Recent research published in Current Science reveals that flood hazard assessments for Uttarakhand have consistently undervalued the danger to towns and villages because they rely on long‑term average rainfall rather than the extreme downpours that trigger disasters.
Key Developments
- Analysis of flood hazard zones for the period 2017‑2021 shows a marked rise in areas classified as ‘high’ or ‘severe’ hazard, with 2021 recording the largest extent of high‑hazard land.
- More than 90 % of the state fell within moderate or high‑hazard categories across all years studied.
- Researchers from Malaviya National Institute of Technology (MNIT), Jaipur used a GIS model that combined six factors—elevation, slope, drainage density, topographic wetness, land use and rainfall—to map flood risk.
- When the model employed the highest annual rainfall recorded in a year, severe and high‑hazard zones expanded dramatically; using three‑decade averages produced a misleadingly smaller risk area.
Important Facts
The six‑factor weighting gave the greatest importance to slope, elevation and rainfall. Land‑use change, drainage density and topographic wetness were treated as secondary factors.
Historical catastrophes such as the Malpa landslide (1998), the Kedarnath disaster (2013)—where Uttarakhand received 375 % of its benchmark monsoon rainfall—and the Chamoli flood (2021) underscore the growing vulnerability. Climate scientists link the rising frequency of cloudbursts and glacial lake outbursts to a warming atmosphere.
Rapid urbanisation has expanded built‑up areas, reducing land’s capacity to absorb runoff and further aggravating flood risk.
Exam Relevance
The study highlights the intersection of disaster management and climate adaptation. Understanding how extreme rainfall reshapes risk maps is crucial for answering questions on climate‑induced hazards, sustainable development and policy planning. The role of research institutions like MNIT illustrates the importance of scientific input in governance.
Way Forward
- Redraw flood hazard maps using extreme rainfall data to reflect true risk.
- Establish buffer zones around high‑hazard zones.
- Incorporate field validation by comparing model outputs with observed flood events before policy adoption.
- Integrate land‑use planning that limits urban spread into flood‑prone areas and promotes watershed management.