Burned slopes are susceptible to runoff-generated debris flows in the years following wildfire due to reductions in vegetation cover and soil infiltration capacity. Debris flows can pose serious threats to downstream communities, so quantifying variations in flow properties along debris-flow runout paths is needed to improve both conceptual and quantitative models of debris-flow behaviour to help anticipate and mitigate the risk associated with these events. Changes in flow properties along the runout paths of the runoff-generated debris flows that follow fire may be particularly dramatic, since they initiate when a water-dominated flow rapidly entrains sediment and later transition back to a water-dominated flow once they reach greater drainage areas and lower slopes. Here, we study the properties of a debris flow that initiated 1 month following the 2022 Pipeline Fire in northern Arizona, USA. We categorized flow type into two classes, granular debris flow and muddy debris flow, along the 7-km runout path and examined how flow properties varied between the phases. Changes in channel gradient and confinement likely facilitated the transition between the flow phases, which were characterized by significant differences in maximum clast size, but similar clay content and fine fractions. We also found that the volume and runout distance of the debris flow were 28 and six times greater, respectively, than that of a debris flow that initiated in the same watershed following a fire 12 years earlier. We attribute these differences to the combined effects of two high-severity fires, suggesting that consideration of recent fire history could improve post-fire debris-flow hazard assessments. Results of this study provide quantitative constraints on changes in post-fire debris-flow properties along a runout path. Data collected in this study add to a small number of debris-flow inundation datasets that can be used to test runout models in post-fire settings. 相似文献
泥石流物源识别与计算是科学评估泥石流规模、危害程度以及综合治理的基础,而传统的地面调查和光学遥感手段难以有效识别山区植被茂密覆盖下的泥石流物源。机载激光雷达(light detection and ranging,LiDAR)技术能有效去除植被获取真实的地表形态,为泥石流物源的识别提供了新的解决方案。以九寨沟震区的日则沟泥石流为例,基于高分辨率机载LiDAR数据结合震前卫星影像,开展泥石流物源识别研究,根据物源所处位置和在山体阴影图像上的色彩及纹理差异,将物源分为崩滑物源、坡面物源和沟道物源,并建立各类型物源的机载LiDAR识别标志与遥感解译方法。共解译出日则沟泥石流物源155处,总面积达1.06 km2,占流域总面积的31.56%,在此基础上分析了各类型物源的发育分布规律。为泥石流物源的精确计算提供理论参考和数据支撑,进一步服务于九寨沟震区泥石流的防治与风险评价。