Baoxiang Pan

and 10 more

Yuan Yang

and 9 more

Accurate global river discharge estimation is crucial for advancing our scientific understanding of the global water cycle and supporting various downstream applications. In recent years, data-driven machine learning models, particularly the Long Short-Term Memory (LSTM) model, have shown significant promise in estimating discharge. Despite this, the applicability of LSTM models for global river discharge estimation remains largely unexplored. In this study, we diverge from the conventional basin-lumped LSTM modeling in limited basins. For the first time, we apply an LSTM on a global 0.25° grid, coupling it with a river routing model to estimate river discharge for every river reach worldwide. We rigorously evaluate the performance over 5332 evaluation gauges globally for the period 2000-2020, separate from the training basins and period. The grid-scale LSTM model effectively captures the rainfall-runoff behavior, reproducing global river discharge with high accuracy and achieving a median Kling-Gupta Efficiency (KGE) of 0.563. It outperforms an extensively bias-corrected and calibrated benchmark simulation based on the Variable Infiltration Capacity (VIC) model, which achieved a median KGE of 0.466. Using the global grid-scale LSTM model, we develop an improved global reach-level daily discharge dataset spanning 1980 to 2020, named GRADES-hydroDL. This dataset is anticipated to be useful for a myriad of applications, including providing prior information for the Surface Water and Ocean Topography (SWOT) satellite mission. The dataset is openly available via Globus.

Michael Durand

and 30 more

Donghui Xu

and 4 more

Floodplain inundation links river and land systems through significant water, sediment, and nutrient exchanges. However, these two-way interactions between land and river are currently missing in most Earth System Models. In this study, we introduced the two-way hydrological coupling between the land component, ELM, and the river component, MOSART, in Energy Exascale Earth System Model (E3SM) to study the impacts of floodplain inundation on land and river processes. We calibrated the river channel geometry and developed a new data-driven inundation scheme to improve the simulation of inundation dynamics in E3SM. The new inundation scheme captures 96% of the spatial variation of inundation area in a satellite inundation product at global scale, in contrast with 7% when the default inundation scheme of E3SM was used. Global simulations including the new inundation scheme performed at resolution with and without two-way land-river coupling were used to quantify the impact of coupling. Comparisons show that two-way coupling modifies the water and energy cycle in 20% of the global land cells. Specifically, riverine inundation is reduced by two-way coupling, but inland inundation is intensified. Wetter periods are more impacted by the two-way coupling at the global scale, while regions with different climates exhibit different sensitivities. The two-way exchange of water between the land and river components of E3SM provides the foundation for enabling two-way coupling of land-river sediment and biogeochemical fluxes. These capabilities will be used to improve understanding of the interactions between water and biogeochemical cycles and their response to human perturbations.