for salmon in the Klamath Basin to p rovide for the assessment of a Biological Opinion The methods used to derive the unimpaired flows as well as their application to ecological models were examined by the National Research Council (NRC) in request of the USBR Through the study, many shor tcomings in the data and methodology were presented There has been considerable criticism of the UIF development and analysis as well as application of the flows for non intended uses There were many NRC criticisms that led to the recommendation that t seriously compromised by several fundamental issues (National Research Council, 2008, p. 3) The methods
53 used in the development of the UIFs were considered too basic to provide meaningful and adequate representation for ecological applications The flows were developed at a monthly timestep which is not useful for most ecological and hydrological applicatio ns based on best professional judgment or loose empirical relationships Evapotranspiration was calculated according to antiquated techniques where more accurate techni ques have been derived Groundwater interactions with surface water were not adequately described in the process Lastly, and probably most important is that the study did not follow standard engineering and scientific practices when calibrating, validat ing, and testing the model (National Research Council, 2008) Some suggestions have been to develop a physical based rainfall runoff model of the watershed in an effort to describe many of the interactions in a more physically accurate way Many rainfall runoff models have been developed and are used consistently in academia and private enterprise However, it has been noted that the amount of research and funding put into developing this model from the USBR is substantial and to change modeling practices would be a substantial investment (National Research Council, 2008) The development of UIFs is a common practice in modeling for various applications Through the process of investigating these UIFs there was no standard for validation method presented in the literature
54 Develo pment of the Unimpaired Fl ow Dataset for the Apalachicola Chattahoochee Fli nt River Basin Creation of c omprehensive i nitial f low d ata After the initiation of the comprehensive study in the ACF in 1997, it was determined that a set of UIF datasets for th e basin would drive the models that were being constructed to evaluate future allocation strategies (Jordan and Wolf, 2006) The initial steps of the development of the UIF datasets were to determine the time series start date as well as which gauges were to be used Flow records were collected from the USGS but were not considered reliable as they were sparse for the period prior to 1935 and, t hus, were not used in the study The flow records contained gaps as well as inconsistencies such as the relocation of gauges during dam construction New gauges were introduced with the construction of water control structures as well Gauges were used in the development of the UIF datasets based on the data quality rankings provided by the USGS (USACOE, 1997) and longevity of the data Where needed, data gaps were filled using five different fill in methods Empirical regression with nearby gauges was the primary method to fill in gaps in the flow time series Other methods included determining ratios of drainage areas from gauges where flo w data was present Moreover stage/storage relationships were investigated on only the most recent curves were used as adding historical relationships proved too burdensome Some disagreements were found when comparing reservoir inflow values with stage and outflow As stage and outflow are considered the most reliable measurements inflows for all the reservoirs were recalculated based on change of storage and outflow
55 Estimating e vaporation/ p recipitation losses from reaches and r eservoirs During the development of the UIF datasets, evaporation estimates from isopleth maps were used to determine evaporation losses The average annual rates were estimated from isopleth lines described in the NOAA Technical Reports NWS 33 and 34 (National Weather Service, 1982a, b) To distribute the annual evaporation rates to a monthly scale, nearby pan evaporati on stations were used to fit the distribution Only three stations were available to assess the four major reservoirs Buford, West Point, W.F. George, and Jim Woodruff (USACOE, 1997) Run of the river reservoirs were not considered in the e vap oration study since inflows were estimated from upstream locations. Other studies maintain that using empirical fitting to estimate reservoir inflows at the reservoirs already includes the effects of evaporation and increased runoff (Leitman, 2010) Open reservoir surfaces also increase the runoff into the r eservoir Open reservoir surfaces are considered impervious surfaces as the time of concentration for rainfall is zero as the rainfall immediately enters the reservoir Instant precipitation runoff was calculated from the average annual precipitation of the region near the reservoir and the average runoff at the reservoir Average precipitation was computed using the 30 year annual average (1951 1980) of the precipitation gage near the reservoir Average runoff was computed by averaging the annual flow using the 30 year annual average flow and dividing it by the total surface area of the subwatershed This percentage is converted to inches and gives the ratio of the amount of precipitation that is represented in surface water The difference between th e annual discharge and annual rainfall is the amount that is lost to infiltration or transpiration The average monthly precipitation on the lake is then multiplied by the ratio of runoff percent that is due to transpiration
56 and infiltration to yield the instantaneous runoff from precipitation The net reservoir loss is computed by subtracting the reservoir evaporation from the instantaneous runoff from precipitation This value is then converted to a flowrate by multiplying it by the surface area of the reservoir and converting this volume to cubic feet per second (USACOE, 1997) Upon further analysis of this method, the net losses to the re servoirs are quite generalized by taking averages over a period of 30 years During periods of drought when flows are below normal and typically temperatures are above normal, the evaporation losses can contribute a more substantial amount to withdrawal f rom the surface water system On the other hand the reduction in the surface are of the lakes due to the less than average precipitation may reduce the total evaporation from the surface of the reservoirs Drought conditions are of the most interest in p erforming this study since allocation and operations are based mainly on low and high flows. Much of the evaporation is based on the four main reservoirs, however small reservoirs, ponds, and stream surface area is accounted for in the evaporation calculat ions Surface area of the four large federal reservoirs accounts for 147,000 acres while the basin has about 228,000 acres of total open water surface area (Ignatius, 2009) This leaves 80,000 acres of open water surface not accounted for represented in ponds and small reservoirs As a response to this criticism, additional investigations by University of Georgia researchers as to the impact of these small water bodies and their effect on the water budget in the basin are underway. Estimation of f low r outing t hrough ACF r iver r eaches The Muskingum routing method (Seth, 1950; Ponce, 1979) was chosen to develop the routing mechanism for the UIF datasets in the ACF The method divides the river
57 reach storage into wedge and prism storage and routes them se parately Prism storage is assumed to be steady flow and given a travel time (K p ) through the reach Wedge storage is assign ed a weighting coefficient (X) and given a travel time (K w ) based on travel time through the reach The weighing factor was deter mined through the combination of USACOE experience; USGS dye flow studies, and the computer software HEC 1 Flood Hydrograph Package Since the UIF datasets were being developed to run for a daily 24 hour time step, the routing equation needed to estimate travel times in daily reaches Reaches with much longer time steps were divided while reaches with much shorter time scale were not routed Local inflows were developed by subtracting the upstream routed flows from the downstream observed flows represent ing the local inflows within the reaches Water availability in the ACF tends to be most critical during low flow periods, therefore conservative low flow hydrographs and routing coefficients were chosen for the development of the UIF datasets (USACOE, 1997) Estimating municipal and industrial water use An inventory of municipal and industrial withdrawals and returns was taken for the period Ja nuary 1, 1980 through December 31, 1993 Withdrawals and returns greater than 0.1 millions of gallons per day (mgd) were identified as a component to be adjusted for in the UIF datasets As the UIF datasets were developed at specific control points, each withdrawal and return had to be placed within an individual control point. An important aspect to note was that groundwater withdrawals or returns were not included in the calculation Very rarely were records comprehensive over this period of time so f ill in methods were adopted. For the period that the inventory was taken three different approaches were taken depending on which method was more suitable Each method
58 involved averaging streamflows from previous years or by averaging the previous and su bsequent months. The data for the 1939 1979 period was filled in using a hind cast method which was calculated with total population and per capita water use to create a decay factor that was applied to the water use (USACOE, 1997) Estimating thermal plant water uses Thermal energy plants water supply can be either consumptive or non consumptive, depending on if the water is returned back to the system after used for cooling Within the ACF basin only the Farley Nuclear Plant, the Wansley Coal/Natural Gas Electrical Plant and the Yates Coal fired Electrical Plant are consumptive users Rough estimates of monthly uses were compiled for the di fferent plants however the information was questionable showing erroneous withdrawal values and duplicate return data (USACOE, 1997) Many of Monthly average withdrawals were assigned to each plant and flows were adjusted during the period of their operation (USACOE, 1997) Estimating agricultural water demand Agricultural land lies primarily in the Flint Basin and in areas in the southern portion of t he ACF basin To develop withdrawal data, the Natural Resources Conservation Service (NRCS) supplied the study with crop coverage statistics for the basin as well as associated water use values for 1970, 1980, and 1990 Withdrawal values were derived onl y after 1970 as it was assumed that agricultural irrigation had no significant impact to river flow in the region prior to 1970 It was also assumed that there were no returns to the surface water from agricultural irrigation However, the relationship b etween groundwater pumping for agricultural irrigation and surface water
59 was considered in the analysis It has been shown that groundwater is a main (GDNR, 2006; Mosner, 2002) Flow reduction to surface water percentages were assigned to each reach to illustrate the surface water inter action Moreover groundwater pumping was shown to provide delayed surface water drawdown which required that values be adjusted to reflect this interaction (USACOE, 1997) In the Upper ACF basin where the Piedmont/Blue Ridge/Cumberland Plateau/Valley and Ridge reside, the groundwater withdrawal was assumed to impact the surface water instantaneously at a ratio of 1:1 while further south, the ra tio was assumed to be a 1:0.6 delay. A later comprehensive study of the groundwater water usage in the Flint River conducted by the Flint River Regional Water Development and Conservation Plan was completed in December of 2005 The study was conducted in response to low flow scenarios in the Flint basin during droughts It was initiated to understand the severe impact of agricultural irrigation on the flows in the Flint River and its tributaries The goal of the study was to (1) define a plan to promote conservation and reuse, (2) guard against water shortage, and (3) to manage the water resources o the Flint River in a sustainable fashion The study found that the amount of agricultural irrigation was highly variable depending on the precipitation event s on a local scale and most agricultural crops were irrigated between the months of April and September Typically irrigation reached its maximum in June, July, or August when the crops were growing Since the development of agricultural irrigation in th e basin, low flows were reached sooner in the year and occurred more frequently throughout the year There were 160,000 acres of agricultural area irrigated by surface water throughout the Flint River
60 Basin and 403,000 acres or agricultural area irrigated by Floridan aquifer wells In a drought year, as much as 250 mgd were withdrawn from surface water and as much as 950 mgd withdrawn from the Floridan aquifer at the peak of irrigation (GDNR, 2006) Other agricultural acreage estimations have come from the University of Georgia, Center of Remote Sensing and Mapping Science in cooperation with the Georgia DNR A survey was completed with an ag ricultural acreage in 1999 resulting in 475,779 acres which was far less than the ACF Comprehensive Study estimated (Litts et al., 2001) Another more rece nt study was carried out by the Georgia Department of Natural Resources Environmental Protection Division which conducted a survey of both groundwater and surface water supply and demand in the Flint River Basin This study produced agricultural land in t he Flint basin to be 563,000 acres in 2004 which was a wet year (GDNR, 2006) Three separate studies produced large disparities between the to tal agricultural acreage found in the Flint Basin. Estimating leakage from dams and reservoirs Dams and reservoirs experience water losses due to leakage around, through and under the water control structures Leakage often causes minor losses however in some cases it can significantly affect flow from a dam or even affect dam structures In the ACF, the West Point, W.F George, and Jim Woodruff have been known to have considerable leakage problems, however due to poor documentation and the complexity of the interaction no flow adjustments were made for the UIF datasets (Crawford et al., 2005; Torak an d McDowell, 1996) A principal source of uncertainty in the UIF dataset lies within the groundwater surface water interaction A specific instance of this is found at Jim Woodruff Dam and the surrounding area The Jim Woodruff dam was constructed at t he confluence of the
61 Chattahoochee and Flint rivers on top of an upcropping of the Upper Floridan Aquifer The Polk Lake Spring is located downstream of the dam is thought to be influenced by groundwater sinking from Lake Seminole Die trace studies were performed to determine the source of this spring and Lake Seminole is established as a source through groundwater conduits (Crawford et al., 2005; Jones and Torak, 2003; Torak, 2003) The boil discharges at a rate from about 140 to 220 cubic feet per second from about 300 yards be low the dam (Crilley and Torak, 2003) This would have significant impacts on the baseflow calculations since a continuous boil is occurring without regard s to flow upstream Creating flow adjustments to represent expected hydrographs in different ACF reaches After the initial adjustments to remove the anthropogenic effects described above, erratic local unimpaired inflows were computed Erratic data and negative flows that were developed during the initial adjustment are thought to be caused by the routing errors and inconsistent flow measurements at tandem sites However, the flows were rograph shapes found in the region (USACOE, 1997) Averaging for daily flow values contributed to inaccuracy of the outputs as well For inst ance, when hydropower generation released 24,000 cfs from a dam over a 4 hour period, this release would be buffered by flows the rest of the day to generate a continuous flow of only 5,000 cfs In addition, routing errors may accumulate as initial flow r outing with the Muskingum method was a relatively simplistic way to describe flows This method was fit to allow for only a 24 hour travel time while the ACF river system has variable travel times occurring in some of the more extreme flood and severe dro ught periods As such, flow rates and travel
62 times were also only allowed one value for travel time and weighting factor To adjust for these factors, running averages were calculated for all the time series over a period of 0, 3, 5, or 7 days Each run ning average was selected respectively to give the lowest incidence and magnitude of negative flows in each local inflow (USACOE, 1997) Mitig ating uncertainty within ACF gauging stations Another consideration when using the actual, recorded flow data is the inherent uncertainty in USGS gauge measurements These uncertainties have been documented and improvements in error reduction methodologi es are always progressing (Hirsch and Costa, 2004) Limitations arise when the uncertainty of inconsistent flow measurements at tandem sites are considered For example, when an upstream gauge shows a greater flow rate than the downstream gage, adjustments must be made based on a constant difference through the full range of flows experienced Systematic error such as this is easier to adjust for than erro rs in reaches that have many dams Factors such as leakage under dams or turbine rating accuracy play a large role in the uncertainty of gauges at dams (USACOE, 1997) The methods used in the development of the unimpaired flows are extremely important to make transparent so that the limitations of the data and methodology can be properly regarded when they are used Moreover the results of the statistical analysis can be more fully explained with a proper understanding of their development Finally, illustrating the strengths and weaknesses of the UIFs demonstrate where gaps in knowledge and data exist so that efforts in research and data accum ulation can be more precise
63 Methodology Four statistical methods were used to compare the ACF UIF dataset with a set of independent flows selected from the USGS HCDN dataset (Slack and Landwehr, 1992) The first method was to compare elements of the UIF dataset with specific HCDN parametric (Helsel and Hirsch, 1993) The second statistical method divided the UIF dataset into pre (1939 1954) and post dam (1970 1988) sub sections and again performed pa rametric and non parametric correlations to gage the covariance between the two datasets Differences between pre and post dam correlations were examined to determine where the unimpaired flows may be inaccurate due to localized adjustments If the UIF d atasets have high correlation with pre dam flows and much lower with post dam flows, this would suggest that there may be problems with the construction of the UIF datasets after the construction of the dams since they no longer correlate well with the nat ural flows On the other hand, if there is shift from a bad correlation pre dam and a good correlation post dam, further investigation would be performed In the third statistical method, the negative flow occurrences in the unimpaired flows were examine d both temporally and spatially to assess where and when they have the most impact Daily negative flows are in large part due to routing mechanisms and mass balance parameters being met, however monthly averages that are negative are much harder to give physical meaning to Within the fourth statistical method, differences explored in pre and post dam correlation were explored using spectral wavelet analysis to better determine where the flows changed the frequency domain.
64 Selection of a Comparative Hydr ological Dataset Using the USGS Hydro Climatic Data Network Long term climate variability patterns and climate change are subject to much scientific research in the past 20 years Investigating long term changes in rainfall, hydrologic conditions, and oth er geophysical data is extremely important to water allocation and land use management The USGS collected records of streamflow that have been considered relatively unaltered by anthropogenic influences such as artificial diversions, storage or other cha nges in stream channels that affect hydrologic conditions Inspecting these datasets can reveal patterns in natural flow regime, extreme hydrologic condition frequencies that would have otherwise been covered by anthropogenic influences on impaired waterw ays The Hydro Climatic Data Network (HCDN) dataset was produced by the United States Geological Survey (USGS) to help gage the effects of climate variability and change on water resources throughout the United States (Slack and Landwehr, 1992) Each streamflow as individually selected based on strict conditions of measurement accuracy and natural conditions No values in the HCDN dataset wer e filled in with empirical algorithms and the minimum length of continuous record was 20 years The data was reviewed jointly with data specialists of each USGS District office The total dataset consists of 1,659 gauges throughout the United States conc entrated mostly in the Northeast where water control structures are relatively less abundant The dataset starts in the late 19th century and extends through September of 1988 (Slack and Landwehr, 1992) The dataset has been used in multiple studies to provide validation for hydrologic and climatic models (Leung et al., 2003; Dai et al., 1998) It has also been used
65 extensively to examine the effects of climate variability on streamflow (Cayan et al., 1999; Barlow et al., 2001; Stone et al., 1999; Piechota et al., 1997) The HCDN dataset was used in this study to compare with the derived UIF dataset from the Comprehensive Study The dataset on ly extends through 1988 however this period is considered the most uncertain as withdrawals before 1988 are far less understood Others have extended the dataset further into the future by appending USGS data to these stations (Small et al., 2006) This study does not attempt to extend the dataset because not enough is known about the stations to assume water control structures or other diversions have affe Parameteric, Non Parameteric and Cross Correlation Analysis of the ACF Unimpaired Flow Dataset with Selected USGS HCDN Stations Linear correlation is used consistently throughout research to understand the co variance between two time series datasets both used to analyze the HCDN and UIF datasets with one another For this study, the five spatially defined zones were created to investigate correlations within each zone T he groups were determined based on location in the reach as well as proximity to other stations Natural zones formed around clusters of HCDN stations. The groups are described in Figure 3 1, 3 2 The zones are defined as 1) Headwater Chattahoochee 2) U pper Chattahoochee 3) Middle Chattahoochee 4) Flint River 5) Lower Basin Entire time series were compared based on the zone that they were placed into in order to initially understand the relationship between entire time series from 1939 1988 The HCDN da taset does not contain values for all the years while the UIF datasets are a complete dataset from 1939 2008 The latest start date for the HCDN dataset is in
66 1968 at Upatoi Creek in group 3 The earliest starts in 1896 however the UIF datasets begin in 1939 so any records before 1939 were not used Many of the HCDN flows ended before 1988 as dams and other water control projects as well as USGS discretion were implemented Inactive HCDN sites are shown in Figure 3 2 as green triangles in while sites th at are still active are blue The HCDN dataset was ended in 1988 and was not extended determine the relationship between the HCDN and UIF datasets in the same zone. Lineari exists Trends outside of the standard linear correlation were not tested since a li near trend was expected When the data lie along a straight line and are directly linearly correlated with a positive slope, then r=1 As the data become less correlated the value decreases to 0 which implies no linear relationship For this study value s greater than (Moriasi et al., 2007) Anything less than 0.8 is considered outliers commonly found in geophysical data It also assumes a normal probability distribution of the data which is not the case with most streamflow data Most streamflow data is skewed right and follow a lognor mal distribution since the mean flows are typically larger than the median flows (3 1) Non parametric correlation was performed on the HC DN and UIF datasets to determine the trends without considering quantitative dynamics The nonparametric
67 Both methods use a nonparametric ranking system however different analysis is performed i and y i as two independent time series and (x 1 y 1 ), (x 2 y 2 n y n ) are a set of joint observations based where i is the time step considered The method determines the difference between concordant and discordant observations The observation is considered concordant when x i > x j and y i > y j or if both x i < x j and y i < y j otherwise the observation is discordant The value for tau has the ra nge 1 correlation lies in the range 0.8 8 and below 0.7 (3 2) where n = total number of paired observations monotonic function een the variables however it uses the difference between ranks The value for rho has the range 1 1 and a good correlation lies in the range 0.8 1 coefficient is defined as: (3 3) its methodology does not fit the distribution of the test statistic well for small sample sizes (n<20),
68 for this reason (Helsel and Hirsch, 1993) Tests of the power of the two different methods suggest that they have similar power when detecting monotonic trends in hydrologic cycles (Yue et al., 2002) To further examine the relationship between the time series, lagged correlations were investigated Cross correlation is the method u correlation of lags between time series A lagged correlation occurs when one time series trails behind another time series in covariance When comparing HCDN data to the UIF datasets, a delay may exist even at a monthly ti me step Both the UIF datasets lagging the HCDN and vice versa were tested The lagged correlation was computed 1) from Equation (3 4) where n is the number of lags, is the mean of the time series, and d is t he lag (Helsel and Hirsch, 1993) (3 4) Other traditional model testing correlations such as Percent Bias (PBIAS) and Room Mean Square Error (RMSE) we re not used in defining correlations since they measure quantitative differences For the correlation, it would not be expected that the flows are the same, only that they fluctuate at the same time with a proportional rate (have strong covariance). Compa rison on Pre (1939 1954) and Post (1970 1988 ) Dam UIF Datasets Construction of dams in the ACF watershed significantly changed the flow and storage of water in the rivers specifically in dry years The earliest known dam is the Langdale Dam built by th e Georgia Power Company in 1860 however many of these early dams did not significantly affect the river system However in 1954 two large projects were completed in the basin that substantially affected both flow and storage
69 A list of dam completion dat es can be found in Table 3 4 The furthest north project is Buford Dam near the headwaters of the Chattahoochee and is the main water source for Atlanta, the largest metropolitan area in Georgia The lake extends of 37,000 acres and at full pool contains an estimate of 1 million acre feet (USACOE, 1997) At the same time, Jim Woodruff Dam was being completed as a run of the river hydropower da m at the confluence of the Chattahoochee and Flint rivers Comparing the correlation between pre and post dam data illustrates if there have been any shifts Pre dam flows have been adjusted less in the development of the UIF datasets since there were fewer anthropogenic influences before the dams On the other hand the data is less reliable since streamflow measuring techniques have improved over time (Hirsch and Costa, 2004) It would be expected that pre dam correlation would be greater than post dam correlation since it has less anthropogenic influence Correlations before the dam construction should be the same as after the dam constru ction in an ideal world assuming that the HCDN data reflect truly natural flows 6, 3 7 Sample sizes less than 48 (4 years) were not considered useful when comparing the two correlations While the correlations may have been significant within the sample size, comparing pre and post correlations must have larger sample sizes Exploration of Negative Flow Months within the A CF UIF Dataset In the development of the ACF UIF dataset, many factors were used to adjust the One of the most difficult calculations to perform is the routing mechanism for the Comprehensive Study In the development of the UIF datasets, Muskingum Routing coefficients were used However, inherent within the use
70 of routing, lies negative daily inflows due to uncertain time steps and the inflexibility of that time step to change depending on flow rate Flow data at lo cal inflows were developed using the flows that were routed from upstream and subtracting the observed flows The largest routing travel time was 120 hours or 5 daily steps from Griffin to Montezuma Moreover, with the adjustments for routing, the mass b alance must be preserved One consequence of routing and mass balance adjustments was the introduction of large negative UIF datasets Even though the largest routing mechanism is 5 days, monthly averages of streamflow still produced negative flows at a monthly scale These flows were losses from the main stem of the river and have no actual physical reality in the system To investigate this occurrence, the UIF datasets were summarized both spatially and temporally. Wavelet Analysis on Pre and Post D am Subsections of the ACF UIF Dataset Time series analysis and correlation provides useful information about how time series correlate on a time domain while other analysis can be explored by looking at times series from a different perspective such as fre quency, extremes and quartiles One popular perspective is the frequency domain where time series are explored by how often an event occurs Other geophysical data has much to explore in the frequency domain that may be otherwise unseen through conventio nal time series analysis A popular method for exploring the frequency domain is Fourier analysis This method deconstructs time series into an orthogonal combination of sines and cosines (Equation 3 a basic waveform (Equation 3 6) (3 5)
71 (3 6) Many limitations have been suggested in the use of Fourier analysis in the analysis of geophysical time series Geophysical time series are often discontinuous, contain sharp peaks, and are non stationary Fo urier analysis is not able to robustly analyze these characterizes Wavelet analysis was formulated to investigate frequencies in a more useful and comprehensive analysis of the time frequency domain over the more traditional Fourier analysis Wavelet an alysis provides a way of continuously looking at time series in both time and frequency space to provide further analysis It is a technique that identifies the dominant localized variations of power throughout the various frequencies It is a method use d to quantify and visualize significant changes in variance over a multi decadal time scale Some of its greatest strengths are the ability to represent functions that have discontinuities, have sharp peaks, are non stationary, and most importantly can de construct finite signals Wavelet transforms can expose the power of many different frequencies of a non stationary time series (Daubechies, 1990) The b asic driver for the wavelet function is a localized, zero mean function with a non dimensional time parameter The wavelet transform, W n (s), is the convolution of the time series, x n Wavelet transform, dimensional time
72 pa where s is the wavelet scale, n is the localized time index, and (*) indicates the complex conjugate: (3 7) Wavelet analysis has been applied across many disciplines of stu dy including signal processing (Mallat, 1999) computer vision (Antonini et al., 1992) hydrology (Wang and Ding, 2003) water quality (Keener et al.; Wang and Ding, 2003) etc. For further technical knowledge of the wavelet transform refer to (Torrence and Compo, 1998) Similar to the Fourier analysis for the whole time series, a Global Wavelet Spectrum (GWS) (Torrence and Webster, 1999) is calculated by integrating the squared wavelet transform coefficients at different scales for all data points This value computes the power of the frequency integrated over the whole time series, sug gesting that frequency is significant Areas that are above the dashed blue line are considered significant at the 95% confidence level. For this research effort, ACF streamflows that showed differences in pre and post dam correlations were analyzed us ing wavelet analysis Comparison between two wavelets graphically illustrates where periodicities are similar or where there is high frequency covariance When the pre and post dam start to exhibit different frequencies, confidence in the flows is reduce d Frequency covariance could be graphically displayed using cross wavelet analysis however for this research a visual inspection of the more conventional wavelets is performed
73 Results As presented in the methodology section, this section details the f indings of the various statistical tests conducted on the ACF UIF dataset as a whole and on selected sub sections (Pre and Post Dam) The data is analyzed for correlation as a whole and pre and post dam as well as selected gages that showed pre and post d am differences were explored in the frequency domain Negative flows were inspected both spatially and temporally. Parametric, Non Parametric and Cross Correlation Tests Correlation analysis is extremely useful for determining how datasets vary with one a nother One would expect that the UIF datasets and HCDN data would have better correlations in the upper part of the watershed since the streamflows would have been least affected by cumulative anthropogenic influences In the Lower Basin (Zone 5), the l east amount of correlation would be expected since flows in this section have been subject to the most amount of routing error, mass balance adjustment, and cumulative withdrawal error Correlations were considered good when either a thresholds were chosen based on conventional hydrologic modeling validation criteria (Moriasi et al., 2007) the nature of the statistic being non parametric ranking (Helsel and Hirsch, 1993) The headwaters of the ACF basin (Zone 1) contains only one UIF at Buford (BU_UI) which was correlated with four different HCDN gauges Three HCDN flows were located in the watershed and one just outside of the water shed boundary The Overall the correlation was extremely high
74 and the Buford UIF has the most confidence of any of the gauges since it has the most HCDN data to compare to as well as good correlations The result of this is that the unimpaired flows in the headwaters of the Chattahoochee are reflective natural flows The Upper Chattahoochee (Zone 2) contained four HCDN station s and four UIF datasets Whitesburg (WHI_UI) has a strong correlation with Big Creek (2335700) which is a very close tributary to the UIF control point Also WHI_UI correlated well with Sweetwater Creek (2337000) and Snake Creek (2337500) which are furth er upstream Norcross (NOR_UI) is not correlated with either the HCDN or the other UIF datasets while Morgan Falls (MF_UI) and Atlanta (ATL_UI) only correlated with one another. Overall, WHI_UI correlated well with all of the HCDN stations except for Lit tle River (2392500) which was very close to reaching the threshold The other UIF control points including NOR_UI, ATL_UI, and MF_UI do not correlate well with the HCDN dataset. This result indicates that Whitesburg reflects natural conditions however no conclusions can be reached about Norcross, Morgan Falls, or Atlanta. The Middle Chattahoochee (Zone 3) has considerable correlations with the UIF datasets and one of the unimpaired flow datasets. Goat Rock (GR_UI), Bartletts Ferry (BF_UI), Columbus (COL_ UI), West Point (WP_UI) all correlated well with Chattahoochee River at West Point (2339500) Goat Rock (GR_UI), Bartletts Ferry (BF_UI), and Columbus (COL_UI) all correlated well with one another On the other hand WF George (WFG_UI) correlated well wit h both Upatoi Creek (2341800) and Uchee Creek (2342500) This result indicates that WF George resembles natural flow and Goat Rock, Bartletts F erry Columbus, and West Point resemble natural flows through 1955 and after this no confidence can be assessed without more HCDN data
75 The Flint River (Zone 4) has three UIFs and four HCDN gauges with 2 of them directly on the main Flint River channel Montezuma ( MON_UI) and Griffin (GRI_UI) correlated well with one another as well as both the Flint gauges Flin t River Near Culloden (2347500) and Flint River Near Montezuma (2349500) The other gauges did not correlate well except for the Turkey Creek (2349900) which was close to the non parametric threshold of tau=0.7 (Table 3 5) The results indicate that Mon tezuma and Griffin have confidence under the assumption that the HCDN gauge stations on the main channels are truly unimpaired. The Lower Basin (Zone 5) is closest to the outlet of the watershed at the confluence of the Chattahoochee and Flint Only HCD N data correlated with one another with the Flint River near Bainbridge (2356000), Ichawaynochaway Creek (2353500), Spring Creek (2357000), and Apalachicola River (2358000) all correlated with one another well Spring Creek (2357000) and the Chipola River (2359000) correlated well The Blountstown (BLO_UI) did not correlate well with any of the UIF datasets or the HCDN with its highest correlation with the Flint River near Bainbridge r of 0.2196 and all the others were much lower than this Jim Woodruff (JW_UI) also did not No confidence can be give to UIFs in the lower basin since not much correlation exists with HCDN data All of these correlations listed in Table 3 5. Comparison of Pre and Post Dam UIF Subsections Pre and post dam comparisons are useful to determine if corre lation with HCDN data changes with the influence of large dams on the development of the UIFs A
76 6 This method will selec t both informative changes in correlation as well as some not so informative noise between less correlated values Some of UIF sections showed large differences between pre and post dam flows Blountstown (BLO_UI) showed a large difference in correlation Pre dam 0.81 with several of the HCDN stations while most of the post dam correlations are negative ranging from 0.2 through 0.35 Morgan Falls (MF_UI) showed large difference as well since it shifted dramatically from pre to post dam Morgan falls in pre dam correlated fairly well with both Little River (2392500) and It was reduced to almost no correlation with post dam Atlanta (ATL_UI) followed the same pattern of losing correlation with both Little River (2392500) and Sweetwater Creek (2337000) It is also important to note that W hitesburg (WHI_UI) correlated well with ATL (UI) and Morgan Falls (MF_UI) pre dam and this was decreased in post dam flows Other differences in correlations were not as substantial and the complete list presented in Table A 1 Some of the correlation di fferences were large however they were weak correlations both pre and post dam For instance Ichawaynochaway Creek (2353500) correlated weakly with Sumatra (SUM_UI) both pre nd 0.3964 The difference in correlation is large (0.6) however the individual pre ( 0.3) and post (0.3) correlations are very weak initially The result of this correlation is that
77 Blountstown, Morgan Falls and Atlanta lose correlation with HCDN in pos t dam flows illustrating that inaccuracy in the post dam flows Negative Flows an d their Potential Influence on O verall S ystem F lows One of the most intriguing aspects about the UIF dataset is the negative flows that are distributed throughout much of th e UIF datasets at a daily time step Upon aggregating the daily time step into average monthly time steps, monthly negative averages were found in abundance In an effort to understand the trends of these flows, they were examined both in time and by gag e Table 3 8 shows where the negative flows are distributed through the different gauges. Blountstown carries a large number of occurrences of negative values showing that there were 221 (18 years) negative flow months where the second highest number of negative flow months occurs at Newton with 50 months (4 years) Norcross, West Point, Bartletts Ferry WF George, Newton, Jim Woodruff and Sumatra contained between 20 and 50 instances of monthly negative flow Whitesburg and Bainbridge had between 1 and 20 instances Negative flows are distributed regardless of proximity to dams since Blountstown, Sumatra, Newton, Whitesburg, Norcross are not directly on dam sites and West Point, Bartletts Ferry, WF George, and Jim Woodruff are located at dam sites W hat is more of interest is the cumulative flow loss on the ACF system The average negative monthly flow for each site was recorded in Table 3 8 and suggests that Blountstown makes up the majority of negative flows in the system followed by Sumatra, Jim W oodruff and Newton Other negative flows are relatively insignificant when compared with these flows. Such significant monthly flow adjustments at Blountstown, this would have large effects on downstream calculations This would also
78 suggest that flows upstream are overestimated since such a large adjustment is needed to reach mass balance. The distribution of negative flow throughout the year was examined to see if a particular month had more negative flows September had the most instances while summ er months had around half as many with most months have between 30 and 50 instances Figure 3 5 The instances were looked at through time and it seems there is a general increasing trend in time where few instances occurring in the first 35 years than the second Suggested reasons for some of the spikes in negative values are suggested in Figure 3 6 There was a spike in 1954 when Jim Woodruff and Buford were constructed There was another spike in 1974 when west point was constructed Finally, two spi kes occur when new unimpaired flow datasets are introduced into the system in 1994 and 2002 Wavelet Analysis of Pre and Post Dam UIF Subsections Spectral wavelet analysis is a method allows for the exploration of the non stationary aspects of the time s eries within the frequency domain When comparing time series using correlation methods, elements of frequency relationships are lost in the analysis Wavelet analysis was performed on several of the UIF datasets and visually compared to a selected HCDN gage station to compare frequencies The UIF datasets and corresponding HCDN data was chosen if the difference in correlation between pre and post dam flows was greater than 0.4 and either pre or post dam correlation was considered fair Wavelet analysis provides more information on the location of changes in correlation exposed by pre and post dam correlation Stations that were compared include Blountstown (BLO_UI) with Chipola Creek, Morgan Falls
79 (MF_UI) and Atlanta (ATL_UI) with Sweetwater Creek, and finally Jim Woodruff (JW_UI) with Ichawaynochaway Creek An example of wavelet analysis was performed on the ENSO 3.4 time series from 1939 2008 When the annual cycle is removed, SST power (Figure 3 7) is concentrated within the ENSO periodicity of 2 7 years The strength (power) of the frequency is expressed by the colors where warm colors (red, orange, yellow) are a greater power and cooler colors (green, teal, blue, white) are less powerful An example of this is during strong El Nino events (1982 1983 and 1997 1998) the most prominent reds are displayed. Significant Wavelet Power Spectra are shown within the cone of influence and GWS gives the significant periods over the entire time period Visual comparison of power (colors) is used in this stu dy by similar colors at similar times periods and time indicating similarity of time series in the frequency domain The idea is to pick out years of differing strength of spectral signals between the HCDN and UIF comparisons Atlanta and Morgan Falls h ave almost the exact same wavelet power spectra plot so only Atlanta will be referred to (Figure 3 8) The most noticeable difference is the increased power from 1980 1988 in the 4 8 year period in Sweetwater Creek This would reduce the correlation in t he post dam series significantly The correlation between Jim Woodruff (JW_UI) and Ichawaynochaway Creek showed reduction from pre to post dam (Figure 3 9) The wavelet analysis exposed an 8 16 year period with much more power in the Ichawaynocheaway Cre ek between 1939 and 1955 (Figure 3 9) This would reduce the correlation for pre dam Chipola Creek and Blountstown
80 showed almost opposite wavelet power after 1955 Power in the 6 10 period from 1970 through 1988 in Blountstown is not seen in the Chip ola wavelet plot (Figure 3 10) Discussion / Conclusion The overall objective of this research effort was to systematically analyze the foundational unimpaired hydrological datasets that provide the inflows to complex, wa ter system models in the ACF H aving little to no validation in the process of developing the unimpaired flow set, this study set out to give some meaningful ways of testing the flows Conventional hydrologic model testing techniques were not applicable to this study since most all ava ilable streamflow datasets were used in the derivation of the unimpaired flows The results of this study indicate that the nearly half of the UIF dataset correlate well with observed HCDN streamflows Moreover through inspection of the negative unimpair ed flows, Blountstown had significant occurrences as well as large negative values that influence downstream flows used for guidance in making allocation decisions Through statistical analysis many of the UIF datasets correlated well with at least one H CDN station giving confidence to this dataset Ten of 24 the UIF datasets Moreover, it was shown that the UIF datasets and HCDN flows at the headwaters of the Chattahoo chee in Zone 1 correlate better than those further downstream In the downstream stretch (Zone 5) there were no UIF datasets which correlated well with the HCDN stream flows It is suspected that much of the reason for the lack of correlation at Sumatra, Blountstown, Jim Woodruff and Newton is due to the cumulative routing errors of flow from the Chattahoochee, Flint, and Apalachicola rivers Another consideration is that during periods of high flow its floodplain is used to provide flood
81 control and cha nges the channel and flow dynamics significantly (Light et al., 2006) Flood conditions are modeled poorly since the Muskingum method has fixed its channel and there are no floodplain considerations Furthermore the flows are routed the same during flood conditions when the travel time may be as much as 96 hours and normal when calculated around 24 hours (USACOE, 1997) This produces large mismatches in peak flow used in the calculation of the incremental local flows Cross correlation was performed on many of the UIF datasets and their respect ive HCDN datasets however the strongest correlations existed with no lag (Figure 3 3) This concludes that there is not more than one month delay between when the flows occur In addition to the correlating UIF datasets with HCDN flows, this part of the study affirmed that many of the unimpaired flows correlated well with one another This is in large part due to the methods used in filling in data Upon development of the local incremental inflows, there were no physical gauges for Bartletts Ferry (BF _UI), Goat Rock (GR_UI), Oliver (OL_UI), North Highlands (NH_UI) The flow data for these control points were empirically derived from correlations with Columbus and West Point gage based on drainage area fill in methods Many of the gauges in the system were used to correlate with one another to provide adequate long term UIF datasets Correlation between UIF datasets should be expected even with withdrawals and routing changing the flows One thing to note is that only gauges on the main channel were used for fill in methods when developing the incremental local inflows so HCDN data not located on the main channels are completely independent datasets
82 The next method used to examine the flows was to divide the flows into pre and post dam correlations Pre dam correlations were taken before 1954 when both Jim Woodruff and Buford were completed Post dam correlation was taken after 1970 when all the dams except for West Point had been constructed Differences between correlations were then taken to l ook at where UIF datasets once correlated with pre Several interesting differences were exposed through this analysis The Blountstown UIF shows some difference between pre and post dam This station is probably one of t he most uncertain considering it is downstream of the every gauge except for Sumatra Blountstown correlated fair and slightly below fair with many of the gauges pre dam however post dam correlations were very weak Much of this is due to the changes in the main channel due to the large dam projects that were put into place Blountstown is located directly below Jim Woodruff and would have been affected significantly by these flow changes and adjustments would have been difficult Blountstown (BL_UI) do es not have much confidence and is considered one of the most critical since it is close the outlet of the river system where many of the flow restrictions and modeling outputs are used On the other hand this control point only contributes an average of 600 cfs to the total flow of the system Table 3 2 The Morgan Falls and Atlanta UIF datasets showed similar characteristics since they are correlated well with one another Table 3 5 Both of these UIF datasets showed significant differences between pre and post dam flow correlations with Sweetwater Creek and Little River One weakness of the Little River correlations is that there is not a relative large amount of time considered since the pre dam correlations only correlate
83 a little more than 6 years e ach However, the difference in correlation would reduce the confidence in both of these unimpaired flows The Jim Woodruff UIF had the opposite correlation as the Morgan Falls, Atlanta and Blountstown Post dam correlations were fair with Ichawaynocha way Creek with less than 6 years of measurements however pre dam correlation was weak with a much larger number of measurements Ichawaynochaway Creek is the farthest north of the Jim Woodruff in Zone 5 however it contributes an average of 800 cfs which i s relatively large compared to other non main channel HCDN flows except for Chipola River as seen in Table 3 5 Negative flows are prevalent in the UIF datasets and were evaluated to determine what where and when they occur and what effect they may have on the flows Even after smoothing the daily flows to reduce the erratic flows left after routing was performed on the flows, there were still substantial negative values at the daily time step Computation of local flows consists of routing upstream dai ly flows to the next downstream control point and subtracting the routed flow from the downstream observed flow Over the period of 1939 2008 large droughts and heavy floods were both recorded giving rise to significantly different flow patterns as well a s timing A single best estimate for routing coefficients was chosen for each reach to represent to range of flow rates For the study, these flow rates were typically chosen on the conservative side to provide more accurate values in times of droughts However, this leads to peak flows routed from upstream not coinciding with peak flows from a downstream point Negative local flows occur since the non coincident peaks are subtracted (USACOE, 1997) Mass balance is preserved when negative flows are
84 considered in this computation however this must be considered when considering modeling application as a limitation As such, daily flows would not be considered a reasonable use of model output Monthly and possibly weekly flows would be a proper use in recognition of accuracy limitations the UIF development routing methodology and subsequent negative flow occurrences Wavelet analysis provid ed a means of looking at where frequencies in the data differed between HCDN and UIF datasets Wavelet analysis confirmed that pre and post dam differences by careful visual inspection of wavelets derived for each streamflow Substantial differences were seen in the pre and post dam power in the three different comparisons This analysis however does not prove to give any numerical values that can be compared for analysis The method provided a means of looking at the time series in the frequency domain however further analysis would need to take place to determine where the differences in correlation take place A more fitting analysis might be a windowed correlation (Boker et al., 2002) that runs a correlation It essentially parses the time series into different periods and runs correlations Windowed correlations are run and the optimal window would r eveal much about where the correlation changes Another way to give more numerical results to the frequency spectrum would be to use cross wavelet analysis This method runs a correlation between the two wavelet plots which results in a more precise rela tionship between the wavelet power spectrums of the two different streamflows (Keener et al.) The purpose of this study was to give added conf idence to the unimpaired flow dataset as an accurate resemblance of natural flows in the basin Through the use or
85 parametric and non parametric statistical techniques correlation between physically HCDN datasets and synthetic UIF datasets were compared Overall confidence in 10 of 24 UIF datasets was established by correlating well with HCDN flows Other flows such as Blountstown, Morgan Falls, Atlanta and Jim Woodruff were shown to have inconsistencies in their record when comparing pre and post dam re lationships with natural HCDN flows Wavelet analysis further confirmed that these flows shifted from correlating well with the natural flows to not having much correlation This study is useful when determining how to use this model and where the incons istencies lie Further studies should correlate rainfall with flow data a monthly data as well as use cumulative UIF datasets in comparison.
86 Figure 3 1. The Apalachicola Chattahoochee Flint with HCDN and control points for UIFs with control points la beled. Zones are also labeled 1 5 and correlations were performed within these zones.
87 Figure 3 2. Map of HCDN stations with USGS gage stations labeled and divided into different zones 1 5 Inactive HCDN stations are no longer recording new observat ion for standard USGS flows as of December 2004 Active stations are still recording as of December 2004.
88 Figure 3 3. Cross correlation between Buford with lags on Chestatee River (2333500) All other gauge stations were cross correlated and revealed highest meaningful correlation at zero lag as suggested in this example
89 Figure 3 4. Generalized system flow diagram for developing UIFs (Modified from (USACOE, 1997) Figure 3 5. Histogram of t otal number of negative flows by month in the UIF dataset Most instances occur in the month of September (9) Develop Flow Data Adjust for Evaporaiton Precipitation Channel Routing / Local Inflows Remove Municipal and Industrial Effects Remove Agricultural Effects Correct for Leakage Smooth Flow Hydrographs Unimpaired Flows
90 Figure 3 6. Instances of negative values by year over all gauges Boxes indicate a corresponding action that may have occurred to justify jumps in negative flow frequency.
91 Figure 3 7. (a) Time series anomaly (not shown in other figures) (b) Significant Wavelet Power Spectra are shown within the cone of influence with by color mapping to indicate high wavelet power with warm colors (red, orange), and low powers in cool colors (blue, white) (c) The power of the period is represented with t he Global Wavelet Spectrum (GWS) by integration over all scales and times The 95% confidence lim it is shown on the GWS (dashed blue line), the periodicities above which show significance (similar to Fourier analysis) (d) Graph of 2 8 year scale averaged variance, which is a weighted sum of the spectrum in (b) at specific scales, in this case, the 2 8 year ones indicative of ENSO activity (not shown in other figures).
92 (a) (b) (c) Figure 3 8. Significant Wavelet Power Spectra are shown within the cone of influence and GWS gives the significant periods over the entire time period Visual comparison of pow er (colors) is used in this study by similar colors at similar times periods and time indicating similarity of time series in the frequency domain (a) Sweetwater Creek (2337000) (b) Atlanta (ATL_UI), (c) Morgan Falls (MF_UI)
93 (a) (b) Figure 3 9. Significant Wavelet Power Spectra are shown within the cone of influence and GWS is shows with significant periods Visual comparison of power (colors) is used in this study by similar colors at similar times periods and time indicating similarity of time series in the frequency domain (a) Ichawaynochaway Creek (2353500) (b) Jim Woodruff (JW_UI) (a) (b) Figure 3 10. Significant Wavelet Power Spectra are shown within the cone of influence and GWS is shows with significant periods Visual comparison of power (colors) is used in this study by similar colors at similar times periods and time indicating similarity of time series in the frequency domain (a) Chipola River (2359000) (b) Blountstown (BLO_UI)
94 Table 3 1. Chattahoochee, Flint, and Apalachicola HCDN river stations used for comparison with UIF stations. Stream Name State HUC6 Station No. Area (mi 2 ) First Year Last Year Years Recorded Latitude Longitude Chattahoochee River GA 31300 2331000 150 1940 1972 32 34:34:37N 083:38:09W Chattahoochee River GA 31300 2331600 315 1957 1988 42 34:32:27N 083:37:14W Chestatee River GA 31300 2333500 153 1929 1988 63 34:31:41N 083:56:23W Etowah River GA 31501 2389000 107 1940 1977 37 34:22:57N 084:03:21W Little River GA 31501 2392500 60 1947 1977 30 34:0 7:09N 084:23:18W Big Creek GA 31300 2335700 72 1960 1988 40 34:03:02N 084:16:10W Sweetwater Creek GA 31300 2337000 246 1904 1988 84 33:46:22N 084:36:53W Snake Creek GA 31300 2337500 35.5 1954 1988 45 33:31:46N 084:55:42W Chattahoochee Riv er GA 31300 2339500 3550 1896 1955 59 32:53:10N 085:10:56W Mountain Oak Creek GA 31300 2340500 61.7 1944 1972 28 32:44:28N 085:04:08W Whitewater Cr GA 31300 2349000 93.4 1952 1972 20 32:28:00N 084:15:58W Upatoi Creek GA 31300 2341800 342 1 968 1988 32 32:24:48N 084:49:12W Uchee Creek AL 31300 2342500 322 1947 1988 53 32:19:00N 085:00:54W Flint River GA 31300 2347500 1850 1911 1989 78 32:43:17N 084:13:57W Flint River GA 31300 2349500 2900 1905 1981 76 32:17:53N 084 :02:38W Turkey Creek GA 31300 2349900 45 1958 1988 41 32:11:44N 083:54:03W Ichawaynochaway Creek GA 31300 2353500 620 1905 1967 62 31:22:58N 084:32:52W Spring Creek GA 31300 2357000 485 1937 1988 53 31:02:23N 084:44:18W Flint River GA 31 300 2356000 7570 1908 1957 49 30:54:41N 084:34:48W Stream Name State HUC6 Station No. Area (mi 2 ) First Year Last Year Years Recorded Latitude Longitude Apalachicola River FL 31300 2358000 17200 1929 1988 71 30:42:03N 084:51:33W Chipola River FL 31300 2359000 781 1922 1987 65 30:32:02N 085:09:55W
95 Table 3 2. Total UI average flows over all years from 1939 2008 and sorted from the smallest to the largest UIF name Average Flow (cfs) CHA_UI 0.523654 OLI_UI 40.07352 COL_UI 78.597 67 NH_UI 113.5417 ATL_UI 166.9586 NOR_UI 230.3377 GRI_UI 350.8613 GR_UI 400.7354 WPG_UI 436.8634 MF_UI 438.8484 NEW_UI 518.1881 BLO_UI 595.8703 BF_UI 750.0044 GA_UI 938.605 WHI_UI 1308.545 WPR_UI 1319.525 WP_UI 1529.636 BAI_UI 1881.289 BU_U I 2032.1 ALB_UI 2439.634 WFG_UI 2807.18 WFG_UI2 2807.18 MON_UI 3128.667 JW_UI 3149.144 SUM_UI 3538.253
96 Table 3 3. Total HCDN average flows over all years available from 1939 1988 and sorted from the smallest to the largest HCDN Gage Average Flo w (cfs) 4 2349900 TURKEY CREEK 46.1 2 2337500 SNAKE CREEK 56.4 3 2340500 MOUNTAIN OAK CREEK 80.5 2 2392500 LITTLE RIVER 85 2 2335700 BIG CREEK 111 4 2349000 WHITEWATER CR 164.1 1 2389000 ETOWAH RIVER 271 2 2337000 SWEETWATER CREEK 334.4 1 233350 0 CHESTATEE RIVER 362 1 2331000 CHATTAHOOCHEE RIVER 407 3 2342500 UCHEE CREEK 435.9 3 2341800 UPATOI CREEK 451.8 5 2357000 SPRING CREEK 485.8 5 2353500 ICHAWAYNOCHAWAY CREEK 798.6 1 2331600 CHATTAHOOCHEE RIVER 818 5 2359000 CHIPOLA RIVER 1476.7 4 2 347500 FLINT RIVER NR CULLODEN GA 2275.2 4 2349500 FLINT RIVER NR MON 3606.8 3 2339500 CHATTAHOOCHEE RIVER 5130.7 5 2356000 FLINT RIVER 8942.4 5 2358000 APALACHICOLA RIVER 22271
97 Table 3 4. List of dams on the ACF with their corresponding completion year and what river reach it is located on. Name Owner Year Complete River Buford Dam Corps of Engineers 1954 Chattahoochee Morgan Falls Dam Georgia Power Company 1903 Chattahoochee West Point Dam Corps of Engineers 1974 Chattahoochee Langdale Dam G eorgia Power Company 1860 Chattahoochee Riverview Dam Georgia Power Company 1906 Chattahoochee Bartletts Ferry Dam Georgia Power Company 1926 Chattahoochee Goat Rock Dam Georgia Power Company 1912 Chattahoochee Oliver Dam Georgia Power Company 1 959 Chattahoochee North Highlands Dam Georgia Power Company 1899 Chattahoochee City Mills Dam City Mills Company 1890 Chattahoochee Eagle and Phenix Dam Eagle and Phenix Hydro Company 1834 Chattahoochee W. F. George Dam Corps of Engineers 1963 Chatt ahoochee G. W. Andrews Dam Corps of Engineers 1963 Chattahoochee Crisp County Dam Crisp County Power Commission 1930 Flint Flint River Dam Georgia Power Company 1921 Flint Jim Woodruff Dam Corps of Engineers 1954 Apalachicola
98 Table 3 5. Selecte years Variable by Variable Count Pearson r 5 2356000 Flint River 5 2353500 Ichawaynochaway Creek 0.7822 180 0.9267 5 2357000 Spring Creek 5 2353500 Ichawaynochaway Creek 0.734 8 372 0.9291 5 2357000 Spring Creek 5 2356000 Flint River 0.7507 189 0.9035 5 2358000 Apalachicola River 5 2353500 Ichawaynochaway Creek 0.7565 432 0.9204 5 2358000 Apalachicola River 5 2356000 Flint River 0.8788 189 0.9825 5 2359000 Chipola River 5 23 57000 Spring Creek 0.7598 324 0.9029 4 MON UI 4 GRI UI 0.7471 840 0.9075 4 2347500 Flint River Nr Culloden Ga 4 GRI UI 0.8362 597 0.9601 4 2347500 Flint River Nr Culloden Ga 4 MON UI 0.8747 597 0.9704 4 2349500 Flint River Nr Mon 4 GRI UI 0.7728 597 0. 9133 4 2349500 Flint River Nr Mon 4 MON UI 0.9733 597 0.999 4 2349500 Flint River Nr Mon 4 2347500 Flint River Nr Culloden Ga 0.888 597 0.972 3 COL UI 3 GR UI 0.6629 840 0.9052 3 2339500 Chattahoochee River 3 GR UI 0.9017 201 0.986 3 2339500 Chattahoo chee River 3 BF UI 0.9649 201 0.9954 3 2339500 Chattahoochee River 3 COL UI 0.8981 201 0.9857 3 2339500 Chattahoochee River 3 WP UI 0.8076 201 0.9409 3 2341800 Upatoi Creek 3 WFG UI 0.7829 240 0.9335 3 2341800 Upatoi Creek 3 2340500 Mountain Oak Creek 0.7355 36 0.9079 3 2342500 Uchee Creek 3 2341800 Upatoi Creek 0.7785 240 0.9313 2 2392500 Little River 2 2335700 Big Creek 0.8658 192 0.9799 2 MF UI 2 ATL UI 0.7559 840 0.9413 2 WHI UI 2 2335700 Big Creek 0.7358 336 0.9103 2 2337000 Sweetwater Creek 2 2335700 Big Creek 0.7886 336 0.9429 2 2337000 Sweetwater Creek 2 2392500 Little River 0.7798 348 0.9326
99 Table 3 5. Continued Variable by Variable Count Pearson r 2 2337000 Sweetwater Creek 2 WHI UI 0.7857 597 0.9421 2 2337500 Snake Creek 2 WHI UI 0.721 408 0.9226 2 2337500 Snake Creek 2 2337000 Sweetwater Creek 0.7472 408 0.9253 1 2331000 Chattahoochee River 1 BU UI 0.8312 373 0.9621 1 2331600 Chattahoochee River 1 BU UI 0.8781 372 0.977 1 2331600 Chattahoochee RIVER 1 2331000 Chattahoochee River 0.9193 169 0.9829 1 2333500 Chestatee River 1 BU UI 0.8731 576 0.975 1 2333500 Chestatee River 1 2331000 Chattahoochee River 0.8781 373 0. 9788 1 2333500 Chestatee River 1 2331600 Chattahoochee River 0.8828 372 0.9814 1 2389000 Etowah River 1 BU UI 0.8341 432 0.961 1 2389000 Etowah River 1 2331000 Chattahoochee River 0.8262 373 0.9533 1 2389000 Etowah River 1 2331600 Chattahoochee River 0 .8211 228 0.9575 1 2389000 Etowah River 1 2333500 Chestatee River 0.8669 432 0.9717
100 Table 3 Variable by Variable Pre Post Pre Pearson r Post Pearson r 5 JW UI 5 BLO UI 0.2161 0.2467 0.3572 0.3934 5 SUM UI 5 JW UI 0.0093 0.2263 0.237 0.2388 5 BAI UI 5 BLO UI 0.4714 0.1094 0.753 0.2446 5 2353500 Ichawaynochaway Creek 5 BLO UI 0.4742 0.1057 0.7682 0.2565 5 2353500 Ichawaynochaway Creek 5 JW UI 0.2252 0.6479 0 .3974 0.8441 5 2353500 Ichawaynochaway Creek 5 SUM UI 0.3615 0.3308 0.3162 0.3964 5 2357000 Spring Creek 5 BLO UI 0.4425 0.5 0.7368 0.7307 5 2357000 Spring Creek 5 JW UI 0.3517 0.8889 0.4834 0.9302 5 2358000 Apalachicola River 5 BLO UI 0.4815 0.15 32 0.8077 0.3367 5 2358000 Apalachicola River 5 JW UI 0.2712 0.6594 0.3976 0.8739 5 2359000 Chipola River 5 BLO UI 0.537 0.0695 0.7252 0.2005 2 2392500 Little River 2 ATL UI 0.7983 0.2374 0.9252 0.5817 2 MF UI 2 2392500 Little River 0.7587 0.1184 0.8914 0.395 2 WHI UI 2 ATL UI 0.7341 0.2601 0.8757 0.5891 2 WHI UI 2 MF UI 0.697 0.1014 0.8519 0.4121 2 2337000 Sweetwater Creek 2 ATL UI 0.7318 0.239 0.8942 0.5806 2 2337000 Sweetwater Creek 2 MF UI 0.6947 0.0722 0.8663 0.392
101 Table 3 7. Selected differences between pre and post dam correlations as well as sample size correlation is based on. Variable by Variable Post Pre Post Pre Pearson r Post Sample Size n Pre Sample Size n 5 JW UI 5 BLO UI 0.4628 0.7506 228 180 5 SUM UI 5 JW UI 0.2356 0.4758 228 180 5 BAI UI 5 BLO UI 0.5808 0.9976 228 180 5 2353500 Ichawaynochaway Creek 5 BLO UI 0.5799 1.0247 69 171 5 2353500 Ichawaynochaway Creek 5 JW UI 0.4227 0.4467 69 171 5 2353500 Ichawaynochaway Creek 5 SUM UI 0.6923 0.7126 69 17 1 5 2357000 Spring Creek 5 BLO UI 0.9425 1.4675 9 180 5 2357000 Spring Creek 5 JW UI 0.5372 0.4468 9 180 5 2358000 Apalachicola River 5 BLO UI 0.6347 1.1444 225 180 5 2358000 Apalachicola River 5 JW UI 0.3882 0.4763 225 180 5 2359000 Chipola River 5 BLO UI 0.6065 0.9257 225 123 2 2392500 Little River 2 ATL UI 0.5609 0.3435 81 75 2 MF UI 2 2392500 Little River 0.6403 0.4964 81 75 2 WHI UI 2 ATL UI 0.474 0.2866 228 180 2 WHI UI 2 MF UI 0.5956 0.4398 228 180 2 2337000 Sweetwater Creek 2 ATL UI 0.4928 0.3136 225 180 2 2337000 Sweetwater Creek 2 MF UI 0.6225 0.4743 225 180
102 Table 3 8. List of UIFs with corresponding instances of negative monthly flows, average negative flows, and cumulative impact of flows Control Point Instance s of N egative Monthly Flows Average Negative Monthly Flow (cfs) Cumulative (cfs*month) BUFORD 0 NORCROSS 47 65.0 3056.29 MORGAN FALLS 0 ATLANTA 0 WHITESBURG 12 112.1 1345.45 WEST POINT G 6 51.8 310.508 WEST POINT R 31 47.9 1486. 02 BARTLETTS FERRY 37 256.3 9484.51 GOAT ROCK 0 OLIVER 0 NORTH HIGHLANDS 0 COLUMBUS 0 W.F.GEORGE 22 266.7 5866.39 GEORGE ANDREWS 0 GRIFFIN 0 MONTEZUMA 0 ALBANY 0 NEWTON 50 482.1 24103.1 BAINBRIDGE 1 36.0 35.9 677 JIM WOODRUFF 24 1005.0 24121.2 CHATTAHOOCHEE N/A BLOUNTSTOWN 221 973.5 215137 SUMATRA 22 1454.7 32003.6
103 CHAPTER 4 A SYSTEMS DYNAMICS M ODEL APPLICATION FOR DROUGHT OPERATIONS I N THE APALACHICOLA/CHA TTAHOOCHEE/FLINT RIV ER WATERSHED In troduction Increasing human demands on water resources have put pressure on river systems to provide consistent and sustainable flows for often competing interests Complex water conflicts can persist at low levels for decades and escalate rapidly under d rought conditions, providing a challenging environment for the systematic analysis and implementation of resolution strategies (Scholz and Stiftel, 2005; Dellapenna, 2006) This dynamic has been evident in the Apalachicola Chattahoochee Flint (ACF) River Basin, covering three southern states with diverse populations and water resource objectives (Leitman and Hatcher, 2005; Jordan et al., 2006) Water conflicts in the Apalachicola Chattahoochee Flint (ACF) River Basin have persisted for over twenty years with ongoing negotiation, discussion, mandated compromise and litigation among Georgia, Alabama and Florida (Jordan et al., 2006) The ACF contains one of the largest rivers in the southeastern United States. After the passage of the River and Harbor Act of 1945 and 1946 the Corps initi ated the construction of several dams along the length of the river; five dams were constructed the river for federal purposes of flood control, navigation, and hydropower Since then the manual has been replaced by several intermediary plans In 1989 after a series of droughts in the basin the Water Control Plan (WCP) was assembled to more adequately address drought and floods Initially, the projects were operated so that hydropow er requirements dictated releases during summer months when energy consumption was high and flows were low Navigation demands provided releases during the fall during
104 low flow Flood control releases have always taken precedence over other authorized us es as they are more urgent and have more financial implications if release requirements are not met During drought conditions water supply and quality dominate operations (USACOE, 1989) The Interim Operations Plan (IOP) was introduced in 2006 to increase the flow from Jim Woodruff dam to protect the endangered species in the river (Zeng and Wen, 2007) After the drought of 2006 2008 the Revised Interim Operation Plan (RIOP) replaced the IOP because it was better able to deal with drought in the basin (Zeng et al., 2009) Increasingly, water resource managers have turned to model representations of the ACF channel/reservoir system with a specific dependence on the use of Unimpaired Flow (UIF) datas ets to provide the baseline flow conditions to model water allocation scenarios (USACOE, 1997) UIF data are described as the historically der ived flows that have been systematically adjusted to remove the effects of anthropogenic influences such as withdrawals, returns, and the effects of water control structures These datasets were input into several water system models to allow simulation o f various historical flows and future projections under various management scenarios designed to balance limited water allocations amongst competing human uses (Jordan & Wolf, 2006). As recent response to more extreme droughts, different drought managemen t plans have been proposed to mitigate ecological damage to downstream ecosystems and endangered species as well as critical, downstream ecosystems and endangered species While various ACF models have been compared in the technical literature (Zeng et al., 2005; Goodman et al., 2001) few aspects of the ACF modeling effort are
105 represent ed in the peer review literature Systematic simulation comparison and analysis would help to build confidence in model simulations The overall objective of this research is to explore the hydrologic system response to the three (WCP, IOP, RIOP) operati onal plans using forecasted demands and a system dynamic model developed through compact negotiations (ACF STELLA) Specific objectives of this research are the following: 1. Review and summarize water resource models applied in the ACF with special emphasis on the ACF STELLA model. 2. Describe the structure of the ACF STELLA model and the interests for which it manages 3. Alter structure of the ACF STELLA model to reflect current operational policies (RIOP). 4. Simulate forecasted conditions for 2050 with WCP, IOP an d RIOP operations and compare the performance in terms of low flow performance and system storage capacity. This research paper is divided into four sections The first section provides a review of the models developed for simulating water resource dynam ics in the ACF This review section places special emphasis on the design features of the ACF STELLA model This section also provides the methodology of how ACF STELLA components were created to simulate different operations A second section discusses the RIOP development in the ACF STELLA model The third section highlights the results of the comparative simulations, and the fourth section discusses the ramifications of the simulation results towards ACF water resource objectives. Literature Review T his review highlights two major areas within water resource modeling in the ACF basin The first section details the water resources modeling efforts in the ACF basin
106 while the second section provides additional detail into the ACF STELLA platform which i s used in further simulations Hydrological and Water Resource Management Modeling in the ACF Basin Several modeling efforts have been initiated in the ACF basin over the course of water management there Originally the USACOE managed the basin using pe ncil and paper calculations (Palmer, 1998) ; however, the advent of computer models quickly changed the methods that managers were able to use in order to mo re accurately represent the system being modeled Many of the early models were FORTRAN models and were only able to be understood by those programming and thus were When computers became available to the public, spreadshe et models were developed by the USACOE to help make viable decisions that contributed to the WCP (Palmer, 1998) Optimization modeling scenari os have been developed to increase efficiency of hydropower production while maintaining the balance in other uses (Georgakakos et al., 1995) A BASINS/HSP F watershed model (Zhang, Wen, et al., 2005) was constructed of the Spring Creek Subbasin in the Lower Flint Basin This model was calibrated and validated and is able to generate unimpaired flow for the Flint River surface water model to be added to the ACF STELLA model (Zhang, Wen, et al., 2005) A Watershed Evaluation and Planning System (WEAP) model (Yates et al., 2005) was developed for the Upper Chattahoochee as a water balance model for deman d and supply for the city of Atlanta (Johnson and CA, 1994) Moreover, the USACOE developed HEC 5 models (Zeng et al., 2005; Labadie and ASCE, 2004) of the entire ACF watershed during the comprehensive study that allowed for parallel modeli ng with the ACF STELLA model Since then, the USACOE have more recently
107 developed the HEC ResSim (Klipsch and Hurst, 2007) model for the ACF b asin that simulates dam release operations under variable regimes much like the ACF STELLA model simulates by using IF THEN statements Advantages of the HEC ResSim model are that it has a GUI interface as well as can simulate more complicated operations (Klipsch and Hurst, 2007) The model was officially considered released in March of 2009 by the United States Fish and Wildlife Services (Carmody, 2009) While the USACOE will primarily use the ResSim model to develop the Water Control Manual to develop new operations for the future, some of the advantages of the ACF STELLA model is that it was developed using the Shared Vision process and is highly transparent in structure and its development methodology. ACF STELLA Model Development Overview In 1990, the federal government approved the funding of the ACT/ACF Comprehensive Study (comprehensive study) of the Alabama Coosa Tallapoosa (ACT) and Apalachicola/Chattahoochee/Flint river basins. The comprehensive study had four objectives stated in its plan of study: 1. of the demands for water resources in 2. 3. Develop implementable strategies for the planning period for the basins to guide water mana 4. (Technical Coordination Group, 1992) The comprehensive study was then divided into three elements of study: Water Demand, Water Resources Availability, and the Comprehensive Management Strategy
108 The purpose of the Water Availability Study was to determine the historic and present availability of water and forecast the future water availability by assuming a stationary availability of water resources (USACOE, 1997) This assumption has been challenged by other papers on climate change (Georgakakos and Yao, 2000) As a result of the comprehensive study, two models were developed by the USACOE and the University of Washington The ACF STELLA and HEC 5 were created to simulate the ACF basin system through parallel modeling efforts to allow the comparison of model operations and results Both models were originally developed to simulate the operational rules and alternatives as well as create management scenarios under future demands The models were designed to balance different authorized purposes, maintain flexibility, and have a re latively transparent interface and operational functions The STELLA platform was chosen to be appealing to both stakeholder and technical participants in the ACF allocation discussions Some disadvantages of the traditionally coded models (such as FORT RAN models) are that they require extensive time to create, extensive operator training, and are often times considered incomprehensible by non technical stakeholders Furthermore, water resource control) style to a considered essential to successful and sustainable allocation formulas (Palmer, 1998) The Shared Vision process takes into account different visions from those who are modeling the system in order to more equally balance the values that do not have as verifiable coefficients To this end, the sh ared vision process required four model
109 developers from four different organizations to work together on a team of twelve participants to produce a final model Moreover, there were 24 meetings and 8 public workshops to publicly introduce and analyze the model in order to receive feedback as well as facilitate understanding of the ACF water resource system (Palmer, 1998) As a result of this st akeholder development and feedback process, the ACF STELLA model was considered a parallel model the USACE HEC RAS model available at the same time The STELLA modeling platform provided the transparency and allowed for the shared vision process to take p lace. Design and Construction of the ACF STELLA Model To navigate the ACF STELLA model, there are four levels in whic h to view and/or manipulate the model The first level is the user interface which allows the user to control a range of model variables ( Figure 4 1 ) The systems dynamic object oriented model is built within the middle level where the protocol and model logic struct ures are programmed (Figure 4 2 ) This layer uses a series of STELLA based objects such as converters, stocks, and connectors to provide the rules and logic to numerous model algorithms Raw code from the object oriented lower level is automatically written by the STELLA software within the equation level Lastly, the map layer provides an interface to look solely at the docum entation of objects within the model layer and gives the user a read only way to view the objects For brevity, only objects in the upper layer will be mentioned in this overview (Leitman and Hamlet, 2000) The design level is set up with a number of sectors that divide the various ACF water resource interests and management The main sector has 23 control points that have local inflow nodes fo und on the main stem of the ACF River and its tributaries (Figure 4 3 ). Reservoir operation conducted through a set of complicated rules which
110 depend predominantly on reservoir action zones In the ACF there are primarily three reservoirs responsible for most of the storage to support various water allocation purposes: Lake Lanier, West Point, and WF George Other federal dams and smaller providing storage or fixed rule c urve operations Morgan Falls, George Andrews, and Private Power Dams operate as simple stocks where outflow equals inflow in order to provide power generation at constant head Oliver, Goat Rock, North Highlands, and Bartletts Ferry dams have been aggre gated into one composite dam since they are only used to calculate hydropower generation assuming constant head Fish and wildlife management, flood control, hydropower and navigation were purposes cited in the al authorizations (USACOE, 1989) Functions such as recreation and water supply are considered purposes under general legislation (USACOE, 1989) M ajor flow targets have been developed to meet demands for water quality at Peachtree Creek (750 cfs), hydropower at Columbus (2,000cfs), and navigation/ecological at Blountstown (5,000cfs) The operational objectives in the ACF (and within the ACF STELLA model) are described within the following sections with a brief summary of model algorithms where practical. Fish and wildlife management Fish and wildlife management was originally only authorized to be supported by West Point Dam (USACOE, 1989) ; however, with the development of endangered species minimum flows by the United States Fish & Wildlife Service (USFWS), the entire ACF system was used to support minimum flows at the Jim Woodruff outflow No operations are specifically designated to protect the Apalachicola estuary to s upport the oyster and seafood industry at the terminus of the basin
111 implemented to set maximum values to which the stage can decrease in a given day in order to smooth abrupt day to day changes in ACF flows Biological organism s often become trapped when the river stage decreases too quickly by reducing releases further upstream (U.S. Fish and Wildlife Service, 2006) Ramping rat es protect the ecosystem from abrupt changes as well as allowing fish and other moving organisms to retreat with slow stage decrease Moreover, through endangered species flow requirements, much of the needed flow volumes for a healthy ecosystem at Apalac hicola Bay are being met. Flood control Reservoirs in the ACF system are designed to reduce downstream water levels by impounding excess flows within their available storage Whenever flooding conditions occur all operations but flood control are temporar ily suspended While only Buford and West Point dams are authorized with flood control operations, the WF George dam also has flood storage available. December through April is typically the flooding season in the ACF and lower pools in the reservoirs ar e maintained to leave flood storage available Flood zones serve as the available storage above the conservation zones up to a critical elevation. For example: When flood conditions are met at Buford a desired release of less than 14,000 cfs is met by ho lding water up to 1085 ft within the flood zone through the ACF conditions continue to increase the BUDamProtectRel object will set an emergency release through the spillway The turbines and slu ice gate can release 10,000 cfs each, and the uncontrolled spillway rating increases with elevation above 1085 ft. For instance, at 1086 ft of elevation 10,000 cfs would be released through the turbines (regardless of downstream flood constraints), 11,500 cfs would be released through the
112 sluice gates, and up to 400 cfs of continuous flow over the spillway until the reservoir dropped below 1085 ft of elevation for a total of 21,900 cfs (Leitman, 1999) Hydropower Most energy in the southeast is provided by thermal sources (such as coal fired and nuclear plants) Hydropower however is a small but essential part of meeting peak energy needs as it instantaneous ly provided during times of elevated power demand or interrupted thermal energy supply Hydropower is produced at Buford, West Point, Walter F George, and Woodruff and with the exception of Woodruff these plants are g power during peak demand Jim Woodruff is considered a run of river plant and provides consistent minimum energy (Leitman, 1999) Navigation Navigation suppo rt is provided from Apalachicola, Florida to Columbus, Georgia The support is only required for the Apalachicola River since Andrews and George provide navigation to Columbus Flows to provide navigation channels of 9 ft by 100 ft are sh ared between the three largest storage reservoirs (Buford, West Point, and WF George) The Blountstown checkpoint below the Jim Woodruff Dam is used to determine depth in the river For the WCP operating system, if West Point and WF George dams can meet the flow requirement by themselves in a single zone then no water is released from the Buford dam If not, then water is shared proportionally according to zone storage in all three reservoirs These releases are made in an attempt to keep the three rese rvoirs at the same zone value (USACOE, 1989) Buford dam releases are only required when the other reservoirs cannot sustain the navigation re quirements so that the upstream storage is preserved When required, Lake Lanier
113 supports navigation releases at a ratio of 0.4 (or 40% percent) when below Zone 3 and at a ratio of 0.1 (10%) when above Zone 3 W F George and West Point have 75% of their respective storages available for navigation support To release the navigation support during proper time periods, navigation months can be set to support these flows in the user interface (upper level) section of the ACF STELLA model Typically navigat ion is supported during weekdays when hydropower production is most valuable However, a n ACF STELLA user interface switch (MoveNavSupToPeak) allows the navigation to be supported throughout the entire week. The relationship between flow and depth is impe rative to for navigation support as well as the desired navigation target. Flow/depth relationships are selected based on latest estimates of required flow for specific depths at different times of the year given a typical dredging schedule The model con tains six estimated options based on channel configuration and Chipola Weir options (Leitman, 1999) Recreation While not originally within the operational guidelines (Leitman, 2003) recreation has become a multimillion dollar industry on the Federal reservoirs in the ACF Since lower lake reserv oir levels may cause for unsightly banks and bottoms to appear, reservoir operations were created to maintain steady, full pools when possible This is reflected in the reservoir action zones being high in the summer months Water supply Water supply wa s originally designed as a supplementary purpose in the ACF, but it has become a priority within the basin More specifically water supply for the metropolitan Atlanta area are met almost entirely by direct withdrawals from Lake Lanier and some withdrawal downstream of Lanier Releases from Lake Lanier specifically for
114 water supply are often made during the weekends in the summer months Most other periods, water supply needs are met without special releases being made Further downstream, minimum flows of 1150 cfs for municipal purposes at Columbus Georgia of have been set (Leitman, 1999) Water quality Like water supply, water quality requir ements are met in normal conditions however during drought conditions releases may be required to meet minimum water quality flows Water quality minimum flows at Peachtree Creek have been set at 750cfs continuous release from Morgan Falls Dam that provid es continuous flows from hydropower releases by Lake Lanier To meet the flow targets at Peachtree Creek and Columbus during non peak periods, releases are made from Buford and West Pont dams Buford Dam continuously provides releases to prevent Morgan F alls from being overdrawn as it supplies flow to Peachtree Creek The WCP (USACOE, 1989) provides for a minimum flow rate of 750 cfs at Peacht ree Creek from Morgan Falls Dam and can be adjusted monthly at a ACF STELLA user defined input (PtreeCrkTarFlow cfs) At West Point dam, operations provide a continuous release of 675 cfs to reliably supply high quality water to users as well as assimilat e wastes discharged downstream In addition, Jim Woodruff releases a minimum of 5,000 cfs to maintain ecological flows and provide adequate freshwater to the estuary. Agriculture Agricultural withd rawals are only significant within the Flint Basin primari ly located in Georgia The ACF STELLA model apportions these withdrawals within the model as it is set to estimate the acreage of agriculture as 621,114 acres during both wet and dry years in the Flint basin This acreage value plays a large role in the agricultural
115 demands as it is converted into a water demand Other values have been introduced by the state of Georgia based on permit review to be 821,000 acres during dry years and 922,000 acres during wet and normal years The disparity between the we t and dry years is due to the Flint River Drought Protection Act purchasing irrigation water rights during dry years Acreage can be adjusted in the interface level with user defined inputs (Flint Wetnorm Year Ag Acres and Flint Drynorm Year Ag Acres) O ther agricultural acreage estimations have come from the University of Georgia, Center of Remote Sensing and Mapping Science in cooperation with the Georgia DNR A survey was completed with an agricultural acreage in 1999 resulting in 475,779 acres which is far less than the Comp Study estimated (Litts et al., 2001) With this in mind, there is a large amount of uncertainty built into the model inputs and agricultural demand dataset. Within ACF STELLA, there are three Agricultural demands set from the demand dataset and can be adjusted in the model to increase in a dry year when irrigation is operating more frequency and conversely decrease in a wet year Net withdrawals are calculated from municipalities, industries, agricultural, and thermal power Parameters are set as to how much groundwater influences surface water through the ACF STELLA parameter (EffGWOnSWPTC) Moreover percentages are assigned to total withdrawals as to how much was pumped from groundwater The time series multipliers are based on historic ratios of actual to average demand (Leitman and Hamlet, 2000) The ACF STELLA parameter, AgDemFactor, is set using the TimeSerAgDemFact demand which is the agricultural demand based on wet and dry years where in wet years there is less of a demand and dry years there is mo re of a demand For dry years, the demand is set to multiply by a factor 1.4 and for wet years
116 0.5 During the negotiations Georgia used the demand factor of 2.2 for the dry year however the Natural Resource Conservation Service of the U.S Department of Agricultural recommend the use of a value between 1.1 and 1.2 (Leitman, 2003) Federal dam operation summary Action zones have been developed in the thr ee major USACOE reservoirs to year, historical pool level / releases relationships, operational limits for conservation, and Resource Impact levels Reservoir releases are guided by these action zone elevations and are managed differently depending on each zone During the summer, zones are raised to provide support to downstream areas and hold more water as the threat of flooding is reduced During the spring and winter, zones are lowered to allow for a larger flood control storage capacity when floods are more prevalent Towards the end of the winter and spring months, action zones are raised to accumulate water to be released during summer months Action z ones for each rese rvoir are defined in Figures 4 4 4 5 and 4 6 The top of the conservation pool is the rule curve which defines the reservoirs being full but still useful for flood mitigation The reservoirs should be kept below the rule curve so that there is storage capacity for flooding event Below the rule curve is Zone 1 and that is the least conservative operating conditions apply When reservoir elevation is in Zone 2 more conservative measure of releases are taken More conservative measure s of release are taken in Zone 3 Finally the basin is considered under drought when the levels decrease to Zone 4 Revising A CF O perations with the Revised Interim Operating Plan (RIOP) Under Section 7 Consultation under the Endangered Species Act of 1 973, the gulf sturgeon ( Acipenser oxyrinchus desotoi ), fat threeridge mussel ( Amblema neislerii ), and
117 the purple bankclimber mussel ( Elliptoideus sloatianus ) and their critical habitat are federally protected by the Department of the Interior To mitigate impacts on these protected species, acceptable flow regimes were established To provide information for these acceptable flows, many adaptive management strategies were performed during the drought in 2000 and 2006 to understand the effects of varying w ater regimes in the Apalachicola River During the drought of 2000, mussels were exposed at flows of 8,000 cfs, however the United States Fish and Wildlife Services (FWS) stated that the continued existence of mussels were at risk under flows less than 5, 000 cfs (U.S. Fish and Wildlife Service, 2006) Ramping rates were introduced to minimize trapping of fish, mortality of mussels, reduce bank sloughing, and to mimic natural flows These flows were captured in the IOP and introduced into the model previously. The IOP was introduced in 2006 to increase the flow from Jim Woodruff dam to protect the Gulf Sturgeon from low flows during the spawnin g season (March through May) (Zeng and Wen, 2007) The IOP included 2 seasons which included spawning and non spawning in which to manage its r eservoirs During the spawning season storage could not be increased in the reservoirs unless basin inflow was greater than 20,400 cfs During the 2006 2008 drought flows were limited to much less than this threshold most of the time and storage in the s ystem was greatly depleted After the reservoirs had dropped to one third of the systems conservation storage in November of 2007 the Exceptional Drought Operation (EDO) was instated to adjust the minimum flow to 4,750 cfs Storage could be increased by storing all inflows over this level After the drought ended in 2008, storage increased and in April of 2008 the Revised Interim Operation Plan (RIOP) was adopted (Zeng et al., 2009)
118 After the drought of 2006 2008, the RIOP was adopted to replace the IOP due to For this research the RIOP operations were introduced into the ACF STELL A model. The RIOP functions under same general principles as the WCP and IOP in that there are zone operations and minimum flow thresholds through the basin The RIOP only addresses the Jim Woodruff release schedule and contains within it the lower releas es when the Drought Contingency Operations have triggered Drought Contingency Operations are triggered to allow for a temporary waiver from the minimum releases of 5,000 cfs from Jim Woodruff Dam for endangered species protection (U.S. Fish and Wildlife Service, 2006) During normal operation WF George and West Point do not change operation if Jim Woodruff Dam releases are adequate to meet man datory environmental flows The IOP defined releases based on two season inflow thresholds while the RIOP modifies releases based on three season inflow threshold levels: spawning season (March May); non spawning season (June November); and winter (Decemb er February) Moreover, the RIOP takes into account the whole system by using composite storage of the three reservoirs to define thresholds for operational decisions (Figure 4 7) The composite storage is defined as the summation of Lake Lanier, West Po int Lake and Walter F. George Lake Composite storage is divided into 4 zones and releases become more conservative as the zones increase by composite volume decreasing Droughts on the system are defined by entering the drought zone in the composite sto rage volume and subsequently the drought contingency operations are triggered and the most conservative approach is taken by reducing Jim Woodruff releases to 4,500cfs Drought contingency operations are not removed until the
119 composite storage reaches Zon e 2 in order to replenish reservoirs (USACOE, 2008a) All of the changes were completed in the ACF STELLA model. Data and Methodology This sec tion details the changes to the ACF STELLA model to incorporate the RIOP operations. In addition, a subset of system performance metrics for analysis of model simulations are selected. Update of the ACF Stella Model t o Incorporate Current Operational Stra tegies The operations in the model were revised in three main ways The introduction of new discharge thresholds, minimum fall rate, and drought contingency operations will be described below and the code can be seen in Figures 4 8 4 9 4 1 0 4 1 1 New d ischarge thresholds based on composite storage zones were introduced in the RIOP and described in Table 4 1 JWRIOP is what Jim Woodruff would release if there were no other considerations such as ramping rates, navigation releases, top of conservation po ol (rule curve) storage capacity, and minimum releases (Figure 4 8 ). Preliminary releases that have not been adjusted for ramping rate are calculated in JW Prelim (Figure 4 9 ) This calculation determines the release that is going to occur based on meetin g thresholds for various considerations such as minimum releases to keep the elevation below the rule curve, the maximum available water in the reservoir, navigation support, and the RIOP released out of the reservoir than is available in the conservation storage through JWRelLimit Next, it determines which has the highest flow requirements for needs to be met between navigation JWNav Rel, staying within the rule curve JWRuleCurve, and RIOP flow requirements JW RIOP NORAMP JWNavRel is the minimum release required to meet the flow requirement at Chattahoochee when navigation is being used
120 JWAvailAfter Withdrawals is the total water available for release after withdrawals and returns including inflow JWRu leCurve is defined as the quantity of water that must be released to keep the reservoir below the rule curve The rule curve elevation is important to maintain so that the integrity of the structure is not compromised Finally JWRelLimit is the release l imit or maximum amount of water that can be released from the conservation volume This amount is only used when the reservoirs are very low to prevent it from going below the conservation volume The release limit should never be used since Jim Woodruff is being supported by supplemental releases from WF George, West Point, and Lanier storage. The Drought Contingency Operations (Figure 4 10 ) are triggered when the composite storage falls into Zone 4 At that time all the composite storage Zone 1 3 provi sions (seasonal storage limitations, maximum fall rate schedule, minimum flow thresholds) are suspended and management decisions are based on the provisions of the drought plan (USACOE, 2008b) The drought plan calls for releases at Jim Woodruff to move to 4,500 cfs Drought Contingency Operations are turned off when composite storage reenters Zone 2 signaling that the basin drought is over T he composite storage is evaluated on the first day of each month when future operations are evaluated and decisions are made (USACOE, 2008b) When Drought Contingency Operations Switch = 0, the drought plan is in effect and when Drought Contingency Operations Switch = 1 then normal operations exist The programming uses the Drought Contingency stock (rectangle) to receive 1 unit (unitless trigg er) when the Composite Zone enters Zone 4 The reservoir is positive until it is emptied by End Drought Contingency upon Composite Zone entering Zone 2
121 The final calculation to determine the release at Jim Woodruff by including fall rates occurs at JW Release cfs (Figure 4 11 ) If Drought Contingency Operations are triggered then the flow is mandated at 4,500 cfs to help maintain the system upstream regardless of fall rate For all other operations the fall rate is maintained and the product of CHATT_ FLOW_STAGE_RELATIONSHIP and RampRate is the maximum drop in flow rate that can occur in the river Maximum fall rate is measured at the Chattahoochee gage. Volumetric balancing described in the proposed action modifications (USACOE, 2008b) were not adopted in the actual operations of the RIOP (personal communication Steve Leitman). Additional provisions during drought contingency operations to allow temporary storage above the winter pool rule curve at Walter F George and West Point to begin spring refilling operations earlier than normal (USACOE, 2008b) were not considered in this update These exact operations were not specified in the proposed action and the purposed ambiguity in operations seems to leave this decision up to the discretion of the Corps water management In revisi ng the model, these vague operations were not included. However, acknowledging the limitations of the modeling efforts Building Confidence in ACF STELLA Model Results Traditional model testing cannot be performed on the ACF STELLA model since there are no independent datasets with which to compare the model outputs The unimpaired flow dataset used to drive the model are an adjustment of USGS streamflow gauges on the river As such they are not considered an independent dataset and cannot be used for val idation purposes As a result of this limitation, model analysis of different management scenarios will be performed on the Water Control Plan (WCP),
122 IOP and RIOP versions of the ACF STELLA model. Relative changes in model performance can give users an i ndication of whether model components are sensitive to changes in user defined inputs. An analysis between the three different operations (WCP, IOP, and RIOP) was developed to understand the relative differences between operations on unimpaired historical flow For the analysis, the 2010 forecasted demand dataset from the comprehensive study were used (USACOE, 1997) The demand dataset from yea r 2010 was chosen since it was currently 2010 during this research Unfortunately there is no switch in one individual model that would allow for the use of different operations so each operation is its own model Adjustable parameters were set the same for all models with navigation turned off, routing turned on, and all other parameters set to default. There are many different outputs from the model that can be analyzed however two of the most critical outputs were selected The two outputs were Lake L anier elevation and Jim Woodruff (Lake Seminole) outflow Lake Lanier is the most critical storage reservoir in the basin due largely to various demands from stakeholders placed on it The dam has more than 60% of the total ACF storage capacity and it su pplies hydropower to supplement electrical needs during peak operating hours by releasing large amounts of water for several hours a day Morgan Falls Dam to the south of Lake Lanier captures these large hydropower releases and releases more continuous fl ows and industrial needs Releases from the reservoir are used to maintain water quality standards further downstream at Peachtree Creek Finally, recreation plays a large part
123 in the local economy and general well being of the area All of these needs rely heavily on the storage and consequently elevation of Lake Lanier Elevation was specifically chosen because it the measure used when managing the reservoir Ele vation zones are the standard measure used to describe reservoir conditions and is more easily gauged and used for management purposes than volume. The Jim Woodruff outflow is at the southern end of the basin near the discharge into the Apalachicola Bay. The default minimum flow is set at 4,5 00 cfs to be m aintained from the Jim Woodruff Dam The original reason to maintain this minimum flow was that it represented the minimum flow tolerable by downstream industrial users (USACOE, 1989) Later this value was prescribed to maintaining the ecological sustainability of river ecosystems for the endangered species found there (U.S. Fish and Wildlife Service, 2006) The model operations were tested against severe drought conditions found in the basin Severe drought conditions were prescr ibed as three years of below average precipitation in 75% of the basin (Arrocha et al., 2005) The events were took place in the periods 1999 2001, 1984 1988, and 1949 1952 Other less severe droughts occurred in the periods 1954 1956, 1930 1935, 1930 1935, and 1908 1910 however most do not lie in the period of record of the unimpaired flows so they were not compared The goal of this model c omparison is to examine the relative difference between the various operations on Lake Lanier level and Jim Woodruff outflow The number of instances where the daily flows were below 5,000 6,000 and 7,000 cfs were recorded as well as the percentage of ti
124 Investigating these two parameters is helpful in understanding how the system interacts as well as gives a relative understanding of the influences of the newly introduced RIOP in comparison to other op erations. Results General comparisons were made between model outputs as well as summarized using basic statistics Lake Lanier elevations can be examined for the different operations in Figures 4 12 4 14 4 16 The three different drought scenarios rep resented in the unimpaired flows dataset were used as a benchmark for drought conditions in the future. It should be noticed that the drought scenarios each had more of an impact in different areas of the watershed. The 1999 2001 lasted 4 years and was sma ller than normal amounts of precipitation in most of the basin (Arrocha et al., 2005) The 1984 1988 drought lasted 5 years and con centrated in the central part of the basin Finally, the 1949 1952 drought extended over the entire river basin (Arrocha et al., 200 5) The different elevations at Lake Lanier and the releases at Jim Woodruff follow the general trends where the RIOP is a more conservative in releases from Jim Woodruff and Lake Lanier than the IOP (Zeng et al., 2009) Over the entire span of the series from 1939 2001 Lake Lanier elevations with the RIOP operations were maintained in higher zones (flood zone zone 2) for more day s than the WCP and IOP operations. The RIOP was in the higher zones for 70% of the time, while the WCP was in the higher zones 60% of the time and finally the IOP 45% of the time (Table 4 2 ). The IOP operations had Lake Lanier Levels in zone 4 for almost 4 5% of the time since the releases under these operations are so liberal (Zeng et al., 2009) Table 4 3 illustrates the different z ones during the RIOP operations and demonstrates that at no time would the composite storage fall into the drought zone.
125 Not even during 2008 at the height of the most recent drought when the RIOP was created did the composite storage fall into drought the drought zone to trigger the drought operations (Figure 4 7). Furthermore, only 94 days was the composite storage located in zone 4 where the most conservative other than the drought operations (Table 4 3 ). During the 1999 2001 drought the WCP consistentl y operated with Lake Lanier elevations lower than IOP and RIOP operations. Moreover, the IOP consistently operated to give elevations lower than the RIOP operations. The RIOP and IOP Lake Lanier elevations entered zone 4 in the beginning of 2000 and did no t return to zone 3 for the duration of the drought. Moreover, the unimpaired flow set ends at the end of 2001 so further evaluation of the droughts effects cannot be made into 2002 (Figure 4 12 ) Overall, the WCP operations had Lake Lanier elevations that were in zone 4 (50% of the time) more than the other operations which were about even at almost 40% of the time (Table 4 4 ). The RIOP and IOP operations were in the other zones for a similar amount of time while the WCP did not stay in these more conserva tive zones for as long. The drought of 1984 1988 showed similarly that the IOP and WCP Lake Lanier elevations stayed consistently below the RIOP elevations. The Lake Lanier elevations entered zone 4 in early 1986 from all operations and did not increase e levations out of zone 4 substantially until both the WCP and RIOP operations went to zone 3 and quickly to zone 1 in 1990. The IOP operations Lake Lanier elevations did not recover into zone 3 until after 1990 (Figure 4 14 ) Most of the drought and the rec overy period at Lake Lanier was slow since during the drought period the IOP operations controlled
126 Lake Lanier Levels to stay in zone 4 almost 75% of the drought while the RIOP and WCP were in zone 4 for around 55% of the time. The IOP also never entered z on e 1 or the flood zone (Table 4 5 ). During the 1949 1952 drought there much of an effect on Lake Lanier elevations until September of 1951 through early 1952. Moreover, at the end of 1952 the WCP Lake Lanier elevations entered zone 3 (Figure 4 16 ) One thing to note is that the WCP had elevations that were in the flood pool almost 10% more than the RIOP operations. The RIOP and IOP Lake Lanier elevations spent more than 50% of the time in zone 1 (Table 4 5). The WCP and the IOP operations had Lake Lanier elevations in zone 3 almost 20% of the time while the more conservative RIOP only entered zone 3 for 44 days (Table 4 6 ). Figures 4 8 4 9 and 4 10 show the values of outflow at Jim Woodruff during the three separate drought events. At no time are the outflows less than 5,000 cfs for any of the droughts from visual inspection. For all droughts the, the IOP operations have the fewest number of instances of low flows from Jim Woodruff (Table s 4 7 through 4 10 ). This is consistent with the overall model t ime period from 1939 2001 which indicates that the IOP has the least instances of releases below 7,000 cfs. The WCP has the highest number of instances in most droughts (Tables 4 8, 4 8, and 4 9 ) as well as over the entire time period (Table 4 7 ) Conclusio ns The goal of this research was to (1) review and summarize the different water resources models in the ACF, (2) review the ACF STELLA model structure, (3) update the model to RIOP operations, and finally (4) run the model and compare model outputs for di fferent operations A comprehensive literature review was done on modeling
127 efforts in the ACF basin to achieve objective 1 There are more than six known modeling initiatives in the basin Objective two was accomplished by looking at the various interes ts the model manages for and showing some of the methodology used to manage for it Objective three was completed by updating the ACF STELLA model to current RIOP operations Finally objective four was achieved by comparing both Jim Woodruff outflow and Lake Lanier elevations to a range of past and current operations. Water resources modeling in the ACF is an important endeavo r for management of sustainable and fair water supplies in the region The ACF STELLA model is one of many modeling endeavors in t he basin and will continue to a useful tool for examining simulations under given operations. The RIOP was introduced into the ACF STELLA modeling environment as an update to represent current water management in the basin Most of the updates from the IO P to the RIOP were made to the Jim Woodruff dam release schedule New provisions were made to manage the system in three separate seasons to encourage the endangered species of the region to recover To help the basin recover from drought, the Corps intr oduced the Drought Contingency Operations as part of the RIOP These provisions allow the discharge at Jim Woodruff to go below 5,000 cfs to 4,500 cfs when the composite storage of the system is in the d rought z one. Given the comparison of ACF STELLA mode l results over the three management strategies, the RIOP operations managed more conservatively at Lake Lanier than did the IOP or the WCP. There was no substantial pattern as to how Lake Lanier elevations react to droughts in the basin as the droughts oc curred at different areas in the basin primarily One of the most interesting observations takes place during the
128 1949 1952 drought where very little change in elevation occurred since t he elevation largely stayed in z one 1 This may be due to the overes timation of the unimpaired flows which drive the model as also mentioned in chapter 3. Lake Lanier elevations in the 1949 1952 drought are in the upper zones for a greater percentage of the time than even the overall percentage of time for the entire time period 1939 2001 (Table 4 2 ). This is a confusing problem since much of the basin was under drought according to precipitation data (Arrocha et al., 2005) and Lake Lanier would support much of the downstream flows by releasing water. Meanwhile in the 1984 1988 drought large Lake Lanier elevation changes occurred especially under IOP operations which remained in Zone 4 for 73.3 % of th e drought period (Table 4 5 ) This value is far above the value of the overall percentage of time for the entire time period in zone 4 as would be expected. A significantly challenging aspect in the development of these models is that there is no way to validate using conventional hydrologic modeling validation methods This method explored three different operations by simply plotting two different outputs over time and comparing them to one another using visual inspection and percentages in zones respe ctively Comparing the differences between the various operations illustrates the relative disparity between the model operations Further study on the model output could be performed on the support release s from WF George, Buford, and West Point. A sens itivity analysis on the model variables would also be helpful in understanding where the model has the most uncertainty. This would be helpful to determine where research should be conducted to collect the most accurate data. Overall, having knowledge of how the ACF STELLA model was developed and how the new operations were placed into the model gives water management
129 simulations more transparency In addition, running simulations over the whole period of record (1939 2001) and analyzing the main drought s illustrates how the different operations manage drought over the historical record These simulations are useful for comparing different operations for future scenarios assuming stationarity in the unimpaired hydrologic regime.
130 Figure 4 1 User Int erface level of the ACF STELLA model
131 Figure 4 2 Systems dynamics level of the ACF STELLA model
132 Figure 4 3 Main sector of the ACF STELLA model
133 Figure 4 4 Lake Lanier reservoir zone elevations Zone ranges are defined below the lines Figure 4 5 WF George reservoir zone elevations Zone ranges are defined below the lines 1 2 3 4 5 6 7 8 9 10 11 12 Zone 1 1070 1070 1071 1071 1071 1071 1071 1071 1071 1070 1070 1070 Zone 2 1068 1068 1068 1068 1068 1068 1067.5 1067 1066.5 1066 1065.5 1065 Zone 3 1067 1067 1067 1067 1067 1067 1066.1 1065.3 1064.5 1063.6 1062.8 1062 Zone 4 1055 1065 1065 1065 1065 1064.9 1063.1 1061.3 1059.6 1057.8 1056 1055 Bot Cons 1035 1035 1035 1035 1035 1035 1035 1035 1035 1035 1035 1035 1010 1020 1030 1040 1050 1060 1070 1080 Elevation (ft) 1 2 3 4 5 6 7 8 9 10 11 12 Zone 1 188 188 188 188 190 190 190 190 190 189 188 188 Zone 2 187.5 187.5 187.5 187.5 189 189 189 188.2 187.5 187.5 187.5 187.5 Zone 3 185.5 185.5 186.4 187.2 188 188 188 187.4 186.7 186.1 185.5 185.5 Zone 4 184.5 184.5 185.8 187 186.5 186 185.5 185 184.9 184.8 184.6 184.5 Bot Cons 184 184 184 184 184 184 184 184 184 184 184 184 181 182 183 184 185 186 187 188 189 190 191 Elevation (ft)
134 Figure 4 6 West Point reservoir zone elevations Zone ranges are defined below the lines Figure 4 7 Composite storage of the ACF system and corresponding zones during 2008. The RIOP operations are largely defined based on composite storage. http://water.sam.usace.army.mil/ACFcomposite.htm 1 2 3 4 5 6 7 8 9 10 11 12 Zone 1 628 629 631 633 635 635 635 635 635 635 628 628 Zone 2 624.1 626.7 629.7 632.5 633 633 632.5 632 629.4 626.6 624 624 Zone 3 623.1 625.9 629 632 632 631.9 630.1 628.3 626.6 624.8 623 623 Zone 4 621.1 623.9 627 630 630 628.5 627 625.5 624 622.5 621 621 Bot Cons 620 620 620 620 620 620 620 620 620 620 620 620 610 615 620 625 630 635 640 Elevation (ft)
135 Figure 4 8 Description of the RIOP operations at Jim Woodruff JW RIOP / RIOP NORAMP IF Drought_Contingency_Operations_Switch < 1 AND Composite_Zone > 4 THEN 4500 ELSE IF Drought_Contingency_Operations_Switch < 1 AND Composite_Zone > 3 THEN 5000 ELSE IF (MonthNumber >=3) AND (MonthNumber <= 5) THEN IF Composite_Zone <= 2 THEN IF JW_Basin_inflow_7_day >= 34000 THEN 25000 ELSE IF JW_Basin_inflow_7_day >= 16000 THEN (16000 + 0.5*(JW_Basin_inflow_7_day 16000)) ELSE MAX (5000, JW_Basin_inflow_7_day) ELSE IF Composite_Zone <= 3 THEN IF JW_Basin_inflow_7_day >= 39000 THEN 25000 ELSE IF JW_Basin_inflow_7_day >= 11000 THEN (11000 + 0.5*(JW_Basin_inflow_7_day 11000)) ELSE MAX (5000, JW_Basin_inflow_7_day) ELSE IF Composite_Zone <=4 THEN 5000 ELSE 99999 ELSE IF (MonthNumber >=6) AND (MonthNumber <= 11) THEN IF Composite_Zo ne <= 3 THEN IF JW_Basin_inflow_7_day >= 24000 THEN 16000 ELSE IF JW_Basin_inflow_7_day >= 8000 THEN 8000 + 0.5*(JW_Basin_inflow_7_day 8000) ELSE IF JW_Basin_inflow_7_day >= 5000 THEN JW_Basin_inflow_7_day ELSE MAX (5000, JW_Basin_inflow_7_day) ELSE IF Co mposite_Zone <=4 THEN 5000 ELSE 99999 ELSE IF (MonthNumber =12) OR (MonthNumber <= 2) THEN IF Composite_Zone <= 3 THEN 5000 ELSE 5000 ELSE 99999
136 Figure 4 9 Description of Jim Woodruff preliminary release JW Prelim Release cfsd MIN(MAX(MIN(JWNav Rel,JWAvailAfter_Withdrawals),JWRuleCurve, JW_RIOP__Op_Final),JWRelLimit) Figure 4 10 Description of the Drought Contingency Operations Switch Start Drought Contingency IF Composite_Zone = 5 THEN 1 ELSE 0 Drought Contingency Switch IF EmptyReservoi r > 0 THEN 0 ELSE 1 End Drought Contingency IF DayofMonth =1 and Composite_Zone = 2 THEN EmptyReservoir ELSE 0 Composite Storage IF SUM_DAILY_CONS_VOL < Drought_Zone THEN 5 ELSE IF SUM_DAILY_CONS_VOL < Composite_Zone_4_rop THEN 4 ELSE IF SUM_DAILY_CONS_ VOL < Composite_Zone_3_top THEN 3 ELSE IF SUM_DAILY_CONS_VOL < Composite_Zone_2_top THEN 2 ELSE IF SUM_DAILY_CONS_VOL < SUM_CONS_STORAGE THEN 1 ELSE 1
137 Figure 4 11 Description of final release with ramping considerations JWRelease cfsd IF Drought_Conti ngency_Operations_Switch <1 THEN 4500 ELSE IF JW_Prelim_Release_cfsd > 30000 THEN MIN (JW_Prelim_Release_cfsd, JWAvailAfter_Withdrawals) ELSE IF JW_Prelim_Release_cfsd JW_OUTFLOW_DELAY > 0 THEN MIN (JW_Prelim_Release_cfsd, JWAvailAfter_Withdrawals) ELSE IF JW_OUTFLOW_DELAY JW_Prelim_Release_cfsd > CHATT_FLOW_STAGE_RELATIONSHIP RampRate THEN MIN (JW_OUTFLOW_DELAY CHATT_FLOW_STAGE_RELATIONSHIP RampRate, JWAvailAfter_Withdrawals) ELSE MIN (JW_Prelim_Release_cfsd, JWAvailAfter_Withdrawals)
138 Fi gure 4 12. Lake Lanier Elevations for 1998 2001 drought under 201 0 demand dataset Red WCP Green IOP Blue RIOP Figure 4 13 Jim Woodruff Outflow for 1998 2001 dr ought under 20 1 0 demand dataset Red WCP Green IOP Blue RIOP 1,030 1,035 1,040 1,045 1,050 1,055 1,060 1,065 1,070 1,075 1,080 Lanier Elevation (ft)
139 Figure 4 14. Lake Lanier Ele vations for 1984 1988 drought under 201 0 demand dataset Red WCP Green IOP Blue RIOP Figure 4 15 Jim Woodruff Outflow for 1984 1988 drought under 20 1 0 demand dataset Red WCP Green IOP Blue RIOP 1,030 1,035 1,040 1,045 1,050 1,055 1,060 1,065 1,070 1,075 1,080 Lanier Elevation (ft)
140 Figure 4 16. Lake Lanier Elevations for 1949 1952 drough t under 20 1 0 demand dataset Red WCP Green IOP Blue RIOP Figure 4 17 Jim Woodruff Outflow for 1949 1952 drought under 20 1 0 demand dataset Red WCP Green IOP Blue RIOP 1,030 1,035 1,040 1,045 1,050 1,055 1,060 1,065 1,070 1,075 1,080 Lanier Elevation (ft)
141 Table 4 1. Composite action zones of the ACF with corresponding basin inflows and releases. Month Composite Storage Zone Basin Inflow (BI) (cfs) Releases from JWLD (cfs) Basin Inflow Available for Storage Mar May Zones 1 and 2 BI >= 34,000 >= 25,000 Up to 100% BI > 25,000 34,000 > BI > 16,000 >= 16,000 + 50% BI > 16,000 Up to 50 % BI > 16,000 16,000 > BI > 5,000 >= BI BI < 5,000 >= 5,000 Zone 3 BI > 39,000 >= 25,000 Up to 100% BI > 25,000 39,000 > BI > 11,000 >= 11,000 + 50% BI > 11,000 Up to 50% BI > 11,000 11,000 > BI > 5,000 >= BI BI < 5,000 >= 5,000 June Nov Zones 1, 2, and 3 BI > 24,000 >= 16,000 Up to 100% BI > 16,000 24,000 > BI > 8,000 >= 8,000 + 50% BI > 8,000 Up to 50% BI > 8,000 8,000 > BI > 5,000 >= BI BI < 5,000 >= 5,000 Dec Feb Zones 1, 2, and 3 BI >= 5,000 >= 5,000 (Store all BI > 5, 000) Up to 100% BI > 5,000 BI < 5,000 >= 5,000 At all times Zone 4 NA >= 5,000 Up to 100% BI > 5,000 At all times Drought Zone NA >= 4,500 Up to 100% BI > 4,500 Table 4 2 Number of days in Lanier reservoir zone elevations 19 39 2001 RIOP (days) % WCP (days) % IOP (days) % Flood Zone 4443 19.3% 5273 22.9% 1662 7.2% Zone 1 10475 45.5% 6356 27.6% 7692 33.4% Zone 2 1222 5.3% 2186 9.5% 946 4.1% Zone 3 2232 9.7% 3604 15.7% 2627 11.4% Zone 4 4638 20.2% 5591 24.3% 10083 43.8%
142 Table 4 3 Number of d ays in RIOP composite storage zones from 19 39 2001 Month Composite Zone Basin Inflow (BI) (cfs) Incidents RIOP Mar May Zones 1 and 2 BI > 34 000 2,178 34 000 > BI > 16 000 2,675 16 000 > BI > 5 000 920 BI < 5 000 7 Zone 3 BI > 39 000 30 39 0 00 > BI > 11 000 375 11 000 > BI > 5 000 175 5 000 > BI 14 June Nov Zones 1, 2, and 3 BI > 24 000 1,161 24 000 > BI > 8 000 8,460 8 000 > BI > 5 000 2,356 5 000 > BI 810 Dec Feb Zones 1, 2, and 3 BI > 5 000 6,303 BI < 5 000 9 At all ti mes Zone 4 94 At all times Drought Zone 0 Table 4 4 Number of days in Lanier reservoir zone elevations 1999 2001 RIOP (days) % WCP (days) % IOP (days) % Flood Zone 132 7.2% 153 8.4% 99 5.4% Zone 1 440 24.1% 81 4.4% 364 19.9% Zone 2 25 1.4% 93 5.1 % 50 2.7% Zone 3 183 10.0% 221 12.1% 259 14.2% Zone 4 680 37.2% 912 49.9% 688 37.7% Table 4 5 Number of days in Lanier reservoir zone elevations 1984 1988 RIOP (days) % WCP (days) % IOP (days) % Flood Zone 175 9.6% 181 9.9% 0 0.0% Zone 1 368 20.1% 372 20.4% 0 0.0% Zone 2 195 10.7% 108 5.9% 162 8.9% Zone 3 91 5.0% 120 6.6% 325 17.8% Zone 4 998 54.6% 1046 57.3% 1340 73.3% Table 4 6 Number of days in Lanier reservoir zone elevations 1949 1952 RIOP (days) % WCP (days) % IOP (days) % Flood Zone 4 27 29.2% 564 38.6% 176 12.0% Zone 1 827 56.6% 496 33.9% 813 55.6% Zone 2 163 11.2% 127 8.7% 220 15.1% Zone 3 44 1.0% 274 18.8% 252 17.2% Zone 4 0 0.0% 0 0.0% 0 0.0%
143 Table 4 7 Number of days less than flow thresholds at Jim Woodruff for 1939 2001 Flo w Threshold (cfs) RIOP (days) WCP (days) IOP (days) 5,000 0 0 0 6,000 508 609 379 7,000 1234 1253 1034 Table 4 8 Number of days less than flow thresholds at Jim Woodruff for 1998 2001 Flow Threshold (cfs) RIOP (days) WCP (days) IOP (days) 5,000 0 0 0 6,000 202 265 165 7,000 316 373 276 Table 4 9 Number of days less than flow thresholds at Jim Woodruff for 1949 1952 Flow Threshold (cfs) RIOP (days) WCP (days) IOP (days) 5,000 0 0 0 6,000 17 27 14 7,000 84 58 58 Table 4 10 Number of days les s than flow thresholds at Jim Woodruff for 1984 1988 Flow Threshold (cfs) RIOP (days) WCP (days) IOP (days) 5,000 0 0 0 6,000 87 94 69 7,000 189 189 165
144 CHAPTER 5 CONCLUSION The Apalachicola Chattahoochee Flint (ACF) River Basin has been home to one of the most contentious debates over water allocation and management in the Eastern United States The three states in the basin, Georgia, Alabama and Florida, have for more than 20 years and after extensive negotiations failed to reach an allocation agre ement as of 2010 Extensive hydrologic modeling efforts and studies have proceeded from the negotiations; however some crucial elements need review This Research from Chapter 2 investigates the correlation between ENSO and both measured HCDN dataset as well as synthetic unimpaired flows Next, the study performed in Chapter 3 reviews the development of the unimpaired flow dataset and through statistical correlations wi th HCDN dataset confidence is built Finally, Chapter 4 consists of a review of the ACF STELLA model structure as well as includes updates to current operations The model was then tested to observe flows during drought years against previous operations. In Chapter 2 statistical correlation and non parametric statistical hypothesis analysis was performed to examine the relationship between sea surface temperatures (SST) and streamflow in the ACF Two datasets were used in this study The USGS collecte d records of streamflow that have been considered relatively unaltered by anthropogenic influences such as artificial diversions, storage or other changes in stream channels that affect hydrologic conditions in a dataset called the Hydro Climatic Data Netw ork (HCDN) The second dataset was derived from a study done of the basin in which USGS gauges on the main channel were adjusted to resemble unimpaired
145 flows This dataset is synthetic and was called the Unimpaired Flows (UIF) dataset in this study. Tes ting of lagged correlation produced results that showed that in the southern part of the basin at Chipola River, lagged ENSO relationship of up to 4 months shows correlation greater than r > 0.4 in JFM However, this amount of correlation is not seen thro ughout the basin Generally winter and spring months have larger correlation and longer correlated lags in the basin Specifically, January through March exhibited statistically larger streamflows during El Nino in the south of the basin (Figures 2 2 and 2 3). It was established that only significant differences between streamflow occurred in the winter and were at the southern end of the watershed The extent of these differences is not investigated in this study, only that streamflow during El Nino is significantly greater than La Nina Water management in the ACF would benefit only slightly in the southern end of the watershed from using these results This may be useful for developing navigation windows during El Nino phases if flows in winter weren If correlation extended further north throughout more of the basin it would be much more useful If the trend existed as far north as the city of Atlanta, water managers would be able to manage with much more foresight Since the genera l trend has been explored in this paper, it would be a useful next step to examine correlations between extreme streamflow events (flood, drought) and ENSO This analysis would be of more use to management since a principle purpose of many of the dams is for flood control and drought mitigation.
146 Overall, streamflow in the ACF is correlated at the very southern end of the basin with ENSO Through the use of both parametric and nonparametric statistical methods the relationship was explored with similar res ults Both main channel synthetic flows and USGS gauged flows confirmed this outcome. The purpose of Chapter 3 was to give added confidence to the unimpaired flow dataset as an accurate resemblance of natural flows in the basin Through the use or par ametric and non parametric statistical techniques correlation between physically HCDN datasets and synthetic UIF datasets were compared Overall confidence in 10 of 24 UIF datasets was established by correlating well with HCDN flows Other flows such as Blountstown, Morgan Falls, Atlanta and Jim Woodruff were shown to have inconsistencies in their record when comparing pre and post dam relationships with natural HCDN flows These particular flows correlated fair with the physically based HCDN dataset bef ore dams were constructed and poorly after suggesting that the UIFs were adjusted inaccurately Wavelet analysis further confirmed that these flows shifted from correlating well with the natural flows to not having much correlation at all This study is useful when determining how to use the UIF dataset as an input into models by exposing where inconsistencies lie Further studies should correlate rainfall with flow data as well as use cumulative UIF datasets in comparison. Negative flows were also inves tigated due to their prevalence in the UIF datasets They were evaluated to determine what where and when they occur and what effect they may have on the flows Even after smoothing the daily flows to reduce the erratic flows left after routing was perfo rmed on the flows, there were still substantial negative values at the daily time step Computation of local flows consists of routing upstream
147 daily flows to the next downstream control point and subtracting the routed flow from the downstream observed f low Over the period of 1939 2008 large droughts and heavy floods were both recorded giving rise to significantly different flow patterns as well as timing A single best estimate for routing coefficients was chosen for each reach to represent to range o f flow rates For the study, these flow rates were typically chosen on the conservative side to provide more accurate values in times of droughts However, this leads to peak flows routed from upstream not coinciding with peak flows from a downstream poi nt Negative local flows occur since the non coincident peaks are subtracted (USACOE, 1997) Mass balance is preserved when negative flows ar e considered in this computation however this must be considered when considering modeling application as a limitation As such, daily flows would not be considered a reasonable use of model output Monthly and possibly weekly flows would be a proper use in recognition of accuracy limitations the UIF development routing methodology and subsequent negative flow occurrences In Chapter 4 a n analysis between the three different operations (WCP, IOP, and RIOP) was developed to understand the relative differ ences between operations on unimpaired historical flow For the analysis, the 2050 forecasted demand dataset was used that were developed during the comprehensive study (USACOE, 1997) The year 2050 was chosen due to the high demands that would be place on the system Unfortunately there is no switch in one individual model that would allow for the use of different operations so each operation is its own model Adjustable parameters were set the same for all models with navigation turned off, routing turned on, and all other parameters set to default.
148 Given the comparison of ACF STELLA model results over the three management strategies, the RI OP operations managed more conservatively at Lake Lanier than did the IOP or the WCP. There was no substantial pattern as to how Lake Lanier elevations react to droughts in the basin as the droughts occurred at different areas in the basin primarily. One of the most interesting observations takes place during the 1949 1952 drought where very little change in elevation occurred since t he elevation largely stayed in z one 1 This may be due to the overestimation of the unimpaired flows which drive the model as also mentioned in chapter 3. Lake Lanier elevations in the 1949 1952 drought are in the upper zones for a greater percentage of the time than even the overall percentage of time for the entire time period 1939 2001 (Table 4 2). This is a confusing prob lem since much of the basin was under drought according to precipitation data (Arrocha et al., 2005) and Lake Lanier would support much of the downstream flows by releasing water. Meanwhile in the 1984 1988 drought large Lake Lanier elevation changes occurred especially under IOP operations which remained in Zone 4 for 73.3 % of the drought period (Table 4 5). This value is far above the value of the overall percentage of time for the entire time period in zone 4 as would be expected. A significantly challenging aspect in the development of these models is that there is no way to validate using conventional hydrologic modeling valida tion methods This method explored three different operations by simply plotting two different outputs over time and comparing them to one another using visual inspection and percentages in zones respectively. Comparing the differences between the variou s operations illustrates the relative disparity between the model operations Further study on the model output could be performed on the support release s from WF George, Buford, and
149 West Point. A sensitivity analysis on the model variables would also be helpful in understanding where the model has the most uncertainty. This would be helpful to determine where research should be conducted to collect the most accurate data. Having knowledge of how the ACF STELLA model was developed and how the new operati ons were placed into the model gives water management simulations more transparency In addition, running simulations over the whole period of record (1939 2001) and analyzing the main droughts illustrates where how the different operations manage drought over the historical record These simulations are useful for comparing different operations for future scenarios assuming stationary in th e unimpaired hydrologic regime. as sumptions of the UIF dataset, and updated and compared the ACF STELLA model under variable operations This study will be very useful for those interested in modeling the ACF basin as well as those interested in model evaluation of empirically based unimp aired flows datasets Moreover, the climate variability study may provide useful information to others for basic predictive purposes and management Other studies that would be useful would be to correlate streamflow and rainfall datasets to determine co rrelation Also groundwater levels may have a role to play in geophysical relationships in this system
150 APPENDIX A LAGGED CORRELATION B ETWEEN NINO 3.4 AND HCDN DATASETS a) b) c) d)
151 e) f) g) h)
152 i) j) k) l)
153 m) n) o) p)
154 q) r) s) t)
155 u) Figure A 1. Lagged correlation between Nino 3.4 and HCDN datasets. Listed from north to south a) 1 2331000 Chattahoochee river b) 1 2331600 Chattahoochee river. c) 1 2333500 Chestatee river d) 1 2389000 Etowah river e) 2 2335700 Big creek. f) 2 2337000 Sw eet water creek g) 2 2337500 Snake creek h) 2 2392500 Little river i) 3 2339500 Chattahoochee river j) 3 2340500 Mountain oak creek k) 3 2341800 Upatoi creek l) 3 3 2342500 Uchee creek m) 4 2347500 Flint river NR Culloden GA n) 4 2349000 White water cr o) 4 2349500 Flint river NR MON p) 4 2349900 Turkey creek q) 5 2353500 Ichawaynochaway Creek r) 5 2356000 Flint river s) 5 5 2357000 Sspring creek t) 5 2358000 Apalachicola river u) 5 2359000 Chipola river
156 LIST OF REFERENCES Ahmad, S., S.P. Simonovic, and others, 2004. Spatial System Dynamics: New Approach for Simulation of Water Resources Systems. Journal of Computing in Civil Engineering 18:331. Antonini, M., M. Barlaud, P. Mathieu, and I. Daubechies, 1992. Image Coding Using Wavelet Transform. IEEE Transactions on Image Processing 1:205 220. Arrocha, G., F.L. Tallahassee, and P. Ruscher, 2005. Analysis of Precipitation Variability and Meteorological Drought in the Apalachicola Chattahoochee Flint River Basin. 16th Conference on Pla nned and Inadvertent Weather Modification. Barlow, M., S. Nigam, and E.H. Berbery, 2001. ENSO, Pacific Decadal Variability, and US Summertime Precipitation, Drought, and Stream Flow. Journal of Climate 14:2105 2128. Beebee, R.A. and M. Manga, 2004. Varia tion in the Relationship Between Snowmelt Runoff in Oregon and ENSO and PDO. Journal of the American Water Resources Association 40:1011 1024. Boker, S.M., J.L. Rotondo, M. Xu, and K. King, 2002. Windowed Cross Correlation and Peak Picking for the Analysi s of Variability in the Association Between Behavioral Time Series. Psychological Methods 7:338 355. Carmody, G., 2009. USFWS Letter (27 Mar 2009). http://www.sam.usace.army.mil/ACF%20Water%20Resources%20Management/ May_2008_Consultation/RPM%20Status%20Let ter%203 27 2009.pdf. Accessed 30 Apr 2010. Cayan, D.R., K.T. Redmond, W.R. Center, N. Reno, and L.G. Riddle, 1999. ENSO and Hydrologic Extremes in the Western United States. Journal of Climate 12. Chiew, F.H.S., T.C. Piechota, J.A. Dracup, and T.A. McMah on, 1998. El Nino/Southern Oscillation and Australian Rainfall, Streamflow and Drought: Links and Potential for Forecasting. Journal of Hydrology 204:138 149. Chiew, F.H.S., S.L. Zhou, and T.A. McMahon, 2003. Use of Seasonal Streamflow Forecasts in Water Resources Management. Journal of Hydrology 270:135 144. Chiew, F.H. and T.A. McMahon, 2002. Global ENSO Streamflow Teleconnection, Streamflow Forecasting and Interannual Variability/Tlconnexion Entre Le Phnomne ENSO Et L'coulement, Les Prvisions D' coulement Et La Variabilit Interannuelle. Hydrological Sciences Journal 47:505 522.
157 Crawford, N.C., D.B. Poiroux, and J.H. Sanders, 2005. Hydrogeologic Investigation of Leakage Through Sinkholes in the Bed of Lake Seminole to Springs Located Downstream from Jim Woodruff Dam. ASCE Conf. Proc. doi:10.1061/40796(177)52. Crilley, D.M. and L.J. Torak, 2003. Physical and Hydrochemical Evidence for Lake Leakage in Lake Seminole, Georgia. Proceedings of the Georgia Water Resources Conference, April., pp. 23 24. Dai, A., K.E. Trenberth, T.R. Karl, and others, 1998. Global Variations in Droughts and Wet Spells: 1900 1995. Geophysical Research Letters 25:3367 3370. Daubechies, I., 1990. The Wavelet Transform, Time Frequency Localization and Signal Analysis. IE EE Transactions on Information Theory 36:961 1005. Dellapenna, J.W., 2006. International Law Applicable to Water Resources Generally. Waters and Water Rights. Dracup, J.A. and E. Kahya, 1994. The Relationships Between US Streamflow and La Ni \ Na Events. Water Resources Research 30:2133 2141. DWR, 2007. California Central Valley Unimpaired Flow Data Fourth Edition 1920 2003. cific Climate (PACLIM) Workshop, Pacific Grove, CA., pp. 35 48. GDNR, 2006. Flint River Basin Regional Water Development and Conservation Plan. Georgakakos, A. and H. Yao, 2000. Climate Change Impacts on Southeastern US Basins. Water Resources Research:0 0 334. Georgakakos, A.P., H. Yao, and Y. Yu, 1995. A Control Model for Hydropower System Analysis and Operation. Proceedings of the 1995 Georgia Water Resources Conference. Goodman, S.J., R. Ritschard, M.G. Estes Jr, and U. Hatch, 2001. National Environ mental Change Information System Case Study Final Report. Nasa/Tm 211410. Green, P.M., D.M. Legler, V. Miranda, and J.J. O'Brien, 1997. The North American Climate Patterns Associated with El Ni \ No Southern Oscillation. Report 97 1. Center for Ocean Atmos pheric Prediction Studies, Tallahassee, FL 32306:17.
158 Hamlet, A.F. and D.P. Lettenmaier, 1999. Columbia River Streamflow Forecasting Based on ENSO and PDO Climate Signals. Journal of Water Resources Planning and Management 125:333 341. Hanley, D.E., M.A Bourassa, J.J. O'Brien, S.R. Smith, and E.R. Spade, 2003. A Quantitative Evaluation of ENSO Indices. Journal of Climate 16:1249 1258. Hansen, J.W., A.W. Hodges, and J.W. Jones, 1998. ENSO Influences on Agriculture in the Southeastern United States. Jour nal of Climate 11:404 411. Helsel, D.R. and R.M. Hirsch, 1993. Statistical Methods in Water Resources. Elsevier Science Ltd. Hirsch, R.M. and J.E. Costa, 2004. US Stream Flow Measurement and Data Dissemination Improve. EOS, Transactions, American Geophys ical Union. Hughes, D.A., 2001. Providing Hydrological Information and Data Analysis Tools for the Determination of Ecological Instream Flow Requirements for South African Rivers. Journal of Hydrology 241:140 151. Ignatius, A., 2009. Big Water, Little Wa ter: Identification of Small and Medium Sized Reservoirs in the Apalachicola Chattahoochee Flint River Basin with a Discussion of Their Ecological and Hydrological Impacts. Florida State University, Tallahassee, FL. ISEE, 2009. ISEE Systems. Software Refe rence Guide: STELLA Software Technical Documentation. Johnson, W.K. and H.E.C.D. CA, 1994. Accounting for Water Supply and Demand. An Application of Computer Program WEAP to the Upper Chattahoochee River Basin, Georgia. Jones, L.E. and L.J. Torak, 2003. Simulated Effects of Impoundment of Lake Seminole on Surface and Ground Water Flow in Southwestern Georgia and Adjacent Parts of Alabama and Florida. Proceedings of the 2003 Georgia Water Resources Conference, April., pp. 23 24. Jordan, J.L. and A.T. Wol f, 2006. Interstate Water Allocation in Alabama, Florida, and Georgia: New Issues, New Methods, New Models. University Press of Florida. Kahya, E. and J.A. Dracup, 1993. US Streamflow Patterns in Relation to the El Nino/Southern Oscillation. Water Resourc es Research 29:2491 2503. Keener, V.W., K.T. Ingram, B. Jacobson, and J.W. Jones, Effects of El Ni \ No/Southern Oscillation on Simulated Phosphorus Loading in South Florida.
159 Klipsch, J.D. and M.B. Hurst, 2007. HEC ResSim, Reservoir System Simulation Use Manual, Version 3.0. US Army Corps of Engineers, Hydrologic Engineering Center (HEC), Davis, Calif. Http://Www.hec.usace.army.mil/Software/Hec Ressim. Knowles, N., 2002. Natural and Management Influences on Freshwater Inflows and Salinity in the San F rancisco Estuary at Monthly to Interannual Scales. Water Resources Research 38:1289. Labadie, J.W. and M. ASCE, 2004. Optimal Operation of Multi Reservoir Systems: State of the Art Review. Management 130. Leitman, S. and K.J. Hatcher, 2005. An Appraisal of the Consumptive Withdrawal Limit for the Upper Chattahoochee Basin. University of Georgia, Institute of Ecology Athens GA 30602 1619 USA, Leitman, S., 1999. Instream Flow Guidelines for the ACT and ACF Basins Interstate Water Allocation Formula. Leitm an, S., 2003. Overview of Consumptive Demands in Teh Apalachicola Chattahoochee Flint Drainage Basin. Leitman, S., 2010. Evaporation Estimates and Modeling in the ACF Basin. Leitman, S. and A.F. Hamlet, 2000. ACF STELLA Users Manual with Appendix. Leung L.R., Y. Qian, X. Bian, and A. Hunt, 2003. Hydroclimate of the Western United States Based on Observations and Regional Climate Simulation of 1981 2000. Part II: Mesoscale ENSO Anomalies. Journal of Climate 16:1912 1928. Light, H.M., K.R. Vincent, M.R. Darst, and F.D. Price, 2006. Water Level Decline in the Apalachicola River, Florida, from 1954 to 2004, and Effects on Floodplain Habitats. US Geological Survey Scientific Investigations Report 5173:83. Lindenmayer, L.E., 2006. Evaluating Experimental Str eamflow Forecasts for Use in Reservoir Modeling on the Colorado River Basin. University of Arizona. Lipp, E.K., N. Schmidt, M.E. Luther, and J.B. Rose, 2001. Determining the Effects of El Nino Southern Oscillation Events on Coastal Water Quality. Estuarie s and Coasts 24:491 497. Litts, T., H. Russell, A. Thomas, and R. Welch, 2001. Mapping Irrigated Lands in the ACF River Basin. Proceedings of the 2001 Georgia Water Resources Conference. Institute of Ecology, University of Georgia, Athens, Georgia., pp. 79 83.
160 Mallat, S.G., 1999. A Wavelet Tour of Signal Processing. Academic Pr. Martinez, C.J., G.A. Baigorria, and J.W. Jones, 2009. Use of Climate Indices to Predict Corn Yields in Southeast USA. International Journal of Climatology 29:1680 1691. Meko, D and D.A. Graybill, 1995. Tree Ring Reconstruction of Upper Gila River Discharge. Water Resources Bulletin 31:605 616. Mennis, J., 2001. Exploring Relationships Between ENSO and Vegetation Vigour in the South East USA Using AVHRR Data. International Jour nal of Remote Sensing 22:3077 3092. Merenlender, A., M.J. Deitch, and S. Feirer, 2008. Decision Support Tool Seeks to Aid Stream Flow Recovery and Enhance Water Security. California Agriculture 62. Meruelo, N., 2006. Considering a Cooperative Water Manag ement Approach in Resolving the Apalachicola Chattahoochee Flint River Basin Water War. Fordham Envtl. L. Rev. 18:335. Moriasi, D.N., J.G. Arnold, M.W. Van Liew, R.L. Bingner, R.D. Harmel, and T.L. Veith, 2007. Model Evaluation Guidelines for Systematic Q uantification of Accuracy in Watershed Simulations. Mosner, M.S., 2002. Stream Aquifer Relations and the Potentiometric Surface of the Upper Floridan Aquifer in the Lower Apalachicola Chattahoochee Flint River Basin in Parts of Georgia, Florida, and Alaba ma, 1999 2000. US Geological Survey Water Resources Investigations Report 4244:45. National Research Council, P., 2008. Hydrology, Ecology, and Fishes of the Klamath River Basin. Nat. Acad., Washington D. C.:250. National Weather Service, 1982a. Evaporat ion Atlas for the Contiguous 48 United States. National Weather Service, 1982b. Mean Monthly, Seasonal, and Annual Pan Evaporation for the United States. Null, S.E., 2008. Improving Managed Environmental Water Use: Shasta River Flow and Temperature Model ing. University of California. Null, S.E., M.L. Deas, and J.R. Lund, 2009. Flow and Water Temperature Simulation for Habitat Restoration in the Shasta River, California. River Research and Applications 9999.
161 Palmer, R.N., 1998. A History of Shared Visio n Modeling in the ACT ACF Management and the 1998 Annual Conference on Environmental Engineering, W. Whipple, Jr. Ed., Chicago, IL., pp. 221 226. Piechota, T.C. and J.A. Dracup, 1999. Long Range Streamflow Forecasting Using El Nino Southern Oscillation Indicators. Journal of Hydrologic Engineering 4:144. Piechota, T.C., J.A. Dracup, and R.G. Fovell, 1997. Western US Streamflow and Atmospheric Circulation Patterns During El Ni \ No Southern Oscillation. Journal of Hydrology 201:249 271. Ponce, V.M., 1979. Simplified Muskingum Routing Equation. J Hydraul. Div 105. Prairie, J. and R. Callejo, 2005. Natural Flow And S alt Computation Methods: Calendar Years 1971 to 1995. United States Department of the Interior, Bureau of Reclamation. Ropelewski, C.F. and M.S. Halpert, 1986. North American Precipitation and Temperature Patterns Associated with the El Ni \ No/Southern Os cillation (ENSO). Monthly Weather Review 114:2352 2362. Schmidt, N., E.K. Lipp, J.B. Rose, and M.E. Luther, 2001. ENSO Influences on Seasonal Rainfall and River Discharge in Florida. Journal of Climate 14. Schmidt, N., M.E. Luther, and R. Johns, 2004. Cl imate Variability and Estuarine Water Resources: A Case Study from Tampa Bay, Florida. Coastal Management 32:101 116. Scholz, J.T. and B. Stiftel, 2005. Adaptive Governance and Water Conflict: New Institutions for Collaborative Planning. Resources for the Future. Streamflow Prediction. Journal of Hydrologic Engineering 9:368. Seth, M., 1950. Comparative Study of Teh Muskingum and Lag and Route Flood Routing Methods. Publication:169. Sla ck, J.R. and J.M. Landwehr, 1992. Hydroclimatic Data Network (HCDN): A USGS Streamflow Data Set for the United States for the Study of Climate Variations, 1874 1988. USGS Open File Report:92 129. Small, D., S. Islam, and R.M. Vogel, 2006. Trends in Precip itation and Streamflow in the Eastern US: Paradox or Perception. Geophysical Research Letters 33.
162 Smith, T.M. and R.W. Reynolds, 2005. A Global Merged Land Air Sea Surface Temperature Reconstruction Based on Historical Observations (1880 1997). Journal of Climate 18:2021 2036. Stevens, K., 2008. Statistical Associations Between Large Scale Climate Oscillations and Mesoscale Surface Meteorological Variability in the Apalachicola Chattahoochee Flint River Basin. Florida State University, Tallahassee, FL. S tone, W., K. Reed, P. Chang, L. Pfeffer, and A. Jacoff, 1999. NIST Research Toward Construction Site Integration and Automation. Journal of Aerospace Engineering 12:50. Sun, H. an d D.J. Furbish, 1997. Annual Precipitation and River Discharges in Florida in Response to El Nio and La Nia Sea Surface Temperature Anomalies. Journal of Hydrology 199:74 87. Technical Coordination Group, 1992. The Comprehensive Study Comprehensive St udy, Alabama Coosa Tallapoosa and Apalachicola Chattahoochee Flint River Basins, Volume 1, Plan of Study, Main Report. Technical Service Center, Denver, CO, 2005. Natural Flow of the Upper Klamath River Phase I. Timilsena, J., T.C. Piechota, H. Hidalgo, and G. Tootle, 2007. Five Hundred Years of Hydrological Drought in the Upper Colorado River Basin. Journal of the American Water Resources Association 43:798 812. Tootle, G.A. and T.C. Piechota, 2004. Suwannee River Long Range Streamflow Forecasts Based o n Seasonal Climate Predictors. Journal of the American Water Resources Association 40:523 532. Tootle, G.A. and T.C. Piechota, 2006. Relationships Between Pacific and Atlantic Ocean Sea Surface Temperatures and US Streamflow Variability. Water Resources R esearch 42:7411. Tootle, G.A., T.C. Piechota, and A. Singh, 2005. Coupled Oceanic Atmospheric Variability and US Streamflow. Water Resour. Res 41:1 11. Torak, L.J., 2003. Assessment of Karst Features Underlying Lake Seminole, Southwestern Georgia and Nor thwestern Florida, Using Orthorectified Photographs of Preimpoundment Conditions and Hydrographic Maps. Proceedings of the 2003 Georgia Water Resources Conference, April., pp. 23 24.
163 Torak, L.J. and R.J. McDowell, 1996. Ground Water Resources of the Lo wer Apalachicola Chattahoochee Flint River Basin in Parts of Alabama. Florida, and Georgia Subarea 4:95 321. Torrence, C. and G.P. Compo, 1998. A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society 79:61 78. Torrence, C. and P.J. Webster, 1999. Interdecadal Changes in the ENSO monsoon System. Journal of Climate 12:2679 2690. Trenberth, K.E., 1997. The Definition of El Ni \ No. Bulletin of the American Meteorological Society 78:2771 2777. U.S. Fish and Wildlife Service, 20 06. Biological Opinion and Conference Report on the U.S. Army Corps of Engineers, Mobile District, Interim Operating Plan for Jim Woodruff Dam and the Associated Releases to the Apalachicola River. USACOE, 1989. Apalachicola Chattahoochee Flint Basin Wate r Control Plan. USACOE, 1997. ACT/ACF Comprehensive Water Resources Study: Surface Water Availability. Volume 1: Unimpaired Flow. USACOE, 2008a. Description of Proposed Action Modification to the Interim Operations Plan at Jim Woodruff Dam. USACOE, 200 8b. Description of Proposed Action Modification to the Interim Operations Plan at Jim Woodruff Dam. USBR, 2004. Colorado River System Consumptive Uses and Losses Report 1996 2000. Wang, W. and J. Ding, 2003. Wavelet Network Model and Its Application to t he Prediction of Hydrology. Science 1:67 71. Yates, D., J. Sieber, D. Purkey, and A. Huber Lee, 2005. WEAP 21 A Demand Priority and Preference Driven Water Planning Model Part 1: Model Characteristics. Water International 30:487 500. Yue, S., P. Pil on, and G. Cavadias, 2002. Power of the Mann Kendall and Spearman's Rho Tests for Detecting Monotonic Trends in Hydrological Series. Journal of Hydrology 259:254 271. Zagona, E.A., T.J. Fulp, R. Shane, T. Magee, and H.M. Goranflo, 2001. RiverWare: A Gener alized Tool for Complex River Basin Modeling.". Journal of the American Water Resources Association 37:913 929.
164 Zeng, W., F. Jiang, and Y. Zhang, 2009. Reservoir Management in the Apalachicola Chattahoochee Flint (ACF) River System Under the Interim Opera tion Plan (IOP) During the Ongoing Drought. Proceedings of the 2009 Georgia Water Resources Conference. University of Georgia. Zeng, W., G.F. McMahon, D.E. Hawkins, and K.J. Hatcher, 2005. Modeling for Conflict Resolution Using Parameterization of Operat ions and Strong Stakeholder Initiatives. University of Georgia, Institute of Ecology Athens GA 30602 1619 USA, Zeng, W. and M. Wen, 2007. Understanding the Interim Operation Plan in the Apalachicola Chattahoochee Flint River Basin. Georgia Water Resources Institute. Zhang, Y., D. Hawkins, W. Zeng, M. Wen, and K.J. Hatcher, 2005. The Framework of GIS Based Decision Support Systems(DSS) for Water Resources Management at the Flint River Basin. University of Georgia, Institute of Ecology Athens GA 30602 1619 USA, Zhang, Y., M. Wen, and K.J. Hatcher, 2005. Watershed Modeling and Calibration for Spring Creek Sub Basin in the Flint River Basin of Georgia Using the EPA BASINS/HSPF Modeling Tool. University of Georgia, Institute of Ecology Athens GA 30602 1619 USA
165 BIOGRAPHICAL SKETCH Nathan Taylor Johnson was born on August 30, 1985 in Ft. Lauderdale, Flor i da In first grade he and his family relocated to Arvada, Colorado He grew up in Colorado and he and his family moved to Bradenton, Florida in 2001 He graduated high school in 2003 having completed the international baccalaureate diploma. He attended the 2007 in a gricultural and b iological e ngine ering with a specializ ation in l and and w ater r esources e ngineering More than a year he worked for the Natural Resources Conservation Services as a civil engineering intern After graduating h e worked for Soil and Water Engi neering Technology as an intern He started gradua te school in 2008 and completed a Master of E ngineering degree in August of 2010 with an emphasis in hydrologic modeling and climate variability He was hired to work in Groundwater oundwater trends