Dynamics and Perceptions of Ultrahazardous Flooding across the Wildland Urban Interface

这个项目探索了野火过后，在美国西部山区的山坡上发展起来的社区和社区面临的洪水风险。随着城市和郊区进入荒野地区，这种开发已经并将继续扩大到整个美国西南部和西部。野火后的洪水可能是极其危险的，因为它的快速、高速和侵蚀潜力，导致充满泥石流的不可预测的路径。该项目将考虑三个相互交织的因素：环境危害(野火、泥土/瓦砾和洪水)、建成环境(雨水基础设施)和人口，从而促进对这些洪水风险的了解，以及提高洪水复原力的机会。利用加利福尼亚州河滨县(洛杉矶大都市区的一部分)的环境和社会经济数据，将与社区合作伙伴共同开发整合火灾、碎片盆地和防洪渠道对洪水和碎片危害的影响的创新模型，并将其应用于估计暴露如何在空间和时间上变化。危险估计将与捕捉危险意识、感知和准备情况的家庭调查数据相结合，以表征该地区不同社会经济人群的风险和风险认知的变化。这项研究支持了NSF的使命，即通过产生工具和见解来提高洪水风险意识和准备，从而促进科学进步和增进国家福利，这将拯救生命，减少未来事件中的经济损失，并增强社区弹性。气候变化和城市扩张正在导致洪水和泥石流风险的升级。全球变暖导致夏季和秋季的野火更加强烈和频繁，冬季和春季的降水更加强烈。此外，假设居民对洪水的认识和准备特别低，特别是因为干旱的西南部以“不可预测的洪水路径”而闻名，这种洪水是在洪水控制系统被碎片堵塞和/或溢出后发生的。该项目将推进一个创新的随机建模框架，用于估计目前和未来发展面临的洪水和泥石流灾害--一种捕获降雨模式、野火、防洪基础设施性能和气候变化的不确定性的系统方法。此外，将使用高分辨率洪水灾害模型绘制灾害的空间分布图，并将其与社会经济数据和从洪水意识和防备情况的家庭调查中提取的脆弱性指标相交，从而对风险和风险认知产生重要的新见解。最后，研究小组将与地方和州政府的公共工程和应急管理人员合作，共同开发洪水风险管理的数据产品和工具，并将这些工具广泛用于野火后的洪水风险管理。项目期间产生的出版物、软件和数据衍生品将在项目网站、GitHub和公共数据存储库中提供。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project explores post-wildfire flooding risks facing communities and neighborhoods that have developed at the base and long the slopes of mountains in the western United States. These kinds of developments have and continue to expand throughout the southwestern and western United States as urban and suburban areas move into wildland areas. Post-wildfire flooding can be ultrahazardous due to its rapid-onset, high-velocity, and erosive potential, leading to debris-laden flows with unpredictable paths. This project will advance understanding of these flood risks, and opportunities for increased flood resilience, considering three interwoven elements: environmental hazards (wildfire, mud/debris, and flooding), the built environment (stormwater infrastructure), and human populations. Working with environmental and socio-economic data from across Riverside County, California – part of the Los Angeles Metropolitan Region– innovative models that integrate the effects of fire, debris basins, and flood control channels on flood and debris hazards will be co-developed with community partners and applied to estimate how exposure varies spatially and over time. Hazard estimates will be combined with household survey data capturing hazard awareness, perceptions, and preparedness to characterize variations in risks and risk perception across the area’s diverse socioeconomic population. This research supports NSF's mission to promote the progress of science and to advance our national welfare by generating both tools and insights to improve flood risk awareness and preparedness, which will save lives, reduce economic losses in future events, and enhance community resilience.Climate change and urban expansion are leading to an escalation of flood and debris risks. Global warming is resulting in more intense and frequent wildfire during Summer and Fall and more intense precipitation in Winter and Spring. Furthermore, awareness and preparedness for flooding is hypothesized to be especially low among residents, particularly because the arid southwest is known for “unpredictable flow path flooding” that occurs after flood controls system are clogged with debris and/or overtopped. This project will advance an innovative stochastic modeling framework for estimating present and future flood and debris hazards facing development – a systematic approach for capturing the uncertainty in rainfall patterns, wildfires, the performance of flood control infrastructure, and climate change. Furthermore, the spatial distribution of hazards will be mapped using fine-resolution flood hazard models and intersected with socioeconomic data and indicators of vulnerability drawn from a household survey of flood awareness and preparedness, yielding important new insights into risks and risk perceptions. Finally, the research team will partner with public works and emergency management personnel from local and state government to co-develop data products and tools for flood risk management, and to make these tools widely available for post-wildfire flood risk management. Publications, software, and data derivatives generated during this project will be made available on the project website, GitHub and public data repositories.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.