Tuesday, April 1, 2008 : 11:20 a.m.
Estimating Sub-regional Ground Water Availability Using Regional Ground Water Flow Models and Decision Analytic Tools
Groundwater management in Texas is largely based on the concept of local control. Groundwater conservation districts (GCDs) are the states preferred approach to aquifer management and there are nearly 90 such districts within Texas . GCDs typically are defined on a county basis and there are several GCDs within an aquifer sub-division. The Texas Water Development Board has developed several regional-scale groundwater flow models (referred to as GAMs) to help facilitate groundwater management and planning. GCDs use these models to understand the aquifer behavior within their district and develop management plans and other regulatory policies. The challenge of using regional-scale groundwater models for sub-regional scale management is two fold: 1) The regional models only simulate average behavior and cannot directly address aquifer dynamics at a local scale. 2) As the groundwater availability within a district is a function of both science and policy, the risk-preferences and values of various stakeholders must be accounted for developing model runs that help formulate pertinent local-scale policies and guidelines. The scaling issues, modeling uncertainties and differing value-preferences of stakeholders make the interpretation of regional groundwater modeling results and their applications to local problems a challenging task. A framework for addressing some of these challenges is developed here. An essential first-step is to ensure that all stakeholders understand the advantages and limitations of using a regional-model for a more local application. The second-step is to integrate groundwater flow models with decision analytic tools that help generate required information for local-scale decision making. Finally, the uncertainties arising from data limitations, scaling issues as well as divergent risk perceptions of stakeholders needs to be ascertained and incorporated into the decision making process. The developed framework will be illustrated and discussed using a case-study from south Texas .
Venki Uddameri, Texas A&M University-Kingsville Venkatesh Uddameri is Associate Professor in the Department of Environmental Engineering at Texas A&M University in Kingsville, Texas. His research interests lie in the use of mathematical models for water resource evaluations, risk assessment, remediation and applying artificial intelligence techniques for water resources management. He is currently a co-principal investigator and Assoc. Director (Research) for the Center for Research Excellence in Science and Technology, focusing on research on environmental sustainability in semi-arid coastal environments (CREST-RESSACA) funded by the National Science Foundation