HR: 1340h
AN: H13A-1316    [Abstracts]
TI: Assessing Recharge Model Uncertainty: Case Study Using the Death Valley Regional Flow System Model
AU: Pohlmann, K
EM: Karl.Pohlmann@dri.edu
AF: Division of Hydrologic Sciences, Desert Research Institute, Nevada System of Higher Education, 755 E. Flamingo Road, Las Vegas, NV 89119 United States
AU: * Ye, M
EM: Ming.Ye@dri.edu
AF: Division of Hydrologic Sciences, Desert Research Institute, Nevada System of Higher Education, 755 E. Flamingo Road, Las Vegas, NV 89119 United States
AU: Pohll, G
EM: Greg.Pohll@dri.edu
AF: Division of Hydrologic Sciences, Desert Research Institute, Nevada System of Higher Education, 755 E. Flamingo Road, Las Vegas, NV 89119 United States
AU: Chapman, J
EM: Jenny.Chapman@dri.edu
AF: Division of Hydrologic Sciences, Desert Research Institute, Nevada System of Higher Education, 755 E. Flamingo Road, Las Vegas, NV 89119 United States
AB: Hydrologic analyses are commonly based on a single conceptual-mathematical model. Yet hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions. Considering conceptual model uncertainty is thus a critical process in hydrologic uncertainty assessment. For the Death Valley Regional Flow System (DVRFS) model, developed by the U.S. Geological Survey (Belcher et al., 2004) and covering portions of southwest Nevada and southeast California, five alternative recharge models have been independently developed to date. These models are (1) the Maxey-Eakin model (Maxey-Eakin, 1949), (2 and 3) a distributed parameter watershed model with and without a runon-runoff component (Hevesi, 2003), and (4 and 5) a chloride mass balance model with two zero-recharge masks, one for alluvium and one for both alluvium and elevation (Russell and Minor, 2003). Whereas these five models are based on different methodologies for estimating recharge and have different levels of complexity, they all have been used for groundwater modeling in Nevada. The objective of our work is to evaluate recharge model uncertainty and quantify its propagation through the groundwater modeling process. We apply the recently developed Maximum Likelihood Bayesian Model Averaging (MLBMA) method (Neuman, 2003; Ye et al., 2004) and formally incorporate prior information and field measurements into the process. The DVRFS model is the numerical modeling framework and the recharge values of the five recharge models are handled by the recharge package of MODFLOW-2000. Conceptual model uncertainty is first evaluated through expert elicitation based on prior information possessed by a panel of seven experts. Their perceptions of model plausibility are quantified as prior model probabilities, which are then updated by the site measurements of head and flux through inverse modeling using the parameter estimation package in MODFLOW-2000. Posterior model probabilities of the five models are then evaluated after the updating process and used as weights in the summation of each model's mean predictions and associated predictive uncertainty. The modeling process provides the mean and variance of groundwater flux with consideration of both parametric and conceptual model uncertainty.
DE: 1829 Groundwater hydrology
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: Fall Meeting 2005