Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

TLDR

LSTM networks provide unprecedented accuracy for predicting hydrologic responses in ungauged basins. The authors trained and validated multiple LSTM models on 531 CAMELS basins using ~30 years of daily rainfall‑runoff data across a wide range of basin sizes and aridity, and benchmarked the ungauged models over a 15‑year period against the calibrated SAC‑SMA and NOAA NWM. The out‑of‑sample LSTM achieved a median Nash‑Sutcliffe Efficiency of 0.69, outperforming both the calibrated SAC‑SMA (0.64) and NOAA NWM (0.58), indicating that catchment attributes contain sufficient information for more accurate predictions and that adding physical constraints could further improve performance.

Abstract

Abstract Long short‐term memory (LSTM) networks offer unprecedented accuracy for prediction in ungauged basins. We trained and tested several LSTMs on 531 basins from the CAMELS data set using k‐fold validation, so that predictions were made in basins that supplied no training data. The training and test data set included ∼30 years of daily rainfall‐runoff data from catchments in the United States ranging in size from 4 to 2,000 km 2 with aridity index from 0.22 to 5.20, and including 12 of the 13 IGPB vegetated land cover classifications. This effectively “ungauged” model was benchmarked over a 15‐year validation period against the Sacramento Soil Moisture Accounting (SAC‐SMA) model and also against the NOAA National Water Model reanalysis. SAC‐SMA was calibrated separately for each basin using 15 years of daily data. The out‐of‐sample LSTM had higher median Nash‐Sutcliffe Efficiencies across the 531 basins (0.69) than either the calibrated SAC‐SMA (0.64) or the National Water Model (0.58). This indicates that there is (typically) sufficient information in available catchment attributes data about similarities and differences between catchment‐level rainfall‐runoff behaviors to provide out‐of‐sample simulations that are generally more accurate than current models under ideal (i.e., calibrated) conditions. We found evidence that adding physical constraints to the LSTM models might improve simulations, which we suggest motivates future research related to physics‐guided machine learning.