View answer copy →Answered a closely related question
Their question: “Floods as recurring disaster — natural & anthropogenic factors + measures (10 marks, 150 words)”
The topper's flood answer splits causes into natural vs anthropogenic and pairs measures with place-specific Indian flood cases, reusable for the PYQ on urban flood causes and the frameworks that tackle them.
Key learnings from their answer
- ›Every single answer opens with a quantified hook that frames the scale before any argument begins, '85% of geography vulnerable', '12% flood-prone', '7576 km coast', '40% of global cyclones occur at the Indian coast'. -> Don't start a GS3 answer with a definition; open with one hard statistic that quantifies the problem's magnitude, so the examiner sees you grasp the scale in the first line.
- ›Answers run on a repeatable two-bucket spine, Natural vs Anthropogenic (and Geological/Natural/Anthropogenic for landslides), and structural vs non-structural for measures (Q5's branching tree: Structural -> embankments, hydroseeding; Non-structural -> afforestation, terrace farming, contour bunds). -> A clean, pre-decided binary categorisation makes a 250-word answer instantly scannable and ensures you cover both sides without missing a dimension under time pressure.
- ›Current examples are dense and place-specific, each tied to a precise failure mode, Wayanad landslides 2024 (>500 dead, flagged as 'lacking bottom-up approach'), Joshimath subsidence ('better land-use planning needed'), Chamoli cloudburst 2021, Sikkim landslide 2021, Krishna river floods 2019 ('steep gradient'). -> Stock 5-6 recent disasters and attach a one-phrase diagnosis to each, so the example doubles as evidence for your specific argument instead of being name-dropped.
What they cited: Krishna river floods 2019 (steep gradient) · Brahmaputra river floods (glacier snowmelt) · Chamoli cloudburst/disaster 2021 (Uttarakhand floods)