Content

1. India’s Multi-Hazard Early Warning Decision Support System
2. PM-WANI Scheme Accelerates Public Wi-Fi Expansion across India with Over 4 Lakh Hotspots

India’s Multi-Hazard Early Warning Decision Support System

Why in News ?

• PIB highlighted measurable gains of MHEW-DSS—~30% improvement in forecast accuracy and 50% reduction in preparation time—showcasing India’s shift to impact-based digital forecasting.
• System now delivers location-specific warnings to ~80% population, significantly reducing evacuation costs (to one-third since 1999) through improved cyclone prediction accuracy.
• Recognition through national and global awards (e-Governance Award 2025, UNDRR Sasakawa Award) underscores India’s leadership in multi-hazard early warning systems.

What is MHEW-DSS?

• MHEW-DSS is an integrated, GIS-enabled digital forecasting platform developed by IMD under Mission Mausam, enabling automated, real-time, impact-based multi-hazard early warning dissemination across sectors and regions.
• It transforms fragmented forecasting into a unified system by integrating satellite, radar, AWS and ocean data, ensuring seamless processing, visualization and decision-support for meteorologists and administrators.
• Over 90% automation in data processing and utilisation of >95% numerical weather prediction inputs enhances forecast reliability, particularly for extreme weather events like cyclones and heatwaves.

Relevance

GS I (Geography + Disaster Management)

• Extreme weather events: cyclones, heatwaves, floods—improved forecasting accuracy.
• Role of monsoon variability and climate change in multi-hazard vulnerability.
• Disaster preparedness and vulnerability mapping (district-level risk profiling).

GS III (Environment, Disaster Management, Science & Tech)

• Climate adaptation and disaster risk reduction (DRR).
• GIS, AI/ML, numerical weather prediction → GovTech integration.
• Alignment with Sendai Framework and SDGs (11, 13).

Practice Questions

Q1.“Early warning systems are the first line of defence against climate-induced disasters.”Examine the role of India’s Multi-Hazard Early Warning Decision Support System (MHEW-DSS) in strengthening disaster preparedness. (250 words)

Spatial Coverage and Reach

• Operational across India with regional extension to North Indian Ocean; IMD as RSMC provides cyclone and marine advisories to countries like Bangladesh, Sri Lanka, Oman and UAE.
• Hyper-local forecasting via Mausamgram covers 1.5 lakh pincodes, 5,700 blocks and 6.2 lakh villages, ensuring last-mile connectivity through SMS, mobile apps, APIs and Panchayati Raj networks.
• Impact-based warnings currently reach nearly 80% of India’s population, including vulnerable coastal, agrarian and urban regions exposed to multiple climate hazards.

Rationale and Need

• India’s high vulnerability: over 75% districts are multi-hazard prone; floods affect ~40 million hectares annually, while cyclones and heatwaves are increasing in intensity and frequency.
• Earlier forecasting systems were fragmented and slow, lacking actionable insights, leading to higher disaster mortality, economic losses and inefficient evacuation strategies.
• Aligns with Sendai Framework, SDGs (11, 13) and UN’s “Early Warning for All” initiative, promoting anticipatory governance and risk-informed disaster management.

Institutional Framework

• Nodal Ministry: Ministry of Earth Sciences; implementing agency: India Meteorological Department (est. 1875), supported by Mission Mausam (2024) for improved modelling and observation infrastructure.
• Core system includes Weather Analysis and Forecast Enabling System (WAFES), enabling GIS-based visualization, real-time data assimilation and forecast generation across IMD’s network.
• Integrated with NDMA, NDRF, SDMAs and over 200 organisations including NITI Aayog, ensuring interoperable data sharing and coordinated disaster response.

Operational Mechanism

• Multi-source real-time data (satellites, radars, AWS) is collected, standardised and processed using ensemble modelling and bias correction, improving accuracy during extreme weather events.
• Forecast lead time increased from 5 to 7 days; preparation time reduced by ~50%, enabling faster dissemination and improved anticipatory action by authorities.
• Provides colour-coded, impact-based warnings and sector-specific advisories (agriculture, health, energy), translating complex meteorological data into actionable information.

Data and Evidence

• Forecast accuracy improved by ~30% and preparation time reduced by 50%, demonstrating efficiency gains from digital transformation.
• Evacuation costs reduced to one-third (1999–2024) due to improved cyclone landfall prediction accuracy in 3–5 day forecasts.
• Economic savings include ₹250 crore from indigenisation, ₹57.6 crore annual manpower savings and ₹1.4 crore/year from reduced paper usage.
• Agriculture benefits: farmers using advisories report 52.5% higher annual income; potential ₹13,331 crore benefit in rain-fed districts.
• Environmental gains include saving ~210,240 kWh electricity annually, 23.4 tonnes paper, ~63 kilolitres water and avoiding ~2.57 tonnes CO₂ emissions.

Governance and Administrative Significance

• Enables anticipatory governance through real-time, evidence-based decision-making, strengthening Centre-State coordination and inter-agency disaster response.
• Enhances last-mile delivery via multi-channel dissemination (SMS, apps, APIs), improving inclusivity and accessibility for vulnerable populations.
• Promotes digital governance with transparency, interoperability and standardised data-sharing across ministries and departments.

Economic Implications

• Reduces disaster-related losses, evacuation costs and infrastructure damage, improving fiscal resilience and public expenditure efficiency.
• Supports weather-sensitive sectors like agriculture, energy and transport through predictive intelligence, enhancing productivity and reducing uncertainty.
• Strengthens Atmanirbhar Bharat by promoting indigenous forecasting technology and reducing dependence on foreign systems.

Social and Ethical Dimensions

• Protects vulnerable communities (farmers, fishermen, women, children) through targeted advisories, promoting equity in disaster preparedness.
• Supports public health through heatwave alerts, disease prediction and preparedness planning for healthcare systems.
• Builds public trust through reliable, timely and accessible warning systems, enhancing community resilience.

Environmental, Security and Technological Dimensions

• Facilitates climate adaptation through improved monitoring of extreme weather, air quality and environmental indicators.
• Strengthens coastal and maritime security through accurate cyclone and marine advisories, reducing risks to fisheries and shipping.
• Represents GovTech advancement integrating GIS, AI/ML models, APIs and digital platforms for scalable disaster risk management.

Challenges and Limitations

• Last-mile connectivity gaps persist in remote and tribal regions, limiting universal access to early warning systems.
• Data integration and interoperability challenges across agencies and states hinder seamless real-time information sharing.
• Capacity constraints at local levels affect interpretation and effective utilisation of impact-based forecasts.
• Requires continuous infrastructure upgrades and faces cybersecurity risks due to increasing digitalisation.
• Behavioural gaps where warnings do not always translate into timely action due to awareness and trust deficits.

Way Forward

• Achieve universal early warning coverage aligned with UN’s “Early Warning for All” initiative by 2027.
• Strengthen last-mile delivery through community institutions (SHGs, Panchayats) and multilingual, localised communication strategies.
• Integrate AI, big data and IoT for hyper-local forecasting and real-time predictive analytics.
• Enhance institutional coordination through unified disaster data platforms and legal frameworks for seamless data sharing.
• Build grassroots capacity through training of district and block-level officials for effective interpretation and response.
• Mainstream MHEW-DSS outputs into urban planning, agriculture policies and climate adaptation strategies for long-term resilience.

Prelims Pointers

• MHEW-DSS launched in January 2024 by IMD under Ministry of Earth Sciences.
• Mausamgram provides forecasts up to 10 days covering over 6.2 lakh villages.
• IMD is a Regional Specialized Meteorological Centre (RSMC) under WMO.
• Forecast lead time increased to 7 days; automation exceeds 90%.
• Impact-based forecasting focuses on consequences rather than only weather parameters.

PM-WANI Scheme Accelerates Public Wi-Fi Expansion across India with Over 4 Lakh Hotspots

Why in News ?

• Government informed Parliament that PM-WANI has crossed 4.09 lakh public Wi-Fi hotspots with 2.44 crore users and ~58.64 petabytes data consumption.
• Recent policy interventions—TRAI tariff cap (2025), roaming, FTTH integration and data offloading—have improved affordability, interoperability and business viability.
• Scheme gaining traction as a key instrument for bridging digital divide and complementing 4G/5G networks under Digital India and National Broadband Mission.

What is PM-WANI Scheme?

• PM-WANI (Prime Minister Wi-Fi Access Network Interface) is a decentralised, market-driven framework enabling public Wi-Fi proliferation through unlicensed PDOs, transforming local shops into low-cost internet access points.
• It follows a four-tier architecture—PDO, PDOA, App Provider, Central Registry (C-DOT)—ensuring interoperability, seamless authentication and scalable last-mile connectivity without licensing barriers.
• Represents a paradigm shift from telecom-centric broadband delivery to community-based internet distribution, reducing entry barriers and promoting digital inclusion.

Relevance

GS II (Governance)

• Digital inclusion and last-mile connectivity.
• De-licensing reform → ease of doing business.
• Cooperative federalism in digital infrastructure expansion.

GS III (Infrastructure)

• Complement to BharatNet and National Broadband Mission.
• Affordable internet as core infrastructure for growth.

Practice Question

Q1.“Affordable internet access is a key enabler of inclusive growth.”Evaluate the role of PM-WANI in bridging India’s digital divide. (250 words)

Spatial Coverage and Current Status

• As of Feb 2026, 4,09,403 public Wi-Fi hotspots operational across States/UTs, with leading contributors being Delhi, Maharashtra, Karnataka and Uttar Pradesh.
• Ecosystem includes 207 PDO Aggregators and 113 App Providers, reflecting expanding private participation and competitive service delivery in public Wi-Fi infrastructure.
• Total users reached 2.44 crore, consuming ~58.64 petabytes of data, indicating growing adoption, especially in urban and semi-urban regions.

Rationale and Need

• Addresses persistent digital divide—while mobile broadband expands, affordability and device constraints limit access for bottom-of-pyramid populations, especially in rural and informal sectors.
• India’s data demand surge (video streaming, e-governance services) requires complementary infrastructure beyond spectrum-based mobile networks to avoid congestion and inefficiency.
• Aligns with Digital India, National Broadband Mission and SDG 9 (Industry, Innovation, Infrastructure), promoting affordable, universal internet access.

Institutional and Regulatory Framework

• Implemented by Department of Telecommunications (DoT) with policy enablement approach, minimising government intervention while encouraging private entrepreneurship.
• Central Registry maintained by C-DOT ensures interoperability, authentication and standardisation across PDOs, PDOAs and App Providers.
• TRAI tariff order (June 2025) mandates FTTH broadband up to 200 Mbps for PDOs at ≤2× residential tariffs, improving affordability and business viability.

Operational Mechanism

• PDOs (local shops) provide Wi-Fi access points using FTTH or broadband connections, while PDOAs manage aggregation, authentication and accounting functions.
• App Providers enable user registration, hotspot discovery and payments, ensuring seamless user interface and interoperability across networks.
• Roaming capability allows users to switch across PDOAs without repeated authentication, improving user experience and network efficiency.

Data and Evidence

• Over 4.09 lakh hotspots operational, indicating rapid infrastructure expansion under a decentralised model with minimal regulatory burden.
• 2.44 crore users and 58.64 petabytes data consumption highlight rising demand for low-cost public internet access.
• TRAI tariff reform ensures cost viability, while FTTH integration reduces infrastructure costs and enhances scalability of PDO operations.

Governance and Administrative Significance

• Promotes cooperative federalism with States leading hotspot deployment, supported by central policy framework and regulatory facilitation.
• Enhances last-mile digital governance by enabling access to e-services (UMANG, DigiLocker, DBT platforms) in underserved regions.
• Reduces regulatory burden through de-licensing, improving ease of doing business and fostering grassroots entrepreneurship.

Economic Implications

• Creates micro–entrepreneurship opportunities for small vendors (kirana stores, tea stalls), generating supplementary income streams without high capital investment.
• Enables telecom operators to offload mobile data traffic, improving spectrum efficiency and reducing network congestion costs.
• Supports digital economy growth by expanding user base for OTT, e-commerce, fintech and digital payments ecosystems.

Social Dimensions

• Bridges digital divide by providing affordable “sachet-sized” internet access, especially benefiting students, migrant workers and low-income households.
• Promotes digital literacy and inclusion, enabling access to online education, telemedicine and government services.
• Strengthens rural empowerment by integrating local communities into the digital ecosystem through decentralised connectivity models.

Technological Dimensions

• Encourages integration of existing home and business Wi-Fi networks into public infrastructure, optimising idle bandwidth utilisation.
• Enables interoperability, roaming and API-based integration, reflecting principles of open digital ecosystems.
• Supports data offloading from 4G/5G networks, enhancing overall telecom infrastructure efficiency and user experience.

Challenges and Limitations

• Uneven distribution of hotspots, with rural and remote regions lagging despite policy push, limiting universal digital inclusion.
• Low awareness and digital literacy among target users restrict utilisation of public Wi-Fi services.
• Revenue sustainability concerns for PDOs in low-demand areas, affecting long-term viability of the model.
• Cybersecurity and data privacy risks due to open public networks require robust safeguards and regulatory oversight.
• Dependence on reliable backhaul (FTTH) infrastructure limits expansion in poorly connected regions.

Way Forward

• Target rural expansion through viability gap funding, incentives and integration with BharatNet to ensure equitable distribution of hotspots.
• Enhance awareness campaigns and digital literacy programmes to increase adoption among underserved populations.
• Strengthen cybersecurity frameworks and user authentication systems to ensure safe public Wi-Fi usage.
• Promote integration with 5G ecosystem for seamless data offloading and improved network efficiency.
• Encourage innovation in business models (ads, data services, partnerships) to ensure financial sustainability for PDOs.
• Align PM-WANI with smart city initiatives, public infrastructure and transport hubs for wider accessibility.

Prelims Pointers

• PM-WANI launched in December 2020; based on de-licensing of public Wi-Fi networks.
• PDOs do not require license or registration fees.
• Central Registry maintained by C-DOT ensures interoperability.
• TRAI (2025) capped FTTH tariffs for PDOs at ≤2× residential rates.
• Four-tier architecture: PDO, PDOA, App Provider, Central Registry.