Monitoring the Development of Potential Hazardous Mountain Lakes Using Remote Sensing in the Kyrgyz Republic

Abstract

The Kyrgyz Republic is a high mountainous country with a high risk of development of debris and mudflows, landslide and other natural hazards. One of such hazards is outbursts of high-mountain lakes (GLOF), which occur much less frequently than debris and mudflows of stormwater genesis and from snowmelt. The hazard of GLOFs in the Kyrgyz Republic is a significant concern due to the region's topography, climate, and the presence of numerous glacier-fed lakes. However, debris flows caused by GLOF are characterized by their catastrophic nature. To monitor and control the development of hazardous lakes, a specialized catalog was created and is used by Ministry of Emergency Situations of KR (MES KR). This catalog is dynamic and requires modern methods and technical solutions for prompt input of information. At present, the catalog of outburst lakes contains 370 lakes, which are assigned different categories of outburst hazard. Most of the outburst lakes are in inaccessible areas, which makes it difficult to survey. During the summer, hiking expeditions with bathymetric measurements are conducted on the most hazardous lakes. To improve the system of monitoring and forecasting of outburst lakes, a tool for monitoring high mountain lakes was created and integrated into the national monitoring system of the MES KR using Google Earth Engine algorithms and scripts, as well as open sources data of meteorological information. The NDWI index calculated from publicly available Sentinel-2 and Landsat-8,9 images and used to detect changes in lake area. The images of all lakes are downloaded in automatic mode, and a shapefile is created for each lake based on the index. Subsequently, all shapefiles are compared with previous measurements, resulting in reports indicating the increase or decrease of areas. Additionally, all meteorological information obtained from satellite data services is also available in the system. This information system will enhance the system of monitoring and forecasting of GLOF.