Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-149-2022
https://doi.org/10.5194/hess-26-149-2022
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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Cited articles

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Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F., Nijssen, B., and Lettenmaier, D. P.: Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments, Water Res. Res., 52, 4209–4225, https://doi.org/10.1002/2015WR017864, 2016. a
Apputhurai, P. and Stephenson, A. G.: Spatiotemporal hierarchical modelling of extreme precipitation in Western Australia using anisotropic Gaussian random fields, Environ. Ecol. Stat., 20, 667–677, https://doi.org/10.1007/s10651-013-0240-9, 2013. a
Atyeo, J. and Walshaw, D.: A region-based hierarchical model for extreme rainfall over the UK, incorporating spatial dependence and temporal trend, Environmetrics, 23, 509–521, https://doi.org/10.1002/env.2155, 2012. a
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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.