Enabling Community Contributions in Formulation and Hydrologic Signature Regionalization for Continental Hydrologic Modeling

As NOAA OWP and its federal and academic partners build the model-agnostic Next Generation Water Resources Modeling Framework (NextGen), we will need to know how to optimally select model formulations and estimate parameter values for ungauged catchments. This problem becomes intractable when considering the unique combinations of current and future model formulations combined with the innumerable possible parameter combinations across the continent. To simplify the model selection problem, we apply an analytical tool that predicts hydrologic formulation performance using community-generated data.

The formulation and signature decision support (FSDS) tool readily predicts how models might perform, or how hydrologic signatures might appear, across ungauged catchments based on catchment attributes such as climate characteristics and geology. FSDS does this by training an algorithm on the relationships between the response variables (model metrics or hydrologic signatures) and the predictor variables (corresponding catchment attributes). The FSDS algorithm then predicts model formulation performance or hydrologic signatures in ungauged catchments based on known catchment attributes. In this application of FSDS, we consider performance metrics from process-based and neural network-based hydrologic models that predict stream discharge. Results suggest that the most performant types of hydrologic models vary across the continent, as some models are more suited for particular catchments than others. The FSDS tool is designed such that as the hydrologic modeling community generates more results, better decisions can be made on where model formulations would be best suited. Here we present the baseline results on formulation selection and demonstrate how the hydrologic modeling community may participate in improving and/or using the FSDS tool to inform NextGen.