18 Hydrologic Science, Geomorphology, and Environmental Geoscience

Tuesday, 4 May 2004
8:00 AM - 12:00 PM

PROBABILITY MAPPING AS A DECISION TOOL FOR GROUND WATER QUALITY MONITORING

Paper number: 18-2
Program: Technical Sessions
Time: 8:20 AM – 8:40 AM

Authors

Abstract

Monitoring is an important tool for formulating management policy where competing resource interests exist. However, if monitoring data are underutilized and/or inadeqately communicated to users, then monitoring's effectiveness as well as its political currency both erode. Idaho's Statewide Ground Water Quality Monitoring Network, a database with 11 years of data on over 400 analytes from 1800 wells and springs statewide, contains a wealth of information to support decisions regarding ground water management. As scientists, we can assist the policy process by developing tools to analyze this data in a manner that policy makers can understand and apply. The ideal decision-making tools need not be quantitative, necessarily, but must be objective and defensible, and they should provide a measure of uncertainty or risk that can be incorporated in policy decisions that weigh competing needs.

Kriging is known as an optimal spatial interpolator that honors all measurements and minimizes estimation uncertainty. It is ideally suited to portraying monitoring information in map form that can be used by decision makers. For example, kriging can be used to produce maps of the likelihood that water quality exceeds a specific threshold (e.g., where is it 90% probable that nitrate exceeds 5 mg/l?). Such exceedance probability maps can be constructed across the life of a monitoring program and summarized in a single map of exceedance frequency (e.g., where is the 90% probability level most often exceeded?). Using nitrate data from the Statewide Network, we show how areas of degraded water quality can be identified via probability mapping, and compare these to subjectively-delineated areas of concern which Idaho regulatory agencies currently use. Probability kriging has the advantages of being quantitative, easy to understand and defend, and statistically rigorous. Furthermore, delineated areas can be regularly updated as new monitoring data accrue.