Abstract
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Dingzhi Peng, Yuwei Gong, Xingtong Chen, Qianxue Yang, Linghua Qiu, Yunqing Xuan
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Continuous streamflow prediction in ungauged basins: Long short-term memory neural networks clearly outperform traditional hydrological models
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Nonlinear and threshold‐dominated runoff generation controls DOC export in a small peat catchment
10.1002/2016jg003621 · 2017
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TreeLSTM: a spatiotemporal machine learning model for rainfall-runoff estimation
2023
When best is the enemy of good – critical evaluation of performance criteria in hydrological models
Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
10.5194/hess-27-2397-2023 · 2023
To what extent does river routing matter in hydrological modeling?
2022
A systematic review of machine learning in groundwater monitoring
10.1016/j.envsoft.2025.106549 · 2025
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
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The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
10.5194/hess-27-2357-2023 · 2023
Differentiable, learnable, regionalized process‐based models with multiphysical outputs can approach state‐of‐the‐art hydrologic prediction accuracy
10.1029/2022wr032404 · 2022
A hydrological process-based neural network model for hourly runoff forecasting
10.1016/j.envsoft.2024.106029 · 2024
The proper care and feeding of CAMELS: how limited training data affects streamflow prediction
10.1016/j.envsoft.2020.104926 · 2021
Unresolved referenced work
Kept as external metadata until matched
Evolution of runoff components and groundwater discharge under rapid climate warming: Lhasa river basin, Tibetan Plateau
10.1016/j.jhydrol.2023.130556 · 2024
Influence of topographic features and stream network structure on the spatial distribution of hydrological response
10.1016/j.jhydrol.2021.126856 · 2021
Unresolved referenced work
Kept as external metadata until matched
A distributed hybrid physics-AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling
2025
SWAT: model use, calibration, and validation
10.13031/2013.42256 · 2012
Introductory overview: Error metrics for hydrologic modelling – a review of common practices and an open source library to facilitate use and adoption
10.1016/j.envsoft.2019.05.001 · 2019
Unresolved referenced work
Kept as external metadata until matched
Semi-supervised classification with graph convolutional networks
2017
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
10.5194/hess-22-6005-2018 · 2018
Towards learning data sparse watersheds under climate change, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
10.5194/hess-23-5089-2019 · 2019
Multivariate power-law models for streamflow prediction in the Mekong Basin
2014
LSTM-FKAN coupled with feature extraction technique for Precipitation–Runoff modeling
10.1016/j.jhydrol.2025.132705 · 2025
Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions
10.3389/frwa.2023.1150126 · 2023
Directed graph deep neural network for multi-step daily streamflow forecasting
10.1016/j.jhydrol.2022.127515 · 2022
Improved streamflow simulations in hydrologically diverse basins using physically-informed deep learning models
10.1080/02626667.2025.2458545 · 2025
Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis
2024
Snow drought to hydrologic drought progression using machine learning and probabilistic analysis
10.1038/s41598-025-05978-y · 2025
What role does hydrological science play in the age of machine learning?
10.1029/2020wr028091 · 2021
A review of hybrid deep learning applications for streamflow forecasting
10.1016/j.jhydrol.2023.130141 · 2023
Storing and managing water for the environment is more efficient than mimicking natural flows
10.1038/s41467-024-49770-4 · 2024
Unresolved referenced work
Kept as external metadata until matched
Spatial proximity, physical similarity, regression and ungaged catchments: a comparison of regionalization approaches based on 913 French catchments
10.1029/2007wr006240 · 2008
A differentiable, physics-based hydrological model and its evaluation for data-limited basins
2025
Development of a distributed physics-informed deep learning hydrological model for data-scarce regions
10.1029/2023wr036333 · doi-reference
Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks
10.1016/j.watres.2024.122142 · doi-reference
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
10.5194/hess-28-2107-2024 · doi-reference
Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics
10.1016/j.watres.2026.125613 · doi-reference
Knowledge-guided graph machine learning improves corn yield mapping in the U.S. Midwest
10.1016/j.rse.2026.115287 · doi-reference
Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology
10.1016/j.earscirev.2025.105276 · doi-reference
Effects of spatial variability and scale with implications to hydrologic modeling
10.1016/0022-1694(88)90090-x · doi-reference
Hydrological post-processing using stacked generalization of quantile regression algorithms: large-scale application over CONUS
10.1016/j.jhydrol.2019.123957 · doi-reference
Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions
10.1016/j.jhydrol.2023.130458 · doi-reference
Explore spatio‐temporal learning of large sample hydrology using graph neural networks
10.1029/2021wr030394 · doi-reference
Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management
10.5194/hess-25-3897-2021 · doi-reference
Differentiable modelling to unify machine learning and physical models for geosciences
10.1038/s43017-023-00450-9 · doi-reference
A transdisciplinary review of deep learning research and its relevance for water resources scientists
10.1029/2018wr022643 · doi-reference
An alternative approach for improving prediction of integrated hydrologic-hydraulic models by assessing the impact of intrinsic spatial scales
10.1029/2020wr027702 · doi-reference
Deep learning and process understanding for data-driven Earth system science
10.1038/s41586-019-0912-1 · doi-reference
Process-guided deep learning predictions of lake water temperature
10.1029/2019wr024922 · doi-reference
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · doi-reference
Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting
10.1007/s11269-021-02937-w · doi-reference
Simulation of snowmelt runoff in ungauged basins based on MODIS: a case study in the Lhasa River basin
10.1007/s00477-013-0837-4 · doi-reference
Quantifying present and future glacier melt-water contribution to runoff in a central Himalayan river basin
10.5194/tc-7-889-2013 · doi-reference
Regionalization for ungauged catchments—lessons learned from a comparative large‐sample study
10.1029/2021wr030437 · doi-reference
Using entity-aware LSTM to enhance streamflow predictions in transboundary and large lake basins
10.3390/hydrology12100261 · doi-reference
Spatial proximity, physical similarity, regression and ungaged catchments: a comparison of regionalization approaches based on 913 French catchments
10.1029/2007wr006240 · doi-reference
Storing and managing water for the environment is more efficient than mimicking natural flows
10.1038/s41467-024-49770-4 · doi-reference
A review of hybrid deep learning applications for streamflow forecasting
10.1016/j.jhydrol.2023.130141 · doi-reference
What role does hydrological science play in the age of machine learning?
10.1029/2020wr028091 · doi-reference
Snow drought to hydrologic drought progression using machine learning and probabilistic analysis
10.1038/s41598-025-05978-y · doi-reference
Improved streamflow simulations in hydrologically diverse basins using physically-informed deep learning models
10.1080/02626667.2025.2458545 · doi-reference
Directed graph deep neural network for multi-step daily streamflow forecasting
10.1016/j.jhydrol.2022.127515 · doi-reference
Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions
10.3389/frwa.2023.1150126 · doi-reference
LSTM-FKAN coupled with feature extraction technique for Precipitation–Runoff modeling
10.1016/j.jhydrol.2025.132705 · doi-reference
Towards learning data sparse watersheds under climate change, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
10.5194/hess-23-5089-2019 · doi-reference
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
10.5194/hess-22-6005-2018 · doi-reference
Introductory overview: Error metrics for hydrologic modelling – a review of common practices and an open source library to facilitate use and adoption
10.1016/j.envsoft.2019.05.001 · doi-reference
SWAT: model use, calibration, and validation
10.13031/2013.42256 · doi-reference
Influence of topographic features and stream network structure on the spatial distribution of hydrological response
10.1016/j.jhydrol.2021.126856 · doi-reference
Evolution of runoff components and groundwater discharge under rapid climate warming: Lhasa river basin, Tibetan Plateau
10.1016/j.jhydrol.2023.130556 · doi-reference
The proper care and feeding of CAMELS: how limited training data affects streamflow prediction
10.1016/j.envsoft.2020.104926 · doi-reference
A hydrological process-based neural network model for hourly runoff forecasting
10.1016/j.envsoft.2024.106029 · doi-reference
Differentiable, learnable, regionalized process‐based models with multiphysical outputs can approach state‐of‐the‐art hydrologic prediction accuracy
10.1029/2022wr032404 · doi-reference