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Risk assessment for optimal freshwater inflow in response to sustainability indicators in semi-arid coastal bay

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Abstract

Coastal zones are the primary interface for the exchange of natural and man-made materials between terrestrial and coastal ecosystems. While continuous industrial development and population growth in the coastal region promote unprecedented economic prosperity, water resource management in bay and estuary areas turns out to be a crucial challenge. Therefore, local, state, and federal water planning groups are attempting to manage the supply of freshwater inflow based on sustainability goals, especially for semi-arid coastal regions like South Texas. Surface and ground water management practices in this semi-arid coastal region are implemented to ensure an ever-lasting water supply on one hand and to maintain ecosystem integrity in the bay and estuary system on the other hand. The aim of this study is to apply a stochastic compromise programming model to identify a compromise solution under uncertainty in terms of two competing objectives: minimizing freshwater release from a coastal reservoir and maximizing fishery harvest in its associated bay—Corpus Christi Bay, South Texas. The global criterion method used in the solution procedure seeks to select a compromise solution that possesses the shortest distance from a positive ideal solution (PIS) and the farthest distance from a negative ideal solution (NIS). Solutions were found using three distance-based functions in conjunction with stochastic constraints reflecting the risk levels involved in decision-making. Results indicate that current flows in the mouth of the Nueces River are not sufficient to maintain the salinity level and to satisfy harvest requirements in the Corpus Christi Bay if water supply goal in the city has higher priority. Therefore, a sustainable management plan of exploring the structure of demand and supply is highly desirable in this fast growing urban region.

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Abbreviations

Q j :

The inflow for jth month (j = 1, ..., 12) (106 m3/month)

QLB j and QUB j :

The lower and upper bounds for jth month (j = 1, ..., 12) (106 m3/month)

H k :

The harvest of kth species (k = 1,..., l) (103 kg/year)

H ratio :

The harvest ratio (unitless)

TH:

The total anticipated annual harvest (103 kg/year)

S ij :

The salinity at location i for month j (ppt)

SLB ij :

The lower bound for the salinity at location i for month j (ppt)

SUB ij :

The upper bound for the salinity at location i for month j (ppt)

AS ij :

The antecedent salinity of preceding month (ppt)

a i , b i and c i :

The regression constants (unitless)

P :

The probability (%)

S ijavg :

The salinity mean value at each location i (ppt)

α:

The predetermined confidence level (unitless)

B s :

The bimonthly inflow (106 m3/bimonth)

s :

An index for seasons (unitless)

a k , b ks , c ks , d ks and e ks :

The regression constants for kth species and sth season (unitless)

x * :

The ideal solution

F(x*):

The ideal objective vector

d PIS :

The PIS distance

d NIS :

The NIS distance

r :

An integer valued exponent (unitless)

d i :

The random variable

Q p :

The sum of inflows for a 2-month period (p=SO for September–October, ND for November–December, JF for January–February, MA for March–April, MJ for May–June, and JA for July–August) (106 m3/bimonth)

PIS:

Positive ideal solution

NIS:

Negative ideal solution

TWDB:

Texas Water Development Board

TCEQ:

Texas Commission for Environmental Protection

TPWD:

Texas Parks and Wildlife Department

TxBLEND:

Texas Estuarine Blend Model

TxEMP:

Texas water development board Estuarine Mathematical Programming Model

SLB:

Salinity lower bound

SUB:

Salinity upper bound

MOP:

Multiobjective optimization problems

SCP:

Stochastic compromise programming

CCP:

Chance constrained programming

VMP:

Vector maximum problem

USGS:

United Sates Geological Survey

TxRR:

Texas rainfall-runoff model

USDOI:

United States Department of Interior

USDOC:

United States Department of Commerce

CPUE:

Catch per unit effort

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Acknowledgements

The authors acknowledge the financial support of NSF grant (CREST Award No: 0206259) as well as the data reports cited and used in this analysis. The authors extend their deep gratitude to the various supports from Dr. Matsumoto J. and the Texas Water Development Board for sharing their invaluable information.

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  1. Department of Environment Engineering, Texas A&M University-Kingsville, Kingsville, TX, USA

    Jae-Hyung Ji & Ni-Bin Chang

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  1. Jae-Hyung Ji
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Correspondence to Ni-Bin Chang.

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Ji, JH., Chang, NB. Risk assessment for optimal freshwater inflow in response to sustainability indicators in semi-arid coastal bay. Stoch Environ Res Ris Assess 19, 111–124 (2005). https://doi.org/10.1007/s00477-004-0219-z

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