Spatial and temporal patterns of sediment storage and erosion following a wildfire and extreme flood

Abstract

Abstract. Post-wildfire landscapes are highly susceptible to rapid geomorphic changes at both the hillslope and watershed scales due to increases in hillslope runoff and erosion, and the resulting downstream effects. Numerous studies have documented these changes at the hillslope scale, but relatively few studies have documented larger-scale post-fire geomorphic changes over time. In this study we used five airborne laser scanning (ALS) datasets collected over four years to quantify valley bottom changes in two ∼15 km2 watersheds, Skin Gulch and Hill Gulch, after the June 2012 High Park fire in northern Colorado and a large mesoscale flood 15 months later. The objectives were to: 1) quantify spatial and temporal patterns of erosion and deposition throughout the channel network following the wildfire and including the mesoscale flood; and 2) evaluate whether these changes are correlated to precipitation metrics, burn severity, or morphologic variables. Geomorphic changes were calculated using a DEMs of difference (DoD) approach for the channel network segmented into 50-m lengths. The results showed net sediment accumulation after the wildfire in the valley bottoms of both watersheds, with the greatest accumulations in the first two years after burning in wider and flatter valley bottoms. In contrast, the mesoscale flood caused large net erosion, with the greatest erosion in the areas with the greatest post-fire deposition. Volume changes for the different time periods were weakly but significantly correlated to, in order of decreasing correlation, contributing area, channel width, percent burned at high and/or moderate severity, channel slope, confinement ratio, maximum 30-minute rainfall, and total rainfall. These results suggest that morphometric characteristics, when combined with burn severity and a specified storm, can indicate the relative likelihood and locations for post-fire erosion and deposition. This information can help assess downstream risks and prioritize areas for post-fire hillslope rehabilitation treatments.

Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

“…Many experts have conducted research related to forest fires with various underlying problems, such as deforestation, declining air quality (Ryan et al 2021), death of flora and fauna (Krebs et al 2019), to health problems from affected residents (Silveira et al 2021). Their research uses various climate indicators such as temperature (Xu et al 2021), soil conditions (Sachdeva et al 2018), precipitation (Brogan et al 2019), wind (Daşdemir et al 2021), El Nino-Southern Oscillation (Cardil et al 2021), and others. The methods used also vary, ranging from regression (Zhao and Xu 2021), classification (Sulova and Arsanjani 2021), clustering (Viana-Soto et al 2020), neural networks (Larsen et al 2021), to copula (Xi et al 2020).…”