U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009-5010

Prepared in cooperation with the

St. Johns River Water Management District

Relations between Municipal Water Use 

and Selected Meteorological Parameters 

and Drought Indices, East-Central and 

Northeast Florida 

-10

-5

0

5

10

15

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

MONTH

DIFFERENCES BETWEEN 

AVERAGE

MONTHLY AND DAILY WATER-USE

RATES (1992-2006), IN PERCENT

Relations between Municipal Water Use 

and Selected Meteorological Parameters 

and Drought Indices, East-Central and 

Northeast Florida

By Louis C. Murray, Jr.

Prepared in cooperation with the 

St. Johns River Water Management District

Scientific Investigations Report 2009–5010

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Murray, L.C., Jr., 2009, Relations between Municipal Water Use and Selected Meteorological Parameters and Drought 

Indices, East-Central and Northeast Florida: U.S. Geological Survey Scientific Investigations Report 2009–5010, 31 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................2

Acknowledgments................................................................................................................................4

Methods of Investigation..............................................................................................................................4

Data Collection and Analyses.............................................................................................................4

Water Use .....................................................................................................................................4

Meteorological Parameters.......................................................................................................4

Drought Indices............................................................................................................................6

Statistical Methods...............................................................................................................................8

Relations between Water Use and Meteorological Parameters/Drought Indices.............................8

Trends .....................................................................................................................................................8

Single-Variant Regression Analyses ...............................................................................................13

Influence of System Memory...................................................................................................15

Nonlinear Relations...................................................................................................................18

Multivariant Regression Analyses...................................................................................................19

Predictive Analyses............................................................................................................................21

Summary........................................................................................................................................................23

Selected References...................................................................................................................................25

Glossary.........................................................................................................................................................26

Appendix—Amounts of Water Treated and Number of Metered Connections at Selected 

Municipal Water-Treatment Plants/Service Areas, 1992-2006.........................................27-31

Figures

1. Map showing locations of selected municipal water-treatment plants and National

 Oceanic and Atmospheric Administration rainfall stations in east-central and 

 northeast Florida...............................................................................................................................3

2. Plot showing residential and commercial water uses at Gainesville Regional 

 Utilities, 1992-2006............................................................................................................................6

3. Graph showing monthly precipitation for 1959-2006 and 1992-2006.........................................7

4. Plots showing total and metered water-use rates for Jacksonville Electric 

 Authority, 1992-2006 .......................................................................................................................10

5. Graphs showing:

5. Differences between monthly and long-term (1992-2006) daily mean

 water-use rates.......................................................................................................................12

6. Relation between percent increase in total monthly water-use rates at

 selected utilities and sensitivity to meteorological parameters, 1992-2006..............15

7. Coefficient of determination R2 from plots of metered water use at Orlando

 Utilities Commission relative to the number of months used to calculate

 moving averages of precipitation at the Orlando NOAA station, 1992-2006..............15
iv

8-11. Plots showing: 

8. Metered water-use rates at Orlando Utilities Commission versus 

 (A) monthly precipitation and (B) the 8-month moving average of precipitation 

 at the Orlando NOAA station, 1992-2006...........................................................................16

9. Metered water-use rates at Eustis and the 3-month moving average 

 of change in temperature at the Lisbon National Oceanic and Atmospheric 

 Administration station, 1992-2006 ......................................................................................18

10. Measured versus predicted water-use rates for the selected utilities, 2007 ........... 21

11. Metered connections for reclaimed water use at Jacksonville 

 Electric Authority, October 2001-December 2006, and reclaimed water use for 

 Orlando Utilities Commission, 1995-2007 ......................................................................... 23

Tables

1. Listing of selected utilities, associated municipal water-supply treatment plants, 

 and National Oceanic and Atmospheric Administration rainfall stations used in 

 this study ..................................................................................................................................................5

2. Precipitation statistics for selected National Oceanic and Atmospheric Administration 

 rainfall stations, 1959-2006 and 1992-2006..........................................................................................7

3. Results of trend tests on total and metered water-use rates and on precipitation at 

 selected National Oceanic and Atmospheric Administration rainfall stations, 

 1992-2006..................................................................................................................................................9

4. Average monthly metered water-use rates (1992-2006) and differences between 

 monthly rates and 15-year daily means............................................................................................11

5. Significant monthly differences in percent departure of monthly water-use rates from 

 the 15-year daily mean.........................................................................................................................12

6. Results of single-variant linear regression analyses between metered water-use 

 rates and selected meteorological parameters and drought indices, 1992-2006......................14

7. Comparison of single-variant regression analyses for monthly and moving-averaged 

 parameter values, 1992-2006 ..............................................................................................................17

8. Results of best fit linear and nonlinear single-variant regression analyses, 1992-2006 ...........18

9. Results of multivariant regression analyses between metered water-use rates and 

 selected meteorological parameters and drought indices, 1992-2006........................................20

10. Summary of descriptive and error statistics for measured and predicted metered 

 water-use rates, 2007...........................................................................................................................22
v

Conversion Factors, Datums, and Acronyms

Multiply By To obtain

inch (in.) 25.4 millimeter

mile (mi) 1.609 kilometer

million gallons per day (Mgal/d) 0.04381 cubic meters per second

Acronyms and Abbreviations

AWC Available water content

COV Coefficient of variation

gpdm gallon per day per metered connection

GRU Gainesville Regional Utilities

JEA Jacksonville Electric Authority

OCU Orange County Utilities

OUC Orlando Utilities Commission

NOAA National Oceanic and Atmospheric Administration

PDSI Palmer Drought Severity Index

SCU Seminole County Utilities

SF Sensitivity factor

SPI Standardized Precipitation Index

STA City of St. Augustine

USGS U.S. Geological Survey

VIF Variance Inflation Factor

WTP Water-treatment plant

Meteorological Parameters

P Precipitation

PET Potential evapotranspiration

P-PET Available water

R2 Coefficient of determination

T Mean air temperature

Vertical coordinate information is referenced to the National Geodetic Vertical 

Datum of 1929 (NGVD 1929).

Abstract

Water-use data collected between 1992 and 2006 at eight 

municipal water-supply utilities in east-central and northeast 

Florida were analyzed to identify seasonal trends in use and 

to quantify monthly variations. Regression analyses were 

applied to identify significant correlations between water use 

and selected meteorological parameters and drought indices. 

Selected parameters and indices include precipitation (P), 

mean air temperature (T), potential evapotranspiration (PET), 

available water (P-PET), monthly changes in these parameters 

(∆P, ∆T, ∆PET, ∆(P-PET)), the Palmer Drought Severity 

Index (PDSI), and the Standardized Precipitation Index 

(SPI). Selected utilities include the City of Daytona Beach 

(Daytona), the City of Eustis (Eustis), Gainesville Regional 

Utilities (GRU), Jacksonville Electric Authority (JEA), 

Orange County Utilities (OCU), Orlando Utilities Commission 

(OUC), Seminole County Utilities (SCU), and the City of 

St. Augustine (STA). Water-use rates at these utilities in 2006 

ranged from about 3.2 million gallons per day at Eustis to 

about 131 million gallons per day at JEA. 

Total water-use rates increased at all utilities throughout 

the 15-year period of record, ranging from about 4 percent 

at Daytona to greater than 200 percent at OCU and SCU. 

Metered rates, however, decreased at six of the eight utilities, ranging from about 2 percent at OCU and OUC to about 

17 percent at Eustis. Decreases in metered rates occurred 

because the number of metered connections increased at a 

greater rate than did total water use, suggesting that factors 

other than just population growth may play important roles in 

water-use dynamics. Given the absence of a concurrent trend 

in precipitation, these decreases can likely be attributed to 

changes in non-climatic factors such as water-use type, usage 

of reclaimed water, water-use restrictions, demographics, and 

so forth. When averaged for the eight utilities, metered wateruse rates depict a clear seasonal pattern in which rates were 

lowest in winter months and greatest in late spring. Averaged 

water-use rates ranged from about 9 percent below the 15-year 

daily mean in January to about 11 percent above the daily 

mean in May. 

Water-use rates were found to be statistically correlated 

to meteorological parameters and drought indices, and to be 

influenced by system memory. Metered rates (in gallons per 

day per active metered connection) were consistently found to be 

influenced by P, T, PET, and P-PET, as well as changes in these 

parameters that occurred in prior months. In the single-variant 

analyses, best correlations were obtained by fitting polynomial 

functions to plots of metered rates versus moving-averaged 

values of selected parameters (R2 values greater than 0.50 at 

three of eight sites). Overall, metered water-use rates were best 

correlated with the 3- to 4-month moving average of ∆T or 

∆PET (R2 values up to 0.66), whereas the full suite of meteorological parameters was best correlated with metered rates 

at Daytona and least correlated with rates at STA. Similarly, 

metered rates were substantially better correlated with 

moving-averaged values of precipitation (significant at all 

eight sites) than with single (current) monthly values (significant at only three sites). Total and metered water-use rates 

were positively correlated with T, PET, ΔP, ΔT, and ∆PET, and 

negatively correlated with P, P-PET, Δ(P-PET), PDSI, and SPI. 

The drought indices were better correlated with total water-use 

rates than with metered rates, whereas metered rates were 

better correlated with meteorological parameters. 

Relations between Municipal Water Use and Selected 

Meteorological Parameters and Drought Indices, 

East-Central and Northeast Florida 

By Louis C. Murray, Jr.
2 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

Multivariant analyses produced fits of the data that 

explained a greater degree of the variance in metered rates 

than did the single-variant analyses. Adjusted R2 values for 

the “best” models ranged from 0.79 at JEA to 0.29 at STA and 

exceeded 0.60 at five of eight sites. The amount of available 

water (P-PET) was the single parameter most common to the 

best models (six of eight sites), whereas ∆T or ∆PET were 

included at five of eight sites. The moving average of at least 

one parameter was present in seven of the eight best models, 

indicating the influence of water-use memory to changing 

climatic conditions. Monthly P and ∆P were better correlated 

with metered water-use rates in the multivariant regression 

analyses (significant at six of eight sites) than in the singlevariant analyses (significant at only three sites). This contrast 

can be attributed to the fact that the multivariant analyses 

better isolate the effects of precipitation on water use by 

factoring out the offsetting effects of other parameters, such as 

temperature, which are directly (and not inversely) related to 

water use. 

The best model equations determined from the multivariant analyses were used to predict metered water-use rates 

for 2007, a relatively dry year. The average error between 

predicted and measured results, an indication of model bias, 

ranged from -53 gpdm (gallons per day per active metered 

connection) at OCU (overpredicted rates) to 42 gpdm at 

GRU (underpredicted rates). These biased results are likely 

due, in part, to factors not considered in the analyses such as 

increased usage of reclaimed water and changes in the ratio of 

commercial-to-residential users in 2007. Based collectively on 

the error and regression statistics developed for the predicted 

results, the best predictions were made at Eustis while the 

poorest predictions were made at GRU.

Introduction

Ground-water withdrawals in east-central and northeast 

Florida have the potential to adversely affect sensitive water 

resources, such as the amounts of water being discharged 

at springs, wetland ecosystems, and canal and lake levels. 

Ground-water withdrawal rates, in turn, are largely dependent on municipal water-supply demands required to meet 

residential, commercial and industrial usages. The amount of 

water used for municipal supply, and changes in usage from 

one month or year to the next, is influenced by numerous 

factors that include changes in population, climatic conditions, 

water-use types, socio-economic conditions, availability of 

reclaimed water, water-conservation practices, and water 

pricing (Marella, 1992a). While municipal water withdrawals 

are known to vary seasonally, little or no research has been 

conducted in east-central and northeast Florida to quantify 

how these withdrawals vary from one month to the next and 

how changes in meteorological conditions may affect usage. 

Similarly, little or no research has been done to determine if 

commonly referenced drought indices, such as the Palmer 

Drought Severity Index (PDSI) or Standardized Precipitation 

Index (SPI), can be related to the amounts of water being withdrawn and used for municipal supply. Drought indices provide 

potentially useful tools for associating predicted water-use 

rates with quantifiable drought conditions of interest. 

The U.S. Geological Survey, in cooperation with the 

St. Johns River Water Management District, conducted a study 

to examine the relations between water use and selected meteorological parameters and drought indices. Statistically relating 

water use to variations in commonly measured parameters and/

or indices may provide resource managers an improved tool for 

predicting the potential effects of climate, and perhaps climate 

change, on associated demands. Additionally, scientists tasked 

with developing transient ground-water flow models can more 

confidently quantify ground-water withdrawal rates subject to a 

broad range of climatic conditions. 

Purpose and Scope

The purposes of this report are to: (1) document seasonal 

(monthly) variations in municipal water withdrawals for 

selected utilities in east-central and northeast Florida, and 

(2) quantify the relations between water use and commonly 

measured meteorological parameters and drought indices. 

Parameters of interest include precipitation (P), air temperature (T), potential evapotranspiration (PET), and available 

water (P-PET). Drought indices of interest include the 

Palmer Drought Severity Index (PDSI) and the Standardized 

Precipitation Index (SPI), both of which are commonly 

referenced across the country for assessing the severity of 

droughts. Because the scope of this report examines just the 

relation between water withdrawals and climate, and does 

not account for other influential factors mentioned above, the 

results discussed herein are somewhat limited for accurately 

predicting future water use. However, results do provide an 

indication of the relative influence of climate in assessing 

changes in short-term (monthly) use. 

The analyses are conducted for eight municipal watersupply utilities dispersed across east-central and northeast 

Florida (fig.1) and include the City of Daytona Beach 

(Daytona), the City of Eustis (Eustis), Jacksonville Electric 

Authority (JEA), Gainesville Regional Utilities (GRU), 

Orange County Utilities (OCU) for the southern service area, 

Orlando Utilities Commission (OUC), Seminole County 

Utilities (SCU) for the northeast and northwest service areas, 

and the City of St. Augustine (STA). Each utility provided at 

least 1 Mgal/d of treated water to respective service areas in 

2006 and is located near a National Oceanic and Atmospheric 

Administration (NOAA) rainfall station. The largest of these 

utilities, JEA and OUC, serve large metropolitan areas with 

numerous and dispersed water-treatment plants (WTPs). 

The smaller utilities (Daytona, Eustis, GRU, and STA) operate 

single plants. 
Introduction 3

Lisbon

Sanford

Clermont 9S

Orlando IAP

High Springs

St. Augustine

Jacksonville IAP

Daytona

Beach

IAP

Gainesville

RAP

Navy

Conway

Eustis

Lynwood

Markham

Vistana

Kirkman

Norwood

Fairfax

Murphee

Heathrow

Sky Lake

Highland

Marietta Ridenour

Greenwood

Arlington

Lovegrove Lakeshore

Highlands

Brierwood

Pine Hills

Deerwood#3

Indian Hills

Country

Club

Meadow Woods

Cypress

Walk

St. Augustine

Community Hall

Southwest

regional

Brennan

Hunters Creek

Orangewood

Southwest

Hanover Woods

Lake Monroe

Main Street

Hendricks

Oak Ridge

Southeast

regional

BAKER

COUNTY

DUVAL

COUNTY

CLAY

COUNTY

ALACHUA

COUNTY

MARION

COUNTY

PUTNAM

COUNTY

LAKE

COUNTY

VOLUSIA

COUNTY

SEMINOLE

COUNTY

ORANGE

COUNTY

OSCEOLA COUNTYBREVARD COUNTY

BRADFORD

COUNTY

UNION

COUNTY

SUMTER

CITRUS COUNTY

COUNTY

HERNANDO

COUNTY

PASCO

COUNTY

LEVY

COUNTY

COLUMBIA

COUNTY

NASSAU

COUNTY

ST JOHN

COUNTY

FLAGLER

COUNTY

POLK COUNTY

82°30 ́ 82°15 ́ 82°00 ́ 81°45 ́ 81°30 ́ 81°15 ́ 81°00 ́

30°30 ́

30°15 ́

30°00 ́

29°45 ́

29°30 ́

29°15 ́

29°00 ́

28°45 ́

28°30 ́

0 10 20 MILES

0 10 20 KILOMETERS

GEORGIA

Base from U.S. Geological Survey digital data,

Universal Transverse Mercator projection, zone 17

ATLANTIC

OCEAN

EXPLANATION

LOCATION

OF STUDY

AREA

CITY OF DAYTONA BEACH

WATER TREATMENT PLANT

CITY OF EUSTIS WATER

TREATMENT PLANT

GAINESVILLE REGIONAL UTILITIES

WATER TREATMENT PLANT

CITY OF ST. AUGUSTINE WATER

TREATMENT PLANT

ORLANDO UTILITIES COMMISSION

WATER TREATMENT PLANT

SEMINOLE COUNTY UTILITIES

WATER TREATMENT PLANT

ORANGE COUNTY UTILITIES

WATER TREATMENT PLANT

NOAA STATION

JACKSONVILLE ELECTRIC AUTHORITY

WATER TREATMENT PLANT

Figure 1. Locations of selected municipal water-treatment plants and National Oceanic 

and Atmospheric Administration rainfall stations in east-central and northeast Florida. 

IAP is International Airport. RAP is Regional Airport.
4 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

Acknowledgments

The author would like to thank several individuals 

for their assistance in obtaining the water-use information 

required for this study. These individuals include Robert 

Briggs and Robert Dehler (Seminole County Environmental 

Services Department); Melanie Abbott, Pat Divecchio, and 

Jacqueline Torbert (Orange County Utilities); Greg Dobbins 

and Erwin Gajentan (City of Eustis); Richard Hutton 

(Gainesville Regional Utilities); Richard Lott and Blanca 

Rodriguez (Florida Department of Environmental Protection); 

Brad Russell (Jacksonville Electric Authority); Chris Russell 

and Robert Teegarden (Orlando Utilities Commission); 

James Thurrott (City of Daytona Beach); and Tom Wallace 

(City of St. Augustine).

Methods of Investigation

The subsequent sections describe the data-collection 

methods and statistical techniques that were applied in this 

study. The analyses were performed on a monthly basis for 

consistency with time frames commonly used in transient 

ground-water flow models. 

Data Collection and Analyses 

Water-use data collected for this study include the 

treated flow rates from the selected WTPs and the associated 

number of active service (metered) connections for each 

WTP (table 1). Average daily flows converted to monthly 

flows or total monthly flows can be considered a reasonable 

approximation of the amounts of water withdrawn from 

ground-water sources at nearby well fields. Treated flow rates 

were used in place of ground-water withdrawals because 

these data were more readily available, and the difference 

between raw water withdrawals and treated flow is typically 

minimal for municipal ground-water systems in east-central 

and northeast Florida. Only those plants that rely on ground 

water as the source were included in this study. The number 

of service connections was used as a surrogate to offset the 

effects of population growth. In addition, the populations 

served by municipal WTPs are most often estimated, whereas 

the number of service connections are recorded from billing 

records and maintained monthly by the utility. The appendix 

provides the amounts of water treated and number of metered 

connections at the selected municipal water-treatment plants 

(service areas) between 1992 and 2006. 

The analyses discussed in this report ideally would be 

applied to a period spanning several decades to quantify longterm average conditions and to account for repeated occurrences of unusually wet and dry periods and climatic extremes. 

Acquiring long-term data, however, particularly for service 

area connections, was problematic. Data on service area 

connections were difficult to obtain beyond the past few years. 

Based on the best available information, a 15-year period of 

record (1992-2006) was selected for analyses. Although this 

period is shorter than would have been desired, it does include 

both wet and dry climatic extremes that are representative of 

longer-term conditions. 

Water Use

Treated flows from the WTPs meet a variety of needs, 

including residential, commercial, irrigation, industrial, fire 

protection, and so forth, and the amount of water required 

from one use type to the next can vary considerably. With 

the exception of GRU, however, information provided by the 

utilities on the total number of active metered connections 

did not explicitly distinguish between use type—a condition 

which unavoidably introduces some level of uncertainty in 

identifying factors contributing to changes in metered rates. 

At GRU, where the total amount of water used for residential 

applications exceeds that for commercial applications, the 

amount of residential use per metered connection is considerably less than that used per commercial connection (fig. 2). 

Additionally, water use at GRU is heavily influenced by 

about 50,000 university students, who live and use water in 

Gainesville. As such, not only can water-use rates vary by 

type, but the ratios of these use types can vary spatially from 

one utility to the next and with time. In Orange County, for 

example, the percentage of water used for residential supply 

decreased from about 63 percent in 1990 to about 56 percent 

in 2000, whereas the amount of water used for commercial 

supply increased from about 19 to 26 percent (Marella, 1992b; 

2004). Because metered water-use rates vary considerably by 

use type, the ratios of which are likely to vary from one utility 

to the next and to change with time, such dynamics invariably 

introduce some scatter (variance) in the data that cannot be 

accounted for in a regression analyses, such as that used in this 

study, which treats all connections “equally.” Only the data 

provided by SCU grossly accounts for varying use types by 

assigning a multiplicative factor, based on meter size, to each 

active connection.

Meteorological Parameters

Monthly precipitation and temperature data were 

collected from eight NOAA stations located near the selected 

utilities (fig.1). These stations include Clermont (for the OCU 

southern service area), Daytona Beach International Airport 

(for Daytona Beach), Gainesville Regional Airport (for GRU), 

Jacksonville International Airport (for JEA), Lisbon (for 

Eustis), Orlando International Airport (for OUC), the Sanford 

Experiment station (for the SCU northeast and northwest 

service areas), and St. Augustine Airport (for STA).

Precipitation data collected between 1992 and 2006 

were statistically contrasted with data collected between 1959 

and 2006 to provide some measure of how representative the 
Methods of Investigation 5

Table 1. Listing of selected utilities, associated municipal water-supply treatment plants, and National 

Oceanic Atmospheric Administration (NOAA) rainfall stations used in this study.

[Mgal/d, million gallons per day; IAP, International Airport]

Utility (service area) Water-treatment 

plant

Treated flows, Mgal/d 

(total all plants) Nearest NOAA station 

providing data

1992 2006

City of Daytona Beach Brennan 12.3 13.1 Daytona Beach IAP

City of Eustis Eustis 2.5 3.2 Lisbon

Gainesville Regional Utilities Murphee 17.6 22.8 Gainesville/High Springs

Jacksonville Electric Authority Arlington 67.7 131.3 Jacksonville IAP

Brierwood

Community Hall

Deerwood#3

Fairfax

Hendricks

Highlands

Lakeshore

Lovegrove

Main Street

Marietta

Norwood

Oak Ridge

Ridenour

Southeast regional

Southwest regional

Orange County Utilities Cypress Walk 4.9 16.5 Clermont 9S

(southern service area) Hunters Creek

Meadow Woods

Orangewood

Vistana

Orlando Utilities Commission Conway 72.3 91.1 Orlando IAP

Highland

Kirkman

Navy

Pine Hills

Sky Lake

Southwest

Seminole County Utilities1 Hanover Woods 4.7 13.3 Sanford Experiment Station

(northeast, northwest service areas) Country Club

Greenwood

Heathrow

Indian Hills

Lynwood

Lake Monroe

Markham

City of St. Augustine St. Augustine 1.8 3.3 St. Augustine

1Total flows include water purchases from cities of Casselberry (Sunshadow, Tanglewood) and Sanford (Chase Groves, 

Five Points).
6 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

selected 15-year period of record was of long-term climatic 

conditions. Monthly averages computed for the two periods 

were relatively close (fig. 3), and the two driest years in the 

15-year record (2000 and 2006) were also the driest years on 

record between 1959 and 2006 at four of the eight NOAA 

stations, and among the two or three driest years at the other 

stations (table 2). Similarly, the wettest years between 1992 and 

2006 occurred in 2002, 2004, and 2005 and were exceeded by 

less than 10 percent of the long-term record at six of the eight 

NOAA sites. 

PET was estimated by the Thornthwaite (1948) method 

based on a set of formulas that relate PET with monthly 

mean air temperature and length of daylight hours, and which 

are indexed or “calibrated” to the long-term record of mean 

monthly temperatures for a given location. Because of its 

limited input data requirements, the method has been widely 

applied in climatic and hydrologic studies and, given adequate 

historical record, can provide reasonable estimates of PET 

(Hagan and others, 1967). In the present study, a computer 

program developed by McCabe and others (1985) was used 

to calculate monthly PET values at each of the eight NOAA 

stations. Input data included the monthly temperatures for 

the investigated period of record, latitude of the station, and 

long-term historic monthly temperature values averaged from 

1959 to 2006. The difference between P and PET is considered 

a gross measure of available water, another of the explanatory 

variables considered in this study. 

Drought Indices

The Palmer Drought Severity Index (PDSI) is a meteorological drought index developed by Palmer (1965) to quantify 

the severity of drought and wet periods. The index uses 

temperature, precipitation, and the available water content 

(AWC) of the soil in a water-budgeting formulation that 

determines the relative wetness or dryness of soils. The index 

is standardized to the local climate so comparisons of soilmoisture conditions can be made among varying locations. 

A given value of the PDSI, which ranges from -4 (extreme 

drought) to +4 (extreme wet conditions), is a combination of 

the current condition and previous PDSI values, so it is somewhat representative of trends whether a drought or wet period. 

The index is best applied in determining relatively long-term 

drought, such as occurred in Florida in 1998-2000. 

The “Self-Calibrated” PDSI applied in this study is a 

modified version of the PDSI that is calibrated to the climate 

of the selected location by replacing the empirical constants 

used in the water-budget equation with dynamic values that 

are based on the historical climate of that location. As such, 

the Self-Calibrating PDSI provides more consistent and 

reliable results for specific locations than does the PDSI. The 

Self-Calibrating PDSI is also designed so that index values at 

or below -4.0 and at or above +4.0 each occur 2 percent of the 

time. Thus, for every 50 years, there will be about 12 months 

of “extreme” drought and 12 months of “extreme” wetness, 

which serves to better define the drought classification system 

0

200

400

600

800

1,000

1,200

1,400

1990 1995 2000 2005 2010

YEAR

WATER USE, IN GALLONS PER DAY

PER METERED CONNECTION

0

2

4

6

8

10

12

14

16

WATER USE, IN MILLION

GALLONS PER DAY

EXPLANATION

RESIDENTIAL, PER METERED CONNECTION

COMMERCIAL, PER METERED CONNECTION

COMMERCIAL, TOTAL WATER USE

RESIDENTIAL, TOTAL WATER USE

Figure 2. Residential and commercial water uses at Gainesville Regional 

Utilities, 1992-2006.
Methods of Investigation 7

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

MONTH

10

9

8

7

6

5

4

3

2

1

0

PRECIPITATION, IN INCHES

1959-2006

1992-2006

EXPLANATION

Table 2. Precipitation statistics for selected National Oceanic and Atmospheric Administration (NOAA) rainfall stations, 1959-2006 

and 1992-2006. 

[IAP, International Airport] 

 NOAA station

Annual average 

precipitation (inches) Driest 

year

Precipitation 

(inches)

Percent greater 

than1

Wettest 

year

Precipitation 

(inches)

Percent 

less than2

1959-06 1992-06

Clermont 51.7 52.4 2000 28.9 100 2002 86.7 0

Daytona IAP 50.0 52.1 2006 31.4 100 2005 65.8 8

Gainesville 50.5 47.8 2000 34.4 98 2004 58.4 21

Jacksonville IAP 52.9 53.9 2006 38.1 96 2004 69.5 4

Lisbon 48.7 50.7 2000 29.3 100 2005 58.7 10

Orlando IAP 50.3 52.6 2000 30.4 100 2002 66.4 4

St. Augustine 51.2 51.5 2006 37.1 94 2005 70.1 4

Sanford 51.6 54.6 2000 32.8 100 2002 66.5 6

Average (all stations) 50.9 51.9

1Percent of years in the long-term (1959-2006) record with precipitation equal to or greater than the driest year between 1992-2006.

2Percent of years in the long-term (1959-2006) record with precipitation equal to or less than the wettest year between 1992-2006.

Figure 3. Monthly precipitation for 1959-2006 and 1992-2006 (averaged from eight project 

NOAA sites).
8 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

and improve spatial comparability. A step-by-step formulation 

for the PDSI and the Self-Calibrating PDSI can be found at 

the National Agricultural Decision Support System web site 

maintained for NOAA by the University of Nebraska-Lincoln 

Department of Computer Science and Engineering. 

Problems associated with the PDSI are summarized 

by Alley (1984) and Karl and Knight (1985). Some of the 

more important issues are as follows: (1) because the index 

is sensitive to the AWC of a given soil type, applying it to a 

climate division that contains multiple soil types may be too 

general; (2) the natural lag that occurs between precipitation 

and the resulting runoff is not considered in the water budget; 

and (3) PDSI values may lag emerging droughts by several 

months and may not be representative of most current conditions. Notwithstanding these limitations, many Federal and 

State government agencies rely on PDSI to initiate drought 

relief programs. 

The Standardized Precipitation Index (SPI) also quantifies deficit or excess moisture conditions at a desired location 

and time interval (McKee and others, 1993). The SPI is 

based on fitting the long-term precipitation record for a given 

location and time interval to a probability distribution which 

is transformed into a normal distribution, such that the mean 

SPI for the desired location and time interval is zero. Thus, the 

value computed for the index represents the number of standard deviations that the measured precipitation deviates from 

the long-term mean. SPI values range from -2 (extremely dry) 

to +2 (extremely wet) and can be calculated for varying time 

scales to reflect the impact of drought on the availability of the 

different water resources. For the purposes of this study, SPI 

values calculated on a single monthly basis were used because 

it is assumed that the majority of municipal water use is 

directed for irrigation needs, both residential and commercial, 

which are sensitive to soil-moisture conditions. The procedure 

for calculating SPI can be found at the National Agricultural 

Decision Support System web site. 

Statistical Methods

Statistical methods described by Helsel and Hirsch 

(2002) for trend testing and for single- and multivariant 

regression analyses were applied in this study. The DurbinWatson statistic (Durbin and Watson, 1951) was used to 

determine if the temporal sequence of monthly water-use 

rates were serially correlated. A Kolmogorov test (Conover, 

1999) was applied to determine if monthly water-use and 

meteorological parameter values were normally distributed 

about respective means to help select appropriate tools for 

subsequent trend and correlation tests. The KolmogrovSmirnov test serves as a goodness-of-fit test in which the 

frequency distribution of a given dataset is compared against 

a theoretical distribution, in this case, a Gaussian normal 

distribution. The test calculates a p-value to determine 

whether or not the null hypothesis can be accepted. P-values 

of less than 0.05, which were quantified for virtually every 

meteorological dataset, indicate that the null hypothesis 

can be rejected. While the water-use data were found to 

be normally distributed, the meteorological data were 

not. Accordingly, the parametric ANOVA test was used to 

determine whether significant differences existed between 

the 15-year mean daily water-use rate and average monthly 

rates, while Tukeys (1977) multiple comparison test was 

applied to identify all of the significant pairwise differences. 

Kendall’s tau (Helsel and Hirsch, 2002) was used to analyze 

temporal variations in water use for trends and to quantify 

the relations between water use and selected meteorological 

variables and drought indices. The Kendall test is a nonparametric procedure that measures the strength of a monotonic 

relation, whether linear or nonlinear, between the dependent 

and explanatory variables and, as a ranked-based method, its 

results are not affected by outliers in the data. A probability 

level of 5 percent (p-value of less than 0.05) was used in all 

tests as the criterion for significance; that is, a p-value of less 

than 0.05 indicates that the probability of a detected correlation or difference not being real is less than 5 percent. 

Commercially available spreadsheet software was used 

to plot the data. A commercially available statistical software 

package, used throughout the USGS, was applied to test the 

data for normality and serial correlation, detrend the water-use 

data for seasonal analyses, and conduct the regression analyses 

and trend tests. 

Relations between Water Use and 

Meteorological Parameters/Drought 

Indices

Water-use data were analyzed to identify significant 

seasonal trends, quantify differences in use, and determine 

interrelations with meteorological parameters and drought 

indices measured at eight NOAA stations in east-central and 

northeast Florida from 1992 to 2006. Both single-variant and 

multivariant regression analyses were applied to quantify these 

relations. Additionally, best model equations determined from 

the multivariant analyses were used to predict metered wateruse rates for 2007, a relatively dry year.

Trends 

While no significant temporal trends were detected 

in precipitation between 1992 and 2006 at any of the eight 

NOAA stations, total water-use rates increased significantly 

at all utilities, from about 4 percent at Daytona to greater 

than 200 percent at OCU and SCU (table 3). When normalized per metered connection, however, which serves to 

reduce the influence of population increases, water-use rates 

in gallons per day per metered connection decreased significantly at five of the eight utilities (Daytona, Eustis, GRU, 

JEA, and OCU), while increasing at SCU and STA (table 3). 

No significant change in metered rates were seen at OUC. 
Relations between Water Use and Meteorological Parameters/Drought Indices 9

Table 3. Results of trend tests on total and metered water-use rates and on precipitation at selected National Oceanic and Atmospheric Administration (NOAA) rainfall stations, 1992-2006. [Percent change in water use calculated from the difference between LOWESS curve values for Jan-92 and Dec-06. Mgal/d, million gallons per day; gpdm, gallons per day per metered connection; Tau, Kendalls tau value; significant trends shown by p-values equal to or less than 0.05]

Rainfall stations

Total water use (Mgal/d) Metered water use (in gpdm) Precipitation

Jan-92 Dec-06 Change Tau p-value Jan-92 Dec-06 Change Tau p-value NOAA station Tau p-value

City of Daytona Beach 12.24 12.76 4 0.19 <.001 533 501 -6 -0.02 0.01 Daytona IAP -0.020 0.69City of Eustis 2.47 3.02 22 .16 <.001 377 314 -17 -.26 <.001 Lisbon -.081 .11Gainesville Regional Utilities 17.0 23.1 36 .38 <.001 409 343 -16 -.43 <.001 Gainesville -.049 .33Jacksonville Electric Authority 66.3 132 99 .64 <.001 464 436 -6 -.26 <.001 Jacksonville -.061 .22Orange County Utilities (Southern) 4.70 16.3 247 .79 <.001 1,002 983 -2 -.14 .007 Clermont -.001 .99Orlando Utilities Commission 73.1 90.0 23 .47 <.001 666 655 -2 -.05 .33 Orlando IAP -.041 .41Seminole County Utilities (NE, NW) 4.41 13.3 202 .64 <.001 352 440 25 .12 .019 Sanford -.067 .18City of St. Augustine 1.88 3.41 81 .73 <.001 280 340 21 .42 <.001 St. Augustine .006 .91
10 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

Given the absence of coincident trends in precipitation, these 

reductions can likely be attributed to variables not considered 

in this study, such as changes in service area demographics 

and water-use type, conservation measures, increased use of 

reclaimed water, and changes in the ratio of single- to multifamily metered connections. 

The plots shown for JEA in figure 4 contrast the trends 

seen between total and metered water-use rates and also depict 

the shorter term and more erratic monthly fluctuations characteristic of all utilities. While the longer 15-year trend in total 

water-use rates can be associated with population increases, 

these shorter term fluctuations are more affected by changes in 

meteorological conditions and thus more suitable for seasonal 

(monthly) analyses. 

Metered water-use rates, such as those depicted for JEA 

in figure 4B, were detrended to better isolate the shorter term 

fluctuations for seasonal analyses. This was performed by 

fitting the data with LOWESS smooth curves (smoothing 

factor = 5), calculating residuals, and adding the value of 

the LOWESS curve at the end of the period of record to the 

residual values. By doing so, the data were adjusted to better 

represent current (end of 2006) conditions.

Average metered water-use rates were calculated at each 

site for each calendar month of the year and compared with the 

respective 15-year daily means (table 4). Given that publicsupply water-use rates are indicative of ground-water withdrawals, and that average monthly climatic conditions between 

1992 and 2006 are representative of long-term (1959-2006) 

conditions, the 15-year daily mean may be associated with a 

steady-state hydrologic stress against which transient monthly 

variations can be quantified. At Daytona, for example, the 

average metered rate in January (464 gpdm) was about 

7.3 percent lower than the 15-year daily mean of 500 gpdm, 

whereas the average rate in August (517 gpdm) exceeded 

the daily mean by about 3.3 percent. Although results vary 

considerably from one utility to the next, the largest positive 

differences generally occur in the spring months (April, May, 

and June), while the greatest negative differences occur in the 

winter months (December, January, and February). 

When spatially averaged across the eight utilities for each 

calendar month, the departures depict a well-defined seasonal 

pattern in which metered water-use rates tended to be least in 

the winter and greatest in the late spring (fig. 5). In January, 

for example, water use averaged about 9 percent below the 

15-year daily mean, whereas water use in May averaged about 

11 percent above the daily mean. Because of the considerable 

scatter in the monthly departures from one utility to the next, 

Tukey’s (1977) test was applied to determine if the monthly 

mean departures were significantly different from one another. 

Results in table 5 indicate, for example, that the mean January 

departure is significantly different from those of April, May, 

June, July, and August, while not significantly different from 

those of September, October, November, December, February, 

and March. These results can provide guidance for quantifying 

seasonally variant ground-water withdrawals rates in predictive transient flow models. 

WATER USE, IN MILLION

GALLONS PER DAY

WATER USE, IN GALLONS PER DAY

PER METERED CONNECTION

180

160

140

120

100

80

60

40

20

0

800

700

600

500

400

300

200

100

Aug

1987

Jan

1993

Jul

1998

Jan

2004

Jul

2009

DATE DATE

Aug

1987

Jan

1993

Jul

1998

Jan

2004

Jul

2009

A B

Figure 4. Total and metered water-use rates for Jacksonville Electric Authority, 1992-2006.
Relations between Water Use and Meteorological Parameters/Drought Indices 11

Table 4. Average monthly metered water-use rates (1992-2006) and differences between monthly rates and 15-year 

daily means.

[Daytona, City of Daytona Beach; Eustis, City of Eustis; GRU, Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange 

County Utilities (southern service area) ; OUC, Orlando Utilities Commission; SCU, Seminole County Utilities (northeast, northwest service areas); 

and STA, City of St. Augustine; Avg. diff., average difference between daily and monthly mean]

Month Daytona Eustis GRU JEA OCU OUC SCU STA Avg. 

diff.

Metered water-use rates, in gallons per day per metered connection

Jan 463.7 333.1 311.2 386.3 878.1 593.8 397.6 326.4 --

Feb 487.2 345.0 317.0 376.4 889.9 601.3 404.7 333.8 --

Mar 517.0 372.9 295.1 405.8 951.9 637.2 378.5 341.2 --

Apr 519.3 429.1 342.0 458.8 1,063 698.1 443.3 344.3 --

May 521.7 461.7 363.5 514.4 1,088 744.3 482.5 346.1 --

June 523.7 397.2 417.7 475.5 1,049 691.1 538.7 343.1 --

July 538.1 375.5 351.9 474.6 1,047 683.7 445.7 350.1 --

Aug 517.1 369.2 311.3 457.9 1,009 670.3 440.1 340.9 --

Sept 493.3 359.6 371.7 428.7 916.9 635.1 445.1 334.5 --

Oct 488.7 362.7 344.8 425.3 975.5 655.1 406.6 332.8 --

Nov 479.8 359.9 349.2 421.5 960.8 641.5 434.1 331.7 --

Dec 455.3 343.1 331.7 393.7 888.9 606.0 440.3 322.3 --

 Daily mean 500.4 375.8 342.3 434.9 976.5 654.8 438.1 337.3 --

Difference between monthly and daily means, in percent

Jan -7.3 -11.4 -9.1 -11.2 -10.1 -9.3 -9.2 -3.2 -8.9

Feb -2.6 -8.2 -7.4 -13.5 -8.9 -8.2 -7.6 -1.0 -7.2

Mar 3.3 -.8 -13.8 -6.7 -2.5 -2.7 -13.6 1.2 -4.5

Apr 3.8 14.2 -.1 5.5 8.9 6.6 1.2 2.1 5.3

May 4.3 22.9 6.2 18.3 11.4 13.7 10.1 2.6 11.2

June 4.7 5.7 22.0 9.3 7.4 5.5 23.0 1.7 9.9

July 7.5 -.1 2.8 9.1 7.2 4.4 1.7 3.8 4.6

Aug 3.3 -1.8 -9.1 5.3 3.3 2.4 .5 1.1 .63

Sept -1.4 -4.3 8.6 -1.4 -6.1 -3.0 1.6 -.8 -.85

Oct -2.3 -3.5 .7 -2.2 -.1 .0 -7.2 -1.3 -2.0

Nov -4.1 -4.2 2.0 -3.1 -1.6 -2.0 -.9 -1.7 -2.0

Dec -9.0 -8.7 -3.1 -9.5 -9.0 -7.5 .5 -4.4 -6.3
12 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

-10

-5

0

5

10

15

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

MONTH

DIFFERENCE BETWEEN

MONTHLY AND LONG-TERM

DAILY RATES, IN PERCENT

Table 5. Significant monthly differences in percent departure of monthly water-use rates from the 

15-year daily mean (averaged for the eight utilities). 

[X, indicates that the difference in percent departure for the two months being compared is significant at the 

α = 0.05 level]

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Jan X X X X X

Feb X X X X

Mar X X X X

Apr X

May X X X X X

June X X X X X

July X

Aug

Sept

Oct

Nov

Dec

Figure 5. Differences between monthly and long-term (1992-2006) daily mean water-use rates (averaged 

for the eight utilities).
Relations between Water Use and Meteorological Parameters/Drought Indices 13

Single-Variant Regression Analyses

Both single- and multivariant least squares regression 

analyses were used to evaluate the relations between municipal 

water use and selected meteorological parameters and drought 

indices which included P, T, PET, P-PET, ∆P, ∆T, ∆PET, 

∆(P-PET), PDSI, and SPI. Explanatory variables were regressed 

against both total and metered water-use rates to determine 

which form of the dependent variable was best correlated with 

meteorological conditions. The same sets of metered water-use 

data analyzed for seasonal trends in the previous section were 

used in these analyses. Total water-use rates were also regressed 

against the number of metered connections. 

Prior to regression analyses, the Durbin-Watson (dL) 

statistic (Durbin and Watson, 1951) was used to determine if 

the temporal sequence of monthly water-use rates was serially 

correlated. Regression analyses using serially correlated data 

will yield erroneous values for the coefficient of determination 

(R2) and standard error, which can result in too easily rejecting 

the null hypothesis; that is, there is no significant relation 

between the explanatory and dependent variables. Results 

indicated that, at a 5-percent significance level, monthly wateruse rates were serially correlated at all utilities (dL < 1.80 

for N = 180 observations). Accordingly, and in line with an 

approach suggested by Helsel and Hirsch (2002) of using a 

regularly sequenced subset of a serially correlated dataset, 

water-use rates measured for every other calendar month were 

used (for example, January, March, May, July, September, and 

November). This approach reduced serial correlation effects to 

acceptable levels (dL > 1.5 for N = 90 observations) while still 

accounting for seasonal variability in meteorological parameters 

and drought indices.

Results of the single-variant regression analyses are 

summarized in table 6 on the basis of R2 values and sensitivity 

factors (SFs). Only those R2 values determined from linear 

regressions with statistically significant slopes (p < 0.05) are 

quantified. The SF is calculated as the product of the geometric 

mean of the R2 values—determined from the plots of water 

use relative to the explanatory variables—by the number of 

variables found to be significantly correlated with the dependent variable. At Daytona, for example, where metered wateruse rates are significantly correlated with T, PET, P-PET, ΔT, 

and ΔPET, the product of the geometric mean of the R2 values 

(0.258) times the number of significantly correlated parameters (5) is equal to 1.290 (table 6). The SF provides a basis 

for determining: (1) which of the eight utilities have water-use 

rates that are most (or least) sensitive to the suite of selected 

meteorological parameters and drought induces, and (2) which 

individual parameter and drought index is best (or least) 

correlated with water-use rates, all utilities considered. Results 

given in table 6 are summarized below.

• In general, the correlations between monthly water-use 

rates (both total and metered) and selected meteorological parameters/drought indices can be described as 

poor (R2 < 0.40). These poor correlations illustrate the 

limits of using any single meteorological parameter or 

drought index for predictive purposes, although some 

parameters/indices are clearly better than others for 

estimating water use at the selected utilities. 

• Metered water-use rates were better correlated with 

the suite of meteorological parameters than were total 

water-use rates. Total water-use rates, however, were 

better correlated with PDSI and SPI and were best 

correlated with the number of metered connections, the 

parameter being used here as a surrogate for population 

growth. 

• Metered water-use rates were best correlated with T, 

PET, and P-PET (sensitivity factors of 1.552, 1.532, 

and 1.424, respectively) and least correlated with P 

and ∆P (sensitivity factors of 0.213 and 0.074, respectively). Precipitation was correlated with metered 

rates at only three of the eight utilities (Eustis, OCU, 

and STA). As discussed later, however, this apparent 

absence of a significant relation between precipitation 

and water use at most utilities could be considered 

counterintuitive and misleading. 

• Metered water-use rates were best correlated with 

meteorological parameters at JEA and Daytona 

(sensitivity factors of 1.290), while least correlated 

with parameters at SCU and STA (sensitivity factors of 

0.445 and 0.504, respectively).

• Both total and metered water-use rates were positively 

correlated with T, PET, ΔP, ΔT, and ∆PET, while 

negatively correlated with P, P-PET, and Δ(P-PET). 

• The relation between total water-use rates and meteorological parameters is affected by growth rate. As shown 

in figure 6, sensitivity factors calculated from plots 

of total water use relative to meteorological parameters decrease logarithmically with a percentage-wise 

increase in total water use between 1992 and 2006. That 

is, water-use rates at utilities with the lowest growth 

rates (Daytona, Eustis, and OUC) were much better 

correlated with the parameters than were water-use rates 

at utilities with the highest growth rates (OCU, SCU, 

and STA). The logarithmic function fitted to the plot 

explains greater than 80 percent of the scatter in the 

data. This result is noteworthy because it suggests that 

metered, as opposed to total, water use is a better choice 

of the dependent variable for assessing correlations with 

meteorological parameters over periods of record in 

which growth is significant. Accordingly, only metered 

water-use rates were used in the multivariant regression 

analyses discussed later in this report. However, if total 

water-use rates are to be used in future analyses (for 

example, in the absence of adequate data on metered 

connections), then it may be best to try and a select a 

period of record over which the growth rate is relatively 

small and unaffected by changes in population. 
14 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

Table 6. Results of single-variant linear regression analyses between metered water-use rates and selected meteorological parameters 

and drought indices, 1992-2006. 

[Values are coefficients of determination, R2. Correlated utilities: The number of the utilities having water-use rates significantly correlated with the given 

parameter. Correlated parameters: The number of the meteorological parameters significantly correlated with water use rates at the given utility. The sensitivity 

factor indicated for each utility is calculated by multiplying the geometric mean of significantly correlated parameter R2 values by the number of parameters 

found to be significantly correlated with water use at the utility. Similarly, the sensitivity factor for each meteorological parameter/drought indice is calculated 

by multiplying the geometric mean of significantly correlated R2 values by the number of utilities found to have water use rates significantly correlated 

with the parameter or drought indice. P, precipitation; T, temperature; PET, Thornthwaite potential evapotranspiration; P-PET, precipitation minus potential 

evapotranspiration; ∆P, change in precipitation; ∆T, change in temperature; ∆PET, change in potential evapotranspiration; ∆(P-PET), change in precipitation 

minus evapotranspiration; PDSI, Palmer Drought Severity Index; SPI, Standardized Precipitation Index; Daytona, City of Daytona Beach; Eustis, City of 

Eustis; GRU, Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange County Utilities (southern service area); OUC, Orlando 

Utilities Commission; SCU, Seminole County Utilities (northeast, northwest service areas); STA, City of St. Augustine]

Daytona Eustis GRU JEA OCU OUC SCU STA Slope Correlated 

utilities

Geometric 

mean

Sensitivity 

factor

Regressions against metered water-use rates, in gallons per day per connection

Meteorological parameter

P 0.126 0.052 0.054 neg 3 0.071 0.213

T 0.342 .120 0.322 0.357 .186 0.225 0.129 .078 pos 8 .194 1.552

PET .326 .109 .311 .345 .191 .214 .132 .088 pos 8 .192 1.532

P-PET .107 .308 .172 .376 .272 .192 .056 .165 neg 8 .178 1.424

∆P .074 neg 1 .074 .074

∆T .306 .155 .058 .125 .118 .145 .057 pos 7 .119 .833

∆PET .313 .227 .026 .199 .158 .183 .074 pos 7 .134 .938

∆(P-PET) .077 .041 neg 2 .056 .112

Geometric mean .258 .162 .121 .258 .145 .190 .089 .072

Correlated parameters 5 6 5 5 6 5 5 7

Sensitivity factor 1.290 .972 .605 1.290 .870 .950 .445 .504

Drought indice

PDSI .106 .109 .039 .041 .142 .068 .089 neg 7 .077 .539

SPI .078 .168 .035 .121 .178 .086 .101 neg 7 .098 .686

Geometric mean .078 .133 .062 .069 .085 .111 .068 .095

Correlated indices 1 2 2 2 2 2 1 2

Sensitivity factor .078 .266 .124 .138 .170 .222 .068 .190

Regressions against total water-use rates, in million gallons per day

Meteorological parameter

P .146 .045 neg 2 .081 .162

T .240 .089 .211 .107 .146 .046 pos 6 .122 .732

PET .226 .072 .213 .086 .137 .045 pos 6 .111 .666

P-PET .087 .314 .118 .189 .174 .063 neg 6 .137 .822

∆P neg 0 .000 .000

∆T .295 .124 .048 .040 .096 pos 5 .092 .460

∆PET .295 .170 .048 .119 pos 4 .130 .520

∆(P-PET) neg 0 .000 .000

Geometric mean .210 .135 .126 .073 .000 .132 .051 .000

Correlated parameters 5 6 4 6 0 5 3 0

Sensitivity factor 1.050 .810 .504 .438 .000 .660 .153 .000

Drought indice

PDSI .032 .210 .371 .184 .273 .290 .167 neg 7 .180 1.260

SPI .076 .248 .042 .156 .104 neg 5 .105 .525

Geometric mean .049 .228 .125 .169 .000 .169 .290 .167

Correlated indices 2 2 2 2 0 2 1 1

Sensitivity factor .098 .456 .250 .338 .000 .338 .290 .167

No. metered connections .092 .131 .294 .691 .849 .104 .705 .849 8 .327 2.616
Relations between Water Use and Meteorological Parameters/Drought Indices 15

Influence of System Memory

Correlations between metered water-use rates and meteorological parameters could be improved by considering the 

influence of system “memory.” In this study, memory refers to 

the fact that the availability of water to plants is affected by the 

depth to the underlying water table and by soil-moisture conditions, both of which can be influenced by climatic variables on 

time scales of greater than a single (current) month. Given that 

water users will provide their lawns, shrubs, commercial ornamentals, and so forth, as much water as needed to keep them 

green, users would tend to use more water in a month where 

the water table and soil-moisture conditions are not as conducive to supplying the plant’s need as they would in a month 

where these conditions have been made more favorable by 

rainfall and temperature conditions that occurred in preceding 

months. That is, watering habits may be influenced by the 

memory of the water table and soil-moisture conditions—and 

hence plants—to varying climatic conditions. 

The duration of each utility’s water-use memory to 

each parameter was examined by plotting monthly water-use 

rates relative to the moving averages of the given parameter 

for 1-, 2-, 3-, ...and 12-month time periods (12 plots were 

constructed). The R2 value obtained from each plot was 

plotted against the number of months used in the moving 

average calculation to identify which time frame provided 

the best correlation between the dependent and explanatory 

variables. Precipitation proved to be particularly sensitive to 

the moving average analyses. While correlated with monthly 

precipitation at only three sites, metered water-use rates were 

correlated with the moving average of precipitation at all 

eight sites. At OUC, for example, metered rates were best 

correlated with the 8-month moving average of precipitation 

at the Orlando NOAA station (fig. 7). Moving averages below 

and above this 8-month mark produced poorer fits of the data. 

OCU

SCU

St. Augustine

JEA

GRU

OUC

Eustis

Daytona

y = -0.263ln(x) + 1.4718

R = 0.844 2

0

0.2

0.4

0.6

0.8

1.0

1.2

0 50 100 150 200 250 300

PERCENT INCREASE IN WATER USE

SENSITIVITY FACTOR, DIMENSIONLESS

City of

St. Augustine

Seminole

County

Utilities Orange

County

Utilities

Jacksonville

Electric

Authority

Gainesville

Regional

Utilities

Orlando Utilities

Commission

City of Eustis

City of Daytona Beach

Figure 6. Relation between 

percent increase in total 

monthly water-use rates at 

selected utilities and sensitivity 

to meteorological parameters, 

1992-2006.

Figure 7. Coefficient of determination R2 from plots 

of metered water use at Orlando Utilities Commission 

relative to the number of months used to calculate 

moving averages of precipitation at the Orlando NOAA 

station, 1992-2006.

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0

COEFFICIENT OF DETERMINATION, R2

NUMBER OF MONTHS USED TO CALCULATE MOVING

AVERAGE OF ORLANDO PRECIPITATION

1 2 3 4 5 6 7 8 9 10 11 12
16 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

The plots shown in figure 8 depict the improved relation 

between water use at OUC and precipitation at Orlando when 

using the moving-averaged value of precipitation (R2 = 0.31, 

significant correlation) in place of the single (current) monthly 

value (R2 = 0.018, nonsignificant correlation). Optimal time 

frames for precipitation at the other utilities ranged from 7 

months at Daytona and Eustis to 11 months at GRU. 

The monthly moving averages of the other meteorological parameters, particularly those for ΔT and ΔPET, also 

improved the correlations when plotted against metered rates 

(table 7). At JEA, for example, regression of metered rates 

relative to the 4-month moving averages of ΔT and ΔPET 

improved R2 values from 0.125 to 0.491 (for ΔT) and from 

0.199 to 0.449 (for ΔPET). Accounting for the eight utilities 

collectively, the sensitivity factor for ΔT increased nearly 

threefold—from 0.833 to 2.320—while the sensitivity factor 

for ΔPET increased from 0.938 to 2.320. 

The results depicted in table 7 do not necessarily represent 

a unique or optimal solution, and the true duration of water-use 

memory to these parameters may be less than the indicated 

time frames. Each month used to calculate the moving average 

was equally weighted, whereas the current month or most 

recent 2 to 3 months may exert more influence on water use 

than do later months. Nonetheless, results indicate that a 

current month’s water use is affected by P, ΔT, and ΔPET on 

time scales of greater than 1 month and may be indicative of 

the cumulative influence of these parameters on the amounts of 

water required to maintain green vegetative covers.

WATER USE, IN GALLONS PER DAY

PER METERED CONNECTION

8-MONTH MOVING AVERAGE OF PRECIPITATION, IN INCHES

MONTHLY PRECIPITATION, IN INCHES

WATER USE, IN GALLONS PER DAY

PER METERED CONNECTION

y = -2.3357x + 665

R = 0.0176 2

1,000

900

800

700

600

500

400

300

200

100

0

1,000

900

800

700

600

500

400

300

200

100

0

0 5 10 15 20

0 2 4 6 8

y = -26.598x + 771.3

R = 0.308 2

p < 0.001

p = 0.35

B

A

(non-significant correlation)

(significant correlation)

Figure 8. Metered water-use rates at Orlando Utilities Commission versus (A) monthly 

precipitation and (B) the 8-month moving average of precipitation at the Orlando NOAA 

station, 1992-2006.
Relations between Water Use and Meteorological Parameters/Drought Indices 17

Table 7. Comparison of single-variant regression analyses for monthly and moving-averaged parameter values, 1992-2006. [Values are coefficients of determination, R2; 1-mo, single month result; ma, moving monthly averaged result (number in parentheses is number of months found to provide highest R2); P, precipitation; T, temperature; PET, Thornthwaite potential evapotranspiration; P-PET, precipitation minus potential evapotranspiration; ∆P, change in precipitation; ∆T, change in temperature; ∆PET, change in potential evapotranspiration; ∆(P-PET), change in precipitation minus potential evapotranspiration; Daytona, City of Daytona Beach; Eustis, City of Eustis; GRU, Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange County Utilities (southern service area); OUC, Orlando Utilities Commission; SCU, Seminole County Utilities (northeast, northwest service areas); STA, City of St. Augustine]

Meteorological 

parameter

Daytona Eustis GRU JEA OCU OUC SCU STA Geometric mean Sensitivity factor

1-mo ma 1-mo ma 1-mo ma 1-mo ma 1-mo ma 1-mo ma 1-mo ma 1-mo ma 1-mo ma 1-mo ma

P 0.234 (8) 0.126 0.254 (9) 0.134 (11) 0.435 (8) 0.052 0.244 (8) 0.373 (8) 0.110 (9) 0.052 0.270 (7) 0.071 0.235 0.213 1.880T 0.342 .342 (1) .120 .106 (1) 0.313 .322 (1) 0.357 .357 (1) .186 .186 (1) 0.225 .225 (1) 0.129 .155 (2) .078 .078 (1) .194 .196 1.552 1.568PET .326 .326 (1) .109 .109 (1) .311 .311 (1) .345 .345 (1) .191 .191 (1) .214 .214 (1) .132 .147 (2) .088 .088 (1) .192 .195 1.532 1.560P-PET .107 .137 (4) .308 .347 (2) .172 .172 (1) .376 .403 (2) .272 .272 (1) .192 .232 (3) .056 .257 (3) .165 .165 (1) .178 .233 1.424 1.864∆P .074 .074 (1) .074 .074 .074 .074∆T .306 .654 (3) .153 .332 (3) .058 .202 (4) .125 .491 (4) .118 .358 (3) .145 .366 (3) .111 (4) .057 .159 (3) .119 .290 .833 2.320∆PET .313 .661 (3) .227 .300 (3) .026 .245 (4) .199 .449 (4) .158 .359 (3) .183 .354 (3) .104 (4) .074 .174 (3) .134 .290 .938 2.320∆(P-PET) .156 (2) .173 (4) .199 (4) .077 .077 (1) .041 .054 (3) .056 .121 .112 .605Geometric mean .258 .340 .162 .202 .121 0.219 .258 .370 .145 .247 .190 .285 .089 .120 .072 .124Sensitivity factor 1.290 2.040 .972 1.414 .605 1.314 1.290 2.590 .870 1.729 .950 1.710 .445 .960 .504 .868
18 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

Nonlinear Relations

In most cases, linear functions provided the best (or very 

nearly so) R2 values for the single-variant regressions. At 

several utilities, however, plots between metered water-use 

rates and the moving averages of ΔT, ΔPET, and P-PET were 

clearly curvelinear. In such cases, R2 values could be further 

improved by fitting the data with nonlinear (polynomial) functions. At Eustis, for example, the R2 value determined from a 

linearly regressed plot of metered rates relative to the 3-month 

moving average of ΔT increased from 0.33 (table 7) to 0.48 

for the second-order polynomial function (fig. 9). Results 

depicted in figure 9 are noteworthy in that water use appears 

to be relatively insensitive to reductions in the 3-month 

moving average of ΔT (negative scale values) as water use 

approaches some constant minimum usage. Positive changes 

in temperature, however, produce increasingly greater wateruse demands. The plot shown for Eustis in figure 9 is similar 

to those found for the other utilities. 

Table 8 quantifies the highest R2 values determined for 

each meteorological parameter from the three single-variant 

regression models discussed above; that is, from either the 

single monthly linear fit, the moving-averaged linear fit, or 

the moving-averaged polynomial fit. At OCU, for example, 

metered water-use rates were best correlated with a linear 

fit of the single monthly values of PET (R2 = 0.19), while 

y = 1.2224x + 7.3842x + 288.16 2

R = 0.4849 2

0

100

200

300

400

500

-10 -5 0 5 10

3-MONTH MOVING AVERAGE OF

TEMPERATURE, IN DEGREES FAHRENHEIT

CHANGE IN

WATER USE, IN GALLONS

PER DAY PER METERED

CONNECTION

Table 8. Results of best fit linear and nonlinear single-variant regression analyses, 1992-2006. 

[Values are coefficients of determination, R2; P, precipitation; T, temperature; PET, Thornthwaite potential evapotranspiration; P-PET, precipitation minus 

potential evapotranspiration; ∆P, change in precipitation; ∆T, change in temperature; ∆PET, change in potential evapotranspiration; ∆(P-PET), change in 

precipitation minus potential evapotranspiration; a, the best R2 value was obtained with a single month linear model; b, the best R2 value was obtained with 

a moving average linear model; c, the best R2 value was obtained with a moving average polynomial model; Daytona, City of Daytona Beach; Eustis, City 

of Eustis; GRU, Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange County Utilities (southern service area); OUC, Orlando 

Utilities Commission; SCU, Seminole County Utilities (northeast, northwest service areas); STA, City of St. Augustine. Single parameter best correlated 

with water use at each utility is in bold]

Meteorological 

parameter Daytona Eustis GRU JEA OCU OUC SCU STA

P 0.234 b 0.254 b 0.134 b 0.435 b 0.244 b 0.373 b 0.110 b 0.270 b

T .342 a .120 a .322 a .357 a .186 a .225 a .155 b .078 a

PET .326 a .109 a .311 a .345 a .191 a .214 a .147 b .088 a

P-PET .137 b .381 c .172 a .475 c .272 a .232 b .257 b .165 a

∆P .074 a

∆T .661 b .485 c .202 b .575 c .478 c .476 c .111 b .159

∆PET .616 b .415 c .245 b .506 c .530 c .484 c .104 b .174 b

∆(P-PET) .156 b .173 b .199 b .077 a .054 b

Figure 9. Metered water-use rates 

at Eustis and the 3-month moving 

average of change in temperature at 

the Lisbon NOAA station, 1992-2006.
Relations between Water Use and Meteorological Parameters/Drought Indices 19

a polynomial function provided the best fit for the 3-month 

moving average of ΔPET (R2 = 0.53). Overall, a single 

explanatory variable could account for greater than 50 percent 

of the variance in the data at three of the eight sites—Daytona 

(66 percent), JEA (58 percent), and OCU (53 percent). In 

general, and when regressed against single meteorological 

parameters, metered rates appear to be most sensitive to 

changes in the moving averages of ∆T or ∆PET. 

In summary, even though single parameters are significantly correlated with water use, the high variance present 

in these relations limits the use of single-variant analyses for 

predictive purposes. However, these results do provide insight 

into which parameters are most likely to influence water-use 

rates, the importance of system memory, and the influence of 

nonlinear relations between water usage and climate. 

Multivariant Regression Analyses

Multivariant linear regression models predict the relation 

between a response (dependent) variable and explanatory 

(independent) variables as:

Y = β0 + β1x1 +.... Βkxk + ε (1)

where 

Y is the dependent variable, 

β0 is the intercept, 

β1 is the slope coefficient for the first explanatory 

variable (x1), 

Bk is the slope coefficient for the kth explanatory 

variable (xk), and 

ε is the error or remaining unexplained “noise” in 

the data.

Guidelines presented by Helsel and Hirsch (2002) were 

used to conduct the multivariant analyses and to select the 

“best” model (regressed equation) for each utility. The data 

were analyzed as follows: 

1. Initial model runs were conducted using all candidate 

explanatory variables, including those moving-averaged 

variables found to improve the single-variant results. 

Residual plots of the dependent variable were examined 

for linearity, constant variance, and normality. Where 

required, the dependent variable was log-transformed to 

conform to these assumptions. 

2. Partial plots of the explanatory variables were examined 

for nonlinearity and log-transformed where necessary.

3. Variables having unacceptable levels of multicollinearity were identified by the Variance Inflation Factor (VIF) 

and grouped separately in subsequent testing. The VIF 

(Marquardt, 1970) is an index which measures how 

much the variance of a coefficient is increased because 

of colinearity. In this study, variables with VIF >3 are 

considered to be highly correlated and included those 

which are mathematically related such as T and PET, P 

and (P-PET), and P and SPI.

4. The best subsets of models at each utility were identified 

for each of “n” explanatory variables; the best model was 

selected as that which minimized Mallows Cp. Mallows 

Cp is a test statistic designed to achieve a compromise 

between explaining as much of the variance in the dependent variable as possible by including all relevant explanatory variables, while minimizing the standard error of the 

resulting estimates by keeping the number of coefficients 

small (Helsel and Hirsch, 2002). All explanatory variables 

included in the best models had statistically significant 

slope coefficients (p < 0.05). 

Results of the multivariant regression analyses are 

given in table 9 and summarized below:

• Relative to the single-variant analyses, multivariant 

regression analyses improved the correlations between 

metered water-use rates and meteorological parameters 

at all eight utilities. Adjusted R2 values for the best 

models exceeded 0.60 (considered to be an acceptable level of correlation) at five of the eight utilities, 

ranging from about 0.79 at JEA to about 0.29 at STA. 

Greatest improvements are seen at GRU (from 0.31 

to 0.62), SCU (from 0.26 to 0.48), and JEA (from 0.58 

to 0.79). Marginal improvements are seen at Daytona 

(from 0.61 to 0.69) and STA (from 0.27 to 0.29). 

• The number of significant meteorological parameters 

included in the eight best models ranged from two 

at STA to four at GRU, JEA, and SCU. The amount 

of available water (P-PET) was the single parameter 

most common to the best models (six of eight utilities), 

while ΔT or ΔPET were present at five of eight sites. 

Neither of the drought indices, PDSI or SPI, was found 

to be significantly correlated to metered rates in any of 

the eight best models;

• The monthly moving average of at least one parameter 

was present in seven of the eight best models, again 

reflective of system memory. 

• P and ∆P were much better correlated with metered 

water-use rates in the multivariant analyses (included 

as significant parameters in six of the eight best 

models) than in the single-variant analyses (significant 

at only three of eight utilities). The contrast here is 

noteworthy. Multivariant results provide a more realistic view of how precipitation is related to water use 

because the analytical procedure factors out any offsetting effects of other parameters, in this case temperature, on the dependent variable. Partial residual plots 

developed between metered rates and precipitation 

and temperature (not shown here) indicate that 
20 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida Table 9. Results of multivariant regression analyses between metered water-use rates and selected meteorological parameters and drought indices, 1992-2006. [R2 adj, adjusted coefficient of determination; Cp, Mallow’s statistic; PRESS, PRESS statistic; VIF, variance inflation factor; gpdm, gallons per day per metered connection; P, precipitation, in inches; PET, Thornthwaite potential evapotranspiration, in inches; T, temperature in degrees Fahrenheit; ∆P, change in precipitation, in inches; ∆T, change in temperature, in degrees Fahrenheit; ΔPET3ma, 3-month moving average of change in PET, in inches; lnP, natural log of precipitation; Daytona, City of Daytona Beach; Eustis, City of Eustis; GRU, Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; 

OCU, Orange County Utilities (southern service area); OUC, Orlando Utilities Commission; SCU, Seminole County Utilities (northeast, northwest service areas); STA, City of St. Augustine]

Daytona Eustis GRU JEA OCU OUC SCU STA

Regression statisticsR2 adj (R2 single variant) 69.0 (61.1) 63.8 (48.4) 62.0 (31.3) 78.8 (57.5) 56.9 (53.0) 63.3 (48.4) 48.2 (25.7) 29.2 (27.0)Cp 3.1 2.6 2.9 4.6 3.0 2.8 3.2 1.9PRESS 24654 87767 .478 57803 409045 .318 1.11 .205VIF <3 <2 <3 <3 <2 <2 <2 <3Significant parameters ΔPET3ma (<.001) ΔT3ma (<.001) T3ma (<.001) T (<.001) ΔPET3ma (<.001) ΔT4ma (<.001) PET (<.001) P7ma (<.001)(p-value) ΔPET (.008) (P-PET)2ma (.001) ΔP (<.001) P8ma (<.001) P-PET (<.001) (P-PET)4ma (<.001) ΔP (<.001) PET (.046)

P (.010) P (.002) P (<.001) P-PET (<.001) Δ(P-PET) (.013) P (.003) P-PET (<.001)

(P-PET)2ma (<.001) ΔP (.003) ΔT (<.001) 

“Best” regressed equations Daytona gpdm = 508 - 1.40*P -7.59*ΔPET + 36.4*∆PET3ma

Eustis1 gpdm = 784 + 6.79*ΔT3ma - 147*ln(P-PET+20)2ma - 19.2*lnPGRU gpdm = exp(3.27 + .615*lnT3ma - .0324*lnP + .00728*ΔP - .0109*(P-PET)2ma)JEA gpdm = 353 - 46.3*lnP8ma + 2.69*ΔP - 9.95*(P-PET) + 2.35*TOCU gpdm = 2137 + 60.0*ΔPET3ma - 388*ln(P-PET+20) + 4.94*Δ(P-PET)OUC gpdm = exp(6.52 + .0191*ΔT4ma - .0205*(P-PET)4ma - .00746*P)SCU gpdm = exp(6.50 + .0367*PET + .0218*ΔP - .0266*(P-PET) - .191*ln(ΔT+20)STA gpdm = exp(5.89 - .0211*P7ma + .00553*PET)1Precipitation measured at City of Eustis WTP.
Relations between Water Use and Meteorological Parameters/Drought Indices 21

metered rates decrease significantly with increasing 

precipitation but increase significantly with temperature. Because these two parameters have the opposite 

effect on metered rates and are seasonally related 

(for example, highest P and T typically occur together 

in the summer months while lowest P and T occur 

together in the winter months), the two parameters 

tend to dampen or offset the effects of one another on 

the dependent variable. Based on comparison with the 

single-variant results, temperature appears to dampen 

more of the influence of precipitation on metered rates 

than vice versa. 

The effect of using locally measured versus the more 

distant NOAA-measured precipitation on correlated results 

was evaluated for the four utilities operating single plants—

Daytona, Eustis, GRU, and STA. At Eustis, where the watertreatment plant and associated Lisbon station are farthest 

apart (about 7 miles), precipitation measured locally at the 

plant improved the correlation. At Daytona, GRU, and STA, 

however, where the plants and associated NOAA stations are 

closer to one another, the correlations were not improved by 

using locally measured precipitation. These results suggest 

that the spatial variability of monthly precipitation may affect 

the relations between water use and precipitation discussed 

here, particularly for those utilities having multiple and more 

geographically dispersed water-treatment plants such as JEA, 

OUC, OCU, and SCU.

Predictive Analyses 

The regressed equations given in table 9 were used to 

predict metered water-use rates for 2007. Results provide 

some indication of how well these equations may be expected 

to predict future rates, within the limits of the parameter-value 

ranges documented in this 15-year study, for hypothetical sets 

of meteorological conditions that may be of interest to waterresource managers and ground-water modelers. Precipitation 

in 2007 averaged 45.5 in., a relatively dry year compared 

with the long-term (1959-2006) average of 50.9 in. Water-use 

restrictions were in effect in 2007.

Measured versus predicted water-use rates are depicted 

graphically in figure 10 and summarized statistically in 

table 10. Overall, some models performed better than others, 

0

200

400

600

800

1,000

1,200

0 200 400 600 800 1,000 1,200

MEASURED WATER USE, IN GALLONS PER DAY PER METERED CONNECTION

PREDICTED 

WATER USE, IN GALLONS PER DAY PER METERED CONNECTION

CITY OF DAYTONA BEACH

CITY OF EUSTIS

GAINESVILLE REGIONAL UTILITIES

JACKSONVILLE ELECTRIC AUTHORITY

ORLANDO UTILITIES COMMISSION

SEMINOLE COUNTY UTILITIES

CITY OF ST. AUGUSTINE

ORANGE COUNTY UTILITIES

LINE OF EQUALITY

EXPLANATION

Figure 10. Measured versus predicted water-use rates for the selected utilities, 2007.
22 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

but most provided fair-to-good results. The average error 

between predicted and measured rates, an indication of model 

bias, ranges from -53 gpdm at OCU (overpredicted rates) 

to 42 gpdm at GRU (underpredicted rates). The coefficient 

of variation (COV), an error statistic normalized to allow 

comparison between utilities having different annually 

averaged water-use rates, is calculated by dividing the 

root-mean square error of the differences between measured 

and predicted rates by the average annual measured rate. 

The COV values ranged from a low of 5.8 percent at Eustis 

to a high of 15 percent at GRU. The remaining two statistics 

given in table 10—the slope of the line-of-best-fit and the 

coefficient of determination—are regression statistics that 

describe just the predicted results. Ideally, a regressed slope 

of 1.0 would indicate that, even though the predicted results 

may be biased in one direction of another, the rate of change 

in predicted water-use rates equals that of the measured rates 

(a desirable attribute). Regressed slopes between 0.90 and 

1.10, such as those determined for Daytona Beach, Eustis, 

and OCU, provide reasonably good approximations of the 

measured changes in rates. Based collectively on the abovedescribed statistics, the most accurate predictions of metered 

rates were made at Eustis while the least accurate predictions 

were made at GRU. 

Two factors not accounted for in the regression analyses 

that probably have significant effects on water use are 

changes in (1) the ratios of water-use type and (2) reclaimed 

water usage. At GRU, for example, where metered rates are 

underpredicted, the ratio of commercial-to-residential service 

connections increased from 8.4 to 9.7 percent in 2007 (Rick 

Hutton, Gainesville Regional Utilities, written commun., 

2008). Given that the amount of water used per commercial 

connection at GRU exceeds that used per residential connection (see fig. 2), the increase in the ratio of commercial-toresidential connections in 2007 may be responsible, at least in 

part, for the biased results at this utility. 

Increased usage of reclaimed water likely contributed to 

the overpredicted results at JEA and OUC. At JEA, the number 

of metered connections for reclaimed water use remained 

nearly constant between 2001 and 2006 but increased sharply 

in 2007 (fig.11A). Volumetrically, reclaimed water usage 

roughly doubled in 2007 (Brad Russell, Jacksonville Electric 

Authority, oral commun., 2008). At OUC, reclaimed-water 

usage has been increasing over the past 10+ years and is 

estimated to have increased from about 2 Mgal/d in 1996 

to 9 Mgal/d in 2007 (fig.11B). In addition to increases in 

reclaimed water usage, both JEA and OUC service areas were 

subject to water-use restrictions in 2007 - another factor not 

accounted for by the models which would tend to contribute to 

overpredicted rates. 

Table 10. Summary of descriptive and error statistics for measured and predicted metered water-use rates, 2007. 

[gpdm, gallons per day per metered connection; R2, coefficient of determination; NOAA, National Oceanic and Atmospheric Administration]

Utility

Average annual water use

Average error 

(gpdm)

Root-mean 

square error 

(gpdm)

Coefficient of 

variation (percent)1

Slope line 

of best fit R2

Measured 

(gpdm)

Predicted 

(gpdm)

City of Daytona Beach 465 507 -42 42.4 9.1 0.92 0.85

City of Eustis2 342 333 9 19.9 5.8 .95 .80

City of Eustis3 342 326 16 22.4 6.6 .74 .84

Gainesville Regional Utilities 392 350 42 58.9 15.0 .25 .12

Jacksonville Electric Authority 428 464 -36 41.1 9.6 .76 .86

Orange County Utilities 945 998 -53 62.0 6.6 .98 .78

Orlando Utilities Commission 634 686 -52 56.9 9.0 1.15 .79

Seminole County Utilities 418 445 -27 39.8 9.5 .64 .53

City of St. Augustine 346 337 9 20.8 6.0 .32 .46

1Calculated as 100*RMSE/measured average annual water-use rate.

2 Using precipitation measured at water-treatment plant.

3Using precipitation measured at Lisbon NOAA station.
Summary 23

Summary 

Future and increased ground-water withdrawals in 

east-central and northeast Florida have the potential to 

adversely affect sensitive resources such as springs, lakes, and 

wetlands. Rates of ground-water withdrawals vary seasonally 

in response to municipal water-supply demands, which include 

residential, commercial and industrial usages. Changes in 

municipal demands are affected by a complex combination 

of climatological, economic, social, and regulatory factors, 

not all of which can be accounted for in a statistical examination of cause-and-effect relations. However, if water use can 

be statistically related to variations in commonly measured 

meteorological parameters and (or) drought indices, these relations would provide resource managers an improved tool for 

predicting the potential effects of climate, and perhaps climate 

change, on associated demands. Additionally, scientists tasked 

with developing transient ground-water flow models could 

more confidently quantify ground-water withdrawal rates 

under varying climatic conditions. 

This study examined the relations between municipal 

water use and several commonly measured meteorological 

parameters and drought indices. Selected parameters include 

precipitation (P), air temperature (T), potential evapotranspiration (PET), and available water (P-PET). Drought indices 

include the Palmer Drought Severity Index (PDSI) and the 

Standardized Precipitation Index (SPI). Water-use rates were 

analyzed for seasonal (monthly) variations at eight utilities 

in east-central and northeast Florida which include the City 

of Daytona Beach (Daytona), the City of Eustis (Eustis), 

Gainesville Regional Utilities (GRU), Jacksonville Electric 

Authority (JEA), Orange County Utilities (OCU) (southern 

service area), Orlando Utilities Commission (OUC), Seminole 

County Utilities (SCU) (northeast and northwest service 

areas), and the City of St. Augustine (STA). The amounts of 

potable water treated at associated WTPs in 2006 ranged from 

about 3.2 Mgal/d at Eustis to about 131 Mgal/d at JEA. 

The water-use data were analyzed to identify significant 

seasonal (monthly) trends, quantify differences in use, and 

determine interrelations. Both single- and multivariant regression analyses were applied to quantify the relations between 

water use and selected meteorological parameters and drought 

indices. Results of the multivariant regressions were used to 

predict monthly rates in 2007. Three types of single-variant 

regression analyses were conducted: (1) a simple linear 

regression of both total and metered water-use rates against 

single (current) monthly parameter values and drought 

indices; (2) a linear regression of metered rates against 

monthly moving-averaged parameter values (to account 

for the influence of system memory); and (3) a nonlinear 

regression of metered rates against monthly moving-averaged 

parameters values. Both total and metered water-use rates 

were used as dependent variables in the single monthly 

analyses, while just the metered rates were used in the latter 

two analyses. Because monthly water-use rates were found to 

be serially correlated, the regression analyses were conducted 

on a subset of the dependent variable, that is, on those rates 

0

50

100

150

200

250

300

350

400

APR

2001

SEP

2002

JAN

2004

MAY

2005

OCT

2006

FEB

2008

JUL

2009

0

2

4

6

8

10

1994 1996 1998 2000 2002 2004 2006 2008

YEAR

NUMBER OF METERED

CONNECTIONS

RECLAIMED 

WATER

USE, IN MILLION

GALLONS PER DAY

B

A

MONTH AND YEAR

Figure 11. Metered connections 

for reclaimed water use at 

Jacksonville Electric Authority, 

October 2001-December 2006, and 

reclaimed water use for Orlando 

Utilities Commission, 1995-2007.
24 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

measured for every other calendar month (for example, 

January, March, May, July, September, and November). 

All trend testing and regression analyses were conducted 

from 1992 to 2006, with precipitation patterns and extremes 

similar to those of the longer term (1959-2006) record. 

Climatological data and drought indice values were obtained 

from National Oceanic and Atmospheric Administration 

(NOAA) rainfall stations closest to the utilities. 

Total water-use rates between 1992 and 2006 increased 

at all utilities, from about 4 percent at Daytona to greater 

than 200 percent at OCU and SCU. Metered rates, however, 

decreased at six of the eight utilities, from about 2 percent at 

OCU and OUC to about 17 percent at Eustis. In December 

2006, metered water-use rates ranged from about 983 gpdm 

at OCU to about 314 gpdm at Eustis. Given the absence of 

significant trends in precipitation, decreases in metered rates 

can likely be attributed to temporal changes in non-climatic 

factors that were not considered in this study such as the 

ratios of water-use types, usage of reclaimed water, effectiveness of water-use restrictions, demographics, water pricing, 

and so forth. 

When averaged for the eight utilities, metered water-use 

rates depict a clear seasonal pattern in which rates tend to be 

least in the winter and greatest in the late spring. In January, 

metered rates averaged about 9 percent below the 15-year 

daily mean but about 11 percent above the daily mean in 

May. These results serve to provide guidance for quantifying 

seasonally variant ground-water withdrawal rates, under 

average meteorological conditions, for predictive groundwater flow models. 

Linear regression of water-use rates versus monthly 

parameter- and drought-indice values generally produced poor, 

though still statistically significant, correlations (R2 < 0.40). 

Metered rates were better correlated with the meteorological 

parameters while total rates were better correlated with PDSI 

and SPI. Metered rates were best correlated with T, PET, 

and P-PET, and least correlated with ∆P and ∆(P-PET). Both 

metered and total water-use rates were positively correlated 

with T, PET, ∆P, ∆T, and ∆PET, while negatively correlated 

with P, P-PET, and ∆(P-PET). Of the eight utilities, metered 

rates were most sensitive to meteorological parameters at JEA 

and Daytona while least sensitive to these parameters at SCU 

and STA. 

The relation between total water use and the meteorological parameters is affected by the growth rate in water use. 

Water-use rates at the utilities with the lowest growth rates 

(Daytona, Eustis, and OUC) were much better correlated with 

the parameters than were water-use rates at utilities with the 

highest growth rates (OCU, SCU, and STA). The logarithmic 

function that quantifies the relation explains greater than 80 

percent of the scatter in the data. This result is noteworthy in 

that it shows metered water use is a better choice of the dependent variable for assessing correlations with meteorological 

parameters over periods of substantial growth.

Correlations between metered water-use rates and 

meteorological variables could be improved by accounting 

for the influence of system memory. Metered rates were 

most sensitive to system memory of precipitation where, 

when averaged for the eight utilities, the sensitivity factor 

increased from 0.213 (for single monthly regressions) to 

1.880 for the monthly moving-averaged regressions. Optimal 

moving-averaged time frames for precipitation ranged from 

7 months at STA to 11 months at GRU. At JEA, regression of 

metered water-use rates versus the 8-month moving average 

of P increased R2 from < 0.10 (insignificant correlation) to 

0.44 (highly significant correlation). Similarly, regression of 

metered rates at Daytona versus the 3-month moving averages 

of ∆T and ∆PET increased R2 from 0.30 and 0.31 (for single 

monthly regressions) to 0.65 and 0.66, respectively. Overall, 

accounting for system memory improved the sensitivity of 

metered rates to the full suite of meteorological parameters 

by greater than 50 percent at all but one utility, and by 100 

percent or greater at four of the eight utilities. 

In the few cases where plots of metered water use versus 

the moving-averaged values of ∆T, ∆PET, and P-PET were 

curvilinear, the R2 values could be further improved by fitting 

the data with polynomial functions. At Eustis, for example, R2

determined from the plot of metered rates versus the 3-month 

moving average of ΔT increased from 0.33 for the linear plot 

to 0.49 when fitted with polynomial function. These results 

are noteworthy in that water use appears to be relatively 

insensitive to reductions in the 3-month moving average of 

ΔT (negative scale values) as water use approaches a constant 

(consumptive) minimum usage rate. Positive changes in the 

3-month moving average of ΔT, however, result in increasingly greater water-use demands. The results for Eustis were 

similar to those found for the other sites. Overall, the highest 

R2 value at each utility was achieved by either a linear or 

nonlinear regression of metered rates against moving-averaged 

values of the meteorological parameters. 

Relative to the single-variant analyses, multivariant 

regression analyses improved the correlations between 

metered water-use rates and meteorological parameters at 

all eight sites. Adjusted R2 values ranged from 0.79 at JEA 

to 0.29 at STA and exceeded 0.60 at five of the eight sites. 

Greatest improvements are seen at GRU (from 0.31 to 0.62), 

SCU (from 0.26 to 0.48), and JEA (from 0.58 to 0.79). 

Marginal improvements are seen at Daytona (from 0.61 to 

0.69) and STA (from 0.27 to 0.29). The amount of available 

water (P-PET) was the parameter most commonly included 

in the best models (six of eight sites), while ΔT or ΔPET were 

present at five of eight sites. Neither of the drought indices, 

PDSI or SPI, were significant parameters in any of the eight 

best models. The moving average of at least one parameter 

was present in seven of the eight best models, again indicative 

of the role of memory in water use.

Monthly P and ∆P were much better correlated with 

metered water-use rates in the multivariant analyses (included 

as significant parameters at six of eight sites) than in the 

single- variant analyses (significant at only three of eight 
Selected References 25

sites). This contrast can be attributed to the fact that the 

multivariant analysis better isolates the effect of precipitation 

on water use by factoring out any offsetting effects from other 

parameters, such as T, that are directly (and not inversely) 

related to water use. 

The “best” model equations determined from the multivariant analyses were used to predict metered water-use rates 

for 2007, a relatively dry year. The average error between 

predicted and measured results, an indication of model 

bias, ranged from -53 gpdm at OCU (overpredicted rates) 

to 42 gpdm at GRU (underpredicted rates). The coefficient 

of variation (COV), which is the error in predicted results 

normalized to allow comparison between utilities with varying 

water-use rates, ranged from a low of 5.8 percent at Eustis to 

a high of 15 percent at GRU. Regressed slopes between 0.90 

and 1.10, such as those determined for Daytona Beach, Eustis, 

and OCU, provide reasonably good approximations of the 

measured changes in rates. Based collectively on the error and 

descriptive statistics, the best predictions were made at Eustis 

while the poorest predictions were made at GRU. Increased 

usage of reclaimed water likely contributed to the biased 

(overpredicted) results at OUC and JEA, while changes in 

the ratio of commercial-to-residential users in 2007 probably 

contributed to the underpredicted rates at GRU.

In future work, researchers may want to consider several 

explanatory variables that were not included in this study. 

Locally measured precipitation could be used in place of 

more distant NOAA station data, particularly for the larger 

utilities having widely dispersed service areas. More highly 

resolved data are needed to differentiate the types of metered 

connections to better account for temporal changes in use 

type. Finally, accounting for changes in reclaimed water use, 

as well as for periods where water-use restriction are in place, 

may help improve the predictive capabilities of regressionbased models. 

Selected References

Alley, W.M., 1984, The Palmer drought severity index: 

Limitations and assumptions: Journal of Climate and 

Applied Meteorology, v. 23, p. 1100-1109.

Conover, W.L., 1999, Practical nonparametric statistics, 

(3d ed.): New York, John Wiley, 584 p.

D’Agostino, R.B., Belanger, A., and D’Agostino, R.B., Jr., 

1990, A suggestion for using powerful and informative tests 

of normality: American Statistician, v. 44, p. 316-321. 

Durbin, J., and Watson, G.S., 1951, Testing for serial correlation in least squares regression II: Biometrika, v. 38. 

Hagan, R.M., Haise, H.R., and Edminster, T.W. (eds.), 1967, 

Irrigation of agricultural lands: No. 11 in series, Agronomy, 

published by the American Society of Agronomy, p. 564-565. 

Helsel, D.R., and Hirsch, R.M., 2002, Statistical methods in 

water resources: U.S. Geological Survey Techniques of 

Water-Resources Investigations, book 4, chap. A3, 510 p. 

Hirsch, R.M., Slack, J.R., and Smith, R.A., 1982, Techniques 

of trend analysis for monthly water-quality data: Water 

Resources Research, v. 18, p. 107-121. 

Karl, T.R., and Knight, RW., 1985, Atlas of monthly Palmer 

hydrological drought indices (1931-1983) for the contiguous United States: Historical Climatology Series 3-7, 

Asheville, N.C., National Climatic Data Center. 

Mann, H.B., 1945, Nonparametric test against trend: 

Econometrica, v. 13, p. 245-259. 

Marella, R.L., 1992a, Factors that affect public-supply water use 

in Florida, with a section on projected water use to the year 

2020: U.S. Geological Survey Water-Resources Investigations Report 91-4123, 35 p.

Marella, R.L., 1992b, Water withdrawals, use, and trends in 

Florida, 1990: U.S. Geological Survey Water-Resources 

Investigations Report 92-4140, 38 p.

Marella, R.L., 2004, Water withdrawals, use, discharge, and 

trends in Florida, 2000: U.S. Geological Survey Scientific 

Investigations Report 2004-5151, 136 p.

Marquardt, D.W., 1970, Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation: 

Technometrics, v. 12, p. 591-612. 

McCabe, G.J., McLaughlin, J.D., and Muller, R.A., 1985, 

Thornthwaite continuous monthly water budget—A computer program for microcomputers: Louisiana State University Climate Paper 85-1, 20 p. 

McKee, T.B., Doesken, N.J., and Kleist, J., 1993, The relationship of drought frequency and duration to time scales: Preprints, 8th Conference on Applied Climatology, p. 179-184. 

Mosteller, F., and Tukey, J.W., 1977, Data analysis and regression: Menlo Park, Calif., Addison-Wesley, 588 p.

National Agricultural Decision Support System web site, 

University of Nebraska-Lincoln Department of Computer 

Science and Engineering http://nadss.unl.edu/PDSIReport/

pdsi/steps

Palmer, W.C., 1965, Meteorological drought: Research Paper 

No. 45, Washington, D.C., U.S. Weather Bureau (NOAA 

Library and Information Services Division).

Thornthwaite, C.W., 1948, An approach towards a rational classification of climate: Geography, Rev. 38, no. 1, p. 55-94. 

Tukey, J.W., 1977, Exploratory data analysis: Reading, Mass., 

Addison-Wesley, 506 p. 
26 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

Glossary (adapted from Marella, 2004)

Commercial water use: Water for motels, hotels, restaurants, office buildings, 

commercial facilities and civilian and military institutions. The water may be obtained 

from a public supply or be self-supplied.

Industrial water use: Water used for industrial purposes such as fabricating, 

processing, washing, and cooling, and includes such industries as steel, chemical and 

allied products, paper and allied products, mining, and petroleum refining. The water 

can be obtained from a public supply or be self-supplied.

Municipal (public) supply: Water withdrawn by public or private water suppliers and 

delivered to users who do not supply their own water. Water suppliers provide water 

for a variety of uses, such as domestic, commercial, industrial, thermoelectric power 

(domestic and cooling purposes), and public water use. According to the Florida 

Department of Environmental Protection, any water system that serves more than 25 

people or has 15-year-round service connections is considered a community public 

supplier. 

Reclaimed water: Water that has received at least secondary treatment and is reused 

after leaving a wastewater treatment facility.

Residential water use: Water for normal household purposes, such as drinking, food 

preparation, bathing, washing clothes and dishes, flushing toilets, and watering lawns 

and gardens. The water can be obtained from a public supply or be self-supplied.

Water use: (1) In a restrictive sense, the term refers to water that is actually used for a 

specific purpose such as domestic use, irrigation, or industrial processing; and (2) more 

broadly, water use pertains to human’s interaction with and influence on the hydrologic 

cycle, and includes elements such as water withdrawals, deliveries, consumptive use, 

wastewater releases, reclaimed wastewater, return flow, and instream use.

Withdrawal: Water removed from the ground or diverted from a surface-water source. 

The amount of water withdrawn may not equal the amount of water used due to water 

transfers or the recirculation or recycling of the same water. For example, a power plant 

may use the same water multiple times but withdraw a significantly different amount.
Appendix 27

Appendix. Amounts of water treated and number of metered connections at selected municipal water-treatment plants/service areas, 1992-2007.

(Mgal/d, million gallons per day; conn, number of active metered connections; ERC, equivalent metered connections; Daytona Beach, City of Daytona Beach; Eustis, City of Eustis; GRU, 

Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange County Utilities (southern service area); OUC, Orlando Utilities Commission; SCU, Seminole County Utilities 

(NE, NW service areas); STA, City of St. Augustine]

Month-year Daytona Beach Eustis GRU JEA OCU (south) OUC SCU (NE, NW) STA

Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d ERC Mgal/d conn

Jan-92 11.374 22992 2.438 6559 17.810 43135 64.320 142958 4.164 4670 68.800 109699 4.092 12441 1.737 6682

Feb-92 12.172 23000 2.392 6576 16.745 42797 62.130 143434 4.173 4742 66.884 109862 4.486 12584 1.827 6706

Mar-92 13.071 23011 2.479 6586 15.681 42459 64.800 143880 4.684 4786 69.694 110112 3.629 12689 1.896 6241

Apr-92 12.983 23021 2.903 6604 16.449 41662 70.660 144357 5.105 4810 71.899 110200 4.342 12615 1.972 6712

May-92 12.345 23146 3.087 6616 18.218 41086 74.060 144818 5.478 4827 87.011 110381 5.193 12710 1.953 6753

Jun-92 12.397 23067 2.325 6631 19.466 41277 67.430 145294 5.217 4863 78.915 110474 5.307 13358 2.046 6866

Jul-92 13.474 23054 2.562 6648 19.546 40917 78.960 145755 5.771 4887 87.715 110567 5.217 12782 2.066 6864

Aug-92 12.565 23092 2.343 6657 17.848 42299 70.840 146232 5.301 4947 76.582 110680 5.358 12768 1.620 6855

Sep-92 12.090 23060 2.400 6663 18.835 43714 65.720 146708 4.808 5083 73.427 110879 4.741 12798 1.625 6867

Oct-92 11.681 23131 2.392 6671 16.525 43865 64.630 144042 4.707 5219 72.676 110863 4.625 12919 1.667 6949

Nov-92 11.783 23134 2.244 6679 17.327 44659 64.820 145449 4.842 5229 70.185 111045 4.088 12973 1.720 6845

Dec-92 11.119 23136 2.259 6688 16.835 43652 63.680 146424 4.783 5297 67.800 111147 4.902 13020 1.705 6849

Jan-93 11.416 23136 2.223 6703 15.348 44098 62.080 146644 4.687 5321 66.173 111222 4.517 13129 1.711 6651

Feb-93 11.914 23136 2.234 6719 16.373 44644 62.640 147255 4.909 5402 67.538 111353 4.821 13281 1.768 6679

Mar-93 12.874 23164 2.230 6724 15.428 44773 65.680 147424 5.015 5469 68.646 111651 4.154 13266 1.826 6713

Apr-93 12.670 23164 2.537 6738 17.225 44949 72.720 148026 5.637 5486 77.384 111800 4.582 13265 1.698 6699

May-93 12.868 23172 2.817 6757 18.944 45584 87.810 148478 6.044 5488 88.836 111831 5.942 13308 1.752 6741

Jun-93 13.637 23224 3.012 6771 24.252 45509 89.620 149606 6.021 5508 83.044 112056 6.984 13409 1.874 6740

Jul-93 13.968 23251 2.436 6778 20.162 46075 80.280 149742 6.089 5529 76.302 112208 5.796 13490 1.812 6776

Aug-93 14.010 23245 3.009 6787 19.343 49330 83.310 150829 6.364 5586 85.039 112322 6.123 13544 1.791 6799

Sep-93 12.923 23319 2.455 6808 22.581 45783 74.730 151617 5.393 5629 76.287 112391 5.898 13526 1.690 6834

Oct-93 12.419 23319 2.536 6817 20.111 45796 68.430 151666 5.367 5642 76.668 112539 5.428 13545 1.653 6821

Nov-93 11.860 23343 2.397 6826 18.466 45932 65.010 152401 5.291 5690 73.873 112850 5.420 13644 1.580 6645

Dec-93 11.194 23358 2.843 6848 17.707 45732 64.560 152320 5.106 5761 73.362 113015 5.235 13861 1.526 6684

Jan-94 11.171 23373 1.964 6857 16.727 46346 63.340 152732 4.999 5796 68.166 113170 4.488 13751 1.549 6704

Feb-94 12.032 23302 2.420 6863 18.518 46052 64.830 153208 5.047 5846 69.485 113467 4.726 13773 1.614 6736

Mar-94 13.458 23378 2.710 6876 17.172 46323 74.340 152945 5.474 5909 77.689 113760 4.417 13864 1.698 6746

Apr-94 13.597 23385 3.400 6887 20.174 46280 78.640 154005 6.481 5915 87.426 113905 5.647 13915 1.714 6755

May-94 12.932 23409 3.445 6895 19.925 47234 83.950 154627 5.986 5967 86.642 114116 5.589 13960 1.739 6783

Jun-94 12.760 23333 2.543 6910 23.605 46773 72.850 155623 5.935 6051 77.425 114323 6.276 14036 1.650 6758

Jul-94 13.323 23447 2.509 6924 18.392 47033 74.520 155505 6.245 6089 80.608 114533 5.227 14123 1.656 6764

Aug-94 12.552 23464 2.220 6936 17.862 49929 74.890 156223 5.698 6142 77.018 114748 5.325 14460 1.697 6800

Sep-94 12.393 23472 2.293 6956 20.709 47889 74.320 156074 4.869 6197 71.986 114918 5.172 14363 1.688 6805

Oct-94 11.790 23492 2.185 6969 18.127 47360 68.020 155798 5.380 6226 73.120 115089 4.346 14345 1.672 6784

Nov-94 11.500 23495 2.180 6972 18.902 47447 65.490 155559 5.455 6253 74.499 115322 5.115 14385 1.720 6812

Dec-94 11.271 23515 1.924 6983 19.143 47850 62.570 155945 5.047 6305 68.112 115473 4.764 14481 1.650 6779

Jan-95 11.506 23529 1.773 6993 18.020 48294 62.470 156682 5.278 6344 66.690 115540 4.341 14539 1.567 6739

Feb-95 12.296 23542 1.868 7001 18.213 47789 65.480 157045 5.497 6385 69.840 115662 4.729 14605 1.633 6699

Mar-95 13.019 23545 2.031 7013 16.748 47976 69.940 157556 5.987 6439 76.510 115766 4.805 14641 1.660 6659

Apr-95 12.723 23558 2.494 7019 19.813 47976 77.370 158066 6.432 6461 83.940 116021 5.992 14695 1.667 6771

May-95 13.013 23562 3.222 7035 19.721 48908 91.370 159291 6.774 6485 92.860 116158 6.692 14772 1.663 6670

Jun-95 12.787 23561 2.633 7044 23.350 48752 80.460 159254 6.542 6503 86.090 116369 7.716 14807 1.591 6660

Jul-95 13.052 23571 2.494 7088 21.332 48798 84.480 159621 6.984 6523 86.240 116467 8.020 14883 1.705 6654

Aug-95 12.645 23563 2.232 7108 19.663 51850 81.030 160530 6.746 6567 80.930 116760 5.222 14951 1.652 6672

Sep-95 11.797 23574 2.330 7118 21.757 48947 75.020 160050 6.474 6626 80.150 116796 6.255 15010 1.628 6689
28 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

Month-year Daytona Beach Eustis GRU JEA OCU (south) OUC SCU (NE, NW) STA

Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d ERC Mgal/d conn

Oct-95 11.745 23591 2.182 7128 19.753 48900 73.310 161061 6.260 6643 74.050 116961 5.727 15030 1.817 6676

Nov-95 11.800 23595 2.348 7141 19.362 48775 71.390 161108 5.891 6667 78.280 117222 5.942 15075 1.738 6689

Dec-95 11.439 23604 2.230 7150 17.703 49070 69.050 162745 6.061 6713 76.490 117259 6.203 15124 1.602 6678

Jan-96 11.348 23609 2.102 7171 18.005 49484 70.150 163508 5.603 6759 72.930 117404 5.473 15202 1.670 6705

Feb-96 12.521 23638 2.343 7185 17.969 49002 74.740 164170 6.001 6817 77.910 117646 5.741 15241 1.749 6703

Mar-96 13.019 23642 2.194 7195 18.012 49192 71.480 164557 6.129 6850 74.380 117857 5.912 15323 1.725 6701

Apr-96 12.673 23569 2.572 7212 18.864 49280 82.360 164941 7.281 6848 84.290 118083 5.443 15403 1.746 6712

May-96 13.045 23676 2.969 7220 21.949 50191 94.880 165763 7.433 6930 91.430 118283 8.350 15505 1.815 6729

Jun-96 12.717 23868 2.509 7234 24.708 49952 84.320 166484 6.760 7002 79.810 118448 7.688 15622 1.701 6742

Jul-96 14.145 23714 2.658 7251 19.954 50756 90.810 167467 7.643 7051 88.530 118663 5.043 15634 1.815 6754

Aug-96 12.897 23720 2.643 7264 21.181 53224 87.700 167920 7.904 7164 87.890 118841 7.244 15701 1.780 6777

Sep-96 12.090 23729 2.433 7270 21.825 50388 85.610 167898 6.870 7218 84.200 119037 7.586 15716 1.770 6793

Oct-96 11.742 23742 2.435 7284 20.420 50609 77.470 168736 6.864 7288 81.900 119215 6.130 15808 1.717 6792

Nov-96 11.523 23755 2.523 7293 20.358 50474 77.560 166989 7.076 7355 83.940 119433 7.042 15875 1.758 6805

Dec-96 11.255 23769 2.289 7301 19.457 51033 72.580 169379 6.564 8253 76.790 119610 6.447 16204 1.676 6812

Jan-97 11.510 23766 2.362 7318 19.172 51302 71.880 168757 7.578 9238 77.240 119686 5.884 16231 1.710 6832

Feb-97 12.404 23762 2.408 7324 18.745 50974 71.680 170084 7.349 9338 78.340 119909 6.566 16266 1.760 6858

Mar-97 13.345 23769 2.774 7336 18.279 51124 79.560 169809 7.734 9474 83.900 120117 6.216 16296 1.870 6876

Apr-97 12.847 23792 2.655 7353 19.327 51246 82.070 171309 7.996 9555 81.120 120497 6.687 16355 1.867 6879

May-97 12.619 23775 2.854 7359 19.592 51758 84.960 171311 8.071 9653 83.760 120620 6.368 16440 1.830 6915

Jun-97 12.507 23778 2.448 7366 21.356 51693 79.030 171926 7.782 9739 73.780 120980 7.685 16504 1.770 6914

Jul-97 13.300 23785 2.648 7385 19.988 52228 85.840 172375 8.014 9810 75.570 121179 7.097 16625 1.946 6931

Aug-97 12.861 23787 2.640 7403 19.185 54646 81.160 172773 7.626 9916 73.620 121261 6.385 16685 1.910 6973

Sep-97 12.517 23805 2.801 7413 23.922 51788 87.970 173388 7.712 10006 75.250 121324 7.357 16728 1.970 6999

Oct-97 12.345 23830 2.693 7463 25.163 51834 79.520 173903 8.720 10058 85.860 121560 8.134 16780 1.900 7023

Nov-97 11.783 23833 2.399 7477 20.717 51706 77.560 173586 8.200 10109 79.130 121796 6.973 16823 1.840 7021

Dec-97 11.442 23822 2.124 7490 18.944 52263 72.580 173397 7.053 10238 70.640 121860 6.638 16880 1.980 7350

Jan-98 11.661 23828 2.162 7496 18.429 52451 66.120 172002 7.584 10321 70.820 121846 5.233 16907 1.869 7341

Feb-98 12.196 23835 2.139 7501 18.613 52115 63.970 173673 7.427 10400 70.750 122068 5.601 17154 1.899 7338

Mar-98 12.719 24532 2.393 7508 16.290 52340 69.450 174558 8.128 10495 77.390 122107 4.874 17243 1.955 7329

Apr-98 13.717 23849 3.386 7521 21.462 52467 85.040 175793 10.187 10561 93.080 122551 6.744 17306 2.067 7353

May-98 13.958 23860 3.737 7529 24.328 53161 96.920 177049 10.654 10586 99.590 119184 8.736 17346 2.095 7378

Jun-98 15.053 23887 4.195 7538 30.901 53332 112.390 178764 12.498 10686 114.130 115816 13.196 17834 2.189 7081

Jul-98 13.455 23889 3.055 7550 27.085 53769 92.030 178727 11.257 10758 98.650 116071 11.778 17916 2.000 7104

Aug-98 12.913 23897 3.042 7555 20.055 56862 81.460 178817 10.689 10887 93.170 116244 8.649 17975 1.864 7133

Sep-98 12.623 23942 2.590 7563 23.020 53570 79.510 178922 9.064 11020 83.110 116095 8.880 18042 1.857 7155

Oct-98 13.026 23932 2.864 7574 20.491 53546 79.380 180507 9.705 11021 90.630 116835 7.734 18174 1.797 7168

Nov-98 12.537 23943 2.928 7581 23.791 53611 79.960 180417 9.759 11058 88.180 116955 9.571 18216 1.767 7177

Dec-98 11.758 23997 2.787 7594 22.957 54209 74.120 180924 9.249 11199 86.340 117223 9.888 18381 1.709 7176

Jan-99 11.823 24016 1.765 7601 20.783 54408 75.920 181299 8.473 11227 82.220 117373 8.128 18597 1.750 7192

Feb-99 12.929 24042 2.687 7615 20.015 54216 75.720 182335 8.858 11388 85.370 117648 8.253 18580 1.890 7220

Mar-99 13.648 24073 3.211 7639 19.610 54475 86.700 182271 11.002 11485 94.790 117806 8.278 18701 2.020 7222

Apr-99 14.040 24086 3.682 7646 25.463 54650 92.640 183326 13.095 11525 105.310 118147 10.863 18812 2.080 7234

May-99 13.032 24108 2.949 7658 25.100 55305 98.490 184093 10.418 11582 96.940 118342 10.528 18919 1.980 7262

Jun-99 13.290 24116 2.615 7668 25.737 55649 90.940 185855 10.160 11666 91.040 118352 10.315 19016 1.990 7250

Appendix. (Continued) Amounts of water treated and number of metered connections at selected municipal water-treatment plants/service areas, 1992-2007.

(Mgal/d, million gallons per day; conn, number of active metered connections; ERC, equivalent metered connections; Daytona Beach, City of Daytona Beach; Eustis, City of Eustis; GRU, 

Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange County Utilities (southern service area); OUC, Orlando Utilities Commission; SCU, Seminole County 

Utilities (NE, NW service areas); STA, City of St. Augustine]
Appendix 29

Month-year Daytona Beach Eustis GRU JEA OCU (south) OUC SCU (NE, NW) STA

Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d ERC Mgal/d conn

Jul-99 13.974 24140 2.776 7678 22.148 55765 94.350 186128 10.655 11809 96.240 118827 7.420 19116 2.200 7271

Aug-99 13.781 24149 3.001 7694 22.643 59449 94.060 189002 11.288 11942 101.340 118892 12.001 19214 2.260 7330

Sep-99 12.810 24178 2.644 7708 24.964 55568 87.800 189626 10.280 12035 91.880 118916 11.838 19299 2.150 7347

Oct-99 12.590 24178 2.408 7721 22.003 55553 85.440 188466 10.176 12086 85.540 118807 7.700 19421 2.100 7381

Nov-99 12.307 24191 2.612 7735 22.001 55750 84.910 189509 10.407 12138 89.070 119125 8.600 19543 2.010 7429

Dec-99 12.084 24191 2.520 7750 22.525 56710 82.380 190349 9.398 12352 85.870 119460 8.523 19664 1.920 7504

Jan-00 13.347 24191 2.539 7765 20.888 56504 77.920 190280 9.818 12374 86.142 119499 9.900 19786 1.980 7539

Feb-00 12.983 24191 2.656 7770 21.154 56328 81.370 190902 10.771 12495 90.332 119437 8.181 19935 2.020 7608

Mar-00 13.874 24245 3.146 7780 21.856 56487 88.140 191873 11.957 12571 99.558 120029 9.672 20223 2.170 7669

Apr-00 13.409 24245 3.250 7789 24.506 56615 96.470 192176 13.066 12652 104.111 120208 11.377 20648 2.120 7766

May-00 14.213 24245 3.978 7808 25.717 57600 115.610 193538 14.952 12743 119.263 120500 10.901 20706 2.160 7861

Jun-00 14.367 24275 3.270 7815 28.272 57648 109.530 194793 15.195 12904 116.269 120836 15.739 20814 2.120 7975

Jul-00 13.903 24171 2.775 7827 21.785 57998 103.520 194673 11.913 12870 100.735 120936 11.617 20857 2.190 8032

Aug-00 13.823 24171 2.968 7842 20.910 61552 103.970 195216 12.093 12987 97.707 121141 10.933 21027 2.160 8135

Sep-00 12.927 24148 2.557 7851 23.532 57998 88.410 196217 11.598 13063 95.200 121063 11.422 21012 2.150 8238

Oct-00 13.003 24148 2.836 7867 22.609 57564 91.340 197284 12.435 13097 100.471 121122 11.586 21107 2.400 8250

Nov-00 12.950 24168 2.895 7875 27.110 57489 91.980 197568 12.407 13186 99.645 121236 10.128 21384 2.390 8296

Dec-00 12.016 24168 2.578 7880 22.898 57720 85.620 198007 10.874 13308 91.458 121457 15.702 21618 2.320 8306

Jan-01 12.374 24623 2.544 7889 22.178 58226 77.840 198614 10.234 13328 83.216 121539 9.328 20435 2.370 8350

Feb-01 13.139 24648 2.472 7900 22.904 57910 78.440 198896 10.504 13472 81.579 121723 12.406 22254 2.460 8426

Mar-01 13.732 24664 2.472 7906 21.483 58065 77.720 199254 10.451 13548 80.955 121882 8.000 22266 2.530 8476

Apr-01 13.670 24679 3.009 7917 20.968 58156 100.210 200166 11.865 13559 89.040 121995 10.333 22278 2.660 8521

May-01 13.816 24693 3.183 7919 24.749 59020 112.120 201523 11.902 13679 92.100 122111 10.483 22059 2.630 8558

Jun-01 13.720 24403 2.625 7934 27.048 58963 95.810 202457 11.016 13813 83.576 122195 11.446 22462 2.560 8600

Jul-01 13.781 24403 2.116 7944 21.466 59622 95.540 203215 9.817 13921 83.027 122094 9.226 22567 2.490 8603

Aug-01 13.694 24432 2.358 7951 20.528 63609 96.200 203157 8.849 13979 84.346 122378 9.700 22916 2.490 8627

Sep-01 13.117 24432 2.154 7959 24.687 59153 77.850 203511 8.272 14152 79.775 122403 9.566 23265 2.000 8642

Oct-01 13.087 24646 1.767 7974 21.981 59418 84.757 203511 10.092 14136 85.409 122425 9.348 23180 2.030 8664

Nov-01 12.817 24693 2.358 7993 23.861 59200 92.427 203511 10.337 14353 81.889 122549 10.035 23189 2.240 8694

Dec-01 12.206 24703 2.370 7997 23.208 59383 80.810 204747 9.666 14436 82.844 122608 10.131 23298 2.280 8715

Jan-02 12.319 24701 2.397 8016 21.468 59654 77.140 204747 8.777 14367 78.683 122805 10.954 23375 2.210 8715

Feb-02 12.804 24745 2.335 8024 20.186 59415 77.030 236409 9.263 14441 78.436 122971 10.253 23451 2.240 8621

Mar-02 13.745 24757 2.675 8047 19.323 59487 85.040 236409 11.371 14495 88.622 123132 9.454 23500 2.050 8641

Apr-02 13.640 24809 2.806 8056 23.032 59720 96.850 237770 12.678 14549 94.067 123285 10.952 23613 2.130 8661

May-02 14.106 24827 3.407 8070 28.036 60747 117.830 239131 15.298 14603 99.673 123421 14.092 23685 2.450 8731

Jun-02 13.697 24827 2.316 8095 27.861 60404 104.350 241705 12.286 14657 84.226 123544 12.504 23779 2.420 8801

Jul-02 13.606 24827 2.102 8107 22.010 61462 107.040 242479 11.452 14707 81.489 123659 8.567 23622 2.630 8877

Aug-02 13.126 24833 2.180 8123 19.850 64289 104.090 242817 11.579 14717 80.578 123798 8.558 24031 2.390 8481

Sep-02 13.323 24825 2.271 8172 22.378 60345 101.150 243549 10.194 14919 79.301 123894 8.551 24161 2.330 8468

Oct-02 13.413 24863 2.774 8208 22.640 60300 102.660 239627 12.517 14873 86.886 123981 9.854 24177 2.500 8473

Nov-02 12.907 24879 2.640 8233 20.564 59910 98.560 239627 11.704 14982 80.515 124159 12.716 24333 2.460 8477

Dec-02 12.006 24904 2.328 8251 21.084 60164 91.010 245588 10.061 15078 76.166 124199 10.051 24325 2.290 8450

Jan-03 12.519 24914 2.548 8273 19.001 60423 95.030 245772 10.074 15074 77.625 124494 9.759 24449 2.510 8476

Feb-03 13.082 24909 2.328 8290 20.451 60093 81.560 246759 10.458 15179 78.741 124629 10.122 24508 2.520 8502

Mar-03 13.242 24924 2.413 8314 19.496 60278 84.350 247171 11.062 15323 77.734 124849 7.556 24544 2.500 8592

Appendix. (Continued) Amounts of water treated and number of metered connections at selected municipal water-treatment plants/service areas, 1992-2007.

(Mgal/d, million gallons per day; conn, number of active metered connections; ERC, equivalent metered connections; Daytona Beach, City of Daytona Beach; Eustis, City of Eustis; GRU, 

Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange County Utilities (southern service area); OUC, Orlando Utilities Commission; SCU, Seminole County 

Utilities (NE, NW service areas); STA, City of St. Augustine]
30 Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices..... Florida

Month-year Daytona Beach Eustis GRU JEA OCU (south) OUC SCU (NE, NW) STA

Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d ERC Mgal/d conn

Apr-03 13.477 24964 3.302 8323 20.619 60553 109.760 247893 13.118 15227 89.970 125054 9.844 24596 2.420 8598

May-03 13.890 24979 3.493 8349 23.373 61124 122.400 249511 14.290 15261 96.216 125266 12.000 24793 2.490 8610

Jun-03 13.443 24987 2.553 8368 25.088 61254 110.040 249485 13.129 15427 86.326 125497 11.491 24990 2.340 8619

Jul-03 13.797 24996 2.511 8386 21.749 62139 101.180 249485 13.725 15410 84.845 125817 10.588 25104 2.420 8620

Aug-03 12.881 25001 2.163 8424 21.351 64806 104.330 249485 11.943 15495 78.576 126095 9.101 25309 2.460 8724

Sep-03 12.583 25007 2.532 8453 21.454 61141 116.200 252613 11.794 15639 81.394 126325 9.673 25723 2.790 8755

Oct-03 12.145 25000 2.511 8474 23.300 61368 99.170 253803 13.261 15590 82.452 126902 9.338 25616 2.509 8771

Nov-03 12.353 25029 2.445 8487 22.985 61167 102.480 261048 12.690 15773 76.912 127049 10.058 25660 2.480 8808

Dec-03 11.958 25040 2.473 8503 21.626 61824 98.270 261542 12.346 15713 70.420 127246 10.494 25731 2.460 8822

Jan-04 12.281 25062 2.493 8516 21.059 62165 97.750 263029 12.686 15695 77.452 127940 9.976 25771 2.640 8822

Feb-04 12.600 25075 2.330 8528 20.423 62116 88.450 263932 12.604 15822 75.807 128441 8.592 25778 2.760 8895

Mar-04 13.529 25104 2.768 8535 18.652 62261 114.240 264054 14.264 15886 84.491 128722 8.852 26425 2.990 8936

Apr-04 13.640 25104 3.153 8554 24.853 62322 131.430 265093 15.903 15855 91.740 129203 12.408 26321 2.770 8884

May-04 13.861 25135 3.417 8569 24.217 62926 141.010 267293 15.706 15926 96.438 129523 11.059 23169 2.620 8923

Jun-04 13.663 25153 2.913 8590 28.624 63619 123.790 268392 16.590 15981 90.338 129859 13.752 26419 2.860 8962

Jul-04 14.135 25168 2.801 8609 23.856 63981 121.160 270079 16.184 16103 90.397 130285 11.421 26690 2.980 8980

Aug-04 12.887 25168 2.458 8652 22.859 67056 94.640 270965 13.938 16067 82.570 130092 9.485 26895 2.620 8967

Sep-04 11.757 25200 2.294 8668 23.079 63016 75.690 271852 11.904 15868 75.161 130461 8.390 26432 2.550 9045

Oct-04 12.735 25227 2.488 8705 19.571 63299 114.360 271852 14.483 15669 86.597 130644 8.658 26659 2.500 9010

Nov-04 12.884 25256 2.643 8729 21.519 63350 118.470 273431 14.943 15469 87.538 130795 10.056 26737 2.570 9046

Dec-04 12.158 25255 2.568 8778 21.260 63589 115.020 274504 14.098 15779 83.505 131230 11.561 27414 2.330 9149

Jan-05 12.271 25287 2.565 8804 20.253 63920 106.730 275695 13.848 15898 82.233 131688 9.725 27463 2.440 9257

Feb-05 12.696 25325 2.711 8831 21.712 63748 105.990 276540 14.699 15948 86.725 132165 9.750 26527 2.460 9284

Mar-05 13.026 25352 2.501 8867 19.405 64085 105.370 277704 14.201 16162 83.802 132555 10.331 27859 2.540 9310

Apr-05 12.880 25397 3.063 8886 20.043 63929 115.010 278739 15.455 16167 92.306 132981 10.877 27258 2.480 9290

May-05 12.932 25436 3.027 8924 20.105 64756 127.510 280161 16.151 16212 94.380 133347 9.612 26846 2.500 9369

Jun-05 12.873 25450 2.401 8958 23.623 65085 121.070 281608 14.068 16291 83.284 133611 12.279 28194 2.470 9366

Jul-05 13.703 25484 2.588 8989 20.959 65606 118.280 282740 16.018 16281 87.556 134118 8.910 27961 2.540 9411

Aug-05 13.200 25515 1.840 9015 20.677 69069 121.640 283286 15.258 16274 88.977 134429 10.274 27414 2.720 9459

Sep-05 12.747 25511 2.611 9043 24.130 65005 107.220 284629 14.912 16283 87.316 134706 12.484 28271 2.670 9542

Oct-05 12.394 25595 2.489 9083 23.228 64969 114.730 286422 14.968 16292 83.456 134800 8.877 28667 2.520 9607

Nov-05 12.700 25595 2.800 9098 21.944 65090 117.360 287133 14.826 16308 85.484 134999 10.945 28696 2.540 9619

Dec-05 11.574 25654 2.376 9133 23.367 65403 104.830 288509 13.982 16341 79.974 135066 10.758 28779 2.490 9635

Jan-06 12.258 25693 2.657 9158 19.974 65874 106.940 289470 14.743 16310 83.283 135169 10.904 28885 2.810 9662

Feb-06 12.586 25675 2.632 9197 20.085 63211 104.990 290470 14.604 16350 82.520 135388 11.428 29429 2.790 9763

Mar-06 13.445 25529 3.415 9216 19.471 68285 121.320 293031 16.986 16433 92.965 135711 14.156 29417 3.000 9806

Apr-06 13.967 25255 3.735 9345 22.776 65792 140.320 294449 18.645 16436 100.576 135979 14.809 29641 3.390 9825

May-06 13.968 25274 3.940 9384 22.816 66624 153.910 295539 18.770 16454 100.028 136127 13.939 29753 3.600 9843

Jun-06 13.853 25287 3.403 9414 25.905 66809 139.540 296567 17.775 16474 93.286 136306 17.056 29819 3.470 9868

Jul-06 13.877 25365 3.236 9455 25.242 67164 146.530 296567 16.871 16494 90.167 136398 13.003 29899 3.440 9900

Aug-06 13.368 25400 3.335 9476 23.948 70998 145.620 297584 17.150 16514 95.853 136452 14.000 29951 3.530 9946

Sep-06 12.587 25418 3.178 9559 24.491 66641 136.870 298271 14.660 16535 85.947 136698 12.448 30003 3.370 10012

Oct-06 12.917 25430 3.632 9654 23.251 66555 138.600 300340 17.370 16555 95.657 136941 11.799 29591 3.400 10070

Nov-06 12.376 25464 3.304 9697 24.293 66509 125.990 300393 16.026 16575 88.584 137027 14.240 30229 3.430 10085

Dec-06 12.051 25468 2.025 9705 20.943 66635 114.480 301927 14.760 16595 84.436 137361 12.049 30256 3.260 10138

Appendix. (Continued) Amounts of water treated and number of metered connections at selected municipal water-treatment plants/service areas, 1992-2007.

(Mgal/d, million gallons per day; conn, number of active metered connections; ERC, equivalent metered connections; Daytona Beach, City of Daytona Beach; Eustis, City of Eustis; GRU, 

Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange County Utilities (southern service area); OUC, Orlando Utilities Commission; SCU, Seminole County 

Utilities (NE, NW service areas); STA, City of St. Augustine]
Appendix 31

Month-year Daytona Beach Eustis GRU JEA OCU (south) OUC SCU (NE, NW) STA

Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d conn Mgal/d ERC Mgal/d conn

Jan-07 11.563 25488 3.008 9711 23.015 67141 107.427 302032 14.590 16605 82.278 137557 10.784 29478 3.275 10207

Feb-07 11.688 25513 2.950 9735 24.449 67081 111.288 302481 14.609 16615 81.132 137807 11.480 29721 3.323 10213

Mar-07 12.322 26930 3.469 9744 26.904 67265 127.523 303227 16.478 16625 90.209 138259 11.781 30467 3.439 10263

Apr-07 12.646 26988 3.744 9758 30.175 66951 147.234 303734 17.325 16635 93.976 140615 12.466 30462 3.642 10276

May-07 12.720 25608 4.147 9776 31.518 67935 156.137 304162 17.553 16645 99.503 140570 14.947 30484 3.979 10340

Jun-07 12.695 27088 3.628 9784 27.706 69863 137.429 304843 16.582 16655 93.022 140955 15.086 30523 4.085 10384

Jul-07 12.576 25648 3.187 9792 26.874 68847 130.050 305209 15.977 16665 87.386 140780 12.688 30668 3.896 10387

Aug-07 12.860 25662 3.557 9799 27.968 70753 152.903 305727 14.897 16675 96.059 141103 30792 3.679 10432

Sep-07 11.724 3.087 9811 28.009 66898 132.042 306062 14.872 16685 87.445 140899 13.521 30844 3.335 10427

Oct-07 11.569 25721 2.922 9822 25.726 70982 117.253 305492 14.822 16695 84.817 140508 10.369 30839 3.500 10420

Nov-07 11.617 25743 3.352 9832 25.961 67919 124.639 304575 15.691 16705 85.722 140694 12.174 30501 3.499 10477

Dec-07 10.817 25734 3.129 9835 23.312 69193 117.521 304245 15.451 16715 83.442 140773 12.064 30748 3.420 10477

Appendix. (Continued) Amounts of water treated and number of metered connections at selected municipal water-treatment plants/service areas, 1992-2007.

(Mgal/d, million gallons per day; conn, number of active metered connections; ERC, equivalent metered connections; Daytona Beach, City of Daytona Beach; Eustis, City of Eustis; GRU, 

Gainesville Regional Utilities; JEA, Jacksonville Electric Authority; OCU, Orange County Utilities (southern service area); OUC, Orlando Utilities Commission; SCU, Seminole County 

Utilities (NE, NW service areas); STA, City of St. Augustine]

L.C. Murray, Jr. — Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices, —SIR 2009-5010

East-Central and Northeast Florida

ISBN

Printed on recycled paper

L.C. Murray, Jr. — Relations between Municipal Water Use and Selected Meteorological Parameters and Drought Indices, —SIR 2009-5010

East-Central and Northeast Florida

Printed on recycled paper