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Forecasting magnitude and frequency of seasonal streamflow extremes using a Bayesian hierarchical framework
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2023
Source: Water Resources Research (2023)
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Journal Title:Water Resources Research
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Description:We develop a space-time Bayesian hierarchical modeling (BHM) framework for two flood risk attributes—seasonal daily maximum flow and the number of events that exceed a threshold during a season (NEETM)—at a suite of gauge locations on a river network. The model uses generalized extreme value (GEV) and Poisson distributions as marginals for these flood attributes with non-stationary parameters. The rate parameters of the Poisson distribution and location, scale, and shape parameters of the GEV are modeled as linear functions of suitable covariates. Gaussian copulas are applied to capture the spatial dependence. The best covariates are selected using the Watanabe-Akaike information criterion (WAIC). The modeling
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Source:Water Resources Research (2023)
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ISSN:0043-1397;1944-7973;
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Rights Information:Other
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Compliance:Library
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