Streamflow Forecasting

Follow this topic Ask about this topic

Latest papers

10 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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
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 · 3Independent 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
Topology enables learning-based hydrodynamic prediction of the global river system
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, Wenyu Ouyang, Chi Zhang +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

Oct 14, 2026J/K move · Enter open · S save

Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes

Justin Gallant, Tyler Wilson

University of British Columbia, Vancouver, BC, Canada

Streamflow Forecasting Uncertainty Quantification Uncertainty Calibration Time Series Forecasting Deep Ensembles

Open paper AskPDF arXiv

Deep learning models, particularly Long Short-Term Memory networks, are increasingly used for operational streamflow forecasting, with ensemble spread providing a signal of predictive uncertainty. As climate change increases the frequency of hydroclimatic extremes, these models will increasingly operate under flow conditions that are rare in their historical records. We therefore evaluate whether ensemble spread remains informative under increasing flows, examining both its magnitude relative to error and its ability to rank errors. Prediction error is approximately

2\times

the corrected spread at ordinary flows and up to

4\times

at the highest flows, with the discrepancy increasing with flow (log-log slope

1.30

, 95% CI:

[1.20,1.41]

). Under-dispersion is strongest in observed extreme flows beyond the training record, reaching an

18\times

discrepancy. This is pervasive and not explainable by conditional mean bias. Area Under the Sparsification Error, a standard distribution-free metric of uncertainty ranking, describing how well a model knows when it is uncertain, indicates near-oracle ranking for ensemble spread (

0.071

), yet ordering by predicted discharge alone nearly matches it. Informative uncertainty ranking can coexist with severe under-dispersion. In deployment, error ranking cannot be verified, yet the spread is available, and it is relied on as the uncertainty signal under increasingly extreme conditions, while systematically understating risk, most severely for the extreme events that matter most.