Post-Processing a Conceptual Rainfall-Runoff Model with an LSTM
Grey Nearing, Alden Keefe Sampson, Frederik Kratzert, Jonathan Frame
Abstract
Machine learning is becoming an increasingly important part of streamflow forecasting, but as these models to date lack a physical basis, there is a potential that they may produce values that are not realistic. We tested a simple post-processing strategy that uses the outputs from a calibrated conceptual model (the Sacramento Soil Moisture Accounting Model with Snow-17; SAC-SMA) as inputs into a a Long Short Term Memory Network (LSTM). Overall, the SAC-SMA model was improved substantially, while post-processing offered only minor improvements relative to the standalone LSTM. SAC-SMA performance was improved in catchments with more snow. The standalone LSTM was improved in terms of long-term bias, which is likely because the LSTM is not constrained by conservation principles.