Himachal Pradesh University (HPU) has developed a satellite-driven hydrodynamic modelling framework for assessing and reducing the impacts of cloudburst-induced flash floods in the Himalayan region.
The model achieved 95.34% validation accuracy against official government damage records, demonstrating its capability to identify severely affected areas, assess infrastructure exposure and estimate direct economic losses in data-scarce mountainous catchments.
This research was led by Mahesh Sharma, deputy director, Himalayan Centre for Disaster Risk Reduction and Resilience (HIM-DR³), along with researchers from the University of Delhi and Imperial College London, United Kingdom.
The study integrated NASA satellite rainfall data, high-resolution terrain information, field investigations, official damage records and two-dimensional HEC-RAS hydrodynamic modelling to simulate flood-inundation pathways, water depth, flow velocity and building-level impacts.
The framework was scientifically validated by reconstructing the 2025 cloudburst-induced flash flood in Thunag village in Himachal’s Mandi district.
The research, titled “Assessment of 2025 Cloudburst-Induced Flash Flooding in Thunag Village, Mandi, Himachal Pradesh,” has been published in the journal Geomatics, Natural Hazards and Risk.
{{/usCountry}}The research, titled “Assessment of 2025 Cloudburst-Induced Flash Flooding in Thunag Village, Mandi, Himachal Pradesh,” has been published in the journal Geomatics, Natural Hazards and Risk.
{{/usCountry}}“The hydrodynamic modelling framework provides a scientifically validated solution for understanding, anticipating and reducing the impacts of cloudbursts and the flash floods triggered by them,” said Dr Mahesh Sharma.
This is particularly important for Himachal Pradesh, Uttarakhand and the wider Himalayan region, where short-duration, high-intensity cloudbursts can generate destructive flash floods within a very limited response time.
“When integrated with real-time rainfall observations, weather forecasts and scientifically defined warning thresholds, the model can support early-warning systems by identifying likely inundation zones, vulnerable settlements and critical infrastructure at risk. It can also assist governments and disaster-management authorities in evacuation planning, emergency preparedness, flood-hazard mapping, infrastructure protection and long-term resilience planning,” added Sharma.
Vice-Chancellor, HPU, Prof Mahavir Singh, said that cloudbursts have emerged as a major challenge for Himalayan states and that scientifically validated models of this nature can play a crucial role in strengthening early-warning support, disaster preparedness and evidence-based decision-making.