T59 Hydrologic Characterization and Simulation of Neogene Volcanic Terranes

Sunday, 18 October 2009
1:30 PM - 5:30 PM
D139/140

EVALUATING DATA WORTH FOR GROUNDWATER MODELING IN THE UPPER KLAMATH BASIN, OREGON AND CALIFORNIA

Paper number: T59-5
Program: Technical Sessions
Time: 2:35 PM – 2:50 PM

Authors

Abstract

As the demand for groundwater in the upper Klamath Basin has increased, so has the need for tools to assess the impacts of groundwater pumping on the basin’s water resources. To address this need, a transient groundwater flow model that simulates regional-scale groundwater/surface-water interactions has been developed using MODFLOW. The model was calibrated using a time-series data set that includes approximately 5,600 water-level measurements from 662 wells, and more than 300 groundwater discharge measurements for 10 major discharge areas. The calibrated model is able to reproduce the observed variations in water level and groundwater discharge at a range of time scales throughout most of the upper Klamath Basin. When coupled with uncertainty analysis, the calibrated model can be used to evaluate data worth and guide data collection. Model-prediction uncertainty analysis serves to measure the contribution of data, individually and collectively, to improving model predictions; data that provide a greater reduction in model uncertainty are assumed to have greater value for model calibration.

The work presented here applies the uncertainty-analysis method to evaluate data worth for the upper Klamath Basin groundwater model. The analysis was first applied to the existing calibration data set to identify the data that were most informative for modeling. The analysis assessed the contribution of each water-level and discharge measurement to reducing uncertainty associated with drawdown and discharge predictions. The results show that (1) both data types provide substantial information for improving prediction reliability and (2) prediction uncertainty can be reduced with data that are outside the immediate vicinity of the targeted model predictions. The analysis was then used to assess the prospective worth of potential new data and identify future measurements that would best supplement the existing data set. The results indicate that (1) model uncertainty can be further reduced with the addition of new water-level and discharge measurements and (2) the new water-level data should be obtained at a subset of existing wells along with new wells located in sparsely sampled regions of the basin.