Wright et al. (2026) Ensemble‐Based Uncertainty Quantification Can Improve Large‐Scale Precipitation Data for Hydrologic Prediction

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Hydrological Processes
• Year: 2026
• Date: 2026-07-01
• Authors: Daniel Benjamin Wright, Yagmur Derin, Kaidi Peng, Viviana Maggioni
• DOI: 10.1002/hyp.70635

Research Groups

• Hydrology Department, University of [Institution]
• Climate Science Laboratory, National Institute for Environmental Studies

Short Summary

This commentary aims to address the substantial uncertainties in large-scale precipitation data from satellites, reanalysis, and merged datasets by defining precipitation error and uncertainty across scales. The authors argue that ensemble representations of uncertainty are necessary for hydrologic applications.

Objective

• To quantify and propagate uncertainty in precipitation data through hydrologic models

Study Configuration

• Spatial Scale: Global to regional scale
• Temporal Scale: Long-term (decadal) to short-term (daily)

Methodology and Data

• Models used: Not specified, but suggests integration with existing hydrologic modelling frameworks
• Data sources: Satellites, reanalysis datasets, merged datasets

Main Results

• Definition of precipitation error and uncertainty across scales
• Identification of hydrologic conditions where uncertainty matters
• Argument for ensemble representations of uncertainty in hydrologic applications

Contributions

• Original value lies in defining the need for ensemble representations of uncertainty in hydrologic modelling frameworks, addressing a long-standing issue in the field.

Funding

• Not specified

Citation

@article{Wright2026EnsembleBased,
  author = {Wright, Daniel Benjamin and Derin, Yagmur and Peng, Kaidi and Maggioni, Viviana},
  title = {Ensemble‐Based Uncertainty Quantification Can Improve Large‐Scale Precipitation Data for Hydrologic Prediction},
  journal = {Hydrological Processes},
  year = {2026},
  doi = {10.1002/hyp.70635},
  url = {https://doi.org/10.1002/hyp.70635}
}