Projecting Flood-Inducing Precipitation with a Bayesian Analogue Model
Projecting Flood-Inducing Precipitation with a Bayesian Analogue Model
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DOI：
10.1007/s13253-020-00391-6
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发表时间：
2020-03-27
影响因子：
1.4
通讯作者：
Mejia，Alfonso
中科院分区：
文献类型：
--
作者：
Bopp，Gregory P.;Shaby，Benjamin A.;Mejia，Alfonso
降雨的时空相关性很大程度上影响着洪涝灾害的危险性。当极端降雨量具有很强的空间相关性时，由于引起径流积累的集水区因素，如地形因素，洪水的风险被放大。时间上的依赖性也会增加洪水风险，因为暴雨排水系统满负荷运转时可能会被多天的强降水淹没。虽然变换的高斯过程是模拟降水的常用方法，但其弱的尾部相关性可能导致对洪水风险的低估。极值模型，如阈值超越的广义Pareto过程和最大稳定模型是有吸引力的替代方案，但当观测点数量较多时很难拟合，并且对大部分分布建模用处不大，这也可能是水资源管理规划者感兴趣的。虽然控制降水的大气动力学是复杂的，很难完全纳入简约的统计模型，但事实证明，近似这些动力学的非机械模拟方法是捕捉降水的时间相关性的很有前途的方法。在本文中，我们提出了一种贝叶斯模拟方法，它利用大的天气尺度大气模式来进行降水预报。通过改变不同强度的空间相关性，将其建模为能够适应强和弱尾相关性的空间学生-t过程的混合体。所提出的模式在捕捉社区大气模式(CAM)5.2上的极端降水分布方面表现出更好的性能。
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.