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\begin{document}

\title{Global Daily Discharge Estimation Based on Grid-Scale Long Short-Term
Memory (LSTM) Model and River Routing}



\author[1]{Yuan Yang}%
\author[2]{Dapeng Feng}%
\author[3]{Hylke E. Beck}%
\author[4]{Weiming Hu}%
\author[4]{Agniv Sengupta}%
\author[1]{Luca Delle Monache}%
\author[5]{Robert Hartman}%
\author[6]{Peirong Lin}%
\author[7]{Chaopeng Shen}%
\author[1]{Ming Pan}%
\affil[1]{University of California San Diego}%
\affil[2]{Stanford University}%
\affil[3]{King Abdullah University of Science and Technology}%
\affil[4]{University of California, San Diego}%
\affil[5]{Robert K. Hartman Consulting Services}%
\affil[6]{Peking University}%
\affil[7]{Pennsylvania State University}%


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  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
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\selectlanguage{ngerman}° 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.%
\end{abstract}\selectlanguage{ngerman}%



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\textbf{Hosted file}

\verb`975488_0_art_file_11460659_s24dmg.docx`  available at \url{https://authorea.com/users/673109/articles/671985-global-daily-discharge-estimation-based-on-grid-scale-long-short-term-memory-lstm-model-and-river-routing}

\textbf{Hosted file}

\verb`975488_0_supp_11456081_s22fyr.docx`  available at \url{https://authorea.com/users/673109/articles/671985-global-daily-discharge-estimation-based-on-grid-scale-long-short-term-memory-lstm-model-and-river-routing}

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