Spatial Water Balance of the Suwannee River Basin, Florida: an Application of El Niño Southern Oscillation Climatology

SPATIAL WATER BALANCE OF THE SUWANNEE RIVER BASIN, FLORIDA: AN APPLICATION OF EL NIÑO SOUTHERN OSCILLATION CLIMATOLOGY

NITESH TRIPATHI

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2006

Nitesh Tripathi

I dedicate this dissertation to my wonderful family, especially…

To my grandparents, particularly, my grand father, who has always wanted me to excel at the highest level and achieve my life goals; whose letters have kept me motivated to accomplish this; who finds pride in my accomplishments.

To my loving parents who are my greatest teachers in life who have taught me how to be a good human being, for opening my eyes to the world; for their countless sacrifices so that I could pursue my dreams; for their diligence in instilling in me the importance of hard work and higher education; for their unconditional support and encouragement throughout my life and through my doctoral program.

To my brother who is a role model in my life; with whom I shared many of our childhood dreams; attaining a PhD abroad is an extension of one of those dreams; who is a big influence on life and my academic career; for his motivation in my pursuit of academic excellence.

To my wife who has been my most faithful encourager; who always stood by me and never lost her smile during many of our stressful times and during the course of my doctorate; for her endurance, patience, love and continued support in every whichever way possible always.

To our precious daughter Navya who continually helps me put life into perspective; whose pranks take away the stress after long hours of work; who may also be motivated and encouraged to reach her dreams; who may recognize the inherent value of dedication and hard work to attain her goals in life. To all of you, I express my deepest love and appreciation for always being there to

encourage me in so many ways. I am blessed to be your grandson, your son, your brother, your husband and your dad.

I share this accomplishment joyfully and gratefully with you.

I love you!

ACKNOWLEDGMENTS

I wish to express my sincere gratitude to my supervisory committee. First of all, I

would like to thank Dr. Grenville Barnes, my committee chair, for his thoughtful

guidance and constant encouragement to me and his unbelievable patience with me

throughout the course of my graduate study at the University of Florida. I would

especially like to thank Dr. Sandra Russo for providing me with continued employment to overcome some of my financial constraints during my graduate studies at UF. My special thanks go to Dr. Peter Hildebrand who helped me select this interesting research topic. His one statement, early on in one of our discussions, “How much water do we have in Suwannee?” set the course for my PhD research. Thanks go to Dr. Jim Jones for his willingness to spend time with me to explain the complex hydrologic concepts necessary to successfully complete this research. I would also like to express my gratitude to him for providing financial support for part of my study program. Thanks go to Dr. Ignacio Porzecanski for his support, motivation and always useful feedback during the entire research.

I would like to thank Dr. Roy Carriker, Department of Food and Resource

Economics, for always being available to discuss about water issues in Florida. I would also like to thank Dr. Peter Waylen, Geography Department, for helping me understand the concept of “spatial streamflow computations” and also imparting knowledge about

ENSO effects on Florida streamflow. I express my thanks to Dr. Anurag Agarwal in the

Department of Decision and Information Sciences for providing a computer program

v crucial for computing the final results for my research. I would like to thank Dr. Clyde

Fraisse, Department of Agricultural and Biological Engineering, for providing weather

data for this research. I would like to express my deep gratitude to Mr. Richard Marella at

USGS-FL office for his always helping attitude, for providing the water use datasets and

reports for the research and for explaining the complexity of “water use” datasets in

Florida. I would like to thank Mr. John Good and Mr. Thomas Mirti at the Suwannee

River Water Management District office for their support in providing me necessary datasets and reports for the research.

I would like to thank Dr. Steve Humphrey for providing me the necessary financial support when it was most needed and Cathy Ritchie and Meisha Wade at SNRE for dexterously handling my paperwork.

I would like to thank my friends at UF, Seemant and Purvi, Vibhuti and Tulika and

Amit and Raman for their help and support in many ways, both related and unrelated to this research, during the course of my PhD. I would also like to thank Ana, Laura, Leslie and Nikki at the UF International Center for providing a wonderful work environment and for their encouragement during this research.

I would like to express my sincere gratitude towards my parents and brother whose encouragement and support have helped me finish this PhD.

Last but most importantly, I would like to thank my wife Shefali and our daughter

Navya for their constant love, support, endurance and encouragement during the course of this research. This dissertation is their accomplishment as much it is mine.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... v

LIST OF TABLES...... xi

LIST OF FIGURES ...... xiii

ABBREVIATIONS ...... 16

ABSTRACT...... xiv

CHAPTER

1 INTRODUCTION ...... 1

Factors Affecting Water Resources in Florida ...... 2 Population and Water Use Demand ...... 2 Climate Variability ...... 5 Water Problems in Florida...... 6 Rationale of the Study ...... 8 Objectives ...... 9 Structure of the Dissertation ...... 10

2 LITERATURE REVIEW...... 13

Introduction...... 13 Review of Water Balance Studies in Florida...... 13 Review of Spatial Water Balance Studies ...... 16 Summary...... 18

3 STUDY AREA...... 20

Suwannee River Basin Characteristics ...... 20 Physical Characteristics and Hydrology...... 20 Population...... 22 Water Use Trends...... 23 Topography and Landuse ...... 25 Climate ...... 26 Summary...... 27

vii 4 SPATIAL APPROACH TO MODELING AND ANALYZING THE WATER BALANCE OF A RIVER BASIN ...... 28

Hydrologic and Weather Data ...... 28 A Spatial Water Balance Modeling Approach ...... 29 Geo-processing of Water Balance Components...... 32 Precipitation...... 32 Streamflow ...... 35 Evapotranspiration (ET) ...... 42 Consumptive Water Use...... 50 Spatial Water Balance Equation ...... 54 Methods for Analyzing Results ...... 54 Assumptions, Limitations and Sources of Error in the Modeling Approach ...... 57 Summary...... 60

5 ANALYSIS OF HYDROLOGIC COMPONENTS IN THE SUWANNEE RIVER BASIN ...... 61

Introduction...... 61 Precipitation...... 61 Historical Monthly Precipitation in the SRB...... 62 Mean Monthly Trends ...... 62 Mean Seasonal Trends...... 64 Annual Trends ...... 65 Streamflow...... 66 Historical Monthly Streamflow in the SRB ...... 66 Mean Monthly Trends ...... 66 Mean Seasonal Trends...... 68 Evapotranspiration...... 70 Historical Monthly ET in the SRB ...... 70 Mean Monthly Trends ...... 70 Mean Monthly ET Trends in Various Landcover Classes ...... 71 Mean Seasonal Trends...... 72 Annual Trends ...... 74 Consumptive Water Use...... 74 Historical Monthly Consumptive Water Use in the SRB...... 74 Mean Monthly Trends ...... 75 Mean Seasonal Trends...... 76 Annual Trends ...... 78 Summary...... 79

6 ANALYSIS OF SPATIAL WATER BALANCE FOR THE SUWANNEE RIVER BASIN ...... 85

Introduction...... 85 Water Balance of the SRB...... 85 Mean Monthly Trends ...... 87

viii Mean Seasonal Trends...... 89 Annual Trends ...... 90 Decadal Trends...... 91 30-Year Water Balance Analysis ...... 92 Comparison of Water Balance Estimates ...... 93 Summary...... 95

7 CONCLUSIONS AND RECOMMENDATIONS...... 94

Conclusion ...... 94 Spatial Water Balance Modeling...... 94 Comparison between SWBA versus Reported Estimates ...... 95 Comparison between SWBA versus Point-and-Average Approach (PAAA)...... 97 Effect of ENSO on Water Balance and its Hydrologic Components...... 97 Behavior of WB and Hydrologic Components in Relation to ENSO ...... 98 Significance of ENSO on WB and hydrologic components based on ANOVA...... 99 Recommendations...... 99

APPENDIX

A AREAS OF MAJOR WATERSHEDS AND COUNTIES IN THE SRB...... 102

B PRECIPITATION GAUGING STATIONS...... 103

C CONVERSIONS ...... 105

D STREAMFLOW GAUGING STATIONS...... 106

E SPECIFIC DISCHARGE AT RESPECTIVE WATERSHED CENTROIDS ...... 108

F EVAPOTRANSPIRATION..STATIONS...... 113

G LAND USE LANDCOVER DATA DESCRIPTION...... 115

H BLANEY-CRIDDLE CROP COEFFICIENTS FOR LEVEL II FLUCCS CLASSES ...... 118

I CONSUMPTIVE WATER USE PERCENTAGES BY WATER USE CATEGORY FOR COUNTIES IN THE SRB ...... 120

J ANNUAL TOTALS OF WATER BALANCE AND HYDROLOGIC COMPONENTS IN THE SRB...... 123

K DECADAL VARIABILITY OF HYDROLOGIC COMPONENTS AND WATER BALANCE ...... 125

ix L ENSO ANALYSIS OF HYDROLOGIC COMPONENTS BASED ON ANNUAL AVERAGES...... 127

M COMPARISON BETWEEN RESULTS OF THE SPATIAL WATER BALANCE APPROACH (SWBA) AND THE POINT-AND-AVERAGE APPROACH (PAAA) ...... 129

N COMPARISON OF ESTIMATES OF HYDROLOGIC COMPONENTS AND WATER BALANCE COMPUTED IN THIS RESEARCH WITH REPORTED ESTIMATES OF FLORIDA AND SRB...... 134

O WATER BALANCE AND HYDROLOGIC COMPONENTS AS A PERCENTAGE OF PRECIPITATION ...... 139

P AN ACCOUNT OF TECHNICAL DATA PROCESSING AND DIFFICULTIES INVOLVED IN ADOPTING SWBA APPROACH...... 140

LIST OF REFERENCES...... 147

BIOGRAPHICAL SKETCH ...... 154

LIST OF TABLES

Table page

3-1 Suwannee River and its major sub-basins...... 21

4-1 Inflow and outflow streamflow stations with their respective drainage areas ...... 37

4-2 Blaney-Criddle crop coefficients for level I LC classes...... 49

4-3 Watersheds and counties, their areas and percent areas in watersheds in the SRB .53

4-4 List of ENSO year type, based on the JMA-SST Index...... 56

4-5 Seasonal classification...... 56

5-1 Summary of maximum and minimum precipitation based on annual totals...... 62

5-2 Summary of maximum and minimum streamflow based on annual totals ...... 66

5-3 Summary of maximum and minimum ET based on annual totals ...... 70

5-4 Summary of maximum and minimum CWU based on annual totals...... 75

5-5 Summary of hydrologic response of water balance components based on ENSO effects on a monthly basis ...... 79

5-6 Summary of hydrologic response of water balance components based on ENSO effects on a seasonal basis...... 79

5-7 Monthly precipitation (mgal) in the Suwannee River Basin (1974/75- 2003/04) ...... 80

5-8 Monthly streamflow (mgal) in the Suwannee River Basin (1974/75- 2003/04) ...... 81

5-9 Monthly actual evapotranspiration losses (mgal) in the Suwannee River Basin (1974/75-2003/04)...... 82

5-10 Monthly consumptive water use (mgal) in the Suwannee River Basin (1974/75- 2003/04) ...... 83

6-1 Summary of maximum and minimum WB based on annual totals ...... 85

xi Table 6-2. Summary of affect of ENSO on monthly water balance...... 96

Table 6-3. Summary of affect of ENSO on seasonal water balance...... 96

Table 6-4. Monthly water balance (mgal) in the Suwannee River Basin (1974/75- 2003/04) ...... 93

Table G-1. LULC datasets used during the 30 year period of the study ...... 116

Table G-2. FLUCCS classes, their areas and percent areas of the SRB in respective years ...... 117

Table L-1: Comparison of El Niño and La Niña year water volumes against Neutral years ...... 128

Table M-1:Comparison of computations using the SWBA and PAAA for the year 1997/1998 (El Niño)...... 130

Table M-2: Comparison of computations using the SWBA and PAAA for the year 1998/1999 (La Niña) ...... 131

Table M-3: Comparison of computations using the SWBA and PAAA for the year 2000/2001 (Neutral) ...... 132

Table M-4: Summary of comparison of computations using the SWBA and PAAA for the three years...... 133

Table N-1: Comparison of hydrologic components and water balance of Florida (reported) vs. SRB (this research) ...... 137

Table N-2: Comparison of hydrologic components and water balance of SRB (reported) vs. SRB (this research) ...... 138

Table P-1: GIS tasks performed and tools used to achieve them...... 141

Table P-2: GIS tasks, measure of processing time and computer storage involved...... 142

Table P-3: Tasks and processing time involved in computing CWU...... 143

xii

LIST OF FIGURES

Figure page

1-1 Top 25 most populated cities in Florida in relation to Interstate 4 ...... 3

1-2 Population and Water Withdrawals for 1995 vs. 2000 in Florida ...... 3

1-3 PSS Water withdrawals and population 1975-2000 for Florida ...... 4

3-1 Suwannee River Basin study area with counties and watersheds ...... 22

3-2 Historical water withdrawal trends for Florida and SRWMD...... 24

3-3 Percentage of fresh water withdrawals by category in SRWMD, 1975-2050...... 24

4-1 Schematic diagram of methodology used for calculating spatial water balance...... 30

4-2 Location of precipitation stations used in the study...... 33

4-3 Geo-processing methodology for calculating precipitation spatially...... 34

4-4 Location of streamflow gauging stations used in the study ...... 37

4-5 Possible scenarios of gauging station location within a watershed...... 38

4-6 Location of inflow and outflow streamflow gauging stations used in the study...... 39

4-7 Drainage basins of inflow and outflow stations of respective watersheds...... 40

4-8 Location of centroids of respective watersheds ...... 41

4-9 Geo-processing methodology for calculating streamflow spatially...... 43

4-10 Location of ET stations used in the study ...... 44

4-11 FLUCCS LC and their areas in various years...... 46

4-12 LULC maps for 1974, 1988, 1995 and 2003 based on FLUCCS ...... 47

4-13 Illustration of 7 FLUCCS classes with respective ET surfaces and their crop coefficients ...... 48

xiii 4-14 Geo-processing methodology for calculating ET spatially...... 51

4-15 Geo-processing methodology to calculate CWU ...... 54

5-1 Mean monthly precipitation trends in the SRB...... 63

5-2 Mean monthly precipitation trends in the SRB based on ENSO phase ...... 63

5-3 Mean seasonal precipitation trends in the SRB...... 64

5-4 Mean seasonal precipitation trends based on ENSO phase...... 65

5-5 Annual precipitation trends based on ENSO phase ...... 65

5-6 Mean monthly streamflow trends in the SRB ...... 67

5-7 Mean monthly streamflow trends in the SRB based on ENSO phase...... 67

5-8 Mean seasonal streamflow trends in the SRB...... 68

5-9 Mean seasonal streamflow trends in the SRB based on ENSO phase ...... 69

5-10 Annual streamflow trends based on ENSO phase...... 69

5-11 Mean monthly evapotranspiration trends in the SRB ...... 70

5-12 Mean monthly evapotranspiration trends in the SRB based on ENSO phase...... 71

5-13 Mean monthly evapotranspiration losses from land cover classes in the SRB...... 72

5-14 Mean seasonal evapotranspiration trends in the SRB ...... 73

5-15 Mean seasonal evapotranspiration trends based on ENSO phase ...... 73

5-16 Annual evapotranspiration trends based on ENSO phase...... 74

5-17 Mean monthly consumptive water use trends in the SRB ...... 75

5-18 Mean monthly consumptive water use trends in the SRB based on ENSO phase...76

5-19 Mean seasonal consumptive water use trends in the SRB ...... 77

5-20 Mean seasonal consumptive water use trends in the SRB based on ENSO phase...77

5-21 Annual consumptive water use trends based on ENSO phase ...... 78

6-1 Mean monthly water balance and hydrologic components in the SRB ...... 87

6-2 Mean monthly water balance trends in the SRB ...... 88

xiv 6-3 Mean monthly water balance trends in the SRB based on ENSO phase ...... 88

6-5 Mean seasonal water balance in the SRB based on ENSO ...... 90

6-6 Annual water balance trends based on ENSO phase...... 91

6-7 Hydrologic components and water balance in the three decades...... 92

G-1 FLUCCS LC and change in their areas over years ...... 117

N-1 Florida’s Water Cycle ...... 135

ABBREVIATIONS

AET Actual evapotranspiration

ANOVA Analysis of variance

C/I/M Commercial/industrial/mining

cfs-1 Cubic feet per second

CUAHSI Consortium of Universities for the Advancement of Hydrologic Studies, Inc.

CWU Consumptive water use

DEP Department of Environmental Protection

DSS Domestic self supply

ENSO El Niño Southern Oscillation

FDOT Florida Department of Transportation

FGDL Florida Geographic Data Library

FLUCCS Florida Landuse Cover and Forms Classification System

FWC Florida Fish and Wildlife Commission

GIS Geographic Information Science

IDW Inverse distance weighted

JMA Japan Metrological Agency

LC Landcover

LULC Landuse land cover mgal Million gallons mgald-1 Million gallons per day

xvi

mgalm-1 Million gallons per month

NCDC National Climatic Data Center

NOAA National Oceanic and Atmospheric Administration

NRC National Research Council

NSF National Science Foundation

PAAA Point-and-average approach

PET Potential evapotranspiration

PSS Public self supply

RI Recreation irrigation

SECC Southeastern Climate Consortium

SD Specific discharge

SRAD Solar radiation

SRB Suwannee River Basin

SRHO Suwannee River Hydrologic Observatory

SRWMD Suwannee River Water Management District

SST Sea surface temperature

SWBA Spatial water balance modeling approach

SWBM Spatial water balance model

SWFWMD Southwest Florida Water Management District

TCU Transportation, communications and utilities

USEPA United States Environmental Protection Agency

USGS United States Geological Survey

WB Water balance

xvii

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

SPATIAL WATER BALANCE OF THE SUWANNEE RIVER BASIN, FLORIDA: AN APPLICATION OF EL NIÑO SOUTHERN OSCILLATION CLIMATOLOGY

Nitesh Tripathi

December 2006

Chair: Grenville Barnes Major Department: Interdisciplinary Ecology

With increasing population and natural climate variability, water resources in

Florida are constantly under stress. As water shortages increase in south Florida, developers are increasingly looking towards north Florida for future expansion. If development were to move northwards, it would put severe stress on the water resources of north Florida.

A spatial water balance (WB) modeling approach has been formulated and implemented to compute monthly water balances in the Suwannee River Basin (SRB) in order to understand the water availability under varying climatic conditions. The methodology uses distributed data on streamflow, precipitation, evapotranspiration (ET) and consumptive water use (CWU) in a geographic information system (GIS) in order to model monthly water balance at a watershed scale.

A water balance has been computed for a period of 30 climatic years (1974/75 –

2003/04). In the process maps of precipitation, streamflow and evapotranspiration were

xviii produced. The advantage of this approach was that it captured the precise spatial nature of the hydrologic components allowing for seasonal analyses of these components in light of ENSO phases (climate variability).

Mean monthly analysis of hydrologic components based on ANOVA revealed that precipitation was significantly affected by ENSO during November and February and streamflow was significantly affected by ENSO during March and April. ET and CWU did not show any ENSO related effects. Based on seasonal analysis only precipitation showed to be significantly affected by ENSO during fall and winter seasons. Hydrologic responses of other hydrologic components for ENSO related affects were not significant.

Mean monthly and seasonal WB was affected by ENSO in the same fashion as precipitation. However, seasonal WB was significantly affected by ENSO during spring in addition to fall and winter as was the case in precipitation.

Comparison of WB computed in this research to those reported in literature for

Florida and SRB revealed that estimates of WB were higher by approximately 17-18%.

Compared to the traditional point-and-average approach of calculating water volumes of hydrologic components in a watershed, the spatial approach developed in this research resulted in a lower estimate of water balance. The WB computed in this research was approximately 64% lesser. Over all the methodology resulted in a detailed estimate of potential water balance for the SRB.

xix

CHAPTER 1 INTRODUCTION

Florida is blessed with abundant water. It has 1,197 miles of coastlines, 7,700 lakes greater than 10 acres, more than 1,700 streams and 3 million acres of wetlands (Kautz

1998; Patton and DeHan 1998). Most of the state’s water is ground water. Florida has more available ground water in aquifers than any other state. Rivers, springs, bays, wetlands and lakes form the surface water systems while wells are the main sources of groundwater in Florida (DEP 2001). There are 33 first-magnitude springs,1 more than in any other state in the country. The state has approximately 16,000 kilometers (10,000 miles) of rivers and streams (Kautz 1998). Of the state’s five largest rivers, four

(Choctawhatchee, Escambia, Apalachicola and Suwannee) are in the drainage basins of northern Florida with headwaters in Alabama or Georgia. The largest, the St. Johns, flows northward from Indian River county, flowing into the Atlantic Ocean near

Jacksonville (Carriker 2000).

Water is an integral part of the environment in Florida. Florida’s natural system must meet its water needs to sustain human demands for water. These systems have specific water requirements in terms of quantity, quality, duration and season. Fernald and Purdum (1998) reported average daily values of water (in billion gallons) input and loss from various hydrologic components for Florida. According to them, on an average

Florida receives 150 billion gallons of rain each day. 26 billion gallons of water flows

1 "First-magnitude" springs are defined as meaning they discharge at least 100 cubic feet of water per second (cfs-1), or about 64.6 million gallons per day (mgald-1)

1 2

into the state from rivers in Georgia and Alabama. 170 billion gallons are lost as ET, 2.7

billion gallons are consumptively used and the remainder 66 billion gallons flows to

rivers or streams or seeps into the ground and recharges aquifers.

In north Florida, water is a high priority for maintaining the river flows, in north

and central Florida for sustaining springs, and in south Florida for the proper functioning

of the Everglades (Department of Environmental Protection [DEP] 2001). Fresh water is

also needed to sustain the wildlife and habitats of valuable sport and commercial fishes in

estuaries along the entire coastline of Florida (DEP 2001). Although more than half of

Florida’s original wetlands have been drained or developed (Noss and Peters 1995), the

state still has vast and diverse wetlands (Purdum 2002).

Factors Affecting Water Resources in Florida

Population and Water Use Demand

Increasing population and growing urbanization is putting a stress on the

environment and economy of the state (Katz and Raabe 2005). According to the Florida

Council of 100, “In Florida, 80 percent of the population and public water consumption is

south of I-42; whereas 80 percent of the water resources are north of I-4.” Of the 25 most

populous cities in Florida, 21 fall south of I-4 and only 4 are north of I-4 (Figure 1-1).

The percent population distribution of these 25 cities is 72.4% (286,000) south of I-4 and

27.6% (1,091,000) north of I-4 (State of Florida website 2006). This situation has been getting worse as Florida’s population increases.

2 Interstate-4

Figure 1-2 shows a comparison of total amount of water withdrawn in the state and population increase between 1995 and 2000. During the 5-year period, Florida’s population increased by 7.5% compared to a 10.7% increase in water withdrawals.

Figure 1-1. Top twenty five most populated cities in Florida in relation to Interstate 4 (Data Source: State of Florida website 2006)

s 21,000 15.4 15.2 n 20,000 15 14.8 14.6 19,000 14.4 14.2 (Mgal/day) 18,000 14 13.8 W ater W ithdraw al 17,000 13.6 Population (Millio 1995 2000 Water Withdrawal 18,200 20,147 (Mgal/day) Population (Million) 14.16 15.23 Years Figure 1-2. Population and Water Withdrawals (mgalday-1) for 1995 vs. 2000 in Florida (Source: US Census 2000, USGS-FL 2003, Marella 1999)

Florida’s population is projected to reach nearly 21.7 million by the year 2020

(Smith and Nogle 2001). Demand for water use is often highly correlated with increases

in population and urban development.

In a restrictive sense, the term water use refers to water that is actually used for a specific purpose such as domestic use, irrigation, or industrial processing. More broadly, water use pertains to human’s interaction with and influence on the hydrologic cycle and thus includes elements such as water withdrawals, deliveries, consumptive use, waste water releases, reclaimed waste water, return flow and instream use. (Marella 1995)

Figure 1-3 shows the trend between population, population served by water for

public self supply3 (PSS), and water withdrawal for PSS between 1975 and 2000 and

expected increases for year 2020. The changes in population, population served by PSS

and water withdrawals for PSS between 1975 and 2000 are 88%, 105%, and 117%

respectively.

20 3000 n 2500 15 2000 10 1500 1000 5 500 Population (Millio Population 0 0 1975 1980 1985 1990 1995 2000 Population (Million) 8.50 9.75 11.35 12.94 14.18 15.98

Population Served by PSS (Million) 6.81 7.79 9.74 11.23 12.21 14.02 (Mgal/day Withdrawals Water

Water Withdrawals PSS 1124.10 1406.40 1685.40 1925.10 2065.27 2436.79 (Mgal/day) Years

Figure 1-3. PSS Water withdrawals and population 1975-2000 for Florida Source: Marella 1995, Marella 1999, USGS 2003, Marella 1992, Florida- business-data.com 2006

3 Public Self Supply (PSS) is water withdrawn by public or private water suppliers and delivered to users who do not supply their own water (Marella 1999)

Between 2000 and 2020, population is estimated to increase by 36% while water

withdrawals for PSS will increase by 19%. These upward going trends indicate that

water withdrawal is on the increase with increasing populations in Florida thereby

affecting water availability. Water availability is also known to be affected by climate

variability.

Climate Variability

Florida’s variable climate is the main cause of rainfall variability in the state.

Although the state receives abundant rainfall, it’s spatial and temporal variability coupled

with natural climate variability have a significant affect on water availability. Climate

variability is defined in many ways by various researchers and agencies working on

climate research. For this research, climate variability is defined as a fluctuation in

climate, which could last for a specified period of time, usually of the order of seasons to

years to decades.

The state receives on average 53 inches of rainfall annually (Geraghty 1973;

Carriker 2000; Mossa 2000; Purdum 2002), compared to a national average of 30 inches

per year, and 9 inches per year for - the driest state in the country (Nevada). Extreme

annual variations in rainfall also occur. Seventy percent of the annual rainfall occurs

during the period from May to October making summer the wettest season in the state

(Florida’s Water: A Shared Resource 1977). North-central Florida experiences more total

rainfall during summer months, but recharge of the aquifer systems usually takes place

during the winter. This is due to reduced evapotranspiration (ET) rates in winter.

Variations in amount and intensity of rainfall also affect flow characteristics of streams, groundwater recharge and water levels of lakes and reservoirs (Carriker 2001). These

unique climatic characteristics play a significant role in water availability in various parts of the state at different times of the year.

Water Problems in Florida

It is interesting to note that with abundant rainfall and vast water resources, Florida still has water problems and issues related to water resource management. Florida water problems can be traced to a few factors (adapted from Betz 1984): a) Florida’s rainfall is unevenly distributed spatially and temporally, b) between 1940 and 2003, Florida’s population increased 845% to 17.01 million, excluding out of state visitors4 (State of

Florida website 2006 ) c) large areas of the state are generally unsuitable for dense human

habitation, and d) the state’s aquatic environment, which is also one of its main tourist

attractions, is one of the most ecologically sensitive areas in North America. Nine out of

the 21 most threatened ecosystems in the country are located in Florida (U.S. EPA 1994).

Problems related to excessive development and water shortage in Florida have

raised issues about moving water from water rich areas to densely populated areas facing water shortages. In Florida, for about three decades, there has been an ongoing debate about water transfers from north Florida to central/south Florida to supplement the depleting water resources in the south and to support the growing population there. In late

2003 this debate heated up and several lead articles were published in local newspapers.

In general, north Florida is viewed as having excess water, while central/south Florida is understood to be facing a water shortage due to rapid urban development and a growing population. Urban portions of Tampa Bay area, for example, have already exhausted their fresh water sources. As a result, Tampa, St. Petersburg and other southwestern

4 Seventy two million people visited Florida from out of state in 2000 (University of Florida 2002)

municipalities of the state have already started drawing water from their north causing

water deprivation in these areas (Gainesville Sun Aug. 29 2003a). Over the years, there

have been many initiatives to both promote and curb water transfer from north Florida to south Florida. In 1996 the Army Corps of Engineers proposed a pipeline to move water

from the Suwannee River to the Tampa area. “Save Our Suwannee,” a local activist

group, did not allow this proposal to materialize (Gainesville Sun May 19 2003b).

Legislation was later passed based on the “local sources first” policy that required each of

the state’s five water management districts to use up local water sources before taking

water from another district.

Towards the end of 2004 there was a shift in focus of the water transfer debate.

During the 29th Annual Conference on Water Management in Orlando, the Florida

Chamber of Commerce's Water Task Force made recommendations to tie growth to availability of local water resources as opposed to long-distance water transfer, as recommended by the Florida Council of 100 (Gainesville Sun Nov 5, 2004c). The Florida

Council of 100 is a private, nonprofit, nonpartisan organization that was formed in 1961 at the request of Governor Farris Bryant to provide advice to him on key Florida issues from a business perspective. CEOs invited into the Council represent a cross-section of key business leadership in Florida.

It is speculated that north Florida could become a future development hub because of its abundant water resources. In light of the water transfer debate and demand for better water resources management in the future, there is a broadly recognized need to better understand the complex water system in the region (Katz and Raabe 2005). This can be appropriately done by calculating regional water balance considering the factors

that affect it. Water balance is defined as net change in water taking into account all the

inflows to and outflows from a hydrologic system. This is assumed to be “available

water” in the regional hydrologic system. Suwannee River Basin is ideally located for

such a pilot study. The basin is in the north Florida region and has had a precedence of a

mass water transfer request in the past. Even though the proposal had been rejected, the region is considered as potential future hotspot for development and is best suited for studying water availability.

Rationale of the Study

Future urban development in Florida has been linked with water availability

(Gainesville Sun Nov 5, 2004c). Considering the above-mentioned factors affecting water availability, an improved understanding of the regional hydrologic system and the science behind it is required. Traditional approach of calculating water contained in a hydrologic component has been the point-and-average method. This method does not give an

accurate estimate of the water volume in a hydrologic component. Therefore there is a

need to develop a methodology to model available water in Florida. This research aims to

shed light on water availability under various climatic conditions. The SRB was selected

as the study area.

The SRB contains an abundance of water that is sought by more developed parts of

the State in addition to water supply demands within the basin itself (Katz and Raabe

2005). The need for effective management of water resources in the SRB has been

recognized by many agencies. A white paper by USGS entailing the issues and research needs in the SRB was published by Katz and Raabe in 2005. The authors believe that increased knowledge about the interactions among hydrologic processes, climate

variability, ecosystem and human health and economic development will greatly benefit

the effective management of water resources in the basin (Katz and Raabe 2005). The

paper details water supply/water budget research needs and opportunities in the SRB.

Competing demands on the natural resources of the SRB require a better understanding of the sources and effects of contamination, water withdrawals, and climate change and the interactions among these stressors. Climate variation alone can result in significant changes in rainfall, with subsequent impacts on surface- water and groundwater supply. Natural fluctuation in water supply, coupled with water consumption, can place added stress on biological communities. Intermittent droughts in Florida over the last two decades have heightened concerns about management of water resources within the watershed. In the future, it will be desirable to improve interagency communication and coordination of groundwater and surface water monitoring, as well as developing predictive capabilities. Katz and Raabe (2005, pp 10)

The USGS, along with Suwannee River Water Management District (SRWMD) and other federal and state agencies (university researchers) have been conducting research studies in the basin for several decades (Katz and Raabe 2005).

Suwannee River Hydrologic Observatory (SRHO) was setup as one of the hydrologic observatories in the country to focus on issues related to the science and management of hydrologic systems. The observatory is supported by a Consortium of

Universities for the Advancement of Hydrologic Studies, Inc. (CUAHSI) in conjunction with the National Science Foundation (NSF). The SRHO addresses long-standing hydrologic science questions such as estimating actual evapotranspiration, groundwater recharge, or groundwater discharge to estuaries over complex spatially variable terrains

(Suwannee Hydrologic Observatory Website 2006). SRHO is housed in the newly instituted Water Institute at the University of Florida.

Objectives

The principal goal of this research was to develop a spatial water balance modeling approach to calculate water balance in the SRB in Florida. The water balance computed 10

was analyzed based on climate variability that would aid in decision making for water

resource planning allowing for seasonal analyses of water availability in different ENSO

phases. Specific objectives were as follows:

• Specific Objective 1: Develop a spatial water balance modeling methodology that describes the existing sources and uses of water to compute water balance in the study area

• Specific Objective 2: Analyze water balance and its individual components (precipitation, streamflow, evapotranspiration and consumptive water use) as affected by climate variability

Structure of the Dissertation

Chapter 1 lays the foundation for this research. The most important water resource

in Florida is its abundant rainfall which is variable in amount and intensity over seasons

and geographic areas in the state. This variability also is one of the main problems facing

the state in terms of its water resources management. Other issues are related to the

state’s population growth that put stress on the water resources. With this as background

the rationale of this research, the study area chosen, and the specific objectives of the

research were discussed.

Chapter 2 is a literature review on the water balance concept and its theoretical

underpinnings. The chapter discusses water balance studies in Florida and reviews

studies on spatial water balance worldwide.

Chapter 3 deals with the characteristics of the study area- Florida part of the SRB.

The aim of this was to understand the landscape of the basin and also the factors

influencing the water resources in the basin. Physical characteristics and hydrology of the

region are discussed. Population growth trends including its distribution among major

population growth and development areas have been discussed. Historical water use and 11

withdrawal trends of the SRWMD and the State starting 1975 up till 2000 have been

studied. Topography, major land uses in the basin and climate are also discussed.

Chapter 4 details the spatial water balance modeling methodology formulated for computing water balance in the SRB. The chapter starts with describing the parent hydrologic and weather datasets used in the study and the sources they were obtained from. Next, an overview of the methodology designed is given. Theoretical foundations of the classical water balance equation by Thornthwaite including the peculiar basin characteristics and the datasets have been discussed which lay the foundation for defining the final spatial water balance equation for SRB. Geo-processing of the datasets of the water balance components is then described in detail including the distinct methodology adopted for each model component. The final spatial water balance equation for the study is defined. The basis for analysis of the results relative to climate variability is laid out. El

Niño Southern Oscillation (ENSO) is briefly discussed and ENSO phases are defined.

Finally, assumptions, limitations and sources of error in the modeling approach are discussed.

Chapter 5 presents the results and analysis of the implementation of the spatial water balance methodology of individual water balance components: precipitation, streamflow, actual evapotranspiration (AET) and consumptive water use (CWU) over

SRB. Results are presented in terms of water volume (mgalmonth-1) computed for each

component. Mean monthly, mean seasonal and annual analysis of each of the components

relative to climate variability (ENSO) is presented.

In Chapter 6 the components that were dealt with individually in Chapter 5 are

brought together in the form of water balance. Water volume (mgalmonth-1) generated as

12 water balance is presented. Similar climate variability analysis of water balance is shown as presented in chapter 5. Analysis is also presented at decadal and 30-year time scale.

Water balance estimates from this research are compared with the traditional point-and- average approach (PAAA) and with WB estimates calculated using reported values of hydrologic components for Florida and SRB.

The final chapter (Chapter 7) concludes the findings of the study. Conclusions are drawn from the spatial modeling methodology and the effect of ENSO on WB and hydrologic components in the SRB. Finally, the implications of the present study are presented.

CHAPTER 2 LITERATURE REVIEW

Introduction

Hydrology at a watershed scale is widely studied using the classical water balance

approach developed by Thornthwaite and Mather (1948). Water balance can be regarded

as a “water budget” or “an accounting” of the amount of water in each component of the hydrologic cycle at a specific temporal scale (e.g. daily, monthly, annual). In simple terms it quantifies water entering the system as recharge and that leaving via discharge.

The water balance approach has also been adopted for modeling water resources in relation to climate, mostly climate change (Wolock and McCabe 1999) and more recently climate variability (Muttiah and Wurbs 2002). Water balance over land is an important measure in assessing the impacts of climate change on important human requirements such as drinking water, sanitation, agriculture, transportation, and energy supply (NRC

2003). The variables used to measure changes in the surface water balance are precipitation, evaporation, and runoff rates, as well as soil and surface water storage.

These quantities are affected by temperature, wind, cloudiness, vegetation characteristics,

and other climate system variables (NRC 2003).

Review of Water Balance Studies in Florida

Several studies have been conducted to calculate water balance at different

locations in Florida. These studies have been done at different spatial scales (e.g. lake,

wetland, basin) and for different temporal scales. The methodology adopted to compute

13 14

water balance in all these studies follows the Thornthwaite and Mather (1948) approach

(German 1997; Bidlake and Boetcher 1996).

Bidlake and Boetcher (1996) quantified water balances of wildland ecosystems for

management of water and other resources of those systems. The near-surface water

balance of a homogenous area of wildland vegetation in west-central Florida was

measured quantitatively for June 1991 to October 1992. The study area was in the dry

prairie vegetation type, which is common to central Florida. The water balance was

defined on a unit area basis for a depth of 5.5 meters. The water balance components

were precipitation, evapotranspiration (ET) and the rate of soil-water storage. Rate of

soil-water storage was computed using a time series of replicated measurements of soil-

water content.

Germen did a study in 1997 to assess the effects of developmental impacts on

wetlands in central Florida. A water budget model was developed as a tool for

understanding the factors affecting water levels in wetlands. The model allowed

simulation of hypothetical hydrological system as a series of tanks. Two hypothetical

cases were simulated to determine the effects of developmental impact on wetland water

levels. The two cases were

1. Effect of lowering the potentiometric surface of the Floridan Aquifer system on water levels in an isolated wetland

2. Effect of reducing the drainage area of a stream feeding a wetland

The model used daily rainfall data for the period being modeled, average potential evapotranspiration (PET) for each month of the year, and specification of parameters describing the watershed. It is worthwhile to note that this study used ET calculations for water table (ETwt) and ET for soil zone (ETsoil) separately as the area has cypress trees

which have their roots reach the water table and hence the ET is extracted from both the

water table and the soil zone at the same time. The model was evaluated by applying to a

wetland area in Volusia County, Florida for June 1992 through June 1994. The author

concluded that the model could give some indication of the magnitude of water-table

changes that might result from various types of development. However, effects on the

ecology of the area were much more difficult to assess. The model could be used as a tool

to understand wetlands hydrology in a semi-quantitative way and to determine relative

sensitivity of a system to developmental impacts.

Wyllie (1977) conducted a grid based (1 square kilometer) spatial ET study for the

Southwest Florida Water Management District (SWFMD). The spatial ET calculated on a

monthly basis was used for spatial water balance estimation using spatial precipitation,

soil moisture capacity and runoff to estimate available water for consumptive use. Actual

ET was calculated using four ET estimation methods: Thornthwaite’s (1948), Blaney-

Criddle’s (1962), Christiansen’s (1966) and Penman’s (1948). Spatial precipitation was

calculated by interpolating the average precipitation values for 60 years at 23 stations.

Soil moisture capacity and runoff were calculated using soil type and landuse of the grid.

It was concluded by the author that the water balance study performed was a crude

estimation. The spatial precipitation estimation was the most accurate available for the

SWFWMD area at the time. The ET estimates were also reported to be exceedingly superior to the values previously available. The runoff values used in the study was understood to be inadequate and it was recommended that analysis of runoff, using well levels, stream runoff, and other variables, was highly recommended. The soils data used in the study were regarded as an excellent starting point for future soils moisture

16 calculations. More information on groundwater inflow and outflow was required. Once these additional parameters were accurately obtained, a reliable estimate of the water balance was understood to be achieved.

Review of Spatial Water Balance Studies

While Thornthwaite and Mather’s approach serves as the theoretical basis for studying water balance on a watershed scale, researchers have noted a number of shortcomings in this approach and have called for changes in the original conceptual framework of the water balance model (Pascual Aguilar 1999; Shrestha 2003). This approach takes point data from water gauges and simply generalizes this up to the watershed level. In other words, this approach ignores the spatial variation of hydrological components across the watershed. In complex watersheds this generalized approach may lead to significant inaccuracies below the scale of the watershed. The parameters considered in computing water balance differ by region, spatial scale and purpose of study. A spatial water balance approach provides a more accurate method of computing water budget at a watershed scale. While there is work published on water balance and its components, fewer studies have been conducted to evaluate water budget spatially. Some of the studies using spatial water balance approach are briefly discussed below.

With the advancement in GIS technology and geostatistical tools, it has become possible to capture, visualize and compute the spatial nature of the hydrologic components of the water balance models. Alemaw and Chaoka (2003) have reported two types of spatially distributed models: one that divides the drainage basin into a regular grid at a specific resolution, and a second that is based on identifying as many as possible recognizable upstream catchments in a watershed. Pimenta (2000) developed a monthly

spatial water balance model (SWBM) using GIS for Cunhus basin in Portugal to identify

and group regions based on the number of months with water stress conditions.

Holdstock et al. (2000) developed a SWBM for Cape Fear River Basin in North Carolina

for evaluating current and future water uses as well as for future water resource decision

making. The model allows analysis of various what-if scenarios at a daily time step for

the 69-year period of study based on changes in withdrawals, discharges and agricultural

demands. Pascual Aguilar (1999) developed a SWBM for Rambla del Poyo, a catchment

in eastern Spain, to automate monthly ET and water balance calculations using raster

GIS. Alemaw and Chaoka (2003) have developed a continental scale distributed GIS-

based hydrological model for Southern Africa to create a GIS-based high resolution data

sets of monthly soil moisture (SM), evapotranspiration (ET) and grid runoff (ROF) in the

region. Regional variation of the mean annual SM, ET and generated ROF across the

Southern Africa has been mapped for the climatic period 1961-1990. Yu et al. (2000)

developed a daily time step SWBM for a small watershed (Darby Creek Watershed,

Ohio) in Pennsylvania. White et al (1996) used GIS modeling techniques to model daily

water balance in the Upper Mississippi River basin in order to study climatic and

hydrologic factors that led to the 1993 Midwest floods. Luijten et al. (2001) developed a daily time scale SWBM for Cabuyal River watershed in southwest Columbia for assessing water availability under different development scenarios to identify potential water security problems.

A spatial water balance approach has several benefits over the traditional water balance modeling approach: (a) it provides better water balance estimates as it accounts for spatial variability of hydrologic components (Pimenta 2000; Yu et. al.1998; Pascual

Aguilar 1999), (b) GIS can be used to link topography, landuse and spatially distributed

hydrological components (Shrestha 2003), (c) it simplifies calculations (Pascual Aguilar

1999), (d) it provides estimates of hydrologic components at ungauged points (Pimenta

2000), (e) it provides qualitative information about the space and time variability of all

the components that are not revealed in an aggregate model (Pimenta 2000), (f) it enables

the computation of water balances using complex spatial and time variant data (Pimenta

2000), (g) it can be applied to different spatial scales, (h) it helps to understand seasonal

dynamics of different components of the hydrologic cycle (Pascual Aguilar 1999), (i) it

facilitates ‘what-if” scenarios, including evaluating impacts of water allocation schemes,

interbasin transfer requests (Pimenta 2000; Holdstock et al. 2000), and climate change

(Pimenta 2000; Alemaw and Chaoka 2003), (j) it provides a spatial representation of the

variability of hydrologic components so that the trend surfaces can be visualized over

time in the form of maps and (k) it enables visualization of the water balance in the form

of maps (White et al. 1996).

Although spatial water balance approaches are becoming common and more and

more spatial hydrologic models are being developed these days, little work has been presented on this topic (Pascual Aguilar 1999). Also, different methodologies within the

spatial water balance modeling concept in capturing the spatial variability of each

hydrologic component exist. Chapter 4 discusses the spatial water balance methodology

developed in this research.

Summary

In this chapter, about Thornthwaite’s classical water balance approach and its

importance in modeling water resources in relation to climate were discussed. Some of

the studies on water balance done in Florida were reviewed and the fact that most of these

19 studies have adopted Thornthwaite’s water balance approach was highlighted. Also, that these studies were conducted at various spatial and temporal scales and for various research purposes ranging from water balances of wildland ecosystems for management of water resources in west-central Florida to assessing effects of developmental impacts on wetlands in central Florida. Studies on spatial water balance were also reviewed and the fact there is a dearth of literature available on spatial water balance studies was highlighted. It was pointed out that the classical water balance approach is known to have shortcomings related to the ‘point and average’ generalization of the hydrologic components to the watershed level. Towards the end of the chapter, studies on spatial water balance were reviewed and the benefits of spatial water balance approach over the traditional ‘point and average’ approach were summarized.

CHAPTER 3 STUDY AREA

Suwannee River Basin Characteristics

The study area incorporates all of the SRB that falls within Florida (Figure 3-1).

The SRB is 11,020 square miles1 (28,541 square kilometers) (Georgia River Network

2006) with 4,230 square miles area (Katz and DeHan 1996) (10,955 square kilometers) within the State of Florida (Bottcher and Hiscock 1996), which is 38% of the total SRB.

It covers about 7% of the total landmass of the state.

In this chapter hydrology, population, water use trends and demand, topography and landuse, and climate of the SRB are discussed. Because of paucity of literature specifically on the Florida part of the SRB, some of the characteristics of the SRWMD are assumed to be the same for SRB. These include topography, water use trends and demand, and climate.

Physical Characteristics and Hydrology

The hydrology of the SRB is driven by climate, and it is modified by the topography, physiography, geology, and land cover characteristics of the drainage area

(SRWMD Technical Report 2005). The SRB is composed of a rich variety of surface waters in the form of rivers and streams, springs, cypress ponds, swamps and estuaries. It is drained by four rivers -The Alapaha, Withlacoochee, Suwannee and Santa Fe. The significant natural feature of the SRB is the Suwannee River which is the second largest

1 Franklin et al. (1995) reported that the SRB encompasses 9,950 mi 2 (25,770 km 2) (10,000 mi 2: Raulston et al., 2000) in Florida and Georgia with approximately 57% (43%) of the basin is in Georgia (Florida).

20 21

river in the state in terms of flow (after the Apalachicola in northwest Florida). The river

is 235 miles long, 207 miles of which is in Florida. The river originates in the

Okefenokee Swamp in southern Georgia, and flows south across the Northern Highlands

into the Gulf Coastal Lowlands. The river (basin) discharges an average of 10,166 cfs-1

(10,500 cfs-1 : Raulston et al. 1998) into the Suwannee River estuary and the Gulf of

Mexico as mean daily discharge into the Gulf of Mexico at Wilcox gauging station which is the most downstream gauging station in the basin2. This discharge is equivalent to 14.8

inches (Franklin et al. 1995) which is about 28% of the 53.4 inches average annual

rainfall across the basin.

The two major tributaries, Alapaha and Withlacoochee, also originate in Georgia.

The Alapaha River meets the Suwannee River southwest of Jasper and the Withlacoochee

flows into the Suwannee River at Ellaville. The Santa Fe River joins Suwannee near

Branford. Figure 3-1 shows watersheds, rivers and county boundaries in the SRB. Table

3-1 shows the description of the Suwannee River and its major sub-basins (Franklin et al.

1995 and Berndt et al. 1996)

Table 3-1. Suwannee River and its major sub-basins SRB sub-basins/ Basin Area Total Length Florida Length Average Characteristics (sq. miles) (miles) (miles) Flow (cfs-1) Suwannee River* 9,950 235 206.7 10,540* Withlacoochee 2,360 120 30 1,714 River Alapaha River 1,840 130 22.6 1,674 Santa Fe River 1,360 79.9 79.9 1,608 * includes contribution from Withlacoochee, Alapaha and Santa Fe basins

2 97 percent of the basin drainage area is upstream of this gage (Figure 4-4)

There are over 150 known springs in the SRWMD, most of which are in the

Suwannee and Santa Fe River Basins. Of these 150 known springs in the SRWMD, 18

are classified as first magnitude springs, 16 of which are in the SRB.

The Florida part of the SRB is divided into five watersheds – Alapaha,

Withlacoochee, Upper Suwannee, Lower Suwannee and Santa Fe. The basin covers a

total of thirteen counties in part or whole (Figure 3-1). Appendix A gives details about

the main watersheds and counties in the SRB along with their areas and percent area they

cover within respective watersheds in the basin.

Figure 3-1. Suwannee River Basin study area with counties and watersheds

Population

Varying topography influences population distribution within the region. Of the total population of Florida in 2000 (15.98 million), 2% (319,600) resided in the

SRWMD (Marella 2004). Historically, the SRB has been largely rural, sparsely populated, and undeveloped (Katz and Raabe 2005). Population growth and development in the SRB have been steady since the 1960s. In 1990, population in the basin was

384,000 and in year 2000, it increased to 430,000 (Katz and Raabe 2005), an increase of

11.97% in a decade. Similar growth projections in population increase are indicated for areas in and around SRB in the coming decade (Katz and Raabe 2005). Most of the population growth and development has been in the rural unincorporated areas of

Alachua, Gilchrist and Columbia Counties and also in the higher, drier counties east of the Suwannee River concentrated around Lake City and Live Oak and along the northern and west sides of Gainesville. Other major population areas are Starke, Chiefland east of the river, and Madison and Perry west of the river (Figure 3-1). Floodplain management ordinances, landuse plans, and land acquisition plans at the state, regional and local levels have restricted growth and development along the region’s rivers, especially the

Suwannee River (Raulston et al. 1998).

Water Use Trends

Total water use in the SRWMD has not changed significantly in the last two decades. However, potable and irrigation water use has increased mainly due to population increase in the area. In 2000, SRWMD used 4% of the total fresh water withdrawn in Florida (Raulston et al. 1998).

Excluding power generation, fresh water withdrawals in all five water management districts have increased between 1975 and 2000 with the SRWMD having the largest percent increase in withdrawals between 1975 and 2000 (61%) (Marella 2004). This is a result of increase in irrigated acreage, population, and tourism. Figure 3-3 shows the trend for water withdrawals for the individual water use categories in the SRWMD.

9000

8000

7000 ) 6000

5000

4000

3000

2000 Water Use (mgal/day 1000

0 1975 1980 1985 1990 1995 2000 Florida Water Use 6772.75 6701.22 6313.34 7583.58 7229.92 8191.77 SRWMD Water Use 310.22 321.45 263.88 334.81 334.93 323.34 Years

Figure 3-2. Historical water withdrawal trends for Florida and SRWMD Sources: Marella 1995 pp. 41; Marella 1999 pp. 32; Marella 2004 pp.33, pp. 43.

100.00

50.00

0.00 2020 2050 1975 1980 1985 1990 1995 2000 Projected Projected Percent Fresh Water Withdrawls Water Fresh Percent Power % 55.90 54.05 25.69 33.06 34.44 32.03 Agriculture % 6.28 9.06 23.00 25.38 25.17 30.05 33.74 34.47 Commercial/Industrial/Mining % 31.81 29.30 37.46 29.93 29.24 26.84 57.00 56.41 Domestic Self Supply % 2.98 4.15 9.22 7.39 6.89 6.20 4.65 4.60 Public Self Supply % 3.04 3.44 4.65 4.24 4.27 4.88 4.61 4.52 Years

Note: 1995, 2000, 2020 and 2050 agriculture values include 1, 1.43, 2.3 and 3.6 mgald-1 of Recreation Irrigation (RI) water withdrawals. For 2020 and 2050, values for Power are included in commercial/Industrial/Mining category.

Figure 3-3. Percentage of fresh water withdrawals by category in SRWMD, 1975-2050 Sources: Marella, 1995 pp 41; Marella 1999 pp 32; Marella 2004 pp 33, SRWMD Technical Report 2005 pp 2-22.

Total water use in 2000 for counties entirely or partly within the SRB in Florida was 259 mgald-1 which is 82% of the 314 mgald-1 total SRWMD water use. Total water use in the SRWMD is projected to be approximately 547 mgald-1 in 2020 and 895

mgald-1 in 2050 (SRWMD Technical Report 2005) which is 41 and 64 percent increase from 2000 respectively.

Seasonal variations in water withdrawals mostly affect agricultural irrigation and public supply. This variation in water withdrawal is mainly a result of seasonal differences in residential demand for lawn irrigation and tourism (Marella 1988).

Topography and Landuse

The basin topography overall does not have any significant undulations. Elevations range from at or near sea level in the coastal swamps, lowlands, and river valleys to just

200 feet above MSL in the Northern Highlands and Tallahassee Hills (Raulston et al.

1998).

The SRB is a mixture of subtropical forests, wetlands, springs, black water rivers, and estuarine habitats. The SRB hosts agriculture, commercial and recreational fisheries, clam farming, and ecotourism (Katz and Raabe 2005). Until the 20th century, towns

developed along the Suwannee River (Raulston et al. 1998). West of the Suwannee River, the predominant land uses are silviculture and agriculture with large plantations of pine.

Vast tracts of timber are also found in the wet flat woods to the east of Alapaha River and upper most Suwannee River. On the eastern side of the river, silviculture and agriculture predominate but still urbanized land is markedly greater than the western side of the

Suwannee River. The region is quite rural in nature with low population density. The two largest industries for the region are forestry and phosphate mining even though agriculture is understood to be the region’s economic base. The region has large corporate dairies and irrigated row-crop operations, as well as small farms that have row crops along with livestock. Corn, tobacco, soybeans, peanuts and vegetables are the primary crops. Within the Suwannee River Water Management District, Suwannee,

Lafayette and Gilchrist counties have intensive agribusiness operations, primarily dairies

and poultry. Aquaculture is also increasing along the coast, particularly in Levy County and the Cedar Key area (Raulston et al. 1998).

Climate

Precipitation and ET are the climatic features most significant to long-term hydrologic conditions in the SRB (SRWMD Technical Report 2005). The climate of the region is humid subtropical with average annual temperatures ranging from 68oF in

Madison County to 72oF at Cedar key in Levy County (Raulston et al. 1998). Although

freezing temperatures occur during the winter, temperatures generally range from 40-

50oF. Average annual rainfall in the SRB is approximately 53.4 inches (NOAA 2002) but

varies spatially from 46 inches in the upper basin to over 60 inches near the Gulf coast

(SRWMD Technical Report 2005) with 50 percent of the rainfall occurring during the summer months (June through September) (Raulston et al. 1998). Summer rainfall is associated with localized thunderstorm activity with both summer and spring rainfall being unevenly distributed. Spring and summer seasons are also known for occurrences of droughts of varying severity. However, in winter, fronts bring rain and cooler temperatures. These frontal rains are more evenly distributed spatially and are of longer

duration than summer rainfall (Raulston et al. 1998). Also these winter rains are crucial in

recharging the ground water, as evaporation and plant transpiration are significantly

lower during this time of the year.

Rates of ET in the region have been estimated with a variety of direct

measurements and/or computational methods (SRWMD Technical Report 2005). The

average annual ET pattern estimated from computed reference ET for Gainesville (Jacobs

and Dukes 2004) multiplied by monthly crop coefficients for pasture (Jacobs and Satti

2001) resulted in mean annual ET of 40.8 inches ( 39 inches: SRWMD Technical Report

2005), with the largest mean monthly value of 5.20 inches in June and a minimum of 1.3

inches in December.

Summary

In this chapter the characteristics of the SRB were discussed. The basin’s

topography is a mixture of forests, wetlands, and springs with agriculture, forestry and phosphate mining being the most important economic bases. The climate of the region is humid subtropical. The basin is drained by four rivers, Suwannee River being the most significant natural feature of the region. It is the second largest river in the state. The

Florida part of the basin is divided into five watersheds- Alapaha, Withlacoochee, Upper

Suwannee, Lower Suwannee and Santa Fe. Population in the region has remained steady

with about 12% increase in the decade 1990-2000 with similar projections for future.

Total water use in the SRB in 2000 was 82% of the total SRWMD water use which is

projected to increase by 41 and 64 percent 2020 and 2050 respectively. The SRB receives

approximately 53.4 inches of average annual precipitation. Of this, 14.8 inches runs off to

Gulf of Mexico and about 39 inches is lost as ET or consumptively used.

CHAPTER 4 SPATIAL APPROACH TO MODELING AND ANALYZING THE WATER BALANCE OF A RIVER BASIN

In this chapter the spatial water balance methodology formulated to model water balance in the SRB is described. Basic details about the parent datasets used in the study, such as the various datasets themselves, their sources and number of stations of each is given. An overview on the foundation of the spatial water balance modeling approach is given along with detailed description of the geo-processing of each of the water balance components. The final spatial water balance equation is presented. Finally, assumptions, limitations and sources of error are discussed.

Hydrologic and Weather Data

The data used in the study were spatially distributed monthly precipitation, streamflow, solar radiation (SRAD), monthly average temperature (Tm) landuse/land cover (LULC), and water withdrawal.

Monthly precipitation data were obtained from the SRWMD. Data from 21 precipitation stations in the SRB were used. GIS coverage of the precipitation stations was acquired from the SRWMD. Streamflow (used interchangeably as discharge also) data were obtained from the USGS website. Data from a total of 15 river gauging stations in the basin (FL and GA parts combined) were used in the study. GIS coverage of the same was also obtained from the SRWMD.

To calculate ET, weather data and landcover (LC) data were used. Weather data used were SRAD and monthly average temperature. Daily SRAD and monthly maximum

28 29

and minimum temperature at the 14 weather stations (referred to as ET stations in this

study hereafter) were obtained from the Southeastern Climate Consortium (SECC).

LULC datasets for four years (1974, 1988, 1995 and 2003) were obtained from Florida

Geographic Data Library (FGDL), Department of Environmental Protection (DEP) website, SRWMD and Florida Fish and Wildlife Commission (FWC) respectively. LULC data were used to compute ET losses from respective LC categories.

Water use and withdrawal data from USGS were used for calculating CWU. Water

withdrawal for individual water use categories: public self supply (PSS), domestic self supply (DSS), commercial/industrial/mining(C/I/M), power generation (P), agriculture

(Ag) and recreational irrigation (RI) from the 13 counties in the SRB were used. The quality of the water withdrawal data ranges from total monthly water withdrawal for individual water use category to average of daily water withdrawals. Water use and ultimately CWU were calculated for the five watersheds in the SRB using the data from the 13 counties. Unfortunately, a complete and consistent set of records for water use data was not available.

As expected, the input data available were in different spatial and temporal scales and thus required preprocessing for use in the research. To accomplish a spatial water balance using these hydrologic components, the datasets were processed in a GIS.

Detailed methodology on the geo-processing of each hydrologic component is described further in the chapter.

A Spatial Water Balance Modeling Approach

A spatial water balance methodology was developed which utilizes a GIS to model space and time varying components of the hydrologic cycle to calculate the water balance over the SRB. Precipitation, streamflow, ET, and CWU were used as the components in

this approach. Figure 4-1 shows a schematic diagram of the spatial water balance

modeling approach developed for the study.

Figure 4-1. Schematic diagram of methodology used for calculating spatial water balance

The methodology developed was needed to analyze hydrologic response to climate variability. The model calculates water balance and individual model components in million gallons per month (mgalm-1).

The water balance equation developed in this study is based on the conservation of

mass theory described by Thornthwaite (Thornthwaite 1948; Mather 1978; 1979) in his

water balance model concept. The equation incorporates the characteristics of the river

basin regional hydrology and accounts for sources and uses/losses of water to the system.

The equation forms the basis of the spatial water balance model and is expressed as

P − Q − ET − CWU − ∆S = 0 (4-1) 31

∆S = P − Q − ET − CWU (4-2)

where

∆S = net change in water storage (residual) Q = streamflow P = precipitation ET = evapotranspiration CWU = consumptive water use

Florida predominantly has sandy soils and most of the water received in the form of precipitation either leaches down to the aquifer or runs off as discharge into rivers.

However, some water remains in the root zone, called soil water storage. Water in the root zone (assumed to be 20 cm in depth in this study) is the water volume held in this layer and is negligible (equal to zero) compared to the total water volume in the regional water system (pers. comm. Jones 2003). ET is the combined loss of water from the hydrologic cycle in the form of transpiration from plants and evaporation from soil surface. In this study, actual ET (AET) was used in the estimation of water balance. AET is the amount of water that is actually removed from a surface due to the processes of evaporation and transpiration from each respective LC type. CWU is generally defined as that part of water withdrawn that is lost through evaporation, transpiration, incorporation into products or crops, consumed by humans or livestock, or otherwise removed from the intermediate water environment. It is also called water consumed or water depleted

(Marella 2004). Additionally, any water withdrawn and transferred out of a county or hydrologic basin for use is considered 100 percent consumptively used in that county or hydrologic basin (Marella 1995). Although ET is accounted for in CWU (by definition), in this research, it is estimated separately as an individual component. The reason for this 32 is that USGS only calculates CWU percentages for the water it pumps for various water uses. This does not account for water lost through ET from various LC over the basin.

The residual (∆S) in equation (2) is water that actually leaches and makes up the water volume in the aquifer. In hydrologic terms, water storage in the aquifer is the net result of water supply and use processes. I refer to this volume as the water balance which is actually assumed to be available water for this study.

Geo-processing of Water Balance Components

Precipitation

Monthly precipitation records were needed. There are a total of 117 precipitation gauging stations in the SRB. Not all stations are currently active or have significant records during the 30 year period. 21 precipitation gauging stations with significant1 records were used. The names of the precipitation gauging stations used and the percentage of missing data at each station between October 1974 and September 2004 is listed in Appendix B. Figure 4-2 shows the geographic distribution of precipitation gauging stations used.

Monthly precipitation values (inches/month) on the gauging stations were stored in a GIS point shapefile. Monthly precipitation values at each gauging station were then interpolated at a default cell size of 500 sqm using Inverse Distance Weighted (IDW) method to create a raster surface of precipitation values over the entire SRB. A rectangular mask covering the SRB was used as analysis extent. Stations with missing monthly precipitation values were not used in the interpolation for that month (using them would have resulted in wrong representation and estimation of precipitation over

1 Not more than 50 percent missing data calculated on a monthly basis for the duration of the study.

33 the basin as a missing value in a GIS is entered as ‘zero’). The raster surface was then converted into a vector layer with polygons so that it could be clipped with the SRB boundary. The result is a collection of polygons each having a precipitation value in inches per month. Precipitation was then converted to meters and multiplied by its respective area (square meter) to get the total precipitation amount (volume in cubic meter) falling on that polygon each month. Precipitation volumes were then converted to million gallons (mgal). The process also resulted in the production of monthly precipitation maps showing spatial variability of precipitation.

Figure 4-2. Location of precipitation stations used in the study

Figure 4-3 illustrates the geo-processing methodology described above. The conversions are shown in Appendix C. Precipitation values of all polygons in the basin were then summed to get the total precipitation in the SRB. This can be mathematically expressed as

(a) (b)

n (e) Pw = ∑ Pi * Ai (f) i Figure 4-3. Geo-processing methodology for calculating precipitation spatially2 a) Stations with precipitation values b) Interpolated surface (GRID) of monthly c) Surface converted to polygons (vector) d) Clipped to SRB e) Volume of water (m3) = Area (A)*Precipitation f) Volume of water (mgal)

2 Figure 4-3 (e) and figure 4-3 (f) differ in their color shades as conversion of water volume from m3 to mgal changes the class (color shade representing a range of water volume) of water volume and hence changes the color of the polygons.

n Pw = ∑ Pi * Ai (4-3) i=1 where

Pw = water volume from precipitation Pi = precipitation falling on polygon i Ai = area of polygon i n = number of polygons

Streamflow

Streamflow was calculated spatially over the basin in a three step process:

1. ‘Specific Discharge’ (SD) for individual watersheds in the SRB was calculated 2. SD was assigned to the centroids of Florida part of the watersheds 3. Streamflow values at the centroids were interpolated over the SRB

In surface water hydrology, SD is defined as discharge per unit area of an upstream watershed. SD is calculated because the discharge value for gauging stations downstream in a stream network is cumulative of the drainage areas of all gauging stations upstream.

To calculate SD, there has to be a representative ‘inflow’ and a representative ‘outflow’ streamflow station to be able to calculate discharge per unit area between these two points. SD is calculated by subtracting the discharge value of the inflow station from the discharge value of the outflow station and dividing that value by the difference in drainage basin areas of the gauging stations. Mathematically, specific discharge can be expressed as

Q = (O − I) / AO − AI (4-4) where

Q = specific discharge O = discharge at outflow gauging station I = discharge at inflow gauging station AO = drainage area of outflow gauging station AI = drainage area of inflow gauging station

To calculate SD for each of the five watersheds in the SRB, 10 gauging stations were required to act as inflow and outflow stations. However, because of data limitations, in the Florida part of SRB, only six stations met this requirement. This resulted in some watersheds not having a representative ‘inflow’ and ‘outflow’ gauging station. Therefore, additional stations from Georgia had to be used to complement the Florida stations. In the entire Alapaha watershed (FL and GA together), only one station was available which had significant data for the 30 year period. The station existed in Georgia part of the

SRB. The station was used as both ‘inflow’ and ‘outflow’ gauging station. Specific discharge was calculated by dividing the discharge values at the station by the drainage basin area of the station, which were then assigned to the centroid of the watershed to be used for interpolating discharge over the entire SRB. Therefore in all, 9 stations were used as inflow and outflow stations for the watersheds, Alapaha having only one.

These 9 inflow/outflow stations also had some missing data. To infill the missing data, monthly streamflow records of additional 6 USGS gauging stations in the SRB

(both FL and GA part) were identified which had streamflow records for a significant period of time during the period of study. Out of these 15 stations, 10 were in the Florida part of SRB and 5 existed in the Georgia part of the basin. Figure 4-4 shows the geographic distribution of streamflow gauging stations used in this research. Monthly streamflow values of the 15 stations were interpolated using IDW method using a default cell size of 1000 sq m. Missing values at 9 stations were in-filled using streamflow values obtained by interpolation. Appendix D lists the names of the streamflow gauging stations used in the study along with the percent missing data at each station. Table 4-1 lists the stations used as representative ‘inflow’ and ‘outflow’ stations for calculating SD for

37 respective watersheds along with their drainage basin areas. Drainage basin areas were extracted from the USGS streamflow dataset (National Water Information System: Web

Interface 2004).

Figure 4-4. Location of streamflow gauging stations used in the study

Table 4-1. Inflow and outflow streamflow stations with their respective drainage areas Watershed Inflow Station IS Outflow Station OS Resultant (IS) Drainage (OS) Drainage Drainage Area (sq. Area (sq. Area (sq. miles) miles) miles) Withlacoochee Withlacoochee 502 Withlacoochee 2120 1618 North river at river near McMillan Pinetta, FL Road near Bemiss, GA Alapaha Alapaha River 1400 Alapaha River at 1400 - at Statenville, Statenville, GA GA

Table 4-1. continued Watershed Inflow Station IS Outflow Station OS Resultant (IS) Drainage (OS) Drainage Drainage Area (sq. Area (sq. Area (sq. miles) miles) miles) Santa Fe Santa Fe River 94.9 Santa Fe river 1017.9 922.1 near Fort near Graham White Lower Suwannee 6970 Suwannee river 9640 1370 Suwannee river at at Wilcox Ellaville

White et al. (1996) has presented three possible scenarios of gauging station locations in a watershed (Figure 4-5) and how their data can be used to represent ‘inflow’ and ‘outflow’ from a watershed. In addition to these, a situation where only one station is located in the middle of the watershed (Figure 4-5D) was encountered in this study.

Scenario in Figure 4-5B represents all watersheds besides Alapaha. Alapaha was represented by Figure 4-5D. Figure 4-6 shows the geographic distribution of inflow and outflow stations identified. Figure 4-7 shows respective drainage areas of each inflow and outflow station.

A B C D Gauge Station

Figure 4-5. Possible scenarios of gauging station location within a watershed (adapted from White et al. 1996) .

Figure 4-6. Location of inflow and outflow streamflow gauging stations used in the study

a) b)

c) d)

– Outflow stations

– Inflow stations

– Inflow station drainage basin

– Outflow station drainage basin

Figure 4-7. Drainage basins of inflow and outflow stations of respective watersheds, a) Withlacoochee drainage basin, b) Alapaha drainage basin, c) Upper Suwannee drainage basin, d) Santa Fe drainage basin, e) Lower Suwannee drainage basin

SD values were then assigned to the centroid of each watershed. In hydrologic terms, the centroid (of entire cross section) of flow is the vertical in the stream cross section at which the discharge is equal on both sides (National Water Information

System: Web Interface 2004). Centroid is used to represent a polygon as a point. For each watershed, a centroid was calculated in GIS. Figure 4-8 shows the geographic distribution of centroids in their respective watersheds.

Figure 4-8. Location of centroids of respective watersheds

To calculate streamflow at each point in a watershed, SD assigned to the centroids

(Appendix E) of all five watersheds were interpolated using the IDW interpolation method at a default cell size of 500 sq m. The SD values were converted to meters

(streamflow records were originally available in cubic feet per second). This resulted in a

42 raster surface of streamflow values over the entire SRB. Further processing to obtain a discharge values for the entire SRB was similar as outlined in the precipitation section.

The final streamflow values calculated for the SRB can be mathematically expressed as

n Qw = ∑Qi * Ai (4-5) i=1 where

Qw = water volume from streamflow Qi = streamflow generated on polygon i Ai = area of polygon i n = number of polygons

Figure 4-9 summarizes the methodology adopted in calculating streamflow spatially over the basin.

Evapotranspiration (ET)

Actual evapotranspiration (AET) for the study was derived from potential evapotranspiration (PET) and was calculated spatially over the entire SRB. This was a four-step process:

1. PET at the ET stations was computed. 2. PET values at the weather stations were then interpolated over the entire SRB. 3. Monthly PET raster surfaces were then clipped out using the SRB boundary and the LC classes in the region. 4. PET was then converted to AET by applying crop coefficients for respective LULC classes in the SRB.

Monthly PET was estimated from SRAD and mean monthly temperature and is defined as the water loss from a large, homogeneous, vegetation-covered area that never lacks water (Thornthwaite 1948; Mather 1978). Thus PET represents the climatic demand for water relative to the available energy.

PET was calculated using the Stephens-Stewart method (1963) which uses an ET estimation method based on SRAD and mean monthly temperature for Florida

(a) (b) (c) (d) 43

n (e) (f) (g) Qw = ∑Qi * Ai (h) i

Figure 4-9. Geo-processing methodology for calculating streamflow spatially a) Stations with streamflow values (cfs) b) Inflow/Outflow gauging stations c) Centroids of watersheds d) Interpolated 3 surface (GRID) of monthly SD e) Surface converted to polygons (vector) f) Clipped to SRB g) Streamflow (Qw) (m ) = Area (A)*Streamflow (Q) h) Volume of water (mgal)

44 conditions. The method was calibrated against a 30 months ET study of St. Augustine grass at Fort Lauderdale, Florida. The Stephens-Stewart ET equation is expressed as

ETp = 0.01476(Tm +4.905)MRs / λ (4-6) where

ETp = monthly potential ET in mm o Tm = monthly mean temperature in C 2 MRs = monthly solar radiation in cal/cm , and 2 . λ = latent heat of vaporization of water, 59.59 – 0.055 Tm , cal/cm mm.

Weather data in the form of daily SRAD (MJsqm-1month-1) and monthly maximum and minimum temperatures ( o F) for 14 ET stations in and around the research area were obtained from SECC. Appendix F lists the names of the ET stations used in the study along with the percent missing data at each station. Figure 4-10 shows the geographic distribution of ET stations.

Figure 4-10. Location of ET stations used in the study 45

Daily values for maximum and minimum temperature maintained by the National

Weather Service's Cooperative Observer Network (NCDC TD 3200 1995) were used to calculate mean monthly temperature (Tm) by averaging the maximum and minimum monthly temperatures. Missing temperature data were in-filled using DSSAT generated daily temperature values (computed monthly). Daily SRAD data used to get the total monthly solar radiation were generated by DSSAT model. Both SRAD and Tm were appropriately converted to the units required in the Stephen-Stewart equation to compute monthly PET. PET values at the ET stations were then interpolated over the entire SRB.

The interpolated ET surfaces with monthly PET values were then clipped out with SRB study area. PET was converted to AET by applying crop coefficients over the LULC classes. LULC data (GIS layers) are available for four years for the duration of the study

-1974, 1988, 1995 and 2003. Appendix G (Table G-1) gives details of the LULC datasets used in the study.

Among the four LULC datasets, LC classes varied in number. To standardize and to reclassify the LC classes, the Florida Landuse Cover and Forms Classification System

(FLUCCS) (Florida Department of Transportation 1999) was adopted. FLUCCS Level I classification system has 9 classes: Agriculture, Barren land, Rangeland, Transportation,

Communications and Utilities (TCU), Upland Forest, Urban and Built-up land, Water,

Wetlands, and Special Classification. Each dataset was reclassified based on FLUCCS

Level I 3 classification system. Datasets for years 1974, 1988 and 1995 each had eight LC classes whereas the datasets for year 2003 had only seven LC classes. Appendix G (Table

3 This class of data is very general in nature. It can be obtained from remote sensing satellite imagery with supplemental information. Level I would normally be used for very large areas, statewide or larger, mapped typically at a scale of 1:1,000,000 or 1:500,000. At these scales, one inch equals 16 miles (one centimeter per ten kilometers) and one inch equals eight miles (one centimeter per five kilometers) respectively.

G-2) lists the LC data years and the FLUCCS classes in each year along with areas of respective LC classes. Figure G-1 (Appendix G) compares trends of areas (change in) of each LC class over years. Figure 4-11 presents a graphical representation of the areas of

FLUCCS classes in each year. The seven common classes in all years were Agricultural land, Barren land, Upland Forest, Rangeland, Urban and Built-up, Water and Wetlands.

In the year 1974, ‘Abandoned Land’ existed as separate (eighth) LC class where as in the years 1988 and 1995 TCU existed as a separate (eighth) LC class. In the year 2003, TCU was included along with Urban and Built-up LC class in the parent dataset and hence there were only seven LC classes. Figure 4-12 shows the LCs of the four years based on

FLUCCS.

To calculate AET, the first step was to clip out each monthly PET interpolated surface (already converted to a vector layer for SRB as described in precipitation section:

Figure: 4-3) using the LC classes mentioned above (Figure 4-13).

Figure 4-11. FLUCCS LC and their areas in various years

2003

1974 1988

2003 1995

Figure 4-12. LULC maps for 1974, 1988, 1995 and 2003 based on FLUCCS

Figure 4-13. Illustration of 7 FLUCCS classes with respective ET surfaces and their crop coefficients

Blaney-Criddle crop coefficients (Blaney and Criddle 1962) were then applied to

PET of the clipped out area of each LC class. AET values of all the polygons in the basin

were then summed to get the total AET over a LC class in the SRB. To calculate AET,

during the years 1974-1987, LC data for the year 1974 were used, for years 1988-1994,

1988 LC data were used, for years 1995-2002, 1995 LC data were used and for years

2003-2004, 2003 LC data were used. The process was repeated using the appropriate LC

layer for each month over 30 year period. Total AET can be mathematically expressed as

n AETw = ∑ PETl * Kcl * Al (4-7) l=1−7 where

AETw = water volume from actual evapotranspiration (mgal) PETi = potential evapotranspiration loss from polygon i (mm) Kc = crop coefficient Ai = area of polygon i (square meter) 7 = number of LC classes

The coefficients used for the above mentioned seven classes are given in Table 4-2.

A detailed list of Blaney-Criddle crop coefficients for Level II 4 FLUCCS classes is given

in Appendix H.

Table 4-2. Blaney-Criddle crop coefficients for level I LC classes S.No. LC Class Crop Coefficient 1 Agriculture 0.73 2 Barren land 0.75 3 Forest 0.68 4 Rangeland 1.0 5 Transportation, Communication and Utilities 0.75 6 Urban and Built Up 0.75 7 Water 1.0 8 Wetlands 0.8 Source: Blaney and Criddle 1962

4 This class of data is more specific than Level I. Level II data is normally obtained from high altitude imagery (40,000 to 60,000 feet) supplemented by satellite imagery and other materials, such as topographic maps. Mapping typically might be at a scale of 1:100,000 or one inch equals 8,333 feet (one centimeter per one kilometer). 50

In the year 1974, ‘Abandoned Land’ LC class accounted for a mere 0.01 percent of the area of the SRB and also no crop coefficient exists for this LC class and hence it was not included in the AET calculations. TCU in the years 1988 (.18% of SRB) and 1995

(.76% of SRB), was merged with Urban and Built-up LC class as both have same crop coefficient (0.75). This resulted in ensuring seven FLUCCS classes across four LC data which were easy to compare. Figure 4-14 shows a process diagram of the methodology adopted for calculating AET spatially.

Consumptive Water Use

Water withdrawal and use data from USGS were used to compute CWU. The data were available in various temporal and spatial scales over the 30 year period. For some years data were available as average daily usage (mgald-1) for particular water use category, whereas for some other categories actual monthly values (mgalm-1) were given.

Data were available at the county, water management district and watershed scales.

However, data in all these spatial scales were not available in the same temporal scale and for each separate water use category. USGS categorizes water withdrawal data according to six main water uses-PSS, DSS, agriculture, RI, C/I/M, and power. A detailed description of these water use categories is available in USGS water withdrawal and use reports (Marella 1988, 1999, 2004).

Water withdrawal data at the county scale for each of the five water use categories separately were used in this research. The first step was to develop a database of historical water withdrawal for the 13 counties (that fall wholly or partially in the SRB) for each of the five water use categories mentioned above. This monthly water withdrawal database was developed for the five watersheds (Alapaha, Withlacoochee,

(a) (b) (c) (d) 51

7 Land cover 7 classes AETw = PETi * Ai *Kci (e) (f) (g) ∑ (h) i=1 Figure 4-14. Geo-processing methodology for calculating ET spatially a) ET Stations b) Interpolated surface (GRID) of monthly PET c) Surface converted to polygons (vector) d) Clipped to SRB e) LULC map f) LULC classes g) LULC PET surfaces h) AET water volume (mgal)

Santa Fe, Upper Suwannee and Lower Suwannee) in the SRB for the years 1975-2004

separately.

The methodology adopted for calculating the CWU for SRB is given below.

1. Historical monthly water withdrawal data for individual water use categories at the county scale was used. For counties and categories where data were available as average daily value (mgald-1), it was converted to monthly water withdrawal value (mgalm-1) by multiplying by number of days in a month keeping leap year considerations in mind.

2. Part/portion of the county area falling in a particular watershed was calculated in GIS (Figure 4-15 c).

3. Monthly water withdrawal for each water use category for a county were multiplied by the percent area of the county (counties) falling in a watershed to calculate water withdrawal for a water use category of a county in a particular watershed (Table 4- 3).

4. Annual consumptive water use percentages (monthly percentages not available) for each individual water use category for a particular county were applied to the portion of the water withdrawal amount of a county (counties) in a watershed (as calculated in Step 3) to calculate consumptive water use (CWU) or (percent water consumed) for each water use category from the portion of the county falling in a watershed.

5. Consumptive water use amounts of each water use category from the portion of all the counties falling in a watershed were summed to get total CWU of a particular watershed. This can be mathematically expressed as 6 (∑ CWU j ) (4-8) j =1 6. CWU for the SRB is calculated by summing up CWU from all five watersheds: The Withlacoochee, Alapaha, Upper Suwannee, Santa Fe and Lower Suwannee.

Water withdrawal estimates are published at an interval of five years and so

estimates were only available up until December 2000 during the time of this research.

CWU estimates for the years 2001 to 2004 were obtained by forecasting the CWU values calculated for years 1995 to 2000 assuming the data reporting to be improved and based

on the current data collection method relative to earlier years. Forecasted values based 53 on CWU estimates from 1975 till 2000 did not give correct estimates because water withdrawal data was not rich during the 1970s and mid 80s.

CWU percentages for each water use category for each county at an interval of five years starting 1975 till 2000 were obtained from the USGS Florida office (Marella, 2006, written communication). Some CWU percentages were only available for Florida.

Consumptive water use percentages are shown in Appendix I. Figure 4-15 summarizes the methodology for calculating CWU.

Table 4-3. Watersheds and counties, their areas and percent areas in watersheds in the SRB S.No. Name of Area of Percent Name of Area of Percent watershed watershed area of county county area of (Acres) SRB (Acres) county in a watershed 1 Alapaha 69102 2 Hamilton 69102 100 2 Withlacoochee 173904 6 Hamilton 46555 27 Madison 127349 73 3 Upper 579082 21 Hamilton 216458 37 Suwannee Madison 3 0 Suwannee 66929 11 Columbia 267561 46 Baker 28130 5 4 Lower 982123 36 Madison 70973 7 Suwannee Suwannee 356767 36 Columbia 4293 0.44 Lafayette 180136 18 Dixie 121353 12 Gilchrist 109892 11 Levy 137565 14 Taylor 1143 0.12 5 Santa Fe 873414 32 Columbia 224141 26 Suwannee 19127 2 Union 152055 17 Bradford 187423 21 Alachua 174901 20 Gilchrist 85451 10 Baker 22360 2

a) a watershed b) water withdrawal data at c) percentage of counties county scale in a watershed

Individual water use categories Example: Sum of 73.23% of IWUCs (IWUC) for Madison + 26.77% of IWUCs for i. Public Self Supply (PSS) Hamilton = Total water withdrawal ii. Domestic Self Supply for Withlacoochee watershed (DSS) d) % of water iii. Agriculture Irrigation (Ag) withdrawal of iv. Recreation Irrigation (RI) individual v. Commercial/Industrial/ water use Mining (CIM) categories vi. Power (P) (IWUC) at county level

e) CWU percentages of f) Sum of CWU of the g) Sum of CWU of the h) Sum of CWU of the 5 IWUCs applied to IWUCs of a county is counties in a watershed watersheds is the CWU respective percentage the CWU of that is the CWU of that of SRB of water withdrawal county watershed from IWUC for each county in a watershed

Figure 4-15. Geo-processing methodology to calculate CWU

Spatial Water Balance Equation

Incorporating each of the components of the water balance equation described

earlier in the chapter, the final spatial water balance equation can be mathematically

expressed as

n n n 6 ( ∆St ) = (∑ Pi * Ai) − (∑Qi * Ai) − ( ∑ PETl * Kcl * Al ) − (∑CWU j ) (4-9) i=1 i=1 l=1−7 j=1

where

∆St = Change in water storage over time l = 7 FLUCCS LC classes j = USGS individual water use categories i,n = number of polygons

Methods for Analyzing Results

The spatial water balance and its individual components were calculated for a period of 30 (1974/75-2003/04) climate years (October 1974 - September 2004) with a 55 monthly time step. A climate year is defined as one starting in October of the current year and ending in September of the following year.

Understanding ENSO patterns of water resources at drainage basin scale has application for Florida’s economy. The water balance and its hydrologic components were analyzed for effects of climate variability based on El-Niño Southern Oscillation

(ENSO). The duration of the study covers numerous sequences of climatic situations

(‘wet/warm- El Niño’, ‘dry/cold- La Niña’ and ‘neutral’) to be analyzed and evaluated. In this study Japan Metrological Agency’s (JMA 2005) index was used for defining ENSO phases: El Niño, La Niña and Neutral. JMA index is based on sea surface temperature

(SST) anomalies (departures from normals) averaged over an area of the eastern Pacific between 4°N and 4°S and between 90°W and 150°W (Bove 1998; O’Brien et al. 1999).

An El Niño is identified when the five-month running average of SST anomalies is greater than 0.5° C (0.9° F) for at least six consecutive months. The event must begin before September and include October, November, and December (Bove 1998; O’Brien

1999). La Niña on the other hand occurs when the JMA SST index is 0.5°C below average for six consecutive months, starting before September and running through

December (Bove 1998; O’Brien 1999). Years which do not meet the definition for either

El Niño or La Niña are considered neutral. Table 4-4 shows the classification of each type of climate year during the period of the study based on JMA and number of occurrences of each.

The results were analyzed on mean monthly, mean seasonal and annual basis, both without and with respect to ENSO. Seasonal analysis is presented based on four seasons: fall, winter, spring and summer (SECC 2006). 30 year mean of seasonal totals (months of

56 each season) were taken. Trends were drawn on a yearly basis by summing together monthly precipitation values of respective seasons. Table 4-5 shows the four seasons along with respective months.

Table 4-4. List of ENSO year type, based on the JMA-SST Index S.No. ENSO Phase Years* Number of Occurrences 1 El Niño 1976/77, 1982/83, 1986/87, 1987/88, 7 1991/92, 1997/98, 2002/03 2 La Niña 1974/75, 1975/76, 1988/89, 1998/99, 5 1999/2000 3 Neutral 1977/78, 1978/79, 1979/80, 1980/81, 18 1981/82, 1983/84, 1984/85, 1985/86, 1989/90, 1990/91, 1992/93, 1993/94, 1994/95, 1995/96, 1996/97, 2000/01, 2001/02, 2003/04 * Years listed in the year of the October when ENSO phase began Source: Hanley 2003

Table 4-5. Seasonal classification Seasons Months Fall October, November, December Winter January, February, March Spring April, May, June Summer July, August, September

Analysis of variance (ANOVA, Steel and Torrie 1980) was applied to test hypothesized influences of ENSO phases for each of the hydrologic components and the water balance. Precipitation, streamflow, ET, CWU and water balance were examined to test the hypothesis that influence of ENSO on each one of these changes with time of the year. A significant response of ENSO on months or seasons was inferred only if the

ENSO phases differed significantly (P<0.05). Tukey-Kramer HSD test identified which

ENSO phases differed significantly in their effects on the hydrologic components or water balance.

Assumptions, Limitations and Sources of Error in the Modeling Approach

As with most modeling approaches, some assumptions were made in this study.

There were also some data limitations for the four components of the hydrologic model.

These assumptions and data limitations are expected to introduce some errors in the final output of the individual component results as well as the final water balance. Because modeling is an activity that mimics a natural system, errors are bound to occur.

Limitations related to missing data, data reliability and use of averages are understood to have an impact on the results of the modeling approach. Assumptions and limitations related to each component of the hydrologic model are discussed here separately.

In the precipitation dataset there were no major limitations. However, out of the 21 stations used for the study, four stations (Appendix B) had missing data between 40-50% for the duration of the study.

Streamflow values from gauging stations were assumed to have no interchange between surface water and ground water, which usually occurs in the entire study area.

This might not reflect an accurate estimate of discharge volume at a particular gauging station. This assumption is likely to cause some errors in the calculation of the accurate streamflow values over the basin and hence over the resultant water balance. Limitations related to availability of gauging stations with significant data for the duration of the study, which could be used as ‘inflow’ and ‘outflow’ stations were encountered. Out of five watersheds, three did not have stations with significant data records to be used as inflow/outflow stations for calculating the SD for the watershed. This problem was overcome by using three stations from Georgia to be used as inflow stations. To infill data for the three stations, a total of five stations in Georgia were selected. Two out of the five stations, had missing records between 40-50%. However, even using gauging

58 stations from Georgia did not ensure having an inflow/outflow station for Alapaha watershed which only had one station in the entire watershed (both GA and FL parts) and so the station had to be used both as inflow and outflow.

Assumptions and limitations related to CWU calculations were primarily related to limitations with the parent water withdrawal data. Limitations were attributed to spatial and temporal scales of the water withdrawal values. The issue of dealing with political boundaries (data available at county level) and hydrological boundaries in calculating water withdrawal and eventually CWU is known to introduce error in the final CWU.

Data availability at varied temporal scales (daily and monthly) led to loss of monthly variation in water withdrawal. The complexity of data, different temporal and spatial scales and assumptions made may be a source of error in estimation of the final CWU and also the final water budget. The parent water withdrawal dataset was not very rich.

The data were sparse during the 1970s and early 1980s. Most data were available as average daily withdrawal, which had to be multiplied by number of days to get monthly water withdrawals. Use of averages resulted in loss of monthly variability in water withdrawals and would have impacted the results of the model.

Data were available (and compiled) once in five years (still till now), and so the missing years were filled using data from the previous year available (usually five years ago), and hence there are intervals of five years which change with changes in water withdrawal data every five years. One of the important limitations with the dataset was the drastic difference in water withdrawal values in 1970s and early 1980s and after 1985 up till 2000. For e.g. in some counties, DSS was reported from only a few agencies, cities

59 or utility companies in early years (70s and early 80s) whereas for the same county, recent water use estimates were reported from many water utility agencies and/or cities.

This is understood to be attributed to:

1. Poor or complete lack of data collection in earlier years versus better data reporting in recent times or as a result of population increase in these counties

2. Reporting of water withdrawals from fewer utility companies in the past to more in the recent years

3. Change in water withdrawal data capture and computation methods starting around 1985 (pers comm., Marella 2006)

Finally, the consumptive water use percentages (for individual water use categories) applied to individual water use category were available on an annual basis

(absence of monthly CU variability) and was applied to water withdrawals at a monthly time scale. For some counties CWU percentage were available only for Florida.

In calculating ET, assumptions related to LULC data were made. It is assumed that there was no LULC change during the years for which no LULC data layers are available. For example, 1974 LC was considered for all years up until 1987 and 1988 LC was used for all years up until 1994 and so forth. Also the LULC was assumed to be the same during all months in a year. The variation in the number of LULC classes was a problem in comparing LULC change over time. The difference in spatial resolution of the

LC datasets and the areas of respective LC classes over years was expected to introduce some error in quantifying ET losses from the individual LC classes and eventually from the SRB e.g., between the datasets 1988 and 1995, rangeland has changed from 20% to

1%. This would have resulted in introducing some error in ET calculation.

Finally, it was expected that GIS processing, conversion of datasets from raster to vector formats and accuracy of GIS datasets itself are also known to introduce some errors to the final water balance.

Summary

In this chapter the methodology developed to calculate spatial water balance over a river basin and the final spatial water balance equation were discussed. The hydrologic datasets (precipitation, streamflow, evapotranspiration and consumptive water use) used in the modeling activity were discussed. Thornthwaite’s classical water balance equation and characteristics of basin hydrology were used as a basis for developing the spatial water balance equation for this research. CWU was calculated using water withdrawals as a separate water loss component. ET, though understood to be accounted for in the CWU

(by definition) was also separately computed as CWU percentages from USGS are only for the water withdrawals. This does not account for ET losses occurring irrespective of water withdrawals.

Geo-processing of the hydrologic datasets was discussed. To calculate discharge spatially, SD was calculated and assigned to centroid of the watersheds for interpolation.

PET was calculated using the Stephens-Stewart equation which required SRAD and mean monthly temperature as input. Blaney-Criddle crop coefficients were used for converting PET to AET.

For analysis of water balance and its hydrologic components, JMA ENSO index was used to classify years in to El Niño and La Niña. ANOVA was chosen for mean monthly, seasonal and annual analysis. The chapter concludes with the assumptions, limitations and sources of error encountered in the modeling approach. Main sources of error were related to data limitations, particularly for CWU.

CHAPTER 5 ANALYSIS OF HYDROLOGIC COMPONENTS IN THE SUWANNEE RIVER BASIN

Introduction

Results and analysis of hydrologic components of the water balance of the

Suwannee River Basin are presented. Monthly precipitation, streamflow, ET and CWU results (water volume in mgal) are presented and analyzed based on ENSO climatology.

Historic monthly water volumes of the hydrologic components are presented. Maximum and minimum values during the 30 year period are discussed. In the sections below detailed analysis of individual hydrologic components in relation to ENSO is presented.

Mean monthly, mean seasonal and annual totals were analyzed, both, without and with respect to ENSO phases using ANOVA. The chapter ends with summary tables of significance of ENSO phases at monthly and seasonal time scales of each hydrologic component based on ANOVA.

Precipitation

Precipitation is the most important hydrologic component of the regional hydrologic cycle. As the only water input in the cycle, it is important to understand how precipitation behaves in relation to ENSO patterns and climate variability. Understanding precipitation patterns in relation to ENSO and climate variability would have implications on understanding the patterns of streamflow and ET and ultimately WB in the region.

61 62

Historical Monthly Precipitation in the SRB

Monthly precipitation results (mgalm-1) for 30 years for SRB are presented (Table

5-7). Table 5-1 summarizes the results from Table 5-7. It is interesting to note that the years with maximum and minimum annual precipitation totals in 30 years were neutral years.

Table 5-1. Summary of maximum and minimum precipitation based on annual totals 30 Year Annual Totals El Niño Years La Niña Years Year ENSO Amount Year Amount Year Amount Phase (mgal) (mgal) (mgal) Maximum 1990/1991 Neutral 5560602 2002/2003 4644745 1975/1976 3928326 Minimum 2001/2002 Neutral 2694291 1991/1992 3657988 1999/2000 2936853

Mean Monthly Trends

Mean monthly precipitation trends were analyzed irrespective of ENSO (Figure 5-

1). July was the rainiest month whereas November received lowest precipitation during the 30 year study period. June, July and August received more precipitation compared to other months.

In general, precipitation was higher during El Niño years and lower in La Niña compared to Neutral years. During El Niño years, precipitation continued to increase from October till August except for April, May and June. In August precipitation during

El Niño years was highest and was higher than La Niña and Neutral year’s precipitation but dropped below La Niña in September month. Precipitation was highest (lowest) in

August (May) for El Niño and in September (November) for La Niña years. November and February months showed strong responses to ENSO phase (P< 0.05). In February, El

Niño precipitation was significantly higher than in Neutral or La Niña years.

Figure: 5-1. Mean monthly precipitation trends in the SRB

Figure: 5-2. Mean monthly precipitation trends in the SRB based on ENSO phase

Mean Seasonal Trends

Irrespective of ENSO, summer season had the highest precipitation and fall had the lowest (Figure 5-3). Mean seasonal analysis of precipitation showed significant affect of

ENSO. During El Niño fall, winter and summer, precipitation was higher than in La Niña or Neutral years (Figure 5-4). La Niña years showed distinctly lower precipitation during fall and winter seasons as compared to El Niño and Neutral years. During summer season precipitation was highest for El Niño, La Niña as well as neutral years. Precipitation was lowest in spring for El Niño and fall for La Nina years. For fall and winter seasons precipitation showed strong responses to ENSO phase (P<0.05) with La Niña showing significantly less precipitation than El Niño or Neutral years in winter season. Spring and summer precipitation was not significantly different from Neutral.

Figure: 5-3. Mean seasonal precipitation trends in the SRB

Figure 5-4. Mean seasonal precipitation trends based on ENSO phase

Annual Trends

Annual precipitation totals were computed based on climate year and were classified according to ENSO years (Figure 5-5).

Figure 5-5. Annual precipitation trends based on ENSO phase

Annual precipitation did not show any distinct trend based on ENSO years in the

SRB. 1982/83, 1986/87, 1987/88 and 2002/03 showed pronounced El Niño effect producing higher precipitation during these years. 1974/75 and 1975/76 showed higher precipitation during La Niña years. ENSO effects were more pronounced in seasonal analysis (Figure 5-4).

Streamflow

Historical Monthly Streamflow in the SRB

Monthly streamflow (mgalm-1) for 30 years for SRB is presented (Table 5-8). Table

5-2 summarizes the results from Table 5-8. Highest and the lowest annual streamflow totals were both in neutral years.

Table 5-2. Summary of maximum and minimum streamflow based on annual totals 30 Year Annual Totals El Niño Years La Niña Years Year ENSO Amount Year Amount Year Amount Phase (mgal) (mgal) (mgal) Maximum 1983/1984 Neutral 1961702 1986/1987 1947524 1974/1975 1457798 Minimum 2001/2002 Neutral 436649 2002/2003 653398 1999/2000 519457

Mean Monthly Trends

Mean monthly analysis of streamflow showed that streamflow was highest during the month of March in the 30 year duration of the study (Figure 5-6). November had the lowest streamflow. In general streamflow was higher during January, February, March and April compared to other months.

Streamflow was distinctly higher during El Niño years and lower in La Niña years as compared against neutral years except during October, November and December months where La Niña was higher than El Niño (Figure 5-7). Streamflow was highest

(lowest) in April (November) with distinctly higher values during March and May for El

Niño years. For La Niña years, streamflow was highest (lowest) in October (August).

Streamflow trends in the SRB indicated strong responses to ENSO (P<0.05) during

March and a weak response during April (P= 0.0574) months.

Figure 5-6. Mean monthly streamflow trends in the SRB

Figure: 5-7. Mean monthly streamflow trends in the SRB based on ENSO phase

Mean Seasonal Trends

Streamflow was highest during winter season and was lowest during fall season

(Figure 5-8). Streamflow during El Niño winter, spring and summer, was higher than in

La Niña or Neutral years (Figure 5-9). It was highest (lowest) in spring (fall) season for

El Niño years and in fall (summer) season for La Niña years. ENSO affects on seasonal streamflow were not significant. Even though, El Niño streamflow in spring was distinctly higher than La Niña and Neutral streamflows, it was not significantly different statistically based on ANOVA. This was because of the high variability in streamflow for

El Niño years in spring season (highest was 620116 mgal compared to lowest of 158435 mgal).

Figure 5-8. Mean seasonal streamflow trends in the SRB

Figure: 5-9. Mean seasonal streamflow trends in the SRB based on ENSO phase

Annual Trends

Annual streamflow did not show any distinct trend based on ENSO. Streamflow peaked during 1986/87 El Niño year and 1974/75 La Niña year (Figure 5-10).

Figure 5-10. Annual streamflow trends based on ENSO phase

It can be inferred from Figure 5-10 that streamflow had decreased after 1990 and drastically after 2000 compared to precipitation (Figure 5-5) which had remained unchanged during the same time period.

Evapotranspiration

Historical Monthly ET in the SRB

Monthly AET (mgalm-1) for 30 years for SRB is presented (Table 5-9). Table 5-3 summarizes the results from Table 5-9.

Table 5-3. Summary of maximum and minimum ET based on annual totals 30 Year Annual Totals El Niño Years La Niña Years Year ENSO Amount Year Amount Year Amount Phase (mgal) (mgal) (mgal) Maximum 1989/1990 Neutral 2253848 1991/1992 2198139 1988/1989 2228767 Minimum 1986/1987 El Niño 1905733 1986/1987 1905733 1975/1976 1967441

Mean Monthly Trends

ET was higher during April through September. ET losses were highest during the month of July and were lowest during January month (Figure 5-11).

Figure 5-11. Mean monthly evapotranspiration trends in the SRB

ET decreased during fall and winter months starting October and continued to decrease until mid winter (February) and then increased throughout before slightly decreasing in

August and September months. Based on ENSO, ET was highest (lowest) in July

(December and January) months for both El Niño and La Niña years (Figure 5-12). Mean monthly ET trends indicated that ENSO did not affect ET process significantly.

Figure 5-12. Mean monthly evapotranspiration trends in the SRB based on ENSO phase

Mean Monthly ET Trends in Various Landcover Classes

Mean monthly ET trends of the individual LC classes followed the same trend as that of the 30 year average (Figure 5-13). However, the trend was more pronounced in the case of ‘upland forest’ as compared to other LC classes. This was because of higher land cover area and hence higher ET loss. ET losses were in descending order for agriculture, wetlands, rangeland, urban and built-up and water. This trend of ET losses corresponded to the area of the respective LC classes.

Figure 5-13. Mean monthly evapotranspiration losses from land cover classes in the SRB

Mean Seasonal Trends

Seasonal analysis of ET showed that ET losses during winter (lowest) and fall were lower than spring (highest) and summer (Figure 5-14). Mean seasonal ET totals did not vary too much during winter and fall. ET losses increased drastically between winter and spring and then decreased slightly during summer. One would expect summer ET losses to be the highest but on the contrary, ET losses in spring were highest. This is explained by the fact that solar radiation, one of the two components of the Stephens-Stewart ET equation, was lower for summer months especially August and September. This was because of cloud cover during these two months which results in lower solar radiation.

During both El Niño and La Niña, ET losses were highest during spring and were lowest during winter. However, ET losses during El Niño and La Niña did not vary distinctly from Neutral during all seasons (Figure 5-15). During fall and winter seasons, ET was slightly lower during El Niño than La Niña while it was slightly higher in spring for El

Niño years. Based on ENSO, El Niño and La Niña did not force any significant variations in ET throughout the year.

Figure: 5-14. Mean seasonal evapotranspiration trends in the SRB

Figure 5-15. Mean seasonal evapotranspiration trends based on ENSO phase

Annual Trends

Annual ET losses did not show any ENSO based trend (Figure 5-16). However, the trend showed increased ET losses between the years 1988/1989 and 1994/1995. This was attributed to increase in the area of “rangeland” LC from 0% in 1975 to 20% in 1988.

Since the vegetation coefficient for “rangeland” was 1, AET equaled PET thereby increasing the total ET during that period as the same data LC were used between years

1988 and 1994.

Figure 5-16. Annual evapotranspiration trends based on ENSO phase

Consumptive Water Use

Historical Monthly Consumptive Water Use in the SRB

Monthly consumptive water use (mgalm-1) for SRB is presented (Table 5-10).

Table 5-4 summarizes the results from Table 5-10.

Table 5-4. Summary of maximum and minimum CWU based on annual totals 30 Year Annual Totals El Niño Years La Niña Years Year ENSO Amount Year Amount Year Amount Phase (mgal) (mgal) (mgal) Maximum 1991/1992 El Niño 30939 1991/1992 30939 1998/1999 27910 Minimum 1974/1975 La Niña 8041 1976/1977 10872 1974/1975 8041

Mean Monthly Trends

CWU trend did not show any distinct variation between the months (Figure 5-17)

However, CWU losses were highest during March and May and lowest during February.

Figure 5-17. Mean monthly consumptive water use trends in the SRB

CWU was highest (lowest) during May (February) in El Niño years whereas in La

Niña years it was highest (lowest) in the month of Oct (September). During El Niño and

La Niña years, less water was consumed than during Neutral years (Figure 5-18).

Between El Niño and La Niña, the later had lesser CWU. This trend continued through all months. This is primarily because 2 of the 5 La Niña years ( 1974/75 and 1975/76) are

76 during early reporting years when data inconsistencies are assumed (refer chapter 4 section: Assumptions, Limitations and Sources of Error in the Modeling Approach, pp

57) February showed a distinct dip in CWU for all El Niño, La Niña as well as Neutral years.

Figure 5-18. Mean monthly consumptive water use trends in the SRB based on ENSO phase

Mean Seasonal Trends

CWU did not show much variation across different seasons. It was highest during spring season and was lowest in fall (Figure 5-19). Seasonal analysis of CWU indicated that more water was consumed during El Niño years than during La Niña years in all seasons. CWU for neutral years was higher than El Niño and La Niña years for all seasons (Figure 5-20).

Figure 5-19. Mean seasonal consumptive water use trends in the SRB

Figure 5-20. Mean seasonal consumptive water use trends in the SRB based on ENSO phase

Annual Trends

As precipitation and streamflow, no distinct ENSO patterns could be associated to annual CWU. However, it is evident from the figure (5-21) that consumption was higher during the decade 1990-1999. During 2000-2004, CWU dipped again. The main reason for higher CWU values during 1990-1999 was that higher water withdrawal values were reported for the years 1990 and 1995 which were the only years when water withdrawal data were reported during 1990 and 1999. Reasons for higher water withdrawal values in these years are discussed in chapter 4 (section on data limitations, pp57).The steep decrease in CWU estimates during 2000-2004 are attributed to lower forecast estimates for the duration based on CWU estimates for years 1995 till 2000, 2000 being a drought year (refer precipitation values in figure 5-5, page). As mentioned in chapter 4, water use data during 2001 and 2004 were not reported at the time of this research and so CWU could not be calculated.

Figure 5-21. Annual consumptive water use trends based on ENSO phase

Summary

In this chapter, climate variability analysis of the hydrologic components was presented. ENSO influenced precipitation, and streamflow. ET was not affected by

ENSO. Based on seasonal analysis, El Niño brought more precipitation during fall, winter season and less in spring as compared to neutral precipitation. Streamflow and precipitation trends were similar in fall, winter and spring seasons. Nevertheless, streamflow was higher in El Niño than in La Niña during winter, spring and summer seasons. ET trend showed a significant increase from winter to spring months and remained steady for the summer months. Seasonal trend of CWU showed a higher trend in El Niño years in all seasons than La Niña years.

Table 5-5 and Table 5-6 summarize the significance (based on ANOVA) of ENSO phase on the four hydrologic components on monthly and seasonal basis respectively.

Table 5-5. Summary of hydrologic response of water balance components based on ENSO effects on a monthly basis Components/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Precipitation NS* S** NS NS S NS NS NS NS NS NS NS Streamflow NS NS NS NS NS S S NS NS NS NS NS Actual ET NS NS NS NS NS NS NS NS NS NS NS NS CWU NS NS NS NS NS NS NS NS NS NS NS NS Not significant ** Significant

Table 5-6. Summary of hydrologic response of water balance components based on ENSO effects on a seasonal basis Components/Seasons Fall Winter Spring Summer Precipitation S S NS NS Streamflow NS NS NS NS Actual ET NS NS NS NS CWU NS NS NS NS * Not significant ** Significant

Table 5-7. Monthly precipitation (mgal) in the Suwannee River Basin (1974/75-2003/04) Years/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Annual Totals 1974/1975 (LN) 20215 73645 314402 421030 135321 211943 357894 390223 371077 696325 480610 451787 3924471 1975/1976 (LN) 240884 89771 239388 204777 135321 289204 158589 767046 435533 367569 578407 421837 3928326 1976/1977 (EN) 228716 393624 396017 318836 289266 201970 87577 176621 303265 396406 704206 362744 3859248 1977/1978 (N) 67103 216809 558721 386930 390209 387146 227287 507391 351318 727936 595946 136846 4553642 1978/1979 (N) 26670 96152 270804 601423 349706 82626 530988 415361 395695 517688 654614 737968 4679694 1979/1980 (N) 28959 188436 268564 332320 163258 721282 418680 407166 347851 849792 304485 350729 4381523 1980/1981 (N) 355106 224370 69668 81535 545880 372669 94102 99371 439767 486266 668902 181469 3619105 1981/1982 (N) 127319 291539 282166 332320 239132 413159 452117 272509 676416 661371 536393 406889 4691329 1982/1983 (EN) 93004 101039 297107 326471 398542 602419 504521 198665 628865 400196 393267 513128 4457224 1983/1984 (N) 189088 351555 506409 172107 324154 561302 378701 324431 353497 835420 218995 356016 4571674 1984/1985 (N) 126274 220363 73232 181984 184609 126544 248955 153741 548833 467489 842131 347176 3521332 1985/1986 (N) 236013 176528 235487 428932 518438 267595 35663 157926 435738 322531 730260 203287 3748398 1986/1987 (EN) 289710 344547 442656 478743 443059 514698 27559 201583 322559 518043 612179 300157 4495494 1987/1988 (EN) 251545 379953 82056 393403 508845 374096 170522 204417 207002 436767 672818 762184 4194489

1988/1989 (LN) 190855 202932 74363 73640 88957 201729 128729 187342 628326 499366 422320 445004 3143563 80 1989/1990 (N) 109802 144306 331953 215787 333212 220097 143018 119248 417117 483522 383970 155132 3057166 1990/1991 (N) 349840 81789 91241 905414 78461 837958 519525 510182 610605 758904 614570 202113 5560602 1991/1992 (EN) 144657 31281 93990 415098 282573 352228 181795 117663 543513 513780 759773 221636 3657988 1992/1993 (N) 516015 264191 86090 470496 294389 300867 100054 115120 477200 350309 293416 358757 3626904 1993/1994 (N) 448684 254004 171288 651273 295524 338302 164373 227180 765543 545140 514872 282637 4658822 1994/1995 (N) 654340 70827 93421 307626 156273 250362 274112 232771 523158 417086 482652 205571 3668198 1995/1996 (N) 278055 236584 104054 170516 115672 651924 263917 172762 413357 666569 618902 213644 3905956 1996/1997 (N) 677865 117234 301462 301626 171676 255012 571969 202621 489054 475091 372708 169175 4105493 1997/1998 (EN) 466531 282086 266029 405853 435267 398967 69214 57479 49577 636653 441774 870923 4380352 1998/1999 (LN) 255733 15027 59815 349486 126292 132048 87536 129253 591684 317282 459078 533773 3057007 1999/2000 (LN) 59967 162487 82680 222989 149593 301352 82424 42950 399060 446829 337085 649437 2936853 2000/2001 (N) 71793 95321 92269 74123 74530 562584 87611 62180 587565 762837 325987 439242 3236042 2001/2002 (N) 13125 76176 133397 331466 72944 403408 128850 100005 315861 429282 421447 268331 2694291 2002/2003 (EN) 270939 301774 413469 145475 487300 685640 140594 207102 686584 558804 554356 192708 4644745 2003/2004 (N) 354978 125311 100000 98843 442904 68109 75288 63935 435307 529068 509275 1077719 3880737 Mean Monthly 238126 186989 217740 326684 274377 369575 223739 227475 458364 535811 516847 393934 118840669

Table 5-8. Monthly streamflow (mgal) in the Suwannee River Basin (1974/75-2003/04) Year/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Annual Totals 1974/1975 (LN) 117291 91879 87007 100603 133776 151744 161438 171120 113271 100750 115061 113859 1457798 1975/1976 (LN) 114653 91866 81333 104727 117221 94457 84835 81553 126948 108522 84996 78959 1170072 1976/1977 (EN) 84431 83007 127304 170817 176436 169087 138125 88796 70950 68423 71666 77146 1326190 1977/1978 (N) 72321 65267 77029 112768 170120 199529 154973 179916 121209 100657 149738 106592 1510116 1978/1979 (N) 86827 78062 79236 86508 130258 148512 112374 111321 91020 74747 74747 92879 1166493 1979/1980 (N) 128480 92073 98061 95976 119879 179257 205943 139830 98876 87877 105074 79365 1430691 1980/1981 (N) 73118 70083 77274 73158 85438 91943 86015 63420 54069 53035 55356 56241 839150 1981/1982 (N) 53728 57701 59084 91401 111262 111701 144976 111127 83428 114224 139320 119144 1197097 1982/1983 (EN) 97838 84369 76901 94021 138981 210463 248946 217069 154100 150145 120347 116454 1709634 1983/1984 (N) 104088 94039 134312 200876 190488 197490 242848 225911 152272 141258 159174 118945 1961702 1984/1985 (N) 101097 95778 85592 85488 92140 85498 83781 73423 70467 75091 96403 204057 1148816 1985/1986 (N) 130808 128792 137780 163244 226168 254078 167219 106842 86946 79489 81159 95104 1657629 1986/1987 (EN) 82639 78378 107334 179270 257017 294957 279589 179186 135725 120793 117178 115458 1947524 1987/1988 (EN) 105414 98277 96786 106834 141771 234580 177086 139432 94600 86239 91720 194048 1566787

1988/1989 (LN) 135867 99531 98910 91971 91375 87380 88080 78466 73681 83736 84362 87120 1100478 81 1989/1990 (N) 86471 73251 82508 96860 95498 99025 91461 64811 63133 66176 65689 66950 951835 1990/1991 (N) 61531 61184 60461 76858 124976 189869 205230 178587 162041 147454 171320 149558 1589070 1991/1992 (EN) 131331 99535 85597 82412 103784 124458 123514 97612 75152 87169 91880 113129 1215573 1992/1993 (N) 192461 106868 96734 131790 150627 150235 150271 99813 75672 71642 68176 67855 1362143 1993/1994 (N) 66005 75217 74601 99055 147517 169876 144543 125320 66336 104030 111580 102622 1286702 1994/1995 (N) 170987 139881 124908 119174 144191 119316 106385 69718 65368 65870 69745 65382 1260924 1995/1996 (N) 69577 70164 69017 70047 71678 87429 111099 82343 65199 86270 76368 70681 929872 1996/1997 (N) 104218 75649 77160 94045 124187 135946 98789 126651 107819 93240 94003 78967 1210674 1997/1998 (EN) 37000 30587 103735 130807 43228 251266 228541 102478 69617 57533 52799 61858 1169450 1998/1999 (LN) 91215 97635 75320 70940 90087 79688 60232 52708 47782 45092 47900 49051 807650 1999/2000 (LN) 51503 48733 45733 44027 45166 44229 53288 38824 36121 34180 35440 42212 519457 2000/2001 (N) 64980 43697 42180 45098 43448 54067 53548 47271 31866 34492 39580 46037 546264 2001/2002 (N) 48324 42882 35429 24756 24724 29407 49339 32536 33221 35903 37346 42782 436649 2002/2003 (EN) 44197 46824 46222 57256 24724 33236 42733 55775 59927 87892 80410 74202 653398 2003/2004 (N) 68953 49590 48892 44354 49490 33013 49339 45475 41336 32093 35493 33056 531082 Mean Monthly 92578 79027 83081 98171 115522 137058 131485 106244 84272 83134 87468 90657 35660922

Table 5-9. Monthly actual evapotranspiration losses (mgal) in the Suwannee River Basin (1974/75-2003/04) Year/Month Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Annual Totals 1974/1975 (LN) 148015 98258 64879 74165 78201 135043 191112 262727 274293 255166 257285 181134 2020277 1975/1976 (LN) 133199 92347 73240 67993 93394 129604 191898 225793 264333 294542 236838 164260 1967441 1976/1977 (EN) 127720 76709 56743 51481 71252 127617 191424 270870 300110 301420 197730 152391 1925465 1977/1978 (N) 139287 85176 62173 58321 60103 123599 198553 255705 273006 271366 249515 201126 1977930 1978/1979 (N) 144725 98113 69703 63038 67713 134946 177037 240021 286874 287244 255497 117483 1942395 1979/1980 (N) 152011 92683 64400 65205 75935 120488 177267 243177 270696 284275 263378 193166 2002679 1980/1981 (N) 129078 85015 68916 58189 70901 120563 190605 262835 279814 305667 223134 191335 1986052 1981/1982 (N) 135496 79369 57982 75380 77837 124446 185815 269436 279610 285926 262276 188599 2022172 1982/1983 (EN) 139109 90906 63714 58651 77970 118186 179282 254824 252896 287823 248378 172244 1943983 1983/1984 (N) 124977 89814 61177 59764 87873 137752 181878 235109 266577 248935 243111 181301 1918268 1984/1985 (N) 129448 84875 83030 64429 79342 141933 188370 276715 298765 305570 244882 182916 2080275 1985/1986 (N) 120900 76369 66742 67442 84429 138007 209007 282234 294138 301045 245602 195131 2081045 1986/1987 (EN) 134914 79206 58562 63559 69484 109872 189519 254935 258688 285122 235761 166113 1905733 1987/1988 (EN) 145227 85799 75612 60725 86589 140837 198311 279860 305844 305961 243500 154611 2082876

1988/1989 (LN) 153413 98546 82770 84602 92018 137634 212163 289670 306062 300724 281783 189382 2228767 82 1989/1990 (N) 145698 107405 62294 92028 86736 163676 208530 288637 301285 297562 282689 217306 2253848 1990/1991 (N) 150320 113656 83151 68757 100637 152885 191994 248372 299848 277356 242583 227287 2156847 1991/1992 (EN) 160624 108487 87791 73200 87334 151431 203698 287280 282716 323697 237841 194040 2198139 1992/1993 (N) 145451 83295 79731 66802 84547 135789 204766 283850 321805 322656 301420 195692 2225805 1993/1994 (N) 139762 89321 72897 72831 93037 158671 218994 292046 282425 302491 262304 202803 2187581 1994/1995 (N) 138146 200773 77123 68935 81923 131169 190644 272700 267246 301097 229049 187233 2146039 1995/1996 (N) 127213 93854 69530 72850 87721 113069 191706 280626 282389 281674 241872 177978 2020481 1996/1997 (N) 104187 85385 66337 77899 92402 140726 185914 247788 252569 274294 241798 200129 1969428 1997/1998 (EN) 150495 95138 64834 78866 85181 138980 199518 276327 327022 270676 241599 132864 2061500 1998/1999 (LN) 149343 101593 86492 80496 88162 148656 195524 260913 248359 288187 235923 180451 2064100 1999/2000 (LN) 128275 96693 70288 71211 96250 142181 190227 281078 269788 280417 232732 121925 1981066 2000/2001 (N) 145229 89297 65797 67287 74516 106828 199894 290883 282165 301002 271337 174631 2068867 2001/2002 (N) 147525 95462 65272 65641 85231 151573 192346 278666 264144 291879 252195 165698 2055633 2002/2003 (EN) 126056 88487 69380 67707 80506 106741 206768 293882 279845 310168 262507 201354 2093402 2003/2004 (N) 140129 101066 73147 78827 65767 160938 209003 292086 292281 308738 225428 148731 2096142 Mean Monthly 138532 95437 70124 69209 82100 134795 195059 269301 282186 291756 248332 178644 61664236

Table 5-10. Monthly consumptive water use (mgal) in the Suwannee River Basin (1974/75-2003/04) Years/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Annual Totals 1974/1975 (LN) 560 543 557 724 654 724 703 726 702 724 724 701 8041 1975/1976 (LN) 719 701 725 725 603 725 706 735 702 724 724 701 8488 1976/1977 (EN) 719 701 725 991 896 991 958 991 958 991 991 958 10872 1977/1978 (N) 986 958 991 1002 902 1009 991 1022 986 1016 1016 990 11869 1978/1979 (N) 1021 988 997 998 898 1005 992 1018 982 1013 1012 991 11915 1979/1980 (N) 1021 985 994 1089 1017 1088 1054 1089 1065 1098 1100 1060 12660 1980/1981 (N) 1089 1055 1091 1141 1058 1142 1106 1143 1108 1144 1144 1106 13328 1981/1982 (N) 1140 1106 1142 1141 1058 1142 1106 1143 1108 1144 1144 1106 13480 1982/1983 (EN) 1140 1106 1142 1141 1058 1142 1106 1143 1108 1144 1144 1106 13480 1983/1984 (N) 1140 1101 1142 1141 1071 1149 1106 1143 1108 1144 1144 1106 13496 1984/1985 (N) 1140 1106 1142 1518 1368 1531 1483 1541 1493 1518 1508 1464 16811 1985/1986 (N) 1516 1458 1506 1520 1374 1533 1486 1539 1491 1525 1515 1470 17933 1986/1987 (EN) 1521 1465 1513 1511 1366 1517 1493 1546 1507 1538 1543 1490 18010 1987/1988 (EN) 1546 1474 1518 1506 1370 1508 1483 1532 1490 1523 1531 1478 17959

1988/1989 (LN) 1529 1464 1509 1627 1548 1595 1474 1541 1511 1557 1554 1501 18409 83 1989/1990 (N) 1589 1559 1653 2625 2412 2731 2526 2667 2568 2634 2657 2512 28131 1990/1991 (N) 2644 2645 2564 2588 2417 2679 2514 2599 2551 2582 2607 2500 30889 1991/1992 (EN) 2611 2640 2525 2588 2477 2678 2521 2625 2555 2604 2612 2503 30939 1992/1993 (N) 2616 2639 2521 2583 2409 2677 2518 2645 2579 2601 2638 2508 30935 1993/1994 (N) 2619 2641 2526 2589 2415 2690 2533 2633 2553 2587 2610 2502 30897 1994/1995 (N) 2608 2639 2519 2299 2158 2406 2245 2295 2195 2322 2292 2214 28191 1995/1996 (N) 2440 2244 2029 2313 2155 2309 2260 2368 2273 2367 2350 2266 27375 1996/1997 (N) 2322 2247 2308 2318 2172 2325 2269 2352 2266 2349 2359 2301 27588 1997/1998 (EN) 2346 2252 2314 2321 2172 2316 2282 2414 2354 2380 2369 2278 27798 1998/1999 (LN) 2354 2279 2342 2348 2188 2342 2312 2394 2304 2372 2375 2300 27910 1999/2000 (LN) 2356 2276 2337 1191 1106 1276 1340 1218 1270 967 963 902 17203 2000/2001 (N) 967 953 963 1588 1476 1606 1682 1649 1666 1453 1463 1396 16862 2001/2002 (N) 1476 1418 1542 1433 1328 1447 1558 1500 1539 1261 1275 1211 16988 2002/2003 (EN) 1295 1241 1398 1278 1181 1289 1433 1350 1412 1069 1088 1026 15059 2003/2004 (N) 1115 1064 1255 1122 1033 1130 1309 1200 1285 877 901 841 13131 Mean Monthly 1605 1565 1583 1632 1511 1657 1618 1659 1623 1608 1612 1550 576651

CHAPTER 6 ANALYSIS OF SPATIAL WATER BALANCE FOR THE SUWANNEE RIVER BASIN

Introduction

The water balance of SRB located in the northern Florida was modeled by establishing the empirical relationships that exist between input and output components of the hydrologic system. Input component consisted of precipitation. Output components consisted of streamflow (river discharge), evapotranspiration (ET) and consumptive water use (CWU). Monthly spatial water balance for the SRB was computed for 30 years using the spatial water balance modeling approach discussed in chapter 4. Water balance was analyzed against its components and also based on ENSO. Mean monthly, mean seasonal, and annual variability of water balance for the SRB was studied.

Water Balance of the SRB

Monthly water balance (mgalm-1) for 30 years for SRB is presented (Table 6-4).

Table 6-1 summarizes the results from Table 6-4.

Table 6-1. Summary of maximum and minimum WB based on annual totals 30 Year Annual Totals El Niño Years La Niña Years Year ENSO Amount Year Amount Year Amount Phase (mgal) (mgal) (mgal) Maximum 2002/2003 El Niño 1882886 2002/2003 1882886 1975/1976 782325

Minimum 1988/1989 La Niña -204092 1991/1992 213336 1988/1989 -204092

Figure 6-1 illustrates the mean monthly WB in the SRB along with the hydrologic components. It was apparent that WB, in general, followed the trend of precipitation.

Nevertheless, WB trend did differ from that of precipitation during some months. On the

85 86 whole WB trend was governed by changes in precipitation and ET. CWU did not show much variability across the months. Precipitation and ET were both highest during July whereas streamflow was highest during March and CWU was highest during March and

May. WB was highest during August and was considerably higher during January and

July months. Precipitation and streamflow followed the same trend in fall, winter and spring seasons (Figure 5-3 and 5-8)

In October WB was near zero as precipitation and ET equaled out. In November all three decreased but even then WB increased. The reason behind this was that even though precipitation decreased in the month of November, ET decreased at a higher rate thereby slightly increasing WB (Figure 6-1). In December, WB increased as precipitation increased and ET decreased. In January WB increased further with increase in precipitation and streamflow and decrease in ET. In February, WB decreased drastically, with decreasing precipitation and increasing streamflow and ET. In the month of March

WB further decreased slightly compared to February as precipitation increased compared to February. This was coupled with increasing streamflow and ET during March. In April and May, although the WB trend followed the same pattern as precipitation, WB actually dipped below zero (negative balance or no recharge). This was attributed to decrease in precipitation during these months. In addition, ET and streamflow were higher in April and in May ET exceeded precipitation even though streamflow dipped slightly. Between

June and September, WB increased and also became positive (before it flattened out during September) with increase in precipitation during these months coupled with initial increase and subsequent decrease in ET during September.

Figure 6-1. Mean monthly water balance and hydrologic components in the SRB

Mean Monthly Trends

Mean monthly WB trends (irrespective of ENSO) (Figure 6-2) indicated that

August had the maximum surplus WB and April and May had deficit WB with May being the lowest. Analyses of climatic patterns relating to ENSO and WB in the SRB indicated strong responses during November and February months. Mean monthly WB in general showed a higher trend for El Niño years than Neutral years. Mean monthly

WB in La Niña years was negative in October, November, February, March, April and

May. September showed a higher WB for La Niña years than El Niño and neutral years.

In El Niño years, October, April and May months had deficit WB. In April, May and

June, WB in El Niño years was remarkably less than La Niña and neutral years. This trend was attributed to lower precipitation for El Niño years in April, May and June and a remarkably higher streamflow in April (highest) and May and a considerably high streamflow in June.

Figure: 6-2. Mean monthly water balance trends in the SRB

Figure: 6-3. Mean monthly water balance trends in the SRB based on ENSO phase

WB maximized (minimum) in August (May) for El Niño and in September (April) for La Niña years. November and February months showed strong responses to ENSO phase (P< 0.05). In both November and February, El Niño WB was significantly higher than in Neutral or La Niña years.

Mean Seasonal Trends

Irrespective of ENSO, WB peaked in summer and was lowest (negative) during spring (Figure 6-4). Summer had the highest precipitation and low streamflow and higher

ET, therefore WB was highest in summer. Mean seasonal analysis of WB in the SRB showed significant affect of ENSO. During El Niño fall and winter, WB was higher than in La Niña or Neutral years (Figure 6-5).

Figure: 6-4. Mean seasonal water balance trends in the SRB

WB in El Niño spring was negative. La Niña years showed lower WB during fall

(negative) and winter season as compared to El Niño and Neutral years. During summer season WB peaked (lowest) for all El Niño (spring), La Niña (fall) as well as neutral

(spring) years. For winter and spring seasons WB showed strong responses to ENSO phase (P<0.05) with La Niña showing significantly less WB than El Niño or Neutral years in winter season. Fall season showed a weak response to ENSO (P=0.069).

Figure 6-5. Mean seasonal water balance in the SRB based on ENSO

Annual Trends

Annual trends in general showed that WB in El Niño years was higher than during

La Niña years (Figure 6-6). Out of the 30 years, during three years (1985/1986-Neutral,

1988/1989-La Niña and 1989/1990-Neutral) WB was negative. In 1985/1986, precipitation was relatively low and ET was high and streamflow was increasing

(Appendix J). In the years 1988/1989 and 1989/1990, precipitation and was less and ET was high (Appendix J).

Figure 6-6. Annual water balance trends based on ENSO phase

Decadal Trends

Variations in WB and its hydrologic components were also studied on a decadal scale. 3 decades constituted the 30 year period. The decades were:

1. Decade I: 1974/75-1983/84 2. Decade II: 1984/85 – 1993/94 3. Decade III: 1994/95 – 2003/04

Decadal analysis based on decadal totals showed that WB was highest in Decade I

(43%) and lowest in Decade II (20%) (Appendix K). Both ET and CWU were the lowest in the first decade compared to the other two decades. In the second decade however, precipitation was lower compared to first decade, but all the remaining components were maximum among other decades.

Figure 6-7: Hydrologic components and water balance in the three decades

30-Year Water Balance Analysis

Based on a 30-year average (1974/75 to 2003/04), WB in the basin was estimated to be 700,000 mgal (700 billion gallons) per annum. WB was negative in years

(1985/1986-Neutral, 1988/1989-La Niña and 1989/1990-Neutral and, average monthly

WB was negative in April and May.

Based on ENSO, average El Niño precipitation (6%), streamflow (15%) and water balance (8%) were higher than neutral years whereas ET (2%) and CWU (5%) were lower. Average La Niña precipitation (18%), streamflow (15%), ET (0.68%), CWU

(26%) and WB (137%) were all lower than average neutral year estimates (Appendix L,

Table L-1). Percentage of precipitation of hydrologic components was computed based on a 30 year average, 7 year El Niño average and 5 year La Niña average. On the average, 30% (32% for El Niño and 30% for La Niña) of precipitation was converted into

93 streamflow, 52% (47% for El Niño and 60% for La Niña) was lost as ET, 0.5% (0.45% for El Niño and 0.47% for La Niña) was consumed or removed from the hydrologic cycle and 17% (19% for El Niño and 9% for La Niña) was left as available water (Appendix L-

2).

Comparison of Water Balance Estimates

Previous studies on WB have focused on point and average estimation of hydrologic components and WB over a study area whereas the spatial water balance modeling approach (SWBA) used in this study captures the precise spatial nature of each hydrologic component and ultimately water balance thereby producing more accurate estimates of water volumes of the hydrologic components. Previous studies on spatial variability focused on point and average estimates at various geographical locations

(usually at precipitation or streamflow gauging stations) whereas this research allows for true spatial visualization and spatial trend surface analysis of hydrologic components over the entire basin.

WB estimates calculated in this research were compared in two ways. One with the estimates obtained from the point-and-average approach (PAAA) and second, with WB estimates calculated from reported values of hydrologic components for Florida and SRB.

Comparisons between the (PAAA) and the SWBA used in this research were important to understand the difference between estimated water volumes from the two approaches.

Comparison of water volume estimates obtained from this study with other reported estimates were important to validate and evaluate the spatial methodology used in this research and to find out any major differences in estimates between respective hydrologic components which would help in pointing out the important sources of error in the modeling approach.

Compared to the PAAA, the water volumes computed using the SWBA presented in this research indicated a conservative (lower) estimate. Precipitation and WB showed greater differences in the estimates with lesser water volumes. Streamflow estimates also were lesser than in PAAA but were not very different. ET estimates however were higher in the SWBA compared to PAAA. Based on a 3-year average, precipitation and WB from

SWBA were 8% (21% lower in El Niño, 2% higher in La Niña and 1% higher in Neutral year) and 64% (85% lower in El Niño, 99% lower in La Niña and 16% lower in Neutral year) lower respectively than computed using PAAA. ET estimates were on an average higher by 6 percent with no variation between the ENSO years. It must however be noted that SWBA resulted in lower estimates in El Niño year and higher estimates in La Niña year compared to respective PAAA estimates. Comparison of monthly water volumes for the hydrologic components and the water balance calculated by the two methodologies are presented in Appendix M).

To validate the results and to evaluate the methodology used in this research, the

WB and water volumes of the hydrologic components computed were compared with the published values of hydrologic components for Florida reported by Fernald and Purdum

(1998) (Chapter 1, pp 1-2) and SRB as reported in SRWMD Technical Report (2005)

(Chapter 3, pp. 27). Precipitation estimates from this research were validated by converting the 30 year average precipitation estimate to inches. Precipitation estimates from this research translated to 54 inches annually compared to 53.4 inches reported for

SRB (SRWMD Technical Report 2005). Average daily WB for Florida and average annual WB for SRB were computed using published values. WB for Florida and SRB were 0.3 billion gallons per day and -0.4 inches per annum respectively (Appendix N).

Comparisons reveal that the percentage of water volumes for SRB computed in this research were higher than Florida for streamflow (3%) and water balance (17 %) and lower for ET (19%) and CWU (1.3%) (Appendix N, Table N-1). Compared to reported estimates of SRB, percentage of water volumes for SRB calculated in this research were higher for streamflow (2%) and water balance (18%) and lower for ET (20%) (Appendix

N, Table N-2). Compared to the WB for Florida and SRB calculated based on reported estimates, the percentage of WB calculated in this research for SRB was approximately

17-18 % higher. Literature does not mention about the type of ET estimates for Florida and SRB, ET estimates for SRB was reported to be the same as the reference ET calculated for north Florida using crop coefficients for pasture (SRWMD Technical

Report 2005).

WB in this research was calculated using AET. Based on a 30 year average, AET

(2055474.52 mgal) for SRB was (-) 37 percent lower than PET (2810561.17 mgal) (also calculated in this research). It is interesting to note that WB calculated using AET was

108% higher with WB calculated with PET (-57125 mgal). As a percentage of precipitation, WB with PET was -1.44 percent which was quite close to 0.2 and -0.75 for

Florida and SRB respectively (Appendix N, Table N-1 and N-2, column B). It can be inferred that the difference between PET and AET was one possible reason for the WB

(with AET) calculated in this research to deviate 17-18% from reported values of Florida and SRB. It can also be inferred that the biggest source of error was in ET estimation.

Summary

In this chapter WB was analyzed and compared with its hydrologic components.

WB was also analyzed based on ENSO. In general, WB trend was the same as precipitation. Trend of WB and hydrologic components as a percentage of precipitation

96 showed that WB trend was a mirror image of ET trend (Appendix O). Analysis showed that ENSO had a pronounced effect on WB. WB showed a negative trend during spring season in all ENSO years while it was highest during summer season with LN years showing a significant increase. During the 30 year period, 3 years showed negative water balance. Table 6-2 and Table 6-3 summarize the significance (based on ANOVA) of

ENSO phase on WB on monthly and seasonal basis respectively. Analysis of WB at a decadal scale showed that WB was highest in Decade I (43%) and lowest in Decade II

(20%). and for 30 years is provided. The chapter ends with a comparison of the estimates of WB and its hydrologic components with the spatial approach versus the traditional

PAAA and with WB estimates calculated from other reports. Compared to PAAA the spatial approach used in this research estimated lower WB where estimates of were higher by 17-18% in comparison with WB estimates from reported literature.

Table 6-2. Summary of affect of ENSO on monthly water balance WB/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept WB NS* S** NS NS S NS NS NS NS NS NS NS * Not significant ** Significant

Table 6-3. Summary of affect of ENSO on seasonal water balance WB/Seasons Fall Winter Spring Summer WB S S S NS

Table 6-4. Monthly Water Balance Volumes (mgal) in the Suwannee River Basin (1974/75-2003/04) Years/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Annual Totals 1974/1975 (LN) -245652 -117034 161958 245539 -77310 -75568 4641 -44349 -17189 339685 107540 156094 438354 1975/1976 (LN) -7687 -95144 84090 31333 -75898 64419 -118849 458964 43550 -36219 255849 177918 782325 1976/1977 (EN) 15846 233208 211245 95546 40681 -95725 -242931 -184036 -68753 25572 433820 132249 596721 1977/1978 (N) -145491 65408 418529 214840 159085 63009 -127230 70748 -43883 354898 195677 -171863 1053727 1978/1979 (N) -205903 -81010 120868 450878 150835 -201837 240585 63001 16819 154684 323357 526614 1558891 1979/1980 (N) -252553 2695 105110 170049 -33573 420449 34415 23070 -22785 476542 -65066 77138 935493 1980/1981 (N) 151820 68216 -77613 -50954 388483 159021 -183624 -228027 104776 126420 389269 -67213 780575 1981/1982 (N) -63046 153364 163957 164398 48975 175869 120220 -109197 312270 260077 133653 98039 1458580 1982/1983 (EN) -145084 -75341 155350 172658 180533 272628 75187 -274371 220761 -38916 23398 223323 790127 1983/1984 (N) -41118 166601 309777 -89675 44722 224911 -47131 -137732 -66460 444083 -184433 54664 678208 1984/1985 (N) -105410 38604 -96532 30549 11759 -102418 -24679 -197939 178108 85309 499338 -41260 275429 1985/1986 (N) -17210 -30091 29460 196725 206467 -126023 -342049 -232689 53163 -59528 401983 -88418 -8210 1986/1987 (EN) 70636 185498 275247 234403 115193 108353 -443042 -234084 -73361 110591 257697 17096 624227 1987/1988 (EN) -249761 194403 -91861 224338 279115 -2830 -206358 -216407 -194933 43044 336067 412047 526866 1988/1989 (LN) -99954 3391 -108826 -104560 -95983 -24879 -172989 -182335 247072 113350 54620 167002 -204092 1989/1990 (N) -123956 -37909 185498 24274 148566 -45335 -159498 -236868 50131 117150 32936 -131637 -176648 93 1990/1991 (N) 135344 -95696 -54934 757211 -149570 492526 119786 80623 146165 331512 198060 -177232 1783795 1991/1992 (EN) -149908 -179381 -81923 256898 88977 73661 -147938 -269854 183090 100309 427440 -88036 213336 1992/1993 (N) 175486 71389 -92897 269321 56806 12165 -257500 -271187 77144 -46590 -78818 92702 8021 1993/1994 (N) 240298 86825 21264 476799 52556 7065 -201696 -192818 414229 136032 138378 -25291 1153641 1994/1995 (N) 342599 -272467 -111130 117219 -72000 -2529 -25161 -111942 188349 47797 181566 -49258 233044 1995/1996 (N) 78825 70322 -36522 25307 -45883 449117 -41148 -192574 63495 296258 298313 -37281 928229 1996/1997 (N) 467138 -46046 155657 127364 -47085 -23985 284998 -174170 126400 105208 34548 -112222 897803 1997/1998 (EN) 276689 154109 95146 193859 304686 6405 -361127 -323741 -349416 306064 145006 673924 1121604 1998/1999 (LN) 12821 -186480 -104339 195702 -54144 -98638 -170533 -186762 293239 -18370 172880 301971 157347 1999/2000 (LN) -122166 14784 -35678 106560 7071 113665 -162431 -278171 91881 131265 67949 484397 419126 2000/2001 (N) -139382 -38627 -16672 -39850 -44910 400082 -167513 -277622 271868 425890 13608 217178 604050 2001/2002 (N) -184201 -63587 31155 239636 -38340 220981 -114393 -212696 16957 100240 130630 58640 185022 2002/2003 (EN) 99391 165223 296469 19234 380890 544374 -110340 -143905 345400 159675 210351 -83874 1882886 2003/2004 (N) 144782 -26409 -23294 -25461 326614 -126971 -184363 -274826 100405 187360 247453 895091 1240383 Mean Monthly -2893 10961 62952 157671 75244 96065 -104423 -149730 90283 159313 179436 123083 20938860

CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS

Conclusion

This is the first study in the SRB that has presented the hydrologic components and water balance (WB) in terms of volume of water (mgal) at a monthly time step as compared to other measures like inches of precipitation and evapotranspiration, cubic feet per second (cfs-1) of streamflow, or average daily values of hydrologic components.

Monthly water volumes of WB, and its constituent hydrological components, quantify estimates of how much water was available over the past 30 years in the SRB and the volume that was contained in each hydrological component.

Conclusions are drawn from the results presented in chapters 5 and 6 and are presented on two accounts, both of which reflect the two primary objectives of the study:

1. the spatial water balance modeling approach (SWBA) designed in this research, and

2. analysis of the water balance and hydrologic components in relation to climate variability (ENSO)

Spatial Water Balance Modeling

This study demonstrated that GIS is an effective platform for modeling water quantity in the Suwannee River Basin (SRB). GIS served as an important and powerful tool to appropriately capture the spatial nature of the hydrologic components.

One of the goals of this study was to provide a spatial estimate of hydrologic components over the SRB area so that a more accurate monthly water balance could be calculated. A total of 1080 (360 each of precipitation, streamflow and ET) monthly maps

95 of spatial variability of precipitation, streamflow and evapotranspiration for the SRB were generated. The maps were used to compute the final water volume of each hydrologic component (e.g. Figure 4-3(f) for precipitation). The maps allow for trend surface analysis of the hydrologic components. The database compiled for this study and the maps generated through this research are likely to be valuable to water agencies in

Florida.

The results obtained from the spatial methodology applied in this research lead to the conclusion that the methodology has successfully resulted in a detailed estimation of potential water balance for SRB. The precipitation, streamflow and Consumptive Water

Use (CWU) estimates generated in this research were probably the most accurate datasets at a monthly scale. Even though the estimates of precipitation, streamflow and CWU either matched or were very close to the previously reported values, the estimates from this study are superior to single annual average values as they account for and present monthly variability in the data. However, streamflow estimates can be improved upon by using well level data.

The methodology was evaluated based on comparisons made with the estimates obtained using the traditional point-and-average approach (PAAA) and with estimates of

WB and hydrologic components of Florida (Fernald and Purdum 1998) and SRB

(SRWMD Technical Report 2005). By way of these comparisons, conclusions also highlight the data limitations and uncertainties that have come to light in using the spatial methodology in this research.

Comparison between SWBA versus Reported Estimates

The water volumes generated in this research are a reflection of the published input datasets as well as the spatial methodology used to model the water balance. Data

96 limitations became obvious when water balances from this research were compared with estimates from previous work in Florida and the SRB (Fernald and Purdum 1998;

SRWMD Technical Report 2005).

Since these other reported estimates either covered a different area (Florida versus

SRB) or were expressed in different measures (inches versus mgal), the hydrological components (ET, CWU, and streamflow) and WB were computed as a percentage of precipitation (Appendix N). A comparison of these percentages across the three studies showed good consistency for streamflow and CWU. However, the WB and ET (AET) percentages differed by 17 to 18% and -19 to -20% respectively. This suggested that the major difference between this study and the other two was the estimate of ET.

Florida and SRB studies both used potential ET (PET) in their water balance calculations, whereas this study used estimates of actual ET (AET) based on reported crop coefficients for each land use. When the WB was recalculated using PET, the comparison of the WB and PET with the other two studies were within 2%. This raises questions about the reliability of the land cover data and the vegetation coefficients used for modeling water balance over the 30 year period. This may also be related to the spatial resolution of the land cover data, but without an alternative source of land cover data, it is difficult to confirm this.

In the computation of CWU, spatial mismatches between the county-scale water withdrawal data and the watershed scale analysis also introduced uncertainties in WB computation, although the effects of this uncertainty are much less significant than ET given that CWU constitutes less than 1% of total precipitation in the water balance model.

Comparison between SWBA versus Point-and-Average Approach (PAAA)

A spatial approach implies greater consideration of spatial variability of hydrologic components at a watershed scale. But how much difference does such an approach make when compared with simple point-average approach (PAAA)? WB was computed using the PAAA method for 3 years (1997/98- El Niño, 1998/99- La Niña, 2000/2001- Neutral) and compared with the spatial WB for those years. WB from the spatial approach was

64% less than the WB computed using PAAA. This indicates a significant difference between the two approaches. Comparisons of the two approaches based on one-year estimates of precipitation and streamflow also revealed differences between the two approaches. When precipitation and streamflow were compared over a single year and between different ENSO phases, the SWBA estimates were lower in El Niño years and higher in La Niña years compared to the respective PAAA estimates. Estimates for

Neutral years did not differ greatly. So it can be concluded that for obtaining estimates of hydrologic components at an annual time scale for a Neutral year, either of the two approaches can be adopted. It can also be concluded that estimates obtained by using

SWBA were lower than that obtained from the PAAA. However, it would be useful to compare the estimates from the two approaches using all 30 year estimates to have a better understanding of the differences in estimates between the two approaches.

Effect of ENSO on Water Balance and its Hydrologic Components

This is also the first study in the SRB to compute and analyze spatial water balance and its components based on climate variability. Effects of ENSO phase on the WB and hydrologic components were analyzed on three different temporal scales – monthly, seasonally and annually – for the 30-year study period. Significance of ENSO on WB and hydrologic components was analyzed at monthly and seasonal scales. Demonstrated

98 effects of ENSO upon mean monthly, seasonal and annual water balance and its components were found in this research. ENSO did not have any effect on annual trends of WB and its hydrologic components. However, based on ENSO, average water volumes of WB and its hydrologic components were higher in El Niño years compared to

La Niña years.

Conclusions about effect of ENSO on hydrologic components are presented under two headings:

1. Behavior of the WB and hydrologic components in relation to ENSO phase

2. Significance of ENSO phase on the WB and hydrologic components based on ANOVA tests

Behavior of WB and Hydrologic Components in Relation to ENSO

Following conclusions can be drawn from the results and analyses on WB and hydrologic components presented in chapters 5 and 6.

1. Precipitation was higher during El Niño fall, winter and summer compared to La Niña and Neutral year estimates. Previous literature has suggested similar trends for fall and winter (Schmidt et al. 2000; O’Brien 1999; Hansen et al 1999, Ropelewski and Halpert 1986, Kiladis and Diaz 1989, Hanson and Maul 1991, Sittel 1994, Zorn and Waylen, 1997 (for Santa Fe basin in north Florida)). Higher precipitation in El Niño summers is a new finding from this study and needs to be verified.

2. Streamflow values were higher during El Niño winter, spring, and summer. This finding was in accordance with the literature (Schmidt 2000; Tootle and Piechota 2004). Streamflow values were also higher in La Niña fall. This was in partial accordance with Schmidt (2000).

3. ET losses were highest in July in El Niño years compared to La Niña and Neutral years. This is also in accordance with previous reporting for North Florida (Tallahassee) (O’Brien 1999). On a seasonal scale, ET losses were highest in El Niño spring compared to La Niña and Neutral fall, winter and summer season ET losses. This is a new contribution from this research as no previous ENSO based analysis for ET on a seasonal scale has been reported for North Florida.

4. CWU trend showed more consumption of water during El Niño years compared to La Niña years.

5. The WB was higher during El Niño fall, winter and summer seasons, similar to that of precipitation. This is also a new finding in this research.

Significance of ENSO on WB and hydrologic components based on ANOVA

The effects of ENSO upon monthly WB and hydrologic components revealed complexities within the temporal (monthly and seasonal scales) patterns. The following conclusions can be made from the analysis presented in chapters 5 and 6.

1. Precipitation was significantly affected by ENSO in November and February and in fall and winter seasons

2. Streamflow was significantly affected by ENSO in March and April months. No significant effect of ENSO was identified on seasonal streamflow

3. ET and CWU were not significantly affected by ENSO phase

4. WB was significantly affected by ENSO phase in November and February months and in fall, winter and spring seasons

Recommendations

Research presented in this dissertation has wider implications for hydrologic and climate based studies. Ramifications of the conclusions are directly related to issues involving water resources and the effect of climate variability on them. Seasonal forecasts of ENSO conditions could be employed in conjunction with the results of this research to provide information pertaining to both anticipated absolute volumes of WB and its likely variability for fall, winter and spring seasons. Understanding potential future climate variability in the context of current natural variability of the hydrologic components and ultimately WB in North Florida can provide an important contribution to the on going discussions of water transfer for these seasons. The methodology can also be adopted for similar analysis of WB and hydrologic components on the individual watersheds within the basin to have a more detailed understanding of spatial patterns associated with these components.

100

Results of this study can also be used as a base dataset in the future for prediction studies for climate sensitive sectors in the region like agriculture and forestry. These industries have serious economic implications in north Florida and in the entire southeast

US. Historic monthly, seasonal, and annual ENSO-based variations in precipitation may be useful in understanding the patterns of extreme climate events in North Florida especially for fall, winter and summer.

Assessing water availability in the Suwannee River Basin and evaluating water availability in light of plausible future scenarios focusing on climate variability, extreme events (e.g. hurricanes), water transfer and land use change is critical for the region. A set of scenarios can be developed, in consultation with stakeholders, to describe how water availability will be impacted in the future. These scenarios could be based on projections of future changes in population and economic development leading to changes in future water use demand and future changes in land use (e.g. massive deforestation and increase in agricultural area, thus increasing irrigation water demand) in relation to climate variability scenarios (e.g. El Niño, La Niña, Neutral year). Such scenarios can provide answers to conceivable future issues like water transfer.

Based on the data limitations encountered in this research, recommendations are made for improving data collection, especially water withdrawals and streamflow. Data compilation and reporting should be at a monthly time scale to study the monthly variability in water withdrawals and ultimately CWU. For calculating streamflow spatially, representative inflow and outflow gauging stations are needed at the river flow entry and exit points of a watershed. In this research, the gauging stations within the

Florida part of SRB did not meet this requirement.

101

The methodology is replicable and can be used in other study areas involving issues related to water resources on a regional scale. The methodology is detailed, yet simple in terms of steps involved. However, it is tedious and requires a lot of time, computer power and storage to process and store the datasets. An account and measure of the data processing involved in adopting this methodology is detailed in Appendix P.

APPENDIX A AREAS OF MAJOR WATERSHEDS AND COUNTIES IN THE SRB

Name of Area of Percent Name of Area of Percent watershed watershed area of county county area of (Acres) Suwannee (Acres) county in River watershed Basin Alapaha 69102.12 2.54 Hamilton 69102.12 100 Withlacoochee 173903.74 6.4 Hamilton 46554.71 26.77 Madison 127349.02 73.23 Upper 579081.74 21.33 Hamilton 216457.8 37.38 Suwannee Madison 3.34 0 Suwannee 66929.35 11.56 Columbia 267561.38 46.2 Baker 28129.84 4.86 Lower 982123.24 36.18 Madison 70973.5 7.23 Suwannee Suwannee 356767.43 36.33 Columbia 4292.96 0.44 Lafayette 180136.53 18.34 Dixie 121352.76 12.36 Gilchrist 109891.85 11.19 Levy 137565.18 14.01 Taylor 1142.99 0.12 Santa Fe 873414.25 32.18 Columbia 224141.16 25.66 Suwannee 19127.4 2.19 Union 152055.07 17.41 Bradford 187422.65 21.46 Alachua 174900.99 20.02 Gilchrist 85450.96 9.78 Baker 22360.36 2.56

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APPENDIX B PRECIPITATION GAUGING STATIONS

Site Percent* Station Maintained Missing S.No Station Name ID County Watershed Latitude Longitude By Data 1 Lake City (WC) 20 Columbia Santa Fe 30°11'06" N 82°35'40" W NOAA 0.56 2 Live Oak (WQHL) 21 Suwannee Lower Suwannee 30°17'14" N 82°57'56" W NOAA 0.84 3 Starke 25 Bradford Santa Fe 29°57'10" N 82°06'50" W SRWMD 12.61 4 Jasper 19 Hamilton Upper Suwannee 30°31'25" N 82°56'47" W NOAA 0.56 5 Benton Insp. 2 Columbia Upper Suwannee 30°29'15" N 82°39'38" W SRWMD 4.76 6 Branford Insp. 3 Suwannee Lower Suwannee 29°57'20" N 82°55'42" W SRWMD 4.76 7 Dowling Park Insp. 5 Lafayette Lower Suwannee 30°14'43" N 83°15'07" W SRWMD 5.32 8 Ellaville Insp. 6 Suwannee Lower Suwannee 30°22'43" N 83°10'25" W SRWMD 5.04 9 High Springs 18 Alachua Santa Fe 29°49'49" N 82°35'56" W NOAA 0.56 10 SRWMD 88 Suwannee Lower Suwannee 30°17'01" N 82°57'20" W SRWMD 41.74 11 Manatee Springs 93 Levy Lower Suwannee 29°29'14" N 82°57'52" W SRWMD 48.18 12 Newburn 97 Suwannee Lower Suwannee 30°18'54" N 83°06'53" W SRWMD 48.18 13 Nobles Ferry Insp. 84 Hamilton Upper Suwannee 30°26'15" N 83°05'28" W SRWMD 42.30 104 14 Madison Tower 68 Madison Withlacoochee North 30°26'42" N 83°24'05" W DOF 5.04 15 Bell Tower 70 Gilchrist Lower Suwannee 29°48'12" N 82°52'06" W DOF 5.32 16 Trenton Tower 71 Gilchrist Lower Suwannee 29°37'56" N 82°49'29" W DOF 5.32 17 Midway Tower 52 Lafayette Lower Suwannee 29°59'57" N 83°03'21" W DOF 4.76 18 Live Oak Tower 54 Suwannee Lower Suwannee 30°18'38" N 83°01'15" W DOF 5.88 19 New River Tower 35 Bradford Santa Fe 29°58'45" N 82°14'21" W DOF 6.16 20 Alapaha Tower 46 Hamilton Alapaha 30°31'50" N 83°02'30" W DOF 6.44 21 Louis Hill Tower 34 Bradford Santa Fe 30°06'28" N 82°03'00" W DOF 5.88

APPENDIX C CONVERSIONS

Precipitation

1 Inch = .0254 meters 1 cubic meter = 264.17 gallon

Streamflow

1 cubic meter = 264.17 gallon

Evapotranspiration

1 oC = 5*( o F -32)/9 1 MJ/sqm/month = 23.89 Cal/sq cm

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APPENDIX D STREAMFLOW GAUGING STATIONS

USGS Drainage Percent S.No Station Name Reference County/State Watershed HUC* Area Latitude Longitude Missing Data Santa Fe River at 1 Worthington Springs, FL 02321500 Alachua, FL Santa Fe 3110206 575.00 29°55'18" N 82°25'35" W 0.28 Suwannee River near 2 Benton, FL 02315000 Columbia,FL Upper Suwannee 3110201 2090.00 30°30'30" N 82°41'50" W 10.28 Suwannee River at White 3 Springs, FL 02315500 Columbia,FL Upper Suwannee 3110201 2430.00 30°19'32" N 82°44'18" W 0.00 Santa Fe River near Fort 4 White, FL 02322500 Gilchrist,FL Santa Fe 3110206 1017.00 29°50'55" N 82°42'55" W 3.33 Suwannee River near 5 Wilcox, FL 02323500 Levy,FL Lower Suwannee 3110205 9640.00 29°35'22" N 82°56'12" W 0.00 Withlacoochee River near 6 Pinetta, FL 02319000 Madison,FL Withlacochee North 3110203 2120.00 30°35'43" N 83°15'35" W 0.00 Suwannee River at 7 Ellaville, FL 02319500 Suwannee,FL Lower Suwannee 3110205 6970.00 30°23'04" N 83°10'19" W 0.00 107 Suwannee River at 8 Branford, FL 02320500 Suwannee,FL Lower Suwannee 3110205 7880.00 29°57'20" N 82°55'40" W 0.00 Withlacoochee River at US 9 84, near Quitman, GA 02318500 Brooks,GA Withlacochee North 3110203 1480.00 30°47'35" N 83°27'13" W 50.28 Suwannee River at US 441, 10 at Fargo, GA 02314500 Clinich,GA Upper Suwannee 3110201 1260.00 30°40'50" N 82°33'38" W 3.33 Okapilco Creek at GA 33, 11 near Quitman, GA 02318700 Brooks,GA Withlacochee North 3110203 269.00 30°49'32" N 83°33'45" W 17.50 Alapaha River at 12 Statenville, GA 02317500 Echols,GA Alapaha 3110202 1400.00 30°42'14" N 83°02'00" W 0.00 Withlacoochee River at McMillan Rd., near 13 Bemiss, GA 023177483 Lowndes,GA Withlacochee North 3110203 502.00 30°57'09" N 83°16'07" W 45.83 Suwannee River at 14 Suwannee Springs, FL 02315550 Suwannee,FL Upper Suwannee 3110201 2630.00 30°23'34" N 82°56'00" W 26.67 Santa Fe River near 15 Graham, FL 02320700 Alachua,FL Santa Fe 3110206 94.90 29°50'46" N 82°13'11" W 20.00

* Hydrologic Unit Code

APPENDIX E SPECIFIC DISCHARGE AT RESPECTIVE WATERSHED CENTROIDS

SD for Alapaha Watershed (meters*1000)

Years/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep 1974/1975 (LN) 7.23 2.13 4.41 48.14 42.73 53.85 129.88 37.82 13.44 25.98 25.59 7.27 1975/1976 (LN) 7.82 3.95 4.49 30.85 34.66 18.14 14.15 40.60 48.73 17.77 6.06 5.44 1976/1977 (EN) 12.25 22.72 94.78 84.70 32.84 89.05 21.30 2.45 1.25 1.87 7.86 9.57 1977/1978 (N) 2.82 4.89 21.43 45.99 82.59 69.36 21.34 31.36 10.24 5.67 5.02 1.78 1978/1979 (N) 0.75 0.57 1.26 15.51 66.29 81.92 23.73 25.02 6.77 11.33 11.33 8.80 1979/1980 (N) 10.18 6.98 16.79 17.35 43.23 89.45 106.24 23.48 5.85 2.63 1.39 0.84 1980/1981 (N) 1.01 1.50 0.97 1.01 5.35 6.12 9.83 1.94 1.19 0.81 1.43 1.11 1981/1982 (N) 0.62 1.25 4.14 40.64 63.76 36.02 28.81 20.26 9.91 12.12 20.78 9.78 1982/1983 (EN) 2.53 1.90 6.77 42.00 98.84 141.90 101.05 27.87 6.29 9.01 6.27 2.28 1983/1984 (N) 1.32 1.67 35.60 60.46 107.80 147.27 78.98 38.51 15.76 15.57 19.80 3.89 1984/1985 (N) 1.36 1.55 1.53 2.61 10.83 13.69 6.42 2.36 1.50 4.56 9.91 6.79 1985/1986 (N) 1.29 3.91 58.76 45.74 152.92 38.15 8.80 1.61 1.35 0.89 1.39 2.38 1986/1987 (EN) 0.77 1.06 20.30 104.48 117.32 97.16 38.23 7.44 8.47 15.68 3.11 5.41 1987/1988 (EN) 1.18 1.92 1.05 6.52 43.25 81.32 23.18 32.07 2.55 1.01 0.98 12.42 1988/1989 (LN) 4.64 1.70 1.82 2.17 2.42 16.08 19.61 6.77 13.15 26.88 9.32 2.65 1989/1990 (N) 1.50 1.50 10.14 62.98 51.61 33.72 14.28 2.42 1.20 1.07 0.82 0.54 1990/1991 (N) 1.18 1.67 3.34 78.43 154.50 152.56 31.46 24.27 31.71 26.55 70.51 10.41 1991/1992 (EN) 1.77 1.16 1.24 17.64 98.06 76.26 42.35 6.00 7.04 4.14 8.82 11.73 1992/1993 (N) 10.79 10.89 34.91 119.47 56.71 77.53 64.62 4.60 1.98 2.11 1.06 1.04 1993/1994 (N) 1.19 5.46 5.77 22.87 58.53 83.85 42.12 7.13 10.39 22.24 35.85 18.60 1994/1995 (N) 80.84 21.74 53.97 44.44 110.29 53.47 17.54 3.89 10.74 3.07 1.33 0.98 1995/1996 (N) 0.79 0.79 0.77 1.78 7.21 30.19 42.81 19.69 3.09 1.21 1.50 1.23 1996/1997 (N) 8.49 2.17 7.80 25.59 68.04 66.66 11.18 15.43 12.52 7.71 10.18 1.33 1997/1998 (EN) 6.90 78.58 115.68 130.38 133.04 165.77 54.77 23.75 3.64 1.74 2.09 10.97 1998/1999 (LN) 25.15 3.20 2.24 10.43 30.27 11.98 3.85 1.30 1.01 4.18 2.15 1.01 1999/2000 (LN) 0.83 0.69 0.71 1.22 7.17 11.87 34.43 4.68 1.20 1.11 1.72 27.57 2000/2001 (N) 9.62 3.09 6.73 16.97 14.51 60.52 53.10 3.18 11.10 13.09 7.71 3.05 2001/2002 (N) 2.78 1.15 0.70 1.73 3.64 17.66 10.43 2.26 0.88 1.20 0.88 2.05 2002/2003 (EN) 1.86 20.78 21.68 29.77 17.66 131.15 68.40 10.79 26.23 23.91 58.70 31.82 2003/2004 (N) 5.50 13.25 7.40 6.35 54.33 25.71 3.83 2.22 1.52 2.95 3.45 53.35

108 109

SD for Withlacoochee Watershed (meters*1000)

Years/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep 1974/1975 (LN) 3.98 2.71 5.53 60.67 30.26 43.95 87.83 11.05 12.70 8.57 -2.59 2.82 1975/1976 (LN) 7.24 -0.34 5.12 32.90 29.68 14.31 9.84 47.34 38.55 18.54 9.10 4.59 1976/1977 (EN) 10.09 15.32 122.11 27.35 8.77 61.59 7.78 2.68 2.60 2.22 4.79 6.24 1977/1978 (N) 2.53 3.31 4.49 19.81 26.90 45.20 15.19 22.86 7.18 8.90 6.26 2.77 1978/1979 (N) 2.37 2.24 3.94 32.23 77.31 71.01 23.24 14.45 1.63 4.70 1.61 -3.65 1979/1980 (N) -3.07 5.52 15.10 13.62 32.32 54.86 56.71 11.20 3.56 4.09 1.77 1.63 1980/1981 (N) 2.30 1.83 2.68 3.27 11.12 4.52 10.38 1.90 1.83 1.45 1.95 2.82 1981/1982 (N) 1.86 3.55 7.78 35.16 52.98 17.85 20.22 18.25 7.16 8.70 16.95 7.63 1982/1983 (EN) 2.37 2.22 7.67 29.03 42.36 65.97 26.10 12.35 14.29 10.55 3.71 3.06 1983/1984 (N) 3.64 6.53 39.03 31.74 58.28 75.59 -68.06 17.74 6.08 7.72 21.25 5.70 1984/1985 (N) 1.75 2.32 2.10 4.38 10.47 9.57 5.23 3.04 2.42 6.89 -2.26 13.49 1985/1986 (N) 2.88 13.11 49.31 25.02 259.49 15.84 6.31 3.15 2.86 2.62 9.03 8.01 1986/1987 (EN) 2.13 2.44 27.78 69.33 20.98 20.06 19.17 12.66 8.47 6.80 5.55 -0.27 1987/1988 (EN) 1.95 2.32 2.22 9.17 38.87 49.27 21.31 13.11 3.20 2.84 2.98 18.40 1988/1989 (LN) 6.11 3.32 3.70 3.42 3.59 12.23 13.64 8.22 20.57 38.20 12.80 4.46 1989/1990 (N) 3.39 3.23 14.87 48.37 52.22 38.22 17.35 4.79 2.96 2.76 2.05 1.99 1990/1991 (N) 2.14 1.78 2.45 60.54 172.72 157.27 46.78 54.75 27.98 78.63 83.00 9.44 1991/1992 (EN) 4.64 3.45 3.14 15.68 98.27 60.85 27.08 6.18 17.45 8.78 11.94 6.15 1992/1993 (N) 7.78 11.38 21.63 108.45 54.86 66.89 54.46 5.68 2.63 4.84 2.00 1.43 1993/1994 (N) 1.50 2.89 2.97 13.48 42.49 80.22 60.88 12.84 22.41 37.06 88.41 28.07 1994/1995 (N) 114.59 25.11 47.37 48.33 111.55 51.41 21.72 5.72 15.99 5.05 4.41 3.03 1995/1996 (N) 3.29 3.40 3.34 7.16 14.69 36.95 52.31 20.38 3.89 3.42 3.82 3.79 1996/1997 (N) 22.39 4.40 13.89 27.71 71.63 56.72 15.74 20.24 17.91 13.73 10.64 3.12 1997/1998 (EN) 7.43 81.00 71.81 101.13 95.99 166.53 38.64 12.64 4.56 4.61 3.96 21.25 1998/1999 (LN) 74.25 6.66 4.02 15.68 23.82 15.10 6.03 3.32 2.57 3.90 3.18 1.66 1999/2000 (LN) 2.37 2.51 1.88 2.20 7.13 11.81 18.02 3.83 2.28 2.18 2.23 17.35 2000/2001 (N) 10.96 3.14 7.24 19.53 12.90 55.53 42.25 4.21 27.08 12.14 9.44 3.20 2001/2002 (N) 1.76 1.78 1.60 1.47 3.10 24.40 9.88 2.71 1.78 1.40 1.30 3.93 2002/2003 (EN) 2.77 26.79 21.54 28.69 18.87 161.63 55.33 17.29 21.22 16.98 45.15 16.77 2003/2004 (N) 5.03 13.20 10.82 8.75 56.05 28.72 6.28 5.69 7.90 17.06 9.44 29.21

110

SD for Santa Fe Watershed (meters*1000)

Years/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep 1974/1975 (LN) 40.08 32.44 29.80 31.42 39.38 38.05 41.12 38.44 31.80 32.55 40.05 49.26 1975/1976 (LN) 51.02 35.53 30.37 32.87 34.89 30.15 28.32 29.04 32.54 34.28 28.90 34.34 1976/1977 (EN) 33.62 27.44 30.89 49.11 53.43 44.73 33.47 29.06 27.16 26.50 26.71 34.80 1977/1978 (N) 29.44 24.75 28.76 42.03 59.81 74.11 47.63 79.20 48.58 43.65 74.65 52.04 1978/1979 (N) 40.14 35.55 32.93 32.12 43.16 44.05 40.73 41.85 36.76 34.83 39.11 52.37 1979/1980 (N) 60.48 37.52 38.66 39.13 46.98 79.58 73.39 51.35 41.91 41.86 52.55 39.91 1980/1981 (N) 33.44 30.79 29.14 27.34 35.59 38.70 33.78 27.52 25.83 25.46 25.97 28.19 1981/1982 (N) 24.70 23.24 21.96 23.55 26.50 28.89 56.43 39.17 37.03 57.47 59.80 57.57 1982/1983 (EN) 49.18 38.71 35.04 34.23 49.47 80.27 92.07 64.88 53.49 62.96 51.23 53.73 1983/1984 (N) 50.63 46.45 56.46 87.92 63.86 87.60 116.32 65.97 51.33 53.31 62.60 48.71 1984/1985 (N) 46.94 43.18 40.88 39.05 40.32 38.38 39.30 36.90 35.61 40.41 57.73 114.86 1985/1986 (N) 58.87 58.94 42.94 54.21 65.73 67.86 50.19 38.66 35.86 34.73 36.68 50.18 1986/1987 (EN) 40.59 35.92 41.06 69.19 96.17 110.32 105.24 61.93 50.05 48.20 52.07 56.86 1987/1988 (EN) 42.86 39.64 37.09 42.75 66.82 100.36 62.91 46.33 39.81 39.31 46.21 114.77 1988/1989 (LN) 62.93 44.72 39.58 36.39 34.91 33.41 31.23 28.33 26.68 28.86 32.03 39.61 1989/1990 (N) 38.01 28.52 26.92 27.54 27.73 29.67 27.89 24.86 22.97 24.47 25.63 24.40 1990/1991 (N) 23.57 21.92 20.32 21.68 33.27 62.51 70.97 78.68 69.74 64.51 63.57 51.63 1991/1992 (EN) 51.47 37.78 32.52 29.93 32.26 36.98 37.76 32.62 32.98 43.14 47.83 57.53 1992/1993 (N) 131.37 51.11 38.72 40.36 48.85 58.62 52.92 36.61 32.16 33.42 28.99 26.94 1993/1994 (N) 25.14 29.93 28.00 31.90 52.75 43.00 31.53 29.20 31.60 36.48 40.67 34.44 1994/1995 (N) 54.51 43.90 34.84 34.85 32.71 33.02 42.62 32.87 33.05 40.03 45.20 45.36 1995/1996 (N) 30.79 27.15 26.81 28.72 29.63 38.88 43.19 33.98 31.84 51.86 39.83 35.80 1996/1997 (N) 63.71 39.41 39.65 43.53 41.69 39.07 35.55 64.34 51.54 40.90 55.99 31.88 1997/1998 (EN) 0.83 2.24 34.50 35.17 -8.98 110.16 37.13 6.91 3.27 2.36 4.04 6.31 1998/1999 (LN) -21.11 24.38 19.93 13.93 9.43 15.90 20.15 20.28 18.25 17.11 18.28 18.95 1999/2000 (LN) 21.04 17.84 17.55 17.55 14.25 10.12 2.44 12.66 15.23 15.01 17.27 3.87 2000/2001 (N) 22.57 14.22 9.27 -0.25 1.43 16.44 -36.09 7.24 -12.19 -11.36 0.57 13.33 2001/2002 (N) 18.15 14.69 13.27 10.35 8.86 -14.22 4.82 9.11 11.04 12.82 13.90 14.06 2002/2003 (EN) 18.44 4.51 2.09 -2.73 10.70 14.95 -71.57 -16.54 -6.28 6.92 -19.27 -6.73 2003/2004 (N) 20.38 1.30 8.86 10.44 26.57 -30.50 10.22 14.06 14.03 3.14 8.54 2.57

111

SD for Upper Suwannee Watershed (meters*1000)

Years/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep 1974/1975 (LN) 8.20 3.38 4.59 16.53 44.24 21.04 55.73 49.88 10.64 24.08 36.68 21.55 1975/1976 (LN) 11.07 4.46 1.99 7.84 19.89 14.31 9.10 16.92 39.75 15.19 5.40 2.63 1976/1977 (EN) 5.15 3.70 43.77 79.38 35.82 42.21 12.90 3.61 2.79 2.43 4.55 10.36 1977/1978 (N) 5.43 2.86 12.95 23.43 48.39 64.28 21.79 18.16 10.53 5.17 12.25 3.55 1978/1979 (N) 1.90 1.48 1.75 1.92 8.72 17.99 10.64 23.67 12.24 11.73 14.80 9.98 1979/1980 (N) 21.30 7.56 10.66 11.04 15.21 51.93 46.29 21.85 8.99 9.46 8.65 4.04 1980/1981 (N) 3.52 8.33 8.33 3.89 20.21 29.54 23.03 4.61 2.81 2.28 5.00 7.43 1981/1982 (N) 2.22 3.80 3.81 18.04 28.24 25.98 31.08 9.91 6.92 23.78 33.99 9.81 1982/1983 (EN) 3.39 1.52 1.45 14.31 42.57 75.54 106.96 46.63 20.14 16.68 20.59 17.54 1983/1984 (N) 7.71 6.33 19.23 37.96 41.72 61.18 148.12 33.58 16.58 31.79 20.87 7.73 1984/1985 (N) 7.75 5.96 4.79 3.10 9.66 6.79 5.96 2.46 2.89 6.88 22.05 26.36 1985/1986 (N) 6.07 10.25 18.20 51.48 89.31 82.97 19.44 7.88 5.56 3.89 5.15 5.98 1986/1987 (EN) 3.87 4.89 26.45 53.58 93.78 98.24 75.81 14.61 8.32 9.68 16.02 22.73 1987/1988 (EN) 6.07 4.22 4.13 6.03 21.85 63.45 13.48 9.55 4.56 2.94 2.54 18.05 1988/1989 (LN) 8.59 2.95 4.66 2.91 2.73 3.15 3.03 2.81 3.69 5.73 6.44 9.53 1989/1990 (N) 5.32 2.65 5.74 15.04 21.73 25.93 12.18 2.63 2.56 2.30 1.80 1.65 1990/1991 (N) 1.62 1.26 2.23 13.91 80.36 102.82 64.09 32.49 54.75 55.31 70.54 36.02 1991/1992 (EN) 11.15 4.59 -1.78 4.59 16.98 28.84 26.92 12.07 13.03 14.72 19.95 20.55 1992/1993 (N) 24.76 10.08 21.15 42.08 45.71 24.69 30.98 10.13 4.42 4.40 3.55 2.79 1993/1994 (N) 3.35 11.75 7.95 29.16 75.54 74.47 24.99 11.49 15.32 14.36 9.06 11.56 1994/1995 (N) 56.50 24.67 10.04 14.76 19.44 23.99 21.64 5.73 2.93 3.89 4.72 3.78 1995/1996 (N) 6.07 4.38 3.40 5.51 5.77 16.75 32.15 7.37 4.27 8.98 10.89 8.67 1996/1997 (N) 15.15 6.41 9.95 19.12 39.58 44.30 17.32 18.09 11.47 11.88 8.67 5.47 1997/1998 (EN) 1.17 -41.10 -9.04 -29.54 -130.12 -37.55 9.06 2.91 5.91 5.86 5.45 7.82 1998/1999 (LN) 23.99 11.26 8.89 10.28 14.68 11.09 7.22 4.70 4.17 4.98 4.79 4.29 1999/2000 (LN) 3.25 2.95 2.68 2.79 5.39 8.30 14.85 5.36 3.06 2.88 4.38 18.88 2000/2001 (N) 12.45 5.26 6.39 12.76 10.88 -7.26 35.59 7.63 15.83 13.50 11.19 7.03 2001/2002 (N) 4.68 3.56 3.16 4.20 5.02 16.32 10.34 5.11 3.35 2.92 2.95 3.36 2002/2003 (EN) 2.75 12.62 11.07 17.69 11.98 -79.36 53.64 12.71 5.53 21.13 33.71 21.58 2003/2004 (N) 9.83 15.25 10.64 9.63 -15.81 17.56 8.72 6.82 5.00 5.47 4.70 -15.72

112

SD for Lower Suwannee Watershed (meters*1000)

Years/Months Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep 1974/1975 (LN) 108.19 88.47 80.57 31.94 66.22 99.34 11.33 129.09 101.32 65.95 73.44 81.97 1975/1976 (LN) 87.39 84.65 74.31 69.03 73.78 69.63 67.30 18.94 60.02 82.08 75.09 65.58 1976/1977 (EN) 65.05 62.32 -14.16 54.88 135.97 67.99 131.05 87.55 65.72 63.05 60.66 52.48 1977/1978 (N) 61.97 57.77 50.99 55.12 69.93 79.49 127.19 120.00 98.63 81.25 111.75 87.21 1978/1979 (N) 74.80 68.06 71.10 58.34 47.84 59.31 78.10 68.16 72.59 47.73 56.11 61.13 1979/1980 (N) 91.82 75.05 68.62 65.22 61.42 38.08 77.19 102.33 80.22 65.79 80.86 62.94 1980/1981 (N) 61.36 55.88 67.81 67.51 54.71 55.58 52.88 53.04 43.61 43.47 43.20 39.55 1981/1982 (N) 45.25 49.89 49.27 44.92 37.76 73.19 87.85 83.53 61.21 65.22 79.90 86.10 1982/1983 (EN) 77.90 72.66 60.41 42.33 24.45 24.79 84.81 172.44 129.97 117.06 89.89 86.82 1983/1984 (N) 81.46 71.26 68.48 101.09 68.16 1.95 77.72 185.90 131.95 100.07 114.55 102.16 1984/1985 (N) 82.35 80.17 69.40 70.91 65.15 59.19 63.03 57.59 55.31 48.78 48.30 126.32 1985/1986 (N) 112.04 98.30 70.52 86.22 -62.13 182.52 155.96 98.88 76.04 68.55 63.79 67.63 1986/1987 (EN) 66.75 64.75 56.32 31.69 96.47 142.86 180.75 161.13 119.83 96.21 89.20 77.41 1987/1988 (EN) 94.58 89.04 90.44 89.67 60.76 91.18 144.08 112.04 82.45 73.60 73.86 113.36 1988/1989 (LN) 108.74 85.71 88.93 84.13 84.84 67.84 68.59 68.82 52.53 44.85 65.83 66.96 1989/1990 (N) 72.75 67.12 67.97 30.45 27.00 44.35 70.29 57.82 59.35 62.44 61.35 64.92 1990/1991 (N) 57.68 59.29 57.93 -6.25 -115.24 -65.15 112.43 92.77 70.64 30.26 24.26 112.14 1991/1992 (EN) 115.56 92.42 84.15 60.55 -14.85 32.58 70.39 84.74 44.70 55.31 47.73 67.99 1992/1993 (N) 93.30 72.98 43.45 -23.90 56.50 44.26 59.67 86.12 64.91 56.51 59.47 62.29 1993/1994 (N) 61.10 58.30 62.69 59.12 27.64 35.94 90.47 124.29 27.00 61.06 35.32 70.87 1994/1995 (N) 22.66 101.71 72.32 65.03 25.85 52.42 62.37 51.70 38.03 40.54 40.81 36.22 1995/1996 (N) 56.71 63.06 62.69 57.49 51.24 28.10 27.76 49.72 49.17 53.77 51.26 49.84 1996/1997 (N) 45.86 53.86 44.16 37.89 16.29 41.08 70.27 71.72 68.11 64.13 51.15 69.12 1997/1998 (EN) 42.63 -15.40 9.54 40.19 52.05 77.02 222.55 114.90 85.74 70.68 62.55 56.11 1998/1999 (LN) 76.84 96.19 73.51 61.65 74.25 70.66 51.22 45.48 41.82 35.98 40.42 43.16 1999/2000 (LN) 44.92 44.85 41.50 38.53 35.46 30.97 30.38 31.44 29.57 27.34 24.88 13.88 2000/2001 (N) 44.60 38.12 36.14 32.93 35.39 -1.22 31.30 48.37 23.25 35.36 36.38 40.86 2001/2002 (N) 41.68 39.68 31.44 18.18 16.94 19.23 31.25 28.89 30.45 32.59 33.66 38.79 2002/2003 (EN) 37.13 23.80 29.53 36.14 37.87 -68.69 24.26 70.68 60.46 75.87 47.63 66.73 2003/2004 (N) 60.64 41.06 40.15 34.51 -9.06 34.56 45.48 38.56 33.41 26.27 29.60 10.27

APPENDIX F ET STATIONS

Percent Missing Temperature S.No Station Name COOPID Crop ID County Watershed Elevation Latitude Longitude Data 1 Cross City 2 WNW 82008 FL16 Dixie * 12.8 29°23'24" N 83°06'00" W 7.2 2 Federal Point 82915 FL24 Clay * 1.5 29°27'00" N 83°19'12" W 7.2 3 Gainesville Regional Arpt. 83326 FL33 Alachua * 40.8 29°25'12" N 83°10'12" W 33.8 4 Glen St Mary 1 W 83470 FL34 Baker * 39 30°09'36" N 82°06'36" W 5.5 5 High Springs 83956 FL36 Gilchrist Santa Fe 19.8 29°30'00" N 82°21'36" W 6.6 6 Jacksonville Intl. Arpt. 84358 FL39 Duval * 7.9 30°18'00" N 81°25'12" W 0 7 Jasper 84394 FL41 Hamilton Upper Suwannee 44.8 30°08'36" N 82°34'12" W 1.38 8 Lake City 2 E 84731 FL45 Columbia Santa Fe 59.4 30°06'36" N 82°21'36" W 1.66 9 Live Oak 85099 FL47 Suwannee Lower Suwannee 36.6 30°10'12" N 82°34'48" W 0.27 10 Madison 85275 FL48 Madison Withlacoochee North 36.6 30°16'12" N 83°15'00" W 4.16 11 Mayo 85539 FL49 Lafayette Lower Suwannee 19.8 30°01'48" N 83°06'00" W 0.27 12 Monticello 3 W 85879 FL55 Jefferson * 44.2 30°19'12" N 83°33'00" W 6.38

114 13 Palatka 86753 FL64 Putnam * 21.3 29°23'24" N 81°24'00" W 55 14 Perry 87025 FL67 Taylor * 13.7 30°03'36" N 83°20'24" W 0.83

APPENDIX G LAND USE LANDCOVER DATA DESCRIPTION

Table G-1. LULC datasets used during the 30 year period of the study Year Resolution Spatial Scale Source Data Type: Number of Imagery Type Image Agency Land Use LULC Data (LU) or Land classes Date Cover (LC) 1974 1:250,000 United States FGDL/FDEP Land Cover 24 NASA High- 1970s (USGS) Altitude and early Photography and 1980s 1:250,000 Topographic Base Maps 1988 1:24,000 SRWMD DEP Website Land 32 Landsat Thematic April 14, Cover/Use Mapper Imagery 1988 (TM) from April 19,

116 EOSAT for North 1988 Florida April 5, 1988 1995 1:40,000 SRWMD SRWMD Land Use 159 Aerial Photo 1994 2003 30 meter grid State (of Florida) FWC Land Cover 43 Landsat 1999- Enhanced 2000, Thematic Mapper mostly satellite imagery 2003

117

Table G-2. FLUCCS classes, their areas and percent areas of the SRB in respective years 1974 1988 1995 2003 Percent Percent Percent Percent FLUCCS Classes Area Area Area Area Area Area Area Area Abandoned Land 782323 0.01 ------Agriculture 3173543526 28.99 2701687144 24.69 2438472304 22.21 2137117756 19.46 Barren Land 85196533 0.78 45437626 0.42 2751635 0.03 595329702 5.42 Rangeland 8653338 0.08 2205984644 20.16 128602768 1.17 1151957552 10.49 Upland Forest 6065520682 55.41 4018820358 36.72 5574517118 50.78 4519742189 41.16 Urban and Built-up (with TCU) 185328986 1.69 275245885 2.51 1007620828 9.18 716176571 6.52 Water 105454991 0.96 127454754 1.16 149047728 1.36 193736120 1.76 Wetlands 1321981132 12.08 1569617298 14.34 1677392457 15.28 1667716078 15.19 All Classes 10946461512 100.00 10944247709 100.00 10978404838 100.00 10981775967 100.00 * includes .18% of TCU land area ** includes .76% of TCU area

Figure G-1. FLUCCS LC and change in their areas over years

APPENDIX H BLANEY-CRIDDLE CROP COEFFICIENTS FOR LEVEL II FLUCCS CLASSES

Landcover Class Crop Coefficient 1 Urban and Built-up Land 11 Residential 0.75 12 Commercial and Services 0.75 13 Industrial 0.75 14 Transportation, communication and Utilities 0.75 15 Industrial and Commercial Complexes 0.75 16 Mixed Urban or Built-up Land 0.75 17 Other Urban or Built-up Land 0.75 2 Agricultural Land 21 Cropland and Pasture 0.73 22 Orchards, Groves, Vineyards, etc. 0.50 23 Confined Feeding Operations 0.80 24 Other Agricultural Land 0.73 3 Rangeland 31 Herbaceous Rangeland 1.0 32 Shrub and Brush Rangeland 1.0 33 Mixed Rangeland 1.0 4 Forest Land 41 Deciduous Forest Land 0.62 42 Evergreen Forest Land 0.75 43 Mixed Forest Land 0.68 5 Water 51 Streams and Canals 1.0 52 Lakes 1.0 53 Reservoir 1.0 54 Bays and Estuaries 1.0 6 Wetlands 61 Forested Wetland 0.8 62 Non-Forested Wetland 0.8

118 119

Landcover Class Crop Coefficient 7 Barren Land 71 Dry Salt Flats 0.75 72 Beaches 0.75 73 Sandy Area Other Than Beaches 0.75 74 Bare Exposed Rock 0.75 75 Strip Mines, Quarries, & Gravel Pits 0.75 76 Transitional Areas 0.75 77 Mixed Barren Land 0.75 8 Tundra 81 Low Density Residential 0.75 82 Medium Density Residential 0.75 83 High Density Residential 0.75 84 Commercial 0.75 85 Industrial 0.75 86 Recreational 0.70 87 Other Quasi-Public Lands 0.75 88 Marshes & Floodlands 0.80 89 Agricultural 0.73 90 Vacant Lands 0.75 91 Forestry 0.62

APPENDIX I CONSUMPTIVE WATER USE PERCENTAGES BY WATER USE CATEGORY FOR COUNTIES IN THE SRB

Alachua Year PSS DSS Com/Ind/Mining Power Ag RI 1975 39 39 43 100 70 0 1980 20 20 51 100 70 0 1985 33 33 25 100 70 0 1990 39 39 15 100 70 70 1995 31 31 17 100 70 70 2000 40 40 19 100 70 70 Baker Year PSS DSS Com/Ind/Mining Power Ag RI 1975 90 90 50 0 70 0 1980 20 20 100 0 70 0 1985 33 33 25 0700 1990 29 29 15 07070 1995 21 21 17 07070 2000 22 22 19 07070

Bradford Year PSS DSS Com/Ind/Mining Power Ag RI 1975 39 39 41 0 70 0 1980 14 14 33 0 70 0 1985 33 33 25 0700 1990 37 37 15 07070 1995 46 46 17 07070 2000 27 27 19 07070

Columbia Year PSS DSS Com/Ind/Mining Power Ag RI 1975 39 39 58 0 70 0 1980 17 17 56 0 70 0 1985 33 33 25 0700 1990 42 42 15 07070 1995 34 34 17 07070 2000 44 44 19 07070

120 121

Dixie Year PSS DSS Com/Ind/Mining Power Ag RI 1975 17 17 5 0 70 0 1980 20 20 33 0 70 0 1985 33 33 25 0700 1990 29 29 15 07070 1995 29 29 17 07070 2000 37 37 19 07070

Gilchrist Year PSS DSS Com/Ind/Mining Power Ag RI 1975 24 24 67 0 70 0 1980 28 28 34 0 70 0 1985 33 33 25 0700 1990 15 15 15 07070 1995 27 27 17 07070 2000 33 33 19 07070

Hamilton Year PSS DSS Com/Ind/Mining Power Ag RI 1975 22 22 10 0 70 0 1980 31 31 30 0 70 0 1985 33 33 25 0700 1990 30 30 62 0 70 70 1995 17 17 55 0 70 70 2000 39 39 55 0 70 70

Jefferson Year PSS DSS Com/Ind/Mining Power Ag RI 1975 32 32 50 0 70 0 1980 24 24 80 0 70 0 1985 33 33 25 0700 1990 24 24 15 07070 1995 24 24 17 07070 2000 24 24 19 07070

Lafayette Year PSS DSS Com/Ind/Mining Power Ag RI 1975 21 21 0 0 70 0 1980 25 25 0 0 70 0 1985 33 33 25 0700 1990 34 34 15 07070 1995 42 42 17 07070 2000 42 42 19 07070

122

Levy Year PSS DSS Com/Ind/Mining Power Ag RI 1975 24 24 0 0 70 0 1980 35 35 45 0 70 0 1985 33 33 25 0700 1990 34 34 15 07070 1995 35 35 17 07070 2000 38 38 19 07070

Madison Year PSS DSS Com/Ind/Mining Power Ag RI 1975 61 61 67 0 70 0 1980 44 44 75 0 70 0 1985 33 33 25 0700 1990 56 56 15 07070 1995 42 42 17 07070 2000 44 44 19 07070

Suwannee Year PSS DSS Com/Ind/Mining Power Ag RI 1975 59 59 14 0.1 70 0 1980 42 42 20 0.1 70 0 1985 33 33 25 0.1 70 0 1990 43 43 15 0.1 70 70 1995 32 32 17 0.1 70 70 2000 38 38 19 0.1 70 70

Taylor Year PSS DSS Com/Ind/Mining Power Ag RI 1975 39 39 19 0 70 0 1980 55 55 27 0 70 0 1985 33 33 25 0700 1990 44 44 15 07070 1995 48 48 17 07070 2000 44 44 19 07070

Union Year PSS DSS Com/Ind/Mining Power Ag RI 1975 49 49 0 0 70 0 1980 28 28 0 0 70 0 1985 33 33 25 0700 1990 32 32 15 07070 1995 45 45 17 07070 2000 38 38 19 07070

RI values were included under Ag for 1975, 1980, and 1985. Red values indicates that the statewide average was used.

APPENDIX J ANNUAL TOTALS OF WATER BALANCE AND HYDROLOGIC COMPONENTS IN THE SRB

124

APPENDIX K DECADAL VARIABILITY OF HYDROLOGIC COMPONENTS AND WATER BALANCE

Table K-1. Decadal totals and percent of 30 year totals of hydrologic components and water balance for SRB Hydrologic Precipitation Streamflow ET CWU WB Components Decades Totals Percent Totals Percent Totals Percent Totals Percent Totals Percent Decade I (1974/75-1983/84) 42666237 36 13768943 39 19706662 32 117631 20 9073002 43 Decade II (1984/85-1993/94) 39913876 34 13826559 39 21400917 35 240915 42 4196366 20 Decade III (1994/95-2003/04) 36509675 31 8065420 23 20556657 33 218105 38 7669492 37 30 Year Totals (1974/75-2003/04) 119089789 35660922 61664236 576651 20938860 * Total values in mgal

126

APPENDIX L ENSO ANALYSIS OF HYDROLOGIC COMPONENTS BASED ON ANNUAL AVERAGES

Table L-1: Comparison of El Niño and La Niña year water volumes against Neutral Years ENSO Phase Precipitation Streamflow ET CWU WB Annual % of Annual % of Annual % of Annual % of Annual % of Averages Neutral Averages Neutral Averages Neutral Averages Neutral Averages Neutral 7 Year El Niño 4276952 6 1369794 15 2030157 -2 19160 -5 822252 8 Average 5 Year La Niña 3398044 -18 1011091 -15 2052330 -1 16010 -26 318612 -137 Average 18 Year Neutral 4008939 1167606 2066194 20138 755002 Average

128

Table L-2: Comparison of water volumes in El Niño and La Niña years against 30 year annual averages for SRB Components 30 year Percentage of El Niño years Percentage of La Niña years Percentage of annual precipitation annual precipitation annual precipitation average (%) average for SRB (%) average (%) (mgal) (mgal) (mgal) Precipitation 3969660 - 4276952 - 3398044 - Streamflow 1188697 30 1369794 32 1011091 30 ET 2055475 52 2030157 47 2052330 60 CWU 19222 0.50 19160 0.45 16010 0.47 WB 697962 17 822252 19 318612 9

APPENDIX M COMPARISON BETWEEN RESULTS OF THE SPATIAL WATER BALANCE APPROACH (SWBA) AND THE POINT-AND-AVERAGE APPROACH (PAAA)

The PAAA methodology adopted to calculate water volumes in each hydrologic component is briefly discussed. Three years, one of each ENSO type was selected close to each other so that comparisons between ENSO phases could be studied. The years were: 1997/98 (El Niño), 1998/99 (La Niña) and 2000/2001(Neutral).

Monthly precipitation (in inches) at 21 stations was averaged and converted to volumes of water (mgal) which was then multiplied by the area of the SRB ( 10,983 sq.km. - calculated using GIS). For calculating streamflow, same procedure was applied using specific discharge values (in cfs-1) at each of the 5 watershed centroids. To calculate AET, PET (in mm) at all 14 stations were averaged and converted to mgal and were then multiplied by the area of the SRB. The water volume from PET over the SRB were then converted to AET by multiplying the PET with crop coefficient for the major landuse (upland forest, Kc = 0.68) in the basin. CWU values were used as described in the spatial water balance approach detailed in Chapter 4 as that would be the only way possible for calculating CWU over a basin. No comparison was done for CWU. For computing the WB, equation 1 (pp 30) was used.

129

Table M-1:Comparison of computations using the SWBA and PAAA for the year 1997/1998 (El Niño) Components Precipitation Streamflow ET Water Balance % % % % Months PAAA SWBA Diff. PAAA SWBA Diff. PAAA SWBA Diff. PAAA SWBA Diff. Oct 490349 466531 -5 34185 37000 8 139674 150495 7 314144 276689 -14 Nov 295329 282086 -5 61064 30587 -100 88765 95138 7 143248 154109 7 Dec 402855 266029 -51 129029 103735 -24 61142 64834 6 210370 95146 -121 Jan 425686 405853 -5 160837 130807 -23 76797 78866 3 185731 193859 4 Feb 944905 435267 -117 82347 43228 -90 81194 85181 5 779193 304686 -156 130 Mar 396963 398967 1 279486 251266 -11 131353 138980 5 -16192 6405 353 Apr 74385 69214 -7 210013 228541 8 186048 199518 7 -323959 -361127 10 May 66431 57479 -16 93423 102478 9 261208 276327 5 -290615 -323741 10 Jun 236410 49577 -377 59788 69617 14 305177 327022 7 -130909 -349416 63 Jul 649576 636653 -2 49437 57533 14 261287 270676 3 336472 306064 -10 Aug 441888 441774 0 45291 52799 14 222406 241599 8 171823 145006 -18 Sep 888196 870923 -2 59411 61858 4 127607 132864 4 698900 673924 -4 Annual Totals 5312972 4380352 -21 1264311 1169450 -8 1942658 2061500 6 2078206 1121604 -85 * units in mgal

Table M-2: Comparison of computations using the SWBA and PAAA for the year 1998/1999 (La Niña) Components Precipitation Streamflow ET Water Balance Months PAAA SWBA % Diff. PAAA SWBA % Diff. PAAA SWBA % Diff. PAAA SWBA % Diff. Oct 257032 255733 -1 103861 91215 -14 139240 149343 7 11577 12821 10 Nov 33878 15027 -125 82173 97635 16 95903 101593 6 -146476 -186480 21 Dec 58918 59815 1 62978 75320 16 80188 86492 7 -86589 -104339 17 Jan 345409 349486 1 64920 70940 8 75989 80496 6 202153 195702 -3 Feb 114891 126292 9 88407 90087 2 83185 88162 6 -58889 -54144 -9

Mar 130357 132048 1 72343 79688 9 139063 148656 6 -83391 -98638 15 131 Apr 77330 87536 12 51293 60232 15 183642 195524 6 -159917 -170533 6 May 128884 129253 0 43551 52708 17 244962 260913 6 -162023 -186762 13 Jun 573719 591684 3 39318 47782 18 232875 248359 6 299221 293239 -2 Jul 319633 317282 -1 38361 45092 15 272171 288187 6 6729 -18370 137 Aug 444834 459078 3 39898 47900 17 226645 235923 4 175917 172880 -2 Sep 523638 533773 2 40043 49051 18 166252 180451 8 315043 301971 -4 Annual Totals 3008524 3057007 2 727144 807650 10 1940114 2064100 6 313355 157347 -99 * units in mgal

Table M-3: Comparison of computations using the SWBA and PAAA for the year 2000/2001 (Neutral) Components Precipitation Streamflow ET Water Balance % % Months PAAA SWBA Diff. PAAA SWBA % Diff. PAAA SWBA Diff. PAAA SWBA % Diff. Oct 67756 71793 6 58107 64980 11 132497 145229 9 -123814 -139382 11 Nov 103107 95321 -8 37027 43697 15 80977 89297 9 -15850 -38627 59 Dec 99425 92269 -8 38129 42180 10 61990 65797 6 -1656 -16672 90 Jan 79540 74123 -7 47523 45098 -5 63330 67287 6 -32902 -39850 17 Feb 61864 74530 17 43551 43448 0 70290 74516 6 -53453 -44910 -19 132 Mar 572246 562584 -2 71908 54067 -33 100595 106828 6 398136 400082 0 Apr 83222 87611 5 73155 53548 -37 187645 199894 6 -179260 -167513 -7 May 56709 62180 9 40970 47271 13 272250 290883 6 -258160 -277622 7 Jun 592131 587565 -1 37723 31866 -18 266788 282165 5 285953 271868 -5 Jul 754893 762837 1 36389 34492 6 284888 301002 5 432162 425890 -1 Aug 318896 325987 2 37868 39580 4 254445 271337 6 25120 13608 -85 Sep 424949 439242 3 39115 46037 15 161914 174631 7 222525 217178 -2 Annual Totals 3214738 3236042 1 561465 546264 -3 1937610 2068867 6 698802 604050 -16 * units in mgal

Table M-4: Summary of comparison of computations using the SWBA and PAAA for the three years Precipitation Streamflow ET Water Balance Components/Years % % % % PAAA SWBA Diff. PAAA SWBA Diff. PAAA SWBA Diff. PAAA SWBA Diff. 1997/1998 (El Niño) Annual 5312972 4380352 -21 1264311 1169450 -8 1942658 2061500 6 2078206 1121604 -85 Totals 1998/1999 (La Niña) Annual 3008524 3057007 2 727144 807650 10 1940114 2064100 6 313355 157347 -99

Totals 133 2000/2001 (Neutral) Annual 3214738 3236042 1 561465 546264 -3 1937610 2068867 6 698802 604050 -16 Totals Three Year 3845411 3557800 -8 850973 841121 -1 1940127 2064822 6 1030121 627667 -64 Average

APPENDIX N COMPARISON OF ESTIMATES OF HYDROLOGIC COMPONENTS AND WATER BALANCE COMPUTED IN THIS RESEARCH WITH REPORTED ESTIMATES OF FLORIDA AND SRB

Florida’s Water Cycle “An average of 150 billion gallons of rain falls each day in Florida. Another 26 billion gallons flows into the state, mostly from rivers originating in Georgia and Alabama. Nearly 70 percent of the rain (107 billion gallons) returns to the atmosphere through evaporation and plant transpiration (evapotranspiration). The remainder flows to rivers or streams or seeps into the ground and recharges aquifers. Each day in Florida, 2.7 billion gallons are incorporated into products or crops, consumed by humans or livestock, or otherwise removed from the immediate environment (consumptive use)”. Fernald and Purdum (1998)

Figure: N-1. Florida’s Water Cycle (Source: Fernald and Purdum, 1998)

134 135

Average Daily Water Balance for Florida

Using the classical water balance equation (equation 4-1 and 4-2, pp 26-27), an average daily water balance for Florida was computed as shown below. The water balance for Florida was 0.3 billion gallons per day.

P − Q − ET − CWU − ∆S = 0

∆S = P − Q − ET − CWU and where Q is (Outflow- Inflow)

∆S = 150 – (66-26) – 107 – 2.7 = 0.3 billion gallons per day

Average Annual Water Balance for SRB

Average annual water balance for SRB was calculated using the values reported in the literature (chapter 3, pp 27). Average annual water balance for SRB was -0.4 inches.

The calculation is shown below:

∆S = 53.4 – 14.8 – 39 = - 0.4 inches per annum

ET (39 inches) has CWU losses accounted for in it.

Comparison of Water Balance Estimates

Water volumes of WB and its hydrologic components for Florida and SRB

(reported in literature) were compared with those of the SRB computed in this research.

Water volumes as a percentage of precipitation were calculated for both Florida and SRB

(reported in literature) and SRB (calculated in this research). Comparisons were then made by way of comparing percentages of water volumes and difference of percentages for each hydrologic component. Comparisons with Florida and SRB are shown in table

(N-1) and (N-2) respectively.

Table N-1: Comparison of hydrologic components and water balance of Florida (reported) vs. SRB (this research)

Florida (reported) SRB (this research)

Average daily Percentage of 30 year annual Percentage of Difference of Components water volumes precipitation (%) average (mgal) precipitation (%) percentages (%) (billion gallons)

(A) (B) (C) (D) (B-D)

Precipitation 3969660 136 150 - -

Streamflow 1188697 40 27 30 3

ET 107 71 2055475 52 -19 CWU 19222 2.70 1.8 0.5 -1.3

WB 0.30 0.20 697962 17 16.8

Table N-2: Comparison of hydrologic components and water balance of SRB (reported) vs. SRB (this research)

SRB (reported) SRB (this research)

Average Annual Hydrologic 30 year annual Hydrologic Components water volumes components and average of water components and Difference of (inches) WB as a volumes (mgal) WB as a percentages (%) percentage of percentage of precipitation (%) precipitation (%)

(A) (B) (C) (D) (B-D) 137 Precipitation 53.4 - 3969660 - Streamflow 14.8 28 1188697 30 2 ET 39 73 2074697* 52.5** -20 CWU 0.5 (Accounted in Accounted in ET - 19222 - ET) WB -0.40 -0.75 697962 17.00 17.75 * with 19222 CWU); ** with 0.5 % CWU

APPENDIX O WATER BALANCE AND HYDROLOGIC COMPONENTS AS A PERCENTAGE OF PRECIPITATION

138 APPENDIX P AN ACCOUNT OF TECHNICAL DATA PROCESSING AND DIFFICULTIES INVOLVED IN ADOPTING SWBA APPROACH

An account and measure of the tasks involved in adopting this methodology is

presented here. The methodology involved GIS intensive data processing. All the

hydrologic components (precipitation, streamflow and ET), besides CWU were processed

entirely in a GIS. Table P-1 summarizes the GIS tasks involved and the tools used to execute them. Table P-2 details the tasks executed, iterations performed, time spent

(required) and the size of the dataset generated after each operation for each of

precipitation, streamflow and ET. In Table P-2, ‘Tasks’ correspond to the tasks from

Table P-1.

Estimation of CWU involved only partial GIS usage. Calculating water withdrawal

for the individual watersheds required percent areas of counties falling in a watershed.

This percent area of counties in a watershed was calculated in a GIS. Accounting of complexity and hardship involved in estimating CWU is dealt with separately in Table P-

139

Table P-1: GIS tasks performed and tools used to achieve them

Task No. GIS Task Software used Tools used

Acquiring, cleaning, arranging data, and 1 ArcGIS 8x - creating/updating GIS Point shapefile 2 Batch Interpolation ArcGIS 8x Autoraster – an inbuilt functionality in ArcGIS 8x Batch ‘Raster to Vector’ 3 ArcGIS 8x Convert to ‘gridshape’ – a inbuilt functionality of ArcGIS 8x Conversion Batch Clip (Masked vector Batch clip Utility (BatchClipper.dll) – a free tool available at 4 ArcView 3.2 layer to study area) Arcscripts: http://arcscripts.esri.com/details.asp?dbid=10239 Batch Update Attribute

Field ‘Gridcode’ which has 140 5 water volume values in Modified python script (BatchCalcAPL.py) – a free script (given inches by multiplying it by at the end of the Appendix) available at: the conversion factor ArcGIS 9x http://forums.esri.com/Thread.asp?c=93&f=1728&t=143726&mc Batch Add/Update Area 6 =2#423322 Field (of each polygon)

Batch Add Field ‘Mgal’ and 7 update it with values from [Gridcode*Area] Batch Summation and extraction of water volume PerformSum – a program written in Visual Basic for Applications [Gridcode*Area] from each (VBA) by Dr. Anurag Agarwal, Assistant Professor. Department 8 MS Excel polygon in each monthly of Decision and Information Sciences, Warrington College of layer to calculate monthly Business. University of Florida. water volume in the SRB

Table P-2: GIS tasks, measure of processing time and computer stoarge involved Approx. size of the dataset Iterations Estimated Time Spent GIS Task generated Output P Q ET P Q ET P Q ET 3-4 3-4 4-5 Final shapefile with 1 1 1 1 50 KB 50KB 50KB months months months monthly values 30 x 12 360 150 300 300 2 360 2 weeks 2 weeks 2 weeks Raster surface (GRID) = 360 360* MB MB MB 3 360 360 360 15 MB 30 MB 20 MB 2 weeks 2 weeks 2 weeks Vector (Gridshape) 360x7 30 1 1 2 4 360 360 30 MB 30 MB Clipped vector = 2520 GB** month month months 5 30 6 360 360 2520 30 MB 30 MB 1 week 1 week 3 weeks Updated shapefile GB**

7 141 Monthly water volume 8 360 360 2520 20 MB 20 MB 2 GB 1 week 1 week 3 weeks in million gallons * Interpolation done twice, once for interpolating input streamflow data and second for interpolating SD at centroids ** 360 iterations were done 7 times for each LC class

Table P-3: Tasks and processing time involved in computing CWU Tasks Task Software used Estimated time spent

Creating monthly water withdrawal database at county 1 scale for individual water use categories (IWUC) Spreadsheet 3 months

Calculating percent county area in a watershed 2 ArcGIS 8x Insignificant

Calculating monthly water withdrawals at watershed 3 scale for IWUC Spreadsheet 1 week

Summation of water withdrawals of counties of a 4 watershed for IWUC Spreadsheet Insignificant 142

Calculating consumptive water use on a watershed 5 scale for IWUC Spreadsheet 1 week

Summation of consumptive water use at a watershed 6 scale to obtain monthly CWU of the SRB for Spreadsheet Insignificant individual water use categories Summation of CWU for individual water use 7 categories to get total water consumptive use Spreadsheet Insignificant

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BatchCalcAPL.py #BatchCalcAPL.py # #Source # Modified from a script by Erin Haney # http://forums.esri.com/Thread.asp?c=93&f=1728&t=143726&mc=2#423322 # #Modified by # Nitesh Tripathi # School of Natural Resources and Environment # University of Florida, Gainesville, Florida, USA # # #Purpose: # Batch calculates Area and Perimeter for polygon layers or Length for polyline layers. It will add the fields if they weren't found (see the cavaet below). # Select a folder. All polyline/polygon feature classes (eg shapefiles etc will be processed. # #Requires # Attach this script to a tool in ArcToolbox. # Developed in ArcGIS ArcView licence Version 9.0 SP 2 and all updates # #Properties (right-click on the tool and specify the following) #General # Name BatchCalcAPL # Label Batch Calc APL # DescBatch Calculates area perimeter,length, CubM and Mgal. The results are returned in native coordinates, hence, if you want projected values ensure that you are using projected data. Select the input folder containing the data that you want values for. # #Source script BatchCalcAPL.py # #Parameter list # Parameter Properties # Display Name Data type Type Direction MultiValue # arvg [1] Select the folder Folder Required Input No #------#Import the standard modules and the geoprocessor from win32com.client import #Dispatch import sys gp = Dispatch("esriGeoprocessing.gpDispatch.1") gp.AddToolbox("C:/Program Files/ArcGIS/ArcToolbox/Toolboxes/Data Management Tools.tbx") # # Get the input workspace folder gp.workspace = sys.argv[1] # #Get a list of the featureclasses in the input folder # Loop through the list of feature classes # Loop through the files in the folder # Add the area or length field if needed or update them otherwise

144 try: featClassLst = gp.ListFeatureClasses() featClassLst.Reset() featClass = featClassLst.Next() gp.AddMessage("\n" + "Begin Processing" + "\n") while featClass: desc = gp.Describe(featClass) shapeField = desc.ShapeFieldName isGood="True" # if desc.ShapeType == "Polygon": type = "poly" try: gp.AddMessage(featClass + " Processing...") gp.AddField_Management(featClass, "Area","double") gp.AddMessage(" Added Area field.") except: gp.AddMessage(" Updating existing Area field.") try: gp.AddField_Management(featClass, "Perim","double") gp.AddMessage(" Added Perimeter field...") except: gp.AddMessage(" Updating existing Perimeter field.") try: gp.AddField_Management(featClass, "CubM","double") gp.AddMessage(" Added CubicMeter field.") try: gp.AddField_Management(featClass, "MGal","double") gp.AddMessage(" Added MGallons field.")

elif desc.ShapeType == "Polyline": type = "line" try: gp.AddMessage(featClass + " Processing...") gp.AddField(featClass, "Length","double") gp.AddMessage(" Added Length field.") except:

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BIOGRAPHICAL SKETCH

Nitesh Tripathi was born in Pantnagar, India, on June 01, 1973. He received his primary and secondary education in his hometown. He has an undergraduate degree in

Agriculture Sciences from G.B. Pant University of Agriculture and Technology, India.

He then studied business management with a specialization in rural marketing at the

Indian Institute of Rural Management, Jaipur, India. In 1999 he was awarded the prestigious Wageningen University Fellowship to undertake Master's degree in Geo-

Information Science from Wageningen University and Research Center, The

Netherlands. Married in 2001, he then came to University of Florida to pursue a doctorate degree where his major course work is in Interdisciplinary Ecology with concentration in

Geographic Information Systems. He and his wife were blessed with a daughter Navya on September 9, 2004.

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