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

野火改变了水文过程，加速了侵蚀，增加了沉积物的输送速率。在美国西部，这些变化对下游基础设施和生态系统构成了相当大的风险，自然资源管理人员迫切需要实用可靠的预测工具来支持野火后的管理。活跃的灰熊溪野火格伦伍德峡谷，CO，提供了一个独特的，时间敏感的机会，收集必要的现场数据，以监测这些过程和开发可转移的分析工具。该流域具有活跃的长期USGS测量仪，从研究区域向上和下游以及高度可变的地形，土地覆盖和燃烧严重程度，使灰熊溪火灾成为捕获同一流域内一系列野火后水文响应的理想候选人。最终，这些数据将直接支持和通知野火后的管理和恢复，这在美国西部每年花费数千万美元。数据收集工作与美国地质勘探局灾后泥石流灾害小组协调进行，以确保各项工作相辅相成，并支持更大规模的水文和地貌研究工作。研究结果将通过向主要利益相关者团体（如USFS、NRCS、犹他州自然资源部、水资源保护区、大坝运营商和其他相关团体）进行演示的方式传达给野火后研究和管理团体。本项目收集的水文现场数据将支持评估和改进野火后水文和沉积物动力学模型，以及对水文过程基本认识的进步。合理的水文强迫仍然是网络规模的建模框架，以评估野火后沉积物动力学的一个主要限制。为了填补这一空白，并提高我们的理解野火后水文响应整个流域需要快速动员，以捕捉在最近烧毁的流域与长期径流记录和可变的流域和燃烧特性的初始响应。监测点捕获类似的流域特征（例如，坡度、土地覆盖）沿着并与附近的模拟未燃烧集水区配对。降雨量，径流量和山坡渗透率将通过整个燃烧区的时间进行监测。这些易腐数据将在一个新的分析框架内使用，以回答与野火后的空间和时间的径流响应变化相关的关键研究问题，从燃烧后立即开始。该研究将有助于确定流域内烧毁区域的空间分布和严重程度如何影响野火后径流，分布式径流响应以及最终的沉积物通量率。这项研究将推动长期泥沙输运和储存预测在这个和其他新兴的建模工作，通知resource management.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.