LSTM-based data integration to improve Snow Water Equivalent prediction and diagnose error sources

Re-cently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydro-logic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to inte-grate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to im-prove future predictions.

SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow-and deep-snow sites. The median Nash-Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values dmax were re-duced by 41% and 57%, respectively.