Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins

Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short-Term Memory (LSTM) is widely used to for such predictions, owing to its excellent migration performance. Traditional LSTM forced by meteorological data and catchment attribute data barely highlight the optimum data integration strategy for LSTM and its migration from data-rich basins to ungauged ones. In this study, we experimented with 1,897 global catchments and found that LSTM-corrected Global Hydrological Models (GHMs) outperformed uncorrected GHMs, improving the median Nash-Sutcliff efficiency (NSE) from 0.03 to 0.66. Notably, there was a large gap between traditional LSTM modeling in ungauged basins and autoregressive modeling in data-rich basins, and GHM-forced LSTM were an effective way to close this gap in ungauged basins. The spatial heterogeneity of the performance of GHM-forced LSTM was mainly influenced by three metrics (dryness, the leaf area index and latitude), which described the hydrological similarity among catchments. Weaker hydrological similarity among continental catchments results in larger variability in GHM-forced LSTM, with the best performance in Siberia (NSE, 0.54) and the worst in North America (NSE, 0.10). However, the migration performance of GHM-forced LSTM was significantly improved (NSE, 0.63) in ungauged basins when hydrological similarity was considered. This study stressed the advantages of GHM-forced LSTM and due significance should be attached to hydrological similarities among catchments to improve hydrological prediction in ungauged catchments.