Expert elicitation of recharge model probabilities for the Death Valley regional flow system

This study uses expert elicitation to evaluate and select five alternative recharge models developed for the Death Valley regional flow system (DVRFS), covering southeast Nevada and the Death Valley area of California, USA. The five models were developed based on three independent techniques: an empirical approach, an approach based on unsaturated-zone studies and an approach based on saturated-zone studies. It is uncertain which recharge model (or models) should be used as input for groundwater models simulating flow and contaminant transport within the DVRFS. An expert elicitation was used to evaluate and select the recharge models and to determine prior model probabilities used for assessing model uncertainty. The probabilities were aggregated using simple averaging and iterative methods, with the latter method also considering between-expert variability. The most favorable model, on average, is the most complicated model that comprehensively incorporates processes controlling net infiltration and potential recharge. The simplest model, and the most widely used, received the second highest prior probability. The aggregated prior probabilities are close to the neutral choice that treats the five models as equally likely. Thus, there is no support for selecting a single model and discarding others, based on prior information and expert judgment. This reflects the inherent uncertainty in the recharge models. If a set of prior probability from a single expert is of more interest, we suggest selecting the set of the minimum Shannon's entropy. The minimum entropy implies the smallest amount of uncertainty and the largest amount of information used to evaluate the models. However, when enough data are available, we prefer to use a cross-validation method to select the best set of prior model probabilities that gives the best predictive performance.