RAPID: Monitoring and modeling watershed-scale post-wildfire streamflow response through space and time

RAPID：通过空间和时间监测和模拟流域规模的野火后水流响应

• 批准号: 2051762
• 负责人: Belize Lane
• 金额: $ 4.98万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-11-15 至 2022-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051762&HistoricalAwards=false
• 关键词: RAPID; Monitoring; modeling; watershed; scale; RAPID; Monitoring; modeling; watershed; scale

Wildfires alter hydrologic processes, accelerate erosion and increase sediment transport rates. In the western U.S, these changes pose considerable risks to downstream infrastructure and ecosystems, and natural resource managers urgently need practical and reliable predictive tools to support post-wildfire management. The active Grizzly Creek wildfire in Glenwood Canyon, CO, presents a unique, time-sensitive opportunity to collect necessary field data to monitor these processes and develop transferable analytical tools. The watershed has active long-term USGS gauges both up and downstream from the study area as well as highly variable topography, land cover and burn severity, making the Grizzly Creek fire an ideal candidate to capture a range of post-wildfire hydrologic responses within the same watershed. Ultimately, this data will directly support and inform post-wildfire management and restoration, which costs tens of millions of dollars across the western U.S. each year. Data collection is in coordination with the USGS Post-Fire Debris-Flow Hazards team to ensure efforts are complementary and support larger hydrologic and geomorphic research efforts. Results will be conveyed to the post-wildfire research and management community through presentations to major stakeholder groups, such as the USFS, NRCS, Utah Division of Natural Resources, water conservation districts, dam operators, and other relevant groups.Hydrologic field data collected for this project will support evaluation and improvement of post-wildfire hydrology and sediment dynamics models, as well as advances in fundamental understanding of hydrologic processes. Reasonable hydrologic forcing remains a major limitation of network-scale modeling frameworks to assess post-wildfire sediment dynamics. To fill this gap and improve our understanding of post-wildfire hydrologic response across a watershed requires rapid mobilization to capture the initial response in a recently burned watershed with long-term streamflow records and variable watershed and burn characteristics. Monitoring sites capture similar watershed characteristics (e.g., slope, land cover) along a burn severity gradient, and are paired with nearby analog unburned catchments. Precipitation, streamflow and hillslope infiltration rates will be monitored through time throughout the burned area. This perishable data will be used within a novel analytical framework to answer critical research questions related to variability in the post-wildfire rainfall-runoff response through space and time, beginning immediately following a burn. The research will help determine how the spatial distribution and severity of burned areas within a watershed impact post-wildfire runoff, distributed streamflow response and, ultimately, sediment flux rates. This research will advance long-term sediment transport and storage predictions in this and other emerging modeling efforts that inform resource management.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.

野火改变水文过程，加速侵蚀并增加沉积物的运输速率。在美国西部，这些变化对下游基础架构和生态系统构成了很大的风险，而自然资源经理急需实用可靠的预测工具来支持后火力管理。 CO的Glenwood Canyon的Active Grizzly Creek Wildfire提出了一个独特的，时间敏感的机会，以收集必要的现场数据来监视这些过程并开发可转移的分析工具。分水岭有活跃的长期USGS在研究区域上游和下游，以及高度可变的地形，地覆盖和燃烧的严重程度，使灰熊溪火成为捕获同一分水岭内野后水力学反应的理想候选者。最终，这些数据将直接支持并为野外火灾的管理和修复提供信息，这每年在美国西部耗资数千万美元。数据收集与USGS后火灾碎片流危害团队进行协调，以确保努力是互补的，并支持更大的水文和地貌研究工作。结果将通过向主要利益相关者群体进行演示，例如USFS，NRCS，犹他州犹他州，自然资源部，自然资源区，DAM运营商和其他相关群体。为此项目收集的水力学现场数据，将支持生命力的水文学和沉积物动力学的改进，以及基础上的基础，该项目将支持遗传和其他相关的集团。合理的水文强迫仍然是网络规模建模框架的主要局限性，以评估野火后沉积物动力学。为了填补这一空白，并提高了我们对野后水文反应的理解，需要快速动员，以捕获最近燃烧的有长期流量记录和可变分水岭和燃烧特征的最近燃烧的流域中的初始反应。监测地点沿燃烧严重性梯度捕获了类似的流域特征（例如，坡度，土地覆盖），并与附近的模拟未燃烧流域配对。整个燃烧区域将在时间上监测降水，水流和山坡渗透率。这些易腐烂的数据将在新颖的分析框架中使用，以回答与野火后降雨 - 跑步响应在燃烧后立即开始的关键研究问题。这项研究将有助于确定流域内燃烧区域的空间分布和严重程度如何影响野火后的径流，分布式流量响应，并最终是沉积物通量率。这项研究将在这项和其他新兴的建模工作中提高长期沉积物的运输和存储预测，以指导资源管理。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。