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

Su-Fen Wang,Chih-Wei Chuang,Chun-Wei Tseng,Cheng-En Song,Jien-Yi Tu,Yi-Chin Chen,Ci-Jian Yang

doi:10.3390/rs12233855

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.

Highlights

Taiwan is characterized by steep terrain and geological weakness co-evolving with seismic activity, monsoons, and typhoons, resulting in the high vulnerability of Taiwan
Compared with the increase of annual rainfall after 2004 and the frequency change of heavy rain, it shows that the increase of annual rainfall is closely related to the occurrence frequency of heavy rain, that is, the main contribution of the increased annual rainfall comes from the form of heavy rainfall
Frequency of heavy rain and extremely heavy rain has increased significantly, which may suggest that satellite images should be updated before the flood season every year for simulating the disaster risk

Summary

Introduction

Taiwan is characterized by steep terrain and geological weakness co-evolving with seismic activity, monsoons, and typhoons, resulting in the high vulnerability of Taiwan. Under the impacts of global climate change, the uneven temporal and spatial distribution of rainfall has intensified the variability of short-duration, high-intensity rainfall events [2,3,4,5]. The climatic and hydrological cycles in Taiwan tend to be more extreme, leading to frequent floods or droughts [2,6,7,8,9,10,11]. During Typhoon Morakot in 2009, the accumulated rainfall of Alishan rainfall station in Chiayi county reached 3060 mm within 3 days, exceeding the highest rainfall in Taiwan. Typhoon Morakot caused 677 deaths, 22 missing, and 4 serious injuries across Taiwan, and caused economic losses of about 3 billion USD in total, accounting for about 0.75% of annual gross domestic product [13]

Objectives

Methods

Results

Conclusion