Geo-spatial Susceptibility Analysis of Glacial Lake Outburst Flood: A Study of Passu Glacier, Hunza Nagar District—Pakistan
摘要
Glacial lake outburst floods (GLOFs) are significant natural hazards characterized by the sudden release of water from glacial lakes. Historical records indicate that there have been 90 destructive outburst floods in the Hunza Nagar district over the past 50 years. The primary objective of this study is to monitor the spatial and temporal changes in glacial lake conditions and assess these lakes’ susceptibility to GLOFs by employing geospatial techniques. An empirical method has also determined the spatial–temporal extent of the lake’s area and volume (2010–2024). Several parameters were identified and analyzed to create susceptibility maps, including slope, elevation, aspect, lake type, area, and temperature. Precipitation, earthquakes, normalized difference water index, normalized difference glacier index, distance to rivers, and avalanches. The weights of these parameters were calculated using Saaty’s Analytical Hierarchy Process (AHP), providing a quantitative framework for assessing their relative importance in contributing to GLOF susceptibility. According to the NDGI studies for the evaluation of temporal variation (2010–2024) within Passu glacier, the rapid increase in glacial retreat that affects the area of glacial lakes causes the glacier area to drop from 81.1253% to 61.1469%. The NDWI findings also suggest that the area of the water body has increased from 59.28% to 93.92%. Temporally the number of lakes increased from 8 to 11 within the study area. The results indicate that the lake’s volume ranges from 907,916.78 to 2,033,338.13 m3, while its area ranges from 77,050 to 138,331 m2. With a consistency ratio of 0.088, the resulting susceptibility for Hunza Nagar generated from AHP has been divided into four zones: very high (14.57 km2), high (23.54 km2), moderate (26.90 km2), low (20.85 km2), and very low (13.90 km2). Stakeholders can take the necessary steps to reduce possible impacts by identifying regions more vulnerable to GLOFs.