Can Dharali village disaster be a lesson?

Why we need to predict the people also hit by disasters, not just the places. In India monsoons bring flash floods in valleys and floods in plains. Pre-disaster preparedness must track people—the more vulnerable.

When a flood strikes, a wildfire spreads, or a heatwave scorches a region, governments act quickly—sending relief teams, alerting communities, and deploying emergency resources. But here lies the fundamental flaw in how most disaster preparedness operates today: we predict where a disaster will hit, but not who it will hit the hardest.

Disasters are often referred to as “natural.” However, their outcomes are deeply social. A cyclone does not discriminate, but the impact it has is far from equal. A high-income family in a concrete home with access to transport and savings is far less vulnerable than a daily-wage labourer living in a low-lying, poorly built structure. Therefore, the focus must shift from mapping risk zones on a geographic map to mapping the human vulnerability embedded within those zones.

This insight should redefine disaster planning across the world, and certainly in India, which ranks among the most disaster-prone countries globally. Whether it’s the recurring floods in Assam and Bihar, the heatwaves in Delhi and Rajasthan, the cyclones battering coastal Odisha and Tamil Nadu, or the boarder villages hit by war, our preparedness will remain partial unless we predict which people are most at risk, not merely which areas.

Traditionally, disaster risk management relies on geographical tools like hazard maps, weather forecasting, and satellite imagery. These tools are useful—they tell us where a river may overflow or which coasts a cyclone may hit. But they fail to capture social and economic factors that shape vulnerability: income, gender, age, disability, education, social exclusion, housing quality, access to healthcare, and even digital connectivity.

For example, during the 2020 floods in Hyderabad, most flood alerts were sent via smartphone notifications. But what about households with no smartphones or internet access? Many elderly people and migrant workers in informal settlements were left unalerted. In other words, geography alone didn’t determine vulnerability—socioeconomic exclusion did.

We need a shift toward people-centered predictive models. This means integrating demographic data, census information, healthcare records, satellite data, and even mobility patterns to understand which communities will be hardest hit—and how.

Consider a model that doesn’t just warn that a district in Kerala is about to flood, but identifies that within that district, there are 2,000 single women-headed households, 1,100 people with disabilities, 5,500 people over the age of 75 and 77,000 residents without bank accounts or identification documents.

This information allows for targeted early evacuation, resource allocation, and post-disaster support. It brings empathy and efficiency into disaster governance.

The technology to do this exists. Data from Aadhaar, PMJAY health cards, local panchayat registers, and mobile location data (with privacy protections) can help identify vulnerabilities. Civil society organizations can aid in ground verification and mapping. What’s needed is the political will and a framework for ethical data use.

COVID-19 taught us many painful lessons—but one of the most significant was how invisible populations suffer in silence. Migrant workers walked hundreds of kilometers, not because the virus made them do so, but because they were excluded from the state’s database systems. They had no access to local rations, housing, or health services.

Similarly, disaster response often fails those who are invisible to data systems. Think of Dalit families in unauthorised colonies, indigenous groups in forest peripheries, or urban slum dwellers in cities like Mumbai or Chennai. These people live in places that may not even be marked on official maps. How will a district collector protect a community the government does not “see”?

That is why disaster management must begin by asking: who is not in our databases? Who is unseen? India’s disaster profile is changing rapidly. Climate change is leading to more frequent and intense hazards—urban floods, forest fires, landslides, and heatwaves are now regular features. According to a 2023 IPCC report, India faces some of the highest climate risks globally. But it is also home to massive inequality—economic, regional, caste-based, and gendered.

If future disaster responses remain place-focused instead of people-focused, we risk perpetuating harm on the same vulnerable populations again and again. A flood may hit a village, but it will drown the poorest. A heatwave may strike a city, but it will kill the homeless. A drought may ruin a region, but it will crush landless farmers first.

Some countries are already reimagining disaster response using people-centric data. In Japan, authorities use detailed household registries to locate and prioritize elderly and disabled citizens during tsunamis. In the Netherlands, flood risk models include income levels and housing type to estimate damage and response costs. In New York City, the city’s “Climate Vulnerability Index” identifies neighbourhoods with overlapping risks like poverty, poor healthcare access, and heat exposure, so that public cooling centers are built in the right places.

India can do this too—and perhaps better, given its digital infrastructure. From Digital India to the Jan Dhan-Aadhaar-Mobile (JAM) trinity, we already have platforms that can track people, if done responsibly. Disaster planning must now move from the “where” to the “who”.

Steps forward. Local vulnerability mapping is very crucial to begin with. Encourage gram panchayats and urban wards to maintain real-time rosters of vulnerable populations. These can be updated quarterly. Build district-level dashboards that merge health, housing, social security, and land-use data to generate predictive models for disaster vulnerability.

Invest in local youth and women’s groups to serve as disaster first responders, especially for vulnerable sections. Training and stipends can ensure sustainability. Strengthen data privacy and consent laws to ensure that predictive analytics do not become instruments of surveillance or discrimination.

Distribute basic digital tools (radios, feature phones, solar lights) in vulnerable communities to close the communication gap during emergencies. Fund disaster preparedness with specific allocations for the poor, the disabled, the elderly, and marginalized castes—not just for equipment and infrastructure.

In an era of climate disruption and cross boarder tension, it is no longer enough to say that a cyclone is expected to hit Andhra Pradesh, a landslide may occur in Himachal, or a cross boarder skirmishes will impact few only. We must be able to say: this is the community of single mothers living in kutcha homes who will suffer most; these are the elderly people without mobility; these are the invisible children with no access to school, food, or care during crises.

Only then can disaster response be called humane, effective, and just. The geography of a disaster is only half the story. The rest lies in the faces, names, and lives of the people it touches.

Dr. Ashraf Zainabi is a teacher and researcher based in Gowhar Pora Chadoora J&K