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Deep learning, hydrological processes and the uniqueness of place
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Multi-criterial validation of TOPMODEL in a mountainous catchment
inferredcites →https://doi.org/10.1002/2013wr014147openalex-snapshot
Storage dynamics in hydropedological units control hillslope connectivity, runoff generation, and the evolution of catchment transit time distributions
inferredcites →https://doi.org/10.1002/2013wr015141openalex-snapshot
Debates—The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph
inferredcites →https://doi.org/10.1002/2015wr017972openalex-snapshot
Spatiotemporal processes that contribute to hydrologic exchange between hillslopes, valley bottoms, and streams
inferredcites →https://doi.org/10.1002/2016wr020117openalex-snapshot
Using SAS functions and high‐resolution isotope data to unravel travel time distributions in headwater catchments
inferredcites →https://doi.org/10.1002/2016wr020283openalex-snapshot
Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental U S
inferredcites →https://doi.org/10.1002/hyp.10511openalex-snapshot
Hysteresis and scale in catchment storage, flow and transport
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Developing observational methods to drive future hydrological science: Can we make a start as a community?
inferredcites →https://doi.org/10.1002/hyp.331openalex-snapshot
On constraining TOPMODEL hydrograph simulations using partial saturated area information
inferredcites →https://doi.org/10.1002/hyp.343openalex-snapshot
Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system
inferredcites →https://doi.org/10.1002/hyp.444openalex-snapshot
On landscape space to model space mapping
inferredcites →https://doi.org/10.1002/wat2.1278openalex-snapshot
On hypothesis testing in hydrology: Why falsification of models is still a really good idea
inferredcites →https://doi.org/10.1002/wrcr.20068openalex-snapshot
Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems
inferredcites →https://doi.org/10.1007/978-3-030-28954-6_19openalex-snapshot
NeuralHydrology – Interpreting LSTMs in Hydrology
inferredcites →https://doi.org/10.1016/j.envsoft.2019.104521openalex-snapshot
Models of everywhere revisited: A technological perspective
inferredcites →https://doi.org/10.1016/j.jhydrol.2003.12.037openalex-snapshot
Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures
inferredcites →https://doi.org/10.1016/j.jhydrol.2009.07.047openalex-snapshot
Connectivity at the hillslope scale: Identifying interactions between storm size, bedrock permeability, slope angle and soil depth
inferredcites →https://doi.org/10.1016/j.jhydrol.2020.124700openalex-snapshot
Deep learning of subsurface flow via theory-guided neural network
inferredcites →https://doi.org/10.1016/s0309-1708(98)00020-7openalex-snapshot
Use of spatially distributed water table observations to constrain uncertainty in a rainfall–runoff model
inferredcites →https://doi.org/10.1029/2001wr000912openalex-snapshot
Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): The death of TOPMODEL?
inferredcites →https://doi.org/10.1029/2004wr003094openalex-snapshot
Nonparametric direct mapping of rainfall‐runoff relationships: An alternative approach to data analysis and modeling?
inferredcites →https://doi.org/10.1029/2007wr006240openalex-snapshot
Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments
inferredcites →https://doi.org/10.1029/2008wr006912openalex-snapshot
Catchments as simple dynamical systems: Catchment characterization, rainfall‐runoff modeling, and doing hydrology backward
inferredcites →https://doi.org/10.1029/2009wr008887openalex-snapshot
Are seemingly physically similar catchments truly hydrologically similar?
inferredcites →https://doi.org/10.1029/2011wr010666openalex-snapshot
Hierarchical controls on runoff generation: Topographically driven hydrologic connectivity, geology, and vegetation
inferredcites →https://doi.org/10.1029/2017wr022304openalex-snapshot
Age‐Ranked Storage‐Discharge Relations: A Unified Description of Spatially Lumped Flow and Water Age in Hydrologic Systems
inferredcites →https://doi.org/10.1029/2018wr022643openalex-snapshot
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
inferredcites →https://doi.org/10.1029/2019wr026065openalex-snapshot
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
inferredcites →https://doi.org/10.1029/2020wr028091openalex-snapshot
What Role Does Hydrological Science Play in the Age of Machine Learning?
inferredcites →https://doi.org/10.1061/taceat.0000694openalex-snapshot
The Relation Between the Rainfall and the Discharge of Sewers in Populous Districts
inferredcites →https://doi.org/10.1098/rspa.2002.0986openalex-snapshot
Towards a coherent philosophy for modelling the environment
inferredcites →https://doi.org/10.1098/rspa.2018.0862openalex-snapshot
Towards a methodology for testing models as hypotheses in the inexact sciences
inferredcites →https://doi.org/10.31223/osf.io/3sx6gopenalex-snapshot
What Role Does Hydrological Science Play in the Age of Machine Learning?
inferredcites →https://doi.org/10.5194/hess-10-609-2006openalex-snapshot
Searching for the Holy Grail of scientific hydrology: Q t =( S, R, Δt ) A as closure
inferredcites →https://doi.org/10.5194/hess-11-460-2007openalex-snapshot
Towards integrated environmental models of everywhere: uncertainty, data and modelling as a learning process
inferredcites →https://doi.org/10.5194/hess-21-3167-2017openalex-snapshot
Landscape-scale water balance monitoring with an iGrav superconducting gravimeter in a field enclosure
inferredcites →https://doi.org/10.5194/hess-22-6005-2018openalex-snapshot
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
inferredcites →https://doi.org/10.5194/hess-23-5089-2019openalex-snapshot
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
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A history of the concept of time of concentration
inferredcites →https://doi.org/10.5194/hess-4-203-2000openalex-snapshot
Uniqueness of place and process representations in hydrological modelling
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Comprehensive Analysis for Long-Term Hydrological Simulation by Deep Learning Techniques and Remote Sensing
inferred← citeshttps://doi.org/10.3389/fenvs.2023.1261239openalex-snapshot
A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting
inferred← citeshttps://doi.org/10.3389/frwa.2023.1112970openalex-snapshot
Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods
inferred← citeshttps://doi.org/10.3390/hydrology8010006openalex-snapshot
Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks
inferred← citeshttps://doi.org/10.3390/hydrology9010005openalex-snapshot
Machine Learning in Assessing the Performance of Hydrological Models
inferred← citeshttps://doi.org/10.3390/w13121696openalex-snapshot
How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting
inferred← citeshttps://doi.org/10.3390/w17131913openalex-snapshot
Utilizing Hybrid Deep Learning Models for Streamflow Prediction
inferred← citeshttps://doi.org/10.34753/hs.2020.2.4.375openalex-snapshot
Prospects for short-term forecasting of river streamflow from small watershed runoff using machine learning methods
inferred← citeshttps://doi.org/10.3850/978-981-18-8071-1_p131-cdopenalex-snapshot
Deep Reinforcement Learning for Space Power Source Regulation
inferred← citeshttps://doi.org/10.46698/vnc.2025.82.17.001openalex-snapshot
Оценка применимости моделей машинного обучения для прогноза гидроэкологического состояния малых рек
inferred← citeshttps://doi.org/10.46698/vnc.2026.94.99.001openalex-snapshot
Геоэкологические аспекты трансформации гидросферы на участках «мокрой» ликвидации угольных шахт (на примере шахты «Вергелевской»)
inferred← citeshttps://doi.org/10.48550/arxiv.2101.05186openalex-snapshot
MC-LSTM: Mass-Conserving LSTM
inferred← citeshttps://doi.org/10.5194/egusphere-2024-1449openalex-snapshot
Robustness of the long short-term memory network in rainfall-runoff prediction improved by the water balance constraint
inferred← citeshttps://doi.org/10.5194/egusphere-2024-1449-rc2openalex-snapshot
Comment on egusphere-2024-1449
inferred← citeshttps://doi.org/10.5194/gmd-14-7545-2021openalex-snapshot
GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models
inferred← citeshttps://doi.org/10.5194/gmd-17-497-2024openalex-snapshot
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
inferred← citeshttps://doi.org/10.5194/gmd-19-1055-2026openalex-snapshot
A hybrid physics–AI approach using universal differential equations with state-dependent neural networks for learnable, regionalizable, spatially distributed hydrological modeling
inferred← citeshttps://doi.org/10.5194/gmd-2023-128-ac3openalex-snapshot
Revision on gmd-2023-128 based on RC1 and RC2, Sept 8, 2023
inferred← citeshttps://doi.org/10.5194/hess-2021-127-ac5openalex-snapshot
Overview of Proposed Manuscript Changes based on Reviewer Comments
inferred← citeshttps://doi.org/10.5194/hess-2021-127-ac6openalex-snapshot
Overview of Proposed Manuscript Changes based on Reviewer Comments
inferred← citeshttps://doi.org/10.5194/hess-2023-148openalex-snapshot
Semi-supervised learning approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions
inferred← citeshttps://doi.org/10.5194/hess-25-4373-2021openalex-snapshot
Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling
inferred← citeshttps://doi.org/10.5194/hess-25-5355-2021openalex-snapshot
How is Baseflow Index (BFI) impacted by water resource management practices?
inferred← citeshttps://doi.org/10.5194/hess-25-5517-2021openalex-snapshot
Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models
inferred← citeshttps://doi.org/10.5194/hess-26-3079-2022openalex-snapshot
Hydrological concept formation inside long short-term memory (LSTM) networks
inferred← citeshttps://doi.org/10.5194/hess-27-1865-2023openalex-snapshot
Hybrid forecasting: blending climate predictions with AI models
inferred← citeshttps://doi.org/10.5194/hess-29-2521-2025openalex-snapshot
Machine learning in stream and river water temperature modeling: a review and metrics for evaluation
inferred← citeshttps://doi.org/10.5194/hess-30-1543-2026openalex-snapshot
DAR-type model based on “long memory-threshold” structure: a competitor for daily streamflow prediction under changing environment
inferred← citeshttps://doi.org/10.5194/hess-30-629-2026openalex-snapshot
When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models
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