Machine Learning for a Heterogeneous Water Modeling Framework

Abstract

This technical note describes recent efforts to integrate machine learning (ML) models, specifically long short-term memory (LSTM) networks and differentiable parameter learning conceptual hydrological models (δ conceptual models), into the next-generation water resources modeling framework (Nextgen) to enhance future versions of the U.S. National Water Model (NWM). We address three specific methodology gaps of this new modeling framework: (1) assess model performance across many ungauged catchments, (2) diagnostic-based model selection, and (3) regionalization based on catchment attributes. We demonstrate that an LSTM trained on CAMELS catchments can make large-scale predictions with Nextgen across the New England region and match the average flow duration curve observed by stream gauges for streamflow with low exceedance probability (high flows), but diverges from the mean in high exceedance probability (low flows). We demonstrate improvements in peak flow predictions when using δ conceptual model, but results also suggest that performance increases may come at a cost of accurately representing hydrologic states within the conceptual model. We propose a novel approach using ML to predict the most performant mosaic modeling approach and demonstrate improved distributions of efficiency scores throughout the large sample of basins. Our findings advocate for the future development of ML capabilities within Nextgen for advancing operational hydrological modeling.