2007 South Florida Environmental Report Appendix 2-3 

Appendix 2-3: Consideration of 

Long-Term Climatic Variability in 

Regional Modeling for SFWMD 

Planning & Operations 

Jayantha Obeysekera, Paul Trimble, Calvin Neidrauer, Chandra Pathak, 

Joel VanArman, Tommy Strowd and Charles Hall 

 App. 2-3-1 
Consideration of Long-Term 

Climatic Variability in 

Regional Modeling for SFWMD 

Planning and Operations

Jayantha Obeysekera, Paul Trimble, Calvin Neidrauer, 

Chandra Pathak, Joel VanArman, Tommy Strowd and 

Charles Hall 

Draft 

September 1, 2006 

(revised October 30, 2006)

South Florida Water Management District 
TABLE OF CONTENTS 

Executive Summary .........................................................................................................................1

I. Introduction.............................................................................................................................2

Background.....................................................................................................................2

Climatic Variability and Climate Change.......................................................................3

Current Management Issues and Concerns.....................................................................4

Purpose and Scope..........................................................................................................5

II. Relevant Climatic Indicators .......................................................................................................6

El Niño/Southern Oscillation..........................................................................................6

Atlantic Multidecadal Oscillation...................................................................................7

Pacific Decadal Oscillation.............................................................................................8

AMO and South Florida’s Climate...............................................................................10

Predictability of AMO ..................................................................................................10

Interacting Effects of Teleconnections.........................................................................12

Other Potential Indicators.............................................................................................13

District Efforts to Understand Teleconnections............................................................14

III. Role of Models in Planning and Operation ..............................................................................14

IV. AMO Trends in Climate and Hydrologic Data ........................................................................16

Data...............................................................................................................................17

Trends in Water-Year Data...........................................................................................17

Trends in Rainfall-Runoff Relationships......................................................................20

Trends in Monthly Data................................................................................................22

Comparison of the SFWMM Modeling Period with the Period of Record ..................28

Lake Okeechobee Net Inflow .......................................................................................30

V. Use of Climate Outlook in Operations......................................................................................34

Lake Okeechobee Regulation Schedule Development.................................................34

Position Analysis..........................................................................................................37

VI. Incorporation of Climatic Variability in Water Resources Planning .......................................38

Sensitivity Analysis......................................................................................................39

Continual Expansion of Simulation Period of SFWMM..............................................40

Role of Adaptive Management in Plan Implementation...............................................40

V. Summary and Conclusions........................................................................................................42

VI. Recommendations....................................................................................................................44

VII. References...............................................................................................................................45

 i
EXECUTIVE SUMMARY 

The South Florida Water Management District (SFWMD or District) is an agency of the state 

of Florida whose mission is to manage and protect water resources of the region by balancing and 

improving water quality, flood control, natural systems, and water supply. Planning of future 

facilities and their operations is a critical District function and for such efforts, regional-scale 

computer models are frequently used. A primary public concern is whether the SFWMD 

appropriately addresses future water management needs in computer modeling for both planning 

and operations. Long-term climatic variability due to natural causes and climate change due to 

anthropogenic causes are clearly important for planning of facilities and their operations. 

During the past decade, the District initiated several investigative studies on the causes of 

climatic variability in southern Florida. In addition, methods have been developed to successfully 

incorporate climatic variability and climate outlooks into the water resource and operational 

planning (planning) modes of the central and southern Florida flood control system for today and 

into the future. 

Regional climatic variability is the result of land/ocean/atmospheric processes interacting at 

various spatial (regional to global) and temporal (inter-annual to multidecadal) time scales. The 

correlation of atmospheric variables around the globe to the equatorial Pacific sea surface 

temperatures (SST) and atmospheric anomalies is one of the most well recognized 

teleconnections, known as El Niño-Southern Oscillation (ENSO). Teleconnections are 

statistically significant correlations between meteorologic variables in various places of the 

world. Two additional teleconnections recently identified as influencing climatic variability in 

South Florida include the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal 

Oscillation (PDO). The AMO and PDO can be recognized by SST anomalies in the north Atlantic 

and north Pacific oceans, respectively. All three of these oscillations are quasi-periodic, with 

varying periods and irregular amplitudes. This makes the prediction of a phase change of any of 

the three in advance very difficult. ENSO has a periodicity that ranges between 3 and 7 years, 

while the AMO and the PDO have periodicities that range between 10 to 60 and 5 to 20 years, 

respectively. Significant investment worldwide has been devoted to predicting the evolution of 

ENSO and its impacts on global climate. Still there is large uncertainty associated with the 

numerous statistical and dynamic models designed to predict the phase of the ENSO 

phenomenon. 

This report presents an overview of the first three climate phenomena, to illustrate their 

influence on South Florida regional climatic conditions. Particular emphasis is placed on AMO, 

which has figured heavily in recent discussions regarding Lake Okeechobee operations and 

planning for the Comprehensive Everglades Restoration Plan (CERP). Along with analyses of 

period-of-record trends for both rainfall and Lake Okeechobee net inflow data, this report 

discusses the method for incorporating climatic variability into planning decisions. The South 

Florida Water Management Model (SFWMM), which has undergone multiple peer reviews to 

confirm its technical credibility, is the primary tool used for system simulation, planning, and 

performance forecasting. This model was also used for analysis of future projects proposed for 

CERP and for the development of regional water supply plans. 

Currently, climatic variability and meteorological conditions that occurred during the 

1965–2000 period are used to test various infrastructure or operational alternatives. The majority 

of this period represents an AMO cool phase that was in place from 1970 to 1994. Approximately 

11 of the 36 years in this period (1965–1969 and 1995–2000) represent conditions from AMO 

warm phases, that include wet (having above-average seasonal rainfall) and some extreme dry 

events. Statistical analysis of the data from the 1965–2000 period indicates that the wet periods 

used in the model are similar to wet periods that may occur during a typical AMO warm phase. 

 1
The conclusion is that the historical period provides appropriate and reasonable estimates of the 

ranges of climatic conditions that are likely to occur in coming years. 

The report recommends the continuing expansion of the period of simulation used in 

computer modeling, and a feasibility investigation for using historical data prior to 1965 and 

possibly synthetic rainfall data for evaluation of plan under a longer period of AMO warm phase. 

Future work should also include a more refined analysis of rainfall-runoff relationships in the 

Kissimmee Basin in order to separate those trends due to climatic variability from the effects of 

upper and lower basin water management. Adaptive management, already a cornerstone of CERP 

implementation, should be continued for re-evaluating facility plans and future operations as new 

data becomes available. 

I. INTRODUCTION 

BACKGROUND 

The South Florida Water Management District (SFWMD or District) is an agency of the state 

of Florida that has primary responsibility for management of water resources in a 16-county 

region that extends from just south of Orlando to Florida Bay. Climate change and variability 

have been driving forces behind the growth and evolution of the District since its formation in 

1949. The agency was created as the Central and Southern Florida Flood Control District, in 

response to massive flooding that occurred due to a 1948 hurricane, with the primary purpose to 

safely manage and dispose of excess floodwaters. The District was also the local sponsor for the 

federal Central and South Florida Project, developed by the U.S. Army Corps of Engineers 

(USACE). In 1968, this project was revised for greater emphasis on the development and 

enhancement of water supplies for public and agricultural uses. In response to this new definition 

of the project and widespread regional droughts that occurred in the 1950s, 1960s, and early 

1970s, the District was renamed in 1973 by the Florida Legislature as the SFWMD and given an 

additional assignment to provide for environmental protection. 

The agency’s mission today is to manage and protect water resources of the region by 

balancing and improving water quality, flood control, natural systems, and water supply. To 

accomplish this mission, the District has broad authority and responsibility to operate and 

maintain a regional system of nearly 2,000 miles of canals and levees, 546 water control 

structures, and 51 pump stations; to conduct planning studies to assess present and future 

environmental, water supply and flood control needs; and to regulate water use and surface water 

management. 

A major, relatively new initiative of the SFWMD, in cooperation with the USACE, 

U.S. Department of the Interior, other state and federal agencies, and local interests, is 

the ongoing effort to restore the Everglades ecosystem. Planning for this long-term 

restoration effort began in 1995 and resulted in development of the Comprehensive 

Everglades Restoration Plan (CERP) (USACE and SFWMD, 1999). The plan is designed to meet 

water management and environmental needs over the next 50 years and is expected to require 

more than 20 years to complete construction of facilities at an estimated cost of more than 

$8 billion. Since 1999, the District and USACE have conducted additional studies to develop 

details of this plan and initiate design and construction of project facilities to capture, store, and 

redistribute excess surface water to meet future needs. Early in the CERP planning process, it was 

recognized that long-term climatic variability due to natural or anthropogenic causes could 

cause significant changes to South Florida’s hydrology across the 50-year lifetime of 

this program. Consequently, an effort was undertaken to assess the importance, impacts, 

and potential implications of climatic variability. The status of climate studies and research 

 2
needed to address these critical issues were summarized in a District report in 1996 (SFWMD). 

Many of these issues and approaches remain relevant today. 

Planning for the future is a critical District function. To conduct such analyses, the SFWMD 

uses state-of-the-art communications and engineering technology, combined with technical 

expertise in a wide range of scientific disciplines. Analysis of alternative planning and operations 

scenarios and planning for future conditions depends heavily on the collection of data concerning 

current and past conditions in the region and the use of computer-based simulation models to 

represent existing and potential future land use, population, environmental conditions, and 

alternative management scenarios. 

The South Florida Water Management Model (SFWMM), a primary tool used to conduct 

these analyses, is a regional-scale computer model that simulates the hydrology and the 

management of the water resources system from Lake Okeechobee to Florida Bay. It covers an 

area of 7,600 square miles using a mesh of cells, each measuring 2 miles square; for this reason, 

the model is sometimes referred to as the “2x2 model.” The model simulates inflows from 

Kissimmee River, runoff and demands in the Caloosahatchee River and St. Lucie Canal basins, 

and major components of the hydrologic cycle in South Florida, including rainfall, 

evapotranspiration, infiltration, overland and groundwater flow, canal flow, canal-groundwater 

seepage, levee seepage, and groundwater pumping. It incorporates current or proposed water 

management control structures and current or proposed operational rules. 

As a planning tool, the SFWMM can be used to predict the response of the water control 

system to climatic conditions and proposed changes in hydraulic infrastructure and/or operating 

rules. The model design takes into consideration distinct hydrologic and geologic features of 

subtropical South Florida, which include (1) the strong interaction between canals and the highly 

permeable surficial aquifer, especially in the eastern portion of the region and (2) the effects of 

rainfall, evapotranspiration, overland flow, and groundwater movement within the Water 

Conservation Areas (WCAs) and Everglades National Park (ENP). The SFWMM integrates 

hydrologic processes with the hydraulic infrastructure and associated policy-based rules and 

guidelines related to water management in South Florida. To develop CERP, the District used the 

SFWMM with historical environmental, land use, and hydrologic data along with the technical 

expertise of numerous scientists and engineers, to evaluate appropriate initial design features for 

regional facilities and operations. 

CLIMATIC VARIABILITY AND CLIMATE CHANGE 

Climate in any region is the result of both short- and long-term phenomena interacting at 

local, regional, and global scales. A weather anomaly that persists for extended periods is 

recognized as a climate shift or as long-term climatic variability. If the climate shift is likely to 

persist to the foreseeable future and is due to anthropogenic causes, it is defined here as a climate 

change. Reasons for climate patterns, shifts, and changes are not completely understood, 

therefore, significant uncertainties are associated with forecasting conditions. 

The ongoing controversy over global warming underscores the challenge in distinguishing 

threshold points at which climate fluctuations become shifts or changes. Once a longer-term trend 

is established, the challenge is to determine whether the trend is due to effects of natural climatic 

variability, various thermal cycles in the oceans and atmosphere, variations in solar flux, changes 

in atmospheric chemical composition due to human activities, or a combination of factors. 

Therefore, the relative influence of anthropogenic and natural factors on global weather patterns 

remains a highly debated topic. 

Not only are the causes of global warming uncertain, but also the effects. Increased thermal 

energy in the oceans and atmosphere does not necessarily lead to increased temperatures across 

 3
the globe, or to increased rainfall in South Florida. In fact, some recent studies suggest that the 

increase in energy due to global warming may cause more extreme temperature fluctuations, 

more severe droughts, and increased storm activity. Wet regions of the earth could become much 

drier and dry regions could become much wetter. In view of the current uncertain state of science, 

this paper does not address the topic of climate change. However, the SFWMD has conducted 

sensitivity analysis of its plans through computer modeling for such climate change scenarios and 

sea-level rise and increased rainfall due to global warming. 

Climate anomalies observed in South Florida during recent years may represent some 

combination of effects due to natural climatic variability and long-term shifts, and possibly an 

overall global warming trend, perhaps modified to some degree by effects of human activities. 

Small shifts in weather patterns can change the net rainfall (rainfall minus evapotranspiration) 

from a surplus to a deficit or vice versa without being noticed. However, if these weather patterns 

persist over the region for an extended period, they can produce significant excesses or deficits 

that water managers must deal with prudently. Decisions are typically based on minimizing 

impacts and/or maximizing benefits to natural systems, water supplies, and flood protection. 

During the 1970s and 1980s, little was understood about global climatic variability or how to 

apply relevant data to water resource planning or systems operation. At that time, engineers and 

scientists at the SFWMD were trying to comprehend a climate shift that occurred throughout 

South Florida during the late 1960s (Shih, 1983). The cause of the shift was highly debated. Most 

scientists hypothesized that increased drainage and development or accumulation of greenhouse 

gases and global warming were the principal factors. Information now suggests that the climate 

shift in the late 1960s was associated with a phase change of the AMO (Trimble et al., 1998b; 

Enfield et al., 2001). Since that time, the District has been actively involved in conducting and 

supporting research and developing new technologies to incorporate of global, regional, and local 

climate factors into District planning and operations. The District has conducted multidecadal 

programs to implement some of these research findings and improve predictive capabilities and 

the efficiency of South Florida’s complex water management systems. Many of these efforts are 

described in this report. 

CURRENT MANAGEMENT ISSUES AND CONCERNS 

A primary public concern is whether the SFWMD has appropriately addressed future water 

management needs in its operations, planning, and design efforts. Has the District has considered 

long-term climate variability, especially rainfall, in its planning and operations of future water 

management facilities, such as those associated with CERP? Most of the proposed CERP projects 

are evaluated based on model simulations that use a 36-year period (1965–2000) of rainfall, 

hydrologic and water management records. Recent data indicate that climatic conditions may be 

shifting in response to warmer North Atlantic sea surface temperatures that tend to produce 

greater annual rainfall in central and southern Florida, and greater variability in annual rainfall. 

This phenomenon, characterized by the AMO index, is long-term in nature, possibly lasting for 

multiple decades. 

The effects of increased rainfall are nonlinear, that is, as the rainfall rate increases, soils 

become saturated, and storage facilities reach capacity, the proportion that becomes surface water 

runoff tends to increase rapidly. These conditions create a correspondingly greater need for 

additional water storage and/or disposal options during extended periods of high rainfall. 

This issue is of special concern for Lake Okeechobee, which is the largest natural reservoir for 

long-term water storage. During the past several years, the Lake has experienced consistently 

high water levels and levels of turbidity due to above-normal rainfall and several major 

hurricanes. These persistent high water and adverse water quality conditions have severely 

damaged Lake fisheries and littoral zone vegetation. Water managers were compelled to 

 4
discharge large amounts of water to major outlets through the Caloosahatchee and St. Lucie rivers 

in 2004 and 2005, resulting in significant damage to downstream coastal and estuarine resources. 

Naturally, a climatic regime characterized by above normal rainfall and consequently runoff from 

tributary watersheds is a concern for management of the water resources system in South Florida. 

Based on these considerations, several key questions concerning how SFWMD has dealt with 

long-term climatic variability in planning and operations were defined as follows: 

1. Has the District adequately addressed the long-term wet and dry periods in modeling for 

(a) facility planning and (b) operational planning? 

2. Is there compelling evidence that the volume of inflows to Lake Okeechobee will be as much 

as double during a wetter regime as they were in the dry regime? 

3. In the current modeling efforts, has the District adequately addressed the variability of 

inflows into Lake Okeechobee? 

4. Does the modeling approach used by the District for both CERP and the Water Supply and 

Environment (WSE) schedule design meet requirements of standard engineering and design 

practices? 

5. Are the steps being taken in the adaptive management/modeling approach used by the District 

adequate to address the uncertainties in climate predictions and to assimilate new 

information? 

6. Except for basic research approaches, are there other facility planning options that the District 

should consider to address the possibility of a continued wetter regime of the climate cycle? 

7. Are the data and models used by the District appropriate (reasonable and adequate) for their 

intended applications? 

The above questions were the basis of questions posed to a group of five independent peer 

reviewers who assessed them using an June 2006 version of this paper. The paper and the peer 

review reports are available at the District web site (www.sfwmd.gov), under the Weather and 

Water Conditions, Long-Term Forecasts section. 

PURPOSE AND SCOPE 

The purpose of this document is to describe climatic indicators that are likely to affect South 

Florida rainfall, the uncertainty associated with predicting the climatic variability, and the data 

available for modeling long-term hydrologic conditions and changes in South Florida, and apply 

this information to address key questions listed in the previous section. Detailed analyses of the 

trends in the long-term datasets on rainfall and net inflows to Lake Okeechobee are also 

presented, along with comparisons to those used in regional modeling. The long-term datasets 

were analyzed to determine whether the modeling dataset adequately represents climatic 

variability that is likely to occur during coming years. The use of climate outlooks based on 

known teleconnections for operational planning is also discussed. The document concludes with a 

set of recommendations for improving the current approaches to addressing long-term climate 

variability in planning and operations. 

It is emphasized that this document focuses on long-term climate variability rather than 

climate change due to such causes as global warming. This focus reflects the recent emphasis on 

long-term climate shifts due to natural causes such as AMO and the lack of clear patterns and the 

considerable uncertainties concerning scientific understanding of climate changes that may be 

due to anthropogenic causes. 

 5
II. RELEVANT CLIMATIC INDICATORS 

Teleconnections are statistical correlations between persistent shifts of atmospheric variables 

occurring in widely separated parts of the world. These statistical correlations generally result 

from shifts in atmospheric and ocean circulations. Of numerous teleconnections that have been 

investigated by the District, three are recognized to have significant impact on the regional 

climatic variability in South Florida. They are (1) the El Niño/Southern Oscillation (ENSO), 

(2) the Atlantic Multidecadal Oscillation (AMO), and (3) the Pacific Decadal Oscillation (PDO). 

These climatic indicators seem to be related to total annual or seasonal rainfall and hydrologic 

conditions across the South Florida region and within particular basins (Figure 1). 

Figure 1. Geographical locations of AMO, PDO, and ENSO teleconnections 

related to climate in South Florida. The map shows the correlation (red for 

positive and blue for negative) between global monthly sea surface 

temperature (SST) anomalies and the Florida Climate Division 4 monthly 

precipitation. (Figure adapted from Mestas-Nunez and Enfield, 2003.)

EL NIÑO/SOUTHERN OSCILLATION 

The ENSO is a slow oscillation in which the atmosphere and ocean in the tropical 

Pacific region interact to produce a slow, irregular variation between two extremes. 

The National Oceanic and Atmospheric Administration (NOAA) Climate Diagnostics Center 

and other El Niño web pages confirm that ENSO is a major source of interannual climatic 

variability in South Florida. (See http://www.cdc.noaa.gov/ENSO/enso.description.html and 

http://www.elnino.noaa.gov/.) 

ENSO variations are more commonly known as El Niño (the warm phase) and La Niña (the 

cool phase). Although ENSO is centered in the tropics, the changes associated with El Niño and 

La Niña events affect climate around the world. ENSO events tend to form between April and 

June and typically reach full strength in December. During El Niño events, South Florida tends to 

 6
experience above-normal rainfall during the winter (dry season) months, whereas dry-season 

rainfall tends be below normal during La Niña. 

District research indicates that ENSO and other climate indices have significant potential to 

predict net inflow volumes into Lake Okeechobee (Trimble et al., 1998b). ENSO events typically 

have a three- to seven-year period and their climatic effects are more pronounced during the 

winter months than during the wet-season summer months. In recent years, artificial neural 

network (ANN) models have been employed by the District to help predict net inflow volumes 

into Lake Okeechobee using ENSO and other climatic indices (Zhang and Trimble, 1996; 

Trimble et al., 1998a,b; Trimble and Trimble, 1998). Using these techniques, scientists have 

gained a greater understanding of how ENSO and other factors affect South Florida weather and 

hydrology. 

ATLANTIC MULTIDECADAL OSCILLATION 

The AMO (http://www.aoml.noaa.gov/phod/amo_faq.php) is a long-range climatic oscillation 

that causes periodic changes in the surface temperature of the Atlantic Ocean, between the 

equator and Greenland, which may persist for several years or decades. AMO describes 

temperature deviations in the ocean surface that appear to be driving shifts (“warm” and “cool” 

phases) in South Florida’s climate (Enfield et al., 2001). There are significant year-to-year 

fluctuations in the instrumental records of ocean temperature (Figure 2), but no clear indications 

of the exact years when AMO switches from a warm to cool phase. The transition between phases 

is generally determined by tracking a multi-year average (Figure 2). 

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Tempeature Anomaly (deg Celsius)

Monthly Temperature Anomaly

10-year moving average

Figure 2. Interannual variability of detrended, monthly sea surface 

temperature anomalies (degrees Celsius) and the AMO index. 

Derived from a 10-year moving average.

 7
During the twentieth century, the 1900–1925 period seems to have been dominated by a cool 

phase of the AMO, followed by a warm phase (1926–1969) and another cool phase (1970–1994). 

Since approximately 1995, the AMO index has been increasing. Although the exact years of 

warm and cool phases of AMO are difficult to identify and vary among researchers, the general 

conclusion drawn from the differing periods remain the same and is not critically important for 

ensuing discussions. Accordingly, the following four periods were used for this analysis: 

AMO1 (cool) 1895–1925 

AMO2 (warm) 1926–1969 

AMO3 (cool) 1970–1994 

AMO4 (warm) 1995–2005 

The magnitude and duration of the current warm phase cannot be predicted solely based on 

historical trends, although Enfield and Cid-Serrano (2006) recently made some probabilistic 

predictions of when the current warm period might end. There is no overwhelming evidence to 

suggest that rainfall conditions during this warm phase will differ significantly from conditions 

experienced during the 1926–1969 warm phase. The duration and magnitude of the warm phase 

are major uncertainties that affect facility planning for such major programs as CERP. 

PACIFIC DECADAL OSCILLATION 

The PDO (http://jisao.washington.edu/pdo/) is a pattern of Pacific climatic variability that 

somewhat resembles El Niño (Figure 3 Panel A). While the two climatic oscillations have 

similar spatial climate “fingerprints,” they have very different behavior in time. Two main 

characteristics distinguish PDO from ENSO: (1) PDO events during the twentieth century have 

persisted for 20 to 30 years, while typical ENSO events last for only a few years, and (2) the 

climatic fingerprints of the PDO are most visible in the North Pacific/North American sector, 

while secondary signatures exist in the tropics. The opposite effects occur for ENSO. 

Several independent studies find evidence for just two full PDO periods since the start of the 

twentieth century: PDO cool regimes prevailed from 1947 to 1976 and briefly during the latter 

half of 1999 through mid-2002, while warm regimes dominated from 1925 to 1946, from 1977 

through 1998, and from late 2002 until the present. Several other shorter periods occurred within 

the longer ones, for example, the 1958–1961 warm phase and the 1988–1991 cool phase. 

Variations in South Florida rainfall data during the longer periods reflect these shorter episodes. 

The PDO affects the South Florida climate much like El Niño except that the frequency of the 

oscillation is on decadal and multidecadal scales instead of the three to seven years of El Niño. 

Thus, during the warm phase of the PDO, South Florida tends to experience above-normal 

rainfall during the dry season (Figure 3 Panel B). 

 8
A Monthly Values for the PDO index: 1900–November 2005. 

B Correlation of November–April precipitation with November–April PDO 

 1948–2004. 

Figure 3. (A) PDO computed as the leading principal component of the monthly 

North Atlantic sea surface temperature anomalies. (B) Correlation map of U.S. 

precipitation to PDO shows that South Florida has a very high correlation between 

rainfall and PDO index during the dry-season months (November–April).

 9
AMO AND SOUTH FLORIDA’S CLIMATE 

The relationship between AMO cycles and rainfall patterns in South Florida is not well 

defined. Figure 4 shows the historical time series of District-wide annual rainfall patterns and the 

forecast volumes of Lake Okeechobee Net Inflow (LONIN) plotted as a deviation from their 

overall statistical means. The variable LONIN is defined here as rainfall (RF) minus 

evapotranspiration (ET) plus inflows. It was computed using the continuity equation as 

LONIN = DS + Outflows, where DS represents Delta (change in) storage. Although no dramatic 

trends appear from these graphs, there are clearly periods of below- and above-normal rainfall 

and LONIN throughout the historical record. In particular, the wetter periods of 1940s, 1950s, 

and the years since 1995, can be identified from the rainfall pattern. These years generally 

correspond to the AMO warm phases. The LONIN shows similar patterns, and is further 

influenced by the nonlinear relationship between rainfall and runoff, the effect of antecedent 

conditions, and water management in the tributary basins of Lake Okeechobee. These influences 

will be discussed later in this paper. 

Another way to identify trends in long-term data records is to inspect cumulative deviations 

from the statistical mean (Figure 5). The AMO1–AMO4 periods defined above correspond 

reasonably well with the increasing or decreasing trends of the cumulative deviations, which 

generally correspond to the below-normal or above-normal periods of the rainfall and/or LONIN 

time series, respectively. 

PREDICTABILITY OF AMO 

There is no known basis at present to estimate the exact duration, magnitude or return 

frequency of AMO phases. The short record of instrumental ocean temperature data show that the 

average length of the cycle is approximately 60 years, but individual cycles may vary 

considerably. As shown in Figure 6, a 450-year history of the AMO index was recently 

reconstructed using tree data (Gray et al., 2004). The average length of a warm phase during this 

period was about 34 years, but individual phases ranged from as short as 11 years to as long as 60 

years. Similarly, cool phases averaged 21 years in length and ranged from 6 to 61 years 

(Enfield and Cid-Serrano, 2006). It is clear from this data that the exact length, and for that matter 

the strength of a warm or cool episode of AMO, may be extremely difficult to predict, 

while it is not clear whether the current warm phase will last as long as the last one 

(approximately 1926–1969). 

Even during a warm phase of the AMO, multi-year droughts can occur in South Florida, 

associated with La Niña conditions or other phenomena. This was clearly demonstrated by 

occurrence of the most severe drought on record in 2000–2001, a year that was fully within the 

latest AMO warm phase. Multi-year drought periods occurred during each decade of the previous 

warm phase (1920s–1960s). 

 10
Figure 4. District-wide long-term rainfall patterns (top panel) and the Lake 

Okeechobee Net Inflow (LONIN) values plotted as deviations from the individual 

statistical means. 

Figure 5. Cumulative deviation from the overall mean for district-wide 

rainfall and LONIN time series. 

 11
A

B

warm 

Figure 6. (A) Reconstructed values of North Atlantic Sea Surface Temperature 

(SST) anomalies (computed as the deviation from the mean in units of 

standard deviations, plotted as bars) using tree-ring technology and 

corresponding AMO index (10-year moving average plotted as a solid line). 

(B) Durations of significant warm (red) and cool (blue) AMO regimes identified 

from data in panel (A). (Data from Gray et al., 2004).

INTERACTING EFFECTS OF TELECONNECTIONS 

Climatic variability and change are the result of complex and nonlinear interactions among a 

number of determining forces. Therefore, a single indicator cannot typically be used to represent 

the entire coupled oceanic-atmospheric weather generating system. For example, if varying ocean 

phases drive atmospheric circulation, then the response of the atmosphere may be delayed by a 

few years after an ocean phase indicator such as the AMO crosses a specific threshold. AMO sea 

surface temperatures may vary according to a sinusoidal wave pattern, while the atmospheric 

response follows a delayed-step function that is not immediately apparent when the AMO ocean 

index goes from negative to positive. For tropical weather and Florida hydrology, this seems to be 

the case. Similar delays also occur in the response of the atmosphere to El Niño events (Barnston 

et al., 1999). This delay in response to global-scale changes adds to the uncertainty in predictions. 

The complexity underlying climatic indicators necessitates caution before using such 

information to make short- or long-range water management decisions. Finally, seemingly minor 

changes in spatial and temporal patterns of rainfall distribution may produce dramatic changes in 

hydrologic variables such as runoff. 

 12
Since there are at least three significant climatic indicators of importance for South Florida, it 

is important to understand and account for interacting effects of these teleconnections. As a part 

of the District’s climate research program, Mestas-Nunez and Enfield (2003) made an exhaustive 

investigation of intra-seasonal to multidecadal variability in South Florida rainfall. From this 

analysis they concluded that the ENSO association with South Florida precipitation varies 

depending on the different phases (warm and cool) of AMO and PDO. For example, Table 1 

shows contingency tables of February–April rainfall and ENSO for AMO cool and warm phases, 

indicating how ENSO–rainfall relationships change depending on the AMO phase. For example, 

there is a 35 percent chance that February–April rainfall will be in the wetter tercile under 

El Niño conditions when AMO is in the cool phase. However, this increases to 81 percent when 

AMO is in the warm phase. 

Table 1. Contingency table of ENSO–rainfall (February, March, and April [FMA]) 

relationship as a function of AMO.*

AMO Cool Phase AMO Warm Phase 

ENSO Phase ENSO Phase Rainfall 

(FMA) La Niña Neutral El Niño

Rainfall 

(FMA) La Niña Neutral El Niño

Wet 0% 19% 35% Wet 10% 35% 81% 

Neutral 20% 50% 60% Neutral 25% 35% 6% 

Dry 80% 31% 5% Dry 65% 10% 13% 

* ENSO – El Niño-Southern Oscillation; AMO – Atlantic Multidecadal Oscillation 

Correlations between South Florida climate and AMO/PDO patterns (the teleconnections 

mentioned above) have been seen in recent decades. For example, during the 1976–1995 period, 

the AMO was in the cool phase and the PDO was in the warm phase. This was a stressful time for 

the natural systems of south-central Florida, as the seasonality of rainfall tended to reverse 

the natural hydroperiod. That is, on average, wetter-than-normal dry seasons and 

drier-than-normal wet seasons occurred. Because evapotranspiration is much lower during the dry 

season, the 1976–1995 period brought frequent, very large dry-season surpluses of net rainfall. 

These surpluses led to large runoff volumes and rising water levels throughout the District. 

Other examples include the 1970–1975 period, in which both the AMO and PDO were in 

their dry phases, resulting in extended dry periods, and 1995–1998, in which both oscillations 

were in their wet phases, producing very wet periods. 

OTHER POTENTIAL INDICATORS 

The SFWMD also uses the climate outlook forecasts developed by the National Oceanic and 

Atmospheric Administration (NOAA) Climate Prediction Center (CPC) to aid operational 

planning efforts. Data sources and tools used for CPC forecasts include statistical interpretations 

of current and historical global sea surface temperatures (including the data that drive PDO and 

AMO periods), precipitation, soil moisture, solar radiation and trends, ENSO events, and outputs 

from various climate models. The CPC provides updated predictions of precipitation and 

temperature, including one-month and three-month outlooks. 

 13
The SFWMD has also conducted research to improve understanding of the effects of drainage 

and development on temporal and spatial distribution of rainfall (Mattocks et al., 1999; Mattocks 

and Trimble, 2000; Pielke et al., 1999; Chen et al., 2001) and potential effects of global warming, 

including sea level rise, on water resource planning (Trimble et al., 1998a). Several recent 

publications summarize the SFWMD experience in applying climate outlook methods to address 

water management issues in South Florida (IRI, 2002; Trimble et al., 2006). 

DISTRICT EFFORTS TO UNDERSTAND TELECONNECTIONS 

The District conducted numerous studies to understand the effects of teleconnections on 

South Florida and determine how climate forecasts could best be applied to improve Lake 

Okeechobee operations (Trimble et al, 1998b,c; Trimble and Trimble, 1998; Cadavid et al., 1999; 

Obeysekera et al., 2000). The work culminated in a landmark study describing the importance of 

the AMO to climatic variability (Enfield et al., 2001). The SFWMD has also investigated the 

application of ANN and data mining tools to characterize and hindcast Lake Okeechobee net 

inflows (Zhang and Trimble, 1996a,b; Trimble et al., 1998b;c Trimble and Trimble, 1998). These 

hindcasts are provided as input data to test the operational rules of the SFWMM (Trimble et al., 

1998b,c) both for current operations and infrastructure and for performance of future alternatives. 

A number of experts in the field of climatology have peer reviewed the SFWMD climate 

outlook methodology (Hewett, et al., 2000; Kolen and Hewitt, 2000; Mestas-Nunez and Enfield, 

2003). More recently, recognizing the importance of long-term climatic regimes, the USACE 

formally approved and implemented the SFWMD recommendation to use AMO and ENSO 

climatic indicators to predict seasonal and multi-seasonal outlooks for use in the operational 

planning of Lake Okeechobee. 

III. ROLE OF MODELS IN PLANNING AND OPERATION 

Climatic indicators are used in the SFWMD planning process primarily through the 

application of regional models such as the South Florida Water Management Model (SFWMM). 

These tools provide a basis to assess water supply and flood control requirements, help determine 

the design and operation of future water management facilities, and determine environmental 

resource needs and impacts. Models used in conjunction with predictions of future climatic 

conditions are particularly important for planning, design and implementation of long-range 

activities such as CERP projects, which are expected to require many years to construct and have 

design life expectancies of 50 years or more. 

In the design and analysis of water resources plans and projects for CERP, computer models 

are used to analyze existing and simulate possible future conditions (Figure 7). Planning 

processes then analyze these conditions to determine planning options that are likely to offer the 

greatest benefits or least impacts (Loucks, 1992). Models provide important information for 

planning, but such information is never complete. As a result, models cannot substitute for the 

judgment of experienced engineers, planners and managers (Loucks, 1992). Moreover, models 

are only one of a number of tools used to design project components. 

More specifically, a particular modeling scenario is assumed to be static with respect to 

facility plans, operating criteria, land use/land cover conditions, and water demands (Figure 8). 

Consequently, this scenario is subject to a variety of hydrologic conditions as extracted from the 

most recent historical period of record, currently assumed to be 1965–2000. A regional-scale 

computer model (SFWMM) is used for evaluating the performance of the plan with respect to 

varying hydrologic conditions. 

 14
Project 

Implementation 

Reports (PIRs)

Alternative plans are evaluated to 

develop the project plan that will be 

recommended to Congress for 

construction authorization. 

Design/ 

Construction 

Project 

Operation 

 Regional 

 Regional 

 Modeling 

 Modeling 

Figure 7. Planning, design, and construction phases of CERP process indicating 

where regional modeling plays a role. 

Figure 8. Scenario depicting South Florida Water Management Model (SFWMM) 

generalized inputs and outputs. 

Plan: Assume “static” conditions: 

- Land Use/Land Cover 

- Water Demands 

- Operating Criteria

Model Outp

Daily time

series o

ut

f

water levels, 

Model Outp

• Daily time 

series of

water levels, 

•

•

•

Climatic Input

Rainfall

ET

Climatic Input

Rainfall

ET

Model Ou

Daily time 

series o

t

f

water levels, 

Model Output

Daily time step 

series of water 

levels, flows

•

•

•

Climatic Input

Rainfall

ET

Climatic Input

Rainfall

ET

SFWMM (2X2)

Climatic Input

Rainfall

ET

Climatic Input

Rainfall 

ET

Boundary Conditions 

1965–2000 period of 

record 

Performance 

Measures

 15
Analysis of project alternatives often relies on performance measures that represent project 

objectives in the form of quantitative criteria. Models allow quantification of hydrologic 

conditions (i.e., water levels and flows) that can be presented as performance measures and 

provided, along with other critical information such as costs and physical constraints, to decision 

makers. 

The exact application of models in water resources planning and management depends on the 

project purposes. When modeling climatic variability and change, selection of an appropriate 

period of record is important. However, parameters that describe climatic variability (magnitude, 

duration, and return frequency) introduce uncertainties into water resources planning efforts, and 

can result in over-design, cost overruns, and poor project performance. Adaptive management 

offers an accepted approach to dealing with uncertainties in water resources modeling and 

planning processes. The basic elements of adaptive management are summarized in Section IV. 

The South Florida Water Management Model (SFWMM) shown in Figure 8 is a regional 

model developed by the SFWMD as a planning tool to help guide regional water supply and 

environmental restoration efforts for the District. A detailed description of this model can be 

found at the District web site (www.sfwmd.gov) under the Simulation Modeling, Hydrologic & 

Environmental Systems Modeling section. The model mathematically represents a physical 

system, and simulates the performance of the existing or proposed water management facilities 

and operations. Operating rules and input datasets can be modified to represent a range of 

planning and forecasting scenarios. The SFWMM is a continuous simulation model that is driven 

by historical hydrologic and meteorological data. Input datasets include daily spatial rainfall and 

evapotranspiration data, as well as local basin runoff and irrigation estimates for the relevant 

basins. The spatial domain of the SFWMM south of Lake Okeechobee is divided into grid cells, 

each 2 miles square. The model generates a description of hydrologic conditions throughout the 

region, which is then used to estimate effects on water supply, flood protection, and natural 

systems. A typical application of a planning tool such as the SFWMM uses an available period of 

reliable historical input data that includes a wide range of both dry and wet years. For Lake 

Okeechobee and its tributaries, the SFWMM uses estimates of inflows based on historical flow 

data or estimates of inflows based on prescribed changes to upstream water management. The 

combined inflows to Lake Okeechobee from the tributary basins may be referred to as net inflow. 

Studies by Trimble et al. (1998b,c) have shown a significant potential for using ENSO and 

other indices to predict net inflow volumes into Lake Okeechobee for operational planning. 

Furthermore, use of ANN models has helped to bring a better understanding of these factors 

(Zhang and Trimble, 1996; Trimble et. al., 1998b,c; Trimble and Trimble, 1998). 

IV. AMO TRENDS IN CLIMATE AND HYDROLOGIC DATA 

Although the records at individual rain gauge locations vary in length, several long-term 

rainfall datasets have been constructed using such gauge records. These datasets typically begin 

in the year 1895. In addition, inflow records at key gauging locations of the Kissimmee Basin and 

a reconstructed time series of net inflow into Lake Okeechobee are also available. This exercise 

was intended to evaluate the long-term records to detect trends that may be associated with 

climate shifts, especially warm and cool phases of AMO. 

 16
DATA 

Two sets of reconstructed monthly rainfall records were used for this analysis: (a) the PRISM 

dataset (Daly et al, 2004), and (b) National Climatic Data Center (NCDC) datasets for Climate 

divisions 4–7 in South Florida (http://www.ncdc.noaa.gov/oa/ncdc.html). This analysis used flow 

records for S-65 and S-65E gauge locations in the Kissimmee Basin as well as the LONIN dataset 

constructed using Lake water levels and outflow records. The grid-based PRISM dataset was 

spatially averaged to represent regions that cover the major basins in South Florida. Details of the 

data are shown in Table 2. 

TRENDS IN WATER-YEAR DATA 

In this study, annualized values were based on a water year, defined as the period from June 1 

to May 31. This date range differs slightly from the standard period used by the SFWMD 

(May 1–April 30). The June–May span was used in this study because May represents the end of 

the dry season; therefore, the June–May span helped to minimize carryover of discharge data 

from one water year to the next. 

Table 2. Monthly climatic data used for trend analysis.

Variable Region/Gauge Location Dataset Period of Record 

Ukiss Upper Kissimmee Basin PRISM 1895–2005 

Lkiss Lower Kissimmee Basin PRISM 1895–2005 

KissRF Entire Kissimmee Basin PRISM 1895–2005 

BigCyp Big Cypress Basin PRISM 1895–2005 

EAA Everglades Agricultural Area PRISM 1895–2005 

Everglades Everglades National Park PRISM 1895–2005 

LOK Lake Okeechobee PRISM 1895–2005 

WCA3 Water Conservation Area 3 PRISM 1895–2005 

WCA12 Water Conservation Areas 1 and 2 PRISM 1895–2005 

WestAG Western Agricultural Areas PRISM 1895–2005 

Caloos Caloosahatchee Basin PRISM 1895–2005 

SWCoast South West Coast PRISM 1895–2005 

Browrd Broward County PRISM 1895–2005 

Dade Miami-Dade County PRISM 1895–2005 

MartSLC Martin, St. Lucie Counties PRISM 1895–2005 

Pbeach Palm Beach County PRISM 1895–2005 

Div4rf South-central Florida NCDC 1895–2005 

Div5rf Everglades and Southwest Coast NCDC 1895–2005 

Div6rf Lower East Coast NCDC 1895–2005 

Rainfall 

Div7rf Florida Keys NCDC 1895–2005 

S-65E S-65E gauge location SFWMD 1928–2005 

S-65 S-65 gauge location SFWMD 1933–2005 

Flow 

LONIN Lake Okeechobee net inflow SFWMD 1914–2005 

Note: All variables represent rainfall except S-65, S65-E, and LONIN, which represent flows. 

 17
Box plots of the water year data for variables for the four AMO periods (Figure 9) show a 

shift in distribution of both rainfall and flow between cool periods (AMO1 and AMO3) and warm 

periods (AMO2 and AMO4). Moreover, rainfall and flow amounts during the warm periods 

appear to shift upward compared to the cool periods. However, comparing of the means of all 

four periods using one-way analysis of variance (ANOVA) shows that not all of the shifts are 

significant. As shown in Table 3, shifts in rainfall for the Ukiss, KissRF, BigCyp, EAA, 

Everglades, LOK, WCA3, WestAG, and MartSCL basins are significant at the 5 percent level. 

Both S-65E and LONIN also show shifts in mean at the 5 percent level. Although rainfall data for 

NCDC Divisions 4–7 show visual shifts in the box plots, they are not significant at the 5 percent 

level. (The hypothesis testing is formulated in such a way that the percent chance of indicating a 

difference in means when in reality they are not is limited to 5 percent.) 

Overall, results show statistically significant shifts in mean values during different AMO 

periods for both rainfall and flow variables. Actual changes in the means and percent values are 

shown in Table 4. 

Figure 9. Box plots of water year (June–May) rainfall and flow variables for four 

AMO periods (1895–2005). Whiskers of the box plots were eliminated to show the 

actual distribution of points outside the interquartile range as indicated by each box. 

The line inside the box is the median value.

 18
Figure 9. Continued.

 19
Table 3. Probability value, p, for the null hypothesis that samples of different AMO 

periods were drawn from populations with the same mean. Also shown are the 

variables for which p-value exceeds the 5 percent significance level.

 Variable ANOVA 

p-value 

Significant

at 5% 

level 

Variable ANOVA 

p-value 

Significant

at 5% 

level 

Ukiss 0.027 Yes Browrd 0.149 No 

Lkiss 0.056 No Dade 0.12 No 

KissRF 0.028 Yes MartSLC 0.038 Yes 

BigCyp 0.003 Yes Pbeach 0.186 No 

EAA 0.038 Yes Div4rf 0.104 No 

Everglades 0.019 Yes Div5rf 0.163 No 

LOK 0.058 Yes Div6rf 0.278 

WCA3 0.012 Yes 

Rainfall

Div7rf 0.672 

No 

No 

WCA12 0.067 No S-65E 0.014 Yes 

WestAG 0.031 Yes S-65 0.08 No 

Caloos 0.137 No 

Flow

LONIN 0.026 Yes 

Rainfall

SWCoast 0.117 No 

TRENDS IN RAINFALL-RUNOFF RELATIONSHIPS 

As shown in Table 4, the annual rainfall reduction in the last transition from an AMO warm 

period (AMO2) to an AMO cool period (AMO3) ranges from 2 to 9 percent. However, the 

percent reduction in the flow variables is much larger; the LONIN was the highest, at about 

48 percent. Although increases in percent reductions in runoff are typically due to decreased 

rainfall, all of the following factors may contribute to this large reduction in the flow variables 

(S-65E, S-65, and LONIN), such as: 

• Changes in rainfall between AMO periods 

• Land use changes in the Kissimmee Basin 

• Post-channelization water management in the Kissimmee Basin 

• Non-homogeneity in long-term data used for trend analysis 

With regard to the non-homogeneity, it is difficult to pinpoint inconsistency in the data, 

particularly from the 1920s and 1930s. In particular, the calculation of LONIN is based on 

changes in Lake stages and outflow variables. While it is challenging to determine whether 

decades-old measurements have inherent inaccuracies, review of rainfall-runoff relationships 

might provide useful for this analysis. 

 20
Table 4. Sample means for different AMO periods and percent change relative to 

other AMO periods.

Mean Percent Change 

Area Unit AMO1 AMO2 AMO3 AMO4 

AMO2 

to 

AMO3 

AMO3 

to 

AMO4 

AMO2 

to 

AMO4 

Ukiss in. 46.89 50.93 48.61 54.48 -4.6 12.1 7.0 

Lkiss in. 45.59 49.14 47.66 52.49 -3.0 10.1 6.8 

KissRF in. 46.50 50.4 48.32 53.89 -4.1 11.5 6.9 

BigCyp in. 52.27 56.54 52.00 60.82 -8.0 17.0 7.6 

EAA in. 49.04 53.19 51.13 55.70 -3.9 8.9 4.7 

Everglades in. 51.96 56.00 51.67 59.38 -7.7 14.9 6.0 

LOK in. 46.44 50.11 48.39 52.58 -3.4 8.7 4.9 

WCA3 in. 55.94 59.21 54.20 63.33 -8.5 16.8 7.0 

WCA12 in. 55.86 59.07 56.28 63.32 -4.7 12.5 7.2 

WestAG in. 49.31 52.90 50.40 56.38 -4.7 11.9 6.6 

Caloos in. 48.58 51.12 49.94 54.91 -2.3 10.0 7.4 

SWCoast in. 49.72 51.99 50.72 56.99 -2.4 12.4 9.6 

Browrd in. 57.47 59.93 57.27 64.49 -4.4 12.6 7.6 

Dade in. 57.38 59.9 55.22 62.66 -7.8 13.5 4.6 

MartSLC in. 48.07 51.62 51.58 56.19 -0.1 8.9 8.9 

Pbeach in. 55.06 58.82 56.81 60.58 -3.4 6.6 3.0 

Div4rf in. 50.33 53.00 50.19 56.06 -5.3 11.7 5.8 

Div5rf in. 51.09 52.81 50.41 56.07 -4.5 11.2 6.2 

Div6rf in. 58.21 59.87 57.75 64.69 -3.5 12.0 

Rainfall 

Div7rf in. 43.02 42.56 40.79 44.38 -4.2 8.8 

8.1 

4.3 

S-65E ac-ft 1,399,271 890,342 1,386,436 -36.4 55.7 -0.9 

S-65 ac-ft 699,608 592,058 1,007,097 -15.4 70.1 44.0 

Flow 

LONIN ac-ft 319 1,966 1,031 1,619 -47.6 57.1 -17.6 

Note: All variables represent water year (June–May) rainfall except S-65, S-65E, and LONIN, which 

represent annual flow volumes. 

 21
In Figure 10, the rainfall-runoff relationship (S-65E flow versus KissRF) for the Kissimmee 

Basin is presented. In this plot, the runoff volume (water year basis) at S-65E is expressed as a 

depth of water over the basin, by using a total area of 1,595 square miles for the upper basin plus 

684 square miles for the lower basin. Based on the availability of flow data, only the scatter plots 

for AMO2, AMO3, and AMO4 periods are shown. Except for a group of “outliers” that are 

identified on the plot, it is difficult see a definite change between AMO cycles from this plot. 

Many of the outlier years correspond to well known times when South Florida experienced very 

wet periods due to tropical events. These years also occurred before the modern system of water 

control structures and improved canals was constructed in the Kissimmee Basin. 

As is seen from the runoff coefficients (runoff as a fraction of rainfall) shown in Figure 11, 

the percentage of runoff due to rainfall was high (above 35 percent) during these seven outlier 

years. A further investigation of outlier years has shown that the wet-season rainfall, particularly 

the months of June and September, were much wetter during these years than normal 

(Figure 12). Under more natural basin conditions such as those that were present during the last 

AMO warm cycle (AMO2), such wet periods could result in long periods of wetter conditions in 

the basin with high runoff in the ensuing months. Occasional wetter-than-normal wet seasons, 

which could occur during an AMO warm cycle, may be responsible for large increases in runoff 

into Lake Okeechobee. The role of tropical storms and/or hurricanes as possible causes of the 

very wet antecedent conditions during wet seasons in these outlier years needs further 

investigation. 

Monthly distributions of S-65 flow for the AMO2 and AMO3 periods are shown in 

Figure 13. The first box plot each month represents the wetter AMO2 period, while the second 

plot represents the drier AMO3 period. Clearly, there is a big difference between the 

monthly flow distributions. As seen from the box plots, during early dry-season months 

(September–December), the flow data at these key gauges in the Kissimmee Basin show a 

substantial reduction. This change is attributed to the regulation of the upper basin, which 

occurred during the 1963–1969 period, coincidentally around the same time (1960s) that the 

AMO appeared to switch from warm to cool. The change in flow regime, from a hydrograph 

characterized by slow recession to that of a managed flow regime typified by flow spikes, can be 

seen clearly in the daily flow hydrograph at S-65 (Figure 14). Consequently, the attribution of the 

drastic change in LONIN solely due to AMO change may not be warranted, particularly on a 

seasonal basis. 

TRENDS IN MONTHLY DATA 

To identify possible changes in seasonal patterns, monthly values of both rainfall and 

flow variables were analyzed. Figures 15 and 16 present the mean monthly distributions of 

District-wide rainfall and LONIN for the four different AMO periods. A careful evaluation of the 

mean rainfall pattern indicates an apparent shift in the mean in June, with a more significant shift 

in September. Flow values also appear to show a similar shift in August through October. Once 

again, as discussed above, the flow values are impacted by causes other than the AMO. 

Differences in the monthly means of AMO2 and AMO3 periods were tested using a standard 

t-test. Table 5 shows the p-value associated with the t-statistic. In the table, values in bold type 

indicate instances where the p-value is less than the 5 percent significance level. In those 

instances, the null hypothesis that the means are the same between AMO2 and AMO3 may be 

rejected. Table 6 shows the results of the Kolmogorov-Smirnov test, which was conducted to 

compare the empirical distributions of the AMO2 and AMO3 periods for each month. Again, 

bold type indicates instances where the null hypothesis that two distributions are identical may be 

rejected. 

 22
Figure 10. Rainfall-runoff relationship between S-65E and Kissimmee Basin 

rainfall (KissRF). Arrows indicate “outlier” years, which have unusually high 

discharge values. 

Figure 11. Runoff coefficient versus rainfall for the Kissimmee Basin. 

 23
Figure 12. Monthly distribution of rainfall during the seven outlier years (34-35,48-

49, 30-31, 47-48, 53-54, 60-61, and 59-60). Also plotted is the mean monthly 

rainfall (solid line) for the AMO2 period. 

Figure 13. Mean monthly patterns of flows out of Lake Kissimmee at S-65 for 

AMO2 and AMO3 periods. 

 24
Post-Channelization 

Figure 14. Daily flow hydrograph at S-65 indicating a change from natural 

recessions to flow spikes as a result of Upper Kissimmee Chain of Lakes regulation 

during the post-channelization period. 

Figure 15. Mean monthly District-wide rainfall for different AMO periods. 

 25
Figure 16. Mean monthly LOK net inflow volumes for different AMO periods. 

Table 5. p-value for the t-statistic used for comparing means of AMO2 and AMO3. 

Values in bold type indicate variables for that month that have significant differences 

in mean at 5 percent level.

Variable Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Ukiss 0.35 0.94 0.94 0.07 0.33 0.29 0.42 0.63 0.13 0.34 0.18 0.72 

Lkiss 0.26 0.72 0.63 0.04 0.91 0.88 0.73 0.98 0.07 0.11 0.07 0.33 

KissRF 0.31 0.96 0.94 0.05 0.46 0.41 0.49 0.73 0.09 0.23 0.14 0.60 

BigCyp 0.50 0.50 0.60 0.50 0.21 0.42 0.19 0.70 0.01 0.01 0.04 0.10 

EAA 0.07 0.71 0.47 0.28 0.45 0.22 0.61 0.59 0.07 0.01 0.02 0.53 

Everglades 0.63 0.39 0.79 0.75 0.57 0.42 0.07 0.32 0.01 0.00 0.05 0.10 

LOK 0.13 0.76 0.41 0.05 0.87 0.45 0.58 0.88 0.06 0.07 0.02 0.40 

WCA3 0.61 0.31 0.61 0.60 0.65 0.32 0.17 0.95 0.01 0.00 0.03 0.21 

WCA12 0.25 0.32 0.29 0.61 0.51 0.34 0.54 0.67 0.07 0.00 0.03 0.78 

WestAG 0.20 0.68 0.41 0.29 0.62 0.40 0.34 0.97 0.02 0.02 0.01 0.32 

Caloos 0.15 0.86 0.29 0.07 0.82 0.65 0.44 0.52 0.08 0.11 0.02 0.41 

SWCoast 0.29 0.83 0.32 0.11 0.61 0.92 0.68 0.31 0.05 0.09 0.10 0.34 

Browrd 0.50 0.29 0.22 0.84 0.60 0.51 0.37 0.97 0.13 0.00 0.03 0.90 

Dade 0.84 0.25 0.95 0.83 0.92 0.33 0.05 0.27 0.06 0.00 0.05 0.18 

MartSLC 0.11 0.33 0.44 0.12 0.22 0.60 0.59 0.83 0.20 0.01 0.01 0.23 

Pbeach 0.10 0.57 0.33 0.60 0.24 0.33 0.72 0.25 0.15 0.00 0.01 0.53 

Div4rf 0.40 0.96 0.91 0.02 0.34 0.39 0.13 0.71 0.12 0.14 0.11 0.50 

Div5rf 0.16 0.86 0.42 0.08 0.83 0.36 0.14 0.60 0.02 0.03 0.02 0.43 

Div6rf 0.39 0.39 0.54 0.69 0.53 0.51 0.69 0.56 0.14 0.00 0.01 

Rainfall 

Div7rf 0.28 0.46 0.57 0.72 0.20 0.70 0.15 0.34 0.11 0.00 0.38 

0.55 

0.63 

S-65E 0.19 0.76 0.75 0.96 0.35 0.05 0.33 0.52 0.02 0.00 0.00 0.00 

S-65 0.84 0.09 0.67 0.13 0.28 0.00 0.14 0.94 0.02 0.00 0.00 

Flow 

LONIN 0.62 0.62 0.39 0.72 0.04 0.09 0.42 0.53 0.02 0.00 0.03 

0.00 

0.02 

 26
Table 6. p-values for the Kolmogorov-Smirnov test for comparing distributions of 

AMO2 and AMO3. Values indicate variables for that month that have significant 

differences in distribution at 5 percent level. 

Variable Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Ukiss 0.49 0.82 0.71 0.18 0.63 0.61 0.77 0.89 0.17 0.78 0.35 0.97 

Lkiss 0.38 0.60 0.85 0.12 1.00 0.53 0.87 0.44 0.19 0.45 0.32 0.59 

KissRF 0.25 0.75 0.78 0.18 0.97 0.59 0.60 0.48 0.27 0.78 0.27 0.93 

BigCyp 0.39 0.34 0.96 0.80 0.65 0.79 0.06 0.81 0.01 0.01 0.10 0.49 

EAA 0.13 0.29 0.45 0.12 0.57 0.15 0.76 0.67 0.24 0.05 0.01 0.59 

Everglades 0.49 0.48 0.90 0.92 0.98 0.85 0.03 0.77 0.03 0.00 0.20 0.41 

LOK 0.12 0.71 0.83 0.12 0.89 0.45 0.48 0.78 0.19 0.05 0.07 0.62 

WCA3 0.52 0.32 0.69 0.81 1.00 0.85 0.01 0.92 0.04 0.00 0.13 0.25 

WCA12 0.32 0.32 0.84 0.43 0.82 0.40 0.53 0.64 0.13 0.01 0.04 0.45 

WestAG 0.15 0.52 0.32 0.25 0.98 0.35 0.38 0.99 0.16 0.11 0.02 0.84 

Caloos 0.33 0.67 0.13 0.39 0.98 0.55 0.83 0.67 0.34 0.36 0.16 0.69 

SWCoast 0.53 0.75 0.64 0.34 0.55 0.93 0.63 0.10 0.08 0.46 0.55 0.54 

Browrd 0.80 0.63 0.61 0.57 0.68 0.52 0.41 0.67 0.60 0.02 0.06 0.45 

Dade 0.83 0.44 0.88 0.89 0.76 0.74 0.01 0.77 0.13 0.00 0.22 0.57 

MartSLC 0.11 0.18 0.91 0.22 0.27 0.71 0.99 0.97 0.27 0.03 0.06 0.41 

Pbeach 0.15 0.55 0.75 0.18 0.38 0.59 0.27 0.09 0.27 0.01 0.02 0.48 

Div4rf 0.45 0.75 0.56 0.12 0.84 0.59 0.35 0.55 0.37 0.25 0.20 0.99 

Div5rf 0.15 0.55 0.75 0.22 0.94 0.32 0.09 0.52 0.04 0.12 0.09 0.74 

Div6rf 0.69 0.92 0.79 0.77 0.45 0.71 0.38 0.96 0.54 0.00 0.03 

Rainfall 

Div7rf 0.77 0.67 0.65 0.43 0.22 0.47 0.06 0.44 0.32 0.00 0.67 

0.72 

0.94 

S-65E 0.02 0.23 0.30 0.35 0.34 0.01 0.06 0.01 0.06 0.00 0.00 0.00 

S-65 0.00 0.02 0.16 0.22 0.24 0.00 0.00 0.01 0.01 0.00 0.00 

Flow 

LONIN 0.27 0.83 0.94 0.45 0.07 0.29 0.39 0.61 0.08 0.00 0.00 

0.00 

0.00 

 27
COMPARISON OF THE SFWMM MODELING PERIOD WITH THE 

PERIOD OF RECORD 

Modeling an existing period of record allows continuous simulation that represents system 

responses on a daily basis to a wide variety of weather patterns actually experienced in South 

Florida. In addition, the 36-year period of record includes a wide range of extreme wet and dry 

events. The system’s response to these extreme events has been documented and can be related to 

an expected frequency of occurrence or return period. The period includes a sample of the 

most likely weather-related impacts to the system and some very rare, extreme events. The 

likelihood of experiencing a weather pattern beyond the range represented in this period of record 

is very low. 

Table 7 compares the SFWMM modeling period with the AMO phases in the entire period of 

record. About one-third of the 36 years within the SFWMM dataset represents AMO positive 

(warm phase) years and the remaining two-thirds represent AMO neutral or negative (cool phase) 

years. Efforts are ongoing to update the model data through 2005. This will provide 41-year 

simulations that include at least 15 years from the AMO warm phase. The variability and range of 

extremes in the datasets that drive the model are much more important than the AMO phase for a 

given year. 

Table 7. Comparison of AMO periods with the SFWMM period of simulation. 

AMO Period 

Name AMO Phase 

Selected year 

Range within the 

period of record 

Number of 

Years 

Number of years in 

current SFWMM 

modeling period 

(1965–2000) 

AMO1 cool 1895–1925 31 Not used 

AMO2 warm 1926–1969 44 5 

AMO3 cool 1970–1994 25 25 

AMO4 warm 1995–2005 11 6 

The current SFWMM input rainfall dataset covers the geographic region south of Lake 

Okeechobee. The dataset uses hundreds of rain gauges to define the 36-year (1965–2000) daily 

time series for each of the model’s 4-square-mile grid cells. The dataset and SFWMM model 

simulations have not been extended to include years before 1965 because of the relatively low 

density of rain gauges prior to that year, lack of other hydrologic and management data for the 

system, and the fact that many of the Central and Southern Florida Flood Control (C&SF) Project 

facilities were not constructed then. 

For this document, PRISM rainfall data were compared to ascertain how well the 36-year 

“sample” represents the 111-year “population” The 36-year sample is the 1965–2000 period 

currently simulated by the SFWMM; and the population is taken as the entire 1895–2005 period 

of the PRISM dataset. Annual and wet-season rainfall for the entire SFWMD region was 

evaluated. Figure 17 Panel A shows the annual PRISM rainfall data (May–April water year) 

series for the SFWMD region as departures from the mean. In the figure, values are ranked from 

highest to lowest and the 36 years used in the current SFWMM are highlighted. Figure 17 Panel 

B is formatted similarly but uses wet-season (May–October) rainfall data. These figures show 

many wet years in the 36-year dataset, and the distribution is representative of the range that 

occurred during the entire 111-year period. In fact, several of the 36 years in the dataset were 

considerably wetter than were the years 2002–2005. 

 28
Figure 17. (A) Distribution of annual rainfall for the SFWMD (May–April water years, 

1895–2005). (B) Distribution of wet-season rainfall (May–October, 1895–2005).

A 

B

 29
LAKE OKEECHOBEE NET INFLOW 

An important aspect of determining potential effects of increased rainfall on water 

management decisions is the relationship between rainfall rates and the resulting amount of 

surface water flow. This is an issue of special concern in the Lake Okeechobee watershed. In 

recent years, excessive rainfall has produced both sustained high water levels in the Lake and 

consequential large releases of excess water to tide. CERP projects have been proposed and 

initially designed to provide additional capacity throughout the regional system to capture and 

store more of this “excess” water. This added capacity will help to lower Lake levels and reduce 

discharges of water to tide. The concern has been raised that the climatic conditions used to 

design CERP and other projects may not represent a sufficiently wide range of Lake runoff 

conditions. Therefore, an initial analysis was conducted to determine whether the period of record 

used for SFWMM simulations represented a broad-enough range, relative to historical conditions, 

to provide a technically sound basis for future planning efforts. 

Figure 18 illustrates the distribution of historical net inflow to Lake Okeechobee available 

from 1914 through 2005. The solid bars represent the 36-year span used in the current SFWMM 

simulation period (1965–2000), and the striped bars represent the recent, relatively wet five years 

(2001–2005). 

Figure 18. Distribution of annual net inflow to Lake Okeechobee, 

water years 1913–2005 (May–April), where NI = RF – ET + inflows.

 30
These data indicate that the four wettest years used in the 1965–2000 simulation period all 

have larger net inflow than any of the recent very wet, AMO warm-phase years (2001–2005). 

Furthermore, the smallest net inflow year (2000), occurred during the current AMO warm phase. 

This helps to confirm that the SFWMM distribution represents a range of net inflow conditions 

sufficiently broad to evaluate the performance of proposed structural and operational plans. 

Questions have been raised as to whether the dataset used for the SFWMM adequately 

represents conditions likely to occur during the current AMO warm period. Because it is unclear 

how long such a warm period may last, it is important to include both cool and warm period data 

in the modeling period. The period of simulation for the SFWMM is 36 years long, from 1965 to 

2000. Plans are under way to extend this period through 2005. If one applies the same criteria that 

were used to define the AMO phases (AMO1–AMO4), then the current SFWMM simulation 

period includes 11 years of warm-period data, which represents about one-third of the entire 

period. 

A statistical analysis was performed to compare the 1965–2000 period to the previous 

36-year period (1929–1964). Both the t-test for mean and the Kolmogorov-Smirnov test for 

distributions were conducted to characterize significant differences between the two periods. The 

results of this analysis are shown in Table 7 and Table 8. 

In the case of rainfall variables, the SFWMM 36-year simulation period was not significantly 

different from the previous AMO warm period. An exception may have occurred during the 

month of April, when many of variables indicated differences between the two AMO periods. 

Flows at S-65 and S-65E show significant differences during most of the wet season and in the 

early dry-season month of November. This difference may be partly due to regulation of the 

Kissimmee Basin. However, such a change due to management is not relevant for the issue of 2x2 

modeling since the current modeling approach already accounts for hydrologic changes due to 

regulation of the Kissimmee Basin lakes and Kissimmee River. 

 31
Table 7. p-values for t-statistic for comparison of means of rainfall and flow values 

between the 2x2 simulation period (1965–2000) and the preceding period 

(1929–1964). Values in bold type indicate variables for that month that have 

significant differences in mean at 5 percent level. 

Variable Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Ukiss 0.29 0.74 0.61 0.01 0.71 0.76 0.55 0.87 0.11 0.52 0.39 0.41 

Lkiss 0.25 0.66 0.87 0.01 0.43 0.60 0.48 0.69 0.10 0.45 0.18 0.43 

KissRF 0.26 0.71 0.74 0.01 0.62 0.95 0.52 0.81 0.09 0.48 0.31 0.40 

BigCyp 0.20 0.41 0.98 0.28 0.26 0.30 0.26 0.72 0.02 0.37 0.16 0.23 

EAA 0.07 0.27 0.67 0.10 0.88 0.74 0.96 0.70 0.10 0.29 0.19 0.86 

Everglades 0.28 0.41 0.99 0.33 0.66 0.18 0.06 0.36 0.06 0.25 0.30 0.27 

LOK 0.15 0.57 0.66 0.01 0.66 0.75 0.61 0.87 0.09 0.46 0.12 0.56 

WCA3 0.14 0.19 0.72 0.24 0.69 0.27 0.15 0.61 0.06 0.36 0.18 0.45 

WCA12 0.06 0.10 0.36 0.15 0.67 0.22 0.95 0.82 0.16 0.41 0.23 0.62 

WestAG 0.12 0.36 0.71 0.14 0.40 0.43 0.66 0.93 0.03 0.54 0.08 0.44 

Caloos 0.14 0.75 0.60 0.03 0.50 0.53 0.82 0.16 0.07 0.68 0.08 0.45 

SWCoast 0.25 0.85 0.71 0.08 0.38 0.42 0.88 0.04 0.05 0.61 0.16 0.36 

Browrd 0.12 0.10 0.24 0.25 0.70 0.12 0.74 0.50 0.29 0.51 0.23 0.48 

Dade 0.27 0.28 0.97 0.26 0.90 0.18 0.02 0.26 0.26 0.17 0.32 0.43 

MartSLC 0.08 0.20 0.48 0.03 0.86 0.59 0.32 0.69 0.14 0.13 0.07 0.72 

Pbeach 0.05 0.21 0.54 0.12 0.60 0.37 0.91 0.59 0.19 0.17 0.08 0.81 

Div4rf 0.32 0.98 0.68 0.00 0.98 0.88 0.26 0.94 0.06 0.43 0.24 0.45 

Div5rf 0.17 0.62 0.71 0.04 0.95 0.76 0.27 0.61 0.03 0.61 0.14 0.69 

Div6rf 0.18 0.18 0.60 0.21 0.69 0.14 0.93 0.32 0.26 0.18 0.10 

Rainfall 

Div7rf 0.20 0.92 0.37 0.42 0.31 0.05 0.10 0.51 0.41 0.35 1.00 

0.80 

0.99 

S-65E 0.24 0.93 0.70 0.85 0.19 0.04 0.25 0.77 0.01 0.00 0.00 0.00 

S-65 0.79 0.20 0.23 0.08 0.41 0.00 0.16 0.56 0.08 0.00 0.00 

Flow 

LONIN 0.74 0.57 0.34 0.21 0.01 0.19 0.77 0.91 0.00 0.03 0.04 

0.02 

0.03 

 32
Table 8. p-value for the Kolmogorov-Smirnov test for comparing rainfall and flow 

distributions of 2x2 simulation period (1965–2000) and the previous 36-year period 

(1929–1964). Values in bold type indicate variables for that month that have 

significant differences in mean at 5 percent level.

Variable Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Ukiss 0.46 0.46 0.30 0.01 0.66 0.85 0.97 0.30 0.30 0.46 0.30 0.85

Lkiss 0.46 0.66 0.85 0.10 0.66 0.85 0.85 0.66 0.18 0.66 0.66 0.85

KissRF 0.46 0.46 0.46 0.06 0.66 0.85 0.97 0.46 0.46 0.85 0.18 0.85

BigCyp 0.18 0.18 0.85 0.66 0.30 0.30 0.30 0.85 0.10 0.30 0.46 0.85

EAA 0.18 0.10 0.46 0.03 0.85 0.66 0.85 0.97 0.30 0.18 0.10 0.66

Everglades 0.10 0.46 0.97 0.66 0.66 0.66 0.01 0.85 0.30 0.18 0.85 0.66

LOK 0.10 0.46 1.00 0.10 0.30 0.85 0.66 0.97 0.18 0.46 0.46 0.66

WCA3 0.10 0.10 0.85 0.46 0.85 0.66 0.03 0.85 0.18 0.10 0.46 0.85

WCA12 0.10 0.10 0.85 0.10 0.85 0.66 0.85 0.85 0.66 0.46 0.66 0.66

WestAG 0.10 0.18 0.66 0.18 0.66 0.46 0.97 0.66 0.18 0.46 0.18 0.66

Caloos 0.30 0.46 0.46 0.30 0.66 0.97 0.97 0.18 0.18 0.85 0.66 0.85

SWCoast 0.30 0.85 0.85 0.30 0.46 0.66 0.85 0.01 0.18 0.85 0.85 0.66

Browrd 0.18 0.30 0.30 0.30 0.66 0.30 0.85 0.46 0.66 0.30 0.66 0.97

Dade 0.30 0.30 0.30 0.30 1.00 0.46 0.01 0.85 0.85 0.18 0.66 0.97

MartSLC 0.03 0.18 0.66 0.06 0.66 1.00 0.85 1.00 0.30 0.18 0.66 0.66

Pbeach 0.10 0.18 0.97 0.10 0.97 0.46 0.66 0.46 0.46 0.30 0.18 1.00

Div4rf 0.66 0.46 0.30 0.01 0.85 0.97 0.66 0.85 0.10 0.46 0.18 0.97

Div5rf 0.30 0.18 0.85 0.18 0.85 0.97 0.30 0.97 0.10 0.46 0.66 0.46

Div6rf 0.10 0.46 0.85 0.18 0.46 0.30 0.46 0.85 0.46 0.18 0.46 

Rainfall 

Div7rf 0.46 0.85 0.85 0.18 0.66 0.46 0.06 0.66 0.46 0.18 0.97 

1.00

0.97

S-65E 0.01 0.18 0.06 0.46 0.46 0.03 0.01 0.18 0.10 0.00 0.00 0.00

S-65 0.02 0.04 0.04 0.08 0.21 0.00 0.00 0.02 0.02 0.00 0.00 

Flow 

LONIN 0.03 0.85 0.85 0.18 0.10 0.46 0.46 0.85 0.03 0.10 0.01 

0.00

0.00

 33
V. USE OF CLIMATE OUTLOOK IN OPERATIONS 

Lake Okeechobee Regulation Schedule Development 

Seasonal and multi-seasonal climate outlooks have also been incorporated into the federal 

operational rules for Lake Okeechobee. Traditional rule curves (or regulation schedules) for 

reservoir operations are “static” in the sense that operational decisions are made only when the 

reservoir stage crosses a predetermined curve. In 1998, the SFWMD developed a “dynamic” 

regulation schedule for Lake Okeechobee known as Water Supply and Environment (WSE) that 

incorporates not only the seasonal and multi-seasonal climate outlooks, but also the near-term 

(two-week) forecasts of tributary inflows into the Lake. The schedule, adopted by the USACE in 

July 2000, consists of (a) a set of regulation schedule lines that define different operational zones; 

and (b) decision trees that support the process for making discharges based on forecasts of 

inflows and climate outlook. The climate-based operational guidelines as incorporated into the 

WSE regulation schedule have emerged as a highly desirable approach for Lake Okeechobee 

water management. 

Diagrams representing the two parts of the WSE schedule are presented in Figure 19. 

The decision to hold water in the Lake or make releases to tide is primarily determined by 

Lake water levels, the time of year, and conditions in other parts of the regional systems 

(Figure 19 Panel A). These water levels were established primarily based on historical data and 

experience with Lake operations over the past several decades. Meteorological conditions, 

including forecasts and long-term trends, are considered in the operational decision tree 

(Figure 19 Panel B) to determine whether discharges to tide are necessary. 

WSE was the first regulation schedule in South Florida that formally and explicitly utilized 

seasonal and multi-seasonal climate outlooks as guidance for regulatory release decisions. The 

WSE schedule has been modified recently to use the AMO and ENSO states in estimating the 

Lake Okeechobee Net Inflow Outlook (LONINO) for the future 6 to 12 months. Adoption of a 

decision tree with climate outlook as a component of the WSE schedule was a unique feature in 

the operation of Lake Okeechobee, and was a major shift from the traditional rule curves that are 

used to operate large reservoirs elsewhere in the country and the world. 

During WSE development, the SFWMD conducted a performance test using available Lake 

net inflow data beginning in 1914. The results of that effort were presented at public meetings 

prior to the adoption of WSE. Although the 1914–1964 period appeared to be somewhat wetter 

than the 1965–2000 period documented in the Environmental Impact Statement (EIS), the WSE 

schedule still outperformed other regulation schedules considered at that time. This evaluation 

was based on the analyses of the type shown in Figure 20, which consider effects on water 

supply, flood protection, and environmental systems. 

Overall operational strategy was simulated to improve conditions with respect to five major 

competing water management objectives (Figure 20). This improvement was estimated for the 

climatological regime during the 1965–1995 period by five performance measures: (1) a decrease 

by 3 percent in the undesirable Lake Okeechobee water level events for the Lake littoral zone, 

(2) an increase by approximately 4 percent of the Lake Okeechobee Service Area water supply 

needs being met during drought years, (3) improved hydropattern for the Everglades, 

(4) a decrease in the number of times undesirable high discharge criteria were exceeded for the 

estuaries, and (5) lower Lake water levels during the peak of the hurricane season. 

 34
A 

 B 

Figure 19. (A) Water level criteria used to identify different lake management zones and 

potential needs for water releases based on water supply, flood control, and environmental

considerations. (B) Operational decision tree used to help determine timing and volume of 

water releases based on water levels, estimated inflows, and climate forecasts.

 35
Figure 20. Multi-objective tradeoff analysis showing improvement with 

climate-based operation of Lake Okeechobee. 

When the regulation schedule was evaluated, it was unclear whether the apparent trend to a 

drier hydrologic condition was due to changes in regional-scale precipitation patterns associated 

with drainage and development in South Florida (Chen et al., 2001; Mattocks and Trimble, 2000; 

Mattocks et al., 1999; Pielke et al., 1999), natural climatic variability (Enfield et al., 2001; Zhang 

and Trimble, 1996a,b; Trimble et al., 1998b,c; Trimble and Trimble, 1998), long-term global 

climate change, or rainfall–runoff relationships altered by infrastructure changes. As data were 

much sparser before 1965, it was not clear whether wetter periods such as the 1940s and 1950s 

would re-occur or whether South Florida’s declining rainfall trend would continue. 

During the 1970s and 1980s, the remnant Everglades experienced prolonged undesirable dry 

conditions. From an ecological perspective, it seemed highly desirable for any new Lake schedule 

to route regulatory discharges southward to the Everglades at lower Lake water levels for the 

benefit of the littoral zone, the Everglades, and the Caloosahatchee and St. Lucie estuaries. 

WSE was designed to achieve these goals. To account for a possible return to persistent, much 

wetter conditions, WSE was designed to allow regulatory releases to tidewater at stages that were 

almost 2 feet lower than the previous schedule allowed. 

 36
The SFWMD and USACE have further modified the WSE schedule to incorporate the 

potential presence of a wetter climatic regime that appears to have begun in the mid-1990s. In 

2003, after four years of WSE operation, the WSE decision trees were adjusted to improve 

performance during wet periods. The re-evaluation led to adjustments of several WSE parameters 

including the LONINO (USACE, December 2004). 

The USACE secured several other minor temporary deviations (2004, 2005, and 2006) to the 

Water Control Plan to allow releases in pulses up to Level 1, if necessary, under conditions that 

the WSE decision tree called for no releases. These temporary deviations are identified in the plan 

as tools that can be used as adaptive management strategies to address unusual conditions. 

Completion of the 1999 C&SF Project Comprehensive Review Study (Restudy) led to 

the development of CERP (USACE and SFWMD, 1999). When CERP planning began in the 

mid-1990s, there was little published information on relationships between global climate states 

and South Florida rainfall. As CERP planning continued through the late 1990s and predictive 

relationships were developed, it was suggested that climate outlook information should be used to 

help guide the design and operation of some CERP components. Those components included the 

regional Lake Okeechobee Aquifer Storage and Recovery (ASR), the Everglades Agricultural 

Area (EAA) Storage Area, and the Lake Okeechobee Regulation Schedule study. 

POSITION ANALYSIS 

Seasonal and multi-seasonal operational planning of major water resources systems requires a 

careful evaluation of likely future scenarios of water and environmental conditions that influence 

management objectives. It is common practice to use computer simulation models to determine 

likely outcomes of both short- and long-term management options. The SFWMD uses position 

analysis as a form of risk analysis that can forecast uncertainties associated with a specific 

operating plan for a basin over a period of many months conditioned on the current state 

(e.g., reservoir storages) of the system (Hirsh, 1978; Smith et al., 1992; Tasker and Dunne, 1997; 

Cadavid et al., 1999). The District relies on generating a large number of possible traces with 

durations of one or more seasons for the hydrologic variable of interest (e.g., reservoir stages or 

flows), using the same initial conditions and broad ranges of meteorological conditions that may 

occur in the future but cannot be forecast accurately. Future likely meteorological conditions may 

be derived using such methods as extended streamflow prediction (Day, 1985), historical data, 

and stochastic simulation models (Tasker and Dunne, 1997). 

Ideally, to include effects of long-term climate cycles such as AMO and PDO, the dataset 

should span 100 years or more. The 36-year dataset that is generally available for the South 

Florida system allows consideration of only shorter-term phenomena, such as ENSO and sunspot 

cycles, that have return frequencies of 10 years or fewer. 

Cadavid et al. (1999) defined the terms Conditional Position Analysis (CPA) and the 

Unconditional Position Analysis (UPA) depending on whether or not the estimated risks 

incorporate climate outlook (e.g., La Niña conditions that affect the likely inflows to the reservoir 

system). The objective of the CPA is to estimate the risks of the future response of the system 

given both the current state and the future climate forecast. The methodology adopted for CPA 

(Cadavid et al., 1999) follows a procedure described by Croley (1996). Croley’s method includes 

an optimization procedure that provides a set of weights for different traces, and these weights 

reflect information provided by the climate forecasts. For example, if an El Niño condition is 

predicted and larger reservoir inflows are possible during this condition, the traces that are 

associated with El Niño years would have larger weights. 

During the past decade, position analysis concepts developed by the District have been used 

with increasing effectiveness to assess risks associated with seasonal and multi-seasonal 

 37
operations of the water management system and communicate the projected outlook to decision 

makers, agency partners, stakeholders, and the public. While the District has used position 

analysis chiefly to project the expected stage of Lake Okeechobee, it has also used the technique 

for other impoundments, including the WCAs. Monthly position analysis has become an 

important tool for making operational decisions that may have implications for multiple seasons. 

VI. INCORPORATION OF CLIMATIC VARIABILITY IN 

WATER RESOURCES PLANNING 

Incorporating uncertain climatic tendencies into a long-range planning process calls for 

careful analysis to allow balance among risks, costs, and benefits. It may be expensive to provide 

areas for open storage for flood control, for example, based on an expectation that the next 

several decades will be wetter. Construction and operation of such storage facilities may also 

cause significant long-term environmental impacts. Plans must also consider the possibility that 

droughts may occur during an overall wetter climatic regime and, therefore, include provisions 

for long-term carryover water storage capacity. 

Figure 18, which shows the distribution of annual net inflow to Lake Okeechobee for the 

period from 1913 to 2005, provides a general indication of relative inflows to the major regional 

storage facilities planned in CERP, and can be used as a basis to estimate how cost effectively the 

proposed CERP projects may address hydrometeorological extremes. This figure indicates that 

during the 1913–2005 period of record, there are only five years (roughly 5 percent of this period) 

when the volumes of inflow had the potential to exceed the inflow extremes reflected in the 

1965–2000 period of record that was used in CERP planning studies. These five years 

experienced extreme inflows, which exceeded the highest flows of the 1965–2000 period, 

and ranged from approximately 5.0 to 5.9 million acre-feet (ac-ft). These values were up to 

20 percent greater than the maximum inflow of 4.9 million ac-ft that occurred during 1969 within 

the 1965–2000 planning period. As previously noted, these extreme flows occurred before major 

water control facilities were completed in the Upper Kissimmee Basin and Kissimmee River 

floodplain. These facilities provide improved capability to control water levels and flows, and 

could have allowed water managers to better regulate the rate of inflow to Lake Okeechobee, 

providing more time for redistribution, evaporation, and infiltration of excess surface water. 

Recent cost estimates for the three largest reservoirs contemplated in CERP total 

approximately $1 billion. For discussion purposes, assuming that capturing this additional peak 

inflow would require a linear increase in reservoir capacity and construction costs, then it would 

cost approximately $200 million more to accommodate an additional inflow that could be 

reasonably expected to occur once every 20 years (USACE and SFWMD, 1999). Facilities 

proposed in CERP typically have a design life span between 50 and 75 years. Therefore, such 

extreme inflow conditions may occur only two to three times over the life of the project. In this 

example, regional facility planning should balance the cost ($200 million) for this protection from 

two or three extreme inflow events against potential damages or losses. The planning and design 

process could involve engineers and water managers in a public process, with input from 

concerned citizens and stakeholders, to determine whether the increased capacity would be cost 

effective over the life cycle of the project. 

 38
There are many ways that water resources planning for future changes to the system may account 

for uncertainties caused by natural climatic variability such as those attributed to AMO. The 

methods that are being used by the District are: 

1. Sensitivity analysis of planning simulations 

2. Continual expansion of the simulation period to include more AMO warm-phase years 

3. Adaptive management in facility planning and implementation 

SENSITIVITY ANALYSIS 

Several efforts have been undertaken to investigate the implications of climate shifts in South 

Florida. This section explains a sensitivity analysis that was conducted to ascertain the impacts of 

climate shifts. The District used the SFWMM in 1996 to simulate system performance for 

precipitation values that were 10 percent higher and lower than the base values. Results are 

shown in Figure 21. A 10 percent shift in average rainfall (i.e., ± 5 inches) is considered as an 

extreme change. The average rainfall District-wide is approximately 52.1 inches. The 

District-wide mean increases during the AMO warm phase by 1.8 inches to about 53.9 inches and 

decreases during the cool phase by 1.5 inches to 50.6 inches. The change of ±10 percent, 

therefore, represents a shift approximately three times greater than can be expected to occur due 

to temperature cycle effects. 

Figure 21. Results of SFWMM model simulation to estimate lake stage in Lake 

Okeechobee using actual precipitation data for the period from 1965 to 1995 

(CB2.7), precipitation data increased by 10 percent (RF_M10), and precipitation 

decreased by 10 percent (RF_P10).

 39
In this simulation, the base alternative (CB2.7) included the 1990 land uses. A second 

alternative (RF_M10) was based on the same period of simulation with the rainfall decreased by 

10 percent, whereas the third alternative (RF_P10) had rainfall increased by 10 percent. This 

study substantiated the importance of including climate forecasts and climate shift indicators in 

future model development. As a result, subsequent model development integrated climatic 

variability as a key parameter and sensitivity analyses as a standard of performance. While this 

study shows the system responses to large changes in rainfall in the tributary basin, it may not be 

practical or feasible to plan for such changes that have large uncertainties. A similar sensitivity 

analysis has been conducted to investigate implications of potential sea level rise. 

CONTINUAL EXPANSION OF SIMULATION PERIOD OF SFWMM 

The choice of a period of simulation in any hydrologic model requires careful analysis of 

temporal and spatial density of data for rainfall, evapotranspiration, and required discharges at the 

boundaries of the model. Sparseness in the data, both temporally and spatially, can introduce 

unrealistic trends in long-term modeling results. Therefore, modelers typically evaluate the 

modeling objectives with respect to data availability to determine reasonable beginning and end 

dates for historical information. 

During the early 1980s, the SFWMM model developers originally chose to use an input 

dataset from the 1969–1982 period to simulate alternative structural and operational changes to 

the system, because the period represented the most accurate and complete combination of 

hydrological, meteorological, and physical system information available at the time. The model 

datasets for the Lower East Coast Regional Water Supply Plan during the early 1990s were 

increased to allow simulations for rainfall years 1965–1990. Before the start of Everglades 

Restudy modeling in the mid-1990s, the 1991–1995 period was added. Further evaluations of the 

WSE schedule and CERP have used the current 36-year simulation period (1965–2000). 

Lack of sufficient spatial coverage of rain gauges and weather stations makes it impractical 

expand the range of years used in simulations to earlier years (prior to 1965). However, District 

modelers are continually investigating methods that allow simulations to include more years 

during both drier and wetter periods. Because proposed infrastructure changes will be 

implemented over decades, the District will be able to evaluate future system changes with 

datasets that include more wet years, if the current AMO warm phase and above-normal rainfall 

conditions continue. 

ROLE OF ADAPTIVE MANAGEMENT IN PLAN IMPLEMENTATION 

The SFWMD uses an adaptive management approach to incorporate new information about 

climate and hydrologic conditions in South Florida into regional modeling for facility planning 

and operations. Information obtained from scientific research and monitoring is evaluated by 

District scientists and managers, and used as a basis to modify or adjust operational policies and 

procedures and potentially to redesign projects and facilities. Water managers use adaptive 

principles as a means to test or experiment with new concepts and effectively “learn by doing.” 

The planning and implementation of projects associated with CERP over a period of decades 

makes adaptive management an effective means for incorporating climatic variability. 

To apply adaptive management effectively, system responses must be continually monitored 

and management actions adjusted to ensure that the desired outcomes are achieved. Application 

of adaptive management principles is especially appropriate, and often required, in situations 

where there is a substantial amount of uncertainty regarding how management activities affect 

hydrologic conditions. 

 40
Effective adaptive management involves a sequenced approach to information types and 

analysis procedures, including: 

• Develop hypotheses about ecosystem structure and function, and relationships between 

particular management goals and actions and anticipated system responses. 

• Identify information needed to test hypotheses and assess overall system responses. 

• Conduct field operations or tests to manipulate/control/or modify water management 

system features. 

• Perform effective monitoring and evaluation of hydrologic and system responses in 

response to natural or induced changes. 

• Conduct research necessary to investigate/define/redefine hypotheses. 

• Implement a process to systematically modify plans features or operations based on 

monitoring data and measured outcomes. 

Modeling, an important tool used by the SFWMD throughout the adaptive management 

process, provides a means to rapidly and relatively inexpensively develop, test, and screen 

hypotheses that can help identify modifications or design features most likely to produce the 

desired results, and determine which aspects are best tested in field-scale experiments, which 

features of the system may be most important to monitor, and what are the likely effects of 

proposed changes on system components or operations. In the case of climatic variability, models 

can be used to simulate alternative future rainfall conditions of higher rainfall or to effectively 

extend the period of hydrologic simulations backward in time to include additional years in an 

AMO warm phase. 

Adaptive management is a cornerstone of CERP, whose implementation will span decades. 

Projects designed and constructed under CERP are required to incorporate adaptive management 

considerations within their operating rules. The CERP project designs and their actual operation 

are evaluated as a composite on a five-year cycle, to test the assumptions that were originally 

used in their development and determine whether changing conditions or improved knowledge 

identify the need for change. Climatic indicators, trends, and global warming issues will play a 

key role in these overall system reviews. 

Modeling is central to CERP adaptive management, as shown in Figure 22. System-wide 

performance measures are defined by scientists participating in the restoration process and 

expressed in quantitative hydrologic terms. These performance measures are provided to the 

modelers and translated into a form that can be generated by output files from the regional 

computer models. Performance measures may be further refined based on results of research or 

monitoring studies. The regional models are used to simulate the effects of various CERP projects 

and alternative scenarios. The consequent model results, expressed in terms of the hydrologic 

performance measures, are then evaluated by Everglades scientists. The final step is an overall 

assessment through the RECOVER process to determine whether the goals of CERP are being 

achieved by the completed projects. This assessment may result in suggested changes to the 

projects themselves, to the monitoring program, and/or to the models and performance measures. 

 41
Figure 22. Geographical locations of AMO, PDO, and ENSO 

teleconnections related to schematic diagram of CERP adaptive 

management, indicating the central role of modeling in this process. 

V. SUMMARY AND CONCLUSIONS 

The choice of climatic conditions used for planning and operations of complex water 

resources systems involving multiple objectives is an important consideration for success of 

future projects. Climate change and variability due to natural and anthropogenic causes are not 

fully understood and their prediction carries considerable uncertainties. Climate change and 

variability, which always have been of interest to the SFWMD, have become increasingly 

important during the last two decades. Applied research conducted and supported by the SFWMD 

has been published in peer-reviewed literature and has been widely recognized by other 

researchers, agencies, and institutions. Results from these studies have been selectively 

incorporated into SFWMD water resource modeling, planning, and operational programs. 

ENSO, AMO, and PDO are large-scale climatic indicators that have implications for 

water resources and planning in South Florida. Of these three indicators, ENSO, which follows a 

three- to seven-year cycle, has the strongest effect on South Florida climatic conditions, and has 

received the most study. The PDO may last for decades and affects South Florida weather in a 

manner similar to ENSO, but with much less influence. The AMO has weaker effects than ENSO, 

but the cycles could span several decades. The AMO warm phase is associated with slightly 

higher, but more variable, rainfall conditions in South Florida. 

 42
Research on AMO and PDO influences indicates a significant variability in the periodicity, 

duration, and magnitude of these multidecadal climatic indicators and their effects on South 

Florida weather. Furthermore, the relationships between these indicators and regional weather 

patterns or rainfall conditions in South Florida cannot be accurately predicted. Hydrologic 

variation not only changes the amount of rainfall but also the spatial and temporal distribution. 

The SFWMD has incorporated consideration of climate trends into its modeling and 

operations decision-making processes. Notable examples include application of the WSE 

schedule and position analysis techniques to guide the regulation of water releases into and out of 

Lake Okeechobee. The SFWMD is also proceeding cautiously to address long-term climate 

changes in its planning processes through an adaptive management approach, collecting new data 

and periodically reviewing project designs and operations based on new information. 

The SFWMD uses complex regional models effectively as tools to aid in water resources 

planning and management in conjunction with standard engineering practices. Its regional model, 

the SFWMM, incorporates a period of the South Florida hydrologic record (1965–2000) that 

includes a broad range of wet and dry years. This period of record is shown to include years with 

both excess and deficient rainfall that are comparable in magnitude and duration to extreme 

conditions that occurred prior to 1965. Although it would be desirable to expand the hydrologic 

basis of the SFWMM to include more years from the AMO warm phase, especially during the 

period from 1927 to 1964, there is a lack of hydrologic data and operational parameters for the 

water management system before 1965. The SFWMD will continue to expand the modeling 

period forward to include the current AMO warm period as new data become available. 

By periodically extending the simulation period, the SFWMD can continually incorporate 

recent climate trends and re-evaluate proposed infrastructure changes, including CERP projects, 

that will be designed and constructed over a period of several decades. This adaptive approach 

follows standard engineering and operational planning practices and provides a means to reduce 

the risks of facility implementation when climate changes are inherently uncertain. 

The SFWMD has also effectively incorporated the potential effects of future climate shifts 

and variability into operations of the regional water management system. The use of climate 

outlooks as a consideration for regulation of water levels in Lake Okeechobee is an example of 

this effort. Moreover, the SFWMD continuously re-evaluates the implementation of this WSE 

regulation schedule to include adjustments that account for changes in climatic indicators such as 

ENSO and AMO. 

The SFWMD continues to rely on timely information about climate patterns and change for 

planning, design, and operations to meet its mission to manage and protect water resources of the 

region by balancing and improving water quality, flood control, natural systems, and water 

supply. 

 43
VI. RECOMMENDATIONS 

Specific recommendations using long-term climate forecasts in District planning and 

operations, not necessarily in order of importance, are as follows: 

1. Continue to incorporate new climatologic data into its modeling efforts to reflect 

developing trends in climate change. 

2. Investigate alternative methods that can be used to extend the modeling period 

backward to incorporate climatic conditions that existed before 1965. 

3. Further refine the analysis of rainfall relationships to the volume of runoff into Lake 

Okeechobee, especially to examine specific antecedent conditions that occurred prior 

to large inflow events during the 1930s, 1940s, and 1950s, and the relative roles of 

tropical storms/hurricanes and water management practices. 

4. Review the adaptive management approach to ensure that climatic variability is 

adequately addressed in CERP implementation. Careful monitoring and evaluation 

during their design, construction, and operational phases, in conjunction with 

information derived from continued hydrologic monitoring and improved climate 

change predictions methods, will be used to refine and modify these projects as 

necessary to address changing conditions. 

5. Explore the use of synthetically generated rainfall data that account for climate shifts 

due to accepted teleconnections such as AMO and ENSO. 

6. Continue the SWFMD climate research program to investigate further the utility of 

global climate teleconnections for improving the management of existing and 

proposed water resources facilities. Results of this applied research should be 

included, as appropriate, in the development of operating plans. 

 44
VII. REFERENCES 

Barnston, A.G., M.H. Glantz and Y. He. 1999. Predictive Skill of Statistical and Dynamical 

Climate Models in SST Forecasts During the 1997-1998 El Niño Episode and the 1998 La 

Niña Onset. Bulletin of the American Meteorological Society, 80(2):217-243. 

Cadavid L.G., R. Van Zee, C. White, P. Trimble and J. Obeysekera. 1999. Operational Hydrology 

in South Florida Using Climate Forecasts. American Geophysical Union, 19th Annual 

Hydrology Days. Colorado State University, August 16-20, 1999. ed. H.J. Morel Seytoux. 

Hydrology Days Pub., Atherton, CA. 

Chen, S.B., B. Albrecht, J. Tenerelli, J. Gottschalk and P.J. Trimble. 2001. Studies of 

Atmospheric Forcing Over South Florida Using a High Resolution Numerical Model and 

Radar. 2001 Florida Bay Conference. 

Croley T.E. 1996. Using NOAA's New Climate Outlook in Operational Hydrology. Journal of 

Hydrologic Engineering. ASCE, 1(3): 93-102. 

Daly, C., Gibson, W.P., M. Doggett, J. Smith and G. Taylor. 2004. Up-to-Date Monthly Climate 

Maps for the Conterminous United States. Proc., 14th AMS Conf. on Applied Climatology, 

84th AMS Annual Meeting Combined Preprints, Amer. Meteorological Soc. Seattle, WA. 

January 13-16. Paper P5.1, CD-ROM. 

Day, G.N. 1985. Extended Streamflow Forecasting Using NWSRFS. Journal of Water Resources 

Planning and Management. ASCE, 111(2): 157-170. 

Enfield, D.B. and L. Cid-Serrano, 2006. Projecting the Risk of Future Climate Shifts. 

International Journal of Climatology 26(7): 885-895.

Enfield, D.B., A. Mestas Nunez, and P.J. Trimble. 2001. The AMO and Its Relationship to 

Rainfall and River Flow in the Continental U.S. Geophysical Research Letter, vol. 28, May. 

Gray W.M., J.D. Sheaffer and C.W. Landea. 1997. Climate Trends Associated with Multidecadal 

Variability of Atlantic Hurricane Activity: Hurricanes, Climate and Socioeconomic Impacts. 

Edited by H.F. Diaz and R.S. Pulwarty. Springer, Berlin. 15-53. Hanson, K. and G.A. Maul, 

1991. Florida Scientist, 54(3/4):160-168. 

Gray, S.T., L.J. Graumlich, J.L. Betancourt and G.D. Pederson. 2004. A Tree-Ring Based 

Reconstruction of the Atlantic Multidecadal Oscillation Since 1567 A.D. Geophysical 

Research Letters, 31:L12205, doi:10.1029/2004GL019932. 

Hewett, R., J. Leuchner and P.J. Trimble. 2000. Discovering Hydrologic Forecasting Rules for 

Water Management: A Preliminary Result. Proceedings of the 5th International Conference on 

Computer Science and Informatics. 

IRI. 2002. Final Report on the Workshop on Applicable Methods for Use of Seasonal Climate 

Forecasts in Water Management. November 6-8, Palisades, New York. 

Kolen, J. and R. Hewett. 2000. Prediction of Lake Inflows with Neural Networks. Proceedings of 

IEEE Systems, Man, Cybernetics. Tennessee. 

Loucks, D.P. 1992. Water Resource Systems Models: Their Role in Planning. Journal of Water 

Resources Planning and Management. 118 (3): 214-223. 

 45
Mattocks, C.A. and P.J. Trimble. 2000. Simulation of the Anthropogenic Impacts to the Regional 

Climatic Patterns of Central and Southern Florida. Speech at the Greater Everglades 

Ecosystem Restoration (GEER) Science Conference. December 11-15, 2000, Naples, FL. 

Mattocks, C.A., P.J. Trimble, M. Hinton, B. Trimble and M. Pietrucha. 1999. Simulation of 

Anthropogenic Generated Microclimates over the Florida Peninsula and their Impact on 

Florida Bay Water Cycle. Florida Bay and Adjacent Marine Systems Conference. 

Mestas-Nunez, A.M. and Enfield, D.B., 2003. Investigation of Intra-seasonal to Multi-decadal 

Variability in South Florida Rainfall. Atlantic Oceanographic and Meteorological Laboratory 

(83 pp). Online at http://www.aoml.noaa.gov/phod/docs/Mestas_Enfield_FR_2003.pdf, 

October 24, 2006. 

Obeysekera J., P. Trimble, L. Cadavid and C. White. 2000. Use of Climate Outlook for Water 

Management in South Florida, U.S.A. Proceedings of the Engineering Jubilee Conference. 

University of Peradeniya, Sri Lanka, June. 

Pielke Sr., R.A., R.L. Walko, L.T. Steyaert, P.L. Vidale, G.E. Liston, W.A. Lyons and T.N. 

Chase. 1999. The Influence of Anthropogenic Landscape Changes on Weather in South 

Florida. Monthly Weather Review, 127 (7): 1663–1673. 

SFWMD. 1996. Climate Change and Variability: How Should the District Respond. Draft 

Report. South Florida Water Management District, Planning Department with Information 

Systems and Operations Divisions. West Palm Beach, FL. 11 pages and appendix. Online at 

http://www.sfwmd.gov/org/pld/hsm/pubs/ptrimble/solar/clim1.pdf, October 24, 2006. 

Shih, G. 1983. Data Analysis to Detect Rainfall Changes in South Florida. Technical 

Memorandum #DRE-165, South Florida Water Management District, West Palm Beach, FL. 

Smith, J.A., G.N. Day and M.D. Kane. 1992. Non-Parametric Framework for Long-Range 

Streamflow Forecasting. Journal of Water Resources Planning and Management. ASCE, 

118(1): 82-92. 

Tasker, G.D. and P. Dunne. 1997. Bootstrap Position Analysis for Forecasting Low Flow 

Frequency. Journal of Water Resources Planning and Management. ASCE, 

November/December. 

Trimble, P. 1997. Workshop on Climate Variability and Water Resource Management in 

Southeastern United States. Vanderbilt University. Nashville, TN, June 25-27 (74 pp). Online 

at http://www.ghcc.msfc.nasa.gov/regional/jun97wkshp.pdf, August 31, 2006. 

Trimble, P.J. and B.M. Trimble. 1998. Recognition and Predictability of Climate 

Variability within South-Central Florida. 23rd Annual Climate Diagnostic and 

Prediction workshop, Rosenstiel School of Marine and Atmospheric 

Science. University of Miami, Florida, October 26-30. Online at 

http://www.sfwmd.gov/org/pld/hsm/pubs/ptrimble/solar/workshop/cpc_paper.htm, October 

24, 2006. 

 46
Trimble, P.J., E.R. Santee and C.J. Neidrauer. 1998a. Preliminary Estimate of Impacts of 

Sea-Level Rise on Regional Water Resources of Southeastern Florida. Proceedings of the 

International Coastal Symposium, Journal of Coastal Research. Special Issue, No. 26 

Trimble, P.J., E.R. Santee and C.J. Neidrauer. 1998b. Special Report. A Refined 

Approach to Lake Okeechobee Water Management: An Application of Climate 

Forecasts. South Florida Water Management District (73 pp). Online at 

http://www.sfwmd.gov/org/pld/hsm/pubs/ptrimble/solar/report/report.pdf, October 24, 2006. 

Trimble, P.J., E.R. Santee and C.J. Neidrauer. 1998c. Including the Effects of Solar 

Activity for More Efficient Water Management: An Application of Neural 

Networks. Second International Workshop on Artificial Intelligence Applications 

in Solar-Terrestrial Physics. Sweden, July. Online at 

http://www.sfwmd.gov/org/pld/hsm/pubs/ptrimble/solar/final_dec3.pdf, October 24, 2006. 

Trimble, P.J., T.B. Obeysekera, L.G. Cadavid and E.R. Santee. 2000. Seasonal to Multidecadal 

Climate Variability and the Contribution to South Florida Hydrology. Greater Everglades 

Ecosystem Restoration Conference. December 12-15. 

Trimble, P.J., T.B. Obeysekera, L.G. Cadavid and E.R. Santee. 2006. Applications of Climate 

Outlooks for Water Management in South Florida. In Climate Variations, Climate Change, 

and Water Resources Engineering. Edited by Jurgen D. Garbrecht and Thomas C. 

Piechota.ASCE/EWRI. Reston, VA. 

USACE and SFWMD. 1999. The Central and Southern Florida Flood Control Project 

Comprehensive Review Study Final Integrated Feasibility Report and Programmatic Impact 

Statement (PEIS). South Florida Water Management District, West Palm Beach, FL. Online 

at http://www.evergladesplan.org/pub/restudy_eis.cfm#mainreport, October 26, 2006. 

Willet, H.C. 1953. Atmospheric and Ocean Circulations as Factors in Glacial-Interglacial 

Changes of Climate. In Climate Change. Harvard University Press. Cambridge, MA, 51-71. 

Zhang, E. and P.J. Trimble. 1996a. Predicting Effects of Climate Fluctuations for 

Water Management by Applying Neural Networks. World Resource Review, 8(3) (17 pp). 

Online at http://www.sfwmd.gov/org/pld/hsm/pubs/ptrimble/prediction/prediction.htm, October 

24, 2006. 

Zhang, E. and P.J. Trimble. 1996b. Forecasting Water Availability by Applying 

Neural Networks with Global and Solar Indices. Proceedings of the 

Sixteenth Annual American Geophysical Union. April. Online at 

http://www.sfwmd.gov/org/pld/hsm/pubs/ptrimble/forecast/forecast.htm, October 24, 2006. 

Zhang, R. and T.L. Delworth. 2005. Impact of Atlantic Multidecadal Oscillations on India/Sahel 

Rainfall and Atlantic Hurricanes. Nature. November. 

 47