A data-driven evaluation of post-fire landslide susceptibility

Abstract

Abstract. Wildfires change the hydrologic and geomorphic response of watersheds, which has been associated with cascades of additional hazards and management challenges. Among these post-wildfire events are shallow landslides and debris flows. This study evaluates post-wildfire mass movement trigger characteristics by comparing the precipitation preceding events at both burned and unburned locations. Landslide events are selected from the NASA Global Landslide Catalog (GLC). Since this catalog contains events from multiple regions worldwide, it allows a greater degree of inter-region comparison than many more localized catalogs. Fire and precipitation histories for each site are established using Moderate Resolution Imagine Spectroradiometer (MODIS) Burned Area and Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation data, respectively. Analysis of normalized 7 d accumulated precipitation for sites across all regions shows that, globally, mass movements at burned sites are preceded by less precipitation than mass movements without antecedent burn events. This supports the hypothesis that fire increases rainfall-driven mass movement hazards. An analysis of the seasonality of mass movements at burned and unburned locations shows that mass-movement-triggering storms in burned locations tend to exhibit different seasonality from rainfall-triggered mass movements in areas undisturbed by recent fire, with a variety of seasonal shifts ranging from approximately 6 months in the Pacific Northwest of North America to 1 week in the Himalayan region. Overall, this paper offers an exploration of regional differences in the characteristics of rainfall-triggered mass movements at burned and unburned sites over a broad spatial scale and encompassing a variety of climates and geographies.

Expanding the Spatial Reach and Human Impacts of Critical Zone Science

“…In montane or steep landscapes, for example, disturbance to the CZ base and thickness may result from landslides (Handwerger et al., 2022; Riebe et al., 2017) that can be triggered by oversteepening of hillslopes from road cuts (Pradhan et al., 2022; Sidle et al., 2014), and slope destabilization due to clear cutting (Saito et al., 2017; Swansen & Dyrness, 1975). Wildfire alters infiltration rates and removes anchoring roots, leading to debris flows and landslides (Culler et al., 2023; McGuire & Youberg, 2019; Rengers et al., 2020). Human activities play an increasing role in such cascading events.…”

Trend Analysis of MODIS Land Surface Temperature and Land Cover in Central Italy

“…Gradual warming, woodland and forest expansions, and dry spells can potentially increase the severity and occurrences of wildfires in the study region, and pose a significant risk to the biodiversity, greenhouse gas emissions, and infrastructure, as well as increasing the risk of post-fire shallow landslides and debris flows [47]. Thus, utilizing machine learning and statistical modeling to creating wildfire susceptibility maps and risk forecasting in this region becomes crucial [48,49].…”

Modeling Fire Boundary Formation Based on Machine Learning in Liangshan, China

“…In situations where there is insufficient fuel or high moisture content in the topography and vegetation [5], the fire intensity will gradually diminish and eventually stop because it cannot generate enough heat to keep the fire alight [6]. The transition zone between the burned and unburned areas is the fire boundary [7].…”