Dynamics and Perceptions of Ultrahazardous Flooding across the Wildland Urban Interface

该项目探讨了美国西部在山脚下和长山坡上开发的社区和社区面临的野火后洪水风险。随着城市和郊区向荒地地区迁移，这种发展已经并将继续在美国西南部和西部扩展。野火后的洪水由于其快速、高速和侵蚀的潜力，可能是极其危险的，导致充满碎片的水流和不可预测的路径。该项目将促进对这些洪水风险的理解，并考虑到三个相互交织的因素：环境危害（野火、泥浆/碎片和洪水）、建筑环境（雨水基础设施）和人口。利用来自加州河滨县（洛杉矶都会区的一部分）的环境和社会经济数据，将与社区合作伙伴共同开发创新模型，整合火灾、碎片盆地和防洪渠道对洪水和碎片危害的影响，并应用于评估暴露程度在空间和时间上的变化。灾害估计将与住户调查数据相结合，收集灾害意识、感知和准备情况，以表征该地区不同社会经济人口的风险变化和风险感知。这项研究支持了美国国家科学基金会的使命，即通过提供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提高我们的国家福利，这将拯救生命，减少未来事件中的经济损失，并增强社区的复原力。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，据推测，居民对洪水的意识和准备程度特别低，特别是因为干旱的西南部以“不可预测的洪水”而闻名，这种洪水发生在防洪系统被碎片堵塞和/或被淹没之后。该项目将推进一个创新的随机建模框架，用于估计当前和未来发展面临的洪水和泥石流灾害，这是一种捕捉降雨模式、野火、防洪基础设施性能和气候变化等不确定性的系统方法。此外，将使用精细分辨率的洪水灾害模型绘制灾害的空间分布，并将其与社会经济数据和从家庭洪水意识和准备调查中得出的脆弱性指标相交叉，从而对风险和风险认知产生重要的新见解。最后，研究小组将与来自地方和州政府的公共工程和应急管理人员合作，共同开发用于洪水风险管理的数据产品和工具，并使这些工具广泛用于野火后洪水风险管理。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和公共数据存储库上提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.