A GIS-based multi-criteria decision-making approach for establishing a regional-scale groundwater monitoring

Venkatesh Uddameri, Tim Andruss

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

A weighted multi-criteria decision-making framework was designed to identify and prioritize areas for monitoring aquifer hydraulic heads within the Victoria County Groundwater Conservation District. The criteria address salient hydrogeological aspects including groundwater variability, recharge, surface water-groundwater interactions, and groundwater fluxes across district boundaries. Texas water statutes require groundwater conservation districts to quantify these factors and utilize them to manage aquifers. A stakeholder survey instrument was used to prioritize these criteria. A monitoring priority index (MPI), whose value theoretically ranges between 0 and 1 was computed as a weighted average of six mutually exclusive criteria. The MPI value was seen to range between 0.2 and 0.6 within the study area. Lower values of MPI indicate areas where the current level of monitoring is adequate and areas with a higher MPI point to locations where additional monitoring is necessary. In particular, monitoring along district boundaries, particularly along the western and southern sections, is recommended. The developed methodology was seen to provide a transparent and simple-to-use approach to identify and prioritize areas within the district for the purposes of groundwater monitoring. The proposed framework demonstrates the utility of GIS in facilitating a scientifically credible and stakeholder-driven approach for establishing groundwater monitoring networks and can be adapted in other similar applications.

Original languageEnglish
Pages (from-to)2617-2628
Number of pages12
JournalEnvironmental Earth Sciences
Volume71
Issue number6
DOIs
StatePublished - Mar 2014

Keywords

  • Desired future conditions
  • Groundwater levels
  • Index and overlay methods
  • Monitoring network
  • Stakeholder-driven monitoring

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