Climate change poses significant economic threats, particularly to countries in the Asia-Pacific region, as they lack sufficient resources to mitigate climate-related problems. Addressing these issues requires comprehensive planning and development of mitigation strategies, which rely on geographically granular data. This data is essential to assess the unique impact of climate change on different regions, ecosystems and communities. In India, region-specific data can help evaluate the impact of climate change on critical sectors such as agriculture, essential for both economic growth and food security. Although the Indian Meteorological Department (IMD) provides some localised data, there are significant gaps and issues with data accessibility and reliability. Improvements in data collection, integration, and accessibility are critical for India to mitigate risks against climate change. Increased budget allocation for technical upgrades, and outsourcing forecasting to academic institutions could boost data accuracy. Building this data infrastructure is vital to ensure a more resilient future for the country.
Climate change poses significant threats to the global economy. Developing countries in the Asia-Pacific, such as India, Nepal, Bangladesh, Indonesia and Maldives are particularly vulnerable as they lack sufficient resources to mitigate climate-related challenges. Because of this, these countries risk facing challenges like reduced labour productivity, rising inflation, increased poverty and financial losses to industries (UN, 2019). Addressing these issues requires comprehensive planning and development of effective mitigation strategies. But these efforts, in turn, depend on the availability of geographically granular climate data - an area where many countries are falling short. A 2024 survey by the Asian Development Bank (ADB) reveals the absence of comprehensive data collection mechanisms, fragmented data ecosystems, and insufficient trained personnel as key reasons behind countries’ data deficiency (ADB, 2024).
Geographically granular data is essential to assess the unique impact of climate change on different regions, ecosystems and communities (ADB, 2024). In India, region-specific data can help assess the impact of climate change on critical sectors such as agriculture, which is essential for both economic growth and food security (ADB, 2024). The effects of climate change can also vary across regions, depending on crop types, geography, and farming practices, and can be severe, affecting not only crop yields but also crop quality, livestock productivity, cropland, and soil quality (Malhi, 2018). To combat this, research such as Burlig et al (2024) demonstrate that region-specific climate data can enable local communities and farmers to develop mitigation strategies, either by selecting appropriate crops, optimising irrigation, fertilisation, or by adopting appropriate pest management strategies. Informed decisions based on granular data can even help farmers diversify into non-farming activities, reducing their exposure to weather-related risks.
India’s data collection efforts on climate change do cover granular-level data. For instance, the Indian Meteorological Department (IMD) provides dynamic, localised climate data, mapping maximum and minimum temperatures, precipitation levels, and warnings for storms or heavy rainfall across different state districts (Weather Nowcasts IMD, n.d.) Agrometeorological services, which study weather and climate in relation to agriculture and farming practices, are also available on IMD with detailed guidelines on the types of data to be collected for agromet advisory (Ministry of Earth Sciences, n.d.)
However a detailed analysis of IMD’s data shows that there are discrepancies and data gaps in many state districts. For instance, while some districts have detailed daily weather reports and agromet advisory bulletins, other state districts either have no data, or only report maximum and minimum temperature. Another challenge is navigating IMD’s website and its array of reports and dashboards on cyclones, precipitation, and forecasts, which are complicated to understand, with no option to download data in bulk, significantly limiting its value for deeper analysis. Although some historical data on temperature changes, rainfall, and cyclones is available for download on the Open Government Data (OGD) portal, and can be utilised for independent research and forecasting, it is not region-specific.
Further complicating matters is the questionable reliability of IMD’s own forecasts. Several studies such as Burlig et al (2024) and Moron et al (2017) highlight that IMD’s precipitation forecasts poorly correlate with India’s agricultural regions, limiting its utility for the intended beneficiaries. Even specialised regional forecasts are negatively correlated with rainfall realisation, putting IMD’s predictions into question (Rosenzweig, 2019).
Several global institutions, including the World Bank, United Nations, and the Asian Development Bank (ADB), are engaged in the collection of climate-related data across countries, including India. The Intergovernmental Panel on Climate Change (IPCC) also provides extensive data and projections, essential for scenario analysis and effective risk assessment (Anthesis, n.d.). However, leveraging this data effectively requires integration into a broader framework. A credible climate risk management entity, for instance the IMD in India, should be able to incorporate such datasets, guiding communities and other stakeholders on how best to utilise them alongside their own data for more informed decision-making.
Building India's in-house climate data infrastructure is not a challenging task, given data collection capacity already exists. The IMD needs to improve accessibility, standardisation, integration and utilisation of datasets. Increased budget allocations for technological upgrades and local capacity-building can ensure more robust and continuous data collection. Further, a standardised format for data collection should be established, and additional support should be provided to states struggling to furnish quality data. Additionally, outsourcing forecasting to academic institutions like the Indian Institutes of Technology (IITs) could provide a short-term boost in forecast accuracy, while building capacity for in-house improvements over time. These improvements will be crucial for mitigating the economic and social impacts of climate change, ensuring a more resilient future for India and the developing world.