groundwater recharge pressure prediction based...
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GROUNDWATER RECHARGE PRESSURE PREDICTION BASED ON DEMOGRAPHIC, LAND USE AND SEA LEVEL CHANGES IN BREVARD COUNTY,
FLORIDA, USA
By
BOWEN LI
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF URBAN AND REGIONAL PLANNING
UNIVERSITY OF FLORIDA
2016
© 2016 Bowen Li
To my family and my friends
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ACKNOWLEDGMENTS
I truly thank all the faculty in the Department of Urban and Regional Planning for
their help and support in my master study. I would like to express my sincere gratitude
to my committee chair Prof. Zhong-ren Peng who not only gave me the chance to
participate in sea-level research and guided me to be a really master student but also
taught me the lesson of life. Co-chair Stanley Latimer assisted me a lot on GIS in my
thesis with great patience.
Furthermore, I appreciate the help from Chao Liu and Yujun Deng. They taught
me how to conduct a science research.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ............................................................................................. 9
ABSTRACT ................................................................................................................... 10
CHAPTER
1 INTRODUCTION .................................................................................................... 12
Problem Statement and Justification ...................................................................... 12
Research Question and Objective .......................................................................... 13
Contribution to the State of the Art and to Practice ................................................. 14
2 LITERATURE REVIEW .......................................................................................... 16
Groundwater ........................................................................................................... 16
Groundwater Recharge........................................................................................... 17
Groundwater Demand............................................................................................. 19
Factors Influencing Groundwater Demand ............................................................. 20
Methodology Review............................................................................................... 22
Saltwater Intrusion .................................................................................................. 26
Summary ................................................................................................................ 27
3 METHODOLOGY ................................................................................................... 31
Framework .............................................................................................................. 31
Study Area .............................................................................................................. 31
Data collection ........................................................................................................ 32
Data Processing ..................................................................................................... 34
Model Setting .......................................................................................................... 36
4 RESULT .................................................................................................................. 44
Groundwater Demand............................................................................................. 44
Groundwater Recharge Result ............................................................................... 47
Saltwater Intrusion Result ....................................................................................... 48
5 DISCUSSION ......................................................................................................... 62
LIST OF REFERENCES ............................................................................................... 66
6
BIOGRAPHICAL SKETCH ............................................................................................ 69
7
LIST OF TABLES
Table page 2-1 Purposes and Sources of Groundwater Supplies ............................................... 17
2-2 Summary of Groundwater-Demand Prediction Method ...................................... 24
3-1 Data Explanation and Sources ........................................................................... 34
4-1 Multicollinearity Test ........................................................................................... 35
4-1 Public Groundwater Demand, OLS Result ......................................................... 44
4-2 Public Groundwater Demand, OLS Model .......................................................... 44
4-3 Commercial-Industrial-Mining Groundwater Demand, OLS Result..................... 45
4-4 Commercial-Industrial-Mining Groundwater Demand, OLS Model ..................... 45
4-5 Recreational Groundwater Demand, OLS Result ............................................... 46
4-6 Recreational Groundwater Demand, OLS Model ............................................... 46
4-7 Groundwater-Demand Predictions ..................................................................... 47
4-8 Groundwater Recharge Change in 2020, 2030 and 2050 .................................. 48
4-9 Saltwater Intrusion in 2020, 2030 and 2050 under Flux Control ......................... 48
4-10 Saltwater Intrusion in 2020, 2030 and 2050 under Head Control ....................... 49
4-11 The Influenced Parcels under Flux Control ........................................................ 50
4-12 The Influenced Parcels under Head Control ....................................................... 50
5-1 Summary of Prediction Results. ......................................................................... 63
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LIST OF FIGURES
Figure page 1-1 Satellite Sea Level Observations. ....................................................................... 15
2-1 2010 Groundwater Supply Distribution ............................................................... 28
2-2 Evapotranspiration Process. ............................................................................... 28
3-1 The Flow Chart of Groundwater Demand Prediction .......................................... 39
3-2 The Flow Chart of Groundwater Recharge and Saltwater Intrusion ................... 40
3-3 Brevard County Map ........................................................................................... 41
3-6 Precipitation in Brevard County in the Past 40 Years. ........................................ 43
4-1 Change in Domestic Groundwater Demand in the Past 40 Years. ..................... 51
4-2 Change in Commercial-Industrial-Mining Groundwater Demand in the Past 40 Years. ............................................................................................................ 51
4-3 Change in Agricultural Groundwater Demand in the Past 40 Years. .................. 52
4-4 Change in Farm Acres in the Past 40 Years....................................................... 52
4-5 Change in Power-Generation Groundwater Demand in the Past 40 Years. ....... 53
4-6 Water Intensity in Brevard County in 2010. ........................................................ 54
4-7 Water Intensity in Brevard County in 2020. ........................................................ 55
4-8 Water Intensity in Brevard County in 2030 ......................................................... 56
4-9 Water Intensity in Brevard County in 2050. ........................................................ 57
4-10 Flux-control Saltwater-Intrusion Model ............................................................... 58
4-11 Head-control Saltwater-Intrusion Model ............................................................. 59
4-12 Flux-Control Saltwater-Intrusion Map ................................................................. 60
4-13 Head-Control Saltwater-Intrusion Map ............................................................... 61
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LIST OF ABBREVIATIONS
GIS Geography Information System
MGD Million Gallons Per Day
OLS Ordinary Least Square
SJRWMDS St. Johns River Water Management District
SLR Sea Level Rise
SPSS Statistical Package
USGS United States Geological Survey
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the
Requirements for the Master of Urban and Regional Planning
GROUNDWATER RECHARGE PRESSURE PREDICTION BASED ON DEMOGRAPHIC, LAND USE AND SEA LEVEL CHANGES IN BREVARD COUNTY,
FLORIDA, USA
By
Bowen Li
August 2016
Chair: Zhong-ren Peng Cochair: Stanley Latimer Major: Urban and Regional Planning
With the population growth and rising sea levels, the coastal areas are facing
critical pressures of sustainable fresh groundwater resources. Furthermore, sea level
rise induced saltwater intrusion increases the salinity of aquifers at annual and decadal
scales. Hence, the coupling effect of increased demand from population growth and
saltwater intrusion place a great pressure on fresh water resources. Existing literatures
have addressed the groundwater resources pressure from the perspective of population
growth, and emerging literatures are exploring the predictions of groundwater pressure
from the perspective of saltwater intrusion. However, it is critical to examine the
coupling effects of these two stresses on ground freshwater resources. To this end, this
study attempted to assess the groundwater resources pressure based on the change
demographics and sea levels.
This study combined geophysical and urban planning domain together to make this
assessment. we proposed a methodology which includes statistical methods and
hydrodynamic models. The future groundwater demand, groundwater recharge and
saltwater intrusion distance will be predicted in this research.
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This interdisciplinary study combines geophysical and urban planning domain
together. To make this prediction, we proposed a methodology which includes statistical
methods and hydrodynamic modeling. Statistical method is used to predict groundwater
recharge, which is defined as precipitation less evapotranspiration and water demand.
First, ordinary least squares regression is employed to determine public groundwater
demand in the future. Water demand data inputs included agriculture, tourism, historical
trends, economics, meteorology and total population. Results indicate that water
demand is most dependent on population variation,
Saltwater intrusion was measured by two scenario including flux control and
head control. The intrusion distance is correlated to the hydraulic condition, sea level
rise and local groundwater recharge. Intrusion distance grows 2.84m under flux control
and grows 24.3m in 30 years, which indicates the intrusion is more serious under head
control than under flux control under head control. The results showed that the
population and meteorological variables are the main driving forces of water demand
and sea level rise is also the main driving force of saltwater intrusion, and the intrusion
length increases 180 meters when sea level rises 1 meters.
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CHAPTER 1 INTRODUCTION
Problem Statement and Justification
Water is one of the most valuable resources there is, not only in Florida but
everywhere in the world. Because of its unique geographical, environmental, and
climatic conditions, Florida has multiple natural water systems, including rivers, lakes,
springs, and wetlands. These water resources ensure the state’s continued growth in
population, tourism, economy, and agriculture.
Groundwater is essential to meet the rapid growth of urban, industrial,
agricultural, and recreational water demand (De Vries & Simmers, 2002). With the
development of the economy and the expansion of the population, the increasing
pressure on the groundwater supply has drawn the attention of the public. In 2012,
groundwater made up 65 percent of the state’s total freshwater withdrawal and was
used to supply 17,699 million residents, or 93 percent of Florida’s population (Marella,
2015).
Groundwater recharge is the process through which water moves downward—
that is, changes from surface water to groundwater. This replenishment of groundwater
is crucial for ensuring adequate supplies for future use and for preserving the quality of
the groundwater (SJRWMD, 1993).
An area’s groundwater demand is its future requirement for groundwater. This
research focuses on the prediction of local groundwater demand to calculate the
groundwater recharge by groundwater demand prediction model and groundwater
recharge prediction model. It tries to simulate future changes to this demand and
discover the functions determining past changes in the groundwater supply. The
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estimation model will be used to identify various driving factors in the groundwater-
demand prediction process.
The International Panel on Climate Change stated that saltwater intrusion from
the sea into aquifers has an important impact on sea level rise (SLR; Kundzewicz et al.,
2007). Satellite measurements have shown that the global mean sea level has risen by
10 to 20 centimeters under the influence of global warming. Human and natural
activities are the two primary reasons for this trend. Water from melting icebergs and
the increase in seawater are becoming more and more serious (NASA, 2016). Saltwater
intrusion will affect coastal areas in several ways, including land use and groundwater
use (Custodio, 1987). Furthermore, exhaustive exploitation of groundwater changes the
hydrological regime in coastal areas and allows salt water to invade freshwater aquifers
(St. John River Management District, 1988). Brevard County, which is on the southeast
coast of Florida, faces this situation. The groundwater comes from the Floridan aquifer
and surficial aquifer systems, which are being influenced by saltwater intrusion.
Research Question and Objective
In order to predict future groundwater recharge in Brevard County, this research
aims at predicting future groundwater demand on the basis of demographic,
meteorological, and land-use changes from 1970 to 2010. Because SJRWMD started to
collect groundwater supply data since 1970.Predictions will be made for 2020, 2030,
and 2050, which represents the short term, middle term and long term .The following
questions will be answered:
1. What demographic, meteorological, and land-use factors might be significant for future changes in groundwater demand?
2. How will the groundwater recharge rate change in response to groundwater demand increase, sea level rise and saltwater intrusion?
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3. What influence will saltwater intrusion have on sea levels and groundwater recharge?
By answering these questions, we would know what factors actually influence the
groundwater demand, which helps to predict the future groundwater demand. The future
saltwater intrusion distance along the shoreline of Brevard county will identify the
influenced land use types and parcels.
Contribution to the State of the Art and to Practice
This research first identifies the factors that are significant to predicting
groundwater demand in Brevard County using the ordinary least square (OLS) method.
Variables represent the influence of demographics, meteorology, and land use to
provide a comprehensive measurement of groundwater demand. A groundwater-
recharge estimation model is formulated on the basis of several past models and the
natural water cycle process. Precipitation adds to groundwater storage, whereas
groundwater withdrawals and evapotranspiration reduce it. This process, which
simplifies the functioning of this natural phenomenon, can be used to simulate future
groundwater recharge at the county level.
This work also provides a practical model for measuring saltwater intrusion. The
distance of intrusion depends on hydrological conditions and groundwater recharge in a
region. By examining groundwater demand, groundwater recharge, and saltwater
intrusion, this research uncovers the consequences of human and nature activities.
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Figure 1-1. Satellite Sea Level Observations. Source: NASA. Retrived from http://climate.nasa.gov/vital-signs/sea-level/
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CHAPTER 2 LITERATURE REVIEW
This literature review synthesizes the history of groundwater demand prediction.
It includes a review of major groundwater demand modeling algorithms for their
advantages and disadvantages, discusses the concepts and modeling of groundwater
recharge, and examines a well-applied saltwater intrusion model and the functions
behind the two intrusion scenarios.
Groundwater
Groundwater is that part of precipitation that infiltrates through the soil into the
water table (Waller, 1982). Our daily water use relies on groundwater to a large extent.
It supplies 51% of our drinking water, for instance.
There are six general categories of water use: public supply, domestic self-
supplied, commercial-industrial-mining self-supplied, agricultural self-supplied,
recreational self-supplied, and power-generation self-supplied (USGS, 2010). Public-
supplied water is water obtained from private and public water utilities, for both
residential and non-residential use, and amounts to more than 0.1 million gallons per
day (mgd). Water use by individuals and not obtained from public utilities is defined as
domestic self-supplied water, and amounts to less than 0.1 mgd. Commercial-industrial-
mining self-supplied water is water used for commercial, industrial, institutional, mining,
or dewatering purposes and not derived from public supply utilities; it may come from
groundwater and surface water. Agricultural self-supplied water is used mainly for
irrigation. Its quantity is estimated from numbers of crops rather than from total water
use. Recreational self-supply is water withdrawn from ground and surface sources to be
used for recreational purposes. Power-generation self-supply is the water used by
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power plants and not supplied by public water facilities (St. Johns River Water
Management District, 2015).
Thus only the public supply uses water from public water facilities; all the other
categories are supported instead by individual wells, surface water, groundwater, or
pumps. Figure 2-1 shows that in 2010, the agricultural groundwater supply made up 65%
of the total groundwater supply, and the public groundwater made up almost a quarter.
The groundwater supply can be divided into two sources: the public groundwater supply
is supported by public facilities, and the remainder is supported by private and other
facilities.
Table 2-1. Purposes and Sources of Groundwater Supplies
Categories Purpose Source
Public Residential and non-residential use, more than 0.1 mgd
Privately and publically owned water supply utilities
Domestic Individual use Individual domestic wells
Commercial-industrial-mining
Business, government, military, schools, etc.
Not from public supply facilities
Agricultural Supplemental crop irrigation
Groundwater and surface water
Recreational Golf course, urban landscape
Not from public supply facilities
Power generation
Power plants Not from public supply facilities
Groundwater Recharge
Groundwater recharge is the part of the surface water that permanently reaches
the water table. It is hard to measure directly (Rushton & Ward, 1978). In previous
studies, the recharge rates of the Floridan aquifer in SJRWMD have been stressed.
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Groundwater recharge to freshwater aquifers comes from surface water or precipitation,
but the surficial water system is absent in Brevard County (SJRWMD,1993). When the
depth of the water table is greater than the elevation of the Floridan aquifer’s
potentiometric surface, recharge happens.
There are three types of recharge: direct, indirect, and localized (De Vries &
Simmers, 2002). The simplified estimation method is the conventional model based on
the studies of Penman (1948; 1949; 1950) and Grindley (1967; 1969). Recharge is
calculated by subtraction of precipitation and evaporation, as in the following equation:
2-1 Where is precipitation, is actual evaporation, is runoff, expressed as millimeters
over the catchment, and is increase in stored water.
But the conventional method has been thought to underestimate recharge
(Kitching & Bridge, 1974; Kitching et al., 1977). A more comprehensive model was
developed that included more factors: interception of water by grass, shrubs, forest, and
agricultural plants; percolation; actual transpiration; geography; and other hydrological
processes (Ampe et al., 2007). The general equation for each land pixel is written:
2-2
Where is the groundwater recharge, is the average seasonal precipitation, is the
interception fraction, is the surface runoff, and is actual transpiration.
Risser et al. (1994) estimated groundwater recharge with a water-balance
equation from the residual term in the general daily water balance:
2-3
Where is recharge, is precipitation, is evapotranspiration, is direct runoff,
and is change in storage.
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Previous studies have explained the process of groundwater recharge as a cycle
of water exchange with potential loss using several different models, but it can be
summarized this way: precipitation is the fundamental source of the recharge; water
usage, evapotranspiration, and runoff are the loss of groundwater. Water usage is the
withdrawal of groundwater to the water supply system. Evapotranspiration is the loss of
water to the atmosphere from the ground surface, through evaporation from the
capillary fringe of the groundwater table and through transpiration from plants whose
roots tap that capillary fringe (USGS, 2016). The hydrological process here can be
simplified in this equation:
2-4
Where is the groundwater recharge, is the precipitation, is the water usage
(groundwater demand), is the evapotranspiration, and is any other factors, such
as surface runoff, interceptions, and leakage, which are assumed not to change in the
near future.
Groundwater Demand
Scholars have not reached a consensus on the methodology for estimating
groundwater demand. Although previous studies focused on industrial and agricultural
water demand, research on urban domestic water demand started in the 1960s with
price variance (Gottlieb, 1963). Then population came to occupy the main stream of the
estimated demand function (Klein et al., 2007). These models took population to be the
determining factor in water demand, although certain other variables, such as income,
weather, and land use can’t be ignored. A state-space multiple regression model was
built to forecast short-term urban water demand with weight given to other economic
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and meteorological variables (Billings & Agthe, 1998). For long term predictions,
seasonal water use was modeled by the effects of seasonal and climatic factors in time
series (Zhou et al., 2000). Obviously, water demand is estimated from the model with
such factors as temperature, precipitation, water price, population, and income.
Factors Influencing Groundwater Demand
Demographics
Theories of water-resource management suggest that water demand is
influenced by demographic factors, including population and income (Shandas &
Parandvash, 2010). Throughout its history, water-demand study has stayed very close
to residential use, which is the major component of water-use prediction.
Human impacts on the water supply increase the water stress through rising
water demand. Domestic and industrial water demand are determined by population
and per-capita use statistics (Vörösmarty et al., 2000). The income used is the per-
capita income of Brevard County. The data were selected as a variable to measure the
economic situation of water users in this area (Billings & Agthe, 1980). Personal income
represents the local economy through individual perspectives rather than the GDP as a
whole, which allows it to consider the economy and population together. But in fact
income is too closely related to other variables, such as population. Jones and Morris
(1984) suggested income as a proxy to be developed by regression techniques with the
explanatory variables, such as assessed property value, construction date, education
level, and number of cars. To be more specific, scholars classified the water demand
model by level of income (Saleth & Dinar, 1997).
Meteorology
Meteorological variables like precipitation, temperature, and evapotranspiration
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affect water demand in different ways. Zhou (2000) took daily evaporation, temperature,
and precipitation in a time-series model to forecast water consumption in Melbourne.
Precipitation in the growing season was used to predict water demand with a four-type
variable model (Foster & Beattie, 1979). Agthe and Billings (1980) used an
evapotranspiration-in-sprinkling model as a variation to measure the influence of
weather. Monthly days without significant rainfall multiplied by the average temperature
gives a measurement of the weather variable, which had not been used previously
(Griffin & Chang,1990). In a short-term water-prediction model, weekly rainfall and
maximum temperature were investigated by regression, time series, and artificial neural
networks (Bougadis et al., 2005). Daily water-use predictions were analyzed in a
nonlinear model with dynamic rainfall data, which filled the gap resulting from the fact
that the peak-to-average approach failed to take time sequence into consideration
(Maidment & Miaou,1986).
Water Price
In the historical research, some scholars have regarded price as the main force
driving water demand. Domestic water demand has an inelastic relationship with water
price (Howe & Linaweaver, 1967). Meta-analysis plays an essential role in the
examination of the variables influencing estimates of price elasticity (Espey et al., 1997).
In this research, the influence of water price was ignored because historical water-price
data were unavailable.
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Methodology Review
Ordinary Least Squares (OLS)
Many studies have used OLS to predict water demand (Howe &
Linaweaver,1967; Gibbs,1978; Foster & Beattie,1979). This is a method for estimating
the dependent variables in linear regression. It indicates the relationships between
potential effective variables and water demand. The functions differ in the format of their
variables but are similar in modeling. Domestic water demand was built into the model
with variables such as dwelling unit value, age of dwelling unit, and block rates (Howe &
Linaweaver, 1967). Gibbs (1978) used both average and marginal price in regression in
household unit size.
Water demand requires a complex prediction model corresponding to economic,
environmental, and demographic components. Four determinants of the quantity of
demand were built to be explanatory variables representing the function of urban
residential water demand (Foster & Beattie, 1979). As mentioned above, water demand
changes with various demographic, meteorological, and environmental variables. The
OLS method adopted in the linear regression model simulates these driving forces with
independent variables that can be customized to the goal of the research. OLS is
usually applied to long-term prediction with several independent variables.
Instrumental Variables (IV)
Instrumental variables were also used in water-demand prediction. This is a
method of estimation to be applied when there are correlations between variables.
Some social and economic factors may not influence water demand directly but may be
correlated with other variables that do. Average, marginal, and inframarginal price were
built as price-instrumental variables for estimations (Jones & Morris, 1984). Instrumental
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variables can be used to forecast short-term groundwater demand. IV is not applied in
this research
Data Sets
Cross-Section
Cross-sectional data are data that were collected without any difference in time.
Cross-sectional analysis is usually applied in statistical models of elasticity. Urban water
demand by different classes of customers was analyzed with a data set collected in
1976 (Williams & Suh,1986). Research on price-related water demand has focused on a
single year or other time period and examined each particular case’s correlation
between price structure and demand for the purposes of future policy-making. But the
problem with this data set is that multicollinearity limits the accuracy of research, and it
is appropriate only for static models.
Time-Series
Time-series data sets arrange the rate of a phenomenon or statistical index into a
sequence by time. To simulate the law governing change in water demand, the time-
series data could be used to reveal the function by a regression model. Current water
use may be strongly influenced by past water use, which means that the dynamic model
might produce more persuasive results about the real world. Monthly water
consumption data were applied to the price elasticity in water demand in Tucson over
three years (Agthe & Billings, 1980), and annual data for estimating water-demand
elasticity were applied in the regression model. The current time-series research is
presented on the temporal dimension. The long term research is missing.
Panel Data
Panel data can be regarded as a combination of cross-sectional and time-series
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data. This approach contains observations of multiple phenomena in a specific time
sequence. Plentiful observations of many types allow for a more reliable model with
fewer restrictions on extensive research (Arbués et al., 2003). A dynamic model with
panel data can fill shortages in the diversity of the data set.
Table 2-2. Summary of Groundwater-Demand Prediction Method
Model function Study author Main variables Data set
OLS Howe and Linaweaver (1967)
Dwelling unit value; water consumption; population; age of dwelling unit; water price
Panel data
Gibbs (1978) Water price; water consumption; income
Time-series
Foster and Beattie (1979)
Water consumption; water price; income; precipitation; population
Cross-section
Carver and Boland (1980)
Water consumption; income; water price; population
Panel data
Cochran and Cotton (1985)
Water price; income; precipitation; temperature; population
Time-series
Schefter and David (1985)
Water consumption; water price; income
Cross-section
Williams (1985) Water consumption; water price
Cross-section
25
Table 2-2. Continued
Model function Study author Main variables Data set
Nieswiadomy (1992)
Water consumption; water price; income; population; precipitation; temperature
Panel data
Williams and Suh (1986)
Water consumption; water price; income; rainfall; temperature; population
Cross-section
Moncur (1987) Water consumption; water price; rainfall; income; household size
Panel data
Griffin and Chang (1990)
Water consumption; water price; income; population; climate
Panel data
Instrumental variables
Agthe and Billings (1980)
Water consumption; marginal price; income; evapotranspiration
Time-series
Jones and Morris (1984)
Water consumption; instrumental water price; income; population
Cross-section
OLS/ instrumental variables
Deller et al. (1986)
Water consumption Panel data
Agthe et al. (1986)
Water consumption; water price; evapotranspiration; income
Time-series
Nieswiadomy and Molina (1989)
Water consumption; water price; income
Panel data
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Saltwater Intrusion
Saltwater intrusion happens when salt water invades freshwater aquifers,
decreasing their freshwater storage (Barlow & Reichard, 2010). Increases in
groundwater withdrawal reduce the flow of fresh water toward the interface between the
freshwater aquifer and salt water and cause salt water to be drawn into the fresh water
(USGS). This can increase the salinity of the fresh water and contribute to land
salinization. In general, human activity and natural will influence the saltwater intrusion.
Previous study gives no guidance on the intrusion distance in quantitative way and what
hydrogeological parameters control this migration before Werner and Simmons (2009).
Only a few researches explored the sea-level rise as a driven force of saltwater
intrusion (Sherif and Singh,1999; Bobba, 2002),
Two limiting conditions are explored to estimate the future saltwater intrusion
length into aquifers. The first model is a flux-control model in which groundwater flow
toward the interface is persistent despite changes in sea level. When sea-level rises,
the driven force of saltwater increases the pressure on saltwater-freshwater interface.
The storage of groundwater aquifer is enough to control the position of saltwater-
freshwater interface and maintain the groundwater discharge to the sea without the
influence of sea level rise. The water-table elevation will rise to the same height with
sea level in order to keep the discharge (Carretero et al, 2013). Flux-control scenario
usually happens at the beginning of saltwater intrusion. The second model is a head-
control model in which the hydrogeology maintains its depth below the mean sea level
of the surficial aquifer system. Then sea level rise will decrease the the freshwater flow
towards the shoreline. It usually happens after the flux-control scenario in the area
where groundwater over-withdrawal occurs (Werner and Simmons, 2009).
27
The length of the saltwater intrusion at the interface between the freshwater
aquifer and salt water can be calculated as (Custodio, 1987):
, 2-5
Where is the depth of the base of the surficial aquifer below the mean sea
level, is the groundwater recharge, is the hydraulic conductivity, is the
groundwater-density ratio, (commonly assumed to be 40), and is the freshwater flow
to the sea per unit length of coastline.
Summary
Hence, the coupling effect of increased demand from population growth and
saltwater intrusion place a great pressure on groundwater resources in the coastal
areas. Existing literatures have addressed the groundwater resources pressure from the
perspective of population growth, and emerging literatures are exploring the predictions
of groundwater pressure from the perspective of saltwater intrusion. However, it is
critical to examine the coupling effects of these two stresses on ground freshwater
resources. To this end, this study attempted to assess the groundwater resources
pressure based on the change demographics and sea levels.
28
Figure 2-1. 2010 Groundwater Supply Distribution
Figure 2-2. Evapotranspiration Process. Source: USGS
29
Figure 2-3. Saltwater Intrusion Process. Source: USGS.
Figure 2-4. Flux Control Scenario. Source: Impact of sea-level rise on saltwater
intrusion length into the coastal aquifer. (Carretero et al, 2013)
30
Figure 2-5. Head Control Scenario. Source: Impact of sea-level rise on saltwater
intrusion length into the coastal aquifer. (Carretero et al, 2013)
31
CHAPTER 3 METHODOLOGY
Framework
This research has three major parts: the groundwater-demand prediction model,
the groundwater-recharge estimation model, and the saltwater-intrusion model. In Part
One, groundwater demand is predicted using OLS after examining the linearity test.
After the independent variables with multicollinearity have been excluded and the
significance of the remaining independent variables examined, the prediction model is
established. In Part Two, the results of Part One are used to estimate the groundwater-
recharge rate with the model including precipitation, groundwater usage, and
evapotranspiration. In Part Three, saltwater-intrusion distance is measured in two
scenarios using the results of Part Two. With the help of ArcGIS 10.3, a groundwater-
demand intensity map and a saltwater-intrusion map are created to make the data
visible. The total process is shown in figure 3-1 and figure 3-2
Study Area
Brevard County is in southeast Florida on the Atlantic Ocean. It is the tenth-
largest county of Florida, with a population of 550,823. Its total area is 4,033 km2, and
the county seat has been in Titusville since 1894. Almost half of Brevard suffers from
flooding due to the geology and climate. The county has a humid subtropical climate
with hot, humid summers and year-round rainfall. The dry season usually runs from
December to May and the wet season from June to November. It is coldest in January,
with an average low of 50.7°F, an average high of 71°F, and 1.6 inches of rainfall. It is
warmest in July and August, with an average low of 72.2°F, high of 90°F, and 6.6 inches
of rainfall (Space Coast Visitor's Guide, 2007). The economy is driven mainly by trade,
32
transportation, utilities, and professional and business services. The county’s water
resources are managed by St. Johns River Water Management District (SJRWMD),
which manages groundwater and surface water resources in all or part of 18 counties in
northeast and east-central Florida.
The hydrogeologic system in southeast Florida is unique. It includes the surficial
system and the Floridan aquifer (SJRWMD, 1995). The surficial system has been
described this way: ―any permeable material, other than that which is part of Floridan
aquifer system, that is exposed at land surface and that contains water under mostly
unconfined conditions‖ (Miller, 1986). The Floridan aquifer system is ―a vertically
continuous sequence of carbonate rocks of generally high permeability that are mostly
of middle and late Tertiary Age and hydraulically connected in varying degrees and
whose permeability is, in general, an order to several orders of magnitude greater than
that of those rocks that bound the system above and below‖ (Miller, 1986). This system
contains two hydrological units, of which the Upper Floridan aquifer is more suitable for
water usage than the Lower Floridan aquifer because it produces higher-quality water.
Floridan is one of the most productive aquifers in the world and provides water to
thousands of people (SJRWMD, 1995). But in Brevard County, the water from the
Floridan aquifer can’t be used for drinking because of saltwater intrusion.
Data collection
The data in this thesis are secondary data, which largely support the research.
The data come from research agency publications, government documents, and online
GIS data. Most of them were collected from 1970 to 2010.
The groundwater supply data were compiled from USGS publications, including
1965–2000, the USGS Scientific Investigations Report 2004-5152; from 2005 the USGS
33
Scientific Investigations Report 2009-5125; and from 2010 the USGS Scientific
Investigations Report 2014-5088. The supply data were classified into six categories:
public supply, domestic self-supplied, commercial-industrial-mining self-supplied,
agricultural self-supplied, recreational self-supplied, and power-generation self-supplied
from 1970 to 2010. These annual data are the basis of this research.
The Census of Agriculture publishes farmland data by county in the United
States every five years. I collected the data from 1970, 1974, 1978, 1982, 1987, 1992,
1997, 2002, 2007 and 2012.
Temperature and precipitation data were collected from Weather Underground, a
scientific website that provides unique meteorological products, including vast amounts
of weather data. The weather-data collection location in Brevard County is Melbourne.
The average mean temperature and sum of precipitation were recorded for this
research.
The U.S. Department of Commerce, Bureau of Economic Analysis collected the
statistics on regional economies, including county-level personal income data. The
population data were acquired from the Office of Economic and Demographic Research.
The total county population data dates back to 1970.Evapotranspiration data were
collected from the USGS National Water Information System. The St. Johns River
Water Management District opened its GIS data for this region freely to the public. I
collected the basic county-boundary shapefile data and land-use data for this research.
34
Table 3-1. Data Explanation and Sources Variables Data
Format Units Data source
Groundwater supply (six categories )
Excel million gallons per day (mgd)
USGS : USGS Scientific Investigations Report
Farmland Excel acres United States Census of Agriculture
Temperature Excel 0F Weather Underground
Precipitation Excel inches Weather Underground
Personal income Excel thousands of dollars
U.S. Department of Commerce, Bureau of Economic Analysis
Population Excel Office of Economic and Demographic Research
Evapotranspiration Excel mm/d USGS National Water Information System: Web interface
County boundary Shapefile St. Johns River Water Management District (SJRWMD)
Land use Shapefile St. Johns River Water Management District (SRWMD)
Data Processing
Groundwater Demand
Temperature and precipitation were classified by season: spring (March to May),
summer (June to August), autumn (September to November), and winter (December to
February). There are thus 8 data categories: spring temperature, summer temperature,
autumn temperature, winter temperature, spring precipitation, summer precipitation,
autumn precipitation, and winter precipitation. In the groundwater-supply categories, the
sources of water are public facilities or private facilities except for the agricultural
groundwater supply, so there is no way to figure out how the different groundwater
supply categories are used according to land-use type. But the agricultural groundwater
35
supply definitely goes to agricultural land use, so farm area is included here among the
independent variables.
Multicollinearity
Table 4-1. Multicollinearity Test
Year Personal income Population
year Pearson Correlation 1 .981** .992
**
Sig. (2-tailed) .000 .000
N 43 43 43
Personal income Pearson Correlation .981** 1 .973
**
Sig. (2-tailed) .000 .000
N 43 43 43
Population Pearson Correlation .992** .973
** 1
Sig. (2-tailed) .000 .000
N 43 43 43
**. Correlation is significant at the 0.01 level (2-tailed).
Multicollinearity occurs when two or more independent variables are highly
correlated. An examination of the correlation between the independent variables shows
that the Pearson correlation between year and personal income is 0.981, between year
and population is 0.992 and between personal income and population is 0.973. All the
value are close to 1.This means there is a high correlation between these variables, and
one of them alone can explain the trend in the demographics. We choose population as
the independent variable.
Groundwater Recharge
The model of groundwater recharge has four independent variables. An
examination of the historical precipitation data shows no obvious tendencies, so the
average annual precipitation data were calculated for the groundwater recharge model.
Saltwater Intrusion
Gallivan et al. (2009) showed that most of the Atlantic Coast and Gulf Coast were
36
experiencing SLR of 2.03 to 3.05 cm per decade during the last century. This trend has
been further documented by tidal data from local tide gauge stations. Under the worst
scenario, National Oceanographic and Atmospheric Agency (NOAA) predicted that SLR
will increase up to 2 meters by 2100, so the SLR will be 200mm by 2020, 300mm by
2030, 660mm by 2050 in southeast Florida.
In the saltwater-intrusion model, the values of the parameters are important for
this calculation:
, 3-1
Where is the horizontal fresh groundwater flow under the shoreline per unit length of
shoreline. Martine et al. (2007) estimated the discharge of fresh water at 0.45 m3/d
(164.25 m3/y). The value of is the groundwater recharge, which will be calculated in
the groundwater recharge model; is the hydraulic conductivity, which was estimated
to be 3027.456 m/y; and is the groundwater density ratio, usually assumed to be 40
(Martine et al., 2007).
Model Setting
Groundwater-Demand Prediction Model
Ordinary least square (OLS) was applied in this model. OLS is used to establish
the relationship between a scalar dependent variable and multiple independent
variables. We set water demand in Brevard County as the dependent variable and
surrounding demographics (population, personal income), farm area, and
meteorological data (temperature, precipitation) as independent variables. The OLS
model was built and the result calculated by statistical package (SPSS) and ArcMap. By
37
examining the significance of the components of the dependent variable (public water
demand, domestic water demand, commercial-industrial-mining water demand,
agricultural water demand, recreational water demand, and power-generation water
demand), we can determine the relationships between them. These variables could
have great influence on different kinds of groundwater demand. The research flow chart
is shown below.
Dependent Variable
The water-supply data for Brevard County were collected from the U.S.
Geological Survey (USGS) and the Florida Water Science Center. There are six
categories of water supply: public supply, domestic supply, commercial-industrial-mining
supply, agricultural supply, recreational irrigation supply, and power-generation supply.
Each of these was divided into ground and surface supply.
Independent Variable
The independent variables fall into two groups: demographic and meteorological
data. Demographic data include population and personal income from 1970 to 2014.
Population is an essential factor in groundwater demand. Water demand has increased
along with the rapid growth of the population. The global water consumption rate
doubles every twenty years, a pace twice the rate of the population growth (Population
Institute, 2010). One study found that personal income correlates with unit water use in
various areas of the South Coast study area (DWR, 1959); water use climbs with
increasing income, as more water-using items such as clothes washers, dishwashers,
and swimming pools become affordable (Billings, 2008). Meteorological data include
temperature and precipitation.
3-2
38
Where is the groundwater demand (public, domestic, commercial-industrial-
mining, etc.), are the coefficients of the independent variables, are the independent
variables (population, personal income, temperature, precipitation, farm acres), are
the unobserved scalar errors. When the OLS model is run with water supply as the
dependent variable, five independent variables enter the model: population, personal
income, temperature, precipitation, and farm acres.
Groundwater-Recharge Estimation Model
3-3
Where is the groundwater recharge, is the average seasonal precipitation,
is the water demand, is the evapotranspiration, and includes other influential
factors, such as surface runoff, interceptions, and leakage, that are assumed not to be
changing in the near future. The OLS model can predict U, the water demand.
Saltwater-Intrusion Model
, 3-4
Where is the depth of the base of the surficial aquifer below the mean sea
level, is the groundwater recharge, is the hydraulic conductivity, is the
groundwater density ratio, assumed to be 40, and is the freshwater flow to the sea
per unit length of coastline. (Werner and Simmons, 2009)
39
Figure 3-1. The Flow Chart of Groundwater Demand Prediction
40
Figure 3-2. The Flow Chart of Groundwater Recharge and Saltwater Intrusion
41
Figure 3-3. Brevard County Map
42
Figure 3-4. Hydrstratigraphic Sequence in East-Central Florida. Source: SJRWND
43
Figure 3-5. Floridan Aquifer System. Source: Florida Department of Environmental Protection
Figure 3-6: Precipitation in Brevard County in the Past 40 Years.
44
CHAPTER 4 RESULT
Groundwater Demand
By running the regression with the groundwater demand data and other
independent variables data, the results are shown below.
Public Groundwater Demand
Table 4-1. Public Groundwater Demand, OLS Result Model Summary
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .906a .820 .815 1.9988221
a. Predictors: (Constant), population Table 4-2. Public Groundwater Demand, OLS Model
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -5.187 1.402 -3.699 .001 population 4.183E-005 .000 .906 12.445 .000
a. Dependent Variable: Public_Ground
For the results above, Model 1 explains 81.5% of the variation in public
groundwater demand. The significance of the population is less than 0.05, which is
significant. Population is the primary factor in this model, which shows a positive
influence on public groundwater demand. The formula is Y = (4.183E-005) * X - 5.187,
where Y is the public groundwater demand and X is the population.
Domestic Groundwater Demand
In Figure 4-1, there is no obvious trend in domestic groundwater demand. The
scatter is divergent. As a result, the annual average value of domestic groundwater
demand was selected. This value is 4.01 mgd.
Commercial-Industrial-Mining Groundwater Demand
Figure 4-2 shows that the commercial-industrial-mining groundwater demand
45
stays steady from 1974 to 1993 and increases in the following years.
Table 4-3. Commercial-Industrial-Mining Groundwater Demand, OLS Result Model Summary
Model R R-square Adjusted R-square
Std. error of the estimate
1 .809a .654 .645 .9574581
2 .842b .708 .691 .8925937
a. Predictors: (constant), population b. Predictors: (constant), population, summer(T) Table 4-4. Commercial-Industrial-Mining Groundwater Demand, OLS Model
Coefficientsa
Model Unstandardized coefficients Standardized coefficients
t Sig.
B Std. error Beta
1 (Constant) -3.618 .643 -5.624 .000 population 1.266E-005 .000 .809 8.141 .000
2 (Constant) -30.906 10.913 -2.832 .008 population 1.382E-005 .000 .883 9.081 .000 summer(T) .328 .131 .243 2.504 .017
a. Dependent variable: CI_Ground
For the results above, Model 2 explains 69.1% of the variation in commercial-
industrial-mining groundwater demand. The significance of population and summer
temperature are less than 0.05, which is significant. The formula is Y = (4.183E-005) *
X1 + 0.328X2 - 30.906, where Y is the commercial-industrial-mining water demand, X1
is population, and X2 is temperature in summer.
Agricultural Groundwater Demand
Agricultural area and groundwater demand show stable characteristics in the
past. No obvious trends were observed. The reason for the annual variation could be
the change of farm acres. Because agricultural groundwater supply data was calculated
based on the number of crops(SJRWMD, 2015). Farm acres from 1970 to 2010
reached peak in 1998 when the agricultural groundwater demand reached peak as well.
The tendency of farm acres and agricultural groundwater demand are similar to each
other in general. Thus the average groundwater demand for the past 15 years (2000–
46
2015) was calculated and used for future demand. This average value is 61 mgd.
Recreational Groundwater Demand
Table 4-5. Recreational Groundwater Demand, OLS Result Model Summary
Model R R-square Adjusted R-square
Std. error of the estimate
1 .552a .304 .275 1.3947490
2 .721b .520 .479 1.1831370
3 .780c .608 .554 1.0939486
a. Predictors: (constant), spring(T) b. Predictors: (constant), spring(T), summer(T) c. Predictors: (constant), spring(T), summer(T), population Table 4-6. Recreational Groundwater Demand, OLS Model
Coefficientsa
Model Unstandardized coefficients Standardized coefficients
t Sig.
B Std. error Beta
1 (Constant) 16.550 4.220 3.921 .001 spring(T) -.193 .059 -.552 -3.241 .003
2 (Constant) 78.618 19.620 4.007 .001 spring(T) -.175 .051 -.501 -3.445 .002 summer(T) -.778 .242 -.467 -3.218 .004
3
(Constant) 75.584 18.192 4.155 .000 spring(T) -.172 .047 -.492 -3.661 .001 summer(T) -.786 .224 -.472 -3.514 .002 population 7.558E-006 .000 .296 2.214 .037
a. Dependent variable: Recre_Ground
In the results above, Model 3 explains 55.4% of the variation.. The significance of
temperature in spring, temperature in summer, and population are each less than 0.05,
which means these independent variables are significant. The formula is Y = -0.172X1 -
0.786X2 + 7.558E-006X3 + 75.584, where X1 is temperature in spring, X2 is
temperature in summer, and X3 is population.
Power-Generation Groundwater Demand
Power-generation groundwater demand for groundwater showed no obvious
tendency in the historical record. The curve is also fluctuating in the last 40 years. So
the average value, 0.26 mgd, was selected for this part.
The total formula for the groundwater-demand model is Y = 6.3208 * X1 - 0.172 *
47
X2 - 0.458X3 + 104.761, where X1 is population, X2 is temperature in spring, and X3 is
temperature in summer. The future groundwater demand was calculated, as shown as
below, to be 96 mgd by 2020, 101 mgd by 2030, and 112 mgd by 2050.
Table 4-7. Groundwater-Demand Predictions
Year Groundwater demand(million gallons/d)
2020 96
2030 101
2050 112
A water-intensity map was made. Future water demand was distributed into block
groups to show the water intensity relative to the population. In figure 4-6, there are four
block groups meeting the highest water-intensity criteria in 2010. In figure 4-7, two more
block groups belong to this set in 2020, and because of population growth, several more
are added in 2030. By 2050, in figure 4-9, the water intensity of most the block groups
have moved to a new level.
Groundwater Recharge Result
Based on the groundwater-demand prediction, the groundwater recharge was
calculated as shown as below. By 2020, the recharge will be 6.1 inches per year; by
2030, 6 inches per year; by 2050, 5.9 inches per year. In previous investigations (St.
Johns River Water Management District, 1993), Brevard County was classified in the
area with a recharge rate of 0 to 4 inches per year. This means the groundwater
recharge is decreasing.
48
Table 4-8. Groundwater Recharge Change in 2020, 2030 and 2050
Year Sea level rise (mm) Groundwater recharge (inches/year)
2020 200 6.1
2030 300 6.0
2050 660 5.9
Saltwater Intrusion Result
Based on the saltwater-intrusion estimation model and the groundwater-recharge
result, a statistical model was built in Matlab to show the relationships between
saltwater intrusion, groundwater recharge, and sea level rise. It simulated the situation
in which the sea level rises by 0 to 1 meters.
Table 4-9. Saltwater Intrusion in 2020, 2030 and 2050 under Flux Control
Year Sea level rise (mm) Intrusion distance (m)
2020 200 2.27
2030 300 3.4
2050 660 7.56
In the flux-control scenario, when the rise in sea level increases, the intrusion
distance increases. This is because in the flux-control scenario, q0 is constant despite
sea level rise, and the penetration of salt water into the freshwater aquifer will be
immeasurable. The freshwater table increases to maintain the balance between the
aquifer and the salt water. In this scenario, the intrusion will be increasing gently when
the sea level rises. Figure 4-10 describes the change of saltwater intrusion when sea
level rises. The intrusion distance is increasing with the sea level rises and increasing
with water recharge decreases. When sea level rises to 1meter, the intrusion distance
49
will be close to 12 meters in the future. In table 4-9, the intrusion distance increase 2.84
meters from 2020 to 2050.
Table 4-10. Saltwater Intrusion in 2020, 2030 and 2050 under Head Control
Year Sea level rise (mm) Intrusion distance (m)
2020 200 15.18
2030 300 25.2
2050 660 82.63
In the head-control scenario, more and more groundwater is pumped out for
usage. When the water table below the freshwater aquifer remains constant, q0
decreases as the sea level rises, because there is not enough fresh water in the aquifer
to maintain a balance with the salt water. The saltwater intrusion is very serious in this
scenario. Figure 4-11 shows that as the sea level rises, the intrusion distance is nearly
six times larger in the head-control than in the flux-control scenario. When sea level
rises 1meter, the intrusion distance will be 183 meters. In table 4-10, intrusion distance
increases 34.3 meters from 2020 to 2050.
Based on the saltwater-intrusion distance result, an intrusion buffer was created
under two scenarios to measure its influence on land use. The three maps in Figure 4-
12 show the intrusion distance under the flux-control scenario at the same scale.
Obviously, the distance increases a lot along the shoreline. When sea level rises
100 mm, 39 wetland parcels will be affected. After 150 mm, 132 urban and built-up
parcels will be affected, covering 2478 acres. After 65 mm, 17 rangeland parcels
covering 424 acres will become unusable for this purpose.
Figure 4-13 shows that the intrusion distance increases greatly under the head-
50
control scenario. When sea level rises 100 mm, 20 more parcels of wetlands along the
coast will be invaded. After 150 mm, 171 urban and built-up parcels may need to be
transferred to types of land use. After 300 mm, 33 rangeland parcels, covering 605
acres, could be affected.
Under both scenarios, urban and built-up land, wetlands, and rangelands are the
three main land-use types affected by the saltwater intrusion. The main reason for the
saltwater intrusion is the over-withdrawal of groundwater, which breaks the balancing
flow between fresh water and salt water. The potential economic loss to the value of the
land parcels has also been estimated. In the flux-control scenario, 172, 198, and 218
million dollars would be lost in 2020, 2030, and 2050. Under the head-control scenario,
these amounts would be 238, 400, and 590 million dollars.
Table 4-11. The Influenced Parcels under Flux Control
Urban and built-up Wetlands Rangeland
Number Acres Number Acres Number Acres
2020 124 2378 39 763 13 386 2030 132 2478 42 767 14 387 2050 137 2831 45 778 17 424
Table 4-12. The Influenced Parcels under Head Control
Urban and built-up Wetlands Rangeland
Number Acres Number Acres Number Acres
2020 143 2899 50 1749 19 442 2030 171 3405 65 1883 29 498 2050 207 3966 93 2372 33 605
51
Figure 4-1. Change in Domestic Groundwater Demand in the Past 40 Years.
Figure 4-2. Change in Commercial-Industrial-Mining Groundwater Demand in the Past
40 Years.
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
1960 1970 1980 1990 2000 2010 2020
Domestic
0.00
1.00
2.00
3.00
4.00
5.00
6.00
19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00
20
03
20
06
20
09
20
12
Commercial-Industrial-Mining
52
Figure 4-3. Change in Agricultural Groundwater Demand in the Past 40 Years.
Figure 4-4 Change in Farm Acres in the Past 40 Years
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
1960 1970 1980 1990 2000 2010 2020
Agricultural
0
50000
100000
150000
200000
250000
300000
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
20
10
20
12
Farm acres
53
Figure 4-5. Change in Power-Generation Groundwater Demand in the Past 40 Years.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00
20
03
20
06
20
09
20
12
Power generation
54
Figure 4-6. Water Intensity in Brevard County in 2010.
55
Figure 4-7: Water Intensity in Brevard County in 2020.
56
Figure 4-8: Water Intensity in Brevard County in 2030
57
Figure 4-9. Water Intensity in Brevard County in 2050.
58
Figure 4-10. Flux-control Saltwater-Intrusion Model
59
Figure 4-11. Head-control Saltwater-Intrusion Model
60
Figure 4-12. Flux-Control Saltwater-Intrusion Map
61
Figure 4-13. Head-Control Saltwater-Intrusion Map
62
CHAPTER 5 DISCUSSION
In this research, groundwater demand is predicted with OLS models built on
potential influencing factors from 1970 to 2014. It finds that temperature and population
are correlated with the water supply in Brevard County. Public groundwater demand
has the highest correlation with population when population grows 10 thousand, the
groundwater demand will increase 4 mgd. The recreational groundwater demand has
the minimum correlation with population. Because the purpose of recreational
groundwater is to supply golf course and urban landscape, which are mainly influenced
by the area of field rather than population. In general, the groundwater demand is
increasing with the population growth. From descriptive analysis, population is the most
powerful factors that control groundwater demand. Despite of other possible impacts on
groundwater demand, the increasing number of population will result in growing water
consumption from different categories. This suggests that there is no way to stop the
increase in water demand, because the population of Brevard County has increased at
a fairly constant rate for the last 40 years. Temperature is the second influencing factor
on groundwater demand. Especially in spring and summer, groundwater demand is
sensitive to average temperature. If the temperature does not remain stable because of
climate change, the increase will grow more serious. If temperature increases by 1
degree, the groundwater demand will decrease 0.63 mgd. Currently, the groundwater
demand in Brevard County is 94.83 mgd.
Planners and governments should develop policies and plans for meeting the
coming gap between demand and supply. Currently, the total water use in Brevard
County is 94.83 mgd, the population is 552,427, and the water supply will increase by
63
1.4%, 6.5%, and 18% in 2020, 2030, and 2050 respectively. The average daily per-
capita water demand is decreasing. Under the current water-supply system, there will
need to be more water-supply sources like St. Johns River to meet the demand. Water
price is also an effective way to control demand. And updating the equipment of the
water supply utilities, by cleaning the pipes for instance, improves the efficiency of the
system. The groundwater recharge is decreasing in the future. By 2020, the recharge is
6.1inches per year; by 2030, 6 inches per year; by 2050, 5.9 inches per year. By
simulating the situation in which the sea level rises by 0 to 1 meter based on estimation
model, the saltwater intrusion distance under flux control scenario is much more less
than under head control. In flux-control scenario, when sea level rises from 2020 to
2050, the intrusion distance increases 2.84 meters from 2020 to 2050. In head control,
the intrusion distance increases 34.3 meters from 2020 to 2050.
Table 5-1. Summary of Prediction Results.
2020 2030 2050
Population 647607 735553 9111305
Sea level rise (mm) 200 300 660
Water demand (mgd) 96 101 112
Water recharge (inches/year) 6.1 6.0 5.9
There are several solutions for preventing saltwater intrusion (Khomine, Janos, &
Balázs, 2011).
Changing the Source of Water: Brevard County and other east-central Florida
counties need to find other sources rather than groundwater to fill their future water
demand. There is a conflict between the limited groundwater resources and the growing
demand for water. Drawing too much groundwater could cause saltwater intrusion,
64
which would endanger the water supply and the wetlands. Alternate water sources like
St. Johns River will alleviate the pressure on the groundwater supply and maintain the
balance of flow between groundwater and salt water.
Setting a Subsurface Barrier: Subsurface barriers are impervious or semi-
impervious underground structures built in freshwater aquifers to impede the infiltration
of seawater and increase groundwater storage. They have been tested as an effective
way to stop and even reverse seawater intrusion. SJRWMD should work with Brevard
County to set down a subsurface barrier in the coastal aquifer.
Artificial Recharge: This is the process of filling the underground formations and
aquifers with surface water. Several techniques are used, such as wear spreading and
recharge wells (Todd, 1980). This approach improves the flow of groundwater and helps
stop the intrusion of salt water. Injection wells were developed in Los Angeles to add
fresh water to the aquifer.
Changing Land-Use Patterns in the Intrusion Area: Saltwater intrusion will harm
the wetland, and the agricultural and urban built land significantly. Figures 4-10 and
Figure 4-11 shows that several land use types will be affected by saltwater intrusion. It
is necessary to transfer these parcels to other functions, such as fishing camps,
marinas, and swimming beaches, which are not harmed by saltwater intrusion.
There are several limitations in this study. For the groundwater demand
prediction, water price is an important factor that influences the water demand, which I
mentioned in literature review. When water price rises, the demand will decrease
because high rates stop people to consume water. But the shortage of water price data
limits this research to be involved with this factor. The prediction of groundwater
65
demand in every land use parcels will increase the accuracy of this research. A higher
resolution of water demand prediction map can tell the high water intensity parcel from
the map. In that case, it is possible to know the specific future water demand change.
But currently only total groundwater supply data is available without the specific land
use parcels with its corresponding water supply type and amount. For the groundwater
recharge, evapotranspiration is an essential parameter, which is the sum of evaporation
and transpiration. Transpiration is different in different plants. More precise groundwater
recharge prediction will be made if there are accuracy transpiration data of land cover.
66
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BIOGRAPHICAL SKETCH
Bowen Li was born in Xi’an, China. He received his bachelor’s degree of GIS
(Geographical Information System) at Wuhan University in 2014. During the study in the
university, several classes aroused his interest in urban planning. Since August 2014,
he started his master degree at the University of Florida in the Department of Urban and
Regional Planning.
During the past two years, Bowen focused on the application of GIS in urban
planning. His research emphasis has been concentrated on spatial analysis and
customizing GIS. After graduation, he will start his career as a professional planner in
China.