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Improving hydrologic models for predictions and process understanding using neural ODEs
Deep learning methods have frequently outperformed conceptual hydrologic models in
rainfall-runoff modelling. Attempts of investigating such deep learning models internally are …
rainfall-runoff modelling. Attempts of investigating such deep learning models internally are …
Karst water resources in a changing world: Review of solute transport modeling approaches
Karst water resources are valuable freshwater sources for around 10% of the world's
population. Nonetheless, anthropogenic impacts and global changes have seriously …
population. Nonetheless, anthropogenic impacts and global changes have seriously …
The impact of meteorological forcing uncertainty on hydrological modeling: A global analysis of cryosphere basins
Meteorological forcing is a major source of uncertainty in hydrological modeling. The recent
development of probabilistic large‐domain meteorological data sets enables convenient …
development of probabilistic large‐domain meteorological data sets enables convenient …
Estimating reservoir inflow and outflow from water level observations using expert knowledge: dealing with an ill‐posed water balance equation in reservoir …
Quantifying reservoir water balance is an essential process for the efficient management of
water resources. Water level records are often the only data available for reservoir analysis …
water resources. Water level records are often the only data available for reservoir analysis …
A process-driven deep learning hydrological model for daily rainfall-runoff simulation
H Li, C Zhang, W Chu, D Shen, R Li - Journal of Hydrology, 2024 - Elsevier
Although deep learning (DL) models, especially long-short-term memory (LSTM),
demonstrate greater accuracy than process-based models in rainfall-runoff simulation, the …
demonstrate greater accuracy than process-based models in rainfall-runoff simulation, the …
Machine learning applications in vadose zone hydrology: A review
Abstract Machine learning (ML) has been broadly applied for vadose zone applications in
recent years. This article provides a comprehensive review of such developments. ML …
recent years. This article provides a comprehensive review of such developments. ML …
Virtual Hydrological Laboratories: Develo** the next generation of conceptual models to support decision making under change
As hydrological systems are pushed outside the envelope of historical experience, the ability
of current hydrological models to serve as a basis for credible prediction and decision …
of current hydrological models to serve as a basis for credible prediction and decision …
Predicting streamflow with LSTM networks using global datasets
Streamflow predictions remain a challenge for poorly gauged and ungauged catchments.
Recent research has shown that deep learning methods based on Long Short-Term Memory …
Recent research has shown that deep learning methods based on Long Short-Term Memory …
[HTML][HTML] HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone–a blueprint for hydrologists
Abstract The “Digital Earth”(DE) metaphor is very useful for both end users and hydrological
modelers (ie, the coders). In this opinion paper, we analyze different categories of models …
modelers (ie, the coders). In this opinion paper, we analyze different categories of models …
Is precipitation responsible for the most hydrological model uncertainty?
Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological
processes are quite complex, and their simplifications in the models lead to inaccuracies …
processes are quite complex, and their simplifications in the models lead to inaccuracies …