RAPID: Wildfire impacts on snowpack, flow paths, and sediment dynamics across an elevation gradient

科罗拉多北方的2020年卡梅隆峰野火是科罗拉多历史上第三大野火，它覆盖了为多个城市和灌溉区供水的该高速缓存拉普德雷盆地的19%。由于火灾后的洪水、侵蚀和泥石流，火灾对供水和基础设施构成风险。在科罗拉多之前的大火之后，这些风险在夏季雷暴期间最大，在冬季和春季融雪季节危害最小。然而，卡梅隆峰的大火是独一无二的，因为它比以前的大火燃烧的海拔高得多。与1984年以来美国西部的所有野火相比，卡梅隆峰的大火烧毁了持续积雪区有记录以来最大的面积。持续积雪区是高山区，从冬天到春天，积雪都停留在地面上。由于这一地区的积雪比低海拔地区大得多，融雪可能导致比其他火灾记录更剧烈的坡度和河道变化。该项目将重点收集关键的火灾后数据，以了解火灾如何影响积雪，径流，边坡失稳，河道和供水水库。数据收集将包括无人机飞行，传感器网络，以及在整个燃烧区域和火灾高海拔和低海拔的重点区域进行的实地调查。将使用从LiDAR和无人机图像收集的无雪和有雪条件下的精细分辨率地形记录积雪变化。在燃烧和未燃烧条件下的重点地区，气象站将记录积雪深度和辐射收支，实地调查将记录雪水当量的空间模式。将使用按海拔、燃烧严重程度和排水面积分布的河流水位传感器网络记录流量变化。侵蚀、边坡破坏和河道变化将通过比较积雪前、融雪后和夏季雷暴后的无人机图像来确定。对河道的实地调查还将记录河道地貌变化和大型木材和沉积物的分布情况。最后，将使用沉积物岩心对供水水库的沉积速率进行量化。这些数据集将在收集后尽快发布，并将促进具有大气科学，水文学和地貌学专业知识的跨学科科学家团队之间的火灾后研究协调。该项目由地貌学和土地利用动力学以及水文科学项目共同资助。该奖项反映了NSF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。

The 2020 Cameron Peak wildfire in northern Colorado is the third largest wildfire in Colorado history, and it covers 19% of the Cache la Poudre basin, which supplies water for multiple cities and irrigation districts. Fires pose risks to water supplies and infrastructure due to post-fire floods, erosion, and debris flows. After prior large fires in Colorado, these risks have been greatest during summer thunderstorms, with minimal hazards during winter and spring snowmelt seasons. However, the Cameron Peak fire is unique in that it has burned at much higher elevations than these previous large fires. Compared to all wildfires across the western U.S. since 1984, the Cameron Peak fire has burned the largest area recorded in the persistent snow zone, the high mountain zone where snow stays on the ground from winter through spring. Because the snowpack is so much greater in this zone than at lower elevations, snowmelt could cause more dramatic slope and channel changes than have been documented for other fires. This project will focus on collecting critical post-fire data for understanding how the fire impacts the snowpack, streamflow, slope failures, stream channels, and water supply reservoirs.Data collection will include drone flights, a sensor network, and field surveys conducted both across the full burn area and at focus areas at the higher and lower elevations of the fire. Snowpack changes will be documented using fine resolution topography for snow-free and snow-on conditions collected from LiDAR and drone imagery. At focus areas in burned and unburned conditions, weather stations will document snow depth and radiation budget, and field surveys will document spatial patterns of snow water equivalent. Streamflow changes will be documented using a network of stream stage sensors distributed by elevation, burn severity, and drainage area. Erosion, slope failures, and channel changes will be identified by comparing drone imagery from before snow accumulation, after snowmelt, and after summer thunderstorms. Field surveys of channels will also document channel geomorphic changes and distributions of large wood and sediment. Finally, sedimentation rates to water supply reservoirs will be quantified using sediment cores. These datasets will be released as soon as possible after collection and will facilitate post-fire research coordination among an interdisciplinary team of scientists with expertise in atmospheric science, hydrology, and geomorphology. This project is co-funded by the Geomorphology and Land-use Dynamics and the Hydrological Sciences programs.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.