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Impact of Atlantic and Pacific Sea Surface Temperature Anomalies on the Magnitude and Timing of Annual Floods in Norther...


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IMPACT OF ATLANTIC AND PACIFIC SEA SURFACE TEMPERATURE ANOMALIES ON THE MAGNITUDE AND TIMING OF ANNUAL FLOODS IN NORTHERN FLORIDA By SALLY ADKINS A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

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Copyright 2004 by Sally Adkins

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This thesis is dedicated to Patrick, my partner in the rapids of this lovely and crazy river of life, and to my father who encourages us along.

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ACKNOWLEDGMENTS I would like to thank Dr. Peter Waylen for his continued support and encouragement in my academic studies, in the thesis process, and also for being a wonderful teacher and mentor. I would also like to thank Joann Mossa for her support and for providing me with employment during my studies; teaching at a college level was one of the many rewarding experiences of earning a masters degree. Thanks go also to Ellen Martin for joining my committee late in the process. Other thanks go to friends and fellow students who have made the graduate journey with me I am glad to have shared this experience with this special group of people. I would also like to thank my father, Jan Adkins, for his support and love he has always encouraged me to live an examined life and I am continually motivated by his example. Last, but never ever least, I would like to thank my boyfriend, Patrick Burger, for his continued help, support, encouragement and love during this process. He is the steady presence that allows me to work toward my loftiest goals, and I feel very lucky to have him beside me. iv

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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES...........................................................................................................viii LIST OF FIGURES...........................................................................................................xi ABSTRACT.....................................................................................................................xvi CHAPTER 1 INTRODUCTION........................................................................................................1 Research Justification...................................................................................................2 Objective of Thesis.......................................................................................................4 Thesis Structure............................................................................................................5 2 LITERATURE REVIEW.............................................................................................6 Annual Flood Series (AFS)..........................................................................................6 El Nio/Southern Oscillation (ENSO)..........................................................................8 Global Effect.........................................................................................................9 Regional Effect-United States.............................................................................10 Local Effect-Southeastern United States.............................................................11 Atlantic Oscillations...................................................................................................13 Global, regional and local effects of NAO and AMO on climate.......................13 Using Atlantic and Pacific SSTs in Hydrologic Prediction........................................15 Summary.....................................................................................................................15 3 STUDY AREA AND DATA.....................................................................................16 Precipitation................................................................................................................16 Temperature and Evaporation.....................................................................................20 Storage........................................................................................................................20 Runoff.........................................................................................................................21 Data.............................................................................................................................23 Discharge Data....................................................................................................23 SST Anomaly Indicies.........................................................................................24 Summary.....................................................................................................................24 v

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4 METHODOLOGY.....................................................................................................28 Water Year Determination..........................................................................................29 Annual Flood Series Determination...........................................................................30 Generalized Extreme Value Distribution....................................................................30 Kolmogorov-Smirnov Goodness-Of-Fit Test.............................................................31 SST relationships........................................................................................................32 Test of Proportions.....................................................................................................32 Fishers Exact Test.....................................................................................................37 Kruskal-Wallis Test....................................................................................................38 ANOVA......................................................................................................................38 Summary.....................................................................................................................39 5 RESULTS...................................................................................................................40 Seasonal Proportions of Annual Flood Events...........................................................40 Generalized Extreme Value (GEV) Fitted Distributions............................................41 Test of Proportions Results.........................................................................................42 Warm Atlantic Vs. Cold Atlantic........................................................................43 Warm Pacific Vs. Cold Pacific............................................................................43 Warm Atlantic Warm Pacific Vs. Warm Atlantic Cold Pacific..........................44 Cold Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific..............................45 Warm Atlantic Cold Pacific Vs. Cold Atlantic Cold Pacific..............................46 Warm Atlantic Warm Pacific Vs. Cold Atlantic Warm Pacific..........................47 Warm Atlantic Cold Pacific Vs. Cold Atlantic Warm Pacific............................49 Warm Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific............................50 Fishers Exact Test Results..........................................................................................50 Warm Atlantic Vs. Cold Atlantic........................................................................51 Warm Pacific Vs. Cold Pacific............................................................................52 Warm Atlantic Warm Pacific Vs. Warm Atlantic Cold Pacific..........................53 Cold Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific..............................54 Cold Atlantic Cold Pacific Vs. Warm Atlantic Cold Pacific..............................55 Cold Atlantic Warm Pacific Vs. Warm Atlantic Warm Pacific..........................56 Kruskal Wallis Test Results........................................................................................57 ANOVA Results.........................................................................................................58 Summary.....................................................................................................................58 6 DISCUSSION AND IMPLICATIONS......................................................................60 Seasonal Proportions of Annual Flood Events...........................................................60 Generalized Extreme Value Fitted Distributions........................................................60 Annual Floods in Relation to Atlantic SSTs...............................................................61 Annual Floods in Relation to Pacific SSTs................................................................62 Annual Floods in Relation to Combined Atlantic and Pacific SSTs..........................63 Chapter Conclusions...................................................................................................65 vi

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7 CONCLUSIONS........................................................................................................67 APPENDIX A TABLES AND FIGURES FOR TIMING AND MAGNITUDE OF THE AFS........70 B TABLES AND FIGURES FOR TEST OF PROPORTIONS....................................81 C TABLES AND FIGURES FOR FISHERS EXACT TEST.....................................115 D TABLES AND FIGURES FOR KRUSKAL WALLIS AND ANOVA TESTS.....140 LIST OF REFERENCES.................................................................................................145 BIOGRAPHICAL SKETCH...........................................................................................153 vii

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LIST OF TABLES Table page 3.1. 40 USGS discharge stations examined in this study. Numbers 1 40 apply to stations listed in Figure 3.3......................................................................................25 4-1. Atlantic and Pacific sea surface temperature anomaly classifications.......................33 5.1: Number of positive and negative z-values* for timing analysis; A comparison of the proportion of annual floods that occur in the summer season between the following SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis...........................................................81 5.2: Number of positive and negative z-values* for 1.5-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 1.5-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis.82 5.3: Number of positive and negative z-values* for 2-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 2-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis.83 5.4: Number of positive and negative z-values* for 2.33-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 2.33-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis.84 5.5: Number of positive and negative z-values* for 5-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 5-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis.85 5.6: Number of positive and negative z-values* for 10-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 10-year return period between the various SST combinations. Discussion of results is viii

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focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis.86 5.7: Number of positive and negative z-values* for 20-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 20-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis.87 5.8 Timing analysis results Fishers Exact Test oneand two-tailed P value test results.116 5.9: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Warm Pacific compared to Cold Pacific...............................................118 5.9: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Warm Pacific compared to Cold Pacific.(continued)............................119 5.10: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Warm Atlantic Warm Pacific compared to Warm Atlantic Cold Pacific.120 5.11: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Warm Pacific compared to Warm Atlantic Warm Pacific122 5.11: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Warm Pacific compared to Warm Atlantic Warm Pacific (continued).............................................................................................................123 5.12: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Warm Pacific compared to Cold Atlantic Cold Pacific..124 5.12: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Warm Pacific compared to Cold Atlantic Cold Pacific (continued).............................................................................................................125 5.13: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Cold Pacific compared to Warm Atlantic Cold Pacific..126 5.13: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Cold Pacific compared to Warm Atlantic Cold Pacific (continued).............................................................................................................127 5.14: Kruskal Wallis results p-values for all combinations at all stations. Shaded cells indicate significant values at the 90% confidence level.....................................141 5.15: ANOVA results p-values for all combinations at all stations. Shaded cells indicate significant values at the 90% confidence level...................................................143 ix

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LIST OF FIGURES Figure page 3.1. Average monthly rainfall for areas across Florida. First graphs represent northern areas and the sequence of graphs continues west and south finishing with the most southern areas...........................................................................................................18 3.2. Monthly hydrographs of rivers across Florida. First hydrographs represent northern areas and the sequence of hydrographs continues finishing with the most southern areas..........................................................................................................................22 3.3. Base map of 40 USGS discharge stations used in this study. Numbers 1 40 apply to stations listed in Table 3.1....................................................................................26 3.4 Two-year series of Mean Monthly Atlantic (N ATL) and Pacific (NINO 3.4) SST Anomalies 27 4-2. Annual flood series at 3 stations spanning Florida in relation to Atlantic and Pacific sea surface temperature anomalies. A) River 27 northern Florida, B) River 4 central Florida, C) River 8 southern Florida, D) Atlantic sea surface temperature anomalies, E) Pacific sea surface temperature anomalies........................................35 5.1: Total proportion of summer events by station............................................................71 5.2: Total proportion of summer events under Warm Atlantic SST conditions by station.72 5.3: Total proportion of summer events under Cold Atlantic SST conditions by station..73 5.4: Total proportion of summer events under Warm Pacific SST conditions by station.74 5.5: Total proportion of summer events under Cold Pacific SST conditions by station...75 5.6: GEV shape parameter, (related to the positioning of the distribution tail) by station.......................................................................................................................76 5.7: GEV location parameter, (related to the mode) by station......................................77 5.8: GEV scale parameter, (related to the variance) by station......................................78 5.9: Locations parameter, plotted against basin area.....................................................79 xi

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5.10 Scale parameter, plotted against basin area...........................................................79 5.11: Normalized locations parameter, plotted against basin area.................................80 5.12: Normalized scale parameter, plotted against basin area......................................80 5.13: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic conditions to Cold conditions........................................................................................................88 5.14: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic conditions to Cold conditions................................................89 5.15: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 2-year return period threshold under Warm Atlantic conditions to Cold conditions................................................90 5.16: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 5-year return period threshold under Warm Atlantic conditions to Cold conditions................................................91 5.17: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Warm Atlantic conditions to Cold conditions................................................92 5.18: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 20-year return period threshold under Warm Atlantic conditions to Cold conditions................................................93 5.19: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic conditions to Cold conditions........................................................................................................94 5.20: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Pacific conditions to Cold conditions.................................................95 5.21: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 5-year return period threshold under Warm Pacific conditions to Cold conditions.................................................96 5.22: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Warm Pacific conditions to Cold conditions.................................................97 xi

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5.23: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 20-year return period threshold under Warm Pacific conditions to Cold conditions.................................................98 5.24: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific conditions..............................................99 5.25: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific conditions...............................................................................................................100 5.26: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions..............................................101 5.27: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions...............................................................................................................102 5.28: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Cold Pacific conditions to Cold Atlantic Cold Pacific conditions..............................................103 5.29: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Cold Pacific conditions to Cold Atlantic Cold Pacific conditions...............................................................................................................104 5.30: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific conditions............................................105 5.31: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific conditions...............................................................................................................106 5.32: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 2-year return period threshold under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific conditions...............................................................................................................107 5.33: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold xii

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under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific conditions...............................................................................................................108 5.34: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific conditions............................................109 5.35: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific conditions...............................................................................................................110 5.36: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 5-year return period threshold under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific conditions...............................................................................................................111 5.37: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific conditions...............................................................................................................112 5.38: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions..............................................113 5.39: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Warm Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions...............................................................................................................114 5.40: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Warm Atlantic conditions to Cold Atlantic conditions...................................................................128 5.41: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Warm Pacific conditions to Cold Pacific conditions.....................................................................129 5.42: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific conditions.....................130 5.43: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions.......................131 xiii

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5.44: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Cold Atlantic Cold Pacific conditions to Warm Atlantic Cold Pacific conditions.......................132 5.45: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Cold Atlantic Warm Pacific conditions to Warm Atlantic Warm Pacific conditions..................133 5.46: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Warm Atlantic conditions to Cold Atlantic conditions................................134 5.47: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Warm Pacific conditions to Cold Pacific conditions...................................135 5.48: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific conditions...............................................................................................................136 5.49: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Cold Atlantic Warm Pacific conditions to Warm Atlantic Warm Pacific conditions...............................................................................................................137 5.50: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions...............................................................................................................138 5.51: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Cold Atlantic Cold Pacific conditions to Warm Atlantic Cold Pacific conditions...............................................................................................................139 5.51: Timing analysis results..........................................................................................115 5.52: Kruskal Wallis results significant p-values for all combinations at all stations..142 5.53: ANOVA results significant p-values for all combinations at all stations............140 xiv

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science IMPACT OF ATLANTIC AND PACIFIC SEA SURFACE TEMPERATURE ANOMALIES ON THE MAGNITUDE AND TIMING OF ANNUAL FLOODS IN NORTHERN FLORIDA By Sally Adkins May 2004 Chair: Peter R Waylen Major Department: Geography Pacific sea surface temperatures (SST) have been shown to be associated with the interannual variability of precipitation in many areas of the United States. The Gulf of Mexico region and particularly North Florida, which experiences frontal precipitation during the winter, is directly affected in this way. Recent studies indicate that Atlantic sea surface temperatures also significantly affect precipitation patterns in south Florida. This study investigates the relationship between Atlantic and Pacific SST and the timing and magnitudes of annual floods in Northern Florida. Discharge is an appropriate indicator of basin precipitation levels since river systems integrate rainfall on a regional scale in a way that no network of precipitation gauges can. Stream flow, then, can be used as a directly observable index of climatic variability as well as providing information about the hydrologic extremes of floods and droughts, which directly affect the lives of people living in any watershed. xv

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Northern Florida was chosen as an appropriate area of study as it experiences two peaks of precipitation which occur throughout much of the state. Summer precipitation peaks result primarily from convective and tropical storm activity, while the winter peaks result from the seasonal shifts of the mid-latitude jet stream which, channels frontal precipitation into the region. Areas that display these semi-annual peaks phenomena are ideal for studying the relationship between SST and the timing and magnitude of annual flood discharge. The USGS has maintained the discharge records for most of the chosen stations from 1950 to 1998, the period of contemporary SST data. Annual flood series based on the Florida water year were extracted. Annual Atlantic and Pacific SSTs are identified as warm (above average) or cold (below average) yielding four categories of combined SST anomalies. Timing and magnitudes of the annual flood series were compared by category. The proportions and magnitudes of annual floods occurring in summer and winter seasons are compared by category using a test of proportions, a Fishers Exact Test and both the Kruskal-Wallis median and ANOVA tests. This study concludes that Atlantic and Pacific SST anomaly combinations may affect the timing and magnitude of annual maximum discharge in North Florida, but patterns of this are not evident from the results of this study. Results suggest that, despite the well documented evidence for an association between SST and monthly stream flow and rainfall totals, oceanic conditions do not have a discernable affect upon the frequency or magnitudes of annual flood events that occur in each season. xvi

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CHAPTER 1 INTRODUCTION We still do not have a full understanding of the climate system. There are many, many important aspects that we dont fully understand, and therefore its crucial that climate research continue until we understand these systems better, understand these problems better, and know how to model them. (Rasmusson, 2004) Hydrologists have dedicated ample research efforts to examining and reporting the workings of climate processes. Most of these studies have dealt with one of the most socially and economically important results of our climate streamflow. The practical import of these studies to local, state and federal land use planning efforts is considerable; decisions concerning such important economic and social factors as natural resource delineation, conservation, protection, and management, engineering construction and design, municipal/agricultural water supply, energy supply and economic development depend on the prediction of water levels, flood volumes and the availability of surface/groundwater supplies. Hydrologic models serve as important prediction tools to help people understand their water resources, manage them in the present and plan for the future. There is a need for reliable climate predictions due to the direct social and economic implications that result from extreme hydro-climatic events. It is important to continue exploring how oceanic, climatic and hydrologic systems interact to better understand how best to model them in order to have the tools to plan for the future. The need for reliable hydrologic flood models is of great importance in planning for Floridas future. The relatively flat topography and substantial coastline makes many areas in the state susceptible to flooding. Careful consideration of the hydrologic cycle 1

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2 which influences literally all parts of the state is the most prudent way to make informed decisions in planning for the future of Floridas communities. Research Justification Historically, hydrologic modeling for city, state and federal planning has been based on a static model of short-term climatic fluctuation such as the Federal Emergency Management Agencys (FEMA) delineation of flood plains based on the annual flood series (Viessman and Lewis, 2002). This series, composed of the largest daily flow observed each year, has been used as a standard for floodplain engineers and is one of the most common measures of flooding. FEMAs flood plain boundaries are based on assumptions inferred from historical data that presuppose stationary, independent and identically-distributed (iid) climatic processes (Jain and Lall, 2001). The annual flood series continues to be a common tool in hydrologic modeling, and is still used by FEMA to delineate floodplains of different return periods. Recently, many hydrologic works suggest that the iid assumption is inaccurate for streamflow prediction. The wide acceptance of weather as a chaotic system with extreme sensitivity to initial conditions suggests that small changes in ocean/atmosphere conditions in one part of the world may have local consequences in another part, a phenomenon known as teleconnections. This science suggests that distant phenomena affect local planning in changing, dynamic patterns. To project conditions and precautions for a locality, it is necessary to connect the region with the effects of distant phenomena, mega-cycles with differing periods and influence. Current meteorological research firmly insists that distant influences, such as large-scale sea surface pressure gradients like the El Nio-Southern Oscillation (ENSO) phenomenon, change the patterns of local processes. This realization is only recently possible because of satellite

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3 imagery, scholarly reconstruction of climatic records, computer modeling, and facility in sharing scientific data internationally. But this science poses a new level of complexity to the once-straightforward method of using the annual flood series alone for such things as floodplain delineation and hydrologic prediction. Part of improving hydrologic models to reflect current scientific discoveries is incorporating patterns of large-scale climatic phenomena such as ENSO and the Atlantic Multidecadal Oscillation into local hydrologic models. ENSO is an equatorial circulation pattern created by a gradient in surface pressure between the eastern and western pressure cells of the Pacific Ocean (Aceituno, 1992). The system oscillates continually between periods of weak and strong pressure gradients. Periods of anomalously high/low gradients are associated with anomalously high/low sea surface temperatures in the western Pacific and produce conditions called La Nia/El Nio. Both affect weather patterns and have been shown to directly influence precipitation and stream flow in regions across the globe. In the southeast United States (including Florida), the warm El Nio phases of ENSO cause more frequent heavy winter rainfalls while the cold La Nia phase is a causal factor in increased tropical storm activity in the North Atlantic. The Atlantic Multidecadal Oscillation (AMO) is a large-scale pressure oscillation located in the North Atlantic (Kushnir, 1996). While ENSO runs on a 3-7 year cycle, this system exhibits oscillations of 20-80 years. The oscillation is associated with fluctuations in Atlantic sea surface temperatures and global climate swings. The affects of positive/negative phases of the AMO are not as extensively explored in the scientific literature as compared to ENSO, probably due to the fact that sea surface temperature and climate data are limited for the longer period of the AMO cycle. But there is a growing

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4 interest in the affects of the AMO on climate and streamflow patterns. Positive phases of the AMO have been shown to cause wetter, warmer conditions in the eastern US, as well as increased Atlantic tropical storm/hurricane activity (Schlesinger and Ramakutty, 1994). These both directly affect the state of Florida. While research efforts outlining the effects of ENSO and AMO on Florida and localities across the globe can pose great benefits to hydrologic and meteorological modeling, the science is sometimes lost in the long lag-time between scientific certainty and administrative implementation. The benefits of ENSO and AMO research have not yet been considered in the determination of FEMAs floodplains and therefore are not considered in the planning efforts of many cities and towns across the United States. Because the annual flood series plays a large role in local planning, this study attempts to find the relationships between existing annual flood data and Atlantic and Pacific sea surface temperature data. Flood generating processes in Florida vary by geographic location from continental to peninsular in a fairly systematic geographic fashion. The importance of understanding the spatial extent of the relative dominance of each process and their mixture is important for planning purposes. Correlating the relationships between spatial and functional hydrologic processes and large-scale climatic anomalies further advances the understanding of streamflow in Florida. Objective of Thesis The objective of this thesis is to examine the effects of the cycles of two large-scale sea surface pressure gradients, ENSO and the AMO, on Floridas hydrologic regime through study of the relationship of sea surface temperature anomalies to the magnitude and timing of annual floods. Annual flood characteristics associated with single-ocean

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5 (warmer/cooler than median temperatures) and combined ocean categories are compared. The timing of annual floods is analyzed according to differences in the proportion of annual flood events occurring in summer (and therefore also, by default, winter), while anomalies of magnitudes of annual floods are studied through comparison of the proportions of floods that exceed defined thresholds and through simple comparisons of means. Thesis Structure A review of scientific literature on use of the annual flood series, an overview of the El Nio Southern Oscillation and Atlantic Oscillations, and the relationships between the two are presented in Chapter 2. This chapter is followed by a description and explanation of the study area and data examined in this study in Chapter 3. Chapter 4 outlines the methodology for organizing and analyzing the data. Chapter 5 outlines the results of these analyses. Chapter 6 presents a discussion of the results and their implications. The thesis ends with a summary and conclusions chapter.

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CHAPTER 2 LITERATURE REVIEW The use of the magnitude and timing of annual flood series (AFS) is first presented, followed by an overview of ENSO and its characteristic phases. Hydrologic prediction work that considers the AFS in relation to ENSO is examined at the global, national (US), and regional (North central Florida) level. Works documenting potential impacts of temperature fluctuations in the North Atlantic, such as consideration of the Atlantic Multidecadal Oscillation are also reviewed. The chapter concludes with an overview of the literature on the teleconnections between Pacific and/or Atlantic SSTs and stream discharge patterns in the southeastern United States. Annual Flood Series (AFS) The AFS is a commonly encountered measure of flood frequency in hydrologic literature (Jain and Lall, 2001; Lecce, 2000a, b; Glaves and Waylen, 1997; Waylen and Woo, 1982). The annual flood is simply defined as the largest daily flow (or instantaneous maximum when available) observed in a year. Although this approach to flood frequency analysis has some disadvantages, it remains a staple of hydrologic prediction. One major disadvantage is that the AFS is limited to a single annual maximum observation, even if that maximum would be insufficient to constitute a flood by any other practical measure. By the same token, the selection of a single maximum may result in the omission of other flood peaks within the same year. These problems may be particularly troublesome in an environment like north central Florida where rainfall is the major flood generating process and where there are two distinct rainy 6

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7 seasons during the year. On the other hand, the AFS provides a simple and unambiguous definition while other definitions of floods are more subjective. The AFS has a potential theoretical parallel in extreme value theory (Gumbel, 1958; Fisher and Tippett, 1928) a branch of statistics dealing with probability distributions of extremes repeatedly drawn from large samples. The combined use of AFS and some form of fitted probability distributions are well established in the hydrologic literature and are used by such agencies as the Federal Emergency Management Agency to delimit flood zone boundaries that influence critical planning decisions (Viessman and Lewis, 2002; Jennings et al., 1993) The timing and magnitude AFS have also been used to identify the geographic variability of flood generating processes. Lecce (2000a,b) conducted cluster analysis on the timing of AFS to examine the changing spatial patterns floods in the southeastern United States and, more specifically, in North Carolina. Other less commonly used types of flood frequency analysis include the partial duration series that records all events above a defined threshold (Madsen et al., 1997), and the annual exceedence series that records the same number of the largest rank ordered historic discharge events as there are years in the discharge record (McKerchar and Mackey, 2001). All the flood frequency methods mentioned above furnish similar estimates of flood discharges for return-periods of over five years (Lecce, 2000a, b; Cruise and Arora, 1990; Cunnane, 1973). Generally, through use of the annual flood series, hydrologists have either explicitly or implicitly, considered the timings and magnitudes of the AFS as stationary, independent and identically distributed (iid) processes. In other words, these statistics of

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8 the magnitude and timings, specifically their mean and variance, do not change over time exhibiting strong stationarity. As many hydrologists recognize now, aspects of the hydrologic cycle are not fixed in either their characteristics of average behavior or their variability exhibiting weak stationarity (Jain and Lall, 2001; National Academy Press, 1999; National Research Council, 1991, 1988). Their timings and magnitudes change in patterns determined by distant events and forces, thereby invalidating the iid assumption. In recent years hydrologists have perceived links between global phenomena and the local climate patterns which are responsible for changes in the magnitude and timing of flood generating processes (Jain and Lall, 2000, 2001; Dettinger et al., 2000, McCabe and Dettinger, 1999; Baldwin and Lall, 1999; Cayan et al., 1999). It now seems apparent that in many parts of the world flood generation arises from a cascade of phenomena operating at a variety of temporal and spatial scales, many of which endure long and subtle cycles. This means that our regional and local water planning, and the zoning for cities and towns, all based on flood zones determined by the AFS and an implicit iid assumption, are potentially based on an invalid stationary model that may not reflect reality. Powerful components of global climate variability driving local hydrologic patterns in many parts of the United States include the ENSO, the North Atlantic Oscillation (NAO) and the Atlantic Multidecadal Oscillation (AMO). Other causes of non-stationarity in the AFS include large-scale land use changes. El Nio/Southern Oscillation (ENSO) ENSO is a large-scale ocean (EN)-atmosphere (SO) phenomena originating in the equatorial Pacific Ocean that affects weather patterns around the globe. While the name El Nio is technically associated only with the warm phases of this system, especially the warming of the ocean waters off the coast of Peru and Ecuador, the term

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9 ENSO has come to represent a comprehensive description of the entire ocean-atmosphere system (Aceituno, 1992). ENSO is an equatorial circulation pattern created by a see-saw shift in surface pressure between two pressure cells of the Pacific Ocean. This shift oscillates continually between periods of weak and strong pressure gradients, creating El Nio and La Nia. The El Nio phase of this system occurs when the pressure gradient between the eastern and western equatorial Pacific is weak, and supports abnormally high sea surface temperatures (SST) in the eastern equatorial Pacific. El Nio, then, is a warming event, or warm phase, of ENSO. Its counterpart, the cold phase, is La Nia. In this phase the pressure gradient is strong and results in anomalously low SSTs off the coast of South America. Both phases have been shown to affect climate patterns across the globe through a series of teleconnections (Dettinger et al., 2000; Hoerling et al., 1997; Ropelowski and Halpert, 1989,1987,1986; Rasmusson, 1985; Yarnal, 1985; Ely et al., 1994;Rasmusson and Carpenter 1983,1982; Walker, 1923). Global Effect The opposing phases of ENSO produce different effects around the globe. Both phases can cause severe droughts in some regions of the world while others receive heavy rain and flooding. ENSO influences many climatic variables: sea surface temperatures, wind patterns, continental surface temperatures, and precipitation. Variation in stream flow is one of the well-documented regional and local results of ENSOs comprehensive climatic effect (see for example, Waylen and Poveda, 2002; Dettinger et al., 2000; Giannini et al., 2000; Hastenrath et al., 1999; George et al., 1998; Kahya and Dracup, 1993; Cayan and Webb, 1992; Hastenrath, 1990a, b; Cayan and Peterson, 1989; Walker, 1923; Walker and Bliss, 1932).

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10 One of the earliest pieces of research in this area examined the association between El Nio events and monsoon rainfall in India (Walker, 1923). Walker and Bliss (1932) linked ENSO to changes in sea level pressure in Santiago, Honolulu, Darwin, Manila, Batavia, and Cairo; temperature in Madras; rainfall in central Chile and India; and stream flow in the Nile River. Ropelowski and Halpert (1987) completed an extensive global study defining the geographical regions and the temporal phases of ENSO-related precipitation. Eltahir and Wang (1999) determined relationships between Nile River discharge levels and El Nio events. Dettinger et al. (2000) examined streamflow responses to ENSO on a global scale carrying on the work of Ropelowski and Halpert (1987). Waylen and Poveda (2002) confirmed associations between extreme precipitation and streamflow levels and ENSO events in western South America. Studies have also been conducted on the direct climatic influences of ENSO on precipitation and stream flow in South America (Waylen et al., 2000; Hastenrath et al., 1999; Mechoso and Perez-Iribarren, 1992; Hastenrath, 1990a,b; Waylen and Caviedes, 1990,1986) and in Central America (Waylen and Laporte, 1999). Regional Effect-United States Many studies have documented the effects of ENSO events on climatic patterns of the United States (Gershunov and Barnett, 1998; Dracup and Kahya, 1994; Ely et al., 1994; Kahya and Dracup, 1993; Redmond and Koch, 1991; Ropelowski and Halpert, 1986; Douglas and Englehart, 1981). Several of these studies have dealt with stream flow since rivers integrate rainfall on a regional scale making them good indicators of basin, and therefore regional, precipitation levels. Rivers, therefore, give us the means to observe and analyze climatic variability, while also having important effects upon human activity.

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11 Redmond and Koch (1991) noted that during warm phases of ENSO, stream flow is anomalously low in the Pacific Northwest and especially high in the desert southwest. These results are consistent with other studies that have documented a very strong relationship between ENSO events and precipitation/stream flow in these regions (Kahya and Dracup, 1993; Cayan and Peterson, 1989; Ropelowski and Halpert, 1986; Englehart and Douglas, 1985). Significant stream flow magnitude and timing responses to ENSO phases have been found in the Gulf of Mexico, the Northeast, and the North Central regions of the U.S. also. In general, El Nio events seem to cause higher stream flows in the Gulf of Mexico, North Central, and Southwest regions and cause diminished stream flows in the Northeast and Pacific Northwest (Kahya and Dracup, 1993; Redmond and Koch, 1991; Cayan and Peterson, 1989; Ropelowski and Halpert, 1986; Englehart and Douglas, 1985). The signal associated with La Nia is generally the opposite to that associated with the El Nio events (Dracup and Kahya, 1994, Ely et al., 1994). These effects of ENSO are commonly transmitted through the climate patterns of the US by means of the displacement of the sub-tropical and mid-latitude jet streams, which carry moisture across much of the US. While the jet stream path naturally oscillates and varies seasonally, it displays considerable inter-annual variability related to conditions in the equatorial Pacific (Douglas and Englehart, 1981). Local Effect-Southeastern United States The southeastern United States has recurrently been mentioned as one of the US regions climatically affected by ENSO (Pielke and Landsea, 2002; Cao, 2000; Sun and Furbish, 1997; Henderson and Robinson, 1994; Kahya and Dracup, 1993; Dracup and Kahya, 1994; Ropelowski and Halpert, 1987,1986; Simard et al., 1985; Douglas and Englehart, 1981). In general, warm El Nio phases of ENSO cause more frequent heavy

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12 winter rainfalls in the Southeast and Gulf coastal regions. In Florida, local precipitation anomalies can be correlated with ENSO events: precipitation is above normal all over the state during winters and springs of years following a warm ENSO event (Hanson and Maul, 1991; Zorn and Waylen, 1997). In south Florida, ENSO warm events can account for winter rainfall that is 45% to 66% above normal. Sun and Furbish (1998) conclude that El Nio and La Nia are direct factors in up to 40% of the annual precipitation variations and 30% of the river discharge variations in Florida. A study by Schmidt et al. (2001) correlates seasonal precipitation and stream flow responses in Florida to ENSO as shown by their relationships to Pacific (ENSO) sea surface temperatures. La Nia may also have a direct relationship with rainfall and discharge in the southeastern US. Atlantic tropical storms are significant contributors to precipitation levels in this area, and particularly to extreme hydrologic events. There is a strong correlation between La Nia and Atlantic tropical storm/hurricane events (Pielke and Landsea, 2002, 1999, 1998; Bove et al., 1998; Gray, 1984a, b). Gray et al. (2003) issue periodic tropical storm forecasts using several global-climate forecasting variables, one of which is ENSO phase. The occurrence of El Nio, according to the forecasting models, inhibits tropical systems from developing into hurricanes, due to an increased strength of the northeast trade winds and concomitant increases in vertical shear and decreases in sea surface temperatures. La Nia has been shown to increase the probability of two hurricanes striking the US to 66% (Bove et al., 1998), but it must be noted that tropical storm land fall is a noisy process; more/less storms in North Atlantic basin do not necessarily, or simply, correlate with more/less storms hitting Florida.

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13 Atlantic Oscillations The Atlantic may also have a critical influence on global climate patterns (Enfield et al., 2001; Visbeck et al., 2001; Kerr, 2000; Kushnir, 1996, 1994; McCartney, 1996; DArrigo et al., 1993) at a longer time scale than ENSO. Two ocean-atmosphere phenomena of the Atlantic that have been identified as influencing climates in various parts of the world are the NAO and AMO. The NAO is an oscillation in atmospheric mass with centers of action around the sub-arctic Icelandic Low and the subtropical Azores High semi-permanent pressure cells. The strength and position of these pressure systems interact to affect climates in adjacent land masses (Kushnir, 1996, 1994) The NAO has a cycle of approximately 10 years. It affects climate around the world in a complex geographic pattern (Wanner et al., 2001; Hurrell, 1995). Since the NAO indicates conditions north of the subtropical Azores High, its effects are more strongly seen in Western Europe than on the eastern coast of the United States. The AMO is a large-scale pressure oscillation pattern located in the north Atlantic. It has a 20-80 year cycle and is associated with fluctuations in North Atlantic sea surface temperatures as well as global climate swings (Delworth and Mann, 2000; Kerr, 2000; Schlesinger and Ramakutty, 1994). North Atlantic SSTs index the oceanic expression of the AMO (Enfield et al., 2001). Global, regional and local effects of NAO and AMO on climate The North Atlantic is important to global climate patterns at various time scales. The thermohaline circulation is a global ocean circulation driven by differences in the density of seawater, which, in turn, is controlled by temperature and salinity (Broecker, 1995). In the North Atlantic, the thermohaline circulation transports warm, salty water

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14 from the equatorial zones to the north. As the temperature changes to become cooler in the northward journey, the water becomes denser. In the vicinity of Western Europe, the circulation sinks and flows south past the African coast. This giant circulation creates considerable flow, and influences climates and hydrology occurring in adjacent continents. Because SST indices indicate stages of the NAO and the AMO, the thermohaline circulation plays an important role in both of these oscillations and subsequent climate patterns (Delworth et al., 1993; Hurrell, 1995). Recent studies have examined the relation of the NAO and the AMO to climate patterns at global and regional scales (Enfield et al., 2001; Goldenburg et al., 2001; Visbeck et al., 2001; Kerr, 2000; DArrigo et al., 1993). On a global scale, Visbeck et al. (2001) reviewed the effects of the warm and cold phases of the decadal NAO on climate patterns around the world. Kerr (2000) discussed both land and sea multidecadal AMO in relation to global climate fluctuations. DArrigo et al. (1993) correlated tree-ring records in continents surrounding the Atlantic to the North Atlantic SST NAO index. In the United States, a positive AMO phase has been found to yield less than normal rainfall in most of the country and a 10% decrease in outflow from the Mississippi River (Enfield et al., 2001). Evidence also suggests that the Atlantic modulates responses in the Southeastern US region. Visbecks team found that during a positive NAO phase, conditions in the eastern United States are wetter and warmer that average. Enfield et al. (2001) found that between warm and cold phases of the AMO, the inflow to Lake Okeechobee, Florida varied by 40%. Goldenburg et al. (2001) detected an increase in Atlantic hurricane activity due to an increase in north Atlantic SSTs.

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15 Using Atlantic and Pacific SSTs in Hydrologic Prediction Since Pacific and Atlantic ocean-atmosphere patterns are both crucial to understanding climate variability around the globe, the interactions between Atlantic and Pacific trends are also important to consider. Recent studies have examined these interactions. Livezey and Smith (1999) discuss the relationships between the U.S. surface temperatures and ENSO, the NAO, and another global signal characterized by a global ocean-warming trend. Guenni et al. (2002) have observed climatic patterns in Venezuela indicating an overall decrease (increase) in rainfall when the Pacific and Tropical Atlantic SSTs are warmer (colder) than normal. Enfield et al. (2001) identify the importance of understanding that the AMO affects the intensity and geographic coverage of inter-annual impacts like those of ENSO. Enfield and his colleagues also discuss the United States patterns of variability driven by both ENSO and the AMO. Summary The annual flood series is commonly used in hydrologic studies. This series is often used with an assumption that the flood series behaves as a stationary, independent and identically distributed process. However, recent studies invalidate the iid assumption by showing that the hydrologic cycle is not fixed in either its variablility or characteristics of average behavior. Large-scale climatic cycles such as ENSO and the AMO affect the variability of the hydrologic processes over time. These systems also affect streamflow patterns in Florida and across the globe.

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CHAPTER 3 STUDY AREA AND DATA The water balance equation is a way of accounting for, and tracing the path of, water as it moves through the hydrologic cycle and is therefore used below as the framework to describe the hydrologic regime in Florida. Singh (1992) presents the hydrologic budget of a drainage basin as: Input = Output Storage. In Florida at the monthly time scale, drainage basin input is predominantly precipitation (P) mostly in the form of rain and in some cases groundwater transfers from other basins. Outputs include runoff (R), evaporation (E), and, again, in some cases, losses to inter-basin transfers of groundwater. Hydrologic stores (S) include lakes and aquifer systems. Singhs equation can therefore be reformulated to fit Floridas hydrologic profile as: Runoff = (Precipitation Evaporation) Storage. The first part of this chapter focuses on the spatial and temporal characteristics of the right hand side of the above equation to reveal the factors that control stream discharge in the study area, the second part discusses the resultant patterns of runoff, and the final section describes the data used in this study. Precipitation In Florida, the major input to a drainage basin is precipitation (P) in the form of rain. Three mechanisms generate precipitation in the region: convection, tropical storms, and fronts. Convective activity and tropical storms dominate the summer precipitation season throughout the state, bringing in the most amount of precipitation as indicated in figure 3.1. Florida has more thunderstorms than any region in North America (Henry et al.., 16

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17 1994). Its peninsular nature, converging sea breezes, and position relative to the Atlantic High pressure system and the tropical/subtropical region make it a prime area for convective activity. This type of precipitation most often occurs in the summer months and exhibits the characteristics of sudden, localized onset. Tropical storms are also an important part of Floridas water budget yielding sizable amounts of rain over affected areas. Of all hurricanes that have affected the United States, almost 40 percent have struck Florida (Henry, 1998). Hurricanes often contribute significant rainfall in the state during the six-month hurricane season of June through November, but the probability of hurricanes and/or tropical storms striking Florida is highly variable. Frontal activity drives winter precipitation particularly in northern Florida. Winter rainfall (most notable in the Panhandle) is brought to the area by large-scale, mid-latitude frontal systems steered by the upper level jet streams. The amount of precipitation for each frontal storm is small, compared to summer storms, but the aggregate rainfall is a reliable seasonal source of water for Floridas far northern watersheds (Henry et al.., 1994). The influence of these winter frontal storms diminishes to the south as the influence of the subtropical jet-stream less frequently drops to these latitudes. Figure 3.1 shows mean monthly precipitation at various stations across the state. North and Central Florida exhibit two precipitation peaksone in summer caused by convective and tropical storm activity, and the other peak corresponding to winter frontal activity. The latter peak declines in magnitude to the south. Most of Florida experiences two precipitation seasons, with precipitation magnitude and timing varying spatially.

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18 Figure 3.1. Average monthly rainfall for areas across Florida. First graphs represent northern areas and the sequence of graphs continues west and south finishing with the most southern areas. Jacksonv ille0.00 2.00 4.00 6.00 8.00 10.00 123456789101112 MonthPrecipitation (inches) Gainesville0.00 2.00 4.00 6.00 8.00 10.00 123456789101112 MonthPrecipitation (inches) Pensacola0.00 2.00 4.00 6.00 8.00 10.00 123456789101112 MonthPrecipitation (inches) Miami0.00 2.00 4.00 6.00 8.00 10.00 123456789101112 MonthPrecipitation (inches) Appalachicola0.00 2.00 4.00 6.00 8.00 10.00 123456789101112 MonthPrecipitation (inches) Tarpon Springs0.00 2.00 4.00 6.00 8.00 10.00 123456789101112 MonthPrecipitation (inches) Daytona Beach0.00 2.00 4.00 6.00 8.00 10.00 123456789101112 MonthPrecipitation (inches) Orlando0.00 2.00 4.00 6.00 8.00 10.00 123456789101112 MonthPrecipitation (inches)

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19 Henry et al. (1994) identifies three seasonal rainfall regimes in the state two of which are addressed below. The third region occurs in the Florida Keys and is not applicable here. The first regime covers Floridas panhandle and most of the northern and central peninsula to approximately the Tampa Bay area. This region experiences two rainfall peaks. Rainfall during the summer peak is greater on average than the winter peaks total. Summer temperatures control this predominance, obeying the basic Clausius Clapeyron equation where summer heating allows the capture and storage of moisture, the heated land generates convective activity that, together with sea breezes on the coast, allows the warm humid air to rise and eventually, rainfall follows. Summer rainfall declines slightly from the coast toward the interior. The winter rainfall season generally peaks in March in most central and northern areas. Rainfall in this season is due to mid-latitude cyclonic storms, cold fronts, and low-pressure systems that move from the Gulf northeastward over the northern half of the state. There is no noticeable coast-to-inland gradient in winter. Henrys (1994) second regime occurs in the southeastern portion of the state. Convective activity is strong for six months and produces large amounts of precipitation. Here, the Gulf Stream approaches land, contributing moisture to the air and aiding in production of instability in the region bettering conditions for rainfall. The southeast rainfall peaks occur in June and September October caused by convective and tropical storm activity. In and around Miami, the June peak is larger; to the north of Miami, the September peak is greater. Again, there is a coastal to inland rainfall gradient with rainfall maxima occurring near the coast. Rainfall decreases in July and August due to the migration of an elongated region of low pressure in the upper atmosphere. In winter, the

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20 southeast has little to no precipitation. Since frontal systems are generally far removed from this region and temperatures are reduced, conditions for rainfall are unfavorable during the winter. Temperature and Evaporation The occurrence and magnitudes of floods are very much a function of antecedent soil moisture content, which in turn is controlled by temperature and evaporation. Floridas average annual maximum daily temperatures range from 77 to 86 degrees from the northern to southern part of the state (Tanner, 1992). In general, evaporation rates increase moving north to south ranging from 39 in the northwest to 53 around Naples (Henry, 1998). Summer evaporation rates are greater than winter rates based on increased temperatures causing an even more dramatic north to south evaporation gradient. During winter, average minimum daily temperatures range from 55 degrees in the north to 71 degrees in the south (Tanner, 1992). Evaporation rates drop dramatically during winter, but maintain a gradient of lower evaporation rates in the north and higher in the south (Henry, 1994). The difference between rainfall and potential evaporation is a direct measure of potential storage and runoff. In Florida, areas along the south coast and panhandle show the greatest differences between annual rainfall and potential evaporation (Mossa, 1998). Storage Floridas water storage takes place in a complex system of wetlands, karstic lakes and groundwater aquifers. This system has developed through long-term geologic processes caused by fluctuating sea-levels. The aquifer systems, including unconfined and semi-confined aquifers, are made up of surficial aquifers of sand and gravel deposits, intermediate semi-confining units of phosphoratic clay and sand mixtures, and a

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21 carbonate semi-confined aquifer known as the Floridan aquifer system. Groundwater constitutes an important input to many streams and rivers in the state, which possesses more than 300 artesian springs. Groundwater storage provides a relatively consistent source of water. At a daily scale, groundwater has no effect on the relationship between precipitation and discharge peaks. Runoff Due to Floridas low peninsular nature and its relatively homogeneous topography, runoff, the output of the water balance equation, varies gradually according to latitude, and to a lesser degree, longitude. There are about ten thousand miles of rivers and streams within Floridas boundaries. Alluvial rivers are found in the panhandle and consist of wide forested floodplains, sandbars, levees, old river channels, sloughs, and oxbow lakes. Slow and sluggish blackwater rivers that contain acidic tannins (thus blackwater) drain the flatwoods and cypress swamps that occur throughout the state. Clear spring run rivers are most common in north central Florida and occur where limestone outcrops allow groundwater to flow from springs. Other stream habitats occur along inshore marine habitats such as the largest river in Florida, the St Johns. In its many rivers and streams, Floridas hydrologic discharge regimes reflect changing temporal and spatial patterns as they vary across the state (Mossa, 1998). Henrys (1994) seasonal regimes are reflected in the hydrographs of mean monthly flows presented in figure 3.2. Northern rivers show considerable winter peaks confirming the strong influence of continental weather systems on the rivers of this area and reduced winter evapotransporation. The response of rivers in the middle of the state is bimodal reflecting both frontal winter and convective/ tropical storm summer rainfall seasons.

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22 Pine Barren Hydrograph050100150200250JFMAMJJASONDMonthQ (cf/s) Choctahatchee Hydrograph02,0004,0006,0008,00010,00012,000JFMAMJJASONDMonthQ (cf/s) Econfina Hydrograph050100150200250300JFMAMJJASONDMonthQ (cf/s) Santa Fe Worthington Hydrograph0100200300400500600700800JFMAMJJASONDMonthQ (cf/s) Charlie Hydrograph0100200300400500600700800JFMAMJJASONDMonthQ (cf/s) Fisheating Hydrograph0100200300400500600700800JFMAMJJASONDMonthQ (cf/s) St Johns Hydrograph050010001500200025003000JFMAMJJASONDMonthQ (cf/s) St Mary's River Hydrograph020040060080010001200JFMAMJJASONDMonthQ (cf/s) Pine Barren Hydrograph050100150200250JFMAMJJASONDMonthQ (cf/s) Choctahatchee Hydrograph02,0004,0006,0008,00010,00012,000JFMAMJJASONDMonthQ (cf/s) Econfina Hydrograph050100150200250300JFMAMJJASONDMonthQ (cf/s) Santa Fe Worthington Hydrograph0100200300400500600700800JFMAMJJASONDMonthQ (cf/s) Charlie Hydrograph0100200300400500600700800JFMAMJJASONDMonthQ (cf/s) Fisheating Hydrograph0100200300400500600700800JFMAMJJASONDMonthQ (cf/s) St Johns Hydrograph050010001500200025003000JFMAMJJASONDMonthQ (cf/s) St Mary's River Hydrograph020040060080010001200JFMAMJJASONDMonthQ (cf/s) Figure 3.2. Monthly hydrographs of rivers across Florida. First hydrographs represent northern areas and the sequence of hydrographs continues finishing with the most southern areas.

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23 Stations located in the northern mid-Florida exhibit a stronger winter peak than those in the south due to proximity to continental frontal activity. Stations on rivers in the southern portion of the peninsula show hydrographs with little if any peaks in the winter months as they are isolated from continental activity, and considerable peaks in the summer months when they are influenced by summer convective and tropical storm activity. Data This study examines the relationship between stream discharge northern Florida and both Atlantic and Pacific sea surface temperatures. Records from discharge stations spanning the area from the panhandle east to Jacksonville and south to Tampa were studied in relation to both Pacific and Atlantic SSTs. Discharge Data Daily discharge records from forty USGS stations available at http://waterdata.usgs.gov/fl/nwis/, in cubic feet/second, are used in this study. While many of these stations have historic discharge data available prior to 1950, the period of data analyzed is controlled by available SST data as described below. Table 3.1 displays the 40 USGS discharge stations analyzed in this study, along with periods of record and basin area. Figure 3.3 displays the 40 rivers geographically. Stations were chosen to provide a geographically distributed group of rivers that have minimal flood control so as to reflect natural flood conditions. While basin areas of the sampled rivers differ, data for each river were only compared relative to measurements at that river; therefore removing basin area as a variable in this study.

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24 SST Anomaly Indicies Sea surface temperature data were obtained from the NOAA National Weather Service Climate Prediction Center website at http://www.cpc.noaa.gov/data/indices/index.html. The site provides average monthly anomalies for sea surface temperatures in different regions from the time period of 1950 to 2002. This study uses the NINO 3.4 region, located between the NINO 3 and 4 at 5N-5S and 170-120W. This region was created from existing data in April of 1996 to help researchers who were trying to gain a better understanding of activity in the critical regions between 3 and 4 (http://www.cpc.noaa.gov/data/indices/index.html). The North Atlantic (NATL) region is located at 5-20 N and 60-30W. Figure 3.4 shows a simple two year monthly time series of these data. Summary This section has presented an overview of the various components of Floridas monthly hydrologic regime using a spatial perspective. Precipitation, evaporation, temperature, storage and runoff patterns across the state are reviewed in order to provide a simple basis for an understanding why the magnitudes (both average levels and variability) and timings of annual floods might be expected to vary seasonally across the state. Data used in this study, including discharge and sea surface temperature data, are also described.

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25 RIVER ID STATION NAME USGS STATION ID PERIOD OF RECORD USED IN THIS STUDY # YEARS OF DATA BASIN AREA (km^2) 1 ST MARYS RIVER NEAR MACCLENNY 2231000 1951 2002 51 1813.0 2 JANE GREEN CREEK NEAR DEER PARK 2231600 1953 2002 49 341.9 3 ST JOHNS RIVER NEAR CHRISTMAS 2232500 1951 2002 51 3986.0 4 WEKIVA RIVER NEAR SANFORD 2235000 1951 2002 51 489.5 5 OCKLAWAHA RIVER NEAR CONNER 2240000 1977 2002 25 3097.6 6 SOUTH FORK BLACK CREEK NEAR PENNEY FARMS 2245500 1951 2002 51 347.1 7 NORTH FORK BLACK CREEK NEAR MIDDLEBURG 2246000 1951 2002 51 458.4 8 FISH EATING CREEK AT PALMDALE 2256500 1951 2002 50 805.5 9 CHARLIE CREEK NEAR GARDNER 2296500 1951 2002 51 854.7 10 JOSHUA CREEK AT NOCATEE 2297100 1951 2002 51 341.9 11 HORSE CREEK NEAR ARCADIA 2297310 1951 2002 51 564.6 12 SOUTH PRONG ALAFIA RIVER NEAR LITHIA 2301300 1962 2002 40 277.1 13 ALAFIA RIVER AT LITHIA 2301500 1951 2002 51 867.7 14 BLACK WATER CREEK NEAR KNIGHTS 2302500 1951 2002 51 284.9 15 BROOKER CREEK NEAR TARPON SPRINGS 2307359 1951 2002 51 77.7 16 ANCLOTE RIVER NEAR ELFERS 2310000 1951 2002 51 187.8 17 SUWANNEE RIVER AT WHITE SPRINGS 2315500 1951 2002 51 6293.7 18 WITHLACO RIVER NEAR PINETTA 2319000 1951 2002 51 5490.8 19 SANTAFE RIVER AT WORTHING SPRINGS 2321500 1951 2002 51 1489.3 20 SANTAFE RIVER NEAR FORT WHITE 2322500 1951 2001 50 2634.0 21 STEINHATEE RIVER NEAR CROSS CITY 2324000 1951 2002 51 906.5 22 FENHOLLOWAY RIVER NEAR FOLEY 2324400 1955 2002 47 155.4 23 ECONFINA RIVER NEAR PERRY 2326000 1951 1992 41 512.8 24 OCHLOCKONEE RIVER NEAR HAVANA 2326000 1951 2002 51 2952.6 25 LITTLE RIVER NEAR QUINCY 2329500 1951 1992 41 613.8 26 TELOGIA CREEK NEAR BRISTOL 2330100 1951 2002 51 326.3 27 CHIPOLA RIVER NEAR ALTHA 2359000 1951 2002 51 2022.8 28 ECONFINA CREEK NEAR BENNETT 2359500 1951 1999 48 512.8 29 CHOCTAWHATCHEE RIVER AT CARYVILLE 2365500 1951 2000 49 9062.4 30 HOLMES CREEK AT VERNON 2366000 1951 1979 28 999.7 31 YELLOW RIVER AT MILLIGAN 2368000 1951 1999 48 1616.2 32 SHOAL RIVER NEAR CRESTVIEW 2369000 1951 2002 51 1227.7 33 BLACK WATER RIVER NEAR BAKER 2370000 1951 1998 47 531.0 34 BIG COLD WATER CREEK NEAR MILTON 2370500 1951 1996 45 613.8 35 ESCAMBIA RIVER NEAR CENTURY 2375500 1951 2002 51 9886.0 36 PINE BARREN CREEK NEAR BARTH 2376000 1952 1995 43 195.0 37 BRUSHY CREEK NEAR WALNUT HILL 2376300 1957 1992 35 126.9 38 PERDIDO RIVER AT BARRINEAU PARK 2376500 1951 1992 41 1020.5 39 ALAPAHA RIVER AT STATENVILLE 2317500 1951 1992 41 3626.0 40 SUWANNEE RIVER AT FARGO 2314500 1951 1992 41 3263.4 Table 3.1. 40 USGS discharge stations examined in this study. Numbers 1 40 apply to stations listed in Figure 3.3.

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26 Figure 3.3. Base map of 40 USGS discharge stations used in this study. Numbers 1 40 apply to stations listed in Table 3.1.

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27 -0.5 0 0.5 1 1.5 2 0123456789101112131415161718192021222324 MonthAnomaly Atlantic SST Anomaly Pacific SST AnomalyJan 2002 Jan 2003 Figure 3.4 Two-year series of Mean Monthly Atlantic (N ATL) and Pacific (NINO 3.4) SST Anomalies

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CHAPTER 4 METHODOLOGY Methods by which the potential effects of temperature fluctuations in the Pacific and the North Atlantic on the timing and magnitude of annual floods are outlined. The definitions of the water year and summer/winter seasons of streamflow are established through inspection of historic mean monthly streamflow data. Annual maximum flows are determined for each water year at each station and their seasonal timing and magnitude noted. Each year is classified according to the corresponding state of Equatorial Pacific (NINO 3.4) and Tropical North Atlantic (NATL) monthly SST anomalies, computed over the appropriate water year. Years of positive anomalies are labeled Warm (W), while those with negative anomalies are labeled Cold (C). This allows the classification of each annual maximum discharge at a station over the period 1950-2002 (water years), into one of four classes based upon the corresponding combined Atlantic and Pacific SSTs, as: CC (both oceans below mean), WW (both oceans above mean), WC (Atlantic above mean, Pacific below mean), and CW (Atlantic below mean, Pacific above mean). Timings of annual maxima are assigned to either the winter or summer seasons as defined a priori. The Generalized Extreme Value (GEV) fitted to magnitudes of the undifferentiated annual flood series determines yields estimates of discharges at the 1.5-, 2-, 2.33-, 5-, 10-, and 20-year return periods. The goodness-of-fit of the GEV is gauged through the use of a Kolmogrov-Smirnov test. Annual flood magnitudes are classified as exceeding or not exceeding each discharge threshold, thereby constituting 6 exceedence/non-exceedence series at each station, which 28

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29 may provide some idea of whether any influence of SSTs is felt at commonly or more rarely experienced levels of annual maxima. A Test of Proportions and the Fishers Exact test are used to compare the relative frequencies with which the annual maximums fall within each season, and separately to compare the relative frequencies with which the annual maximums exceed the 6 return periods under the various ocean conditions for each river. Kruskal-Wallis and one-way ANOVA tests were used to compare populations of annual maxima under the various ocean conditions in order to determine whether these populations differ significantly and whether the magnitude of one population exceeds another. All tests were set at a 90% significance level unless otherwise stated. Water Year Determination As discussed in Chapter 3, Florida has two hydrologic seasons one in the summer driven by convective and tropical storm activity and one in the winter associated with frontal activity. While the degree to which each of these meteorological mechanisms contribute precipitation to each area varies across the state, the two seasons remain distinct throughout. The hydrographs in figure 3.2 illustrate both the presence of these seasonal peaks and their varying magnitudes at stations across the state. On the basis of mean monthly hydrographs the winter hydrologic season is defined as beginning on November 1 and ending April 31. The summer hydrologic season begins May 1 and ends October 30. While the USGS traditionally defines the regional water year as beginning in October of one year and ending in September of the next, the water year described above is more practical for the purposes of this study as the summer tropical cyclone season frequently extends into October. The magnitude and Julian date (which begins day 1 on May 1 of one year and ends on day 365 on April 31 of the following year) of each annual maximum flow is determined. Figure 3.2 shows that annual maxima

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30 occur in both winter and summer seasons with a large cluster occurring during the North Atlantic tropical storm season of June-November. Ninety-seven percent of all historical North Atlantic tropical storms and hurricanes fall in the defined summer season (Landsea, 1993). Annual Flood Series Determination After data were broken up into water years as described above, the annual flood series, which is defined as the highest discharge value of the year, was extracted from each year of data at each river. To test for the stationarity of the AFS data, the annual flood series were plotted against time for each station. Stations geographically distributed across the state analyzed in this study show that these rivers do indeed display stationarity. Generalized Extreme Value Distribution A Generalized Extreme Value (GEV) distribution was fitted to the annual maxima data at each station. The distribution was developed by Jenkinson (1955), combining all three forms of the distribution derived from extreme value theory (Fisher and Tippett, 1928); the Gumbel (Type I), Frechet (Type II) and Weibull (Type III) distributions, all of which are commonly used in hydrologic modeling. The advantage of using Jenkinsons GEV distribution is that it does not require the user to, a priori, determine which of the three forms is most appropriate, allowing instead, this to be determined by the shape parameter, estimated in the following cumulative distribution functions: 0.......}],......./)([exp{exp)(0.,.........)/)(1{exp[)()/1(xxXFxxXF The parameters of the distribution can be estimated by the L-moments (Hosking et al.., 1985), which reduces the errors when dealing with small samples (Vogel and

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31 Fenessey, 1993). The parameter, is related to the mode and to the variance. The value of is controlled by the tail behavior of the distribution; =0 indicates a behavior similar to the Gumbel distribution (neutral tail), <0 indicates a Freshet fat-tailed behavior, and >0 a thin tail like the Weibull distribution. Kolmogorov-Smirnov Goodness-Of-Fit Test The Kolmogorov-Smirnov test is used to determine the goodness-of-fit of the GEV to each data set. This is a non-parametric test that makes no assumption about the distribution of the data (distribution-free) and is therefore a good test to use with a diverse distribution like the GEV. The test was used to compare the observed data sets to corresponding GEV functions and tests the null hypothesis that the population distribution from which the observed data is drawn conforms to (is not significantly different from) the hypothesized GEV distribution. The test statistic of the Kolmogorov-Smirnov is D and determines whether the maximum difference between the data sets is sufficiently large to be unlikely to have occurred because of chance fluctuations. The following equation is used to calculate the test statistic D where F is the theoretical cumulative distribution of the distribution being tested given N ordered data points Y1, Y2, YN: D = max |F (Yi) i /(N+1)| for 1iN Observed plotting position is here calculated using the Weibull formula. From the fitted GEV distributions, discharge thresholds corresponding to fixed return periods of 1.5, 2, 2.33, 5, 10 and 20 years were estimated at each station.

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32 SST relationships Monthly sea surface temperature anomalies for Nino 3.4 and the North Atlantic were combined (averaged) over each water year. Years of positive anomalies were labeled Warm (W), while those with negative anomalies were labeled Cold (C) thereby providing four possible classifications: CC, WW, WC, and CW. As described in chapter 3, Nino 3.4, located in the mid-Pacific near the International Date Line, starts to warm Pacific SSTs from July October. Documented meteorological results of this warming in the eastern areas of the United States do not occur until December March of the following year (Kahya and Dracup, 1993). Because of this teleconnection lag, Nino 3.4 anomalies were offset by one year in relation to annual maximum discharge values and North Atlantic anomalies. In other words; for each year, North Atlantic anomalies were classified as W or C based on the same year as the annual maximum discharge, while Nino 3.4 anomalies were classified for the year prior. For instance, an annual maximum discharge for the year 1951 would be classified as WC; Warm Atlantic since the average SST anomalies for that water year resulted in a positive value; and Cold Pacific since the average SST anomalies for the previous water year (1950) resulted in a negative value. Table 4.1 shows the yearly SST classifications determined in this study. Figure 4.1 displays AFS time series for three stations spanning the different hydrologic regimes of Florida and correlates the AFS at those stations to North Atlantic and Nino 3.4 anomaly time series for the corresponding years as they are classified in this study. Test of Proportions A test of proportions (TOP) evaluates the null hypothesis that the proportion of annual floods at each station falling in one timing or magnitude category (e.g. summer, or exceeding the 10-year return period flow) under one oceanic classification is not

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33 Table 4-1. Atlantic and Pacific sea surface temperature anomaly classifications. Year Monthly North Atlantic SST Anomaly Class Monthly NINO 3.4 SST Anomaly Class Final Atlantic Class Final Pacific Class (with offset Pacific) Final Combined Class 1950 -0.07 C -0.72 C C 1951 0.09 W 0.38 W W C WC 1952 0.26 W 0.04 W W W WW 1953 0.11 W 0.29 W W W WW 1954 -0.20 C -0.80 C C W CW 1955 0.22 W -1.14 C W C WC 1956 -0.24 C -0.31 C C C CC 1957 0.37 W 1.02 W W C WC 1958 0.35 W 0.30 W W W WW 1959 -0.13 C -0.16 C C W CW 1960 0.03 W -0.11 C W C WC 1961 0.02 W -0.25 C W C WC 1962 0.37 W -0.31 C W C WC 1963 0.11 W 0.51 W W C WC 1964 -0.15 C -0.68 C C W CW 1965 -0.02 C 1.15 W C C CC 1966 0.17 W -0.16 C W W WW 1967 -0.14 C -0.40 C C C CC 1968 0.16 W 0.50 W W C WC 1969 0.48 W 0.64 W W W WW 1970 -0.01 C -1.06 C C W CW 1971 -0.25 C -0.49 C C C CC 1972 -0.18 C 1.23 W C C CC 1973 -0.27 C -1.24 C C W CW 1974 -0.52 C -0.56 C C C CC 1975 -0.42 C -1.18 C C C CC 1976 -0.24 C 0.38 W C C CC 1977 0.04 W 0.37 W W W WW 1978 0.01 W -0.15 C W W WW 1979 0.29 W 0.29 W W C WC 1980 0.38 W -0.02 C W W WW 1981 0.14 W -0.07 C W C WC 1982 -0.01 C 1.77 W C C CC 1983 0.04 W -0.24 C W W WW 1984 -0.40 C -0.76 C C C CC 1985 -0.23 C -0.46 C C C CC 1986 -0.25 C 0.83 W C C CC 1987 0.44 W 1.09 W W W WW 1988 -0.09 C -1.57 C C W CW 1989 0.00 W -0.17 C W C WC 1990 0.10 W 0.27 W W C WC 1991 -0.20 C 1.23 W C W CW

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34 Table 4-1. (continued) Year Monthly North Atlantic SST Anomaly Class Monthly NINO 3.4 SST Anomaly Class Final Atlantic Class Final Pacific Class (with offset Pacific) Final Combined Class 1992 -0.11 C 0.32 W C W CW 1993 -0.20 C 0.34 W C W CW 1994 -0.14 C 0.69 W C W CW 1995 0.58 W -0.52 C W W WW 1996 0.17 W -0.23 C W C WC 1997 0.45 W 2.00 W W C WC 1998 0.46 W -1.03 C W W WW 1999 0.16 W -1.13 C W C WC 2000 -0.10 C -0.52 C C C CC 2001 0.34 W 0.05 W W C WC 2002 0.02 W 1.01 W W W WW 2003 0.45 W 0.25 W W W WW significantly different from the proportion of annual floods falling in that same category under another oceanic classification. It compares the assumed population proportions, 1 and 2, by computing the difference between their sample estimates, p1 and p2. This is done by using a pooled proportion estimate, p, which estimates the common value of p1 and p2, and tests whether they are equal. The test statistic, z, and standard error, are calculated as shown below: z = 120)(21pppp p2-p1 = 2111)1(nnpp where n1 denotes the first sample size; n2 denotes the second sample size; p denotes the pooled proportion estimate for the whole population (calculated by first adding the numerators of p1 and p2, then the denominators, resulting in the pooled fraction); p1 denotes the estimated proportion for the first population the summer ratio

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35 0200400600800 195019601970198019902000 0100200300400500 Annual Flood (m3s-1) 01020304050 -0.4-0.20.00.20.40.6 195019601970198019902000 SST Anomaly (C) -1012 (a)(b)(c)(d)(e) Summer Winter Figure 4-2. Annual flood series at 3 stations spanning Florida in relation to Atlantic and Pacific sea surface temperature anomalies. A) River 27 northern Florida, B) River 4 central Florida, C) River 8 southern Florida, D) Atlantic sea surface temperature anomalies, E) Pacific sea surface temperature anomalies.

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36 of the first significantly different from the proportion of annual floods falling in that same category under another oceanic classification. It compares the assumed population proportions, 1 and 2, by computing the difference between their sample estimates, p1 and p2. This is done by using a pooled proportion estimate, p, which estimates the common value of p1 and p2, and tests whether they are equal. The test statistic, z, and standard error, are calculated as shown below: z = 120)(21pppp p2-p1 = 2111)1(nnpp where n1 denotes the first sample size; n2 denotes the second sample size; p denotes the pooled proportion estimate for the whole population (calculated by first adding the numerators of p1 and p2, then the denominators, resulting in the pooled fraction); p1 denotes the estimated proportion for the first population the summer ratio of the first classification; and p2 denotes the estimated proportion for the second population the summer ratio of the second classification (Agresti and Finlay, 1997). Ratios were compared between classifications using the test of proportions to detect significant differences. Since the time period analyzed consists of 50 years of data broken down into categories that, in some cases, amount to an n1 + n2 of less than 30, a small sample inference for comparing proportions is also performed using the Fishers Exact Test.

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37 Fishers Exact Test The Fishers Exact Test (Fisher, 1934) is used in place of the chi-square test when sample sizes are small. Like the Test of Proportions (TOP) above, it compares members of two independent groups that fall into one of two mutually exclusive categories to determine whether the proportions of those falling into each category differs by group; it differs from the TOP in that it is a specialized test for small sample sizes. The test starts with a 2 by 2 table, called the observed table, and tests the probability of getting a table as strong or stronger, than the observed table due to random chance of sampling. A strong table is one that contains two groups that differ greatly. An example of a strong table for Warm vs. Cold Atlantic when comparing the number of summer and winter annual flood events, for instance, might show the following: the first row of the table represents the Warm Atlantic and may have 10 summer events and 0 winter events; the second row, represents the Cold Atlantic events, and may contain 0 summer events and 10 winter events. In this example, there is a clear difference in these groups in respect to seasonal timing of annual flood events, and therefore this table would be considered strong. But not all tables start out strong the test is calculated by determining the exact probability for each possible outcome that is as strong or stronger than the observed table, then it adds up the probabilities to get a P-value. The test returns exact oneand two-tailed P-values for a given frequency table. The P-value determines whether there are nonrandom associations between the two categorical variables. This test was conducted to analyze the likelihood that the population of annual floods falling in one timing or magnitude category (e.g. summer) under one classification (e.g. Warm Atlantic) are different from or larger than the

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38 proportion of annual floods falling in that same category under another classification (e.g. Cold Atlantic). The calculations for this test have been described as quite laborious and about as pleasant as an afternoon of root-canal surgery (Lowry, 2004), so luckily it was calculated using the STATISTICA 5.1 software package (StatSoft, 1995) to analyze the timing data and a specialized FORTRAN program to analyze the magnitude data. Kruskal-Wallis Test The Kruskal-Wallis test was used to determine whether certain SST combinations have discernable affects on the magnitudes of annual floods according to Atlantic and Pacific SST conditions. The test was used to compare populations of annual maxima under the various ocean conditions in order to determine whether these populations differ significantly and whether the magnitude of one population exceeds another. It is a nonparametric test used to assay the null hypothesis that samples-in this case one sample representing annual floods falling in one SST category and the other sample representing another SST category-come from identical populations. The alternative hypothesis is that samples are drawn from different populations. It is based on ranking all of the observations and comparing the mean rank of the sample group based on SSTs. The STATISTICA 5.1 software package (StatSoft, 1995) returns values of the probability that the observed differences came about by chance. ANOVA The one-way ANOVA (analysis of variance) test compares means of categories of a single qualitative variable. It compares groups based on independent random samples from those groups. This test was used in conjunction with the Kruskal-Wallis test (STATISTICA 5.1) to compare populations of annual maxima under the various ocean

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39 conditions. In this case, the mean under consideration is that under each SST condition. The null hypothesis is that the groups have identical means. Summary To identify any relationship between Atlantic and Pacific SSTs and the timing and magnitude of annual flood events on rivers across Florida, it is necessary to determine a set water year including seasons, to categorize SST states, and to determine appropriate, common levels of flood frequency (comparable levels of flooding in the frequency domain) across basins of varying area, in order to facilitate spatial comparisons of the relative magnitudes of events. The latter is accomplished through the GEV distribution, backed by a Kolmogrov-Smirnov goodness-of-fit test. A Test of Proportions and the Fishers Exact test are used to isolate any changes in the seasonal timings of the annual maxima under the various ocean conditions, and the Kruskal-Wallis and one-way ANOVA tests are employed to compare the magnitude of annual maxima between classifications. The results of these tests are presented in the next chapter.

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CHAPTER 5 RESULTS This chapter presents the results of analyzing the timing and magnitude of annual maxima in relation to Atlantic and Pacific SST anomalies. First, observations on the geographical distribution of proportions of summer AFS events are described. Then the results of the GEV fitted distribution are outlined. Results from the Test of Proportions and the Fishers Exact test analyses are presented to compare the relative frequencies with which the annual maximums fall within each season, and separately to compare the relative frequencies with which the annual maximums exceed the 1.5-, 2-, 2.33-, 5-, 10-, and 20-year return periods under the various ocean conditions for each river. The Kruskal-Wallis and one-way ANOVA results are also presented to determine whether certain SST combinations have discernable affects on the magnitudes of annual floods. The results are discussed in the next chapter. Seasonal Proportions of Annual Flood Events To prepare for timings analyses, annual flood series data were compiled and organized to reflect the proportion of annual flood events that occurred in the summer season at each station. Figure 5.1 of Appendix A displays the total proportion of summer events by station. Largest proportions of summer events occur in southern parts of the state and diminish categorically moving north. Those stations showing lower proportions of summer annual flood events in turn should be assumed to display higher proportions of winter flood events. Figures 5.2 5.5 display the proportions according to Warm and Cold Atlantic, then Warm and Cold Pacific conditions respectively. There is little 40

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41 difference between those figures exhibiting different SST conditions with the exception of the Cold Atlantic, which seems to cause a slight increase in summer events, especially as compared to the number of summer events under Warm Atlantic conditions. This is merely a visual observation; this study relies on the tests below to determine whether this difference is significant. Generalized Extreme Value (GEV) Fitted Distributions The GEV fitted distribution was utilized to determine threshold levels according to annual flood return periods in order to determine, through a series of tests discussed below, whether the magnitudes of floods are influenced by various SST conditions. In addition to providing threshold levels, the GEV also generates parameters; results that describe the annual flood data at each station. The three parameters of interest are the shape parameter, related to the positioning of the distribution tail; the location parameter, related to the mode; and the scale parameter, related to the variance. Tables and figures concerning results of this test are shown in Appendix A. Figures 5.6-5.8 of Appendix A display these parameters by station. Additional analyses of these parameters were conducted to test the stationarity of the data sets. Figures 5.9-5.10 of Appendix A display the location and scale parameters plotted against basin area. These graphs display general linear trends. Figures 5.11-5.12 of Appendix A display these same parameters normalized by basin area plotted against basin area. The graphs show that while several of the rivers show relatively similar alpha and epsilon characteristics, many of the smaller basins form a separate group with relatively larger alpha and epsilon characteristics and may constitute a distinct category of rivers.

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42 Test of Proportions Results The Test of Proportions (TOP) was conducted to evaluate the null hypothesis that the proportion of annual floods at each station falling in one timing or magnitude category (e.g. summer or exceeding the 5-year return period)) under one classification is not different from the proportion of annual floods falling in that same category under another classification. It compares the proportions 1 and 2 using the difference between them and produces a z-value. Tables and figures concerning results of this test are shown in Appendix B. Table 5.1 of Appendix B displays the z-values for the Test of Proportions conducted to evaluate the timing of annual floods under the different SST conditions. Tables 5.2 5.7 display the z-values for the Test of Proportions conducted to evaluate the magnitude of annual floods under the different SST conditions. These tables show z-values for the 1.5-, 2-, 2.33-, 5-, 10-, and 20-year return periods. Below are descriptions of the TOP results for 8 category comparisons. Under each section, the annual flood timing analysis is first presented. This test compared the proportion of annual floods occurring in the summer between SST categories and presents significant positive and negative z-values. Then there is a description of the TOP results of the annual flood magnitude analysis. This test compared the proportion of annual floods that exceeded defined 1.5-, 2-, 2.33-, 5-, 10-, and 20-year return period thresholds between SST categories and presents significant positive and negative z-values for those tests. Figures in Appendix B display maps of the z-values for both timing and magnitude analyses by station.

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43 Warm Atlantic Vs. Cold Atlantic Figure 5.13 of Appendix B displays the 14 stations (35% of all of the stations) at which significant differences of proportions of annual floods conditioned upon the state of the Atlantic. Thirteen stations, mostly in northern Florida, indicate a greater likelihood of summer events when the Atlantic is warm and only one when it is cold. Figures 5.14-5.18 display significant z-values for the comparison of the proportions of annual floods exceeding different return-period thresholds under Warm Atlantic conditions to those under Cold Atlantic conditions. In general, the 1.5and 10-year return periods show the largest number of z-values, where 3 and 4 stations show positive z-values, contradicting the null hypothesis that there is no difference in the proportions of annual floods exceeding these return-period thresholds between the Warm and Cold Atlantic categories. The positive z-values indicate that the proportion of events that exceed the 1.5and 10-year return period thresholds under Warm Atlantic conditions is, at positive z-value stations, likely to be greater than it would be under Cold conditions. The negative z-values indicate that the proportion of events that exceed the 1.5and 10-year return period thresholds under Warm Atlantic conditions is likely to be smaller than it would be under Cold conditions. For all magnitude categories, out of 40 rivers, results tend to represent less than 10% of all of the stations tested. Warm Pacific Vs. Cold Pacific Figure 5.19 displays significant z-values for the comparison of the proportion of annual floods occurring in the summer under Warm Pacific conditions to that occurring under Cold Pacific conditions. Only 1 river shows a positive z-value, contradicting the null hypothesis that there is no difference in the proportion of summer annual floods between the Warm and Cold Pacific categories. Another 4 are negative z-values

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44 indicating that the proportion of summer events under Warm Pacific conditions is, at these 4 stations, likely to be smaller than it would be under Cold conditions. Out of 40 rivers, these results only represent 12.5% of all of the stations tested. Figures 5.205.23 display significant z-values for the comparison of the proportions of annual floods exceeding different return-period thresholds under Warm Pacific conditions to those under Cold Pacific conditions. In general, the 5and 10-year return periods show the largest number of z-values, both where 4 stations show negative z-values, contradicting the null hypothesis that there is no difference in the proportions of annual floods exceeding these return-period thresholds between the Warm and Cold Pacific categories. The negative z-values indicate that the proportion of events that exceed the 5and 10-year return period thresholds under Warm Pacific conditions is, at negative z-value stations, likely to be less than it would be under Cold conditions. For all magnitude categories, out of 40 rivers, the results represent less than 10% of all of the stations tested. Warm Atlantic Warm Pacific Vs. Warm Atlantic Cold Pacific Only 3 rivers show positive z-values for the comparison of the proportion of annual floods occurring in the summer under Warm Atlantic Warm Pacific conditions to that occurring under Warm Atlantic Cold Pacific conditions (see Figure 5.24), contradicting the null hypothesis that there is no difference in the proportion of summer annual floods between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific categories. Positive z-values indicate that the proportion of summer events under Warm Atlantic Warm Pacific conditions is, at these 3 stations, likely to be greater than it would be under Warm Atlantic Cold Pacific conditions. There are no negative z-values. Out of 40 rivers, these results only represent 7.5% of all of the stations tested.

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45 Figure 5.25 displays significant z-values for the comparison of the proportion of annual floods exceeding the 1.5-year return-period threshold under Warm Atlantic Warm Pacific conditions to that occurring under Warm Atlantic Cold Pacific conditions. In general, the 1.5year return period shows the largest number of z-values of all return periods analyzed, where 4 stations show negative z-values, contradicting the null hypothesis that there is no difference in the proportions of annual floods exceeding these return-period thresholds between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific categories. The positive z-values indicate that the proportion of events that exceed return period thresholds under Warm Atlantic Warm Pacific conditions is, at positive z-value stations, likely to be greater than it would be under Warm Atlantic Cold Pacific conditions. The negative z-values indicate that the proportion of events that exceed the return period thresholds under Warm Atlantic Warm Pacific conditions is likely to be smaller than it would be under Warm Atlantic Cold Pacific conditions. For all magnitude categories, out of 40 rivers, results tend to represent less than 10% of all of the stations tested. Cold Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific Figure 5.26 displays significant z-values for the comparison of the proportion of annual floods occurring in the summer under Cold Atlantic Warm Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. Only 1 river shows a positive z-value, contradicting the null hypothesis that there is no difference in the proportion of summer annual floods between the Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific categories. The 4 negative z-values indicate that, at those 4 stations, the proportion of summer events under Cold Atlantic Warm Pacific conditions is likely to be

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46 smaller than it would be under Cold Atlantic Cold Pacific conditions. Out of 40 rivers, these results only represent 12.5% of all of the stations tested. Figures 5.27 displays significant z-values for the comparison of the proportions of annual floods exceeding the 10-year return-period threshold under Cold Atlantic Warm Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. In general, the 5-, 10 and 20-year return period show the largest number of z-values, where 5, 12, and 11 stations (respectively) show negative z-values, contradicting the null hypothesis that there is no difference in the proportions of annual floods exceeding these return-period thresholds between the Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific categories. The positive z-values indicate that the proportion of events that exceed return period thresholds under Cold Atlantic Warm Pacific conditions is, at positive z-value stations, likely to be greater than it would be under Cold Atlantic Cold Pacific conditions. The negative z-values indicate that the proportion of events that exceed the return period thresholds under Cold Atlantic Warm Pacific conditions is likely to be smaller than it would be under Cold Atlantic Cold Pacific conditions. For all magnitude categories, out of 40 rivers, results tend to represent 30% or less of all of the stations tested. Warm Atlantic Cold Pacific Vs. Cold Atlantic Cold Pacific Figure 5.28 displays significant z-values for the comparison of the proportion of annual floods occurring in the summer under Warm Atlantic Cold Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. 4 rivers show positive z-values, contradicting the null hypothesis that there is no difference in the proportion of summer annual floods between the Warm Atlantic Cold Pacific and Cold Atlantic Cold Pacific categories. The positive z-values indicate that the proportion of summer events

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47 under Warm Atlantic Cold Pacific conditions is, at these 4 stations, likely to be greater than it would be under Cold Atlantic Cold Pacific conditions. There are two negative z-values indicating that, at those 2 stations, the proportion of summer events under Warm Atlantic Cold Pacific conditions is likely to be smaller than it would be under Cold Atlantic Cold Pacific conditions. Out of 40 rivers, these results represent 15% of all of the stations tested. Figures 5.29 displays the significant z-values for the comparison of the proportions of annual floods exceeding the 1.5-year return-period threshold under Warm Atlantic Cold Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. In general, the 1.5-year return period shows the largest number of z-values of all of the magnitude thresholds, where 4 stations show positive z-values, contradicting the null hypothesis that there is no difference in the proportions of annual floods exceeding these return-period thresholds between the Warm Atlantic Cold Pacific and Cold Atlantic Cold Pacific categories. The positive z-values indicate that the proportion of events that exceed return period thresholds under Warm Atlantic Cold Pacific conditions is, at positive z-value stations, likely to be greater than it would be under Cold Atlantic Cold Pacific conditions. The negative z-values indicate that the proportion of events that exceed the return period thresholds under Warm Atlantic Cold Pacific conditions is likely to be smaller than it would be under Cold Atlantic Cold Pacific conditions. For all magnitude categories, out of 40 rivers, results represent less than 10% of all of the stations tested. Warm Atlantic Warm Pacific Vs. Cold Atlantic Warm Pacific Figure 5.30 displays the z-values for the comparison of the proportion of annual floods occurring in the summer under Warm Atlantic Warm Pacific conditions to that

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48 occurring under Cold Atlantic Warm Pacific conditions. 15 rivers show positive z-values, contradicting the null hypothesis that there is no difference in the proportion of summer annual floods between the Warm Atlantic Warm Pacific and Cold Atlantic Warm Pacific categories. The positive z-values indicate that the proportion of summer events under Warm Atlantic Warm Pacific conditions is, at these 15 stations, likely to be greater than it would be under Cold Atlantic Warm Pacific conditions. There are no significant negative z-value results for this comparison. Out of 40 rivers, these results represent 37.5% of all of the stations tested. Figures 5.315.33 display significant z-values for the comparison of the proportions of annual floods exceeding different return-period thresholds under Warm Atlantic Warm Pacific conditions to that occurring under Cold Atlantic Warm Pacific conditions. In general, the 10-year return period shows the largest number of z-values, where 5 stations show positive z-values, contradicting the null hypothesis that there is no difference in the proportions of annual floods exceeding these return-period thresholds between the Warm Atlantic Warm Pacific and Cold Atlantic Warm Pacific categories. The positive z-values indicate that the proportion of events that exceed return period thresholds under Warm Atlantic Warm Pacific conditions is, at positive z-value stations, likely to be greater than it would be under Cold Atlantic Warm Pacific conditions. The negative z-values indicate that the proportion of events that exceed the return period thresholds under Warm Atlantic Warm Pacific conditions is likely to be smaller than it would be under Cold Atlantic Warm Pacific conditions. For all magnitude categories, out of 40 rivers, results represent less than 12.5% of all of the stations tested.

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49 Warm Atlantic Cold Pacific Vs. Cold Atlantic Warm Pacific Figure 5.34 displays the z-values for the comparison of the proportion of annual floods occurring in the summer under Warm Atlantic Cold Pacific conditions to that occurring under Cold Atlantic Warm Pacific conditions. 10 rivers show positive z-values, contradicting the null hypothesis that there is no difference in the proportion of summer annual floods between the Warm Atlantic Cold Pacific and Cold Atlantic Warm Pacific categories. The positive z-values indicate that the proportion of summer events under Warm Atlantic Cold Pacific conditions is, at these 10 stations, likely to be greater than it would be under Cold Atlantic Warm Pacific conditions. There are no significant negative z-value results for this comparison. Out of 40 rivers, these results only represent 25% of all of the stations tested. Figures 5.355.37 display significant z-values for the comparison of the proportions of annual floods exceeding different return-period thresholds under Warm Atlantic Cold Pacific conditions to that occurring under Cold Atlantic Warm Pacific conditions. In general, the 1.5and 10-year return periods show the largest number of z-values, each where 4 stations show positive z-values, contradicting the null hypothesis that there is no difference in the proportions of annual floods exceeding these return-period thresholds between the Warm Atlantic Cold Pacific and Cold Atlantic Warm Pacific categories. The positive z-values indicate that the proportion of events that exceed return period thresholds under Warm Atlantic Cold Pacific conditions is, at positive z-value stations, likely to be greater than it would be under Cold Atlantic Warm Pacific conditions. The negative z-values indicate that the proportion of events that exceed the return period thresholds under Warm Atlantic Cold Pacific conditions is likely to be smaller than it would be under Cold Atlantic Warm Pacific conditions. For all

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50 magnitude categories, out of 40 rivers, results represent less than 10% of all of the stations tested. Warm Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific Figure 5.38 displays the z-values for the comparison of the proportion of annual floods occurring in the summer under Warm Atlantic Warm Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. 5 rivers show positive z-values, contradicting the null hypothesis that there is no difference in the proportion of summer annual floods between the Warm Atlantic Warm Pacific and Cold Atlantic Cold Pacific categories. The positive z-values indicate that the proportion of summer events under Warm Atlantic Warm Pacific conditions is, at these 5 stations, likely to be greater than it would be under Cold Atlantic Cold Pacific conditions. There is one significant negative z-value result for this comparison. Out of 40 rivers, these results only represent 15% of all of the stations tested. Figures 5.39 displays significant z-values for the comparison of the proportions of annual floods exceeding the 10-year return-period thresholds under Warm Atlantic Warm Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. In general, the analyses for this category show very little (0-3 z-values) to contradict the null hypothesis that there is no difference in the proportions of annual floods exceeding these return-period thresholds between the Warm Atlantic Warm Pacific and Cold Atlantic Cold Pacific categories. For all magnitude categories, out of 40 rivers, results represent less than 5% of all of the stations tested. Fishers Exact Test Results In addition to the Test of Proportions, a small sample inference for comparing proportions is performed using the Fishers Exact test which compares members of two

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51 independent groups that fall into one of two categories to determine whether the proportions of those falling into each category differs by group. This test was conducted to analyze the likelihood that the population of annual floods falling in one timing or magnitude category under one classification are different from or larger than the population of annual floods falling in that same category under another classification. The Fishers Exact test returns exact oneand two-tailed P-values for a given frequency table. Tables 5.8-5.14 of Appendix C display Fishers Exact 1and 2-tail P-values for the timing and magnitude analyses of annual floods. Tables highlight 1-tail P-values in yellow and 2-tail P-values in green to represent those values that are significant at the 90% confidence level. Figures 5.43-5.54 of Appendix C display these results to give geographic perspective. Below are descriptions of the Fishers Exact results for 8 category comparisons. Under each section, the annual flood timing analysis is first presented. This test compared the population of annual floods occurring in the summer between SST categories and presents significant positive and negative z-values. Then there is a description of the Fishers Exact results of the annual flood magnitude analysis. This test compared the populations of annual floods that exceeded defined 1.5-, 2-, 2.33-, 5-, 10-, and 20-year return period thresholds between SST categories and presents significant 1and 2-tail P-values for those tests. Warm Atlantic Vs. Cold Atlantic Figure 5.43 shows 1and 2-tailed P-values of the annual flood timing analysis used to compare Warm and Cold Atlantic conditions. Ten stations, 5 of which are scattered about the state and 5 in the panhandle area, have significant 2-tail P-values contradicting the null hypothesis that there is no difference between the proportion of summer annual

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52 floods between the Warm and Cold Atlantic categories. Five significant 1-tail P-values show up in the panhandle stations, and show that for these stations, there is a greater likelihood of larger proportions of summer events under Warm Atlantic conditions than under Cold conditions. Figure 5.44 shows 1and 2-tailed P-values of the annual flood magnitude analysis used to compare Warm and Cold Atlantic conditions. This test does not show a considerable difference in magnitude at any return period between the Warm and Cold Atlantic. Three stations, again in the panhandle area, show significant 2-tail P-values at the 1.5-yr return period. Only two stations, also in the panhandle area, show 1-tail P-values at this return period level. One station in the 2-year category shows significant results for the 1and 2-tail P-values; it also is located in the panhandle. Three stations in the 10-year category show significant 2-tail P-values and one significant 1-tail, all in the panhandle. Those stations that show a significant 2-tail P-value indicate a contradiction to the null hypothesis that there is no difference between the proportion of summer annual floods between the Warm and Cold Atlantic categories. Those stations that show a significant 1-tail P-value indicate a greater likelihood of larger proportions of events that exceed the given return period threshold under Warm Atlantic conditions than under Cold conditions at the stations. Warm Pacific Vs. Cold Pacific Figure 5.45 shows 1and 2-tailed P-values of the annual flood timing analysis used to compare Warm and Cold Pacific conditions. Only 1 station, located in the panhandle, had significant 2-tail P-values contradicting the null hypothesis that there is no difference between the proportion of summer annual floods between the Warm and Cold Pacific categories. The same station had a significant 1-tail P-value meaning that there is a

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53 greater likelihood of larger proportions of summer events under Warm Pacific conditions than under Cold conditions at that station. Figure 5.46 shows 1and 2-tailed P-values of the annual flood magnitude analysis used to compare Warm and Cold Pacific conditions. Again, this test does not show a considerable difference in magnitude at any return period between the Warm and Cold Pacific. Two stations bordering the panhandle area show significant 1and 2-tail P-values at the 1.5-yr return period. One station, also in the panhandle area, shows a 1-tail P-value at this return period level. One station in the 2.33-year category shows a significant 2-tail P-value; it also is located in the panhandle. Two stations in the 5-year category show significant 1and 2-tail P-values; one in mid-east Florida and one in the panhandle. There is one station showing significant 1and 2-tail P-values in north-central Florida. Those stations that show significant 2-tail P-values indicate a contradiction to the null hypothesis that there is no difference between the proportion of summer annual floods between the Warm and Cold Pacific categories. Those stations that show a significant 1-tail P-value indicate a greater likelihood of larger proportions of events that exceed the given return period threshold under Warm Pacific conditions than under Cold conditions at the stations. Warm Atlantic Warm Pacific Vs. Warm Atlantic Cold Pacific Figure 5.47 shows 1and 2-tailed P-values of the annual flood timing analysis used to compare Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific conditions. Two stations, located in southern Florida, had significant 2-tail P-values contradicting the null hypothesis that there is no difference between the proportion of summer annual floods between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific categories. One of these stations also had a significant 1-tail P-value indicating a greater

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54 likelihood of larger proportions of summer events under Warm Atlantic Warm Pacific conditions than under Warm Atlantic Cold Pacific conditions at that station. Figure 5.48 shows 1and 2-tailed P-values of the annual flood magnitude analysis used to compare Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific conditions. This test does not show a considerable difference in magnitude at any return period between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific. Three stations in the panhandle area show significant 1and 2-tail P-values at the 1.5-yr return period. One station in the 5-year category and one in the 10-year category show significant 1and 2-tail P-values; both stations are located in southern Florida. One station in the panhandle shows a significant 2-tail P-value in the 20-year return period category. Those stations that show significant 2-tail P-values indicate a contradiction to the null hypothesis that there is no difference between the proportion of summer annual floods between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific categories. Those stations that show a significant 1-tail P-value indicate a greater likelihood of larger proportions of events that exceed the given return period threshold under Warm Atlantic Warm Pacific conditions than under Warm Atlantic Cold Pacific conditions at the stations. Cold Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific Figure 5.49 shows 1and 2-tailed P-values of the annual flood timing analysis used to compare Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific conditions. Three stations, located in southern Florida, had significant 2-tail P-values contradicting the null hypothesis that there is no difference between the proportion of summer annual floods between the Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific categories. Two of these stations also had significant 1-tail P-values indicating a greater likelihood of

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55 larger proportions of summer events under Cold Atlantic Warm Pacific conditions than under Cold Atlantic Cold Pacific conditions at those stations. Figure 5.50 shows 1and 2-tailed P-values of the annual flood magnitude analysis used to compare Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific conditions. This test does not show a considerable difference in magnitude at any return period between the Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific. One station in the panhandle area shows significant 1and 2-tail P-values at the 1.5-yr return period. One station in the 5-year category shows a significant 2-tail P-value located in southern Florida. Four stations in the center of the state show significant 2-tail P-values in the 10-year return period category; one of these shows a significant 1-tail P-value. Two stations, one in southern Florida and one in the panhandle, show significant 2-tail P-values in the 20-year return period category. Those stations that show significant 2-tail P-values indicate a contradiction to the null hypothesis that there is no difference between the proportion of summer annual floods between the Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific categories. Those stations that show a significant 1-tail P-value indicate a greater likelihood of larger proportions of events that exceed the given return period threshold under Cold Atlantic Warm Pacific conditions than under Cold Atlantic Cold Pacific conditions at the stations. Cold Atlantic Cold Pacific Vs. Warm Atlantic Cold Pacific Figure 5.51 shows 1and 2-tailed P-values of the annual flood timing analysis used to compare Cold Atlantic Cold Pacific and Warm Atlantic Cold Pacific conditions. Five stations, 4 in the panhandle and 1 in the central Florida area, had significant 2-tail P-values contradicting the null hypothesis that there is no difference between the proportion of summer annual floods between the Cold Atlantic Cold Pacific and Warm Atlantic

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56 Cold Pacific categories. The four stations located in the panhandle also had significant 1-tail P-values indicating a greater likelihood of larger proportions of summer events under Cold Atlantic Cold Pacific conditions than under Warm Atlantic Cold Pacific conditions at those stations. Figure 5.52 shows 1and 2-tailed P-values of the annual flood magnitude analysis used to compare Cold Atlantic Cold Pacific and Warm Atlantic Cold Pacific conditions. This test does not show a considerable difference in magnitude at any return period between the Cold Atlantic Cold Pacific and Warm Atlantic Cold Pacific. One station in the panhandle area shows significant 1and 2-tail P-values at the 1.5-yr return period. One station in the 10-year category shows a significant 2-tail P-value located at the east end of the panhandle. Those stations that show significant 2-tail P-values indicate a contradiction to the null hypothesis that there is no difference between the proportion of summer annual floods between the Cold Atlantic Cold Pacific and Warm Atlantic Cold Pacific categories. Those stations that show a significant 1-tail P-value indicate a greater likelihood of larger proportions of events that exceed the given return period threshold under Cold Atlantic Cold Pacific conditions than under Warm Atlantic Cold Pacific conditions at the stations. Cold Atlantic Warm Pacific Vs. Warm Atlantic Warm Pacific Figure 5.53 shows 1and 2-tailed P-values of the annual flood timing analysis used to compare Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific conditions. Seven stations spanning the state had significant 2-tail P-values contradicting the null hypothesis that there is no difference between the proportion of summer annual floods between the Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific categories. Three stations located in central Florida also had significant 1-tail P-values indicating a

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57 greater likelihood of larger proportions of summer events under Cold Atlantic Warm Pacific conditions than under Warm Atlantic Warm Pacific conditions at those stations. Figure 5.54 shows 1and 2-tailed P-values of the annual flood magnitude analysis used to compare Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific conditions. This test does not show a considerable difference in magnitude at any return period between the Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific. Two stations in the panhandle area show significant 2-tail P-values and one of them a significant 1-tail P-value at the 1.5-yr return period. One station in the 2-year category shows significant 1and 2-tail P-values located in southern Florida. Four stations spanning the state show a significant 2-tail P-value in the 10-year return period category; two of these in the panhandle also show significant 1-tail P-values. Two stations, one in southern Florida and one in the panhandle, show a significant 2-tail P-values in the 20-year return period category; two of these in the panhandle also show significant 1-tail P-values. Those stations that show significant 2-tail P-values indicate a contradiction to the null hypothesis that there is no difference between the proportion of summer annual floods between the Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific categories. Those stations that show a significant 1-tail P-value indicate a greater likelihood of larger proportions of events that exceed the given return period threshold under Cold Atlantic Warm Pacific conditions than under Warm Atlantic Warm Pacific conditions at the stations. Kruskal Wallis Test Results The Kruskal-Wallis test was used to determine whether certain SST combinations have discernable affects on the magnitudes of annual floods according to Atlantic and Pacific SST conditions. The test was used to compare populations of annual maxima

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58 under the various ocean conditions in order to determine whether these populations differ significantly. It evaluates the null hypothesis that samples representing annual floods falling in one or another SST category come from identical populations. The alternative hypothesis is that samples are drawn from different populations. Table 5.15 and Figure 5.55 of Appendix D display the P-values produced by this test. The results produced from comparison of the various SST combinations revealed few significant P-values. In fact, only three stations; one under the Warm vs. Cold Pacific, one under the Cold Atlantic Cold Pacific vs. Warm Atlantic Cold Pacific, and one in the Cold Atlantic Warm Pacific vs. Warm Atlantic Warm Pacific; revealed significant results. In general, the null hypothesis that that samples representing annual floods falling in one or another SST category come from identical populations is upheld by this test. ANOVA Results The one-way ANOVA test was used in conjunction with the Kruskal-Wallis test to compare populations of annual maxima under the various ocean conditions. The null hypothesis was that the various SST groups have identical means. Like the results of the Kruskal Wallis, the ANOVA revealed only two stations with significant results in all of the tests conducted, upholding the null hypothesis. Results are shown in Table 5.16 and Figure 5.56 of Appendix D. Summary Results from the Test of Proportions and the Fishers Exact test are presented determine any changes in the seasonal timings or magnitudes of the annual maxima under the various ocean conditions. Kruskal-Wallis and one-way ANOVA tests results are also

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59 presented to compare the magnitude of annual maxima between classifications. A discussion of these results is presented in the next chapter.

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CHAPTER 6 DISCUSSION AND IMPLICATIONS This chapter provides a general overview and discussion of some of the most interesting of the results presented in the previous chapter. These results are discussed in relation to the general framework of previous studies on relationships between ENSO, the AMO, and streamflow. The objective of this thesis is to examine the effects of ENSO and the AMO on 40 streamflow discharge stations in Florida through study of the relationship of SST anomalies to the magnitude and timing of the annual flood series. The results of the statistical analyses performed in this study show little evidence to support similar studies on ENSO, the AMO and streamflow. Seasonal Proportions of Annual Flood Events Summer proportions of annual floods shown in figure 5.1 correlates with Henrys (Henry et al., 1994) seasonal rainfall regimes discussed in chapter 3 of this study. This map shows the dominance of summer events in the southern areas of the state where there is little winter precipitation and a less dominating proportion in the northern parts of the state where winter frontal activity is an important part of the water year. Generalized Extreme Value Fitted Distributions The GEV fitted distribution parameters provided results describing the annual flood data at each station. Figures 5.6 display the shape parameter, by station and confirms that the GEV was a good distribution to use for these data; while most of the stations display a Frechet-like distribution with many outlying values, some also show thin-tailed 60

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61 behaviors more like a Weibull distribution. None of the stations display Gumbel-like distributions. If only one of these distributions had been utilized in this study, variation in distribution style would have been lost. Figure 5.8 displays the scale parameter, which is related to the variance of the data. While areas in the south show the magnitudes of annual flood events to be fairly consistent, areas in the north and the panhandle display greater variance in magnitudes of the AFS suggesting that these areas are more prone to greater flux. This flux could be due to the stochastic nature of frontal storms that influence discharge levels in the area. Annual Floods in Relation to Atlantic SSTs As disussed in Chapter 2, a positive Atlantic SST anomaly is associated with increased precipitation and tropical storm/hurricane activity in the Southeast (Enfield et al., 2001; Goldenburg et al., 2001; Visbeck et al., 2001). The majority of the results of the Test of Proportions do not indicate that this translates in a significant way to the timing and magnitude of annual floods. About 30% of the results support a correlation between Warm Atlantic SST conditions and discharge. Thirteen of 40 stations (located mostly in the panhandle), illustrate that Warm Atlantic conditions were more likely to have higher proportions of summer annual flood events than Cold Atlantic conditions. While most of the significant results of the Test of Proportions of the magnitude of annual flood events analyzed at various time periods show that it is more likely for Warm Atlantic to have higher proportions of events that exceed return-period thresholds than Cold Atlantic conditions, the strength of these results is minimal; the most positive result (at the 10-year return period) shows only 4 out of 40 statistically significant z-values supporting this hypothesis.

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62 In tandem, the Fishers Exact test gives little evidence to support previous studies. Twenty-five percent of the discharge stations tested show a significant difference between Warm and Cold Atlantic proportions of summer annual flood events. Twelve percent indicate that there are a greater proportion of summer events under Warm than Cold Atlantic conditions. An average of 15% of the magnitude analyses displayed significant values, with the most (3 out of 40) significant values showing up in the 1.5-year return period category. As mentioned in Chapter 2, the AMO has an average oscillation period of 20-80 years, so results for the Warm Atlantic alone in this category appear to be random. The period of available record examined in this study may have been too brief to pull out AMO patterns and recognize correlations between SST anomalies and discharge through statistical analyses. The Kruskal Wallis and ANOVA tests evaluate the null hypothesis that populations of annual floods do not differ under Warm and Cold Atlantic conditions. Again, results of this study do not support previous bodies of work that would contradict the null hypothesis. The ANOVA test shows significant values for only one of 40 stations (again in the panhandle) contradicting the null hypothesis. The Kruskal Wallis test shows no significant difference between Warm and Cold conditions. Annual Floods in Relation to Pacific SSTs Previous studies provide strong evidence that positive Pacific SST anomalies increase streamflow in Florida. Kahya and Dracup (1993) indicate that positive Pacific SST anomalies of year 0 result in higher stream flows around the Gulf of Mexico region of the United States in year 1. According to many studies, warm phases of ENSO cause more frequent heavy winter rainfalls in the Southeast (Cao, 2000; Sun and Furbish, 1997; Henderson and Robinson, 1994; Kahya and Dracup, 1993; Dracup and Kahya, 1994;

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63 Ropelowski and Halpert, 1987,1986; Douglas and Englehart, 1981), and above normal precipitation during winters and springs in Florida correlate with years following a warm ENSO event (Hanson and Maul, 1991; Zorn and Waylen, 1997). Results from this study do not support the framework of current literature on ENSO in relation to the Southeast United States and Florida. In fact, results of the Test of Proportions show that 5 of 40 stations produce significant z-values, 4 of which indicate that there are a greater proportion of summer events under Cold than Warm Pacific conditions. Magnitude Test of Proportions show the same results at most return-period levels. Fishers Exact test results are scarce for Warm versus Cold Pacific conditions in relation to the annual flood series in Florida. Only one river of 40 produces significant results for the timings analysis. This test indicates that, at this one river located in the panhandle, there is a greater proportion of summer events under Warm than Cold Pacific conditions. Magnitude analyses show no significant results for any of the 40 rivers. Again, the Kruskal Wallis and ANOVA tests provide few significant results to support previous bodies of work that would contradict the null hypothesis. The ANOVA test shows significant values for only one of 40 stations (in North Central Florida) contradicting the null hypothesis. The Kruskal Wallis test shows one significant difference between Warm and Cold conditions at the same station. Annual Floods in Relation to Combined Atlantic and Pacific SSTs Chapter 2 outlines studies that support evidence to suggest that both the Atlantic and Pacific oscillations affect streamflows in Florida. One of the objectives of this study is to attempt to detect the relationship between streamflow and the combinations of Atlantic and Pacific SST anomalies. While few studies outline the streamflow reactions

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64 to various combined Atlantic and Pacific SST combinations, studies on the separate oscillations suggest that the combined conditions may have magnified effects on streamflow. The Test of Proportions show that Warm Atlantic Warm Pacific conditions are more likely to have higher proportions of summer annual flood events than Cold Atlantic Warm Pacific conditions at about 37% of the stations sampled. This test also indicates that Warm Atlantic Cold Pacific conditions are more likely to have higher proportions of summer annual flood events than Cold Atlantic Warm Pacific conditions at 25% of the stations. Magnitude analyses show few results except at the 10and 20 year return periods which give z-values at about 30% of the stations suggesting that, at 12 stations, there is a higher probability of exceeding the 10and 20-year return period thresholds under Cold Atlantic Cold Pacific conditions than under Cold Atlantic Warm Pacific conditions. The latter analysis does not support the larger body of ENSO work, while the former analysis may support AMO studies. The Fishers Exact test revealed little difference between of proportions of summer annual flood events occurring under the different SST anomaly combinations. The largest difference occurred between Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific and the second largest between Cold Atlantic Cold Pacific and Warm Atlantic Cold Pacific; both of these comparisons pick up the differences in the Atlantic with a steady Pacific, but several of the significant 1-tailed P-values results indicate that, in these conditions, a Cold Atlantic, whether coupled with a Warm or Cold Pacific, would likely have greater proportions of summer annual flood events than a Warm Atlantic. This may be the case, but these results, at the most, represent only 17% or less of the 40

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65 stations analyzed. As for the Fishers Exact magnitudes analyses, even fewer significant results reveal a difference in the magnitude of annual flood events under various combined Atlantic and Pacific SST anomaly combinations. Those showing the largest number of significant results, between the Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific and also between the Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific, occurred at the 10-year return period level and both represented only 10% of the stations analyzed. These results do not indicate a measurable difference in the timing or magnitude of annual floods between Atlantic/ Pacific SST anomaly combinations. The Kruskal Wallis and ANOVA tests gave no evidence to dispute the null hypothesis that populations of annual floods do not differ between various Atlantic and Pacific SST combinations. Chapter Conclusions The intent of this study was to recognize connections between local hydrologic regimes and large-scale climate patterns. While the results of the study do not pull out the patterns suggested by current literature on the relationships between ENSO, the AMO and streamflow, neither do they strongly dispute these studies. They simply add to the growing body of work on these subjects that the annual flood series, as analyzed through the methods used in this study, does not reveal the relationships one would expect with ENSO and the AMO. This may be due to the methods of analysis or to the noisy nature of the annual flood series; because this series is limited to a single annual maximum observation of discharge per year, even if that maximum is insufficient to constitute a flood, and by the same token, may result in the omission of other flood peaks within the same year, it poses some problems to creating a full picture of climatic and

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66 hydrologic patterns. As mentioned in Chapter 2, the problems posed through use of the annual flood series may be particularly troublesome in an environment like north central Florida where rainfall is the major flood generating process and where there are two distinct rainy seasons during the year. While the tests performed in this study did not reveal the expected patterns caused by global phenomena, it does reveal that seasonal patterns of annual flood events reflect the physical geographical variance in precipitation suggested in previous studies. The Generalized Extreme Value fitted distribution also contributes interesting results reflecting scale, location and shape of the bodies of discharge data examined.

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CHAPTER 7 CONCLUSIONS I never failed once. It just happened to be a 2000-step process." -Thomas Edison One of the primary goals of geographic science is to make connections between seemingly separated phenomena. This study seeks to define a connection between the powerful, distant macro-scale conditions of Pacific and Atlantic oscillations in sea-surface temperatures, and local streamflow conditions of the low-lying, peninsular state of Florida. Relatively recent scientific interest in these oscillations has provided adequate sources of available data. In correlating these data with records of Floridian streamflow, this study attempts to make a connection between the distant conditions of ENSO and the AMO to patterns of extremes in local stream flow. Subtle and complex, the interactions between the widely separated phenomena of ENSO and the AMO and local annual flood series (AFS) have proved, in this study, to be somewhat statistically elusive. Signature rhythms and shapes are not brought out of the data by the methods used in this study and do not produce convincing conclusions about the relationship between ENSO, the AMO and local patterns of flooding. The intent of this study was to discern whether previously recognized patterns of association of association between monthly rainfall and streamflow totals, and SSTs, could be extended to the characteristics of the annual flood series; a variable of longstanding hydrologic research and a staple of public policy. The absence of any clear pattern is perhaps due to the noisy nature of the annual flood variable itself. 67

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68 Representing, as it does, the largest event in a year, the variable may be reflective of unusual meteorological events rather than any consistent change. For instance, rainfall related to tropical storms may be very important. Research indicated that the number of tropical storms in the North Atlantic and Carribean basins is related to SSTs in both oceans, however uncertainties concerning the paths of such storms provides no guarantees that a greater number of storms will result in any of them actually striking the areas of Florida examined in this study. The fact that the AFS fails to show any patterns in relation to the large-scale climate indices included in this study that are known to affect precipitation and streamflow around the world may pose some interesting questions in itself; maybe the AFS is not the best tool to use for local hydrologic models. If this is true, it might point to serious flaws in federal and local government planning models and investigations of other high (and low) flow frequency measures such as partial durations series might be more sensitive. This study is the first of its kind to analyze the affects of ENSO and the AMO on the annual flood series in this region. It has contributed positive evidence that seasonal patterns of annual flood events reflect the physical geographical variations in dominance of precipitation generating mechanisms as suggested in previous studies. It also is the first of its kind to use the Generalized Extreme Value fitted distribution applied on a large scale to the AFS at many discharge stations across Florida. Use of this fitted distribution allows the examination and definition of the scale, location and shape of the bodies of AFS discharge data across Florida. While contributions of this study are important, many questions remain including the question: what are the local effects of ENSO and the AMO on the AFS in Florida and

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69 how can permutations of local action recognize their importance to modeling and planning in responsible, intelligent ways? The effect of scientific data on public policy is always complicated, but geography and meteorology are so intimately linked to human habitation and advancement that an understanding of larger climate patterns and local effects and reactions is necessary. Critical differences of action and consequence might result from utilizing current meteorological understanding as presented in established literature on ENSO and the AMO in "accepted" administrative standards and models. Significant harm may be avoided by a more contemporary, broader, more informed set of standards and models that include the influences of larger climate patterns. The final goal is to understand the meteorological connections and processes critical to Florida's future.

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APPENDIX A TABLES AND FIGURES FOR TIMING AND MAGNITUDE OF THE AFS The following tables and figures of this appendix were compiled in preparation for the various statistical tests performed in this study. The first maps presented in this appendix (Figures 5.1-5.5) represent the proportions of annual flood events occurring in the summer season at each station; first overall, then by SST category. Other maps and graphs presented (Figures 5.1-5.5) represent the Generalized Extreme Value (GEV) fitted distribution analysis discussed in chapter 5 and performed in preparation for analysis of the magnitudes of annual floods with different conditions. Figures 5.6-5.8 display the parameters of the GEV by station. 5.9-5.10 display the location and scale parameters plotted against basin area, and 5.11-5.12 show these same graphs with normalized basin areas. These tables and figures supplement and are referenced in Chapter 5 of this thesis. 70

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71 Figure 5.1: Total proportion of summer events by station.

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72 Figure 5.2: Total proportion of summer events under Warm Atlantic SST conditions by station.

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73 Figure 5.3: Total proportion of summer events under Cold Atlantic SST conditions by station.

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74 Figure 5.4: Total proportion of summer events under Warm Pacific SST conditions by station.

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75 Figure 5.5: Total proportion of summer events under Cold Pacific SST conditions by station.

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76 Figure 5.6: GEV shape parameter, (related to the positioning of the distribution tail) by station.

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77 Figure 5.7: GEV location parameter, (related to the mode) by station.

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78 Figure 5.8: GEV scale parameter, (related to the variance) by station.

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79 GEV Epsilon Parameter vs Basin Area0100200300400500600010002000300040005000600070008000900010000Area (km^2) Figure 5.9: Locations parameter, plotted against basin area. GEV Alpha Parameter vs Basin Area020406080100120140160180200010002000300040005000600070008000900010000Area (km^2)Alpha Figure 5.10 Scale parameter, plotted against basin area.

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80 Normalized GEV Epsilon Parameter vs Basin Area0.0000.0500.1000.1500.2000.250010002000300040005000600070008000900010000Area (km^2)Normalized Epsilon Figure 5.11: Normalized locations parameter, plotted against basin area. Normalized GEV Alpha Parameter vs Basin Area0.0000.0200.0400.0600.0800.1000.1200.1400.1600.1800.200010002000300040005000600070008000900010000Area (km ^2)Normalized Alpha Figure 5.12: Normalized scale parameter, plotted against basin area.

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APPENDIX B TABLES AND FIGURES FOR TEST OF PROPORTIONS The following tables and figures of this appendix are results of the Test of Proportions comparisons of the timing and magnitudes of annual floods of differing SST categories. These tables and figures supplement and are referenced in Chapter 5 of this thesis. Table 5.1: Number of positive and negative z-values* for timing analysis; A comparison of the proportion of annual floods that occur in the summer season between the following SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis. p2 1 2 3 4 5 6 7 8 p1 z-value WA CA WP CP WAWP WACP CAWP CACP 1 WA +z x 13 2 1 0 1 11 6 -z x 1 0 0 1 0 0 1 2 CA +z 1 x 0 0 1 0 0 0 -z 13 x 1 2 10 8 0 0 3 WP +z 0 1 x 1 0 1 1 1 -z 2 0 x 4 0 7 0 1 4 CP +z 0 2 4 x 0 0 6 0 -z 1 0 1 x 4 0 1 0 5 WAWP +z 1 10 0 4 x 3 15 5 -z 0 1 0 0 x 0 0 1 6 WACP +z 0 8 7 0 0 x 10 4 -z 1 0 1 0 3 x 0 2 7 CAWP +z 0 0 0 1 0 0 x 1 -z 11 0 1 6 15 10 x 4 8 CACP +z 1 0 1 0 1 2 4 x -z 6 0 1 0 5 4 1 x *90% confidence 81

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82 Table 5.2: Number of positive and negative z-values* for 1.5-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 1.5-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis. p2 1 2 3 4 5 6 7 8 p1 z-value WA CA WP CP WAWP WACP CAWP CACP 1 WA +z x 3 1 0 1 0 2 1 -z x 1 0 0 0 0 0 0 2 CA +z 0 x 0 0 2 0 0 0 -z 3 x 1 0 1 5 0 0 3 WP +z 0 0 x 0 0 0 0 1 -z 1 0 x 3 0 4 0 1 4 CP +z 0 0 3 x 3 0 2 0 -z 0 0 0 x 0 0 0 0 5 WAWP +z 0 1 0 0 x 0 1 1 -z 1 2 0 3 x 4 3 2 6 WACP +z 0 5 4 0 4 x 4 4 -z 0 0 0 0 0 x 0 0 7 CAWP +z 0 0 0 0 3 0 x 1 -z 2 0 0 2 1 4 x 0 8 CACP +z 0 0 1 0 2 0 0 x -z 1 0 1 0 1 4 1 x *90% confidence

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83 Table 5.3: Number of positive and negative z-values* for 2-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 2-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis. p2 1 2 3 4 5 6 7 8 p1 z-value WA CA WP CP WAWP WACP CAWP CACP 1 WA +z x 2 0 0 0 0 1 0 -z x 1 0 0 0 0 0 1 2 CA +z 1 x 0 0 1 0 0 0 -z 2 x 0 0 0 1 0 0 3 WP +z 0 0 x 0 0 0 0 0 -z 0 0 x 0 0 0 0 1 4 CP +z 0 0 0 x 1 0 0 0 -z 0 0 0 x 0 0 0 0 5 WAWP +z 0 0 0 0 x 0 1 0 -z 0 1 0 1 x 1 1 2 6 WACP +z 0 1 0 0 1 x 1 0 -z 0 0 0 0 0 x 0 0 7 CAWP +z 0 0 0 0 1 0 x 0 -z 1 0 0 0 1 1 x 1 8 CACP +z 1 0 1 0 2 0 1 x -z 0 0 0 0 0 0 0 x *90% confidence

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84 Table 5.4: Number of positive and negative z-values* for 2.33-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 2.33-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis. p2 1 2 3 4 5 6 7 8 p1 z-value WA CA WP CP WAWP WACP CAWP CACP 1 WA +z x 1 0 0 0 0 0 1 -z x 0 0 0 0 0 0 1 2 CA +z 0 x 0 0 0 0 0 0 -z 1 x 0 0 0 1 0 0 3 WP +z 0 0 x 0 0 0 0 1 -z 0 0 x 1 0 0 0 2 4 CP +z 0 0 1 x 0 0 0 0 -z 0 0 0 x 0 0 0 0 5 WAWP +z 0 0 0 0 x 0 0 0 -z 0 0 0 0 x 0 0 2 6 WACP +z 0 1 0 0 0 x 0 1 -z 0 0 0 0 0 x 0 1 7 CAWP +z 0 0 0 0 0 0 x 0 -z 0 0 0 0 0 0 x 1 8 CACP +z 1 0 2 0 2 1 1 x -z 1 0 1 0 0 1 0 x *90% confidence

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85 Table 5.5: Number of positive and negative z-values* for 5-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 5-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis. p2 1 2 3 4 5 6 7 8 p1 z-value WA CA WP CP WAWP WACP CAWP CACP 1 WA +z x 1 0 0 0 0 4 0 -z x 0 0 0 0 0 0 0 2 CA +z 0 x 0 0 0 0 0 0 -z 1 x 0 0 0 1 0 0 3 WP +z 0 0 x 0 0 0 0 0 -z 0 0 x 4 0 2 0 3 4 CP +z 0 0 4 x 1 0 6 0 -z 0 0 0 x 0 0 0 0 5 WAWP +z 0 0 0 0 x 0 0 0 -z 0 0 0 1 x 2 0 1 6 WACP +z 0 1 2 0 2 x 3 0 -z 0 0 0 0 0 x 0 1 7 CAWP +z 0 0 0 0 0 0 x 0 -z 4 0 0 6 0 3 x 5 8 CACP +z 0 0 3 0 1 1 5 x -z 0 0 0 0 0 0 0 x *90% confidence

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86 Table 5.6: Number of positive and negative z-values* for 10-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 10-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis. 1 2 3 4 5 6 7 8 p1 z-value WA CA WP CP WAWP WACP CAWP CACP 1 WA +z x 4 1 0 0 0 8 1 -z x 0 0 0 0 0 0 0 2 CA +z 0 x 0 0 0 0 0 0 -z 4 x 0 2 2 2 0 0 3 WP +z 0 0 x 0 0 1 0 0 -z 1 0 x 4 0 1 0 2 4 CP +z 0 2 4 x 1 0 10 0 -z 0 0 0 x 1 0 0 0 5 WAWP +z 0 2 0 1 x 1 5 1 -z 0 0 0 1 x 1 0 0 6 WACP +z 0 2 1 0 1 x 4 1 -z 0 0 1 0 1 x 1 1 7 CAWP +z 0 0 0 0 0 1 x 0 -z 8 0 0 10 5 4 x 12 8 CACP +z 0 0 2 0 0 1 12 x -z 1 0 0 0 1 1 0 x *90% confidence p2

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87 Table 5.7: Number of positive and negative z-values* for 20-year return period magnitude analysis; A comparison of the proportion of annual floods that exceed the 20-year return period between the various SST combinations. Discussion of results is focused on cells highlighted in yellow (largest # of z-values) and white (least # of z-values). Those cells shaded in gray are extraneous, although part of the analysis. p2 1 2 3 4 5 6 7 8 p1 z-value WA CA WP CP WAWP WACP CAWP CACP 1 WA +z x 0 0 0 0 0 11 0 -z x 1 0 0 0 0 0 0 2 CA +z 0 x 0 0 0 2 0 0 -z 0 x 4 0 0 0 0 0 3 WP +z 0 0 x 1 0 2 0 0 -z 0 0 x 1 0 0 0 0 4 CP +z 0 0 1 x 0 0 10 0 -z 0 0 1 x 0 0 0 0 5 WAWP +z 0 0 0 0 x 0 2 0 -z 0 0 0 0 x 0 0 0 6 WACP +z 0 0 0 0 0 x 2 0 -z 0 2 2 0 0 x 0 0 7 CAWP +z 0 0 0 0 0 0 x 0 -z 11 0 0 10 2 2 x 11 8 CACP +z 0 0 0 0 0 0 11 x -z 0 0 0 0 0 0 0 x *90% confidence

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88 Figure 5.13: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic conditions to Cold conditions.

PAGE 105

89 Figure 5.14: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic conditions to Cold conditions.

PAGE 106

90 Figure 5.15: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 2-year return period threshold under Warm Atlantic conditions to Cold conditions.

PAGE 107

91 Figure 5.16: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 5-year return period threshold under Warm Atlantic conditions to Cold conditions.

PAGE 108

92 Figure 5.17: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Warm Atlantic conditions to Cold conditions.

PAGE 109

93 Figure 5.18: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 20-year return period threshold under Warm Atlantic conditions to Cold conditions.

PAGE 110

94 Figure 5.19: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic conditions to Cold conditions.

PAGE 111

95 Figure 5.20: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Pacific conditions to Cold conditions.

PAGE 112

96 Figure 5.21: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 5-year return period threshold under Warm Pacific conditions to Cold conditions.

PAGE 113

97 Figure 5.22: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Warm Pacific conditions to Cold conditions.

PAGE 114

98 Figure 5.23: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 20-year return period threshold under Warm Pacific conditions to Cold conditions.

PAGE 115

99 Figure 5.24: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific conditions.

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100 Figure 5.25: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific conditions.

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101 Figure 5.26: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions.

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102 Figure 5.27: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions.

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103 Figure 5.28: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Cold Pacific conditions to Cold Atlantic Cold Pacific conditions.

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104 Figure 5.29: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Cold Pacific conditions to Cold Atlantic Cold Pacific conditions.

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105 Figure 5.30: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific conditions.

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106 Figure 5.31: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific conditions.

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107 Figure 5.32: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 2-year return period threshold under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific conditions.

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108 Figure 5.33: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific conditions.

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109 Figure 5.34: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific conditions.

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110 Figure 5.35: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific conditions.

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111 Figure 5.36: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 5-year return period threshold under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific conditions.

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112 Figure 5.37: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 1.5-year return period threshold under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific conditions.

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113 Figure 5.38: Test of Proportions results positive and negative z-values produced from comparing the proportion of summer events under Warm Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions.

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114 Figure 5.39: Test of Proportions results positive and negative z-values produced from comparing the proportion of events that exceed the 10-year return period threshold under Warm Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions.

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APPENDIX C TABLES AND FIGURES FOR FISHERS EXACT TEST The following tables and figures of this appendix are results of the Fishers Exact Test comparisons of the timing and magnitudes of annual floods of differing SST categories. These tables and figures supplement and are referenced in Chapter 5 of this thesis. 115

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Table 5.8 Timing analysis results Fishers Exact Test oneand two-tailed P value test results. WA vs CA WP vs CP WAWP vs WACP CAWP vs CACP CACP vs WACP CAWP vs WAWP river 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1 0.1320 0.2640 0.6103 1.0000 0.3626 0.7032 0.3631 0.6693 0.5000 1.0000 0.0992 0.1984 2 0.3531 0.5152 0.2698 0.4911 0.0711 0.0840 0.4037 0.6036 0.3962 0.6322 0.0937 0.1486 3 0.5952 1.0000 0.2788 0.4946 0.1720 0.1981 0.6332 1.0000 0.3673 0.6278 0.4171 0.6462 4 0.4766 0.7778 0.3785 0.5755 0.6151 1.0000 0.3631 0.6693 0.4098 0.7106 0.6161 1.0000 5 0.0697 0.1008 0.1649 0.2635 0.6647 1.0000 0.2619 0.5238 0.0862 0.1026 0.4545 0.5804 6 0.1033 0.1620 0.2489 0.4012 0.4131 0.7104 0.3368 0.6500 0.1668 0.2595 0.3350 0.6699 7 0.1033 0.1620 0.2489 0.4012 0.1649 0.2635 0.6632 1.0000 0.4214 0.7084 0.0992 0.1984 8 0.0506 0.0618 0.0851 0.1458 0.0775 0.1296 0.6703 1.0000 0.5667 1.0000 0.0827 0.1038 9 0.4926 1.0000 0.4588 0.7084 0.3295 0.6221 0.6019 1.0000 0.5906 1.0000 0.3684 0.5940 10 0.4926 1.0000 0.1924 0.2597 0.0067 0.0067 0.1615 0.2292 0.0704 0.0704 0.0301 0.0396 11 0.1575 0.2104 0.5059 0.6890 0.3295 0.6221 0.5652 1.0000 0.6030 1.0000 0.1429 0.2208 12 0.6445 1.0000 0.1906 0.3725 0.6120 1.0000 0.1930 0.2421 0.5000 1.0000 0.5294 1.0000 13 0.5074 1.0000 0.5412 1.0000 0.5562 1.0000 0.4037 0.6036 0.3673 0.6278 0.5714 1.0000 14 0.4468 0.7455 0.6139 1.0000 0.4869 0.6693 0.4632 0.6600 0.3400 0.6656 0.5940 1.0000 15 0.5787 1.0000 0.3555 0.5401 0.4325 0.6828 0.5368 1.0000 0.6600 1.0000 0.6161 1.0000 16 0.0580 0.0770 0.5624 1.0000 0.6373 1.0000 0.5963 1.0000 0.1298 0.2293 0.2678 0.3808 17 0.1122 0.1571 0.0758 0.1442 0.4325 0.6828 0.0736 0.0995 0.4853 0.6976 0.0992 0.1984 18 0.0733 0.1020 0.3861 0.7402 0.5562 1.0000 0.4899 0.6850 0.2035 0.3598 0.2291 0.3476 19 0.0658 0.0962 0.5511 1.0000 0.2535 0.4515 0.3132 0.4050 0.4616 0.7213 0.0380 0.0427 20 0.0777 0.1463 0.5717 1.0000 0.2883 0.4495 0.1711 0.3408 0.5579 1.0000 0.0263 0.0310 21 0.1135 0.1905 0.4225 0.7795 0.2973 0.4621 0.5521 1.0000 0.5384 1.0000 0.3350 0.6699 22 0.0734 0.1468 0.5000 1.0000 0.3905 0.6834 0.2455 0.3802 0.4336 0.7152 0.0513 0.0698 23 0.1838 0.3523 0.0697 0.1177 0.5464 1.0000 0.0249 0.0281 0.6666 1.0000 0.0566 0.0656 24 0.1193 0.2335 0.3414 0.5580 0.3693 0.6645 0.5521 1.0000 0.2870 0.4539 0.2291 0.3476 25 0.0609 0.0956 0.4834 0.7482 0.3684 0.5940 0.5350 1.0000 0.3334 0.6668 0.0882 0.1176 26 0.4876 0.7742 0.2648 0.3883 0.5137 1.0000 0.0783 0.1588 0.3406 0.4713 0.1484 0.1778 116

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Table 5.8 Timing analysis results Fishers Exact Test oneand two-tailed P value test results.(continued) WA vs CA WP vs CP WAWP vs WACP CAWP vs CACP CACP vs WACP CAWP vs WAWP river 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 27 0.5613 1.0000 0.4965 0.7573 0.6302 0.9995 0.5368 1.0000 0.6600 1.0000 0.5829 1.0000 28 0.5627 1.0000 0.2400 0.3961 0.4266 0.7064 0.3350 0.6699 0.6319 1.0000 0.5521 1.0000 29 0.2227 0.2770 0.4983 1.0000 0.1709 0.2381 0.4060 0.6241 0.6427 1.0000 0.0779 0.0779 30 0.3742 0.6665 0.6258 1.0000 0.5000 1.0000 0.4895 1.0000 0.2846 0.5692 0.6853 1.0000 31 0.3160 0.5377 0.0631 0.1192 0.1609 0.2087 0.2214 0.3777 0.4217 0.6348 0.4479 0.6850 32 0.4513 0.7791 0.5858 1.0000 0.5137 1.0000 0.5521 1.0000 0.4098 0.7106 0.6393 1.0000 33 0.2448 0.3551 0.4034 0.7502 0.5087 1.0000 0.5464 1.0000 0.3638 0.6618 0.4381 0.6594 34 0.0199 0.0349 0.4022 0.7652 0.3202 0.6404 0.6393 1.0000 0.0447 0.0894 0.2064 0.3913 35 0.5532 1.0000 0.3861 0.7402 0.6302 0.9995 0.3668 0.6175 0.4690 0.6725 0.5829 1.0000 36 0.0235 0.0307 0.5335 1.0000 0.2556 0.3108 0.4211 0.6843 0.0188 0.0272 0.3916 0.6594 37 0.0429 0.0858 0.3212 0.4992 0.2059 0.2745 0.6478 1.0000 0.0426 0.0635 0.4048 0.6193 38 0.0155 0.0265 0.5551 1.0000 0.4060 0.6241 0.3883 0.6479 0.0180 0.0361 0.3522 0.6305 39 0.0554 0.0806 0.1031 0.1586 0.4286 0.5714 0.1132 0.1675 0.2950 0.5901 0.0882 0.1448 40 0.0421 0.0633 0.0203 0.0274 0.2291 0.3476 0.0249 0.0281 0.3202 0.6404 0.0566 0.0656 total 5 10 1 1 1 2 2 3 4 5 3 7 117

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Table 5.9: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Warm Pacific compared to Cold Pacific. 1.5-year RP 2-year RP 2.33-year RP 5-year RP 10-year RP 20-year RP river 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1 0.1388 0.3804 0.1041 0.2640 0.1137 0.2689 0.0896 0.1499 0.3125 1.0000 0.5004 1.0000 2 0.1668 0.5145 0.1839 0.5715 0.2290 1.0000 0.2447 1.0000 0.3116 1.0000 0.5000 1.0000 3 0.1826 0.5661 0.1960 0.7770 0.1462 0.4050 0.2056 0.4740 0.3125 1.0000 0.3753 1.0000 4 0.2083 0.7619 0.1860 0.5810 0.1564 0.4098 0.0102 0.0129 0.1787 0.3030 0.1787 0.3030 5 0.7586 1.0000 0.5000 1.0000 0.5200 1.0000 0.3118 1.0000 0.2310 0.6882 0.5000 1.0000 6 0.1785 0.5590 0.2184 1.0000 0.1785 0.5590 0.1276 0.3201 0.0923 0.2163 0.5004 1.0000 7 0.2168 0.7687 0.2163 1.0000 0.2230 1.0000 0.2728 0.7128 0.3159 1.0000 0.2662 0.6407 8 0.1906 0.5565 0.1461 0.3980 0.2251 1.0000 0.2768 1.0000 0.3314 0.5029 0.5800 1.0000 9 0.2283 1.0000 0.2150 0.7837 0.1671 0.5640 0.2397 0.7373 0.1723 0.4399 0.0950 0.1243 10 0.1826 0.5661 0.2230 1.0000 0.1955 0.7745 0.2233 0.7411 0.3217 0.6247 0.4289 1.0000 11 0.1388 0.3804 0.2085 0.7816 0.1785 0.5590 0.2445 1.0000 0.1723 0.4399 0.4289 1.0000 12 0.2089 0.5048 0.2384 1.0000 0.2319 0.7547 0.1785 0.4284 0.2288 0.3725 0.4353 1.0000 13 0.1671 0.5640 0.2085 0.7816 0.2230 1.0000 0.0850 0.1728 0.0903 0.1521 0.3217 0.5711 14 0.1270 0.3623 0.1960 0.7770 0.1137 0.2689 0.2728 0.7128 0.2662 0.6407 0.3217 0.5711 15 0.1826 0.5661 0.2255 1.0000 0.1748 0.5519 0.1978 0.5178 0.2728 0.7128 0.3217 0.5711 16 0.1896 0.5790 0.2184 1.0000 0.1156 0.2659 0.2627 1.0000 0.0903 0.1521 0.3217 0.5711 17 0.2343 1.0000 0.2255 1.0000 0.1680 0.5730 0.3159 1.0000 0.2662 0.6407 0.3217 0.5711 18 0.1896 0.5790 0.2085 0.7816 0.2163 1.0000 0.2397 0.7373 0.3159 1.0000 0.3159 1.0000 19 0.2343 1.0000 0.1770 0.5771 0.1235 0.3894 0.0896 0.1499 0.2662 0.6407 0.3753 1.0000 20 0.2258 1.0000 0.1532 0.4074 0.0956 0.2533 0.1487 0.3023 0.0124 0.0124 0.1886 0.1886 21 0.1388 0.3804 0.0669 0.1599 0.0506 0.0964 0.0488 0.0789 0.1787 0.3030 0.1812 0.1812 22 0.0253 0.0339 0.2255 1.0000 0.1426 0.3801 0.3261 1.0000 0.2953 0.5571 0.2953 0.5571 23 0.1583 0.3092 0.2402 1.0000 0.2359 0.7598 0.2647 0.6975 0.3366 0.5024 0.3366 0.5024 24 0.2056 0.7717 0.2207 1.0000 0.2230 1.0000 0.2233 0.7411 0.3593 1.0000 0.5004 1.0000 25 0.2516 1.0000 0.1804 0.5303 0.2355 0.7533 0.3025 1.0000 0.3061 0.6786 0.2504 0.6327 118

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1192.33-year RP 5-year RP 10-year RP 20-year RP Table 5.9: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Warm Pacific compared to Cold Pacific.(continued) 1.5-year RP 2-year RP 1-tail 2-tail 1-tail 2-tail 1-tail river 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 26 0.0299 0.0471 0.1306 0.3876 0.18390.57150.22830.44270.08790.15010.3191 0.5691 27 0.1654 0.5361 0.0945 0.2588 0.15890.56870.24470.72810.31251.00000.4289 1.0000 28 0.0517 0.1179 0.2153 0.7761 0.22751.00000.20640.42900.41341.00000.2881 0.4929 29 0.1828 0.5304 0.2052 0.7761 0.22931.00000.17080.43970.32211.00000.3385 0.5809 30 0.1319 0.2530 0.2823 1.0000 0.29781.00000.32701.00000.37611.00000.4286 0.4286 31 0.2289 1.0000 0.1751 0.5570 0.21260.76760.11280.30150.18140.44170.2583 0.6873 32 0.2115 0.7704 0.0750 0.1643 0.08300.16870.18600.49760.16490.38290.3753 1.0000 33 0.1966 0.5624 0.1761 0.5612 0.18370.56430.05610.11520.18120.38560.1939 0.3112 34 0.2364 1.0000 0.2106 0.7667 0.23641.00000.06950.12050.20280.39550.3890 1.0000 35 0.2140 0.7780 0.1770 0.5771 0.18600.58100.26271.00000.23690.68410.3217 0.6247 36 0.2369 1.0000 0.2201 0.7636 0.20900.74920.09720.17530.27600.66880.5094 1.0000 37 0.1130 0.2027 0.2174 0.7282 0.26681.00000.11300.20270.44921.00000.4941 1.0000 38 0.2038 0.5387 0.2150 0.7520 0.24021.00000.26120.71130.16070.37250.4402 1.0000 39 0.3214 1.0000 0.2887 1.0000 0.22960.74170.12630.34220.19130.51940.2099 0.4797 40 0.2038 0.5387 0.2612 0.7113 0.16120.28960.49761.00000.58541.00000.7593 1.0000 total 2 2 0 0 0 1 2 2 1 1 0 0

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24 0.1319 0.2530 0.1694 0.4454 0.18790.44100.15770.35410.42860.42860.4286 0.4286 120Table 5.10: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Warm Atlantic Warm Pacific compared to Warm Atlantic Cold Pacific. 1.5-year RP 2-year RP 2.33-year RP 5-year RP 10-year RP 20-year RP river 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1 0.2637 0.7047 0.1694 0.4454 0.24320.70220.26860.62390.43961.00000.6915 1.0000 2 0.1346 0.2183 0.2858 1.0000 0.21990.45270.34241.00000.50771.00000.6874 1.0000 3 0.3021 1.0000 0.2637 0.7047 0.22590.70360.32701.00000.43961.00000.5079 1.0000 4 0.3130 1.0000 0.2964 1.0000 0.29641.0000 0.0336 0.0573 0.57141.00000.5714 1.0000 5 0.7586 1.0000 0.5000 1.0000 0.50001.00000.38071.00000.39161.00000.5000 1.0000 6 0.3021 1.0000 0.2978 1.0000 0.30211.00000.32701.00000.43961.00000.5714 1.0000 7 0.2953 1.0000 0.2259 0.7036 0.29781.00000.22220.35530.22220.35530.4396 1.0000 8 0.1700 0.4010 0.0647 0.1144 0.27220.71770.32701.00000.57141.00000.5714 1.0000 9 0.2551 0.6870 0.2964 1.0000 0.24320.70220.30921.00000.31880.67300.1709 0.2381 10 0.3092 1.0000 0.2823 1.0000 0.28231.00000.33821.00000.43961.00000.4396 1.0000 11 0.2551 0.6870 0.2964 1.0000 0.29781.00000.24350.6618 0.0444 0.0525 0.3175 0.4921 12 0.3576 1.0000 0.2311 0.4059 0.23110.40590.19160.31890.28950.47890.5500 1.0000 13 0.2823 1.0000 0.2823 1.0000 0.27220.71770.23410.41840.32230.56040.5714 1.0000 14 0.1093 0.2232 0.2978 1.0000 0.22590.70360.43961.00000.43961.00000.5714 1.0000 15 0.1093 0.2232 0.3021 1.0000 0.25510.68700.22220.35530.43961.00000.5714 1.0000 16 0.2094 0.4589 0.2953 1.0000 0.30211.00000.38681.00000.50791.00000.5714 1.0000 17 0.3021 1.0000 0.2722 0.7177 0.29531.00000.38681.00000.32230.56040.4286 0.4286 18 0.2259 0.7036 0.2953 1.0000 0.30211.00000.31880.67300.43961.00000.4396 1.0000 19 0.2551 0.6870 0.2094 0.4589 0.16940.44540.38681.00000.43961.00000.3175 0.4921 20 0.3172 1.0000 0.2932 1.0000 0.13680.25760.33850.56920.18800.18800.7075 1.0000 21 0.1918 0.4343 0.1512 0.2761 0.22590.70360.26860.62390.22850.30410.6915 1.0000 22 0.1041 0.1794 0.2021 0.4283 0.20210.42830.35000.50000.60001.00000.6000 1.0000 23 0.0301 0.0396 0.2358 0.6699 0.32511.00000.30080.62410.54551.00000.5455 1.0000

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121Table 5.10: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Warm Atlantic Warm Pacific compared to Warm Atlantic Cold Pacific. (continued) 25 0.3001 0.6914 0.3344 1.0000 0.32511.00000.37591.00000.40601.00000.4060 1.0000 26 0.0442 0.0882 0.2259 0.7036 0.28231.00000.38681.00000.32230.56040.5079 1.0000 27 0.2435 0.6618 0.1512 0.2761 0.26370.70470.37611.00000.43961.00000.5714 1.0000 28 0.0373 0.0511 0.2287 0.6951 0.30781.00000.42001.00000.28000.48310.2800 0.4831 29 0.2976 0.6957 0.2932 1.0000 0.29321.00000.28610.62800.39491.00000.5128 1.0000 30 0.0783 0.1189 0.3263 0.6084 0.32630.60840.36920.56920.53331.00001.0000 1.0000 31 0.3078 1.0000 0.2739 0.6652 0.27390.66520.10540.14780.20600.30610.3554 0.5800 32 0.3021 1.0000 0.2722 0.7177 0.22590.70360.33821.00000.17190.28500.5079 1.0000 33 0.3049 1.0000 0.3113 1.0000 0.28300.69680.33480.56480.56001.00000.5600 1.0000 34 0.3198 1.0000 0.1142 0.2138 0.26650.66680.37270.59010.52171.00000.5217 1.0000 35 0.2259 0.7036 0.1694 0.4454 0.24320.70220.38681.00000.43961.00000.4396 1.0000 36 0.3301 1.0000 0.3301 1.0000 0.27240.65940.27370.63510.52381.00000.7400 0.1000 37 0.2941 0.4771 0.3225 0.6641 0.32250.66410.31370.58820.52291.00000.4444 0.4444 38 0.3001 0.6914 0.3344 1.0000 0.32511.00000.37591.00000.28570.48050.5455 1.0000 39 0.3759 1.0000 0.3483 1.0000 0.29720.67490.30010.69140.34831.00000.3759 1.0000 40 0.3538 1.0000 0.4060 1.0000 0.19480.19480.45450.45450.72001.00000.7200 1.0000 total 3 3 0 0 0 0 1 1 1 1 0 1

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24 0.0929 0.1920 0.2358 0.6699 0.30010.69140.29850.64620.51951.00000.5455 1.0000 122Table 5.11: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Warm Pacific compared to Warm Atlantic Warm Pacific 1.5-year RP 2-year RP 2.33-year RP 5-year RP 10-year RP 20-year RP river 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1 0.2985 0.6462 0.3344 1.0000 0.30010.69140.35381.00000.35060.57140.4545 0.4545 2 0.1788 0.3615 0.3301 1.0000 0.28290.66990.36491.00000.37220.58650.4762 0.4762 3 0.3483 1.0000 0.2508 0.6656 0.30861.00000.37591.00000.35060.57140.5195 1.0000 4 0.2985 0.6462 0.3301 1.0000 0.33011.00000.29720.67490.0779 0.0779 0.0779 0.0779 5 0.6691 1.0000 0.6154 1.0000 0.61541.00000.27200.56490.39161.00000.6691 1.0000 6 0.2508 0.6656 0.3086 1.0000 0.25080.66560.27090.65170.09570.13530.4545 0.4545 7 0.2972 0.6749 0.3344 1.0000 0.30861.00000.19690.29320.35060.57140.3506 0.5714 8 0.1533 0.3310 0.0272 0.0318 0.18860.38700.33080.60300.69671.00000.6967 1.0000 9 0.1734 0.3777 0.3001 0.6914 0.30010.69140.30080.59400.28570.48050.7200 1.0000 10 0.2709 0.6517 0.3001 0.6914 0.30010.69140.35381.00000.54551.00000.5455 1.0000 11 0.3538 1.0000 0.3301 1.0000 0.33441.00000.18580.34760.19480.19480.4545 0.4545 12 0.2824 0.5765 0.0968 0.1534 0.30230.63720.52941.00000.47060.47060.4706 0.4706 13 0.3001 0.6914 0.3301 1.0000 0.30861.00000.34831.00000.40601.00000.1948 0.1948 14 0.3008 0.6241 0.3344 1.0000 0.30861.00000.19690.29320.35060.57140.1948 0.1948 15 0.0975 0.1718 0.2508 0.6656 0.35381.00000.19690.29320.19690.29320.1948 0.1948 16 0.3251 1.0000 0.3086 1.0000 0.34831.00000.30080.62410.19690.29320.1948 0.1948 17 0.2985 0.6462 0.3344 1.0000 0.33441.00000.42861.00000.42861.00000.5195 1.0000 18 0.2972 0.6749 0.2358 0.6699 0.25080.66560.40601.00000.35060.57140.3506 0.5714 19 0.3538 1.0000 0.1608 0.3913 0.23580.66990.18580.34760.35060.57140.1948 0.1948 20 0.3251 1.0000 0.3344 1.0000 0.30861.00000.18580.34760.30080.62410.1948 0.1948 21 0.3538 1.0000 0.3251 1.0000 0.33441.00000.27090.65170.0779 0.0779 0.1948 0.1948 22 0.5263 1.0000 0.3501 1.0000 0.35011.00000.21050.21050.21050.21050.2105 0.2105 23 0.0515 0.0515 0.3779 1.0000 0.30230.63720.35290.60290.26090.31680.6857 1.0000

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123Table 5.11: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Warm Pacific compared to Warm Atlantic Warm Pacific (continued) 25 0.3779 1.0000 0.3779 1.0000 0.35631.00000.46321.00000.46321.00000.4632 1.0000 26 0.5143 1.0000 0.3269 1.0000 0.33011.00000.39701.00000.39701.00000.5143 1.0000 27 0.3008 0.6241 0.3251 1.0000 0.32511.00000.30080.62410.35060.57140.4545 0.4545 28 0.4060 1.0000 0.3344 1.0000 0.23580.66990.51951.00000.45450.45450.7200 1.0000 29 0.3759 1.0000 0.3086 1.0000 0.33441.00000.37591.00000.42861.00000.5195 1.0000 30 0.3182 0.5227 0.3788 1.0000 0.44191.00000.47731.00000.53031.00000.4167 0.4167 31 0.3344 1.0000 0.1608 0.3913 0.25080.66560.34831.00000.37591.00000.4060 1.0000 32 0.3483 1.0000 0.3344 1.0000 0.30861.00000.35381.00000.30080.59400.5195 1.0000 33 0.0397 0.0635 0.1886 0.3870 0.27510.66990.21280.3615 0.0351 0.0351 0.0902 0.0902 34 0.3301 1.0000 0.1179 0.1984 0.11790.19840.21280.3615 0.0351 0.0351 0.2143 0.2143 35 0.2972 0.6749 0.3086 1.0000 0.33011.00000.30080.62410.51951.00000.5455 1.0000 36 0.3501 1.0000 0.3437 1.0000 0.37151.00000.39471.00000.52631.00000.5000 1.0000 37 0.4286 0.4286 0.4196 1.0000 0.24480.59210.57141.00000.57141.00000.5714 1.0000 38 0.3563 1.0000 0.3779 1.0000 0.37791.00000.46321.00000.41180.41180.4118 0.4118 39 0.4632 1.0000 0.3529 0.6029 0.40721.00000.35631.00000.40721.00000.4632 1.0000 40 0.3394 0.6437 0.4632 1.0000 0.46321.00000.58821.00000.68571.00000.6857 1.0000 total 1 2 1 1 0 0 0 0 2 4 0 2

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124Table 5.12: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Warm Pacific compared to Cold Atlantic Cold Pacific 1.5-year RP 2-year RP 2.33-year RP 5-year RP 10-year RP 20-year RP river 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1 0.2362 0.4050 0.2665 0.6802 0.19990.41360.17620.28060.33030.55960.5138 1.0000 2 0.3825 1.0000 0.2835 0.6850 0.22050.41730.34001.00000.39641.00000.5138 1.0000 3 0.2520 0.6693 0.3198 1.0000 0.28350.68500.3303 0.5596 0.3303 0.5596 0.5138 1.0000 0.2362 0.4050 0.2205 0.4173 0.1470 0.2213 0.1762 0.2806 0.0678 0.0678 0.0678 0.0678 0.7692 1.0000 0.3062 0.4173 0.5000 1.0000 0.5556 1.0000 0.2381 0.5238 0.7692 1.0000 0.1470 0.2213 0.3198 1.0000 0.1470 0.2213 0.1623 0.3413 0.0237 0.0237 0.4348 0.4348 0.3400 1.0000 0.3062 0.6850 0.3198 1.0000 0.0678 0.0678 0.1779 0.1779 0.1779 0.1779 0.3260 1.0000 0.2898 0.6740 0.2898 0.6740 0.3519 0.6161 0.5909 1.0000 0.6769 1.0000 0.2835 0.6850 0.3062 0.6850 0.3062 0.6850 0.4404 1.0000 0.3083 0.4862 0.5652 1.0000 0.2665 0.6802 0.3150 1.0000 0.3062 0.6850 0.2782 0.6175 0.5652 1.0000 0.7009 1.0000 0.2520 0.6693 0.2835 0.6850 0.1623 0.3413 0.1623 0.3413 0.3303 0.5596 0.4348 0.4348 0.2554 0.6027 0.1320 0.1968 0.3301 1.0000 0.4632 1.0000 0.4632 1.0000 0.5053 1.0000 0.2450 0.6500 0.3198 1.0000 0.2835 0.6850 0.1762 0.2806 0.1779 0.1779 0.1779 0.1779 0.3500 1.0000 0.2665 0.6802 0.2205 0.4173 0.2782 0.6175 0.1779 0.1779 0.1779 0.1779 0.2835 0.6850 0.3198 1.0000 0.3150 1.0000 0.3500 1.0000 0.2782 0.6175 0.1779 0.1779 0.3307 1.0000 0.2835 0.6850 0.1142 0.2138 0.3500 1.0000 0.0678 0.0678 0.1779 0.1779 0.3825 1.0000 0.3062 0.6850 0.1312 0.2215 0.4404 1.0000 0.5138 1.0000 0.5138 1.0000 0.3500 1.0000 0.3150 1.0000 0.3198 1.0000 0.3825 1.0000 0.3964 1.0000 0.3964 1.0000 0.3400 1.0000 0.3307 1.0000 0.3062 0.6850 0.0811 0.1269 0.1779 0.1779 0.1779 0.1779 0.3150 1.0000 0.1999 0.4136 0.2835 0.6850 0.2450 0.6500 0.0678 0.0678 0.1779 0.1779 0.3150 1.0000 0.1999 0.4136 0.0735 0.1023 0.0811 0.1269 0.1762 0.2806 0.1779 0.1779 0.1857 0.2403 0.2817 0.6619 0.3065 1.0000 0.3839 1.0000 0.1558 0.1558 0.1558 0.1558 0.2838 0.6169 0.3438 1.0000 0.3501 1.0000 0.4768 1.0000 0.6316 1.0000 0.6316 1.0000 0.0334 0.0393 0.1999 0.4136 0.1999 0.4136 0.3187 0.6600 0.3230 0.6036 0.5652 1.0000 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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125Table 5.12: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Warm Pacific compared to Cold Atlantic Cold Pacific (continued) 0.3684 25 0.3438 1.0000 0.2292 0.3765 0.2567 0.6332 0.4768 1.0000 0.4912 1.0000 0.3684 26 0.3308 0.6030 0.2641 0.6605 0.2829 0.6699 0.3248 0.5534 0.1714 0.1714 0.4286 0.4286 27 0.3150 1.0000 0.2665 0.6802 0.2665 0.6802 0.2782 0.6175 0.3303 0.5596 0.5138 1.0000 28 0.3759 1.0000 0.3251 1.0000 0.3344 1.0000 0.3008 0.5940 0.4545 0.4545 0.7200 1.0000 29 0.2985 0.6462 0.3086 1.0000 0.3251 1.0000 0.3506 0.5714 0.5195 1.0000 0.4545 0.4545 30 0.4351 1.0000 0.4351 1.0000 0.3263 0.5921 0.4895 1.0000 0.4895 1.0000 0.3846 0.3846 31 0.3251 1.0000 0.3001 0.6914 0.3301 1.0000 0.3538 1.0000 0.4060 1.0000 0.4060 1.0000 32 0.3150 1.0000 0.0735 0.1023 0.1470 0.2213 0.1762 0.2806 0.5138 1.0000 0.5138 1.0000 33 0.1880 0.3233 0.2229 0.4149 0.3001 0.6914 0.0957 0.1353 0.0957 0.1353 0.0779 0.0779 34 0.3344 1.0000 0.2358 0.6699 0.2358 0.6699 0.0957 0.1353 0.0957 0.1353 0.3506 0.5714 35 0.3400 1.0000 0.3198 1.0000 0.3198 1.0000 0.3500 1.0000 0.3230 0.6036 0.5652 1.0000 36 0.3344 1.0000 0.3086 1.0000 0.3483 1.0000 0.1880 0.3233 0.3008 0.5940 0.5195 1.0000 37 0.3200 0.6000 0.3563 1.0000 0.2851 0.6199 0.2426 0.5147 0.6471 1.0000 0.6471 1.0000 38 0.3301 0.6562 0.3001 0.6499 0.3001 0.6499 0.3973 1.0000 0.3973 1.0000 0.4912 1.0000 39 0.4912 1.0000 0.4912 1.0000 0.3831 1.0000 0.1800 0.3498 0.2567 0.6332 0.2980 0.6027 40 0.2292 0.3765 0.2980 0.6027 0.4912 1.0000 0.6316 1.0000 0.6316 1.0000 0.6403 1.0000 total 1 1 0 0 0 0 1 1 4 0 2 0

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126Table 5.13: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Cold Pacific compared to Warm Atlantic Cold Pacific 5-year RP 1.5-year RP 2-year RP 2.33-year RP 10-year RP 20-year RP river 1-tail 2-tail l 1-tail 2-tail 1-tail 2-tail 1-tail 2-tail 1-tail 2-tai 1-tail 2-tail 1 0.2837 1.0000 0.2648 0.7163 0.2979 1.0000 0.4269 1.0000 0.4269 1.0000 0.4483 0.4483 2 0.3454 1.0000 0.2722 0.7177 0.1512 0.2761 0.3297 1.0000 0.3571 0.5833 0.4643 0.4643 3 0.2648 0.7163 0.2531 0.7152 0.2837 1.0000 0.3065 0.6059 0.4269 1.0000 0.5123 1.0000 4 0.2807 0.7141 0.1773 0.4515 0.1064 0.2490 0.5123 1.0000 0.5517 1.0000 0.5517 1.0000 5 0.6691 1.0000 0.6691 1.0000 0.3846 0.3846 0.2720 0.5649 0.3263 0.5921 0.6154 1.0000 6 0.2287 0.4543 0.2531 0.7152 0.2287 0.4543 0.3678 1.0000 0.2956 0.4877 0.5517 1.0000 7 0.2287 0.4543 0.1205 0.2642 0.2893 1.0000 0.0766 0.1067 0.0766 0.1067 0.2956 0.4877 8 0.2979 1.0000 0.2848 1.0000 0.2848 1.0000 0.3372 1.0000 0.5123 1.0000 0.5517 1.0000 9 0.2979 1.0000 0.1773 0.4515 0.2859 1.0000 0.2183 0.4100 0.2989 0.6628 0.3065 0.6059 10 0.1444 0.2742 0.2893 1.0000 0.2364 0.7021 0.2989 0.6628 0.4269 1.0000 0.2956 0.4877 11 0.2648 0.7163 0.2441 0.7107 0.0891 0.1296 0.2183 0.4100 0.1195 0.1834 0.2956 0.4877 12 0.3332 1.0000 0.2706 0.6843 0.2706 0.6843 0.2158 0.3707 0.4099 1.0000 0.5217 1.0000 13 0.1633 0.4335 0.2893 1.0000 0.2441 0.7107 0.3065 0.6059 0.5517 1.0000 0.5517 1.0000 14 0.2364 0.7021 0.2531 0.7152 0.2364 0.7021 0.3941 1.0000 0.2956 0.4877 0.5517 1.0000 15 0.2837 1.0000 0.2648 0.7163 0.2979 1.0000 0.3032 1.0000 0.3941 1.0000 0.5517 1.0000 16 0.2441 0.7107 0.1329 0.2723 0.1329 0.2723 0.1806 0.3640 0.5517 1.0000 0.5517 1.0000 17 0.2287 0.4543 0.1773 0.4515 0.0738 0.1394 0.3941 1.0000 0.5123 1.0000 0.4483 0.4483 18 0.1205 0.2642 0.2025 0.4667 0.2025 0.4667 0.3335 1.0000 0.3941 1.0000 0.3941 1.0000 19 0.2287 0.4543 0.2441 0.7107 0.2859 1.0000 0.4269 1.0000 0.2956 0.4877 0.2956 0.4877 20 0.2400 0.7000 0.2212 0.4757 0.2945 1.0000 0.2095 0.3111 0.2200 0.2200 0.7258 1.0000 21 0.2837 1.0000 0.2837 1.0000 0.1633 0.4335 0.4269 1.0000 0.4483 0.4483 0.7258 1.0000 22 0.2072 0.4348 0.0956 0.1513 0.2823 1.0000 0.4000 1.0000 0.5357 1.0000 0.5357 1.0000 23 0.2369 0.6668 0.1571 0.4136 0.2332 0.6802 0.4099 1.0000 0.5217 1.0000 0.5217 1.0000 24 0.0501 0.1142 0.1329 0.2723 0.0828 0.1436 0.1806 0.3640 0.0783 0.0783 0.4483 0.4483

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127Table 5.13: Magnitudes analysis results Fishers Exact Test oneand two-tailed P value test results for Cold Atlantic Cold Pacific compared to Warm Atlantic Cold Pacific (continued) 0.4136 25 0.2332 0.6802 0.1571 0.2332 0.6802 0.3416 1.0000 0.3913 1.0000 0.2391 0.4783 26 0.1918 0.4343 0.2823 1.0000 0.2978 1.0000 0.4396 1.0000 0.5714 1.0000 0.5714 1.0000 27 0.2807 0.7141 0.2893 1.0000 0.2531 0.7152 0.3678 1.0000 0.4269 1.0000 0.5123 1.0000 28 0.0855 0.1302 0.1539 0.2671 0.2287 0.6951 0.3043 0.6348 0.2800 0.4831 0.2800 0.4831 29 0.3172 1.0000 0.2541 0.7036 0.2443 0.6960 0.4308 1.0000 0.4308 1.0000 0.5556 1.0000 30 0.2437 0.6193 0.1371 0.3147 0.1371 0.3147 0.4000 1.0000 0.4000 1.0000 0.7632 1.0000 31 0.2798 0.7015 0.1076 0.2177 0.1076 0.2177 0.2060 0.3061 0.3554 0.5800 0.3554 0.5800 32 0.2979 1.0000 0.1064 0.2490 0.1633 0.43350.51231.00000.5123 1.0000 0.1992 0.3432 33 0.2199 0.4527 0.2873 1.0000 0.23940.69220.51691.00000.51691.00000.5385 1.0000 34 0.3198 1.0000 0.2320 0.6843 0.31981.00000.52171.00000.52171.00000.5217 1.0000 35 0.0630 0.1297 0.2025 0.4667 0.26480.71630.18060.36400.28900.63220.4269 1.0000 36 0.3198 1.0000 0.3198 1.0000 0.27980.68020.33321.00000.27330.59010.5217 1.0000 37 0.2737 0.6351 0.3301 1.0000 0.33011.00000.36491.00000.52381.00000.5238 1.0000 38 0.2932 1.0000 0.2320 0.6843 0.15710.41360.35961.00000.34161.00000.5217 1.0000 39 0.2484 0.5901 0.1398 0.3168 0.29981.00000.29321.00000.23320.68020.3146 1.0000 40 0.0436 0.0995 0.2427 0.6404 0.50001.00000.50001.00000.50001.00000.7593 1.0000 total 1 1 0 0 0 0 0 0 0 1 0 0

PAGE 144

128 Figure 5.40: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Warm Atlantic conditions to Cold Atlantic conditions.

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129 Figure 5.41: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Warm Pacific conditions to Cold Pacific conditions.

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130 Figure 5.42: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific conditions.

PAGE 147

131 Figure 5.43: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions.

PAGE 148

132 Figure 5.44: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Cold Atlantic Cold Pacific conditions to Warm Atlantic Cold Pacific conditions.

PAGE 149

133 Figure 5.45: Fishers Exact Test results Timing analysis, significant two tailed P values produced from comparing the proportion of summer events under Cold Atlantic Warm Pacific conditions to Warm Atlantic Warm Pacific conditions.

PAGE 150

134 Figure 5.46: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Warm Atlantic conditions to Cold Atlantic conditions.

PAGE 151

135 Figure 5.47: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Warm Pacific conditions to Cold Pacific conditions.

PAGE 152

136 Figure 5.48: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific conditions.

PAGE 153

137 Figure 5.49: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Cold Atlantic Warm Pacific conditions to Warm Atlantic Warm Pacific conditions.

PAGE 154

138 Figure 5.50: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific conditions.

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139 Figure 5.51: Fishers Exact Test results Magnitudes analysis, significant two tailed P values produced from comparing the proportion of events that exceed defined thresholds under Cold Atlantic Cold Pacific conditions to Warm Atlantic Cold Pacific conditions.

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APPENDIX D TABLES AND FIGURES FOR KRUSKAL WALLIS AND ANOVA TESTS The following tables and figures of this appendix are results of the Kruskal Wallis and ANOVA test comparisons of the magnitudes of annual floods of differing SST categories. These tables and figures supplement and are referenced in Chapter 5 of this thesis. 140

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141 Table 5.14: Kruskal Wallis results p-values for all combinations at all stations. Shaded cells indicate significant values at the 90% confidence level. A C vs W P C vs W CA C vs W WA C vs W CP C vs W WP C vs W river P P P P P P 1 0.7692 0.1101 0.2386 0.2458 0.7756 0.8432 2 0.6449 0.6861 0.8039 0.4832 0.9449 0.4812 3 0.3585 0.7900 1.0000 0.8709 0.4427 0.7168 4 0.6354 0.1613 0.1916 0.3645 0.4414 0.6679 5 0.7318 0.5725 0.6752 0.7927 1.0000 0.8836 6 0.8056 0.9016 0.8040 0.8892 0.8779 0.8951 7 0.7190 0.8715 0.7097 0.9629 0.9127 0.5977 8 0.3481 0.5956 0.8412 0.5460 0.8093 0.1263 9 0.5259 0.8417 0.8041 1.0000 0.6931 0.5978 10 0.3684 0.4523 0.6641 0.5932 0.3234 0.7919 11 0.5075 0.5618 0.3519 0.8164 0.3346 0.8690 12 0.9245 0.6122 0.7280 0.2686 0.5794 0.3341 13 0.7836 0.4874 0.5554 0.7276 0.8780 0.5308 14 0.9772 0.2342 0.3516 0.4571 0.9300 0.9474 15 0.9849 0.8334 0.6844 0.9252 0.7914 0.7651 16 0.3017 0.9468 0.8523 0.7447 0.2818 0.7162 17 0.3585 0.9925 0.4381 0.5616 0.2540 0.9474 18 0.2185 0.8417 0.9753 0.4576 0.2540 0.5097 19 0.8945 0.2164 0.5351 0.2962 0.9301 0.8690 20 0.5658 0.1453 0.2149 0.2720 0.6954 0.6442 21 0.9397 0.0785 0.1927 0.3530 1.0000 0.6681 22 0.3750 0.3227 0.6392 0.3872 0.3085 0.7738 23 0.8342 0.5872 0.5824 0.1759 0.4020 0.4347 24 0.5830 0.9621 0.1725 0.1636 0.0832 0.2623 25 0.8445 0.8427 0.4218 0.4683 0.6235 0.3291 26 0.6420 0.1347 0.2002 0.3068 0.6590 0.6958 27 0.8424 0.7464 0.9013 0.7102 0.8780 0.7415 28 0.2297 0.6487 1.0000 0.3811 0.2794 0.5526 29 0.7707 0.4449 0.5749 0.7510 0.7883 0.8950 30 0.6784 0.3530 0.8836 0.1321 0.3717 0.8072 31 0.2261 0.7328 0.9212 0.8976 0.3284 0.4683 32 0.5573 0.1483 0.1725 0.4299 0.5393 0.8951 33 0.1051 0.2145 0.1294 0.8053 0.6250 0.0486 34 0.5625 0.6247 0.3225 0.7817 0.6860 0.2749 35 0.2641 0.5813 0.8041 0.3296 0.2194 0.7416 36 0.7521 0.9903 0.8689 0.8880 0.8294 0.9097 37 0.3998 0.9867 0.4815 0.8239 0.8880 0.2195 38 0.3268 0.8842 0.7353 1.0000 0.3556 0.9222 39 0.4966 0.5783 0.5260 0.9474 0.4024 0.8453 40 0.3466 0.5875 0.2719 0.8174 0.2254 1.0000

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142 Figure 5.52: Kruskal Wallis results significant p-values for all combinations at all stations.

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143 Table 5.15: ANOVA results p-values for all combinations at all stations. Shaded cells indicate significant values at the 90% confidence level. A C vs W P C vs W CA C vs W WA C vs W CP C vs W WP C vs W river P P P P P P 1 0.6830 0.2102 0.3053 0.4450 0.5884 1.0000 2 0.8793 0.8690 0.8548 0.6914 0.7047 0.8350 3 0.8772 0.4917 0.8548 0.4450 0.8367 1.0000 4 0.4376 0.5400 0.3489 1.0000 0.3404 1.0000 5 0.4201 0.6949 0.5271 1.0000 0.4285 0.7249 6 0.5400 0.7796 0.8548 1.0000 0.8970 1.0000 7 0.6830 0.4917 0.8548 0.4450 0.8970 0.3918 8 0.2545 0.7745 0.6646 0.6617 0.8367 0.2563 9 0.3039 0.8415 0.5099 1.0000 0.3404 0.3918 10 0.8772 0.4917 0.8548 0.4450 0.8367 0.6390 11 0.6424 0.7140 0.8548 1.0000 0.3404 1.0000 12 0.5271 0.7491 0.7136 0.3428 0.5368 0.4859 13 0.6830 0.6573 0.8548 1.0000 0.8367 1.0000 14 0.4731 0.4917 0.5800 0.4450 0.5344 0.6959 15 0.2376 0.7796 0.6728 0.8199 0.3787 0.4285 16 0.2198 0.8415 0.1334 1.0000 0.1976 0.6959 17 0.1250 0.6573 0.1334 0.7428 0.1976 1.0000 18 0.2198 0.7140 0.8548 0.4450 0.3787 0.3918 19 0.2004 0.4917 0.8548 0.4450 0.3404 0.3918 20 0.7766 0.2545 0.3053 0.3435 0.2556 0.3918 21 0.4731 0.0690 0.3053 0.4450 0.8367 1.0000 22 0.8911 0.3115 0.6646 0.3268 0.4638 0.8087 23 0.4529 0.4757 0.7636 0.3918 0.4076 0.4859 24 0.6830 0.9029 0.5099 0.1266 0.1976 0.3918 25 0.2779 0.4123 0.2101 0.3918 0.1025 0.7715 26 0.6800 0.3222 0.3085 0.4450 1.0000 0.5283 27 0.6830 0.3129 0.5099 0.4450 0.5884 1.0000 28 0.1655 0.8267 0.3918 0.4314 0.4314 0.2110 29 0.3077 0.4817 0.6959 0.8632 0.5466 1.0000 30 0.2556 1.0000 0.7249 0.8327 0.3173 0.5582 31 0.5623 1.0000 1.0000 1.0000 1.0000 0.3918 32 0.4731 0.2102 0.3053 0.4450 0.8367 0.3918 33 0.4705 0.6711 0.3918 0.8213 0.7157 0.2787 34 0.4578 0.4490 0.3918 0.2921 0.4142 0.2787 35 0.6830 0.4917 0.8548 0.4450 0.1976 1.0000 36 0.8759 0.8869 1.0000 0.8350 0.8274 1.0000 37 0.3950 0.8902 0.4024 1.0000 0.8350 0.2801 38 0.6466 0.8527 0.2101 1.0000 0.4142 0.7715 39 0.8665 0.4123 0.2101 1.0000 0.4142 0.2014 40 0.0870 0.8527 0.2101 1.0000 0.1025 0.4859

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144 Figure 5.53: ANOVA results significant p-values for all combinations at all stations.

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BIOGRAPHICAL SKETCH Sally Adkins was born in New Bedford, Massachusetts, and grew up in Massachusetts and Washington, D.C. She worked for several years in the botanical field for the Missouri Botanical Garden in St. Louis, Missouri, and the National Museum of Natural History at the Smithsonian Institution in Washington, D.C. This led her to an interest in riparian habitats and a job at River Network, a national river advocacy organization. Since moving to Florida, she has become involved in the restoration and preservation of local creeks in Gainesville, Florida. She currently works as the National Pollutant Discharge Elimination System Coordinator for a Clean Water Act partnership between the City of Gainesville, Alachua County, and the Florida Department of Transportation. 153


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Permanent Link: http://ufdc.ufl.edu/UFE0004941/00001

Material Information

Title: Impact of Atlantic and Pacific Sea Surface Temperature Anomalies on the Magnitude and Timing of Annual Floods in Northern Florida
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0004941:00001

Permanent Link: http://ufdc.ufl.edu/UFE0004941/00001

Material Information

Title: Impact of Atlantic and Pacific Sea Surface Temperature Anomalies on the Magnitude and Timing of Annual Floods in Northern Florida
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0004941:00001


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IMPACT OF ATLANTIC AND PACIFIC SEA SURFACE TEMPERATURE
ANOMALIES ON THE MAGNITUDE AND TIMING OF ANNUAL FLOODS IN
NORTHERN FLORIDA

















By

SALLY ADKINS


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

UNIVERSITY OF FLORIDA


2004

































Copyright 2004

by

Sally Adkins




























This thesis is dedicated to Patrick,
my partner in the rapids of this lovely and crazy river of life,
and to my father who encourages us along.















ACKNOWLEDGMENTS

I would like to thank Dr. Peter Waylen for his continued support and

encouragement in my academic studies, in the thesis process, and also for being a

wonderful teacher and mentor. I would also like to thank Joann Mossa for her support

and for providing me with employment during my studies; teaching at a college level was

one of the many rewarding experiences of earning a master's degree. Thanks go also to

Ellen Martin for joining my committee late in the process.

Other thanks go to friends and fellow students who have made the graduate journey

with me I am glad to have shared this experience with this special group of people.

I would also like to thank my father, Jan Adkins, for his support and love he has

always encouraged me to live an examined life and I am continually motivated by his

example.

Last, but never ever least, I would like to thank my boyfriend, Patrick Burger, for

his continued help, support, encouragement and love during this process. He is the steady

presence that allows me to work toward my loftiest goals, and I feel very lucky to have

him beside me.















TABLE OF CONTENTS

page

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

LIST OF TABLES ..................._......... .................... ......... ...... ....... ... viii

LIST OF FIGURES ......... ......................... ...... ........ ............ xi

ABSTRACT .............. .......................................... xvi

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

R research Ju stification ......................................................................... ........ ...........2
Objective of Thesis .............. ................ ................................... .4
T h e sis S tru ctu re ....................................................... ................ 5

2 LITER A TU R E REV IEW ............................................................. ....................... 6

A annual Flood Series (A F S) ............................................................... .....................6
El N ifio/Southem O scillation (EN SO)...................................... ........................ 8
Global Effect ........................................ ........9
Regional Effect-United States .................................. ............... 10
Local Effect-Southeastern United States.................... ........................11
A tlantic O scillation s ............................... .... .... .... .. .. ...... ... ..................... 13
Global, regional and local effects of NAO and AMO on climate......................13
Using Atlantic and Pacific SSTs in Hydrologic Prediction.................... ........ 15
Sum m ary ............. ............................................. .......... ...... .. 15

3 STUD Y AREA AN D D A TA ............................................. ............................ 16

P re c ip itatio n ........................................... .. ........................................................... 1 6
T em perature and E vaporation................................................................ ...............20
S to ra g e ...................... .. ............. .. ........................................................2 0
R u n o ff ...................... .. ............. .. .......................................................2 1
D a ta .......................................................................2 3
D isch arg e D ata ..............................................................2 3
S S T A n om aly In dicies................................................................................... 24
S u m m a ry ...................... .. ............. .. .....................................................2 4



v









4 M E T H O D O L O G Y ............................................................................ ................... 28

W ater Y ear D eterm nation ...................... .. .. ......... .. ........... ..............................29
A annual Flood Series D eterm nation ........................................ ....... ............... 30
Generalized Extreme Value Distribution......................................... ............... 30
Kolmogorov-Smirnov Goodness-Of-Fit Test......................................................31
SST relationships .................................... ............................... ......... 32
Test of Proportions ...................................................... ................. 32
F isher's E xact T est ......................... .... ........ ................................. ...... ........ ...... ... 37
K ruskal-W allis Test .................. ................................. .. .. .. .. .............. 38
A N O V A .........................................................................................3 8
S u m m a ry ......................................................................................................3 9

5 R E S U L T S .............................................................................4 0

Seasonal Proportions of Annual Flood Events .................................................... 40
Generalized Extreme Value (GEV) Fitted Distributions ............... ............ 41
Test of Proportions R results .................................................................................. .... 42
Warm Atlantic Vs. Cold Atlantic ............................. ...............43
W arm Pacific V s. C old Pacific .................................... ........... ................ 43
Warm Atlantic Warm Pacific Vs. Warm Atlantic Cold Pacific ..........................44
Cold Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific ...............................45
Warm Atlantic Cold Pacific Vs. Cold Atlantic Cold Pacific ..............................46
Warm Atlantic Warm Pacific Vs. Cold Atlantic Warm Pacific..........................47
Warm Atlantic Cold Pacific Vs. Cold Atlantic Warm Pacific ..........................49
Warm Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific ...........................50
Fishers Exact Test Results ............................................................. .. .............50
W arm Atlantic Vs. Cold Atlantic ................. ...............................................51
Warm Pacific Vs. Cold Pacific.............................................52
Warm Atlantic Warm Pacific Vs. Warm Atlantic Cold Pacific..........................53
Cold Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific ............................54
Cold Atlantic Cold Pacific Vs. Warm Atlantic Cold Pacific ............................55
Cold Atlantic Warm Pacific Vs. Warm Atlantic Warm Pacific ..........................56
K ruskal W allis T est R results ......................................................................... ......57
A N O V A R esu lts .............................................................................................. 5 8
Sum m ary ................................... ......................................................... 58

6 DISCUSSION AND IMPLICATIONS ................................................. 60

Seasonal Proportions of Annual Flood Events .................................................... 60
Generalized Extreme Value Fitted Distributions ..................................................... 60
Annual Floods in Relation to Atlantic SSTs................... ...........................61
Annual Floods in Relation to Pacific SSTs .................... ............... .................. 62
Annual Floods in Relation to Combined Atlantic and Pacific SSTs..........................63
Chapter Conclusions ....................................................................... ........ 65









7 C O N C L U SIO N S ............................................................................. .......... .. .. ... 6 7

APPENDIX

A TABLES AND FIGURES FOR TIMING AND MAGNITUDE OF THE AFS........70

B TABLES AND FIGURES FOR TEST OF PROPORTIONS ..................................81

C TABLES AND FIGURES FOR FISHERS EXACT TEST ................................115

D TABLES AND FIGURES FOR KRUSKAL WALLIS AND ANOVA TESTS .....140

L IST O F R E F E R E N C E S ...................................................................... ..................... 145

BIOGRAPHICAL SKETCH ............................................................. ............... 153















LIST OF TABLES


Table pge

3.1. 40 USGS discharge stations examined in this study. Numbers 1 40 apply to
stations listed in Figure 3.3. ............................................ ............................. 25

4-1. Atlantic and Pacific sea surface temperature anomaly classifications .....................33

5.1: Number of positive and negative z-values* for timing analysis; A comparison of the
proportion of annual floods that occur in the summer season between the following
SST combinations. Discussion of results is focused on cells highlighted in yellow
(largest # of z-values) and white (least # of z-values). Those cells shaded in gray
are extraneous, although part of the analysis. .................................. .................81

5.2: Number of positive and negative z-values* for 1.5-year return period magnitude
analysis; A comparison of the proportion of annual floods that exceed the 1.5-year
return period between the various SST combinations. Discussion of results is
focused on cells highlighted in yellow (largest # of z-values) and white (least # of
z-values). Those cells shaded in gray are extraneous, although part of the analysis.82

5.3: Number of positive and negative z-values* for 2-year return period magnitude
analysis; A comparison of the proportion of annual floods that exceed the 2-year
return period between the various SST combinations. Discussion of results is
focused on cells highlighted in yellow (largest # of z-values) and white (least # of
z-values). Those cells shaded in gray are extraneous, although part of the analysis.83

5.4: Number of positive and negative z-values* for 2.33-year return period magnitude
analysis; A comparison of the proportion of annual floods that exceed the 2.33-year
return period between the various SST combinations. Discussion of results is
focused on cells highlighted in yellow (largest # of z-values) and white (least # of
z-values). Those cells shaded in gray are extraneous, although part of the analysis.84

5.5: Number of positive and negative z-values* for 5-year return period magnitude
analysis; A comparison of the proportion of annual floods that exceed the 5-year
return period between the various SST combinations. Discussion of results is
focused on cells highlighted in yellow (largest # of z-values) and white (least # of
z-values). Those cells shaded in gray are extraneous, although part of the analysis.85

5.6: Number of positive and negative z-values* for 10-year return period magnitude
analysis; A comparison of the proportion of annual floods that exceed the 10-year
return period between the various SST combinations. Discussion of results is









focused on cells highlighted in yellow (largest # of z-values) and white (least # of
z-values). Those cells shaded in gray are extraneous, although part of the analysis.86

5.7: Number of positive and negative z-values* for 20-year return period magnitude
analysis; A comparison of the proportion of annual floods that exceed the 20-year
return period between the various SST combinations. Discussion of results is
focused on cells highlighted in yellow (largest # of z-values) and white (least # of
z-values). Those cells shaded in gray are extraneous, although part of the analysis.87

5.8 Timing analysis results Fishers Exact Test one- and two-tailed P value test results. 116

5.9: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for W arm Pacific compared to Cold Pacific..............................................118

5.9: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for Warm Pacific compared to Cold Pacific.(continued)............................119

5.10: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for Warm Atlantic Warm Pacific compared to Warm Atlantic Cold Pacific. 120

5.11: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for Cold Atlantic Warm Pacific compared to Warm Atlantic Warm Pacific122

5.11: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for Cold Atlantic Warm Pacific compared to Warm Atlantic Warm Pacific
(continued) ........................................... ........................... 123

5.12: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for Cold Atlantic Warm Pacific compared to Cold Atlantic Cold Pacific.. 124

5.12: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for Cold Atlantic Warm Pacific compared to Cold Atlantic Cold Pacific
(continued) ........................................... ........................... 125

5.13: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for Cold Atlantic Cold Pacific compared to Warm Atlantic Cold Pacific.. 126

5.13: Magnitudes analysis results Fishers Exact Test one- and two-tailed P value test
results for Cold Atlantic Cold Pacific compared to Warm Atlantic Cold Pacific
(continued) ........................................... ........................... 127

5.14: Kruskal Wallis results p-values for all combinations at all stations. Shaded cells
indicate "significant" values at the 90% confidence level. ...................................141

5.15: ANOVA results p-values for all combinations at all stations. Shaded cells indicate
"significant" values at the 90% confidence level............................143
















LIST OF FIGURES


Figure pge

3.1. Average monthly rainfall for areas across Florida. First graphs represent northern
areas and the sequence of graphs continues west and south finishing with the most
southern areas. .................................................. ................. ...... ......... 18

3.2. Monthly hydrographs of rivers across Florida. First hydrographs represent northern
areas and the sequence of hydrographs continues finishing with the most southern
areas........... .... ..................... .......... ........ ..... ....... ......... 22

3.3. Base map of 40 USGS discharge stations used in this study. Numbers 1 40 apply
to stations listed in Table 3.1.......................................... .............................. 26

3.4 Two-year series of Mean Monthly Atlantic (N ATL) and Pacific (NINO 3.4) SST
Anomalies27

4-2. Annual flood series at 3 stations spanning Florida in relation to Atlantic and Pacific
sea surface temperature anomalies. A) River 27 northern Florida, B) River 4
central Florida, C) River 8 southern Florida, D) Atlantic sea surface temperature
anomalies, E) Pacific sea surface temperature anomalies................... ............35

5.1: Total proportion of summer events by station. .................... ......................... 71

5.2: Total proportion of summer events under Warm Atlantic SST conditions by station.72

5.3: Total proportion of summer events under Cold Atlantic SST conditions by station..73

5.4: Total proportion of summer events under Warm Pacific SST conditions by station. 74

5.5: Total proportion of summer events under Cold Pacific SST conditions by station. ..75

5.6: GEV shape parameter, K (related to the positioning of the distribution tail) by
station .............................................................................. 76

5.7: GEV location parameter, (related to the mode) by station. ............................77

5.8: GEV scale parameter, ao (related to the variance) by station................... ................78

5.9: Locations parameter, ,, plotted against basin area. .................................................79









5.10 Scale param eter, a, plotted against basin area................................... ... ..................79

5.11: Normalized locations parameter, plotted against basin area .............................. 80

5.12: Normalized scale parameter, a, plotted against basin area. ....................................80

5.13: Test of Proportions results positive and negative z-values produced from
comparing the proportion of summer events under Warm Atlantic conditions to
C old conditions. .................................................... ................. 88

5.14: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 1.5-year return period threshold
under Warm Atlantic conditions to Cold conditions ....................... ............89

5.15: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 2-year return period threshold
under Warm Atlantic conditions to Cold conditions ......................... ............90

5.16: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 5-year return period threshold
under Warm Atlantic conditions to Cold conditions ......................... ............91

5.17: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 10-year return period threshold
under Warm Atlantic conditions to Cold conditions............................ ............92

5.18: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 20-year return period threshold
under Warm Atlantic conditions to Cold conditions............................ ............93

5.19: Test of Proportions results positive and negative z-values produced from
comparing the proportion of summer events under Warm Atlantic conditions to
C old conditions. .................................................... ................. 94

5.20: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 1.5-year return period threshold
under W arm Pacific conditions to Cold conditions. .............................................. 95

5.21: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 5-year return period threshold
under Warm Pacific conditions to Cold conditions. ..............................................96

5.22: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 10-year return period threshold
under Warm Pacific conditions to Cold conditions. ..............................................97









5.23: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 20-year return period threshold
under W arm Pacific conditions to Cold conditions. .............................................. 98

5.24: Test of Proportions results positive and negative z-values produced from
comparing the proportion of summer events under Warm Atlantic Warm Pacific
conditions to Warm Atlantic Cold Pacific conditions..............................99

5.25: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 1.5-year return period threshold
under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific
con edition s. ....................................................................... 10 0

5.26: Test of Proportions results positive and negative z-values produced from
comparing the proportion of summer events under Cold Atlantic Warm Pacific
conditions to Cold Atlantic Cold Pacific conditions......... .............. ............... 101

5.27: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 10-year return period threshold
under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific
con edition s. ....................................................................... 102

5.28: Test of Proportions results positive and negative z-values produced from
comparing the proportion of summer events under Warm Atlantic Cold Pacific
conditions to Cold Atlantic Cold Pacific conditions......... .............. ............... 103

5.29: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 1.5-year return period threshold
under Warm Atlantic Cold Pacific conditions to Cold Atlantic Cold Pacific
con edition s. ....................................................................... 104

5.30: Test of Proportions results positive and negative z-values produced from
comparing the proportion of summer events under Warm Atlantic Warm Pacific
conditions to Cold Atlantic Warm Pacific conditions.................. ............ 105

5.31: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 1.5-year return period threshold
under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific
con edition s. ....................................................................... 10 6

5.32: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 2-year return period threshold
under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific
con edition s. ....................................................................... 10 7

5.33: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 10-year return period threshold









under Warm Atlantic Warm Pacific conditions to Cold Atlantic Warm Pacific
con edition s. ....................................................................... 10 8

5.34: Test of Proportions results positive and negative z-values produced from
comparing the proportion of summer events under Warm Atlantic Cold Pacific
conditions to Cold Atlantic Warm Pacific conditions..............................109

5.35: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 1.5-year return period threshold
under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific
conditions. .............. .. ........ ..................... ....... 110

5.36: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 5-year return period threshold
under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific
condition s. ......... ................ ............. ......... ................................ 111

5.37: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 1.5-year return period threshold
under Warm Atlantic Cold Pacific conditions to Cold Atlantic Warm Pacific
conditions. .............. .. ........ ..................... ....... 112

5.38: Test of Proportions results positive and negative z-values produced from
comparing the proportion of summer events under Warm Atlantic Warm Pacific
conditions to Cold Atlantic Cold Pacific conditions ................. ........ .......... 113

5.39: Test of Proportions results positive and negative z-values produced from
comparing the proportion of events that exceed the 10-year return period threshold
under Warm Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific
condition s. ......... .... ........... .......... ..... ..... ................................... 114

5.40: Fishers Exact Test results Timing analysis, significant two tailed P values
produced from comparing the proportion of summer events under Warm Atlantic
conditions to Cold Atlantic conditions ................................ ............ ............. 128

5.41: Fishers Exact Test results Timing analysis, significant two tailed P values
produced from comparing the proportion of summer events under Warm Pacific
conditions to Cold Pacific conditions.................................. ................. 129

5.42: Fishers Exact Test results Timing analysis, significant two tailed P values
produced from comparing the proportion of summer events under Warm Atlantic
Warm Pacific conditions to Warm Atlantic Cold Pacific conditions.....................130

5.43: Fishers Exact Test results Timing analysis, significant two tailed P values
produced from comparing the proportion of summer events under Cold Atlantic
Warm Pacific conditions to Cold Atlantic Cold Pacific conditions.......................131









5.44: Fishers Exact Test results Timing analysis, significant two tailed P values
produced from comparing the proportion of summer events under Cold Atlantic
Cold Pacific conditions to Warm Atlantic Cold Pacific conditions .......................132

5.45: Fishers Exact Test results Timing analysis, significant two tailed P values
produced from comparing the proportion of summer events under Cold Atlantic
Warm Pacific conditions to Warm Atlantic Warm Pacific conditions ................133

5.46: Fishers Exact Test results Magnitudes analysis, significant two tailed P values
produced from comparing the proportion of events that exceed defined thresholds
under Warm Atlantic conditions to Cold Atlantic conditions.............................134

5.47: Fishers Exact Test results Magnitudes analysis, significant two tailed P values
produced from comparing the proportion of events that exceed defined thresholds
under Warm Pacific conditions to Cold Pacific conditions. .................................135

5.48: Fishers Exact Test results Magnitudes analysis, significant two tailed P values
produced from comparing the proportion of events that exceed defined thresholds
under Warm Atlantic Warm Pacific conditions to Warm Atlantic Cold Pacific
con edition s. ....................................................................... 13 6

5.49: Fishers Exact Test results Magnitudes analysis, significant two tailed P values
produced from comparing the proportion of events that exceed defined thresholds
under Cold Atlantic Warm Pacific conditions to Warm Atlantic Warm Pacific
con edition s. ....................................................................... 13 7

5.50: Fishers Exact Test results Magnitudes analysis, significant two tailed P values
produced from comparing the proportion of events that exceed defined thresholds
under Cold Atlantic Warm Pacific conditions to Cold Atlantic Cold Pacific
con edition s. ....................................................................... 13 8

5.51: Fishers Exact Test results Magnitudes analysis, significant two tailed P values
produced from comparing the proportion of events that exceed defined thresholds
under Cold Atlantic Cold Pacific conditions to Warm Atlantic Cold Pacific
con edition s. ....................................................................... 13 9

5.51: Tim ing analysis results .................................................................... ..115

5.52: Kruskal Wallis results significant p-values for all combinations at all stations. .142

5.53: ANOVA results significant p-values for all combinations at all stations ..........140















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

IMPACT OF ATLANTIC AND PACIFIC SEA SURFACE TEMPERATURE
ANOMALIES ON THE MAGNITUDE AND TIMING OF ANNUAL FLOODS IN
NORTHERN FLORIDA

By

Sally Adkins

May 2004

Chair: Peter R Waylen
Major Department: Geography

Pacific sea surface temperatures (SST) have been shown to be associated with the

interannual variability of precipitation in many areas of the United States. The Gulf of

Mexico region and particularly North Florida, which experiences frontal precipitation

during the winter, is directly affected in this way. Recent studies indicate that Atlantic sea

surface temperatures also significantly affect precipitation patterns in south Florida. This

study investigates the relationship between Atlantic and Pacific SST and the timing and

magnitudes of annual floods in Northern Florida.

Discharge is an appropriate indicator of basin precipitation levels since river

systems integrate rainfall on a regional scale in a way that no network of precipitation

gauges can. Stream flow, then, can be used as a directly observable index of climatic

variability as well as providing information about the hydrologic extremes of floods and

droughts, which directly affect the lives of people living in any watershed.









Northern Florida was chosen as an appropriate area of study as it experiences two

peaks of precipitation which occur throughout much of the state. Summer precipitation

peaks result primarily from convective and tropical storm activity, while the winter peaks

result from the seasonal shifts of the mid-latitude jet stream which, channels frontal

precipitation into the region. Areas that display these semi-annual peaks phenomena are

ideal for studying the relationship between SST and the timing and magnitude of annual

flood discharge. The USGS has maintained the discharge records for most of the chosen

stations from 1950 to 1998, the period of contemporary SST data.

Annual flood series based on the Florida water year were extracted. Annual

Atlantic and Pacific SSTs are identified as warm (above average) or cold (below average)

yielding four categories of combined SST anomalies. Timing and magnitudes of the

annual flood series were compared by category. The proportions and magnitudes of

annual floods occurring in summer and winter seasons are compared by category using a

test of proportions, a Fishers Exact Test and both the Kruskal-Wallis median and

ANOVA tests.

This study concludes that Atlantic and Pacific SST anomaly combinations may

affect the timing and magnitude of annual maximum discharge in North Florida, but

patterns of this are not evident from the results of this study. Results suggest that, despite

the well documented evidence for an association between SST and monthly stream flow

and rainfall totals, oceanic conditions do not have a discernable affect upon the frequency

or magnitudes of annual flood events that occur in each season.














CHAPTER 1
INTRODUCTION

We still do not have a full understanding of the climate system. There are many,
many important aspects that we don't fully understand, and therefore it's crucial
that climate research continue until we understand these systems better, understand
these problems better, and know how to model them. (Rasmusson, 2004)

Hydrologists have dedicated ample research efforts to examining and reporting the

workings of climate processes. Most of these studies have dealt with one of the most

socially and economically important results of our climate streamflow. The practical

import of these studies to local, state and federal land use planning efforts is

considerable; decisions concerning such important economic and social factors as natural

resource delineation, conservation, protection, and management, engineering construction

and design, municipal/agricultural water supply, energy supply and economic

development depend on the prediction of water levels, flood volumes and the availability

of surface/groundwater supplies. Hydrologic models serve as important prediction tools

to help people understand their water resources, manage them in the present and plan for

the future. There is a need for reliable climate predictions due to the direct social and

economic implications that result from extreme hydro-climatic events. It is important to

continue exploring how oceanic, climatic and hydrologic systems interact to better

understand how best to model them in order to have the tools to plan for the future.

The need for reliable hydrologic flood models is of great importance in planning

for Florida's future. The relatively flat topography and substantial coastline makes many

areas in the state susceptible to flooding. Careful consideration of the hydrologic cycle









which influences literally all parts of the state is the most prudent way to make informed

decisions in planning for the future of Florida's communities.

Research Justification

Historically, hydrologic modeling for city, state and federal planning has been

based on a static model of short-term climatic fluctuation such as the Federal Emergency

Management Agency's (FEMA) delineation of flood plains based on the annual flood

series (Viessman and Lewis, 2002). This series, composed of the largest daily flow

observed each year, has been used as a standard for floodplain engineers and is one of the

most common measures of flooding. FEMA's flood plain boundaries are based on

assumptions inferred from historical data that presuppose stationary, independent and

identically-distributed (iid) climatic processes (Jain and Lall, 2001). The annual flood

series continues to be a common tool in hydrologic modeling, and is still used by FEMA

to delineate floodplains of different return periods.

Recently, many hydrologic works suggest that the iid assumption is inaccurate for

streamflow prediction. The wide acceptance of weather as a chaotic system with extreme

sensitivity to initial conditions suggests that small changes in ocean/atmosphere

conditions in one part of the world may have local consequences in another part, a

phenomenon known as teleconnections. This science suggests that distant phenomena

affect local planning in changing, dynamic patterns. To project conditions and

precautions for a locality, it is necessary to connect the region with the effects of distant

phenomena, mega-cycles with differing periods and influence. Current meteorological

research firmly insists that distant influences, such as large-scale sea surface pressure

gradients like the El Nifio-Southern Oscillation (ENSO) phenomenon, change the

patterns of local processes. This realization is only recently possible because of satellite









imagery, scholarly reconstruction of climatic records, computer modeling, and facility in

sharing scientific data internationally. But this science poses a new level of complexity to

the once-straightforward method of using the annual flood series alone for such things as

floodplain delineation and hydrologic prediction.

Part of improving hydrologic models to reflect current scientific discoveries is

incorporating patterns of large-scale climatic phenomena such as ENSO and the Atlantic

Multidecadal Oscillation into local hydrologic models. ENSO is an equatorial circulation

pattern created by a gradient in surface pressure between the eastern and western pressure

cells of the Pacific Ocean (Aceituno, 1992). The system oscillates continually between

periods of weak and strong pressure gradients. Periods of anomalously high/low gradients

are associated with anomalously high/low sea surface temperatures in the western Pacific

and produce conditions called La Nifia/El Nifio. Both affect weather patterns and have

been shown to directly influence precipitation and stream flow in regions across the

globe. In the southeast United States (including Florida), the warm El Nifio phases of

ENSO cause more frequent heavy winter rainfalls while the cold La Nifia phase is a

causal factor in increased tropical storm activity in the North Atlantic.

The Atlantic Multidecadal Oscillation (AMO) is a large-scale pressure oscillation

located in the North Atlantic (Kushnir, 1996). While ENSO runs on a 3-7 year cycle, this

system exhibits oscillations of 20-80 years. The oscillation is associated with fluctuations

in Atlantic sea surface temperatures and global climate swings. The affects of

positive/negative phases of the AMO are not as extensively explored in the scientific

literature as compared to ENSO, probably due to the fact that sea surface temperature and

climate data are limited for the longer period of the AMO cycle. But there is a growing









interest in the affects of the AMO on climate and streamflow patterns. Positive phases of

the AMO have been shown to cause wetter, warmer conditions in the eastern US, as well

as increased Atlantic tropical storm/hurricane activity (Schlesinger and Ramakutty,

1994). These both directly affect the state of Florida.

While research efforts outlining the effects of ENSO and AMO on Florida and

localities across the globe can pose great benefits to hydrologic and meteorological

modeling, the science is sometimes lost in the long lag-time between scientific certainty

and administrative implementation. The benefits of ENSO and AMO research have not

yet been considered in the determination of FEMA's floodplains and therefore are not

considered in the planning efforts of many cities and towns across the United States.

Because the annual flood series plays a large role in local planning, this study attempts to

find the relationships between existing annual flood data and Atlantic and Pacific sea

surface temperature data.

Flood generating processes in Florida vary by geographic location from

"continental" to "peninsular" in a fairly systematic geographic fashion. The importance

of understanding the spatial extent of the relative dominance of each process and their

mixture is important for planning purposes. Correlating the relationships between spatial

and functional hydrologic processes and large-scale climatic anomalies further advances

the understanding of streamflow in Florida.

Objective of Thesis

The objective of this thesis is to examine the effects of the cycles of two large-scale

sea surface pressure gradients, ENSO and the AMO, on Florida's hydrologic regime

through study of the relationship of sea surface temperature anomalies to the magnitude

and timing of annual floods. Annual flood characteristics associated with single-ocean









(warmer/cooler than median temperatures) and combined ocean categories are compared.

The timing of annual floods is analyzed according to differences in the proportion of

annual flood events occurring in summer (and therefore also, by default, winter), while

anomalies of magnitudes of annual floods are studied through comparison of the

proportions of floods that exceed defined thresholds and through simple comparisons of

means.

Thesis Structure

A review of scientific literature on use of the annual flood series, an overview of

the El Nifio Southern Oscillation and Atlantic Oscillations, and the relationships between

the two are presented in Chapter 2. This chapter is followed by a description and

explanation of the study area and data examined in this study in Chapter 3. Chapter 4

outlines the methodology for organizing and analyzing the data. Chapter 5 outlines the

results of these analyses. Chapter 6 presents a discussion of the results and their

implications. The thesis ends with a summary and conclusions chapter.














CHAPTER 2
LITERATURE REVIEW

The use of the magnitude and timing of annual flood series (AFS) is first presented,

followed by an overview of ENSO and its characteristic phases. Hydrologic prediction

work that considers the AFS in relation to ENSO is examined at the global, national

(US), and regional (North central Florida) level. Works documenting potential impacts of

temperature fluctuations in the North Atlantic, such as consideration of the Atlantic

Multidecadal Oscillation are also reviewed. The chapter concludes with an overview of

the literature on the teleconnections between Pacific and/or Atlantic SSTs and stream

discharge patterns in the southeastern United States.

Annual Flood Series (AFS)

The AFS is a commonly encountered measure of flood frequency in hydrologic

literature (Jain and Lall, 2001; Lecce, 2000a, b; Glaves and Waylen, 1997; Waylen and

Woo, 1982). The annual flood is simply defined as the largest daily flow (or

instantaneous maximum when available) observed in a year. Although this approach to

flood frequency analysis has some disadvantages, it remains a staple of hydrologic

prediction. One major disadvantage is that the AFS is limited to a single annual

maximum observation, even if that maximum would be insufficient to constitute a

"flood" by any other practical measure. By the same token, the selection of a single

maximum may result in the omission of other flood peaks within the same year. These

problems may be particularly troublesome in an environment like north central Florida

where rainfall is the major flood generating process and where there are two distinct rainy









seasons during the year. On the other hand, the AFS provides a simple and unambiguous

definition while other definitions of floods are more subjective.

The AFS has a potential theoretical parallel in extreme value theory (Gumbel,

1958; Fisher and Tippett, 1928) a branch of statistics dealing with probability

distributions of extremes repeatedly drawn from large samples.

The combined use of AFS and some form of fitted probability distributions are

well established in the hydrologic literature and are used by such agencies as the Federal

Emergency Management Agency to delimit flood zone boundaries that influence critical

planning decisions (Viessman and Lewis, 2002; Jennings et al., 1993)

The timing and magnitude AFS have also been used to identify the geographic

variability of flood generating processes. Lecce (2000a,b) conducted cluster analysis on

the timing of AFS to examine the changing spatial patterns floods in the southeastern

United States and, more specifically, in North Carolina.

Other less commonly used types of flood frequency analysis include the partial

duration series that records all events above a defined threshold (Madsen et al., 1997),

and the annual exceedence series that records the same number of the largest rank

ordered historic discharge events as there are years in the discharge record (McKerchar

and Mackey, 2001). All the flood frequency methods mentioned above furnish similar

estimates of flood discharges for return-periods of over five years (Lecce, 2000a, b;

Cruise and Arora, 1990; Cunnane, 1973).

Generally, through use of the annual flood series, hydrologists have either

explicitly or implicitly, considered the timings and magnitudes of the AFS as stationary,

independent and identically distributed (iid) processes. In other words, these statistics of









the magnitude and timings, specifically their mean and variance, do not change over time

exhibiting "strong stationarity." As many hydrologists recognize now, aspects of the

hydrologic cycle are not fixed in either their characteristics of average behavior or their

variability exhibiting "weak stationarity" (Jain and Lall, 2001; National Academy

Press, 1999; National Research Council, 1991, 1988). Their timings and magnitudes

change in patterns determined by distant events and forces, thereby invalidating the iid

assumption. In recent years hydrologists have perceived links between global phenomena

and the local climate patterns which are responsible for changes in the magnitude and

timing of flood generating processes (Jain and Lall, 2000, 2001; Dettinger et al., 2000,

McCabe and Dettinger, 1999; Baldwin and Lall, 1999; Cayan et al., 1999). It now seems

apparent that in many parts of the world flood generation arises from a cascade of

phenomena operating at a variety of temporal and spatial scales, many of which endure

long and subtle cycles. This means that our regional and local water planning, and the

zoning for cities and towns, all based on flood zones determined by the AFS and an

implicit iid assumption, are potentially based on an invalid stationary model that may not

reflect reality. Powerful components of global climate variability driving local hydrologic

patterns in many parts of the United States include the ENSO, the North Atlantic

Oscillation (NAO) and the Atlantic Multidecadal Oscillation (AMO). Other causes of

non-stationarity in the AFS include large-scale land use changes.

El Niiio/Southern Oscillation (ENSO)

ENSO is a large-scale ocean (EN)-atmosphere (SO) phenomena originating in the

equatorial Pacific Ocean that affects weather patterns around the globe. While the name

"El Niho" is technically associated only with the "warm phases" of this system,

especially the warming of the ocean waters off the coast of Peru and Ecuador, the term









ENSO has come to represent a comprehensive description of the entire ocean-atmosphere

system (Aceituno, 1992). ENSO is an equatorial circulation pattern created by a see-saw

shift in surface pressure between two pressure cells of the Pacific Ocean. This shift

oscillates continually between periods of weak and strong pressure gradients, creating "El

Niho" and "La Nifia." The "El Niho" phase of this system occurs when the pressure

gradient between the eastern and western equatorial Pacific is weak, and supports

abnormally high sea surface temperatures (SST) in the eastern equatorial Pacific. El

Niho, then, is a warming event, or warm phase, of ENSO. Its counterpart, the cold phase,

is La Nifia. In this phase the pressure gradient is strong and results in anomalously low

SSTs off the coast of South America. Both phases have been shown to affect climate

patterns across the globe through a series of teleconnections (Dettinger et al., 2000;

Hoerling et al., 1997; Ropelowski and Halpert, 1989,1987,1986; Rasmusson, 1985;

Yarnal, 1985; Ely et al., 1994;Rasmusson and Carpenter 1983,1982; Walker, 1923).

Global Effect

The opposing phases of ENSO produce different effects around the globe. Both

phases can cause severe droughts in some regions of the world while others receive heavy

rain and flooding. ENSO influences many climatic variables: sea surface temperatures,

wind patterns, continental surface temperatures, and precipitation. Variation in stream

flow is one of the well-documented regional and local results of ENSO's comprehensive

climatic effect (see for example, Waylen and Poveda, 2002; Dettinger et al., 2000;

Giannini et al., 2000; Hastenrath et al., 1999; George et al., 1998; Kahya and Dracup,

1993; Cayan and Webb, 1992; Hastenrath, 1990a, b; Cayan and Peterson, 1989; Walker,

1923; Walker and Bliss, 1932).









One of the earliest pieces of research in this area examined the association between

El Nifio events and monsoon rainfall in India (Walker, 1923). Walker and Bliss (1932)

linked ENSO to changes in sea level pressure in Santiago, Honolulu, Darwin, Manila,

Batavia, and Cairo; temperature in Madras; rainfall in central Chile and India; and stream

flow in the Nile River. Ropelowski and Halpert (1987) completed an extensive global

study defining the geographical regions and the temporal phases of ENSO-related

precipitation. Eltahir and Wang (1999) determined relationships between Nile River

discharge levels and El Nifio events. Dettinger et al. (2000) examined streamflow

responses to ENSO on a global scale carrying on the work of Ropelowski and Halpert

(1987). Waylen and Poveda (2002) confirmed associations between extreme precipitation

and streamflow levels and ENSO events in western South America. Studies have also

been conducted on the direct climatic influences of ENSO on precipitation and stream

flow in South America (Waylen et al., 2000; Hastenrath et al., 1999; Mechoso and Perez-

Iribarren, 1992; Hastenrath, 1990a,b; Waylen and Caviedes, 1990,1986) and in Central

America (Waylen and Laporte, 1999).

Regional Effect-United States

Many studies have documented the effects of ENSO events on climatic patterns of

the United States (Gershunov and Barnett, 1998; Dracup and Kahya, 1994; Ely et al.,

1994; Kahya and Dracup, 1993; Redmond and Koch, 1991; Ropelowski and Halpert,

1986; Douglas and Englehart, 1981). Several of these studies have dealt with stream flow

since rivers integrate rainfall on a regional scale making them good indicators of basin,

and therefore regional, precipitation levels. Rivers, therefore, give us the means to

observe and analyze climatic variability, while also having important effects upon human

activity.









Redmond and Koch (1991) noted that during warm phases of ENSO, stream flow is

anomalously low in the Pacific Northwest and especially high in the desert southwest.

These results are consistent with other studies that have documented a very strong

relationship between ENSO events and precipitation/stream flow in these regions (Kahya

and Dracup, 1993; Cayan and Peterson, 1989; Ropelowski and Halpert, 1986; Englehart

and Douglas, 1985). Significant stream flow magnitude and timing responses to ENSO

phases have been found in the Gulf of Mexico, the Northeast, and the North Central

regions of the U.S. also. In general, El Nifio events seem to cause higher stream flows in

the Gulf of Mexico, North Central, and Southwest regions and cause diminished stream

flows in the Northeast and Pacific Northwest (Kahya and Dracup, 1993; Redmond and

Koch, 1991; Cayan and Peterson, 1989; Ropelowski and Halpert, 1986; Englehart and

Douglas, 1985). The signal associated with La Nifia is generally the opposite to that

associated with the El Nifio events (Dracup and Kahya, 1994, Ely et al., 1994). These

effects of ENSO are commonly transmitted through the climate patterns of the US by

means of the displacement of the sub-tropical and mid-latitude jet streams, which carry

moisture across much of the US. While the jet stream path naturally oscillates and varies

seasonally, it displays considerable inter-annual variability related to conditions in the

equatorial Pacific (Douglas and Englehart, 1981).

Local Effect-Southeastern United States

The southeastern United States has recurrently been mentioned as one of the US

regions climatically affected by ENSO (Pielke and Landsea, 2002; Cao, 2000; Sun and

Furbish, 1997; Henderson and Robinson, 1994; Kahya and Dracup, 1993; Dracup and

Kahya, 1994; Ropelowski and Halpert, 1987,1986; Simard et al., 1985; Douglas and

Englehart, 1981). In general, warm El Nifio phases of ENSO cause more frequent heavy









winter rainfalls in the Southeast and Gulf coastal regions. In Florida, local precipitation

anomalies can be correlated with ENSO events: precipitation is above normal all over the

state during winters and springs of years following a warm ENSO event (Hanson and

Maul, 1991; Zorn and Waylen, 1997). In south Florida, ENSO warm events can account

for winter rainfall that is 45% to 66% above normal. Sun and Furbish (1998) conclude

that El Nifio and La Nifia are direct factors in up to 40% of the annual precipitation

variations and 30% of the river discharge variations in Florida. A study by Schmidt et al.

(2001) correlates seasonal precipitation and stream flow responses in Florida to ENSO as

shown by their relationships to Pacific (ENSO) sea surface temperatures.

La Nifia may also have a direct relationship with rainfall and discharge in the

southeastern US. Atlantic tropical storms are significant contributors to precipitation

levels in this area, and particularly to extreme hydrologic events. There is a strong

correlation between La Nifia and Atlantic tropical storm/hurricane events (Pielke and

Landsea, 2002, 1999, 1998; Bove et al., 1998; Gray, 1984a, b). Gray et al. (2003) issue

periodic tropical storm forecasts using several global-climate forecasting variables, one

of which is ENSO phase. The occurrence of El Nifio, according to the forecasting

models, inhibits tropical systems from developing into hurricanes, due to an increased

strength of the northeast trade winds and concomitant increases in vertical shear and

decreases in sea surface temperatures. La Nifia has been shown to increase the probability

of two hurricanes striking the US to 66% (Bove et al., 1998), but it must be noted that

tropical storm land fall is a "noisy" process; more/less storms in North Atlantic basin do

not necessarily, or simply, correlate with more/less storms hitting Florida.









Atlantic Oscillations

The Atlantic may also have a critical influence on global climate patterns (Enfield

et al., 2001; Visbeck et al., 2001; Kerr, 2000; Kushnir, 1996, 1994; McCartney, 1996;

D'Arrigo et al., 1993) at a longer time scale than ENSO. Two ocean-atmosphere

phenomena of the Atlantic that have been identified as influencing climates in various

parts of the world are the NAO and AMO.

The NAO is an oscillation in atmospheric mass with centers of action around the

sub-arctic Icelandic Low and the subtropical Azores High semi-permanent pressure cells.

The strength and position of these pressure systems interact to affect climates in adjacent

land masses (Kushnir, 1996, 1994) The NAO has a cycle of approximately 10 years. It

affects climate around the world in a complex geographic pattern (Wanner et al., 2001;

Hurrell, 1995). Since the NAO indicates conditions north of the subtropical Azores High,

its effects are more strongly seen in Western Europe than on the eastern coast of the

United States.

The AMO is a large-scale pressure oscillation pattern located in the north Atlantic.

It has a 20-80 year cycle and is associated with fluctuations in North Atlantic sea surface

temperatures as well as global climate swings (Delworth and Mann, 2000; Kerr, 2000;

Schlesinger and Ramakutty, 1994). North Atlantic SSTs index the oceanic expression of

the AMO (Enfield et al., 2001).

Global, regional and local effects of NAO and AMO on climate

The North Atlantic is important to global climate patterns at various time scales.

The thermohaline circulation is a global ocean circulation driven by differences in the

density of seawater, which, in turn, is controlled by temperature and salinity (Broecker,

1995). In the North Atlantic, the thermohaline circulation transports warm, salty water









from the equatorial zones to the north. As the temperature changes to become cooler in

the northward journey, the water becomes denser. In the vicinity of Western Europe, the

circulation sinks and flows south past the African coast. This giant circulation creates

considerable flow, and influences climates and hydrology occurring in adjacent

continents. Because SST indices indicate stages of the NAO and the AMO, the

thermohaline circulation plays an important role in both of these oscillations and

subsequent climate patterns (Delworth et al., 1993; Hurrell, 1995).

Recent studies have examined the relation of the NAO and the AMO to climate

patterns at global and regional scales (Enfield et al., 2001; Goldenburg et al., 2001;

Visbeck et al., 2001; Kerr, 2000; D'Arrigo et al., 1993). On a global scale, Visbeck et al.

(2001) reviewed the effects of the warm and cold phases of the decadal NAO on climate

patterns around the world. Kerr (2000) discussed both land and sea multidecadal AMO in

relation to global climate fluctuations. D'Arrigo et al. (1993) correlated tree-ring records

in continents surrounding the Atlantic to the North Atlantic SST NAO index. In the

United States, a positive AMO phase has been found to yield less than normal rainfall in

most of the country and a 10% decrease in outflow from the Mississippi River (Enfield et

al., 2001). Evidence also suggests that the Atlantic modulates responses in the

Southeastern US region. Visbeck's team found that during a positive NAO phase,

conditions in the eastern United States are wetter and warmer that average. Enfield et al.

(2001) found that between warm and cold phases of the AMO, the inflow to Lake

Okeechobee, Florida varied by 40%. Goldenburg et al. (2001) detected an increase in

Atlantic hurricane activity due to an increase in north Atlantic SSTs.









Using Atlantic and Pacific SSTs in Hydrologic Prediction

Since Pacific and Atlantic ocean-atmosphere patterns are both crucial to

understanding climate variability around the globe, the interactions between Atlantic and

Pacific trends are also important to consider. Recent studies have examined these

interactions. Livezey and Smith (1999) discuss the relationships between the U.S. surface

temperatures and ENSO, the NAO, and another "global" signal characterized by a global

ocean-warming trend. Guenni et al. (2002) have observed climatic patterns in Venezuela

indicating an overall decrease (increase) in rainfall when the Pacific and Tropical Atlantic

SSTs are warmer (colder) than normal. Enfield et al. (2001) identify the importance of

understanding that the AMO affects the intensity and geographic coverage of inter-annual

impacts like those of ENSO. Enfield and his colleagues also discuss the United States

patterns of variability driven by both ENSO and the AMO.

Summary

The annual flood series is commonly used in hydrologic studies. This series is often

used with an assumption that the flood series behaves as a stationary, independent and

identically distributed process. However, recent studies invalidate the iid assumption by

showing that the hydrologic cycle is not fixed in either its variablility or characteristics of

average behavior. Large-scale climatic cycles such as ENSO and the AMO affect the

variability of the hydrologic processes over time. These systems also affect streamflow

patterns in Florida and across the globe.














CHAPTER 3
STUDY AREA AND DATA

The water balance equation is a way of accounting for, and tracing the path of,

water as it moves through the hydrologic cycle and is therefore used below as the

framework to describe the hydrologic regime in Florida. Singh (1992) presents the

hydrologic budget of a drainage basin as: Input = Output + A Storage. In Florida at the

monthly time scale, drainage basin input is predominantly precipitation (P) mostly in the

form of rain and in some cases groundwater transfers from other basins. Outputs include

runoff (R), evaporation (E), and, again, in some cases, losses to inter-basin transfers of

groundwater. Hydrologic stores (S) include lakes and aquifer systems. Singh's equation

can therefore be reformulated to fit Florida's hydrologic profile as: Runoff =

(Precipitation Evaporation) + A Storage. The first part of this chapter focuses on the

spatial and temporal characteristics of the right hand side of the above equation to reveal

the factors that control stream discharge in the study area, the second part discusses the

resultant patterns of runoff, and the final section describes the data used in this study.

Precipitation

In Florida, the major input to a drainage basin is precipitation (P) in the form of

rain. Three mechanisms generate precipitation in the region: convection, tropical storms,

and fronts.

Convective activity and tropical storms dominate the summer precipitation season

throughout the state, bringing in the most amount of precipitation as indicated in figure

3.1. Florida has more thunderstorms than any region in North America (Henry et al..,









1994). Its peninsular nature, converging sea breezes, and position relative to the Atlantic

High pressure system and the tropical/subtropical region make it a prime area for

convective activity. This type of precipitation most often occurs in the summer months

and exhibits the characteristics of sudden, localized onset.

Tropical storms are also an important part of Florida's water budget yielding

sizable amounts of rain over affected areas. Of all hurricanes that have affected the

United States, almost 40 percent have struck Florida (Henry, 1998). Hurricanes often

contribute significant rainfall in the state during the six-month hurricane season of June

through November, but the probability of hurricanes and/or tropical storms striking

Florida is highly variable.

Frontal activity drives winter precipitation particularly in northern Florida. Winter

rainfall (most notable in the Panhandle) is brought to the area by large-scale, mid-latitude

frontal systems steered by the upper level jet streams. The amount of precipitation for

each frontal storm is small, compared to summer storms, but the aggregate rainfall is a

reliable seasonal source of water for Florida's far northern watersheds (Henry et al..,

1994). The influence of these winter frontal storms diminishes to the south as the

influence of the subtropical jet-stream less frequently drops to these latitudes.

Figure 3.1 shows mean monthly precipitation at various stations across the state.

North and Central Florida exhibit two precipitation peaks-one in summer caused by

convective and tropical storm activity, and the other peak corresponding to winter frontal

activity. The latter peak declines in magnitude to the south. Most of Florida experiences

two precipitation seasons, with precipitation magnitude and timing varying spatially.










Pensacola





a.

1 2 3 4 5 6 7 8 9 10 11 12
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Gainesville

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Orlando


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1 2 3 4 5 6 7 8 9 10 11 12
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Tarpon Springs






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Figure 3.1. Average monthly rainfall for areas across Florida. First graphs represent
northern areas and the sequence of graphs continues west and south finishing
with the most southern areas.


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Henry et al. (1994) identifies three seasonal rainfall regimes in the state two of which are

addressed below. The third region occurs in the Florida Keys and is not applicable here.

The first regime covers Florida's "panhandle" and most of the northern and central

peninsula to approximately the Tampa Bay area. This region experiences two rainfall

peaks. Rainfall during the summer peak is greater on average than the winter peak's total.

Summer temperatures control this predominance, obeying the basic Clausius Clapeyron

equation where summer heating allows the capture and storage of moisture, the heated

land generates convective activity that, together with sea breezes on the coast, allows the

warm humid air to rise and eventually, rainfall follows. Summer rainfall declines slightly

from the coast toward the interior. The winter rainfall season generally peaks in March in

most central and northern areas. Rainfall in this season is due to mid-latitude cyclonic

storms, cold fronts, and low-pressure systems that move from the Gulf northeastward

over the northern half of the state. There is no noticeable coast-to-inland gradient in

winter.

Henry's (1994) second regime occurs in the southeastern portion of the state.

Convective activity is strong for six months and produces large amounts of precipitation.

Here, the Gulf Stream approaches land, contributing moisture to the air and aiding in

production of instability in the region bettering conditions for rainfall. The southeast

rainfall peaks occur in June and September October caused by convective and tropical

storm activity. In and around Miami, the June peak is larger; to the north of Miami, the

September peak is greater. Again, there is a coastal to inland rainfall gradient with

rainfall maxima occurring near the coast. Rainfall decreases in July and August due to the

migration of an elongated region of low pressure in the upper atmosphere. In winter, the









southeast has little to no precipitation. Since frontal systems are generally far removed

from this region and temperatures are reduced, conditions for rainfall are unfavorable

during the winter.

Temperature and Evaporation

The occurrence and magnitudes of floods are very much a function of antecedent

soil moisture content, which in turn is controlled by temperature and evaporation.

Florida's average annual maximum daily temperatures range from 77 to 86 degrees from

the northern to southern part of the state (Tanner, 1992). In general, evaporation rates

increase moving north to south ranging from 39" in the northwest to 53" around Naples

(Henry, 1998). Summer evaporation rates are greater than winter rates based on increased

temperatures causing an even more dramatic north to south evaporation gradient. During

winter, average minimum daily temperatures range from 55 degrees in the north to 71

degrees in the south (Tanner, 1992). Evaporation rates drop dramatically during winter,

but maintain a gradient of lower evaporation rates in the north and higher in the south

(Henry, 1994). The difference between rainfall and potential evaporation is a direct

measure of potential storage and runoff. In Florida, areas along the south coast and

panhandle show the greatest differences between annual rainfall and potential

evaporation (Mossa, 1998).

Storage

Florida's water storage takes place in a complex system of wetlands, karstic lakes

and groundwater aquifers. This system has developed through long-term geologic

processes caused by fluctuating sea-levels. The aquifer systems, including unconfined

and semi-confined aquifers, are made up of surficial aquifers of sand and gravel deposits,

intermediate semi-confining units of phosphoratic clay and sand mixtures, and a









carbonate semi-confined aquifer known as the Floridan aquifer system. Groundwater

constitutes an important input to many streams and rivers in the state, which possesses

more than 300 artesian springs. Groundwater storage provides a relatively consistent

source of water. At a daily scale, groundwater has no effect on the relationship between

precipitation and discharge peaks.

Runoff

Due to Florida's low peninsular nature and its relatively homogeneous topography,

runoff, the output of the water balance equation, varies gradually according to latitude,

and to a lesser degree, longitude. There are about ten thousand miles of rivers and

streams within Florida's boundaries. Alluvial rivers are found in the panhandle and

consist of wide forested floodplains, sandbars, levees, old river channels, sloughs, and

oxbow lakes. Slow and sluggish blackwater rivers that contain acidic tannins (thus

"blackwater") drain the flatwoods and cypress swamps that occur throughout the state.

Clear spring run rivers are most common in north central Florida and occur where

limestone outcrops allow groundwater to flow from springs. Other stream habitats occur

along inshore marine habitats such as the largest river in Florida, the St Johns. In its many

rivers and streams, Florida's hydrologic discharge regimes reflect changing temporal and

spatial patterns as they vary across the state (Mossa, 1998).

Henry's (1994) seasonal regimes are reflected in the hydrographs of mean monthly

flows presented in figure 3.2. Northern rivers show considerable winter peaks confirming

the strong influence of continental weather systems on the rivers of this area and reduced

winter evapotransporation. The response of rivers in the middle of the state is bimodal

reflecting both frontal winter and convective/ tropical storm summer rainfall seasons.














Pine Barren Hydrograph


250

200

S150

0 100

50

0
J F M A M J J A S O N D
Month


Econfina Hydrograph


250-
200 \

S150 -
100-

50

0
J F M A M J J A S O N D
Month




Santa Fe Worthington Hydrograph

800
700
600
500
t400
a 300 -
200 -
100
0
J F M A M J J A S O N D
Month




Charlie Hydrograph

800
700
600
500
S400
O 300
200
100 -
0
J F M A M J J A S O N D
Month


Choctahatchee Hydrograph

12,000
10,000
8,000 -
' 6,000
4,000 -
2,000
0-
J F M A M J J A S O N D
Month





St Marys River Hydrograph

1200
1000
800 /
S600
400
200-
0
J F M A M J J A S O N D
Month





St Johns Hydrograph

3000
2500
2000
t 1500 -
1000 I
500
0-
J F M A M J J A S N D
Month


800
700
600
500
t 400
O 300
200
100
0


Fisheating Hydrograph













J F M A M J J A S O N D
Month


Figure 3.2. Monthly hydrographs of rivers across Florida. First hydrographs represent

northern areas and the sequence of hydrographs continues finishing with the

most southern areas.









Stations located in the northern mid-Florida exhibit a stronger winter peak than those in

the south due to proximity to continental frontal activity. Stations on rivers in the

southern portion of the peninsula show hydrographs with little if any peaks in the winter

months as they are isolated from continental activity, and considerable peaks in the

summer months when they are influenced by summer convective and tropical storm

activity.

Data

This study examines the relationship between stream discharge northern Florida

and both Atlantic and Pacific sea surface temperatures. Records from discharge stations

spanning the area from the panhandle east to Jacksonville and south to Tampa were

studied in relation to both Pacific and Atlantic SSTs.

Discharge Data

Daily discharge records from forty USGS stations available at

http://waterdata.usgs.gov/fl/nwis/, in cubic feet/second, are used in this study. While

many of these stations have historic discharge data available prior to 1950, the period of

data analyzed is controlled by available SST data as described below. Table 3.1 displays

the 40 USGS discharge stations analyzed in this study, along with periods of record and

basin area. Figure 3.3 displays the 40 rivers geographically. Stations were chosen to

provide a geographically distributed group of rivers that have minimal flood control so as

to reflect natural flood conditions. While basin areas of the sampled rivers differ, data for

each river were only compared relative to measurements at that river; therefore removing

basin area as a variable in this study.









SST Anomaly Indicies

Sea surface temperature data were obtained from the NOAA National Weather

Service Climate Prediction Center website at

http://www.cpc.noaa.gov/data/indices/index.html. The site provides average monthly

anomalies for sea surface temperatures in different regions from the time period of 1950

to 2002. This study uses the NINO 3.4 region, located between the NINO 3 and 4 at 50N-

5S and 1700-120W. This region was created from existing data in April of 1996 to help

researchers who were trying to gain a better understanding of activity in the critical

regions between 3 and 4 (http://www.cpc.noaa.gov/data/indices/index.html). The North

Atlantic (NATL) region is located at 50-200 N and 600-30W. Figure 3.4 shows a simple

two year monthly time series of these data.

Summary

This section has presented an overview of the various components of Florida's

monthly hydrologic regime using a spatial perspective. Precipitation, evaporation,

temperature, storage and runoff patterns across the state are reviewed in order to provide

a simple basis for an understanding why the magnitudes (both average levels and

variability) and timings of annual floods might be expected to vary seasonally across the

state. Data used in this study, including discharge and sea surface temperature data, are

also described.














USGS PERIOD OF BASIN
RIVER STATION RECORD USED IN # YEARS AREA
ID STATION NAME ID THIS STUDY OF DATA (km^2)
1 ST MARYS RIVER NEAR MACCLENNY 2231000 1951 -2002 51 1813.0
2 JANE GREEN CREEK NEAR DEER PARK 2231600 1953 2002 49 341.9
3 ST JOHNS RIVER NEAR CHRISTMAS 2232500 1951 -2002 51 3986.0
4 WEKIVA RIVER NEAR SANFORD 2235000 1951 -2002 51 489.5
5 OCKLAWAHA RIVER NEAR CONNER 2240000 1977-2002 25 3097.6
SOUTH FORK BLACK CREEK NEAR PENNEY
6 FARMS 2245500 1951 -2002 51 347.1
NORTH FORK BLACK CREEK NEAR
7 MIDDLEBURG 2246000 1951 -2002 51 458.4
8 FISH EATING CREEK AT PALMDALE 2256500 1951 -2002 50 805.5
9 CHARLIE CREEK NEAR GARDNER 2296500 1951 -2002 51 854.7
10 JOSHUA CREEK AT NOCATEE 2297100 1951 -2002 51 341.9
11 HORSE CREEK NEAR ARCADIA 2297310 1951 -2002 51 564.6
12 SOUTH PRONG ALAFIA RIVER NEAR LITHIA 2301300 1962 -2002 40 277.1
13 ALAFIA RIVER AT LITHIA 2301500 1951 -2002 51 867.7
14 BLACK WATER CREEK NEAR KNIGHTS 2302500 1951 -2002 51 284.9
15 BROKER CREEK NEAR TARPON SPRINGS 2307359 1951 -2002 51 77.7
16 ANCLOTE RIVER NEAR ELFERS 2310000 1951 -2002 51 187.8
17 SUWANNEE RIVER AT WHITE SPRINGS 2315500 1951 -2002 51 6293.7
18 WITHLACO RIVER NEAR PINETTA 2319000 1951 -2002 51 5490.8
19 SANTAFE RIVER AT WORTHING SPRINGS 2321500 1951 -2002 51 1489.3
20 SANTAFE RIVER NEAR FORT WHITE 2322500 1951 -2001 50 2634.0
21 STEINHATEE RIVER NEAR CROSS CITY 2324000 1951 -2002 51 906.5
22 FENHOLLOWAY RIVER NEAR FOLEY 2324400 1955 -2002 47 155.4
23 ECONFINA RIVER NEAR PERRY 2326000 1951 1992 41 512.8
24 OCHLOCKONEE RIVER NEAR HAVANA 2326000 1951 -2002 51 2952.6
25 LITTLE RIVER NEAR QUINCY 2329500 1951 1992 41 613.8
26 TELOGIA CREEK NEAR BRISTOL 2330100 1951 -2002 51 326.3
27 CHIPOLA RIVER NEAR ALTHA 2359000 1951 -2002 51 2022.8
28 ECONFINA CREEK NEAR BENNETT 2359500 1951 1999 48 512.8
29 CHOCTAWHATCHEE RIVER AT CARYVILLE 2365500 1951 -2000 49 9062.4
30 HOLMES CREEK AT VERNON 2366000 1951 1979 28 999.7
31 YELLOW RIVER AT MILLIGAN 2368000 1951 1999 48 1616.2
32 SHOAL RIVER NEAR CRESTVIEW 2369000 1951 -2002 51 1227.7
33 BLACK WATER RIVER NEAR BAKER 2370000 1951 1998 47 531.0
34 BIG COLD WATER CREEK NEAR MILTON 2370500 1951 1996 45 613.8
35 ESCAMBIA RIVER NEAR CENTURY 2375500 1951 -2002 51 9886.0
36 PINE BARREN CREEK NEAR BARTH 2376000 1952- 1995 43 195.0
37 BRUSHY CREEK NEAR WALNUT HILL 2376300 1957- 1992 35 126.9
38 PERDIDO RIVER AT BARRINEAU PARK 2376500 1951 -1992 41 1020.5
39 ALAPAHA RIVER AT STATENVILLE 2317500 1951 1992 41 3626.0
40 SUWANNEE RIVER AT FARGO 2314500 1951 -1992 41 3263.4
mt 1.-l ')> 1 An/ TT CCC -' -1 -i-. -- --J- il K -it.1 -. -1- XVT ---- 1 Af r\ ---.


- t apply LU


Table 3.1. 40 USGS discharge stations ex
station s listed in Figure 3.3.







































Legend
River Station Location
-- Rivers
| County

N
0 45 90 Miles


S


Figure 3.3. Base map of 40 USGS discharge stations used in this study. Numbers 1 40
apply to stations listed in Table 3.1.


























~-/_


/


2 3 4 8 9 10 11 12 13 14 15 16 19 20 21 22 23 24
2 1. 1'1 2 13 14 15 1


Month


S Atlantic SST Anomaly


Pacific SST Anomaly


Figure 3.4 Two-year series of Mean Monthly Atlantic (N ATL) and Pacific (NINO 3.4)
SST Anomalies


"
7














CHAPTER 4
METHODOLOGY

Methods by which the potential effects of temperature fluctuations in the Pacific

and the North Atlantic on the timing and magnitude of annual floods are outlined. The

definitions of the water year and summer/winter seasons of streamflow are established

through inspection of historic mean monthly streamflow data. Annual maximum flows

are determined for each water year at each station and their seasonal timing and

magnitude noted. Each year is classified according to the corresponding state of

Equatorial Pacific (NINO 3.4) and Tropical North Atlantic (NATL) monthly SST

anomalies, computed over the appropriate water year. Years of positive anomalies are

labeled Warm (W), while those with negative anomalies are labeled Cold (C). This

allows the classification of each annual maximum discharge at a station over the period

1950-2002 (water years), into one of four classes based upon the corresponding

combined Atlantic and Pacific SSTs, as: CC (both oceans below mean), WW (both

oceans above mean), WC (Atlantic above mean, Pacific below mean), and CW (Atlantic

below mean, Pacific above mean). Timings of annual maxima are assigned to either the

"winter" or "summer" seasons as defined apriori. The Generalized Extreme Value

(GEV) fitted to magnitudes of the undifferentiated annual flood series determines yields

estimates of discharges at the 1.5-, 2-, 2.33-, 5-, 10-, and 20-year return periods. The

goodness-of-fit of the GEV is gauged through the use of a Kolmogrov-Smirnov test.

Annual flood magnitudes are classified as exceeding or not exceeding each discharge

threshold, thereby constituting 6 exceedence/non-exceedence series at each station, which









may provide some idea of whether any influence of SSTs is felt at commonly or more

rarely experienced levels of annual maxima. A Test of Proportions and the Fisher's Exact

test are used to compare the relative frequencies with which the annual maximums fall

within each season, and separately to compare the relative frequencies with which the

annual maximums exceed the 6 return periods under the various ocean conditions for

each river. Kruskal-Wallis and one-way ANOVA tests were used to compare populations

of annual maxima under the various ocean conditions in order to determine whether these

populations differ significantly and whether the magnitude of one population exceeds

another. All tests were set at a 90% significance level unless otherwise stated.

Water Year Determination

As discussed in Chapter 3, Florida has two hydrologic seasons one in the summer

driven by convective and tropical storm activity and one in the winter associated with

frontal activity. While the degree to which each of these meteorological mechanisms

contribute precipitation to each area varies across the state, the two seasons remain

distinct throughout. The hydrographs in figure 3.2 illustrate both the presence of these

seasonal peaks and their varying magnitudes at stations across the state. On the basis of

mean monthly hydrographs the "winter" hydrologic season is defined as beginning on

November 1 and ending April 31. The "summer" hydrologic season begins May 1 and

ends October 30. While the USGS traditionally defines the regional water year as

beginning in October of one year and ending in September of the next, the water year

described above is more practical for the purposes of this study as the summer tropical

cyclone season frequently extends into October. The magnitude and Julian date (which

begins day 1 on May 1 of one year and ends on day 365 on April 31 of the following

year) of each annual maximum flow is determined. Figure 3.2 shows that annual maxima









occur in both winter and summer seasons with a large cluster occurring during the North

Atlantic tropical storm season of June-November. Ninety-seven percent of all historical

North Atlantic tropical storms and hurricanes fall in the defined "summer" season

(Landsea, 1993).

Annual Flood Series Determination

After data were broken up into water years as described above, the annual flood

series, which is defined as the highest discharge value of the year, was extracted from

each year of data at each river. To test for the stationarity of the AFS data, the annual

flood series were plotted against time for each station. Stations geographically distributed

across the state analyzed in this study show that these rivers do indeed display

stationarity.

Generalized Extreme Value Distribution

A Generalized Extreme Value (GEV) distribution was fitted to the annual maxima

data at each station. The distribution was developed by Jenkinson (1955), combining all

three forms of the distribution derived from extreme value theory (Fisher and Tippett,

1928); the Gumbel (Type I), Frechet (Type II) and Weibull (Type III) distributions, all of

which are commonly used in hydrologic modeling. The advantage of using Jenkinson's

GEV distribution is that it does not require the user to, apriori, determine which of the

three forms is most appropriate, allowing instead, this to be determined by the shape

parameter, K, estimated in the following cumulative distribution functions:

F(X < x) = exp[-l{- K(x- K )/a)(1', ......K 0
F(X < x) = exp- [exp{-(x- )/ a}],............. K = 0

The parameters of the distribution can be estimated by the L-moments (Hosking et

al.., 1985), which reduces the errors when dealing with small samples (Vogel and









Fenessey, 1993). The parameter, is related to the mode and ca, to the variance. The

value of K is controlled by the tail behavior of the distribution; K=O indicates a behavior

similar to the Gumbel distribution (neutral tail), K
behavior, and K>O a thin tail like the Weibull distribution.

Kolmogorov-Smirnov Goodness-Of-Fit Test

The Kolmogorov-Smirnov test is used to determine the goodness-of-fit of the GEV

to each data set. This is a non-parametric test that makes no assumption about the

distribution of the data (distribution-free) and is therefore a good test to use with a

diverse distribution like the GEV. The test was used to compare the observed data sets to

corresponding GEV functions and tests the null hypothesis that the population

distribution from which the observed data is drawn conforms to (is not significantly

different from) the hypothesized GEV distribution. The test statistic of the Kolmogorov-

Smirnov is D and determines whether the maximum difference between the data sets is

sufficiently large to be unlikely to have occurred because of chance fluctuations. The

following equation is used to calculate the test statistic D where F is the theoretical

cumulative distribution of the distribution being tested given N ordered data points Y1, Y2,

.. :, Y :

D = max F (Y) i (N+1) for l
Observed plotting position is here calculated using the Weibull formula. From the

fitted GEV distributions, discharge thresholds corresponding to fixed return periods of

1.5, 2, 2.33, 5, 10 and 20 years were estimated at each station.









SST relationships

Monthly sea surface temperature anomalies for Nino 3.4 and the North Atlantic

were combined (averaged) over each water year. Years of positive anomalies were

labeled Warm (W), while those with negative anomalies were labeled Cold (C) thereby

providing four possible classifications: CC, WW, WC, and CW. As described in chapter

3, Nino 3.4, located in the mid-Pacific near the International Date Line, starts to warm

Pacific SSTs from July October. Documented meteorological results of this warming in

the eastern areas of the United States do not occur until December March of the

following year (Kahya and Dracup, 1993). Because of this teleconnection lag, Nino 3.4

anomalies were offset by one year in relation to annual maximum discharge values and

North Atlantic anomalies. In other words; for each year, North Atlantic anomalies were

classified as W or C based on the same year as the annual maximum discharge, while

Nino 3.4 anomalies were classified for the year prior. For instance, an annual maximum

discharge for the year 1951 would be classified as WC; Warm Atlantic since the average

SST anomalies for that water year resulted in a positive value; and Cold Pacific since the

average SST anomalies for the previous water year (1950) resulted in a negative value.

Table 4.1 shows the yearly SST classifications determined in this study. Figure 4.1

displays AFS time series for three stations spanning the different hydrologic regimes of

Florida and correlates the AFS at those stations to North Atlantic and Nino 3.4 anomaly

time series for the corresponding years as they are classified in this study.

Test of Proportions

A test of proportions (TOP) evaluates the null hypothesis that the proportion of

annual floods at each station falling in one timing or magnitude category (e.g. summer, or

exceeding the 10-year return period flow) under one oceanic classification is not










Table 4-1. Atlantic and Pacific sea surface temperature anomaly classifications.
Monthly Final Final Pacific Final
North Atlantic Monthly NINO Atlantic Class (with Combined
Year SST Anomaly Class 3.4 SST Anomaly Class Class offset Pacific) Class
1950 -0.07 C -0.72 C C
1951 0.09 W 0.38 W W C WC
1952 0.26 W 0.04 W W W WW
1953 0.11 W 0.29 W W W WW
1954 -0.20 C -0.80 C C W CW
1955 0.22 W -1.14 C W C WC
1956 -0.24 C -0.31 C C C CC
1957 0.37 W 1.02 W W C WC
1958 0.35 W 0.30 W W W WW
1959 -0.13 C -0.16 C C W CW
1960 0.03 W -0.11 C W C WC
1961 0.02 W -0.25 C W C WC
1962 0.37 W -0.31 C W C WC
1963 0.11 W 0.51 W W C WC
1964 -0.15 C -0.68 C C W CW
1965 -0.02 C 1.15 W C C CC
1966 0.17 W -0.16 C W W WW
1967 -0.14 C -0.40 C C C CC
1968 0.16 W 0.50 W W C WC
1969 0.48 W 0.64 W W W WW
1970 -0.01 C -1.06 C C W CW
1971 -0.25 C -0.49 C C C CC
1972 -0.18 C 1.23 W C C CC
1973 -0.27 C -1.24 C C W CW
1974 -0.52 C -0.56 C C C CC
1975 -0.42 C -1.18 C C C CC
1976 -0.24 C 0.38 W C C CC
1977 0.04 W 0.37 W W W WW
1978 0.01 W -0.15 C W W WW
1979 0.29 W 0.29 W W C WC
1980 0.38 W -0.02 C W W WW
1981 0.14 W -0.07 C W C WC
1982 -0.01 C 1.77 W C C CC
1983 0.04 W -0.24 C W W WW
1984 -0.40 C -0.76 C C C CC
1985 -0.23 C -0.46 C C C CC
1986 -0.25 C 0.83 W C C CC
1987 0.44 W 1.09 W W W WW
1988 -0.09 C -1.57 C C W CW
1989 0.00 W -0.17 C W C WC
1990 0.10 W 0.27 W W C WC
1991 -0.20 C 1.23 W C W CW










Table 4-1. (continued)
Monthly Final Final Pacific Final
North Atlantic Monthly NINO Atlantic Class (with Combined
Year SST Anomaly Class 3.4 SST Anomaly Class Class offset Pacific) Class
1992 -0.11 C 0.32 W C W CW
1993 -0.20 C 0.34 W C W CW
1994 -0.14 C 0.69 W C W CW
1995 0.58 W -0.52 C W W WW
1996 0.17 W -0.23 C W C WC
1997 0.45 W 2.00 W W C WC
1998 0.46 W -1.03 C W W WW
1999 0.16 W -1.13 C W C WC
2000 -0.10 C -0.52 C C C CC
2001 0.34 W 0.05 W W C WC
2002 0.02 W 1.01 W W W WW
2003 0.45 W 0.25 W W W WW

significantly different from the proportion of annual floods falling in that same category

under another oceanic classification. It compares the assumed population proportions, 7tn

and 7t2, by computing the difference between their sample estimates, pi and p2. This is

done by using a pooled proportion estimate, p, which estimates the common value of pi

and p2, and tests whether they are equal. The test statistic, z, and standard error, ro are

calculated as shown below:

(P, P) 0
-P2 P,


I 1
Ocp2-pl= IP(1- P) -I+-


where ni denotes the first sample size; n2 denotes the second sample size; p denotes the

pooled proportion estimate for the whole population (calculated by first adding the

numerators of p and p2, then the denominators, resulting in the "pooled" fraction); pi

denotes the estimated proportion for the first population the summer ratio










1950 1960 1970 1980 1990 2000
500 I I I
400 (a) o o

300 o 0
0 0
200 0 0 0 00000 0
200 0 0o o *
100 000oo 0 00 0 0 0 0 o
000 o* 0 0 o
0
CO 50 (b) 0. 0
E *
40-
S3 0 O* 0 O0
L 20 a* 00 0 0 *o

800 00. 0- -
800 (C) Summer

600 o Winter

400
0 .





200 o o o o
S ** a*** .* *.o ** .*9. o



-0.2

E -0.4
0
c: 2
1- 1
C/) 0
-1


1950 1960 1970 1980 1990 2000

Figure 4-2. Annual flood series at 3 stations spanning Florida in relation to Atlantic and
Pacific sea surface temperature anomalies. A) River 27 northern Florida, B)
River 4 centralFlorida,C)River8 southern Florida, D) Atlanticseasurface
temperature anomalies, E) Pacific sea surface temperature anomalies.
temperature anomalies, E) Pacific sea surface temperature anomalies.










of the first significantly different from the proportion of annual floods falling in that same

category under another oceanic classification. It compares the assumed population

proportions, nti and 712, by computing the difference between their sample estimates, pi

and p2. This is done by using a pooled proportion estimate, p, which estimates the

common value of pi and p2, and tests whether they are equal. The test statistic, z, and

standard error, oc are calculated as shown below:

(P, P) 0
P2 P,


I 1
Sp2-pl= P(1-P) -+-


where ni denotes the first sample size; n2 denotes the second sample size; p denotes the

pooled proportion estimate for the whole population (calculated by first adding the

numerators of p and p2, then the denominators, resulting in the "pooled" fraction); pi

denotes the estimated proportion for the first population the summer ratio of the first

classification; and p2 denotes the estimated proportion for the second population the

summer ratio of the second classification (Agresti and Finlay, 1997).

Ratios were compared between classifications using the test of proportions to detect

significant differences. Since the time period analyzed consists of 50 years of data broken

down into categories that, in some cases, amount to an nl + n2 of less than 30, a small

sample inference for comparing proportions is also performed using the Fisher's Exact

Test.









Fisher's Exact Test

The Fisher's Exact Test (Fisher, 1934) is used in place of the chi-square test when

sample sizes are small. Like the Test of Proportions (TOP) above, it compares members

of two independent groups that fall into one of two mutually exclusive categories to

determine whether the proportions of those falling into each category differs by group; it

differs from the TOP in that it is a specialized test for small sample sizes.

The test starts with a 2 by 2 table, called the "observed" table, and tests the

probability of getting a table as "strong" or "stronger", than the observed table due to

random chance of sampling. A "strong" table is one that contains two groups that differ

greatly. An example of a strong table for Warm vs. Cold Atlantic when comparing the

number of summer and winter annual flood events, for instance, might show the

following: the first row of the table represents the Warm Atlantic and may have 10

summer events and 0 winter events; the second row, represents the Cold Atlantic events,

and may contain 0 summer events and 10 winter events. In this example, there is a clear

difference in these groups in respect to seasonal timing of annual flood events, and

therefore this table would be considered "strong". But not all tables start out strong the

test is calculated by determining the exact probability for each possible outcome that is as

strong or stronger than the observed table, then it adds up the probabilities to get a P-

value.

The test returns exact one- and two-tailed P-values for a given frequency table. The

P-value determines whether there are nonrandom associations between the two

categorical variables. This test was conducted to analyze the likelihood that the

population of annual floods falling in one timing or magnitude category (e.g. summer)

under one classification (e.g. Warm Atlantic) are different from or larger than the









proportion of annual floods falling in that same category under another classification (e.g.

Cold Atlantic). The calculations for this test have been described as "quite laborious and

about as pleasant as an afternoon of root-canal surgery" (Lowry, 2004), so luckily it was

calculated using the STATISTICA 5.1 software package (StatSoft, 1995) to analyze the

timing data and a specialized FORTRAN program to analyze the magnitude data.

Kruskal-Wallis Test

The Kruskal-Wallis test was used to determine whether certain SST combinations

have discernable affects on the magnitudes of annual floods according to Atlantic and

Pacific SST conditions. The test was used to compare populations of annual maxima

under the various ocean conditions in order to determine whether these populations differ

significantly and whether the magnitude of one population exceeds another. It is a

nonparametric test used to assay the null hypothesis that samples-in this case one sample

representing annual floods falling in one SST category and the other sample representing

another SST category-come from identical populations. The alternative hypothesis is that

samples are drawn from different populations. It is based on ranking all of the

observations and comparing the mean rank of the sample group based on SSTs. The

STATISTICA 5.1 software package (StatSoft, 1995) returns values of the probability that

the observed differences came about by chance.

ANOVA

The one-way ANOVA (analysis of variance) test compares means of categories of

a single qualitative variable. It compares groups based on independent random samples

from those groups. This test was used in conjunction with the Kruskal-Wallis test

(STATISTICA 5.1) to compare populations of annual maxima under the various ocean









conditions. In this case, the mean under consideration is that under each SST condition.

The null hypothesis is that the groups have identical means.

Summary

To identify any relationship between Atlantic and Pacific SSTs and the timing and

magnitude of annual flood events on rivers across Florida, it is necessary to determine a

set water year including seasons, to categorize SST states, and to determine appropriate,

common levels of flood frequency (comparable levels of flooding in the frequency

domain) across basins of varying area, in order to facilitate spatial comparisons of the

relative magnitudes of events. The latter is accomplished through the GEV distribution,

backed by a Kolmogrov-Smimov goodness-of-fit test. A Test of Proportions and the

Fishers Exact test are used to isolate any changes in the seasonal timings of the annual

maxima under the various ocean conditions, and the Kruskal-Wallis and one-way

ANOVA tests are employed to compare the magnitude of annual maxima between

classifications. The results of these tests are presented in the next chapter.














CHAPTER 5
RESULTS

This chapter presents the results of analyzing the timing and magnitude of annual

maxima in relation to Atlantic and Pacific SST anomalies. First, observations on the

geographical distribution of proportions of summer AFS events are described. Then the

results of the GEV fitted distribution are outlined. Results from the Test of Proportions

and the Fisher's Exact test analyses are presented to compare the relative frequencies

with which the annual maximums fall within each season, and separately to compare the

relative frequencies with which the annual maximums exceed the 1.5-, 2-, 2.33-, 5-, 10-,

and 20-year return periods under the various ocean conditions for each river. The

Kruskal-Wallis and one-way ANOVA results are also presented to determine whether

certain SST combinations have discernable affects on the magnitudes of annual floods.

The results are discussed in the next chapter.

Seasonal Proportions of Annual Flood Events

To prepare for timings analyses, annual flood series data were compiled and

organized to reflect the proportion of annual flood events that occurred in the summer

season at each station. Figure 5.1 of Appendix A displays the total proportion of summer

events by station. Largest proportions of summer events occur in southern parts of the

state and diminish categorically moving north. Those stations showing lower proportions

of summer annual flood events in turn should be assumed to display higher proportions of

winter flood events. Figures 5.2 5.5 display the proportions according to Warm and

Cold Atlantic, then Warm and Cold Pacific conditions respectively. There is little









difference between those figures exhibiting different SST conditions with the exception

of the Cold Atlantic, which seems to cause a slight increase in summer events, especially

as compared to the number of summer events under Warm Atlantic conditions. This is

merely a visual observation; this study relies on the tests below to determine whether this

difference is significant.

Generalized Extreme Value (GEV) Fitted Distributions

The GEV fitted distribution was utilized to determine threshold levels according to

annual flood return periods in order to determine, through a series of tests discussed

below, whether the magnitudes of floods are influenced by various SST conditions. In

addition to providing threshold levels, the GEV also generates parameters; results that

describe the annual flood data at each station. The three parameters of interest are the

shape parameter, K, related to the positioning of the distribution tail; the location

parameter, related to the mode; and the scale parameter, a, related to the variance.

Tables and figures concerning results of this test are shown in Appendix A.

Figures 5.6-5.8 of Appendix A display these parameters by station. Additional

analyses of these parameters were conducted to test the stationarity of the data sets.

Figures 5.9-5.10 of Appendix A display the location and scale parameters plotted against

basin area. These graphs display general linear trends. Figures 5.11-5.12 of Appendix A

display these same parameters normalized by basin area plotted against basin area. The

graphs show that while several of the rivers show relatively similar alpha and epsilon

characteristics, many of the smaller basins form a separate group with relatively larger

alpha and epsilon characteristics and may constitute a distinct category of rivers.









Test of Proportions Results

The Test of Proportions (TOP) was conducted to evaluate the null hypothesis that

the proportion of annual floods at each station falling in one timing or magnitude

category (e.g. summer or exceeding the 5-year return period)) under one classification is

not different from the proportion of annual floods falling in that same category under

another classification. It compares the proportions tin and n72 using the difference between

them and produces a z-value. Tables and figures concerning results of this test are shown

in Appendix B.

Table 5.1 of Appendix B displays the z-values for the Test of Proportions

conducted to evaluate the timing of annual floods under the different SST conditions.

Tables 5.2 5.7 display the z-values for the Test of Proportions conducted to evaluate the

magnitude of annual floods under the different SST conditions. These tables show z-

values for the 1.5-, 2-, 2.33-, 5-, 10-, and 20-year return periods.

Below are descriptions of the TOP results for 8 category comparisons. Under each

section, the annual flood timing analysis is first presented. This test compared the

proportion of annual floods occurring in the summer between SST categories and

presents significant positive and negative z-values. Then there is a description of the TOP

results of the annual flood magnitude analysis. This test compared the proportion of

annual floods that exceeded defined 1.5-, 2-, 2.33-, 5-, 10-, and 20-year return period

thresholds between SST categories and presents significant positive and negative z-values

for those tests. Figures in Appendix B display maps of the z-values for both timing and

magnitude analyses by station.









Warm Atlantic Vs. Cold Atlantic

Figure 5.13 of Appendix B displays the 14 stations (35% of all of the stations) at

which significant differences of proportions of annual floods conditioned upon the state

of the Atlantic. Thirteen stations, mostly in northern Florida, indicate a greater likelihood

of summer events when the Atlantic is warm and only one when it is cold.

Figures 5.14-5.18 display significant z-values for the comparison of the proportions

of annual floods exceeding different return-period thresholds under Warm Atlantic

conditions to those under Cold Atlantic conditions. In general, the 1.5- and 10-year return

periods show the "largest" number of z-values, where 3 and 4 stations show positive z-

values, contradicting the null hypothesis that there is no difference in the proportions of

annual floods exceeding these return-period thresholds between the Warm and Cold

Atlantic categories. The positive z-values indicate that the proportion of events that

exceed the 1.5- and 10-year return period thresholds under Warm Atlantic conditions is,

at positive z-value stations, likely to be greater than it would be under Cold conditions.

The negative z-values indicate that the proportion of events that exceed the 1.5- and 10-

year return period thresholds under Warm Atlantic conditions is likely to be smaller than

it would be under Cold conditions. For all magnitude categories, out of 40 rivers, results

tend to represent less than 10% of all of the stations tested.

Warm Pacific Vs. Cold Pacific

Figure 5.19 displays significant z-values for the comparison of the proportion of

annual floods occurring in the summer under Warm Pacific conditions to that occurring

under Cold Pacific conditions. Only 1 river shows a positive z-value, contradicting the

null hypothesis that there is no difference in the proportion of summer annual floods

between the Warm and Cold Pacific categories. Another 4 are negative z-values









indicating that the proportion of summer events under Warm Pacific conditions is, at

these 4 stations, likely to be smaller than it would be under Cold conditions. Out of 40

rivers, these results only represent 12.5% of all of the stations tested.

Figures 5.20- 5.23 display significant z-values for the comparison of the

proportions of annual floods exceeding different return-period thresholds under Warm

Pacific conditions to those under Cold Pacific conditions. In general, the 5- and 10-year

return periods show the "largest" number of z-values, both where 4 stations show

negative z-values, contradicting the null hypothesis that there is no difference in the

proportions of annual floods exceeding these return-period thresholds between the Warm

and Cold Pacific categories. The negative z-values indicate that the proportion of events

that exceed the 5- and 10-year return period thresholds under Warm Pacific conditions is,

at negative z-value stations, likely to be less than it would be under Cold conditions. For

all magnitude categories, out of 40 rivers, the results represent less than 10% of all of the

stations tested.

Warm Atlantic Warm Pacific Vs. Warm Atlantic Cold Pacific

Only 3 rivers show positive z-values for the comparison of the proportion of annual

floods occurring in the summer under Warm Atlantic Warm Pacific conditions to that

occurring under Warm Atlantic Cold Pacific conditions (see Figure 5.24), contradicting

the null hypothesis that there is no difference in the proportion of summer annual floods

between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific categories.

Positive z-values indicate that the proportion of summer events under Warm Atlantic

Warm Pacific conditions is, at these 3 stations, likely to be greater than it would be under

Warm Atlantic Cold Pacific conditions. There are no negative z-values. Out of 40 rivers,

these results only represent 7.5% of all of the stations tested.









Figure 5.25 displays significant z-values for the comparison of the proportion of

annual floods exceeding the 1.5-year return-period threshold under Warm Atlantic Warm

Pacific conditions to that occurring under Warm Atlantic Cold Pacific conditions. In

general, the 1.5- year return period shows the "largest" number of z-values of all return

periods analyzed, where 4 stations show negative z-values, contradicting the null

hypothesis that there is no difference in the proportions of annual floods exceeding these

return-period thresholds between the Warm Atlantic Warm Pacific and Warm Atlantic

Cold Pacific categories. The positive z-values indicate that the proportion of events that

exceed return period thresholds under Warm Atlantic Warm Pacific conditions is, at

positive z-value stations, likely to be greater than it would be under Warm Atlantic Cold

Pacific conditions. The negative z-values indicate that the proportion of events that

exceed the return period thresholds under Warm Atlantic Warm Pacific conditions is

likely to be smaller than it would be under Warm Atlantic Cold Pacific conditions. For all

magnitude categories, out of 40 rivers, results tend to represent less than 10% of all of the

stations tested.

Cold Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific

Figure 5.26 displays significant z-values for the comparison of the proportion of

annual floods occurring in the summer under Cold Atlantic Warm Pacific conditions to

that occurring under Cold Atlantic Cold Pacific conditions. Only 1 river shows a positive

z-value, contradicting the null hypothesis that there is no difference in the proportion of

summer annual floods between the Cold Atlantic Warm Pacific and Cold Atlantic Cold

Pacific categories. The 4 negative z-values indicate that, at those 4 stations, the

proportion of summer events under Cold Atlantic Warm Pacific conditions is likely to be









smaller than it would be under Cold Atlantic Cold Pacific conditions. Out of 40 rivers,

these results only represent 12.5% of all of the stations tested.

Figures 5.27 displays significant z-values for the comparison of the proportions of

annual floods exceeding the 10-year return-period threshold under Cold Atlantic Warm

Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. In

general, the 5-, 10 and 20-year return period show the "largest" number of z-values,

where 5, 12, and 11 stations (respectively) show negative z-values, contradicting the null

hypothesis that there is no difference in the proportions of annual floods exceeding these

return-period thresholds between the Cold Atlantic Warm Pacific and Cold Atlantic Cold

Pacific categories. The positive z-values indicate that the proportion of events that

exceed return period thresholds under Cold Atlantic Warm Pacific conditions is, at

positive z-value stations, likely to be greater than it would be under Cold Atlantic Cold

Pacific conditions. The negative z-values indicate that the proportion of events that

exceed the return period thresholds under Cold Atlantic Warm Pacific conditions is likely

to be smaller than it would be under Cold Atlantic Cold Pacific conditions. For all

magnitude categories, out of 40 rivers, results tend to represent 30% or less of all of the

stations tested.

Warm Atlantic Cold Pacific Vs. Cold Atlantic Cold Pacific

Figure 5.28 displays significant z-values for the comparison of the proportion of

annual floods occurring in the summer under Warm Atlantic Cold Pacific conditions to

that occurring under Cold Atlantic Cold Pacific conditions. 4 rivers show positive z-

values, contradicting the null hypothesis that there is no difference in the proportion of

summer annual floods between the Warm Atlantic Cold Pacific and Cold Atlantic Cold

Pacific categories. The positive z-values indicate that the proportion of summer events









under Warm Atlantic Cold Pacific conditions is, at these 4 stations, likely to be greater

than it would be under Cold Atlantic Cold Pacific conditions. There are two negative z-

values indicating that, at those 2 stations, the proportion of summer events under Warm

Atlantic Cold Pacific conditions is likely to be smaller than it would be under Cold

Atlantic Cold Pacific conditions. Out of 40 rivers, these results represent 15% of all of

the stations tested.

Figures 5.29 displays the significant z-values for the comparison of the proportions

of annual floods exceeding the 1.5-year return-period threshold under Warm Atlantic

Cold Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. In

general, the 1.5-year return period shows the "largest" number of z-values of all of the

magnitude thresholds, where 4 stations show positive z-values, contradicting the null

hypothesis that there is no difference in the proportions of annual floods exceeding these

return-period thresholds between the Warm Atlantic Cold Pacific and Cold Atlantic Cold

Pacific categories. The positive z-values indicate that the proportion of events that

exceed return period thresholds under Warm Atlantic Cold Pacific conditions is, at

positive z-value stations, likely to be greater than it would be under Cold Atlantic Cold

Pacific conditions. The negative z-values indicate that the proportion of events that

exceed the return period thresholds under Warm Atlantic Cold Pacific conditions is likely

to be smaller than it would be under Cold Atlantic Cold Pacific conditions. For all

magnitude categories, out of 40 rivers, results represent less than 10% of all of the

stations tested.

Warm Atlantic Warm Pacific Vs. Cold Atlantic Warm Pacific

Figure 5.30 displays the z-values for the comparison of the proportion of annual

floods occurring in the summer under Warm Atlantic Warm Pacific conditions to that









occurring under Cold Atlantic Warm Pacific conditions. 15 rivers show positive z-values,

contradicting the null hypothesis that there is no difference in the proportion of summer

annual floods between the Warm Atlantic Warm Pacific and Cold Atlantic Warm Pacific

categories. The positive z-values indicate that the proportion of summer events under

Warm Atlantic Warm Pacific conditions is, at these 15 stations, likely to be greater than it

would be under Cold Atlantic Warm Pacific conditions. There are no significant negative

z-value results for this comparison. Out of 40 rivers, these results represent 37.5% of all

of the stations tested.

Figures 5.31- 5.33 display significant z-values for the comparison of the

proportions of annual floods exceeding different return-period thresholds under Warm

Atlantic Warm Pacific conditions to that occurring under Cold Atlantic Warm Pacific

conditions. In general, the 10-year return period shows the "largest" number of z-values,

where 5 stations show positive z-values, contradicting the null hypothesis that there is no

difference in the proportions of annual floods exceeding these return-period thresholds

between the Warm Atlantic Warm Pacific and Cold Atlantic Warm Pacific categories.

The positive z-values indicate that the proportion of events that exceed return period

thresholds under Warm Atlantic Warm Pacific conditions is, at positive z-value stations,

likely to be greater than it would be under Cold Atlantic Warm Pacific conditions. The

negative z-values indicate that the proportion of events that exceed the return period

thresholds under Warm Atlantic Warm Pacific conditions is likely to be smaller than it

would be under Cold Atlantic Warm Pacific conditions. For all magnitude categories, out

of 40 rivers, results represent less than 12.5% of all of the stations tested.









Warm Atlantic Cold Pacific Vs. Cold Atlantic Warm Pacific

Figure 5.34 displays the z-values for the comparison of the proportion of annual

floods occurring in the summer under Warm Atlantic Cold Pacific conditions to that

occurring under Cold Atlantic Warm Pacific conditions. 10 rivers show positive z-values,

contradicting the null hypothesis that there is no difference in the proportion of summer

annual floods between the Warm Atlantic Cold Pacific and Cold Atlantic Warm Pacific

categories. The positive z-values indicate that the proportion of summer events under

Warm Atlantic Cold Pacific conditions is, at these 10 stations, likely to be greater than it

would be under Cold Atlantic Warm Pacific conditions. There are no significant negative

z-value results for this comparison. Out of 40 rivers, these results only represent 25% of

all of the stations tested.

Figures 5.35- 5.37 display significant z-values for the comparison of the

proportions of annual floods exceeding different return-period thresholds under Warm

Atlantic Cold Pacific conditions to that occurring under Cold Atlantic Warm Pacific

conditions. In general, the 1.5- and 10-year return periods show the "largest" number of

z-values, each where 4 stations show positive z-values, contradicting the null hypothesis

that there is no difference in the proportions of annual floods exceeding these return-

period thresholds between the Warm Atlantic Cold Pacific and Cold Atlantic Warm

Pacific categories. The positive z-values indicate that the proportion of events that

exceed return period thresholds under Warm Atlantic Cold Pacific conditions is, at

positive z-value stations, likely to be greater than it would be under Cold Atlantic Warm

Pacific conditions. The negative z-values indicate that the proportion of events that

exceed the return period thresholds under Warm Atlantic Cold Pacific conditions is likely

to be smaller than it would be under Cold Atlantic Warm Pacific conditions. For all









magnitude categories, out of 40 rivers, results represent less than 10% of all of the

stations tested.

Warm Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific

Figure 5.38 displays the z-values for the comparison of the proportion of annual

floods occurring in the summer under Warm Atlantic Warm Pacific conditions to that

occurring under Cold Atlantic Cold Pacific conditions. 5 rivers show positive z-values,

contradicting the null hypothesis that there is no difference in the proportion of summer

annual floods between the Warm Atlantic Warm Pacific and Cold Atlantic Cold Pacific

categories. The positive z-values indicate that the proportion of summer events under

Warm Atlantic Warm Pacific conditions is, at these 5 stations, likely to be greater than it

would be under Cold Atlantic Cold Pacific conditions. There is one significant negative

z-value result for this comparison. Out of 40 rivers, these results only represent 15% of

all of the stations tested.

Figures 5.39 displays significant z-values for the comparison of the proportions of

annual floods exceeding the 10-year return-period thresholds under Warm Atlantic Warm

Pacific conditions to that occurring under Cold Atlantic Cold Pacific conditions. In

general, the analyses for this category show very little (0-3 z-values) to contradict the null

hypothesis that there is no difference in the proportions of annual floods exceeding these

return-period thresholds between the Warm Atlantic Warm Pacific and Cold Atlantic

Cold Pacific categories. For all magnitude categories, out of 40 rivers, results represent

less than 5% of all of the stations tested.

Fishers Exact Test Results

In addition to the Test of Proportions, a small sample inference for comparing

proportions is performed using the Fisher's Exact test which compares members of two









independent groups that fall into one of two categories to determine whether the

proportions of those falling into each category differs by group. This test was conducted

to analyze the likelihood that the population of annual floods falling in one timing or

magnitude category under one classification are different from or larger than the

population of annual floods falling in that same category under another classification.

The Fisher's Exact test returns exact one- and two-tailed P-values for a given

frequency table. Tables 5.8-5.14 of Appendix C display Fishers Exact 1- and 2-tail P-

values for the timing and magnitude analyses of annual floods. Tables highlight 1-tail P-

values in yellow and 2-tail P-values in green to represent those values that are significant

at the 90% confidence level. Figures 5.43-5.54 of Appendix C display these results to

give geographic perspective.

Below are descriptions of the Fisher's Exact results for 8 category comparisons.

Under each section, the annual flood timing analysis is first presented. This test compared

the population of annual floods occurring in the summer between SST categories and

presents significant positive and negative z-values. Then there is a description of the

Fisher's Exact results of the annual flood magnitude analysis. This test compared the

populations of annual floods that exceeded defined 1.5-, 2-, 2.33-, 5-, 10-, and 20-year

return period thresholds between SST categories and presents significant 1- and 2-tail P-

values for those tests.

Warm Atlantic Vs. Cold Atlantic

Figure 5.43 shows 1- and 2-tailed P-values of the annual flood timing analysis used

to compare Warm and Cold Atlantic conditions. Ten stations, 5 of which are scattered

about the state and 5 in the panhandle area, have significant 2-tail P-values contradicting

the null hypothesis that there is no difference between the proportion of summer annual









floods between the Warm and Cold Atlantic categories. Five significant 1-tail P-values

show up in the panhandle stations, and show that for these stations, there is a greater

likelihood of larger proportions of summer events under Warm Atlantic conditions than

under Cold conditions.

Figure 5.44 shows 1- and 2-tailed P-values of the annual flood magnitude analysis

used to compare Warm and Cold Atlantic conditions. This test does not show a

considerable difference in magnitude at any return period between the Warm and Cold

Atlantic. Three stations, again in the panhandle area, show significant 2-tail P-values at

the 1.5-yr return period. Only two stations, also in the panhandle area, show 1-tail P-

values at this return period level. One station in the 2-year category shows significant

results for the 1- and 2-tail P-values; it also is located in the panhandle. Three stations in

the 10-year category show significant 2-tail P-values and one significant 1-tail, all in the

panhandle. Those stations that show a significant 2-tail P-value indicate a contradiction to

the null hypothesis that there is no difference between the proportion of summer annual

floods between the Warm and Cold Atlantic categories. Those stations that show a

significant 1-tail P-value indicate a greater likelihood of larger proportions of events that

exceed the given return period threshold under Warm Atlantic conditions than under Cold

conditions at the stations.

Warm Pacific Vs. Cold Pacific

Figure 5.45 shows 1- and 2-tailed P-values of the annual flood timing analysis used

to compare Warm and Cold Pacific conditions. Only 1 station, located in the panhandle,

had significant 2-tail P-values contradicting the null hypothesis that there is no difference

between the proportion of summer annual floods between the Warm and Cold Pacific

categories. The same station had a significant 1-tail P-value meaning that there is a









greater likelihood of larger proportions of summer events under Warm Pacific conditions

than under Cold conditions at that station.

Figure 5.46 shows 1- and 2-tailed P-values of the annual flood magnitude analysis

used to compare Warm and Cold Pacific conditions. Again, this test does not show a

considerable difference in magnitude at any return period between the Warm and Cold

Pacific. Two stations bordering the panhandle area show significant 1- and 2-tail P-values

at the 1.5-yr return period. One station, also in the panhandle area, shows a 1-tail P-value

at this return period level. One station in the 2.33-year category shows a significant 2-tail

P-value; it also is located in the panhandle. Two stations in the 5-year category show

significant 1- and 2-tail P-values; one in mid-east Florida and one in the panhandle. There

is one station showing significant 1- and 2-tail P-values in north-central Florida. Those

stations that show significant 2-tail P-values indicate a contradiction to the null

hypothesis that there is no difference between the proportion of summer annual floods

between the Warm and Cold Pacific categories. Those stations that show a significant 1-

tail P-value indicate a greater likelihood of larger proportions of events that exceed the

given return period threshold under Warm Pacific conditions than under Cold conditions

at the stations.

Warm Atlantic Warm Pacific Vs. Warm Atlantic Cold Pacific

Figure 5.47 shows 1- and 2-tailed P-values of the annual flood timing analysis used

to compare Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific conditions.

Two stations, located in southern Florida, had significant 2-tail P-values contradicting the

null hypothesis that there is no difference between the proportion of summer annual

floods between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific

categories. One of these stations also had a significant 1-tail P-value indicating a greater









likelihood of larger proportions of summer events under Warm Atlantic Warm Pacific

conditions than under Warm Atlantic Cold Pacific conditions at that station.

Figure 5.48 shows 1- and 2-tailed P-values of the annual flood magnitude analysis

used to compare Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific

conditions. This test does not show a considerable difference in magnitude at any return

period between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific. Three

stations in the panhandle area show significant 1- and 2-tail P-values at the 1.5-yr return

period. One station in the 5-year category and one in the 10-year category show

significant 1- and 2-tail P-values; both stations are located in southern Florida. One

station in the panhandle shows a significant 2-tail P-value in the 20-year return period

category. Those stations that show significant 2-tail P-values indicate a contradiction to

the null hypothesis that there is no difference between the proportion of summer annual

floods between the Warm Atlantic Warm Pacific and Warm Atlantic Cold Pacific

categories. Those stations that show a significant 1-tail P-value indicate a greater

likelihood of larger proportions of events that exceed the given return period threshold

under Warm Atlantic Warm Pacific conditions than under Warm Atlantic Cold Pacific

conditions at the stations.

Cold Atlantic Warm Pacific Vs. Cold Atlantic Cold Pacific

Figure 5.49 shows 1- and 2-tailed P-values of the annual flood timing analysis used

to compare Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific conditions. Three

stations, located in southern Florida, had significant 2-tail P-values contradicting the null

hypothesis that there is no difference between the proportion of summer annual floods

between the Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific categories. Two

of these stations also had significant 1-tail P-values indicating a greater likelihood of









larger proportions of summer events under Cold Atlantic Warm Pacific conditions than

under Cold Atlantic Cold Pacific conditions at those stations.

Figure 5.50 shows 1- and 2-tailed P-values of the annual flood magnitude analysis

used to compare Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific conditions.

This test does not show a considerable difference in magnitude at any return period

between the Cold Atlantic Warm Pacific and Cold Atlantic Cold Pacific. One station in

the panhandle area shows significant 1- and 2-tail P-values at the 1.5-yr return period.

One station in the 5-year category shows a significant 2-tail P-value located in southern

Florida. Four stations in the center of the state show significant 2-tail P-values in the 10-

year return period category; one of these shows a significant 1-tail P-value. Two stations,

one in southern Florida and one in the panhandle, show significant 2-tail P-values in the

20-year return period category. Those stations that show significant 2-tail P-values

indicate a contradiction to the null hypothesis that there is no difference between the

proportion of summer annual floods between the Cold Atlantic Warm Pacific and Cold

Atlantic Cold Pacific categories. Those stations that show a significant 1-tail P-value

indicate a greater likelihood of larger proportions of events that exceed the given return

period threshold under Cold Atlantic Warm Pacific conditions than under Cold Atlantic

Cold Pacific conditions at the stations.

Cold Atlantic Cold Pacific Vs. Warm Atlantic Cold Pacific

Figure 5.51 shows 1- and 2-tailed P-values of the annual flood timing analysis used

to compare Cold Atlantic Cold Pacific and Warm Atlantic Cold Pacific conditions. Five

stations, 4 in the panhandle and 1 in the central Florida area, had significant 2-tail P-

values contradicting the null hypothesis that there is no difference between the proportion

of summer annual floods between the Cold Atlantic Cold Pacific and Warm Atlantic









Cold Pacific categories. The four stations located in the panhandle also had significant 1-

tail P-values indicating a greater likelihood of larger proportions of summer events under

Cold Atlantic Cold Pacific conditions than under Warm Atlantic Cold Pacific conditions

at those stations.

Figure 5.52 shows 1- and 2-tailed P-values of the annual flood magnitude analysis

used to compare Cold Atlantic Cold Pacific and Warm Atlantic Cold Pacific conditions.

This test does not show a considerable difference in magnitude at any return period

between the Cold Atlantic Cold Pacific and Warm Atlantic Cold Pacific. One station in

the panhandle area shows significant 1- and 2-tail P-values at the 1.5-yr return period.

One station in the 10-year category shows a significant 2-tail P-value located at the east

end of the panhandle. Those stations that show significant 2-tail P-values indicate a

contradiction to the null hypothesis that there is no difference between the proportion of

summer annual floods between the Cold Atlantic Cold Pacific and Warm Atlantic Cold

Pacific categories. Those stations that show a significant 1-tail P-value indicate a greater

likelihood of larger proportions of events that exceed the given return period threshold

under Cold Atlantic Cold Pacific conditions than under Warm Atlantic Cold Pacific

conditions at the stations.

Cold Atlantic Warm Pacific Vs. Warm Atlantic Warm Pacific

Figure 5.53 shows 1- and 2-tailed P-values of the annual flood timing analysis used

to compare Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific conditions.

Seven stations spanning the state had significant 2-tail P-values contradicting the null

hypothesis that there is no difference between the proportion of summer annual floods

between the Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific categories.

Three stations located in central Florida also had significant 1-tail P-values indicating a









greater likelihood of larger proportions of summer events under Cold Atlantic Warm

Pacific conditions than under Warm Atlantic Warm Pacific conditions at those stations.

Figure 5.54 shows 1- and 2-tailed P-values of the annual flood magnitude analysis

used to compare Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific

conditions. This test does not show a considerable difference in magnitude at any return

period between the Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific. Two

stations in the panhandle area show significant 2-tail P-values and one of them a

significant 1-tail P-value at the 1.5-yr return period. One station in the 2-year category

shows significant 1- and 2-tail P-values located in southern Florida. Four stations

spanning the state show a significant 2-tail P-value in the 10-year return period category;

two of these in the panhandle also show significant 1-tail P-values. Two stations, one in

southern Florida and one in the panhandle, show a significant 2-tail P-values in the 20-

year return period category; two of these in the panhandle also show significant 1-tail P-

values. Those stations that show significant 2-tail P-values indicate a contradiction to the

null hypothesis that there is no difference between the proportion of summer annual

floods between the Cold Atlantic Warm Pacific and Warm Atlantic Warm Pacific

categories. Those stations that show a significant 1-tail P-value indicate a greater

likelihood of larger proportions of events that exceed the given return period threshold

under Cold Atlantic Warm Pacific conditions than under Warm Atlantic Warm Pacific

conditions at the stations.

Kruskal Wallis Test Results

The Kruskal-Wallis test was used to determine whether certain SST combinations

have discernable affects on the magnitudes of annual floods according to Atlantic and

Pacific SST conditions. The test was used to compare populations of annual maxima









under the various ocean conditions in order to determine whether these populations differ

significantly. It evaluates the null hypothesis that samples representing annual floods

falling in one or another SST category come from identical populations. The alternative

hypothesis is that samples are drawn from different populations. Table 5.15 and Figure

5.55 of Appendix D display the P-values produced by this test.

The results produced from comparison of the various SST combinations revealed

few significant P-values. In fact, only three stations; one under the Warm vs. Cold

Pacific, one under the Cold Atlantic Cold Pacific vs. Warm Atlantic Cold Pacific, and

one in the Cold Atlantic Warm Pacific vs. Warm Atlantic Warm Pacific; revealed

significant results. In general, the null hypothesis that that samples representing annual

floods falling in one or another SST category come from identical populations is upheld

by this test.

ANOVA Results

The one-way ANOVA test was used in conjunction with the Kruskal-Wallis test to

compare populations of annual maxima under the various ocean conditions. The null

hypothesis was that the various SST groups have identical means. Like the results of the

Kruskal Wallis, the ANOVA revealed only two stations with significant results in all of

the tests conducted, upholding the null hypothesis. Results are shown in Table 5.16 and

Figure 5.56 of Appendix D.

Summary

Results from the Test of Proportions and the Fishers Exact test are presented

determine any changes in the seasonal timings or magnitudes of the annual maxima under

the various ocean conditions. Kruskal-Wallis and one-way ANOVA tests results are also






59


presented to compare the magnitude of annual maxima between classifications. A

discussion of these results is presented in the next chapter.














CHAPTER 6
DISCUSSION AND IMPLICATIONS

This chapter provides a general overview and discussion of some of the most

interesting of the results presented in the previous chapter. These results are discussed in

relation to the general framework of previous studies on relationships between ENSO, the

AMO, and streamflow.

The objective of this thesis is to examine the effects of ENSO and the AMO on 40

streamflow discharge stations in Florida through study of the relationship of SST

anomalies to the magnitude and timing of the annual flood series. The results of the

statistical analyses performed in this study show little evidence to support similar studies

on ENSO, the AMO and streamflow.

Seasonal Proportions of Annual Flood Events

Summer proportions of annual floods shown in figure 5.1 correlates with Henry's

(Henry et al., 1994) seasonal rainfall regimes discussed in chapter 3 of this study. This

map shows the dominance of summer events in the southern areas of the state where

there is little winter precipitation and a less dominating proportion in the northern parts of

the state where winter frontal activity is an important part of the water year.

Generalized Extreme Value Fitted Distributions

The GEV fitted distribution parameters provided results describing the annual flood

data at each station. Figures 5.6 display the shape parameter, K, by station and confirms

that the GEV was a good distribution to use for these data; while most of the stations

display a Frechet-like distribution with many outlying values, some also show thin-tailed









behaviors more like a Weibull distribution. None of the stations display Gumbel-like

distributions. If only one of these distributions had been utilized in this study, variation in

distribution style would have been lost. Figure 5.8 displays the scale parameter, ca, which

is related to the variance of the data. While areas in the south show the magnitudes of

annual flood events to be fairly consistent, areas in the north and the panhandle display

greater variance in magnitudes of the AFS suggesting that these areas are more prone to

greater flux. This flux could be due to the stochastic nature of frontal storms that

influence discharge levels in the area.

Annual Floods in Relation to Atlantic SSTs

As discussed in Chapter 2, a positive Atlantic SST anomaly is associated with

increased precipitation and tropical storm/hurricane activity in the Southeast (Enfield et

al., 2001; Goldenburg et al., 2001; Visbeck et al., 2001).

The majority of the results of the Test of Proportions do not indicate that this

translates in a significant way to the timing and magnitude of annual floods. About 30%

of the results support a correlation between Warm Atlantic SST conditions and discharge.

Thirteen of 40 stations (located mostly in the panhandle), illustrate that Warm Atlantic

conditions were more likely to have higher proportions of summer annual flood events

than Cold Atlantic conditions. While most of the significant results of the Test of

Proportions of the magnitude of annual flood events analyzed at various time periods

show that it is more likely for Warm Atlantic to have higher proportions of events that

exceed return-period thresholds than Cold Atlantic conditions, the strength of these

results is minimal; the most positive result (at the 10-year return period) shows only 4 out

of 40 statistically significant z-values supporting this hypothesis.









In tandem, the Fishers Exact test gives little evidence to support previous studies.

Twenty-five percent of the discharge stations tested show a significant difference

between Warm and Cold Atlantic proportions of summer annual flood events. Twelve

percent indicate that there are a greater proportion of summer events under Warm than

Cold Atlantic conditions. An average of 15% of the magnitude analyses displayed

significant values, with the most (3 out of 40) significant values showing up in the 1.5-

year return period category. As mentioned in Chapter 2, the AMO has an average

oscillation period of 20-80 years, so results for the Warm Atlantic alone in this category

appear to be random. The period of available record examined in this study may have

been too brief to pull out AMO patterns and recognize correlations between SST

anomalies and discharge through statistical analyses.

The Kruskal Wallis and ANOVA tests evaluate the null hypothesis that populations

of annual floods do not differ under Warm and Cold Atlantic conditions. Again, results of

this study do not support previous bodies of work that would contradict the null

hypothesis. The ANOVA test shows significant values for only one of 40 stations (again

in the panhandle) contradicting the null hypothesis. The Kruskal Wallis test shows no

significant difference between Warm and Cold conditions.

Annual Floods in Relation to Pacific SSTs

Previous studies provide strong evidence that positive Pacific SST anomalies

increase streamflow in Florida. Kahya and Dracup (1993) indicate that positive Pacific

SST anomalies of year 0 result in higher stream flows around the Gulf of Mexico region

of the United States in year 1. According to many studies, warm phases of ENSO cause

more frequent heavy winter rainfalls in the Southeast (Cao, 2000; Sun and Furbish, 1997;

Henderson and Robinson, 1994; Kahya and Dracup, 1993; Dracup and Kahya, 1994;









Ropelowski and Halpert, 1987,1986; Douglas and Englehart, 1981), and above normal

precipitation during winters and springs in Florida correlate with years following a warm

ENSO event (Hanson and Maul, 1991; Zorn and Waylen, 1997).

Results from this study do not support the framework of current literature on ENSO

in relation to the Southeast United States and Florida. In fact, results of the Test of

Proportions show that 5 of 40 stations produce significant z-values, 4 of which indicate

that there are a greater proportion of summer events under Cold than Warm Pacific

conditions. Magnitude Test of Proportions show the same results at most return-period

levels.

Fishers Exact test results are scarce for Warm versus Cold Pacific conditions in

relation to the annual flood series in Florida. Only one river of 40 produces significant

results for the timings analysis. This test indicates that, at this one river located in the

panhandle, there is a greater proportion of summer events under Warm than Cold Pacific

conditions. Magnitude analyses show no significant results for any of the 40 rivers.

Again, the Kruskal Wallis and ANOVA tests provide few significant results to

support previous bodies of work that would contradict the null hypothesis. The ANOVA

test shows significant values for only one of 40 stations (in North Central Florida)

contradicting the null hypothesis. The Kruskal Wallis test shows one significant

difference between Warm and Cold conditions at the same station.

Annual Floods in Relation to Combined Atlantic and Pacific SSTs

Chapter 2 outlines studies that support evidence to suggest that both the Atlantic

and Pacific oscillations affect streamflows in Florida. One of the objectives of this study

is to attempt to detect the relationship between streamflow and the combinations of

Atlantic and Pacific SST anomalies. While few studies outline the streamflow reactions









to various combined Atlantic and Pacific SST combinations, studies on the separate

oscillations suggest that the combined conditions may have magnified effects on

streamflow.

The Test of Proportions show that Warm Atlantic Warm Pacific conditions are

more likely to have higher proportions of summer annual flood events than Cold Atlantic

Warm Pacific conditions at about 37% of the stations sampled. This test also indicates

that Warm Atlantic Cold Pacific conditions are more likely to have higher proportions of

summer annual flood events than Cold Atlantic Warm Pacific conditions at 25% of the

stations. Magnitude analyses show few results except at the 10- and 20 year return

periods which give z-values at about 30% of the stations suggesting that, at 12 stations,

there is a higher probability of exceeding the 10- and 20-year return period thresholds

under Cold Atlantic Cold Pacific conditions than under Cold Atlantic Warm Pacific

conditions. The latter analysis does not support the larger body of ENSO work, while the

former analysis may support AMO studies.

The Fishers Exact test revealed little difference between of proportions of summer

annual flood events occurring under the different SST anomaly combinations. The largest

difference occurred between Cold Atlantic Warm Pacific and Warm Atlantic Warm

Pacific and the second largest between Cold Atlantic Cold Pacific and Warm Atlantic

Cold Pacific; both of these comparisons pick up the differences in the Atlantic with a

steady Pacific, but several of the significant 1-tailed P-values results indicate that, in

these conditions, a Cold Atlantic, whether coupled with a Warm or Cold Pacific, would

likely have greater proportions of summer annual flood events than a Warm Atlantic.

This may be the case, but these results, at the most, represent only 17% or less of the 40









stations analyzed. As for the Fishers Exact magnitudes analyses, even fewer significant

results reveal a difference in the magnitude of annual flood events under various

combined Atlantic and Pacific SST anomaly combinations. Those showing the largest

number of significant results, between the Cold Atlantic Warm Pacific and Warm

Atlantic Warm Pacific and also between the Cold Atlantic Warm Pacific and Cold

Atlantic Cold Pacific, occurred at the 10-year return period level and both represented

only 10% of the stations analyzed. These results do not indicate a measurable difference

in the timing or magnitude of annual floods between Atlantic/ Pacific SST anomaly

combinations.

The Kruskal Wallis and ANOVA tests gave no evidence to dispute the null

hypothesis that populations of annual floods do not differ between various Atlantic and

Pacific SST combinations.

Chapter Conclusions

The intent of this study was to recognize connections between local hydrologic

regimes and large-scale climate patterns. While the results of the study do not pull out the

patterns suggested by current literature on the relationships between ENSO, the AMO

and streamflow, neither do they strongly dispute these studies. They simply add to the

growing body of work on these subjects that the annual flood series, as analyzed through

the methods used in this study, does not reveal the relationships one would expect with

ENSO and the AMO. This may be due to the methods of analysis or to the "noisy" nature

of the annual flood series; because this series is limited to a single annual maximum

observation of discharge per year, even if that maximum is insufficient to constitute a

"flood", and by the same token, may result in the omission of other flood peaks within

the same year, it poses some problems to creating a full picture of climatic and









hydrologic patterns. As mentioned in Chapter 2, the problems posed through use of the

annual flood series may be particularly troublesome in an environment like north central

Florida where rainfall is the major flood generating process and where there are two

distinct rainy seasons during the year. While the tests performed in this study did not

reveal the expected patterns caused by global phenomena, it does reveal that seasonal

patterns of annual flood events reflect the physical geographical variance in precipitation

suggested in previous studies. The Generalized Extreme Value fitted distribution also

contributes interesting results reflecting scale, location and shape of the bodies of

discharge data examined.














CHAPTER 7
CONCLUSIONS

"I never failed once. It just happened to be a 2000-step process." -Thomas Edison


One of the primary goals of geographic science is to make connections between

seemingly separated phenomena. This study seeks to define a connection between the

powerful, distant macro-scale conditions of Pacific and Atlantic oscillations in sea-

surface temperatures, and local streamflow conditions of the low-lying, peninsular state

of Florida. Relatively recent scientific interest in these oscillations has provided adequate

sources of available data. In correlating these data with records of Floridian streamflow,

this study attempts to make a connection between the distant conditions of ENSO and the

AMO to patterns of extremes in local stream flow.

Subtle and complex, the interactions between the widely separated phenomena of

ENSO and the AMO and local annual flood series (AFS) have proved, in this study, to be

somewhat statistically elusive. Signature rhythms and shapes are not brought out of the

data by the methods used in this study and do not produce convincing conclusions about

the relationship between ENSO, the AMO and local patterns of flooding.

The intent of this study was to discern whether previously recognized patterns of

association of association between monthly rainfall and streamflow totals, and SSTs,

could be extended to the characteristics of the annual flood series; a variable of

longstanding hydrologic research and a staple of public policy. The absence of any clear

pattern is perhaps due to the "noisy" nature of the annual flood variable itself.









Representing, as it does, the largest event in a year, the variable may be reflective of

unusual meteorological events rather than any consistent change. For instance, rainfall

related to tropical storms may be very important. Research indicated that the number of

tropical storms in the North Atlantic and Carribean basins is related to SSTs in both

oceans, however uncertainties concerning the paths of such storms provides no

guarantees that a greater number of storms will result in any of them actually striking the

areas of Florida examined in this study. The fact that the AFS fails to show any patterns

in relation to the large-scale climate indices included in this study that are known to

affect precipitation and streamflow around the world may pose some interesting

questions in itself; maybe the AFS is not the best tool to use for local hydrologic models.

If this is true, it might point to serious flaws in federal and local government planning

models and investigations of other high (and low) flow frequency measures such as

partial durations series might be more sensitive.

This study is the first of its kind to analyze the affects of ENSO and the AMO on

the annual flood series in this region. It has contributed positive evidence that seasonal

patterns of annual flood events reflect the physical geographical variations in dominance

of precipitation generating mechanisms as suggested in previous studies. It also is the

first of its kind to use the Generalized Extreme Value fitted distribution applied on a large

scale to the AFS at many discharge stations across Florida. Use of this fitted distribution

allows the examination and definition of the scale, location and shape of the bodies of

AFS discharge data across Florida.

While contributions of this study are important, many questions remain including

the question: what are the local effects of ENSO and the AMO on the AFS in Florida and









how can permutations of local action recognize their importance to modeling and

planning in responsible, intelligent ways? The effect of scientific data on public policy is

always complicated, but geography and meteorology are so intimately linked to human

habitation and advancement that an understanding of larger climate patterns and local

effects and reactions is necessary. Critical differences of action and consequence might

result from utilizing current meteorological understanding as presented in established

literature on ENSO and the AMO in "accepted" administrative standards and models.

Significant harm may be avoided by a more contemporary, broader, more informed set of

standards and models that include the influences of larger climate patterns. The final goal

is to understand the meteorological connections and processes critical to Florida's future.














APPENDIX A
TABLES AND FIGURES FOR TIMING AND MAGNITUDE OF THE AFS

The following tables and figures of this appendix were compiled in preparation for

the various statistical tests performed in this study. The first maps presented in this

appendix (Figures 5.1-5.5) represent the proportions of annual flood events occurring in

the summer season at each station; first overall, then by SST category.

Other maps and graphs presented (Figures 5.1-5.5) represent the Generalized

Extreme Value (GEV) fitted distribution analysis discussed in chapter 5 and performed in

preparation for analysis of the magnitudes of annual floods with different conditions.

Figures 5.6-5.8 display the parameters of the GEV by station. 5.9-5.10 display the

location and scale parameters plotted against basin area, and 5.11-5.12 show these same

graphs with normalized basin areas.

These tables and figures supplement and are referenced in Chapter 5 of this thesis.














































0 45 90 Miles
I I I


'S-t


Figure 5.1: Total proportion of summer events by station.


Legend
Proportion Summer Events (Total)
+ 0-25
25-50
50-75
O 75-100


N


S
S












































N
0 45 90 Miles
I I

S


Figure 5.2: Total proportion of summer events under Warm Atlantic SST conditions by
station.




































Legend

Proportion Summer Events (CA)
+ 0-25
25-50
+ 50-75
0 75- 100


N
W 0 45 90 Miles


S


Figure 5.3: Total proportion of summer events under Cold Atlantic SST conditions by
station.














































N

WS
S


0 45 90 Miles
I I I


Figure 5.4: Total proportion of summer events under Warm Pacific SST conditions by
station.


Legend
Proportion Summer Events (WP)
+ 0-25
+ 25-50
+ 5075
0 75100













































N

WS
S


0 45 90 Miles
I I I


Figure 5.5: Total proportion of summer events under Cold Pacific SST conditions by
station.


Legend
Proportion Summer Events (CP)
+ 16.7-250
+ 25.1-500
* 501 750
o 751-1000









































Negative K values Fresheli
o Neutral K values (Gumbel)
+ Positive K values (Weibull)
S 0 45 900 Miles 060 .
_0.2T -0. 1

-0.264 0.194


-0.194




0.246







Legend









Figure 5.6: GEV shape parameter, K (related to the positioning of the distribution tail) by
station. Negative K values re
O Neutral K values (Gurbel)
+ Positive K values (Weibull)


W0 45 90 Miles
















Figure 5.6: GEV shape parameter, K (related to the positioning of the distribution tail) by
station.




































Legend
GEV MODE (Epsilon)
0-100
S101 -300
301-600

N
E 0 45 90 Miles

S


Figure 5.7: GEV location parameter, t (related to the mode) by station.













































N
S0 45 90 Miles

S


Figure 5.8: GEV scale parameter, a (related to the variance) by station.


Legend
GEV VARIANCE (Alpha)
A 0-43
A 44-95
A 96-199








79




GEV Epsilon Parameter vs Basin Area


500


400


S300


200


100


Eu


*mmm
mg **


0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Area (km^2)


Figure 5.9: Locations parameter, ,, plotted against basin area.



GEV Alpha Parameter vs Basin Area

200 A


180

160

140

120

100

80

60

40

20

0


2000 3000 4000 5000
Area (km^2)


6000 7000 8000 9000 10000


Figure 5.10 Scale parameter, ca, plotted against basin area.


A A A



AAA AA
AL A A A A
A AA A A

A J~A
A'
It A -A

^r


^________


0 1000








80




Normalized GEV Epsilon Parameter vs Basin Area


[p
o O
00 O

DD 0


oB 00
OO

OB D DD



1000 2000 3000 4000


5000
Area (km 2)


6000 7000 8000 9000 10000


Figure 5.11: Normalized locations parameter, plotted against basin area.



Normalized GEV Alpha Parameter vs Basin Area


0200

0 180

0 160

0 140

S0120 '

0 100
E

0060

0040
A A
0020 ---

0000 *A A A


0 1000 2000 3000


4000 5000
Area (km A2)


6000 7000 8000 9000 10000


Figure 5.12: Normalized scale parameter, a, plotted against basin area.


0250


0 200 J [


0050



0000
0
















APPENDIX B
TABLES AND FIGURES FOR TEST OF PROPORTIONS

The following tables and figures of this appendix are results of the Test of

Proportions comparisons of the timing and magnitudes of annual floods of differing SST

categories. These tables and figures supplement and are referenced in Chapter 5 of this

thesis.


Table 5.1:


Number of positive and negative z-values* for timing analysis; A comparison
of the proportion of annual floods that occur in the summer season between
the following SST combinations. Discussion of results is focused on cells
highlighted in yellow (largest # of z-values) and white (least # of z-values).
r-l 11 1 1- - - - -('.1 1


nose cells snaa
p2
z-value
WA +z


2 CA

3 WP

4 CP

5 WAWP


6 WACP

7 CAWP


-z
+z
-z
+z
-z
+z
-z
+z
-z
+z
-z
+z


I-z
8 CACP +z
_-z
*90% confidence


dea in are extraneous, atnough part o me anal sis.
1 23 45 6 7 8
WA CA WP CP WAWP WACP CAWP CACP


1 1


11 1 1






1 0 0

I 1 0 1 4
10

15










Number of positive and negative z-values* for 1.5-year return period
magnitude analysis; A comparison of the proportion of annual floods that
exceed the 1.5-year return period between the various SST combinations.
Discussion of results is focused on cells highlighted in yellow (largest # of z-
values) and white (least # of z-values). Those cells shaded in gray are
extraneous, although part of the analysis.


WA +z-2
WA +


2 CA


3 WP


4 CP


5 WAWP


6 WACP

7 CAWP


-z
+2
-z
+2
-z
+2
-z
+2
-z
+2
-z
+2


8 CACP +
-z90% confident
*90% confident


1 2 34 5 6 7 8
value WA CA WP CP WAWP WACP CAWP CACP



x MI 0 1 0 2
x 0 0 0O 0 0


0 30 0
0 0 x MO 0 0 1
1 0 x O 0 1 1
0 1 0 3 x2






0 0 0 0 0
2 0 I 4 4


0 0 1 0 2 0 0
1 0 1 0
Ice


Table 5.2:










Number of positive and negative z-values* for 2-year return period magnitude
analysis; A comparison of the proportion of annual floods that exceed the 2-
year return period between the various SST combinations. Discussion of
results is focused on cells highlighted in yellow (largest # of z-values) and
white (least # of z-values). Those cells shaded in gray are extraneous,
although part of the analysis.


p2
z-value
WA +z


2 CA

3 WP

4 CP

5 WAWP

6 WACP

7 CAWP


-z
+z
-z
+z
-z
+z

+z
-z
+z
-z
+z


I __ -z
8 CACP +z
I -z
*90% confidence


1 2 34 5 6 7 8
WA CA WP CP WAWP WACP CAWP CACP
2 =0 0 0 0 1 0





1 ~ ~ 1
1 x 0 1 0 0 0
2 x 0 1 0 0

0 0 x MO 0 0 0



0 1 0 1 1
01 0 0 1 0

0 0 0 0 1 0 0
1 0 0 0 1 10


10 0 0 0 0 0 0
S S
S S S S S


Table 5.3:






84


Table 5.4: Number of positive and negative z-values* for 2.33-year return period
magnitude analysis; A comparison of the proportion of annual floods that
exceed the 2.33-year return period between the various SST combinations.
Discussion of results is focused on cells highlighted in yellow (largest # of z-
values) and white (least # of z-values). Those cells shaded in gray are
extraneous, although part of the analysis.
2 1 2 3 4 5 6 7 8
pl z-value WA WP CP WAWPWACPCAWPCACP
1 WA +z 1I

2 CA
3 WP + 0 0 0 1

-z S 66 16
4 CP +z i I I I I

5 WAWP 0 0 0

6 WACP + M 0 0 0 1

7 CAWP+ 1 x 0 0 0 0


8 CACP

*90% confidence