Abstract
The 2017 Mw 6.5 Jiuzhaigou earthquake (Sichuan, China) is the first strong ground motion that struck the famous world heritage site, causing widespread landslides and severe rock mass damage effects and landscapes undergoing rapid evolution in the Jiuzhaigou National Geopark. However, the understanding of the variability of pre- and post-earthquake landslide susceptibility and landslide conditioning factor effects over time remains limited. This study aims to carry out multi-temporal statistical landslide susceptibility modeling at the slope-unit level related to this event. To achieve this, we initially used a set of remote sensing imageries in GIS to obtain systematic landslide inventories across the pre-, co-, and post-seismic periods. Based on three landslide inventory datasets, we developed three statistical models by incorporating 14 landslide conditioning (seismic, topographic, and geologic) factors into a binary logistic regression (BLR) model. Finally, we utilized the area under the receiver operating characteristic (AUC) (QA) curve to assess each model’s calibration and validation performance. The results show that the BLR model has good prediction applicability for both normal and seismic landslides in the study area with outstanding to excellent predictive accuracy for Mod1 (pre-seismic, AUC = 0.801), Mod2 (co-seismic, AUC = 0.942), and Mod3 (post-seismic, AUC = 0.880) periods. There are variations in both the importance of landslide conditioning factors and susceptibility maps through time, and the number of slope units with a mean probability over 0.8 from only one (pre-seismic) increased to 21 (post-seismic). The dynamic susceptibility maps are of great significance for identifying potentially unstable slopes and providing references for hazard and risk assessment, which could provide new insights into geo-environmental protection and regional landslide evaluation in scenery spots, even for those world heritage sites in the tectonic active mountainous region. Moreover, more frequent or extended observation periods could contribute a further understanding of the post-seismic landslide developments in the Jiuzhaigou area.
Highlights
The likelihood of the landslide occurrence in a given area based on a set of slope failure conditioning factors is known as landslide susceptibility (Brabb, 1985; Varnes, 1984), and it can be obtained through different approaches and mapping units (Carrara et al, 1995; Guzzetti et al, 1999)
relative slope position (RSP), topographic wetness index (TWI), and profile curvature (PRC) showed no significance in all models
Our results show that the binary logistic regression (BLR) model has good applicability in predictive performance with outstanding area under the receiver operating characteristic (AUC) results both for nonseismic, co-seismic, and post-seismic landslide datasets, which is consistent with the grid-based Jiuzhaigou coseismic landslide susceptibility assessment outcomes in Fan et al (2018b) (AUC = 0.851) and Ma et al (2019) (AUC = 0.89)
Summary
The likelihood of the landslide occurrence in a given area based on a set of slope failure conditioning factors is known as landslide susceptibility (Brabb, 1985; Varnes, 1984), and it can be obtained through different approaches and mapping units (Carrara et al, 1995; Guzzetti et al, 1999). It is generally recognized that the assumption “future landslides will be more likely to occur under the conditions which led to the landslides past and present” is at the base of statistical landslide susceptibility modeling (Varnes, 1984; Furlani and Ninfo, 2015; Samia et al, 2017). In this regard, the susceptibility maps for a given area can be considered to be static (Segoni et al, 2018). The long-term effects could lead to considerable and severe loss of lives and property in mountainous and tectonic-active areas (Keefer, 1984, Keefer, 2002; Fan et al, 2019b), and it is necessary to track the response of slopes to a landslide for a significant period after the earthquake (Khattak et al, 2010)
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