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SIH 2026

Smart India Hackathon · sih.gov.in

226 statements
SoftwareMiscellaneousSIH26086

Hyperlocal Monsoon Onset & Break Prediction System (Block/Village Scale)

Ministry of Earth Sciences (MoES)

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Organisation
Ministry of Earth Sciences (MoES)
Department
National Centre for Medium Range Weather Forecasting (NCMRWF)
Category
Software
Theme
Miscellaneous
Submission deadline
20 September 20262026-09-20
Ideas submitted
0/500
Serial number
86
Data captured on
2026-08-23
Dataset link
No dataset link published
Contact info
No contact published
Youtube link
No video published
Problem brief

The Indian Summer Monsoon dictates the economic livelihood of millions of farmers, particularly during the Kharif sowing season. While macro-scale monsoon forecasts across large meteorological subdivisions have improved, Indian agriculture remains highly vulnerable to the unpredictable nature of intra-seasonal variations. Specifically, the exact dates of monsoon onset,prolonged dry spells (break-monsoon phases), and subsequent revival cycles vary drastically from one district to another. Standard regional forecasts lack the spatial granularity required for localized agricultural planning.If a farmer sows seeds during a false onset just before a major breakthrough pause, entire crops fail due to moisture stress, leading to crushing financial losses. The challenge is to build a hybrid predictive framework capable of delivering a 7-to-30-day probabilistic outlook of monsoon behavior at the Block and Panchayat (Village cluster) scale. The system must bridge the gap between global climate teleconnections and hyper-local weather outcomes. Participants should design a solution that ingests large-scale climate indices—such as the El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden-Julian Oscillation (MJO)—and downscales their signatures using advanced machine learning models to predict localized precipitation behavior, onset thresholds, and active/break durations.Develop a hybrid mathematical or machine learning model that pairs global planetary boundary conditions (ENSO, IOD, MJO phases) with regional atmospheric data to predict local rainfall anomalies. Generate dynamic, color-coded risk maps at the block/panchayat level illustrating the statistical probability percentage of monsoon onset, continuous dry spells (breaks), or heavy downpours 1 to 4 weeks in advance. Build an expert-system engine that translates rainfall probabilities into localized crop-specific agronomic advisories (e.g., advising farmers to delay sowing, prepare irrigation alternatives, or alter crop choices based on upcoming break phases). A mobile-optimized web application or automated SMS/WhatsApp API gateway that pushes clear,actionable text-based advisories in regional Indian languages directly to farmers and local agricultural extension officers.

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