Abstract:
Extreme precipitation events are intensifying globally due to climate change, often leading to unprecedented
f lood risks and challenges in water resource management. In August 2023, Punjab, India, experienced cata
strophic flooding, impacting approximately 12,000 villages and resulting in 65 reported fatalities. The flood
highlighted the need to understand better the multifaceted drivers of such extreme events, especially in regions
dependent on major dams. This study analyzes the hydrometeorological drivers, dam operations, hydrological
responses, and socioeconomic impacts associated with the event. A detailed spatiotemporal meteorological
analysis demonstrates that heavy and very heavy rainfall events in July significantly elevated antecedent soil
moisture levels, heightening the region's flood susceptibility in August despite seasonal rainfall deficits. Using
HEC-RAS hydrodynamic modeling coupled with high-resolution demographic data, the study demonstrates the
Pong Dam's critical role in mitigating flood impacts, reducing population exposure by approximately 80 %
compared to unregulated conditions. A deterministic population exposure assessment was carried out using 2011 village-level census data. The results showed disproportionate impacts on vulnerable groups, with flood exposure rising from August 15–17 by ~49 % among children, ~46 % among women, and ~47 % among non-working populations. Genetic Algorithm-based optimization with a piecewise penalty function improved the balance between flood mitigation and water conservation. It underscores the importance of integrating real-time hydrological data to enable adaptive reservoir management. The study recommends that policymakers prioritize advanced flood forecasting systems incorporating soil moisture data, high-resolution rainfall forecasts, and demographic vulnerability indices, while addressing the critical operational challenges of data availability,
monitoring density, and institutional capacity.
