Linking Vulnerability and Risk for Flood Assessment in the Himalayas

Technical Summary: Linking Vulnerability and Risk for Flood Assessment in the Himalayas

Problem Statement
Floods in the Kashmir Himalayan region have become a primary cause of disaster-related losses, with frequency and intensity expected to increase due to climate change and shifting precipitation patterns. While traditional Flood Risk Assessment (FRA) often focuses on hydrological processes, inundation mapping, and physical damage, there is a significant gap in integrated assessments that simultaneously consider hazard, exposure, and the socio-economic dimensions of vulnerability. Conventional approaches frequently overlook inherent vulnerability factors—such as income, education, health, and institutional capacity—that determine a community's ability to respond to and recover from flood events. This study addresses the need for a comprehensive, village-level assessment in Kulgam, a complex and environmentally sensitive district in the Kashmir Himalaya, to better understand the interplay between physical hazards and socio-economic conditions.

Methodology
The study employs an integrated Multi-Criteria Decision Analysis (MCDA) framework, specifically utilizing the Analytic Hierarchy Process (AHP), to evaluate flood risk across 275 inhabited villages in Kulgam. The methodology proceeds through the following technical steps:

1. Indicator Selection and Categorization: A set of 15 indicators was selected based on established conceptual frameworks and validated through expert consultation. These were divided into two sub-dimensions of inherent vulnerability:
   • Sensitivity (8 indicators): Factors positively correlated with vulnerability, including population density, Scheduled Tribe/Caste populations, illiteracy rates, net sown area, culturable wasteland, unirrigated land, drainage availability, and the proportion of agricultural workers.
   • Adaptive Capacity (7 indicators): Factors negatively correlated with vulnerability (higher capacity reduces risk), including literacy rates, forest cover, net irrigated area, access to education and healthcare, drinking water availability, road connectivity, and non-agricultural workers.
2. Data Sources: Socio-economic and demographic data were derived from the 2011 Census of India. Flood hazard and exposure data were sourced from field surveys, revenue records, and physical Highest Flood Level (HFL) markings on buildings from the 2014 Kashmir flood event.
3. Weighting and Normalization: AHP was used to assign relative weights to indicators through pairwise comparisons, ensuring logical consistency (Consistency Ratio < 0.1). All variables were normalized to a 0–1 scale using linear min–max normalization. Sensitivity indicators were normalized directly, while adaptive capacity indicators were inverted to reflect their negative relationship with vulnerability.
4. Risk Calculation:
   • Inherent Vulnerability: Calculated by aggregating weighted sensitivity and adaptive capacity indices.
   • Hazard and Exposure: Hazard was categorized based on HFL depths (Low: ~3 ft; High: ~6 ft). Exposure was treated as relatively uniform across the study area due to the villages' location within similar flood-prone zones.
   • Overall Risk: Derived by integrating Hazard, Exposure, and Vulnerability, adjusted for coping capacity.
5. Classification: Sensitivity, adaptive capacity, vulnerability, and risk were classified into low, medium, and high categories based on percentile thresholds (below 33rd, 34th–66th, and above 66th percentiles, respectively).

Key Results
The assessment reveals significant spatial variability in flood risk factors across Kulgam:

• Sensitivity: 92 out of 275 villages are classified as highly sensitive, primarily driven by dense populations, extensive unirrigated land, and a high proportion of marginal agricultural workers.
• Adaptive Capacity: 91 villages exhibit low adaptive capacity, characterized by limited access to literacy, healthcare, road networks, and irrigation. Conversely, 94 villages show high adaptive capacity.
• Inherent Vulnerability: 93 villages fall into the high-vulnerability category, resulting from the combination of high sensitivity and low adaptive capacity.
• Hazard and Exposure: While exposure is broadly uniform, hazard levels vary significantly. 200 villages experienced flood depths exceeding 6 feet (high hazard), while 75 villages recorded depths around 3.3 feet (low hazard).
• Overall Flood Risk: The integration of these factors identifies 93 villages as high-risk. These high-risk areas are typically characterized by the convergence of high inherent vulnerability and significant physical exposure.

Significance and Claims
The paper posits that flood risk in the Himalayas is not determined solely by physical proximity to river systems or inundation depth but is critically shaped by the interaction between environmental conditions and socio-economic characteristics. By integrating sensitivity and adaptive capacity into the risk framework, the study provides a more realistic and comprehensive understanding of flood risk than traditional physical-only assessments.

The authors claim that this village-level analysis is essential for designing effective, targeted mitigation strategies. The findings suggest that interventions must address both physical dimensions (e.g., drainage restoration, embankment strengthening) and socio-economic dimensions (e.g., improving literacy, road connectivity, and access to services). The study concludes that such location-specific assessments are vital for policymakers to prioritize interventions and build sustainable resilience in vulnerable Himalayan communities, offering a framework that can be extended to other parts of the Jhelum basin. The authors acknowledge limitations regarding data availability and the challenges of collecting field-based HFL data, noting that addressing these would further enhance the robustness of future assessments.