Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies.
Coupling physical understanding and statistical modeling to estimate ice jam flood frequency in the northern Peace-Athabasca Delta under climate change
Contributions of geolocated weather and building related data for insurance assessment of flood risks
Laboratoire de Mathématiques de Bretagne Atlantique, UBO（布列塔尼大西洋数学实验室，布勒斯特大学）；Laboratoire des Sciences du Climat et de l’Environnement, PSL & U Paris-Saclay（气候与环境科学实验室，巴黎文理研究大学与巴黎萨克雷大学）
MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
University of Oxford（牛津大学）；Montreal Polytechnique（蒙特利尔理工学院）；Columbia Climate School（哥伦比亚气候学院）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）；Universität Leipzig（莱比锡大学）；IRI THESys；Humboldt-Universität zu Berlin（柏林洪堡大学）；Institute Pierre-Simone-Laplace（皮埃尔-西蒙·拉普拉斯研究所）
Machine Learning for Generalizable Prediction of Flood Susceptibility
Stanford University（斯坦福大学）；Carnegie Mellon University（卡内基梅隆大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Oxford（牛津大学）；University of Warsaw（华沙大学）
Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking
高分辨率区域气候模式的动态降尺度可能提供在气候模拟中真实再现降水和天气事件的可能性。当分辨率降到千米级时，使用显式对流而不是参数化对流可以提供更高的保真度。然而，这些增加的模型分辨率允许并且需要越来越复杂的诊断来评估模型保真度。在这项研究中，我们使用了一套动态缩小规模的模拟夏季美国（WRF驱动的NCEP）与系统的变化参数和治疗对流作为一个测试案例模型降水的评估。特别是，我们使用了一种新的暴雨识别和跟踪算法，该算法基本上将所有降雨分配给单个降水事件（Chang et al. 2016）。这种方法允许多方面的见解，包括，至少在这些运行中，模型湿偏差是由过度的面积范围的降水事件。偏差是时间依赖性的，产生过度的昼夜周期振幅。我们发现，这种效果是由新的生产的事件，但在白天的长期降水事件的过度放大，并在域平均，降水偏差出现最好的表示为添加剂的偏移。在所有评估的模型配置中，对流允许模拟最一致地减少了降水事件特征的偏差。
Assessment of Future Changes in Intensity-Duration-Frequency Curves for Southern Ontario using North American (NA)-CORDEX Models with Nonstationary Methods
