ABSTRACT

Bayesian hierarchical models have been increasingly used in regional flood frequency analysis due to their flexibility and ability to accommodate the spatial variability of flooding processes in distribution parameters. Hierarchical models based on the generalized extreme value (GEV) distribution are useful since they may combine scaling properties and distinct degrees of pooling in the shape parameter for improving quantile estimation. In this paper, we evaluate the benefits of combining a partial pooling approach and a formal description of the spatial latent processes that govern the distribution parameters. The application of the model in the Alto do São Francisco River catchment (Brazil) suggests that, despite obtaining similar estimates at gauged sites, prediction at ungauged counterparts may be substantially improved in densely gauged regions, in terms of accuracy and precision, by accounting for spatial dependency. In poorly gauged areas, however, no benefits in utilizing latent spatial processes for inference were verified.

摘要

由于贝叶斯层次模型的灵活性和适应分配参数中洪水过程的空间变异性的能力，已越来越多地用于区域洪水频率分析。基于广义极值（GEV）分布的分层模型很有用，因为它们可以在形状参数中结合缩放属性和不同的合并度，以改善分位数估计。在本文中，我们评估了结合部分池化方法和控制分布参数的空间潜在过程的形式化描述的好处。该模型在Alto doSãoFrancisco River集水区（巴西）中的应用表明，尽管在指定地点获得了类似的估算，通过考虑空间依赖性，在准确度和精确度方面，未精加工的对应物的预测可以在密度较大的区域中得到显着改善。但是，在测量范围较差的区域，没有证明利用潜在的空间过程进行推理的好处。