The hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment factors such as topography that cause runoff accumulation. Temporal dependence can also increase flood risk as storm water drainage systems operating at capacity can be overwhelmed by heavy precipitation occurring over multiple days. While transformed Gaussian processes are common choices for modeling precipitation, their weak tail dependence may lead to underestimation of flood risk. Extreme value models such as the generalized Pareto processes for threshold exceedances and max-stable models are attractive alternatives, but are difficult to fit when the number of observation sites is large, and are of little use for modeling the bulk of the distribution, which may also be of interest to water management planners. While the atmospheric dynamics governing precipitation are complex and difficult to fully incorporate into a parsimonious statistical model, non-mechanistic analogue methods that approximate those dynamics have proven to be promising approaches to capturing the temporal dependence of precipitation. In this paper, we present a Bayesian analogue method that leverages large, synoptic-scale atmospheric patterns to make precipitation forecasts. Changing spatial dependence across varying intensities is modeled as a mixture of spatial Student-t processes that can accommodate both strong and weak tail dependence. The proposed model demonstrates improved performance at capturing the distribution of extreme precipitation over Community Atmosphere Model (CAM) 5.2 forecasts. Supplementary materials accompanying this paper appear online.

中文翻译：

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使用贝叶斯类比模型预测洪水诱导降水

洪水泛滥的危害在很大程度上受降水的时空依赖性特征的影响。当极端降水具有很强的空间依赖性时，由于地形等集水因素导致径流聚集，洪水风险被放大。时间依赖性也会增加洪水风险，因为在满负荷运行的雨水排水系统可能会被多天发生的强降水所淹没。虽然转换后的高斯过程是建模降水的常见选择，但它们弱的尾部依赖性可能会导致对洪水风险的低估。极值模型，例如阈值超出的广义帕累托过程和最大稳定模型是有吸引力的替代方案，但在观测点数量较多时难以拟合，并且对于对大部分分布进行建模几乎没有用，这也可能引起水资源管理规划者的兴趣。虽然控制降水的大气动力学复杂且难以完全纳入简约统计模型，但近似这些动力学的非机械模拟方法已被证明是捕捉降水时间依赖性的有前途的方法。在本文中，我们提出了一种贝叶斯类比方法，该方法利用大型天气尺度大气模式进行降水预报。在不同强度之间改变空间依赖性被建模为可以适应强尾依赖性和弱尾依赖性的空间学生-t 过程的混合。提议的模型展示了在捕获极端降水分布方面的改进性能，超过了社区大气模型 (CAM) 5.2 预测。本文随附的补充材料出现在网上。