The historical disasters of the study region, the Garhwal Himalaya, were collected, and the types of hydrometeorological disasters (HMD) were tabulated with location, attribute, morbidity, and extent from 1803 to 2025. The Garhwal region has been divided into 58 tehsils (sub-administrative regions). For analysing past HMDs and to map Multi-Hazard Susceptibility Zonation on the tehsil level, QGIS, Google Earth Engine, satellite data, k-means clustering, and AHP techniques were used.

Requirements

Data

• Survey of India map of the study area
• Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM)
• Tropical Rainfall Measuring Mission (TRMM) rainfall data
• Sentinel-2 Land Use / Land Cover (LULC) data
• Global Land Ice Measurements from Space (GLIMS) Glacier data
• National Bureau of Soil Survey & Land Use Planning (NBSS&LUP) Soil data
• Disaster data from Emergency Events Database (EM-DAT)
• National Disaster Management Authority (NDMA)
• Various research publications
• Along with regional newspapers

Software

• QGIS
• Google Earth Engine (GEE)

Steps to a solution

1. Study Area

The Garhwal region is spread over approximately 32,366 square kilometres in northwestern Uttarakhand and comprises 58 sub-administrative divisions (tehsils). This region is a major part of the Indian Himalayan Region (IHR) and has steep slopes, rugged terrain, and a geologically fragile structure, and hence is highly vulnerable to natural hazards. Though associated with steep topography, its intense monsoonal rainfall, changing land use patterns, and glacial influence all make the region highly vulnerable to hydrometeorological disasters (HMDs) such as floods, flash floods, landslides, GLOFs, cloud bursts, and avalanches.

2. Collecting and processing  

Historical HMD data for the Garhwal region (1803–2025) have been collected from a variety of sources, including EM-DAT, scientific publications, NDMA, SDMA, and regional media reports.

3. How Thematic Layer Preparation Works

Seven thematic layers were created for the Garhwal region using satellite remote sensing data in QGIS and the GEE environment:

1. Slope
2. Elevation
3. Rainfall
4. Land Use/Land Cover (LULC)
5. Soil Erosion
6. The region’s closeness to rivers
7. Glacier Proximity

The thematic layers were created using the data sourced from Table 1. Thematic layers were brought to the same scale (1–5) and brought together using AHP to develop a single risk zonation map.

The k-means clustering is done on the QGIS 3.42.3 platform using K-Means Clustering ABC (Attribute-based clustering) tab in the processing toolbox. The attributes were selected like location, elevation, and impact severity.

4. Application of AHP

To evaluate HMD susceptibility using AHP, the main influencing factors were selected: slope, elevation, rainfall, LULC, soil erosion, rivers and glacier proximity. To create these layers, data from DEM for slope and elevation, image data from satellites for LULC, and hydrological data are used. Based on AHP, a table is filled, with one factor compared to another according to Saaty’s 1–9 scale to decide their relative weight. Weights are calculated with an eigenvector analysis, and a small consistency ratio (less than 0.1) indicates sensible conclusions. Finally, using an AHP weighted overlay in GIS, all the relevant layers are combined, and the outcome is a map showing where HMD susceptibility is highest.

Results

• Most (77.6 per cent) HMDs happened during the Monsoon season, followed by pre-monsoon (14.3 per cent), Winter season (6.1 per cent), and the post-monsoon season (2 per cent).   
• The K-means clustering of disaster events in the Garhwal Himalayas yielded the clusters-based partitioning them based on shared characteristics (e.g., elevation, impact severity, location). 
• The multi-hazard zonation using the AHP system shows that the north eastern or north tehsils like Joshimath and Chamoli have very high levels of risk, while places like Haridwar and Roorkee in the south have much lower risks.

Future work  

• Access and incorporation of socio-economic and infrastructure vulnerability cum exposure data for risk zonation 
• Combining GIS outputs with participatory approaches to validate and refine vulnerability maps on the ground 
• Make the methodology suitable and easily workable for the entire Himalayan Region to strengthen resilience against disasters 
• Future climatic scenario along with ML to recognize and forecast disaster patterns  
• Using space-based techniques for ecosystem-based disaster reduction