Nonstationarity in peaks‐over‐threshold river flows: A regional random effects model
Nonstationarity in peaks‐over‐threshold river flows: A regional random effects model
复制标题
河道流量峰值的非平稳性：区域随机效应模型
DOI：
10.1002/env.2560
复制
发表时间：
2019
期刊：
影响因子：
1.7
通讯作者：
E. Eastoe
中科院分区：
文献类型：
--
作者：
E. Eastoe
在局地和大尺度气候过程的影响下，极端河流径流事件往往表现出长期趋势、季节性、年际变化和其他时间非平稳性特征。正确解释这种非平稳性对于准确预测未来洪水至关重要。在本文中，一个区域模型的基础上，广义帕累托分布的峰值超过阈值的河流流量数据集时，事件的大小是非平稳的。如果观测值是非平稳的并且协变量可用，则可以使用极值（半）参数回归模型。不幸的是，必要的协变量很少被观察到，即使观察到了，也常常不清楚模型中应该包括哪个过程或过程的组合。在统计学文献中，潜在过程（或随机效应）模型经常用于这种情况。我们开发了一个区域时变随机效应模型，该模型通过汇集空间均匀区域中所有站点的信息来识别事件大小的时间非平稳性。所提出的模型是贝叶斯分层模型的一个实例，可用于预测无条件极端事件，如m年最大值和以特定年份为条件的极端事件。估计的随机效应也可以告诉我们可能的候选人的气候过程，导致洪水过程中的非平稳性。该模型适用于英国洪水数据分布在81个水文区域的817个站点。
Under the influence of local‐ and large‐scale climatological processes, extreme river flow events often show long‐term trends, seasonality, interyear variability, and other characteristics of temporal nonstationarity. Properly accounting for this nonstationarity is vital for making accurate predictions of future floods. In this paper, a regional model based on the generalised Pareto distribution is developed for peaks‐over‐threshold river flow data sets when the event sizes are nonstationary. If observations are nonstationary and covariates are available, then extreme‐value (semi)parametric regression models may be used. Unfortunately, the necessary covariates are rarely observed, and if they are, it is often not clear which process, or combination of processes, to include in the model. Within the statistical literature, latent process (or random effects) models are often used in such scenarios. We develop a regional time‐varying random effects model that allows identification of temporal nonstationarity in event sizes by pooling information across all sites in a spatially homogeneous region. The proposed model, which is an instance of a Bayesian hierarchical model, can be used to predict both unconditional extreme events such as the m‐year maximum and extreme events that condition on being in a given year. The estimated random effects may also tell us about likely candidates for the climatological processes that cause nonstationarity in the flood process. The model is applied to UK flood data from 817 stations spread across 81 hydrometric regions.
影响因子：
5.4
作者：
E. Eastoe;J. Tawn
通讯作者：
E. Eastoe;J. Tawn