Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5685–5695, 2022
https://doi.org/10.5194/hess-26-5685-2022
Hydrol. Earth Syst. Sci., 26, 5685–5695, 2022
https://doi.org/10.5194/hess-26-5685-2022
Technical note
11 Nov 2022
Technical note | 11 Nov 2022

Technical note: Modeling spatial fields of extreme precipitation – a hierarchical Bayesian approach

Bianca Rahill-Marier et al.

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Cited articles

Asquith, W. H. and Famiglietti, J. S.: Precipitation areal-reduction factor estimation using an annual-maxima centered approach, J. Hydrol., 230, 55–69, 2000. 
Denwood, M. J.: runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS, J. Stat. Softw., 71, 1–25, 2016. 
Devineni, N., Lall, U., Pederson, N., and Cook, E.: A tree-ring-based reconstruction of Delaware River basin streamflow using hierarchical Bayesian regression, J. Climate, 26, 4357–4374, 2013. 
Dyrrdal, A. V., Lenkoski, A., Thorarinsdottir, T. L., and Stordal, F.: Bayesian hierarchical modeling of extreme hourly precipitation in Norway, Environmetrics, 26, 89–106, 2015. 
Gelman, A. and Hill, J.: Data Analysis Using Regression and Multilevel/Hierarchical Models (Analytical Methods for Social Research), Cambridge, Cambridge University Press, https://doi.org/10.1017/CBO9780511790942, 2007. 
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Short summary
We present a new approach to modeling extreme regional rainfall by considering the spatial structure of extreme events. The developed models allow a probabilistic exploration of how the regional drainage network may respond to extreme rainfall events and provide a foundation for how future risks may be better estimated.