Spatial-temporal multivariate semi-Bayesian hierarchical framework for extreme precipitation frequency analysis
Spatial-temporal multivariate semi-Bayesian hierarchical framework for extreme precipitation frequency analysis
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DOI：
10.1016/j.jhydrol.2021.126499
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发表时间：
2021-05
影响因子：
6.4
通讯作者：
Á. Ossandón;B. Rajagopalan;W. Kleiber
中科院分区：
文献类型：
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作者：
Á. Ossandón;B. Rajagopalan;W. Kleiber
我们提出了一个半贝叶斯分层建模框架进行时空频率分析的降水极端在一个大的域。在这个框架中，数据层，降水极端-即，季节最大降水量，在每个站在每年使用广义极值（GEV）分布的时间变化的参数，这是分解为协变量的线性函数。通过最大似然（ML）估计协变量的系数。在过程层中，跨站的每个协变量的估计的ML系数用高斯多变量过程在空间上建模，这使得能够捕获空间结构和空间模型参数之间的相关性。合适的先验被用于空间模型超参数以完成贝叶斯公式化。由于贝叶斯公式仅处于第二水平，因此我们的模型是半贝叶斯的，因此后验是条件后验分布。利用各时刻GEV参数空间场的条件后验分布，得到了极端降水非平稳时空返回水平的条件后验分布。我们证明了这一框架的应用程序夏季降水极端在73个站，覆盖了美国西南部的大域亚利桑那州，新墨西哥州，科罗拉多，和犹他州。拟合和交叉验证的结果表明，我们的模型很好地捕捉到了台站的历史变化。条件后验分布的回报水平上的域，这将是巨大的效用在自然资源和基础设施的管理上的网格模拟。
We present a semi-Bayesian hierarchical modeling framework for conducting space–time frequency analysis of precipitation extremes over a large domain. In this framework, the data layer, the precipitation extreme – i.e., seasonal maximum precipitation, at each station in each year is modeled using a generalized extreme value (GEV) distribution with temporally varying parameters, which are decomposed as linear functions of covariates. The coefficients of the covariates are estimated via maximum likelihood (ML). In the process layer, the estimated ML coefficients of each of the covariates across the stations are spatially modeled with a Gaussian multivariate process which enables capturing the spatial structure and correlation between the spatial model parameters. Suitable priors are used for the spatial model hyperparameters to complete the Bayesian formulation. Since the Bayesian formulation is only at the second level, our model is semi-Bayesian and thus, the posteriors are conditional posterior distributions. With the conditional posterior distribution of spatial fields of the GEV parameters for each time, conditional posterior distribution of the nonstationary space–time return levels of the precipitation extremes are obtained. We demonstrate this framework by application to summer precipitation extreme at 73 stations covering a large domain of Southwest US consisting of Arizona, New Mexico, Colorado, and Utah. The results from fitting and cross-validation indicate that our model captures the historical variability at the stations very well. Conditional posterior distributions of return levels are simulated on a grid over the domain, which will be of immense utility in management of natural resources and infrastructure.