Journal article
Ensemble-Based Uncertainty Quantification Can Improve Large-Scale Precipitation Data for Hydrologic Prediction
Hydrological processes, Vol.40(7), e70635
07/01/2026
DOI: 10.1002/hyp.70635
Abstract
Substantial uncertainties have hindered uptake of large-scale precipitation data from satellites, reanalysis, and ‘merged’ datasets in hydrologic applications. Whilst this problem could be addressed by quantifying uncertainty and propagating it through hydrologic models, there is little consensus on what form of uncertainty information is needed. In this commentary, we define precipitation error and uncertainty across scales. We also describe the hydrologic conditions in which this uncertainty matters. We argue that progress requires ensemble representations of uncertainty, which can be readily integrated into existing hydrologic modelling frameworks.
Details
- Title: Subtitle
- Ensemble-Based Uncertainty Quantification Can Improve Large-Scale Precipitation Data for Hydrologic Prediction
- Creators
- Daniel B. Wright - University of Wisconsin–MadisonYagmur Derin - University of IowaKaidi Peng - University of Wisconsin–MadisonViviana Maggioni - George Mason University
- Resource Type
- Journal article
- Publication Details
- Hydrological processes, Vol.40(7), e70635
- DOI
- 10.1002/hyp.70635
- ISSN
- 0885-6087
- eISSN
- 1099-1085
- Publisher
- Wiley
- Grant note
- NASA Earth and Space Science and Technology 80NSSC24K1689 / National Aeronautics and Space Administration (100000104) 80NSSC22K0600 / National Aeronautics and Space Administration (100000104) 80NSSC22K0600; 80NSSC24K1689 / National Aeronautics and Space Administration (http://data.elsevier.com/vocabulary/SciValFunders/100000104) NASA's Precipitation Measurement Mission
- Language
- English
- Date published
- 07/01/2026
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9985183577702771
Metrics
1 Record Views