Global Daily Discharge Estimation Based on Grid Long Short-Term Memory (LSTM) Model and River Routing

To expand the spatial coverage of the conventional Basin Long Short-Term Memory (LSTM) model for river discharge estimation beyond pre-selected individual locations, we developed a discharge modeling scheme, Grid LSTM-RAPID, to estimate discharge for every river reach worldwide. Grid LSTM-RAPID applies LSTM runoff estimation to the grids (0.25°), small rectangular hydrological response units (HRUs) rather than basins (irregularly shaped HRUs of any size), and then routes the grid runoff over all reaches on a global river network using the RAPID routing model. It largely maintains the strong performance of Basin LSTM over gauged basins and achieves a median Kling-Gupta Efficiency (KGE) of 0.653 for small basins out-of-sample both temporally and spatially (0.688 for out-of-sample temporally), and a median KGE of 0.592 for other basins with larger areas and less data quality. Compared to Basin LSTM, Grid LSTM-RAPID loses about 0.03 in median KGE for basins out-of-sample in both time and space in exchange for global all-reach coverage without heavy cost. Despite this tradeoff, it significantly outperforms a well-calibrated process-based benchmark model. Using the new scheme, we create an improved global reach-level daily discharge data set from 1980 to near present named GRADES-hydroDL, which is openly shared at https://www.reachhydro.org/home/records/grades-hydrodl.