Localised Monsoon Forecasting: Need & Challenges

Table of contents

“In a land where the monsoon shapes lives and livelihoods, precision is not a privilege—it is a necessity.” India’s dependence on the monsoon spans agriculture, hydropower, drinking water, and disaster management. While forecasts of a ‘normal monsoon’ offer broad optimism, they often conceal localised distress; drought in one region, floods in another. As climate unpredictability grows, monsoon data must shift from broad and symbolic to granular, strategic, and action-driven.

Uneven Monsoon Trends: The Regional Reality

Dimension	Key Insights
National Trends (1980–2020)	29 Normal, 8 Above-Normal, 3 Below-Normal monsoon years — but national averages hide regional distress.
District-Level Deficits	~30% of districts face recurring rainfall shortages. E.g., Vidarbha remains vulnerable despite “normal” national forecasts.
Excess Rainfall Zones	~38% districts—such as in Konkan, Assam, and Kerala—see repeated excess rain, increasing flood risks.
Tehsil-Level Rainfall Drop	11% of tehsils have seen >10% rainfall decline in the last decade, impacting micro-level irrigation and cropping patterns.
Fragmented Patterns	Rainfall is now more erratic and localised, with dry spells broken by cloudbursts, undermining resilience.
Rise in Extremes	IMD reports a 30% increase in extreme rain events over 20 years, while moderate rain days are reducing, intensifying drought and flood exposure.

Credit: ResearchGate

Need for Localised Monsoon Forecasting

Masked Regional Distress: Despite IMD’s forecast of a ‘normal’ monsoon in 2022, Bihar experienced a sharp 41% rainfall deficit (Down to Earth, 2022), exposing regional blind spots.
Rain-fed Agricultural Dependence: Over 52% of India’s cultivated land is entirely rain-fed, making accurate local forecasts vital for cropping decisions.
Disaster Preparedness and Loss Prevention: Kerala’s ₹30,000 crore flood losses highlight the urgent need for hyper-local rainfall alerts and early warning systems.
Rising Climate Extremes: With a 30% rise in extreme rainfall events over the past two decades, localised forecasting is now essential for climate-resilient planning.
Precision in Policy Implementation: Granular, district-level data enables better targeting and efficiency in schemes like PMFBY, MSP operations, and drought relief.

Challenges in Localised Monsoon Forecasting

Inadequate Infrastructure: In regions like Chhattisgarh and Northeast India, the lack of Automated Weather Stations (AWS) and Doppler radars hampers accurate, real-time forecasting.
Technological Gaps: Existing models fail to predict extreme weather, as seen in Himachal Pradesh’s 2021 cloudburst, which caused widespread devastation due to inaccurate forecasts.
Last-Mile Connectivity: Tribal areas like Kandhamal and Odisha lack weather advisories in local languages, leaving communities unprepared for severe weather events.
Institutional Fragmentation: In Tamil Nadu, poor coordination between IMD, Krishi Vigyan Kendras (KVKs), and Panchayats results in the exclusion of valuable local knowledge from forecasts.
Funding & Capacity Constraints: Agromet units in states like Rajasthan and Uttar Pradesh face staffing and funding shortages, limiting the effectiveness of weather predictions and early warnings.

Strategies & Reforms for Improvement

Strengthen Ground-Level Infrastructure: Deploy AWS at the block level and expand Doppler radar coverage in underserved geographies. E.g., Odisha’s MGNREGA-backed agro-met units.
Adopt Advanced Forecasting Technologies: Integrate AI/ML algorithms with ISRO satellite inputs to improve hyper-local predictions. E.g., IMD’s AI-based Nowcasting in Delhi.
Enhance Last-Mile Delivery: Use vernacular languages, community radio, SMS alerts, and WhatsApp groups for inclusive outreach. E.g., Mahavedh sends Marathi alerts to 12+ lakh farmers.
Build Capacity & Institutional Convergence: Promote synergy among IMD, ICAR, KVKs, Panchayats, & State Disaster Authorities for integrated action. E.g., National Weather Literacy Mission across 15 states.
Promote Public-Private Partnerships (PPP): Collaborate with agri-tech startups & leverage CSR funds for micro-level monitoring and forecasts. E.g., Skymet–IBM tie-ups in Rajasthan & Andhra Pradesh.
Integrate Indigenous Knowledge: Combine local traditional wisdom with modern tools for contextual forecasts. Example: Kerala’s fisherfolk use lunar cues with GPS apps.
Create a Dedicated Funding Mechanism: Establish a Weather Resilience Fund under NAFCC for decentralised weather infrastructure and outreach. E.g., Himachal’s climate-resilient village model (2022).

“To be truly atmanirbhar in climate risk management, forecasting must begin at the grassroots.” Localised monsoon forecasting is not just a technological fix but a strategic imperative. It is key to agricultural resilience, climate preparedness, and inclusive economic planning in a monsoon-dependent India.

Reference: The Economic Times

PMF IAS Pathfinder for Mains – Question 157

Q. Monsoon forecasting is crucial for promoting climate-resilient agriculture and enhancing disaster preparedness. In this context, analyse the significance of localised monsoon forecasting for effective policy implementation. (250 Words) (15 Marks)

Approach

Introduction: Write briefly about monsoon and the importance of Monsoon forecasting for effective agricultural and water policy implementation.
Body: Examine the significance of localised monsoon forecasting and mention the way forward for this..
Conclusion: Conclusion should be comprehensive and forward-looking, focusing on sustainability, inclusivity, and climate science orientation.