Climate-risk and early-warning system for agriculture and hazards

The problem

Early-warning services, agricultural advisory programmes and index-based insurance schemes all depend on the same thing: monitoring indicators such as temperature, rainfall or vegetation condition, and acting when they cross a defined line. When this is done with ad-hoc scripts and spreadsheets, triggers are hard to audit, alerts arrive late, and different teams end up working from different numbers.

What you get

• Documented indicator and threshold (or index) definitions, agreed with domain experts
• Automated pipelines for the required forecast, observation and satellite inputs
• A trigger and alerting engine with a clear, reproducible record of what was computed and when
• Map-based dashboards showing current status, recent history and areas at or near threshold
• Data services and exports for other systems and reports
• Deployment, operations documentation and user training

How it works

1. 1

   Define the triggers

   Work with scientists, programme staff and users to write down each indicator, its data source, its threshold or index formula, and who needs to know when it is crossed.

2. 2

   Build the data pipeline

   Automate ingestion and processing of the inputs, with QA/QC and checks for missing or late data.

3. 3

   Implement and test the logic

   Code the trigger and alert rules, test them against historical periods, and review the results with the domain experts before going live.

4. 4

   Interface and rollout

   Build the dashboard and alert outputs, demonstrate them to users, collect feedback and adjust.

5. 5

   Operate and hand over

   Deploy, monitor the first operational season, and hand over with documentation and training.

Tools

• Python (xarray, Pandas)
• Google Earth Engine
• PostgreSQL/PostGIS
• GeoServer
• NetCDF / GRIB processing (CDO, NCO, GDAL)
• Django REST Framework
• React / Next.js