Atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan at the beginning of the 21st century

• Main Authors: X. Wang, M. Otto, D. Scherer
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-07-01
• Series: Natural Hazards and Earth System Sciences
• Online Access: https://nhess.copernicus.org/articles/21/2125/2021/nhess-21-2125-2021.pdf

<p>Landslide is a major natural hazard in Kyrgyzstan and Tajikistan. Knowledge about atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan is limited even though this topic has already been investigated thoroughly in other parts of the world. In this study, the newly developed, high-resolution High Asia Refined analysis version 2 (HAR v2) data set generated by dynamical downscaling was combined with historical landslide inventories to analyze the atmospheric conditions that initialized landslides in Kyrgyzstan and Tajikistan. The results indicate the crucial role of snowmelt in landslide-triggering processes since it contributes to the initialization of 40 % of landslide events. Objective thresholds for rainfall, snowmelt, and the sum of rainfall and snowmelt (rainfall <span class="inline-formula">+</span> snowmelt) were defined. Thresholds defined by rainfall <span class="inline-formula">+</span> snowmelt have the best predictive performance. Mean intensity, peak intensity, and the accumulated amount of rainfall <span class="inline-formula">+</span> snowmelt events show similar predictive performance. Using the entire period of rainfall <span class="inline-formula">+</span> snowmelt events results in better predictive performance than just considering the period up to landslide occurrence. Mean annual exceedance maps were derived from defined regional thresholds for rainfall <span class="inline-formula">+</span> snowmelt. Mean annual exceedance maps depict climatic disposition and have added value in landslide susceptibility mapping. The results reported in this study highlight the potential of dynamical downscaling products generated by regional climate models in landslide prediction.</p>