资源环境科技发展态势分析平台

We present a general Bayesian hierarchical framework for conducting nonstationary frequency analysis of multiple hydrologic variables. In this, annual maxima from each variable are assumed to follow a generalized extreme value (GEV) distribution in which the location parameter is allowed to vary in time. A Gaussian elliptical copula is used to model the joint distribution of all variables. We demonstrate the utility of this framework with a joint frequency analysis model of annual peak snow water equivalent (SWE), annual peak flow, and annual peak reservoir elevation at Taylor Park dam in Colorado, USA. Indices of large-scale climate drivers-El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) are used as covariates to model temporal nonstationarity. The Bayesian framework provides the posterior distribution of the model parameters and consequently the return levels. Results show that performing a multivariate joint frequency analysis reduces the uncertainty in return level estimates and better captures multivariate dependence compared to an independent model.

Plain Language Summary In this study, we develop a method for determining the probability of occurrence of rare hydrologic events (e.g., floods). Utilizing modern statistical methods, we are able to estimate occurrence probabilities for multiple hydrologic variables simultaneously while incorporating climate information that changes in time. We apply this technique to estimate occurrence probabilities for stream-flow, reservoir elevation, and snow levels for the Taylor Park reservoir in Colorado, USA. This method provides several benefits over traditional methods including reduction of uncertainty and a flexible model structure which allows for the incorporation of climate information.