THEMATIC ISSUE

A multi-period optimization model for conjunctive surfacewater–ground water use via aquifer storage and recoveryin Corpus Christi, Texas

E. Annette Hernandez • Venkatesh Uddameri •

Marcelo A. Arreola Jr.

Received: 24 April 2013 / Accepted: 28 October 2013 / Published online: 10 December 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract The present study develops and evaluates a

decision support system for the conjunctive management of

the current surface and proposed aquifer storage and

recovery (ASR) facility of the city of Corpus Christi, TX

using a simulation–optimization approach. The objective of

the model is to maximize water storage in the surface and

subsurface storage units while meeting (1) the freshwater

inflow requirements to the Corpus Christi estuary and (2)

the water demands of the city and its service area. The

model is parameterized using streamflow data from the

U. S. Geological Survey gauging stations on the Nueces

River and its tributaries as well as long-term climatic data

and regional hydrogeologic information. Results indicate

that a single-well field ASR facility is capable of storing

approximately 925 ha-m (7,500 ac-ft) of water over a

5-year period in the Evangeline Aquifer with a total

potential storage of about 2,715 ha-m (22,000 ac-ft) of

water over the jurisdictional area of the Corpus Christi

Aquifer Storage and Recovery Conservation District. Sur-

plus surface water sources are seen to contribute approxi-

mately 49–96 % of the water stored in the ASR during the

simulation period. The remaining storage came from either

Choke Canyon Reservoir or Lake Corpus Christi, which

also resulted in a slight reduction in evapotranspiration in

both reservoirs. The analysis indicates that the proposed

ASR system is not limited on the supply side but multiple

well fields may be required to increase the storage capacity

within the aquifer.

Keywords Conjunctive management � Water storage �Water budget � Simulation–optimization � Water supply

augmentation

Introduction

Water is a scarce resource in arid and semi-arid regions. As

such, managers and planners seek to diversify resource

portfolios to minimize risks from drought (Basagaoglu and

Marino 1999; Barlow et al. 2002; Ahlfeld and Hoque

2008). In particular, reservoirs and dams are constructed to

capture excess creek and river water during periods of high

rain and are drawn from during dry periods. However, the

construction of reservoirs and dams can lead to ecological

imbalances as they alter fresh water inflow patterns

(TWDB 2007). An increased environmental awareness has

generally led to public resistance against dams and spurred

the search for alternative storage facilities and water

management strategies.

Aquifers are regarded as viable water storage structures

and are therefore viewed as an alternative to construction

of above-ground water storage facilities (Pyne 1995;

Basagaoglu et al. 1999; Sheng 2005; Uddameri 2007). The

storage of water in aquifers and its subsequent use is

referred to as ‘‘aquifer storage and recovery (ASR)’’. Many

cities are seeking to conjunctively manage their water

resources by using both available surface and groundwater

supplies. In particular, there is a growing push towards

storing surplus surface water underground to meet the

needs of the future. Effectual conjunctive management

however requires proper understanding of both surface and

E. A. Hernandez (&) � V. Uddameri

Department of Civil and Environmental Engineering, Texas

Tech University, Box 41023, Lubbock, TX 79409, USA

e-mail: annette.hernandez@ttu.edu

M. A. Arreola Jr.

Independent Consultant, Boerne, TX 78006, USA

123

Environ Earth Sci (2014) 71:2589–2604

DOI 10.1007/s12665-013-2900-3

groundwater dynamics. Mathematical models are particu-

larly useful in discerning how surface and groundwater

systems can be managed conjunctively in an optimal

manner. The coupling of mathematical models based on

conservation principles (simulation model) with optimiza-

tion routines are noted to be useful for supporting regional-

scale water planning and management decisions (Ahlfeld

and Mulligan 2000; Barlow et al. 2002; Uddameri and

Kuchanur 2007).

In recent years, there have been a number of case studies

on the conjunctive management of surface and subsurface

water resources using a simulation–optimization approach

to determine optimal operating policies, augment water

supplies and prevent deleterious effects on aquifers and

ecosystems (Morel-Seytoux 1975; Matsukawa et al. 1992;

Azaiez and Hariga 2001; Azaiez 2002; Barlow et al. 2002;

de Wrachien and Fasso 2002; Azaiez et al. 2005; Khare

et al. 2007; Uddameri and Kuchanur 2007; Ahlfeld and

Hoque 2008; Safavi et al. 2010). For example, Basagaoglu

and Marino (1999) demonstrated the coupling of surface

(reservoirs) and subsurface (aquifers) water resources by

considering their temporal and spatial hydraulic interaction

to obtain optimal reservoir and ASR operations. In addi-

tion, Safavi and Esmikhani (2013) applied a simulation–

optimization approach to optimize conjunctive water use

for irrigation purposes in a semi arid region. These semi

arid climates experience erratic precipitation patterns

increasing reliance on groundwater stores.

In the semi-arid south Texas region, the City of Cor-

pus Christi is seeking to supplement its current water

resources by implementing an ASR project in conjunc-

tion with its existing reservoir system. Because of the

erratic climate, the City of Corpus Christi faces a tem-

poral disconnect in seasonal supply and demand. Cur-

rently, the City operates two interconnected reservoirs on

neighboring rivers to store water to meet the City’s and

other service area’s water demands. The reservoir sys-

tems are operated such that the freshwater inflow

requirements prescribed for the ecologically sensitive and

economically important Corpus Christi estuary system are

met. Moreover, in cases of severe drought, the city must

employ municipal restrictions in order to meet these

ecological demands (Agreed Order 2001). A proposed

ASR system in the region would alleviate these imposed

stresses to the current reservoir system by routing

available surplus surface water to an ASR system during

wet periods and storing this water for periods of high

demand (e.g. severe drought, industrial growth). To that

end, The City of Corpus Christi formed the Corpus

Christi Aquifer Storage and Recovery Conservation Dis-

trict to alleviate the stresses on the current water

resources (CCASRCD 2008). It is necessary, however, to

address the associated ecological and operational

uncertainties prior to implementation. The focus of this

research is to provide sustainable water resource solu-

tions by developing a decision support system (DSS) for

the conjunctive management of the City of Corpus

Christi’s reservoir system with a proposed aquifer storage

and recovery (ASR) facility utilizing a simulation–opti-

mization approach. The developed model addresses ASR

storage capabilities under various hydrogeologic, eco-

logical and physical constraints and guides field studies

to support ASR implementation.

Background

Study area

The selected study area consists of seven counties located

in the South Texas Coastal Bend region—Bee, Jim Wells,

Kleberg, Live Oak, McMullen, Nueces and San Patricio

Counties (Fig. 1). This area is characterized as a semi-arid/

sub-humid region subject to erratic rainfall and high

intensity storms (Uddameri 2007). The counties inland of

the coast are classified as dry sub-humid based on the

aridity index (Onta et al. 1991; Fig. 1). The region’s pop-

ulation as well as industrial entities rely heavily on surface

water from the Nueces River and its tributaries to meet

their water demands. On the coastal front, estuaries and

bays also rely on the freshwater inflow from the Nueces

River to maintain the freshwater/saltwater balance.

An interconnected reservoir system comprised of the

Choke Canyon Reservoir on the Frio River and Lake

Corpus Christi on the Nueces River (Fig. 2) provides

water for the Cities of Corpus Christi, Mathis, Alice and

Beeville as well as supplying water to the San Patricio

Water District, Nueces County Water Control and

Improvement District (WCID) #3, and the Koch-Flint

Hills and Celanese industrial plants. The Choke Canyon

Reservoir (CCR) receives inflow from both the Frio

River and the San Miguel Creek while Lake Corpus

Christi (LCC) is fed by the Atascosa and Nueces Rivers

which further flows into the Nueces Delta (Fig. 2). The

U.S. Geological Survey (USGS) maintains gauging sta-

tions on each of the rivers upstream of the receiving

water bodies (Table 1) with the USGS #5 station (USGS

# 08211500) used to monitor freshwater releases into the

Nueces Delta. The City of Corpus Christi is required to

meet ecological demands governed by the Agreed Order

of 2001 to mitigate freshwater inflow disruptions to the

estuaries and bays caused by construction of the reser-

voirs (TDWR 1981). The Texas Parks and Wildlife

Department (TPWD) has recommended freshwater inflow

requirements that would sustain the marine life and

maintain the saltwater/freshwater balance of the bays

2590 Environ Earth Sci (2014) 71:2589–2604

123

(Pulich Jr et al. 2002). While these recommendations are

currently not in effect, the future possibility of releasing

greater amounts of water to bays and estuaries to

maintain salinity balance and maximize harvest exists.

These policy changes can add additional stresses on

available water supplies in the future.

The City of Corpus Christi formed the Corpus Christi

Aquifer Storage and Recovery Conservation District

(CCASRCD), in an effort to assuage the stress on the current

water resources with the objectives of augmenting peak

water supplies, expanding water system infrastructure, mit-

igating stream flow diversion and facilitating estuary man-

agement (CCASRCD 2008). The district has a surface area

of approximately 1,368 km2 (528 sq. mi) and crosses the

Nueces and San Patricio counties (Fig. 3) and is underlain by

the Gulf Coast Aquifer (Baker 1979). The Evangeline

Aquifer is a confined sub-aquifer of the Gulf Coast aquifer

with salient hydrogeologic characteristics as summarized in

Table 2. The Evangeline Aquifer is viewed as a suitable

formation for implementing the ASR due to low hydraulic

gradients that help minimize horizontal migration of the

injected water and the presence of high salinity water that

helps create a storage bubble for the freshwater (Pyne and

Howard 2004; Lowry and Anderson 2006).

Methodology

The model framework developed in this paper seeks to

optimize the current reservoir system coupled with a pro-

posed aquifer storage and recovery (ASR) system using a

combined simulation–optimization. The simulation–opti-

mization has proven useful in reservoir and groundwater

management (Cheng et al. 2011; Raul et al. 2011; Georg-

akakos et al. 2012; Raul and Panda 2013). The simulation

model consists of two main forcings: (1) reservoir volumes

based on conservation principles, and (2) functionality of

the ASR based on the radial groundwater flow. The opti-

mization model seeks to maximize the combined storage

within the two reservoirs and the ASR facility while

meeting the area’s water demands within the hydrogeo-

logic constraints of the ASR. A sensitivity analysis was

performed to identify the most critical parameters in the

developed framework. The variables used in the model

development can be found in the appendix under

‘‘Nomenclature.’’

Simulation of reservoir ? ASR storage

A conceptual model of the combined reservoir ? ASR

system is presented in Fig. 4. The model was divided into

three sub-systems: Choke Canyon Reservoir, Lake Corpus

Christi and the ASR. Mathematically, the mass balance for

Choke Canyon Reservoir (CCR), Lake Corpus Christi

(LCC) and the ASR facility can be compactly expressed

using the following ordinary differential equations:

dVCCR

dt¼X

Qin �X

Qout � ET ð1Þ

dVLCC

dt¼X

Qin �X

Qout � ET ð2Þ

Fig. 1 Study region and aridity

index of study region

Environ Earth Sci (2014) 71:2589–2604 2591

123

dVASR

dt¼X

IASR �X

EASR ð3Þ

Equations 1, 2, and 3 can be solved for future time steps

as follows:

CCR: Vtþ1 ¼ Vt þ QSMC þ QFRð Þt�RCCR; t � ETCCR

� �Dt

ð4Þ

LCC: Vtþ1 ¼ Vt þ QNR þ QAR þ RCCRð Þt�

� RLCC þ QAlice þ QBee þ QMathisð Þt�ETLCC�Dt ð5Þ

ASR : Vtþ1 ¼ Vt þ IASR;t � EASR;t

� �Dt ð6Þ

where,

RLCC ¼ QWCID þ QO:N: þ RNB ð7ÞQO:N: ¼ QCC þ QSPWD þ QCel þ QFH þ IASR ð8ÞQCC;Demand ¼ QCC;1 þ QCC;2 ð9Þ

The initial conditions are specified as antecedent

reservoir levels, VCCR,t and VLCC,t with known municipal

and industrial water demands, hydrogeologic

characteristics of the ASR and radius values for potential

ASR well locations (TWDB 2002, 2003a, b). The

hydrologic and climatic variability in the model is

captured by three different simulation time-periods each

of 5 years in duration (Table 3; TWDB 2011). To address

Fig. 2 Reservoir inflow

gauging stations along the Frio

and Nueces River (adapted from

the U.S. Geological Survey)

Table 1 Choke Canyon reservoir and Lake Corpus Christi inflow

characteristics

Water body Storage

capacity

(ha-m)

Inflow feed USGS

gauging

station

Choke Canyon reservoir 85,760 San Miguel Creek 08206600

Frio River 08206700

Lake Corpus Christi 85,760 Nueces River 08194500

Atascosa River 08208000

Nueces Delta/Bay – Nueces River 08211500

2592 Environ Earth Sci (2014) 71:2589–2604

123

the uncertainties associated with potential freshwater

release policies, the release into the bay, RNB is modeled

using three release policies: (1) The ‘‘Current’’ average raw

water release policy, (2) the ‘‘Min Q’’ which is the

minimum flow required to maintain the freshwater–

saltwater balance, and (3) the ‘‘Max H’’ policy that

allows for maximizing the Nueces Bay harvesting. These

freshwater release policies (Table 4) were recommended

by the Texas Parks and Wildlife Department (Pulich Jr

et al. 2002).

Simulation of ASR water availability

ASR units are considered a viable method of capturing

seasonally available water and holding it for drier months

especially in aquifers of limited beneficial use (Pyne 1995).

The limitation of the functionality of the ASR is based on

the hydrogeologic and geologic characteristics of the sub-

surface which govern the water movement (Dillon 2005).

The developed model framework explicitly considered the

hydrogeologic and geologic characteristics of the Evange-

line Aquifer through the application of the Theis formu-

lation (Eqs. 10–12). Applying Theis solution in

conjunction with the superposition principle, the draw-

down, as a function of both extraction and injection of

groundwater, in the aquifer was calculated for the ASR

operations at each monitoring well assuming fully pene-

trating wells in a confined aquifer (Todd and Mays 2005).

s ¼ Q

4pT

Z1

u

e�udu

u¼ Q

4pTW uð Þ ð10Þ

The well function, W (u), in Eq. 10 was defined as:

W uð Þ ¼ �0:5772� ln uð Þ þ u� u2

2 � 2!þ u3

3 � 3!

� �ð11Þ

and

u ¼ r2S

4Ttð12Þ

Equation (10) assumes that the pumping rate remains

constant. If the pumping rate varies from month to month

the drawdown can be computed using the principle of

superposition in time as in Eq. (13). Therefore, the total

drawdown is computed as the injection (I) amount minus

the extraction (E) amount at each monitoring well

location.

Fig. 3 Corpus Christi aquifer

storage and recovery

conservation district

(CCASRCD 2008)

Environ Earth Sci (2014) 71:2589–2604 2593

123

st ¼Z t

0

Qw;s

4pT

� �W usð Þds ð13Þ

st ¼Z t

0

Qw;s

4pT

� �W usð Þds

2

4

3

5

I

�Z t

0

Qw;s

4pT

� �W usð Þds

2

4

3

5

E

ð14Þ

Because the hydrogeology of the CCASRCD is not

known with certainty, a data range for the aquifer

parameters namely hydraulic conductivity, storage

coefficient and transmissivity was collected from

previous hydrogeologic investigations in the surrounding

areas (Shafer 1968; Myers 1969; Baker 1979). To address

the uncertainty in specifying these parameters, three

scenarios were considered and are summarized in

Table 5. The ‘‘Lower’’ scenario considers the lower limit

of the given parameters while the ‘‘Upper’’ scenario

utilizes the upper limit of the given parameters. The

‘‘Baseline’’ scenario was considered to be the most likely

of all parametric configurations. The defined jurisdictional

area of the CCASRCD (Fig. 3) dictates the distance the

monitoring well can be placed from the ASR well.

Optimization of combined reservoir ? ASR

The simulation model was embedded in an optimization

model that maximized the storage in the two reservoirs and

the ASR facility while meeting the service area water

demands (Eq. 15). The combined reservoir ? ASR is

subject to storage (Eqs. 16, 17), ecological demand

(Eqs. 18, 19, 20, 21), ASR property (Eqs. 22, 23, 24), mass

balance (Eqs. 25, 26, 27), and municipal demand (Eqs. 28,

29) constraints. Therefore, the combined simulation–opti-

mization model incorporates the physics based models that

constrain the amount of water available for release and the

operation of the ASR and the ecological and the municipal

demands of the region.

Table 2 Evangeline aquifer hydrogeologic and geologic characteristics

Transmissivity

(m2/month)

Storage

coefficient

Hydraulic conductivity

(m/month)

Aquifer media

characteristics

Reference

20,903–27,871 0.01–0.1 23–46 Inter-bedded sand, silt and clay Baker (1979); Myers (1969); Shafer (1968)

Fig. 4 City of Corpus Christi’s

conjunctive water resources

2594 Environ Earth Sci (2014) 71:2589–2604

123

Simplifying assumptions were made to isolate the per-

formance of the combined reservoir ? ASR system. These

include no other pumping or recharge within the delineated

jurisdictional area so that there is no external influence

upon the ASR storage facility. Furthermore, the hydro-

geologic parameters for both injection and extraction in the

ASR are assumed to be the equal throughout the process.

Sensitivity analysis

Sensitivity analysis has been extensively used in many

groundwater applications to gain insight into the workings of

the developed models (Wagner and Gorelick 1987; William

and Sun 1990; Sun and Yeh 1990; Muleta and Nicklow 2005;

Cheng et al. 2011). Relative sensitivity coefficients are used

to measure the relative change in model behavior from

changing a single parameter value. The greater the sensi-

tivity value, the greater the deviation of the state variables of

the model (outputs) from the model input parameters.

Evaluation of model outputs with respect to parameter

influences on ASR storage provides guidance on manage-

ment strategies (White and Chaubey 2005). Relative sensi-

tivity coefficients (Eq. 31) were used to identify the

significant parameters within the modeled system.

Table 3 Climate characteristics

of the simulation time-periodsSimulation

time-period

Antecedent reservoir conditions Hydrologic characteristics

Choke Canyon

reservoir (percent

capacity) (%)

Lake Corpus

Christi (percent

capacity) (%)

2001–2005 38.92 41.45 1 drought year (2001); 2 wet years (2002 and

2004)

2003–2007 100.00 100.00 2 wet years (2004 and 2007); 1 drought year

(2006)

2005–2009 99.90 100.00 3 Drought Years (2006, 2008 and 2009); 1

wet year (2007)

Optimization Model

Maximize V ¼Pn

t¼0

P2i¼1 Vi þ w

PIASR �

PEASR½ �

�

tþ1

Objective function (15)

Storage constraints

Vi; t � Vmax 8i Reservoir max storage (16)

Vi; t �Vmax 8i Reservoir min storage (17)

Ecological Constraints

Ri; j�RMin 8i; j Reservoir minimum monthly release (18)

RNB�Current Current Bay release policy (19)

RNB�Min Q Maintain freshwater/saltwater balance (20)

RNB�Max H Maximize Bay harvesting (21)

ASR constraints

Ei�Pi�1

m¼1 Im �Pi�1

m¼1 Em8i ¼ 1; . . .; n Extraction policy constraint (22)

Ii� Ii; supp 8i ¼ 1; . . .; n ASR water supply constraint (23)

si;min� si� si; max 8i Drawdown constraint at monitoring well (24)

Mass balance constraints

Vtþ1 ¼ Vt þ QSMC þ QFRð Þt�RCCR; t þ ET� �

Dt Mass balance for Choke Canyon Reservoir (25)

Vtþ1 ¼ Vt þ QNR þ QAR þ RCCRð Þt� RLCC þ QAlice þ QBee þ QMathisð Þt�ETLCC

� �Dt Mass balance for Lake Corpus Christi (26)

Vtþ1 ¼ Vt þ IASR;t � EASR;t

� �Dt Mass balance constraint for the ASR (27)

Demand constraints

RLCC ¼ QFH þ QSP þ QCel þ QWCID þ QCC þ IASR þ RNB Release at Lake Corpus Christi (28)

QCC;Demand ¼ QCC;1 þ QCC;2 City of Corpus Christi water demand (29)

Ii;Ei� 0 Non-negativity constraint (30)

Environ Earth Sci (2014) 71:2589–2604 2595

123

Sr ¼x

y

� �y2 � y1

x2 � x1

� �: ð31Þ

Results and discussion

Existing reservoir system

The developed model was first run as the system without

the proposed ASR. This provides a baseline for a com-

parative analysis of possible future management decisions.

The baseline was explored for the reservoir storage

behavior given the three release policies under the various

climatic and hydrologic conditions. The results of this

application are given in Tables 6, 7, 8 which are repre-

sentative of freshwater release policies corresponding to

levels signified by ‘‘Current,’’ ‘‘MinQ,’’ and ‘‘MaxH,’’

respectively. The model was applied across 3- and 5-year

periods which encompass different climatic conditions.

Furthermore, the reported ‘‘Total Net Evapotranspiration’’

value represents the 5 years sum of the direct input and

output processes in the reservoir (Eq. 32).

X5

i¼1

Evapotranspiration� Precipitationð Þ ð32Þ

The results indicate the Choke Canyon Reservoir acts as a

‘‘buffer’’ or secondary water source for the region as Lake

Corpus Christi serves as the primary source for raw water

withdrawal. Furthermore, both reservoirs experience

significant loss of water due to evapotranspiration (Fig. 5).

Drought years in the third model are seen to have a significant

impact on median reservoir storage indicating that alternative

sources of water become particularly important during

prolonged droughts. As expected, the reservoirs are

relatively full during wet years; therefore, diverting surplus

water during wet years to meet demands, such as an ASR

system, during dry periods is certainly beneficial and also

possible.

Relative to the ‘‘Current’’ release policy the ‘‘Min Q’’

policy which requires a minimum freshwater release to

maintain a balanced salinity level in the Nueces Bay

requires an additional *6,950 ha-m/year (56,336 ac-ft/

year). Therefore, less water is stored in the reservoirs

(Fig. 6), decreasing the surface area and resulting in a

proportional decrease in the total net evapotranspiration

(Table 7). Similarly, the ‘‘Max H’’ policy which requires a

freshwater release such that maximum harvest in the

Nueces Bay is achieved requires an additional release of

*9,770 ha-m/year (79,186 ac-ft/year). This policy has a

similar affect of decreasing the surface area and thereby

decreasing the total net evapotranspiration from the reser-

voirs (Table 8). In general, an analysis of the water budgets

indicated that a large amount of freshwater was available

beyond the demands of municipalities, industries, and the

ecology. This surplus amount, between 123,350 and

431,719 ha-m (1–3.5 million ac-ft) over a 5 year period

(Table 9), would be available for storage in an ASR and

recovery during periods of drought.

Single well field ASR model

The ASR model results seek to determine how much of the

available freshwater ‘‘excess’’ can be stored in a single-

well field ASR within the Corpus Christi ASR Conserva-

tion District. A hypothetical location for a single-well field

ASR was chosen within the conservation district with a

Table 4 Monthly freshwater release policies (adapted from TPWD)

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

Current 2,533 1,922 3,001 1,327 1,875 9,503 12,021 4,759 9,289 5,540 3,796 3,738 59,304

MinQ 2,230 2,780 4,410 5,180 32,140 19,990 6,980 9,750 11,040 8,690 7,780 4,670 115,640

MaxH 2,230 2,780 4,920 5,180 37,770 36,430 9,820 9,750 9,600 7,560 7,780 4,760 138,490

Table 5 Aquifer design parameters adapted from Baker (1979); Myers (1969); Shafer (1968)

Aquifer storage and recovery parameters

Distance from

ASR to MW (m)aDrawdown at

MW (ft)bTransmissivity,

T (m2/month)

Storage

coefficient, S

Hydraulic conductivity,

K (m/month)

Lower 4,023 0.30 20,903 0.01137 7

Baseline 4,828 0.91 24,415 0.01666 10

Upper 5,742 1.52 27,871 0.02340 14

a Based on CCASRCD areab Acceptable drawdown

2596 Environ Earth Sci (2014) 71:2589–2604

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radius of influence of 3 miles to maximize the available

area within the district (Fig. 7). Under ‘‘Current’’ release

policies, the combined reservoir ? ASR model results

indicate that a single-well field ASR is capable of storing

on average 910 ha-m (7,381 ac-ft) of water during a 5-year

period (Table 10). While there is sufficient freshwater

availability, the storage in the aquifer was limited by the

allowable radius of influence. As expected, this decreases

the total net evapotranspiration resulting from the smaller

reservoir surface areas caused by less reservoir storage.

This decrease in ET is an amount of water that is in

practice part of the water routed to the ASR. The first and

second simulation periods allocate water to the ASR lar-

gely from excess ‘‘pass thru’’ from the system and sec-

ondarily from Lake Corpus Christi. Of interest is the

difference from the third simulation period which requires

a minimal allotment from Lake Corpus Christi with a

primary allotment from excess ‘‘pass thru’’ water and the

Choke Canyon Reservoir (Fig. 8). The driver in these

simulations appears to be the climatic conditions and the

availability of water in the respective reservoirs. Histori-

cally, water use for the City of Corpus Christi is

*9,251 ha-m/year (75,000 ac-ft/year). Over a 5-year per-

iod, a single-well field ASR within the confines of the

CCASRCD can provide anywhere from 4 to 6 weeks of

fresh water supply to the city of Corpus Christi. This is an

indication of the beneficial services provided by a com-

bined reservoir ? ASR system.

The uncertainty associated with the amount of fresh-

water released to the Nueces Bay was enumerated through

the application of the ‘‘Min Q’’ and ‘‘Max H’’ freshwater

release policies along with the ‘‘Baseline’’ hydrogeological

parameters of the Evangeline Aquifer. The results indicate

similar allotment strategies relative to the ‘‘Current’’

Table 6 Choke Canyon reservoir and Lake Corpus Christi volumes—‘‘Current’’ freshwater release policy

Simulation

time period

(climate

variability)

Antecedent reservoir storage (ha-m) Median reservoir storage capacity Total net reservoir evapotranspiration

(ha-m)

Choke Canyon

reservoir

Lake Corpus

Christi

Choke Canyon

reservoir (%)

Lake Corpus

Christi (%)

Choke Canyon

reservoir

Lake Corpus

Christi

1 2001–2005 33,380 12,335 99 99 27,443 11,022

2 2003–2007 85,760 29,757 100 97 36,822 9,818

3 2005–2009 85,696 29,757 89 60 50,764 16,825

Average 96 85 38,343 12,555

Table 7 Choke Canyon reservoir and Lake Corpus Christi volumes—‘‘Min Q’’ freshwater release policy

Simulation

time period

(climate

variability)

Antecedent reservoir storage (ha-m) Median reservoir storage capacity Total net reservoir evapotranspiration

(ha-m)

Choke Canyon

reservoir

Lake Corpus

Christi

Choke Canyon

reservoir (%)

Lake Corpus

Christi (%)

Choke Canyon

reservoir

Lake Corpus

Christi

1 2001–2005 33,380 12,335 99 97 26,667 9,560

2 2003–2007 85,760 29,757 99 96 36,020 8,144

3 2005–2009 85,696 29,757 87 49 48,359 14,176

Average 95 81 37,015 10,627

Table 8 Choke Canyon reservoir and Lake Corpus Christi volumes—‘‘Max H’’ freshwater release policy

Simulation time period

(climate variability)

Antecedent reservoir storage

(ha-m)

Median reservoir storage capacity Total net reservoir evapotranspiration

(ha-m)

Choke Canyon

reservoir

Lake Corpus

Christi

Choke Canyon

reservoir (%)

Lake Corpus

Christi (%)

Choke Canyon

reservoir

Lake Corpus

Christi

1 2001–2005 33,380 12,335 99 97 26,193 9,025

2 2003–2007 85,760 29,757 99 96 35,564 7,651

3 2005–2009 85,696 29,757 84 50 47,249 13,439

Average 94 81 10,038 10,038

Environ Earth Sci (2014) 71:2589–2604 2597

123

freshwater releases to the ASR from the various reservoirs

including the ET savings from the system (Fig. 9). It is

interesting to note that while there was no explicit binary

constraint on diversions, the model tended to route water

from a single reservoir to the ASR as a more optimal

routing scheme. Along with the uncertainty due to the

amount of freshwater released, there is uncertainty intro-

duced from incompletely characterizing the hydrogeology

of the Evangeline aquifer (Shafer 1968; Myers 1969; Baker

1979). The range of hydrogeological parameters (‘‘Lower’’,

‘‘Baseline’’ and ‘‘Upper’’) and freshwater policies (‘‘Cur-

rent’’, ‘‘Min Q’’ and ‘‘Max H’’) provided ASR storage as

illustrated in Fig. 10. The ‘‘Lower’’ hydrogeological

parameters allowed less volume of water to be stored in the

ASR; however, between the freshwater policies, little

variance in the ASR storage is expected. This result

Table 9 Excess freshwater (ha-m) due to max capacity of reser-

voirs under various freshwater release policies

Seasonal variability Current Min Q Max H

2001–2005 434,476 407,571 396,746

2003–2007 299,322 267,786 254,509

2005–2009 143,244 125,507 117,802

Fig. 5 Average reservoir

evapotranspiration under each

freshwater release policy

Fig. 6 Average median

reservoir percent capacity under

each freshwater release policy

2598 Environ Earth Sci (2014) 71:2589–2604

123

indicates a reliance of the ASR system on the antecedent

reservoir storage along with the number of drought and wet

periods.

The ASR storage partitioning is shown in Fig. 11 across

all simulation periods for all freshwater policies and

hydrogeological makeup. The first two simulation periods

followed similar trends for water allocation to the ASR

with 5–23 % of the water coming from Lake Corpus

Christi or ET savings under the ‘‘Current’’ and ‘‘Min Q’’

policies. That same amount of water, 5–23 %, was allo-

cated to the ASR from the Choke Canyon Reservoir or ET

savings under the ‘‘Max H’’ freshwater release policy.

Most of the water was excess ‘‘pass thru’’ water so that re-

allocation from the reservoirs was minimally warranted.

Table 10 ASR storage—‘‘Current’’ freshwater release policy

Simulation

time period

Antecedent reservoir storage

(ha-m)

Change in median reservoir storage

capacity

Reduction in reservoir

evapotranspiration (ha-m)

ASR storage

(ha-m)

Choke Canyon

reservoir

Lake Corpus

Christi

Choke Canyon

reservoir (%)

Lake Corpus

Christi (%)

Choke Canyon

reservoir

Lake Corpus

Christi

1 2001–2005 33,380 12,335 0.000 -0.106 2.2 43.5 922

2 2003–2007 85,760 29,757 -0.008 -0.068 0.0 7.0 985

3 2005–2009 85,696 29,757 0.000 -0.013 17.0 5.6 824

Average -0.003 -0.062 52 152 7,381

Fig. 7 Single ASR well-field location

Fig. 8 ASR storage by volume

under variable seasonal flow

Environ Earth Sci (2014) 71:2589–2604 2599

123

The re-allocation of water from the Choke Canyon Res-

ervoir and Lake Corpus Christi to the ASR reduces the

evapotranspiration (ET) by minimizing the surface area of

the reservoirs thereby limiting mass transfer of water

vapor. This reduction in evaporation accounts for a small

portion of water stored in the ASR facility. In the last

simulation period, the water is largely re-allocated from the

Choke Canyon Reservoir, 35–45 %, under all policy and

hydrogeological configurations. Additionally, there is the

water associated with saving from minimized ET-approx-

imately 10 %. In this period, approximately half of the

water allotted to the ASR is from excess ‘‘pass thru’’ water

which is far less than what was apportioned in the first two

simulation periods. Again, this result is driven by both the

antecedent reservoir storage and the number of dry and wet

years.

Fig. 10 Five-year ASR storage profile under climatic regimes and flow release policies

Fig. 9 ASR storage by volume under variable freshwater release policies

2600 Environ Earth Sci (2014) 71:2589–2604

123

Multiple well field ASR model

A multiple well-field ASR was considered to investigate

the potential for increasing the storage of water for use

during drought conditions. Figure 12 illustrates four

potential well field locations within the Corpus Christi

ASR Conservation District delineated using ArcGIS soft-

ware (ESRI, Inc. Redwood, CA). The four single-well

fields were located reasonable distances apart to prevent

interference in drawdown from adjacent wells. Application

of the ‘‘Baseline’’ hydrogeological characteristics of the

Evangeline Aquifer to the multiple well field gives an

estimate of the total volume of water that the CCASRCD

can store (Table 11). The addition of three more ASR units

enhanced the water storage capacity for the City of Corpus

Christi to approximately 3,750 ha-m (30,000 ac-ft) repre-

senting 24 weeks or nearly a half-year of water supply

under drought contingency measures. Thus, the CCASR

district has significant potential to buffer the city against

water non-availability risks.

Sensitivity analysis parameters

The relative sensitivity analysis was used to identify the

sensitive inputs to the model that have significant impacts

on the outputs. A ranking of ‘‘1’’ indicates the parameter

with the largest influence on the output relative to all

other parameters. The sensitivity of all parameters is

tabulated in Table 12. The drawdown at the monitoring

well was the most sensitive parameter in the model across

all hydrogeologic parameters and freshwater release pol-

icies. The driver for the drawdown is the hydrogeologic

parameters which determine flow through the subsurface.

The effect of drawdown on the model can most readily be

seen in Fig. 10 comparing the various hydrogeological

parameter ranges (i.e., ‘‘Lower,’’ ‘‘Baseline,’’ and

‘‘Upper.’’ As the ability of water to flow through the

aquifer increases, so does the stored amount (Fig. 10).

The second most influential parameter in the model was

the distance from the ASR well-field to the monitoring

well (Table 12). This result indicates that the set radius of

influence from the ASR to the monitoring well constrains

the model. These two constraints are critical to ASR

Fig. 11 Five-year components profile of ASR storage under different climatic regimes and flow release policies

Fig. 12 Multiple-well field ASR location

Environ Earth Sci (2014) 71:2589–2604 2601

123

operation as there is an available 123,350–431,719 ha-m

(1–3.5 million ac-ft) of excess ‘‘pass thru’’ that could

potentially be stored in the ASR. Therefore, multiple-well

fields are suggested for the district area given its config-

uration and the sensitivity of the storage to the hydraulic

radius of the cone of influence.

Summary and conclusion

The primary goal of the present study was to develop a

decision support system (DSS) that would assist with ASR

planning and operation in Corpus Christi, TX. The DSS

uses the simulation–optimization approach parameterized

with streamflow data from the U. S. Geological Survey

gauging stations on the Nueces River and its tributaries as

well as long term climatic data and hydrogeologic infor-

mation obtained from the Texas Water Development

Board. When coupled with the two interconnected reser-

voirs, a single-well field ASR could provide approximately

900 ha-m (7,500 ac-ft) of water within the designated ASR

district over a 5-year period. Historically, water use for the

City of Corpus Christi is *9,250 ha-m/year (75,000 ac-ft/

year). The prescribed modeled ASR system would provide

a buffer of approximately 5 weeks for the City of Corpus

Christi alone. The modeled system continues to show that

ASR storage is not constrained by water availability as

49–96 % of the water stored in the ASR comes from excess

‘‘pass thru’’ surface water. The re-allocation of water from

the surface reservoirs to the ASR resulted in a slight

reduction in evapotranspiration (ET) as the injected water

was not subjected to this process and reallocation from the

reservoirs decreased the surface area. Furthermore, the bay

release policies were seen to have little effect on ASR

storage for the conditions simulated. In fact, a sensitivity

analysis of the inputs indicated the allowable drawdown at

the monitoring well along with the influencing well radius

were the largest drivers for determining the ASR storage.

Based on this limitation and given the single well ASR

field presented here uses a relatively small fraction of the

designated ASR district, multiple well-fields and/or hori-

zontal drilling are suggested to provide greater storage.

Preliminary results on such a configuration indicate the

Corpus Christi ASR Conservation District is capable of

storing a total volume of 3,750 ha-m (29,767 ac-ft) of

water using four-well ASR fields which is 55 % of the

annual water use by the City of Corpus Christi. Overall, the

DSS proved to be beneficial in providing guidance on

management of a combined reservoir ? ASR system.

Furthermore, the model provided insights on what param-

eters were most influential in driving the model results and

as such can guide future data collection and hydrogeolog-

ical investigations.

Acknowledgments Portions of this work were completed while the

authors were at Texas A&M University-Kingsville and was partially

supported by the Center of Research Excellence in Science and

Technology—Research on Environmental Sustainability of Semi-

Arid Coastal Areas (CREST-RESSACA) at Texas A&M University-

Kingsville through a Cooperative Agreement (No. HRD-0734850)

from the National Science Foundation. Any opinions, findings and

conclusions or recommendations expressed in this material are those

of the author and do not necessarily reflect the views of the National

Science Foundation.

Appendix

List of variables

Table 11 ASR Storage for multiple well fields with varying radii of influence

Well # 1 2 3 4 Total

Radius (km) 4.0 4.8 5.6 5.6 20.1

Storage (ha-m) 789 912 985 985 3,670

Table 12 Hydrogeological parameter sensitivity ranking

Hydrogeological parameter Rankinga

Current Min Q Max H

Storage coefficient 4 3 3

Transmissivity 3 4 4

Drawdown at the monitoring well 1 1 1

Distance from ASR to monitoring well 2 2 2

a Ranking of 1 is equal to the highest calculated coefficient (Sr),

Sr ¼ xy

� y2�y1

x2�x1

�

2602 Environ Earth Sci (2014) 71:2589–2604

123

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QSMC ha-m/month Choke Canyon reservoir inflow from San Miguel Creek

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QNR ha-m/month Lake Corpus Christi inflow from Nueces River

QAR ha-m/month Lake Corpus Christi inflow from Atascosa River

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