Streamflow Forecasting

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13 papers2 papers in the last four weeks, against 2 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
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
Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes
Oct 14, 2026Justin Gallant, Tyler WilsonStreamflow Forecasting Uncertainty Quantification
University of British Columbia, Vancouver, BC, Canada
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
A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration
Aug 9, 2026Wei Wang, Yaosen Chen, Han Yang +5Streamflow Forecasting World Model Learning
Sobey Media Intelligence Laboratory
Community HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows
Jul 27, 2026Qingyi Yang, Siqian Qiu, Bing Li +20Agentic Workflows Spatiotemporal Forecasting
Department of Civil and Environmental Engineering (D.I.C.A.), Politecnico di Milano, 20133 Milano MI, Italy · College of Hydraulic and Environmental Engineering, China Three Gorges University, 443002 Yichang, China · Independent Researcher, Shandong, 252300, China +16
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
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
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
AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS
Feb 18, 2026Maria Luisa Taccari, Kenza Tazi, Oisín M. Morrison +7Streamflow Forecasting Time Series Forecasting
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom · European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany
Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings
Jan 4, 2026Pengfei Qu, Lei Ye, Wenyu Ouyang +6Streamflow Forecasting Time Series Forecasting
School of Computer Science and Technology, Dalian University of Technology, Dalian, China · School of Infrastructure Engineering, Dalian University of Technology, Dalian, China · School of Information and Communication Engineering, Dalian University of Technology, Dalian, China +2

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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

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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.