Extreme precipitation events are often localized, difficult to predict, and available records are often sparse. Improving frequency analysis and describing the associated uncertainty are essential for regional hazard preparedness and infrastructure design. Our primary goal is to evaluate incorporating Bayesian model averaging (BMA) within a spatial Bayesian hierarchical model framework (BHM). We compare results from two distinct regions in Oregon with different dominating rainfall generation mechanisms, and a region of overlap. We consider several Bayesian hierarchical models from relatively simple (location covariates only) to rather complex (location, elevation, and monthly mean climatic variables). We assess model predictive performance and selection through the application of leave-one-out cross-validation; however, other model assessment methods were also considered. We additionally conduct a comprehensive assessment of the posterior inclusion probability of covariates provided by the BMA portion of the model and the contribution of the spatial random effects term, which together characterize the pointwise spatial variation of each model’s generalized extreme value (GEV) distribution parameters within a BHM framework. Results indicate that while using BMA may improve analysis of extremes, model selection remains an important component of tuning model performance. The most complex model containing geographic and information was among the top performing models in western Oregon (with relatively wetter climate), while it performed among the worst in the eastern Oregon (with relatively drier climate). Based on our results from the region of overlap, site-specific predictive performance improves when the site and the model have a similar annual maxima climatology—winter storm dominated versus summer convective storm dominated. The results also indicate that regions with greater temperature variability may benefit from the inclusion of temperature information as a covariate. Overall, our results show that the BHM framework with BMA improves spatial analysis of extremes, especially when relevant (physical and/or climatic) covariates are used.

中文翻译：

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在极端日降水的贝叶斯分层模型中整合气候和物理信息

极端降水事件通常是局部的，难以预测，可用的记录通常很少。改进频率分析和描述相关的不确定性对于区域灾害准备和基础设施设计至关重要。我们的主要目标是评估在空间贝叶斯分层模型框架 (BHM) 中合并贝叶斯模型平均 (BMA)。我们比较了俄勒冈州具有不同主要降雨生成机制和重叠区域的两个不同区域的结果。我们考虑了几个贝叶斯分层模型，从相对简单（仅位置协变量）到相当复杂（位置、海拔和月平均气候变量）。我们通过应用留一法交叉验证来评估模型预测性能和选择；然而，还考虑了其​​他模型评估方法。我们还对模型的 BMA 部分提供的协变量的后验包含概率和空间随机效应项的贡献进行了综合评估，它们共同表征了每个模型的广义极值 (GEV) 分布参数的逐点空间变化。 BHM 框架。结果表明，虽然使用 BMA 可以改进对极端情况的分析，但模型选择仍然是调整模型性能的重要组成部分。包含地理和信息的最复杂模型在俄勒冈州西部（气候相对湿润）表现最佳，而在俄勒冈东部（气候相对干燥）表现最差。根据我们重叠区域的结果，当站点和模型具有相似的年度最大值气候学时，特定站点的预测性能会提高 - 冬季风暴主导与夏季对流风暴主导。结果还表明，温度变化较大的地区可能受益于将温度信息作为协变量包含在内。总的来说，我们的结果表明，带有 BMA 的 BHM 框架改进了极端情况的空间分析，尤其是在使用相关（物理和/或气候）协变量时。