Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan

Abstract

Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model’s sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change.

Developmental characteristics of rainfall-induced landslides from 1999 to 2016 in Wenzhou City of China

“…But, more detailed rainfall data with higher resolution such as hourly rainfall data would be more helpful for analyzing the occurrence of rainfall-induced landslides (Kirschbaum et al, 2009). Hence, considering the application of remote sensing rainfall instruments with various multitemporal and spatial resolutions in the future has excellent prospects for identifying rainfall-induced landslide characteristics (Kocaman et al, 2020;Tseng et al, 2020).…”

Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China

“…Landslide is one of the most frequent and serious geological hazards entire the globe [ 1 , 2 , 3 ], and rainfall is an important factor that affects the occurrence of landslides. Landslide susceptibility is an estimate to show “where” the landslides may occur in an area, which is strongly influenced by regional characteristics, especially in the frequency and intensity of rainfall [ 4 , 5 ]. Currently, a range of studies around the world has assessed the susceptibility of landslides with the consideration of rainfall.…”

Cluster and Redundancy Analyses of Taiwan Upstream Watersheds Based on Monthly 30 Years AVHRR NDVI3g Data

“…Coupled with the Central Mountain Range (CMR), Taiwan's precipitation patterns are complex, with extensive local variations [19]. In addition, Taiwan is characterized by geological weakness with frequent seismic activities resulting in the high vulnerability of Taiwan [20]. In recent years, Taiwan has encountered increasing pressure from various extreme climate events due to global climate change, such as severe drought [21][22][23], intensive typhoons [24], and associated natural disasters [25].…”