Global Daily Discharge Estimation Based on Grid Long Short‐Term Memory (LSTM) Model and River Routing - Yuan Yang; Dapeng Feng; Hylke E. Beck; Weiming Hu; Ather Abbas; Agniv Sengupta; Luca Delle Monache; Robert Hartman; Peirong Lin; Chaopeng Shen; Ming Pan (Water Resources Research)
Abstract 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.