Detection and attribution of urbanization effect on flood extremes using nonstationary flood-frequency models.
Detection and attribution of urbanization effect on flood extremes using nonstationary flood-frequency models.
复制标题
使用非组织洪水频率模型对城市化对极端洪水的效果的检测和归因。
DOI：
10.1002/2015wr017065
复制
发表时间：
2015-06
影响因子：
5.4
通讯作者：
Miller JD
中科院分区：
文献类型：
--
作者：
Prosdocimi I;Kjeldsen TR;Miller JD
本研究通过非平稳洪水频率分析，探讨了观测到的高流量序列的长期变化是否可归因于土地利用的变化。使用阈值超标的点过程表征，这允许在模型中直接包含协变量；以及块极大值序列的非平稳模型。特别地，从测量的瞬时流量记录中提取的年、冬季和夏季块最大值和超过阈值的峰值的变化，在英格兰北部两个水文相似的集水区彼此靠近，进行了调查。研究流域的特点是近几十年来城市化水平大幅提高，而配对对照流域在研究期间（1970-2010年）仍未得到开发。为了避免自然变率的潜在混淆效应，在洪水频率模型中加入了一个协变量，该协变量概括了关键的气候特性。已发现城市化水平不断提高对高流量的显著影响，特别是在夏季。在检测城市化水平增加对高流量的影响方面，点过程模型似乎优于块极大值模型。研究发现，城市化对城市化流域的高流量有影响，提倡使用点过程进行趋势检测和归因，使用过程相关协变量可以更好地表示变化
This study investigates whether long‐term changes in observed series of high flows can be attributed to changes in land use via nonstationary flood‐frequency analyses. A point process characterization of threshold exceedances is used, which allows for direct inclusion of covariates in the model; as well as a nonstationary model for block maxima series. In particular, changes in annual, winter, and summer block maxima and peaks over threshold extracted from gauged instantaneous flows records in two hydrologically similar catchments located in proximity to one another in northern England are investigated. The study catchment is characterized by large increases in urbanization levels in recent decades, while the paired control catchment has remained undeveloped during the study period (1970–2010). To avoid the potential confounding effect of natural variability, a covariate which summarizes key climatological properties is included in the flood‐frequency model. A significant effect of the increasing urbanization levels on high flows is detected, in particular in the summer season. Point process models appear to be superior to block maxima models in their ability to detect the effect of the increase in urbanization levels on high flows. Urbanization is found to have an impact on high flows in a urbanized catchment The use of point processes is advocated for trend detection and attribution The use of process‐related covariates gives a better representation of change