Predicting hydrologic response through a hierarchical catchment knowledgebase: A Bayes empirical Bayes approach

Abstract

Making useful Predictions in Ungauged Basins is an incredibly difficult task given the limitations of hydrologic models to represent physical processes appropriately across the heterogeneity within and among different catchments. Here, we introduce a new method for this challenge, Bayes empirical Bayes, that allows for the statistical pooling of information from multiple donor catchments and provides the ability to transfer parametric distributions rather than single parameter sets to the ungauged catchment. Further, the methodology provides an efficient framework with which to formally assess predictive uncertainty at the ungauged catchment. We investigated the utility of the methodology under both synthetic and real data conditions, and with respect to its sensitivity to the number and quality of the donor catchments used. This study highlighted the ability of the hierarchical Bayes empirical Bayes approach to produce expected outcomes in both the synthetic and real data applications. The method was found to be sensitive to the quality (hydrologic similarity) of the donor catchments used. Results were less sensitive to the number of donor catchments, but indicated that predictive uncertainty was best constrained with larger numbers of donor catchments (but still adequate with fewer donors). Key Points Useful predictions in ungauged catchments require uncertainty quantification The Bayes empirical Bayes (BEB) approach pools catchments to estimate parameters Results demonstrate the ability of BEB to reliably quantify uncertainty © 2014. American Geophysical Union. All Rights Reserved.