Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 1
Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 1
Research article
 | 
12 Jan 2022
Research article |  | 12 Jan 2022

A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber

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Short summary
Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
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