Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective

Abstract

The post-fire debris flow (PFDF) is a commonly destructive hazard that may persist for several years following the wildfires. Susceptibility mapping is an effective method for mitigating hazard risk. Yet, the majority of susceptibility prediction models only focus on spatial probability in the specific period while ignoring the change associated with time. This study improves the predictive model by introducing the temporal factor. The area burned by the 30 March 2020 fire in Xichang City, China is selected as an illustrative example, and the susceptibility of the PFDF was predicted for different periods of seven months after the wildfires. 2214 hydrological response events, including 181 debris flow events and 2033 flood events from the 82 watersheds are adopted to construct the sample dataset. Seven conditioning factors consist of temporal factors and spatial factors are extracted by the remote sensing interpretation, field investigations, and in situ tests, after correlation and importance analysis. The logistic regression (LR) is adopted to establish prediction models through 10 cross-validations. The results show that the susceptibility to PFDF has significantly reduced over time. After two months of wildfire, the proportions of very low, low, moderate, high, and very high susceptibility are 1.2%, 3.7%, 24.4%, 23.2%, and 47.6%, respectively. After seven months of wildfire, the proportions of high and very high susceptibility decreased to 0, while the proportions of very low to medium susceptibility increased to 35.4%, 35.6%, and 28.1%, respectively. The reason is that the drone seeding of grass seeds and artificial planting of trees accelerated the natural recovery of vegetation and soil after the fire. This study can give insight into the evolution mechanism of PFDF over time and reflect the important influence of human activity after the wildfire.

Effects of Bed Hydrophobicity on Post‐Fire Debris Flow Entrainment and Momentum Growth

“…Apart from generating or enhancing water repellency in soil, wildfires remove vegetation and cause dry ravels, which can lead to accumulation of hydrophobic soil deposits in channels with a thickness over 500 mm (Palucis et al., 2021). In the following rainfall seasons, increased soil hydrophobicity at the soil surface can lead to higher rates of surface runoff that transports sediment downslope (Cerdà, 1998; Prosser & Williams, 1998), gradually forming debris flows that surge down steep channels, entraining loose sediment (Jin et al., 2022). In regions with hydrophobic soil layer deposited at some depth, perched water table is created and shallow slope failures can be triggered, which tend to slide along the hydrophobic layer and disintegrate into thin debris flows (Gabet, 2003; Wells, 1987).…”

A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data

“…As PFDFs are documented outside of the US (e.g., García‐Ruiz et al., 2013; Jin et al., 2022; Nyman et al., 2019), model assessments to catchments on the global scale will represent an exciting opportunity for future research. Additional PFDF inventories would provide further insight as to which catchment characteristic thresholds are most relevant for PFDF activity in burned areas worldwide, with a strong focus on understanding which catchments simply do not have the appropriate characteristics to facilitate PFDF initiation.…”

“…Further, this work details the first PFDF hazard assessment model solely from publicly available, global scale remote sensing data and demonstrates the utility that it can provide for vulnerable communities and infrastructure. Although PFDF activity is well documented and actively monitored in the United States, PFDFs also occur in other regions (e.g., García‐Ruiz et al., 2013; Jin et al., 2022; Nyman et al., 2019). To our knowledge, no work has attempted to reconcile the entire spatial extent of these events to quantify and dynamically monitor PFDF hazard potential in all areas where they occur.…”

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

“…Notably, the representation of identical candidate variables varies across the papers, reflecting the nuances of individual research intentions. Therefore, with reference to relevant studies [24,[78][79][80][81][82], the candidate variables from the 84 papers were systematically categorized into 12 groups: topography factors, morphology factors, geomorphology factors, geology factors, meteorology factors, hydrology factors, soil factors, vegetation factors, fire factors, material source factors, human activity factors, and past debris flow characteristic factors (Table 3).…”