Hydrology

Follow this topic Ask about this topic

Latest papers

26 papers2 papers in the last four weeks, down 60% on the four weeks before. 0.0% of all new papers.

From hydrological laws to catchment models: A process-based model matches deep-learning accuracy on three continents
Nov 4, 2026Xilin XiaHydrology Streamflow Forecasting
School of Engineering, University of Birmingham, Birmingham, UK
Fusing Earth observation embeddings and spaceborne terrain with meteorological forcing for flood peak depth prediction from high-water marks
Nov 3, 2026Wen Zhou, Shaowen Wang, Upmanu Lall +2Flood Forecasting Hydrology
Department of Geography and Geographic Information Science · CyberGIS Center for Advanced Digital and Spatial Studies · Siebel School of Computing and Data Science +2
Basin2Vec: Learning Basin-Centric Embeddings for Scalable Hydrological Modeling
Nov 3, 2026Muhammad Talha, Xiaofeng Liu, Yuan Yang +2Hydrology Streamflow Forecasting
Michigan State University · University of Michigan · University of California, San Diego
EvoBench: An Evolving, Diagnostic Benchmark Framework for Environmental Modeling
Oct 26, 2026Yiming Sun, Yuan Zhang, Qi Cheng +4Benchmark Design Benchmark Validity
Rutgers University, New Brunswick · Apple Inc. · University of Alabama +1
A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes
Oct 10, 2026M Jawad, HV Gupta, MA Farmani +2Hydrology Interpretable ML
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, USA, 85721.
Physics-Refined Spatiotemporal Forecasting on Open-Boundary Hydrologic Graphs
Oct 1, 2026Haoyang Jiang, Zhengui Wang, Shenghan Gao +3Spatiotemporal Forecasting Physics-Informed GNNs
William & Mary, Williamsburg, VA, USA · Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA, USA · Florida Atlantic University, Boca Raton, FL, USA
C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories
Sep 30, 2026Yanjie Tong, Phillip Si, Yuan Qiu +1Surrogate Modeling Flood Inundation Mapping
School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street Northwest, Atlanta, 30308, GA, USA
Distributed Hydrological Modeling in the Feature Space
Sep 26, 2026Mohamad Hakam Shams Eddin, Maria Luisa Taccari, Yikui Zhang +3Streamflow Forecasting Hydrology
Max Planck Institute for Biogeochemistry, Germany · European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom · Institute of Bio- and Geosciences Agrosphere (IBG-3), Research Centre Jülich, Germany +2
Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale
Sep 19, 2026Hong Zhang, John K. Hutchison, Rao Kotamarthi +7Streamflow Forecasting Time Series Forecasting
Argonne National Laboratory, Lemont, IL 60439, USA
Ranking Competing geologic interpretations via foundation-model-assisted generative hydrologic inversion
Sep 17, 2026Harun Ur Rashid, Daniel O'MalleyInverse Problems Hydrology
Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Supraglacial Lake Fate Is Knowable Long Before the Season Ends
Aug 31, 2026Emam Hossain, Md Osman GaniTime Series Classification Climate Science
University of Maryland, Baltimore County Baltimore, Maryland, USA
Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference
Aug 15, 2026Yi Yu, Jian Peng, Yucheng Lin +2Geospatial Foundation Models Hydrology
Precision Agriculture, Hydrology and Geoinformation Science Laboratory, School of Life and Environmental Sciences, The University of Sydney, Eveleigh, NSW, Australia · Department of Remote Sensing, Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany · Institute for Earth System Science and Remote Sensing, Leipzig University, Leipzig, Germany +3
Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods
Aug 12, 2026Matthew Kahn, Milan Arjel, Nirmala Adhikari +2Event Time Prediction Landslide Susceptibility Mapping
Applied Economics and Management, Cornell University, New York, United States of America · Department of Electronics and Computer Engineering, Purwanchal Campus, Institute of Engineering, Tribhuvan University, Dharan, Nepal · Department of Physics, University of Alabama at Birmingham, Alabama, United States of America +2
Effective catchment hydrologic representations are low-dimensional but interaction-dependent
Jul 29, 2026Yuan-Heng Wang, Hoshin V. GuptaNeural Network Interpretability Hydrology
Department of Hydrology and Atmospheric Science, University of Arizona, Tucson, AZ
Physics-Informed CNN-LSTM for Street-Scale Urban Flood Prediction: Reconciling Aggregate Accuracy and Street-Level Plausibility
Jul 27, 2026Luc DCosta, Yidi Wang, Jonathan L. Goodall +1Convolutional RNNs Flood Inundation Mapping
Chandra Robot Autonomy Lab, Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA · Link Lab, Multidisciplinary Research Center, University of Virginia, Charlottesville, VA 22904, USA · Hydroinformatics Research Group, Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA 22904, USA
Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models
Jul 3, 2026Karan Ruparell, Tristan Hascoet, Takemasa Miyoshi +4Streamflow Forecasting Uncertainty Quantification
University of Reading, Department of Meteorology, Reading, UK · European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK · RIKEN Center for Computational Sciences, Kobe, Japan +3
Spatial Support Matters: Geometry-Aware Graph Fusion for Rainfall Field Reconstruction
Jul 2, 2026Low Jun Yu, Niramay Kachhadiya, Herath Mudiyanselage Viraj Vidura Herath +2Heterogeneous GNNs Multimodal Sensor Fusion
School of Computing, National University of Singapore · Faculty of Engineering, The University of Sydney
Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America
Jun 22, 2026Lukas Arzoumanidis, Lara Johannsen, Klara Middendorf +2Climate Science Hydrology
HafenCity University, Computational Methods Lab Hamburg, Germany · HafenCity University, Geodesy and Adjustment Theory Hamburg, Germany · GFZ Helmholtz Centre for Geosciences, Global Geomonitoring and Gravity Field Potsdam, Germany
Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins
Jun 1, 2026Taye Akinrele, James Halgren, Noorbakhsh Amiri Golilarz +2Streamflow Forecasting Transformer
Dept. of Computer Science, The University of Alabama, Tuscaloosa, USA · Alabama Water Institute, USA
HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL
May 18, 2026Zhi Li, Songkun Yan, Jie Cao +4Simulation-Based RL Reinforcement Learning
Civil, Environmental, and Architectural Engineering, University of Colorado Boulder · Civil Engineering and Environmental Sciences, University of Oklahoma · Department of Computer Science, University of Oklahoma +3
Mini-JEPA Foundation Model Fleet Enables Agentic Hydrologic Intelligence
May 13, 2026Mashrekur RahmanGeospatial Foundation Models Geospatial Representation Learning
Dartmouth Libraries, Dartmouth College, 6025 Baker-Berry Library, Hanover, 03755, NH, USA
Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations
May 13, 2026Phillip Si, Yuan Qiu, Omar Sallam +4Digital Twins PDE Surrogate Modeling
School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street Northwest, Atlanta, 30308, GA, USA · Environmental Science Division, Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
Improving Model Performance by Adapting the KGE Metric to Account for System Non-Stationarity
Apr 5, 2026M Jawad, HV Gupta, YH Wang +3Hydrology Non-Stationary Time Series
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, USA, 85721. · Earth and Environmental Science Area, Lawrence Berkeley National Lab, Berkeley, CA
Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints
Mar 26, 2026Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi +3Interpretable ML Physics-Informed ML
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA · Department of Geosciences, University of Arizona, Tucson, AZ, USA
Learning global river system hydrodynamics with a topology-informed foundation model
Feb 25, 2026Hancheng Ren, Gang Zhao, Shuo Wang +12Hydrology Streamflow Forecasting
College of Water Sciences, Beijing Normal University, Beijing, China. · School of Geography and the Environment, University of Oxford, Oxford, UK. · Department of Transdisciplinary Science and Engineering, Institute of Science Tokyo, Tokyo, Japan. +6
Unraveling Groundwater Stress: A Model Generalizable Across Space and Time Reveals Co-Declining Water Tables and Recharge Across Bangladesh
Mar 28, 2025Saleh Sakib Ahmed, Rashed Uz Zzaman, Saifur Rahman Jony +5Hydrology Spatial Generalization
Department of Computer Science and Engineering (CSE), BUET, Dhaka,& 1205, Bangladesh. · Institute of Water and Flood Management, BUET, Dhaka & 1000 , Bangladesh. · Bangladesh Agricultural Development Corporation (BADC), Dhaka, Bangladesh +1

Nov 4, 2026J/K move · Enter open · S save

From hydrological laws to catchment models: A process-based model matches deep-learning accuracy on three continents

Xilin Xia

School of Engineering, University of Birmingham, Birmingham, UK

Hydrology Streamflow Forecasting

Open paper AskPDF arXiv

Dooge asked whether hydrology has laws at the catchment scale. We ask whether a low-dimensional system built under physical laws can match the accuracy of neural networks that outperform process-based models on the same inputs. Three laws constrain a lumped catchment model. Conservation makes it a flux-connected storage network, dissipation fixes the sign of every flux and aggregation determines how the unresolved composition enters the fluxes, through effective laws over threshold distributions and a memory of the forcing. These laws admit four kinds of refinement. A parsimonious model under them, CaRT (11 parameters per catchment), is refined in a loop assisted by AI agents. Each refinement addresses an observed deficit through a hypothesis from the literature. The result, CaRT+, has fourteen states, 35 parameters per catchment and no daily neural network. Most refinements follow from aggregation over thresholds or from variables that carry no water but modulate fluxes. On 602 British and 593 US gauged catchments, single CaRT+ models reach median Nash-Sutcliffe efficiencies of 0.863 and 0.765 in held-out years. A stateful LSTM reaches 0.869 and 0.751 and an entity-aware LSTM 0.862 and 0.754, while three-seed LSTM ensembles remain ahead of the CaRT+ ensemble by 0.003-0.012 in median paired NSE. On 505 drier Australian gauged catchments unused in development, single CaRT+ models reach 0.778 against 0.762 and 0.753 for the two LSTMs. The results suggest that catchment-scale laws exist as laws of form, with the constitutive families shared and only their parameters learned.