A heatwave early warning system (EWS) is an integrated platform that combines weather forecast data, spatial vulnerability assessments, and multi-channel alert dissemination to give governments, health departments, and communities advance notice of dangerous heat conditions.It also provides the spatial intelligence needed to deploy life-saving interventions and target resources effectively before peak temperatures arrive.
India’s heatwave problem has reached a threshold that demands exactly this kind of systematic, spatially grounded early warning capability.
On April 27, 2026, all 50 of the world’s hottest cities were located in India, a concentration of extreme heat that AQI, the air quality monitoring platform, described as having no modern precedent. Between 1993 and 2024, India recorded a 15-fold increase in extreme heatwave days.
The combination of rising baseline temperatures, a large population with significant outdoor and agricultural occupational exposure, and uneven health infrastructure creates a heat mortality risk that is as much a governance challenge as a climate one.
India’s Heatwave Crisis Is Intensifying
With over 24,000 deaths attributed to heatwaves from 1992 to 2015, there is an urgent need to understand India’s vulnerabilities and prepare adaptive strategies. Severe heatwaves can exacerbate chronic health conditions, vector-borne diseases, air pollution, droughts, and other socio-economic pressures causing higher mortality and morbidity.
The geographic and social distribution of heat mortality is not random. Agricultural labourers, construction workers, gig delivery riders, ASHA and ANM field workers, street vendors, the elderly, and communities in informal settlements without access to cooling all face disproportionate risk. A heatwave early warning system that cannot reach these populations where they are, and cannot spatially identify which neighbourhoods and districts are most at risk, will save fewer lives than one that can.
What Is a Heatwave Early Warning System?
A heatwave early warning system is a multi-component platform that combines numerical weather prediction, spatial hazard and vulnerability mapping, colour-coded alert protocols, and multi-channel dissemination to give administrators and at-risk populations enough lead time to take protective action before dangerous heat conditions peak.
Effective systems share three characteristics: they issue alerts early enough for protective action to be taken, they communicate risk in terms citizens and officials can act on, and they are spatially specific enough to target resources where they are most needed. GIS is the spatial intelligence layer that enables all three.
India’s Heatwave Governance: IMD, NDMA, and State Heat Action Plans
The India Meteorological Department (IMD) is the primary agency for heatwave forecasting and early warning. IMD’s criteria for declaring a heat wave requires the maximum temperature at a station to reach at least 40°C for plains and at least 30°C for hilly regions. A Heat Wave is declared when the departure from normal maximum temperature is 4.5°C to 6.4°C, and a Severe Heat Wave when the departure is greater than 6.4°C. A Heat Wave is also declared when the actual maximum temperature reaches 45°C or more, and a Severe Heat Wave when it reaches 47°C or more.
IMD, in collaboration with National Disaster Management Authority (NDMA) and local health departments, has implemented Heat Action Plans (HAPs) in 23 states prone to high temperatures leading to heatwave conditions. The heat wave bulletin is issued daily at 1600 hrs IST providing heat wave warnings with 5-day forecasts. The impact of a heat wave expected over a region is indicated in colour codes (Green, Yellow, Orange, and Red), with specific impacts described in text as per NDMA guidelines. Bulletins are issued by Meteorological Centres and Regional Meteorological Centres at district levels.
Ahmedabad pioneered South Asia’s first comprehensive Heat Action Plan in 2013. Since then, states including Odisha, Telangana, Maharashtra, Rajasthan, and Delhi have developed their own HAPs with varying levels of sophistication. The NDMA Heatwave Guidelines 2017 provide the national framework for HAP preparation, covering early warning, inter-agency coordination, cooling shelter activation, and heat mortality surveillance.
How GIS Powers Heatwave Forecasting and Mapping
The IMD Climate Hazards and Vulnerability Atlas
As part of providing impact-based early warning services for various meteorological disaster events, IMD has prepared a Climate Hazards and Vulnerability Atlas of India for the thirteen most hazardous meteorological events, including Heat Waves. The web Atlas is depicted using Geographic Information System (GIS) tools and is available on the IMD Pune website. It provides district maps on hazard events and vulnerability for all calendar months and at annual scale. Hazard maps are prepared based on climatological data, census data on population and housing density, and different statistical and mathematical methods.
This GIS-based atlas is India’s foundational spatial reference for heat hazard planning. It gives every district a monthly hazard profile, showing when heatwave probability is highest and how that probability has varied historically, expressed in spatial terms that planners can cross-reference with their own district’s population distribution, health infrastructure, and occupational vulnerability data.
The vulnerability atlas for heat waves indicates that 13% of districts and 15% of population are moderate to very highly vulnerable, and 4% of districts and 7% of population are highly vulnerable. The states of Rajasthan (15 districts) and Andhra Pradesh (13 districts) are the most vulnerable states for heat waves. The occupational profile of most victims was ascertained as agricultural labourers, coastal community dwellers, and people living below poverty level (BPL) with mostly outdoor occupations.
ArcGIS Pro provides the analytical environment for state and district-level planners to overlay IMD’s district hazard surfaces with their own vulnerability data, creating compound risk scores that identify which specific blocks and wards face both high heat hazard probability and low adaptive capacity.
Impact-Based Forecasting: Beyond Temperature Thresholds
IMD’s most significant operational evolution in recent years is the shift from purely temperature-threshold-based warnings to impact-based forecasting. An impact-based warning does not just say “maximum temperature will be 45°C in Nagpur tomorrow.” It says what that temperature will mean for specific at-risk populations and activities in specific locations: that outdoor construction workers in specific zones face life-threatening heat stress, that wheat harvesting operations in identified districts should be suspended between 11 AM and 4 PM, that hospitals in named districts should prepare for increased heat illness admissions.
ArcGIS Pro and ArcGIS Velocity enable this impact layer by combining:
- Numerical weather prediction outputs for maximum temperature, minimum relative humidity, and heat index at grid cell level
- Population density surfaces from Census data
- Occupational vulnerability layers mapping the spatial distribution of outdoor workers, agricultural labourers, and construction sites
- Health infrastructure access layers showing hospital density and distance-to-facility surfaces
- Historical mortality data from IMD’s Annual Disastrous Weather Reports, spatially indexed to confirm where heat fatalities actually occurred in past events
The resulting impact forecast map does not describe weather alone. It describes where the weather will be dangerous for specific populations, expressed at the district and sub-district level that HAP officials can act on.
Building the Heat Vulnerability Layer
Heat vulnerability mapping in the context of a regional heatwave EWS is distinct from urban heat island mapping. The goal here is not to identify which street is hottest within a city, but to identify which districts and communities across a state or region have the highest risk of mortality and morbidity when a regional heatwave event occurs.
The spatial vulnerability index that GIS produces for heatwave HAP planning integrates:
- Occupational exposure layers: Agricultural labour density by block from Census and NSSO data; construction worker concentration from urban local body project maps; outdoor vendor distributions from municipality surveys
- Social vulnerability: BPL household density, female-headed household concentration, elderly population share (65+), and slum settlement footprints from Census
- Adaptive capacity gaps: Household-level AC penetration from NSSO data; distance to the nearest hospital or primary health centre; Jal Jeevan Mission tap water coverage (to identify areas where ORS preparation depends on reliable water supply)
- ASHA and ANM coverage: The spatial distribution of frontline health workers whose field outreach during heatwaves depends on them being both present in vulnerable areas and physically safe themselves
- Livestock and crop exposure: For agricultural districts, wheat cultivation area maps and livestock density grids from the Department of Animal Husbandry, since heatwaves impose significant agricultural losses that compound community economic stress alongside health impacts
Indo ArcGIS Living Atlas provides the demographic, land cover, and climate baseline layers that anchor this vulnerability composite in verified spatial data. ArcGIS Pro runs the weighted overlay and spatial clustering analysis that converts these individual vulnerability dimensions into a single, actionable Heat Vulnerability Index map at the sub-district level.
From Alert to Action: GIS in Heatwave Response
Multi-Channel Alert Dissemination Through CAP
IMD conveys heat wave information through daily and weekly video messages, with significant emphasis on social media including YouTube, Facebook, WhatsApp, X, and Instagram. Dedicated state websites have been operationalized to give district-wise and localized information on heatwave warning services. Sector-specific bulletins for health and agriculture sectors are also provided.
The Common Alerting Protocol (CAP) is the international standard that enables a single IMD warning to automatically propagate across the Mausam app, the Sachet civil protection app, RSS feeds, SMS broadcast systems, and state emergency operations centers simultaneously. GIS enables the geo-targeting dimension: a CAP-based alert issued for specific districts automatically reaches only the Mausam users in those districts, the Sachet subscribers in those blocks, and the SMS broadcast recipients whose registered mobile numbers are geolocated to the affected area.
ArcGIS Velocity provides the real-time stream processing layer that ingests IMD’s automated weather station data feeds, identifies when temperature readings cross heatwave declaration thresholds at stations across a district, and triggers alert generation through the district-level dashboard before the formal 1600 hrs bulletin is issued.
ArcGIS Dashboards give state Emergency Operation Centers and District Collectors a live operational view combining real-time temperature station readings, forecast model outputs, current alert status by district, cooling shelter activation status, and health department incident reports in a single spatial interface.
Cooling Shelter and Resource Siting
HAP implementation converts a heatwave alert from an information event into a physical response. GIS supports the resource siting decisions that make this response accessible to the people who need it:
- ArcGIS Pro network analysis identifies the locations for cooling shelters, water kiosks, ORS distribution points, and mist-spray installations that minimize maximum walking distance for the vulnerable population in each ward, given the existing distribution of public buildings, schools, and government facilities available for activation
- ArcGIS Survey123 enables field workers, ASHAs, and ward-level health staff to report real-time shelter occupancy, water point functionality, and heat illness incident locations from mobile devices during active heatwave events
- ArcGIS Dashboards aggregates these field reports spatially, giving district health officers a live situational picture that shows where resources are functioning, where demand is exceeding capacity, and where a new shelter or water point needs immediate activation
India-Specific Edge Cases That Demand Pre-Positioned Resources
India’s heatwave calendar overlaps with events that create concentrated outdoor population exposure that standard HAP designs do not always adequately address:
Summer elections
The 2024 General Elections were conducted in April and May, requiring millions of voters and election officials to remain outdoors in peak heat. GIS-based pre-positioning of water and ORS distribution around polling stations in high-vulnerability wards is an election management function that HAP-implementing states can activate from the same spatial platform used for general heatwave response.
Religious gatherings
Char Dham pilgrimages, Kumbh and Ardh Kumbh gatherings, and summer temple festivals concentrate hundreds of thousands of pilgrims in outdoor settings during peak heat months. GIS analysis of gathering sites against IMD’s district hazard maps identifies which pilgrimage routes and gathering points fall within the highest hazard zones, informing the advance deployment of mobile medical teams, water points, and shaded rest areas along those corridors.
Wheat harvest season
The wheat belt districts of Punjab, Haryana, Rajasthan, and Uttar Pradesh conduct mechanical and manual harvesting between April and June, precisely when heatwave probability is highest in these states. IMD already issues agriculture-sector heat bulletins. GIS overlays of wheat cultivation area maps with forecast heatwave impact zones enable state agriculture departments to issue farm-level advisories for harvest timing adjustments, helping minimize crop loss and worker exposure.
How Indian States and Cities Are Operationalising Early Warning Systems
Odisha has developed one of India’s most robust state-level heatwave response systems, integrating IMD forecast data with district-level HAP trigger mechanisms. The state activates cooling centres, directs ASHA workers to visit elderly and BPL households proactively, and deploys water tankers to identified vulnerable communities when Orange or Red alerts are issued.
Telangana issues heatwave advisories linked to district-specific vulnerability profiles and maintains a state-level heat response command through its Emergency Operations Center.
Ahmedabad Municipal Corporation continues to expand its 2013 HAP model, incorporating ward-level heat vulnerability mapping, worker outreach through the construction sector, and hospital preparedness protocols triggered by daily IMD alerts.
The Esri India Disaster Management platform, built on ArcGIS Enterprise, provides the integrated spatial operations framework that state EOCs need to move from monitoring to coordinated response, connecting IMD alert feeds, field crew deployment, resource status reporting, and health incident surveillance in a single, location-aware operational environment.
Challenges and the Road Ahead
Alert reach to the most vulnerable
HAP dissemination through smartphones and state websites reaches populations with digital access. Agricultural labourers in remote fields, elderly residents without smartphones, and homeless populations in urban areas may not receive CAP-based alerts at all. Reaching these groups requires ground-level outreach networks, ASHA field visits, and community loudspeaker systems that GIS can map and verify.
HAP plan-to-practice gap
Rajasthan, Andhra Pradesh, and other highly vulnerable states have HAPs on paper. The operationalization gap between a documented plan and a funded, practiced, monitored response system is significant in many districts. GIS dashboards that track HAP activation against alert color codes give state heat nodal officers a real-time accountability mechanism, converting the plan into a measurable performance system.
Humid heatwave coverage
IMD’s current criteria are primarily temperature-based and do not fully account for humid heatwaves, where the combination of high temperature and high humidity creates fatal heat stress at temperatures below the 40°C threshold in coastal and inland areas. Incorporating IMD’s experimental Heat Index into the GIS vulnerability layer and adjusting alert thresholds for humidity-adjusted apparent temperature is a methodological evolution that will significantly improve the accuracy of impact forecasts in coastal states.
FAQs
1.What is a heatwave according to IMD?
IMD declares a heat wave when maximum temperature reaches at least 40°C for plains or 30°C for hilly regions, with a departure from normal exceeding 4.5°C. A Severe Heat Wave is declared when departure exceeds 6.4°C or actual maximum temperature reaches 45°C, at two or more stations for at least two consecutive days.
2.How does GIS help in heatwave early warning?
GIS combines IMD forecast outputs with district-level vulnerability data to generate impact-based alert maps showing where dangerous heat will affect at-risk populations. ArcGIS Velocity processes real-time temperature feeds and triggers geo-targeted alerts, while ArcGIS Dashboards gives state EOCs a live view of alert status, shelter activation, and field incidents.
3.What is a Heat Action Plan in India?
A Heat Action Plan is a pre-prepared multi-agency response protocol that activates protective measures when IMD issues color-coded heatwave alerts. Ahmedabad developed South Asia’s first HAP in 2013, and HAPs have since been implemented across 23 states using NDMA’s 2017 national framework.
4.Which Indian states have heatwave early warning systems?
HAPs have been implemented in 23 heat-prone states, with Gujarat, Odisha, Telangana, Maharashtra, and Andhra Pradesh having the most advanced systems. IMD’s Climate Hazard and Vulnerability Atlas identifies Rajasthan and Andhra Pradesh as the states with the highest district-level heat vulnerability.
5.How are heatwave alerts disseminated to citizens?
IMD disseminates alerts through the Common Alerting Protocol framework, with daily color-coded bulletins and geo-targeted push notifications via the Mausam and Sachet apps. Alerts also go out across social media platforms, sector-specific health and agriculture bulletins, and regional language video messages for lower-literacy audiences.
Written by
Esri India Marketing
