A Hybrid, Non‐Stationary Stochastic Watershed Model (SWM) for Uncertain Hydrologic Simulations Under Climate Change

A Hybrid, Non‐Stationary Stochastic Watershed Model (SWM) for Uncertain Hydrologic Simulations Under Climate Change

Abstract Stochastic Watershed Models (SWMs) are emerging tools in hydrologic modeling used to propagate uncertainty into model predictions by adding samples of model error to deterministic simulations. One of the most promising uses of SWMs is uncertainty propagation for hydrologic simulations under...

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Bibliographic Details
Main Authors: Zach Brodeur, Sungwook Wi, Ghazal Shabestanipour, Jon Lamontagne, Scott Steinschneider
Format: Article
Language:English
Published: Wiley 2024-05-01
Series:Water Resources Research
Subjects:
stochastic watershed modeling
non‐stationary hydrology
non‐stationary uncertainty
climate change
hybrid modeling
machine learning
Online Access:https://doi.org/10.1029/2023WR035042
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https://doi.org/10.1029/2023WR035042

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