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A Causal Inference Framework for Catchment Boundary Delineation

https://doi.org/10.2139/ssrn.5389768
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Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors
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Most Global Gauging Stations Present Biased Estimations of Total Catchment Discharge
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Are Deep Learning Models in Hydrology Entity Aware?
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Towards Interpretable Physical‐Conceptual Catchment‐Scale Hydrological Modeling Using the Mass‐Conserving‐Perceptron
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An Attention‐Based Explainable Deep Learning Approach to Spatially Distributed Hydrologic Modeling of a Snow Dominated Mountainous Karst Watershed
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The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data
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Limitation of automatic watershed delineation tools in coastal region
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Global changes in the spatial extents of precipitation extremes
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Functional and Effective Connectivity: A Review
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Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables
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Granger Causality: A Review and Recent Advances
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Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks
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PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies
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[Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and Intervention
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Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
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HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
inferredcites →https://doi.org/10.5194/hess-29-447-2025openalex-snapshot
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
inferredcites →https://doi.org/10.5281/zenodo.8170383openalex-snapshot
Most global gauging stations present biased estimations of total catchment discharge
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