Stockholm University, Department of Physical Geography and Bolin Centre for Climate Research, SE-106 91 Stockholm, Sweden.
CERNAS, Agrarian School of Coimbra, Polytechnic Institute of Coimbra, Coimbra, Portugal.
Sci Total Environ. 2019 Apr 15;661:393-406. doi: 10.1016/j.scitotenv.2019.01.009. Epub 2019 Jan 16.
Flooding may damage important transportation infrastructures, such as roads, railways and bridges, which need to be well planned and designed to be able to withstand current and possible future climate-driven increases in flood frequencies and magnitudes. This study develops a novel approach to predictive statistical modelling of the probability of flooding at major road-stream intersection sites, where water, sediment and debris can accumulate and cause failure of drainage facilities and associated road damages. Two areas in south-west Sweden, affected by severe floods in August 2014, are used in representative case studies for this development. A set of physical catchment-descriptors (PCDs), characterizing key aspects of topography, morphology, soil type, land use, hydrology (precipitation and soil moisture) and sediment connectivity in the water- and sediment-contributing catchments, are used for the predictive flood modelling. A main novel contribution to such modelling is to integrate the spatiotemporal characteristics of remotely-sensed soil moisture in indices of sediment connectivity (IC), thereby also allowing for investigation of the role of soil moisture in the flood probability for different road-stream intersections. The results suggest five categories of PCDs as especially important for flood probability quantification and identification of particularly flood-prone intersections along roads (railways, etc.) These include: channel slope at the road-stream intersection and average elevation, soil properties (mainly percentage of till), land use cover (mainly percentage of urban areas), and a sediment connectivity index that considers soil moisture in addition to morphology over the catchment.
洪水可能会破坏重要的交通基础设施,如道路、铁路和桥梁,这些基础设施需要经过精心规划和设计,以承受当前和未来可能因气候驱动而增加的洪水频率和规模。本研究提出了一种新的方法,用于对主要道路-溪流交叉口处洪水发生概率进行预测性统计建模,在这些地方,水、泥沙和碎片可能会积聚,并导致排水设施和相关道路损坏。瑞典西南部两个受 2014 年 8 月严重洪水影响的地区被用作该方法的代表性案例研究。一组物理集水区描述符(PCD)用于预测性洪水建模,这些描述符用于描述集水区的地形、形态、土壤类型、土地利用、水文(降水和土壤湿度)和泥沙连通性的关键方面。这种建模的一个主要新颖贡献是将遥感土壤湿度的时空特征整合到泥沙连通性指数(IC)中,从而还可以研究不同道路-溪流交叉口处土壤湿度在洪水概率中的作用。研究结果表明,五个类别 PCD 对于洪水概率量化和识别道路沿线特别容易发生洪水的交叉口(铁路等)尤为重要,这些 PCD 包括:道路-溪流交叉口处的河道坡度和平均海拔、土壤特性(主要是耕作层百分比)、土地利用覆盖(主要是城市面积百分比)以及考虑集水区内土壤湿度的泥沙连通性指数。