Natural hazards are complex phenomena that can occur independently, simultaneously, or in a series as cascading events. For any particular region, numerous single hazard maps may not necessarily provide all information regarding impending hazards to the stakeholders for preparedness and planning. A multi-hazard map furnishes composite illustration of the natural hazards of varying magnitude, frequency, and spatial distribution. Thus, multi-hazard risk assessment is performed to depict the holistic natural hazards scenario of any particular region. To the best of the authors’ knowledge, multi-hazard risk assessments are rarely conducted in Nepal although multiple natural hazards strike the country almost every year. In this study, floods, landslides, earthquakes, and urban fire hazards are used to assess multi-hazard risk in Kathmandu Valley, Nepal, using the Analytical Hierarchy Process (AHP), which is then integrated with the Geographical Information System (GIS). First, flood, landslide, earthquake, and urban fire hazard assessments are performed individually and then superimposed to obtain multi-hazard risk. Multi-hazard risk assessment of Kathmandu Valley is performed by pair-wise comparison of the four natural hazards. The sum of observations concludes that densely populated areas, old settlements, and the central valley have high to very high level of multi-hazard risk.

The people of Nepal today are exposed to perennial local disaster events and profound vulnerability to disaster. The combined efforts of government, donors, UN agencies, NGOs, and Nepalese communities are needed to avert the impacts of disaster events. Much more can be done immediately to reduce the impacts by reviewing the scope and distribution of past disaster events. This article provides an overview of Nepal’s disaster vulnerability through an analysis of the record of disaster events that occurred from 1900 to 2005. The data were generated from historical archives and divided into incidents at the district, subnational, and national levels. Statistical and Geographical Information System (GIS) analyses were carried out to generate district level disaster vulnerability maps. It is concluded that small-scale, local disasters have a greater cumulative impact in terms of casualties than large-scale, national disasters.

The study of land use land cover has become increasingly significant with the availability of remote sensing data. The main objective of this study is to delineate geohazard-prone areas using semi-automatic classification technique and Sentinel-2 satellite imagery in Bhutan. An open-source, semi-automatic classification plugin tools in QGIS software enabled efficient and rapid conduct of land cover classification. Band sets 2-8, 8A, and 11-12 are utilized and the virtual colour composites have been used for the clustering and creation of training samples or regions of interest. An iterative self-organizing data analysis technique is used for clustering and the image is classified by a minimum distance algorithm in the unsupervised classification. The Random Forest (RF) classifier is used for the supervised classification. The unsupervised classification shows an overall accuracy of 85.47% (Kappa coefficient = 0.71) and the RF classifier resulted in an accuracy of 92.62% (Kappa coefficient = 0.86). A comparison of the classification shows a higher overall accuracy of the RF classifier with an improvement of 7.15%. The study highlights 35.59% (512,100 m²) of the study area under the geohazard-prone area. The study also overlaid the major landslide polygons to roughly validate the landslide hazards. Article highlights: Semi-automatic classification technique was applied to delineate the geohazard-prone area in the heterogeneous region of Bhutan Himalaya. Unsupervised and supervised classification technique were used to perform land cover classification using the semi-automatic classification plugin (SCP). The Random Forest classifier predicted higher accuracy and the application is rapid and efficient compared to the unsupervised classification.