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The sensitivity of simulated streamflow to individual hydrologic processes across North America
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Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties. This enables seamless quantification of model process sensitivities and parameter sensitivities across a continuous set of models. It also leads to high-level conclusions about the importance of water cycle components on streamflow predictions, such as quickflow being the most sensitive process for streamflow simulations across the North American continent. The results of the 3000 basins are used to derive an approximation of sensitivities based on physiographic and climatologic data without the need to perform expensive sensitivity analyses. Detailed spatio-temporal inputs and results are shared through an interactive website.
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The Great Plains have historically had poor performance for all kinds of models.
“The Great Plains have historically had poor performance for all kinds of models (Mai et al., 2022)”
From Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation (2025) · cites this paper as Mai et al., 2022
The Results section (“Preliminary calibration and validation of the blended model”) reports weaker validation performance over the high plains and says this is consistent with the other hydrologic models it lists. That supports a pattern of poor performance for several streamflow models, but not the broader claim about the entire Great Plains or “all kinds of models.” No page number is provided in our copy. (LLM, conf=0.86)
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Metadata
- Authors
- Mai, J., Craig, J. R., Tolson, B. A., & Arsenault, R.
- Affiliations
- University of Waterloo (CA)
- École de Technologie Supérieure (CA)
- Corresponding author
- Mai, Juliane
- Venue
- Nature Communications
- Published
- 2022-01-24
- Volume
- 13
- Issue
- 1
- Pages
- 455-455
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8786896
- OpenAlex
- W4206949136
- Cited by
- 63
- References
- 75
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 15 · 2023: 23 · 2022: 4
- FWCI
- 4.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Espace ÉTS (ETS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Canarie (RS-332)
- Compute Canada
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Monitoring and Data Management
- Keywords
- streamflow sensitivity, parameter sensitivity, structural uncertainty, quickflow, North American Basin
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