Journal article
Advancing Ensemble Streamflow Prediction Through Satellite‐Based Precipitation Product and Model Parameter Uncertainty Quantification
Journal of Advances in Modeling Earth Systems, Vol.17(10), e2025MS004953
10/04/2025
DOI: 10.1029/2025MS004953
Abstract
Satellite‐based quantitative precipitation estimates (QPE), such as NASA's Integrated Multi‐satellitE Retrievals for GPM (IMERG), provide easily accessible continental‐to‐global precipitation forcings for flood prediction and other hydrologic applications. Nevertheless, when used in hydrologic prediction, uncertainty in satellite‐based QPE often leads to significant bias. This forcing uncertainty is further blended with other error sources, including process representation, parameter values, and their interactions. The identification and decoupling of these uncertainties can enhance our understanding of their respective impacts, thereby improving hydrologic prediction. Addressing this issue worldwide is challenging, however, largely due to the scarcity of precipitation ground truth and complex uncertainty interactions. Therefore, we propose an efficient uncertainty quantification framework for ensemble streamflow prediction, which keeps different uncertainty sources separable through hierarchical Bayesian inference. Satellite‐based QPE uncertainty is characterized by a novel near‐realtime quasi‐global satellite‐only ensemble precipitation data set (STREAM‐Sat), which is completely independent of ground‐based precipitation measurements. Model parameter uncertainty in a distributed physics‐based hydrologic model is inferred by an Iterative Ensemble Smoother (IES). To illustrate the impact and limitations of precipitation uncertainty, we compared ensemble streamflow predictions driven by both model parameter and satellite precipitation uncertainties and ensemble streamflow predictions driven by model parameter uncertainty and deterministic QPE. We demonstrate that the quantification of satellite‐based QPE uncertainty notably improves the accuracy and reliability of near‐realtime streamflow predictions in data scarce regions. This study also lays a foundation for satellite‐based streamflow prediction in ungauged regions.
Details
- Title: Subtitle
- Advancing Ensemble Streamflow Prediction Through Satellite‐Based Precipitation Product and Model Parameter Uncertainty Quantification
- Creators
- Kaidi Peng - University of Wisconsin–MadisonDaniel B Wright - University of Wisconsin–MadisonYagmur Derin - University of Wisconsin–MadisonG. Aaron Alexander - University of Wisconsin–MadisonAnkita Pradhan - University of Wisconsin–MadisonDavide Zoccatelli - Luxembourg Institute of Science and TechnologySamantha H Hartke - NSF National Center for Atmospheric ResearchZhe Li - Colorado State UniversityJackson Tan - Goddard Space Flight Center
- Resource Type
- Journal article
- Publication Details
- Journal of Advances in Modeling Earth Systems, Vol.17(10), e2025MS004953
- DOI
- 10.1029/2025MS004953
- ISSN
- 1942-2466
- eISSN
- 1942-2466
- Publisher
- John Wiley & Sons, Inc
- Grant note
- University of Wisconsin-Madison
K. Peng was supported by the NASA Precipitation Measurement Mission (Grant 80NSSC22K0600) and the NASA Future Investigators in NASA Earth and Space Science and Technology program (Grant 80NSSC24K1689). D.B. Wright, Y. Derin, and J. Tan were supported by the NASA Precipitation Measurement Mission (Grant 80NSSC22K0600). The authors thank the Center for High Throughput Computing (2006) at the University of Wisconsin-Madison for computational resources and support. The authors would like to thank three anonymous reviewers for their thoughtful suggestions to improve this manuscript.
- Language
- English
- Date published
- 10/04/2025
- Description audience
- Academic
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9984968455402771
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