Ticket Contents

Description

Downscaled climate projections provide high-resolution (~5–10km) datasets for analyzing climate impacts on agriculture, forests, health, and hazard patterns. By computing metrics such as max/mean rainfall, drought incidence, extreme rainfall, flood incidence, max/mean temperature, and heatwave frequency, stakeholders can assess spatial and temporal trends over the years. Google Earth Engine (GEE) will be used to generate raster outputs, vectorize them at MWS-level, and produce reports for analysis.

Goals

Goals

• Implement downscaled climate projections for AoI and MWS at ~5–10km resolution.
• Compute climate indicators:
  • Max/mean rainfall
  • Drought incidence
  • Extreme rainfall
  • Flood incidence
  • Max/mean temperature
  • Heatwave incidence
• Generate raster outputs for each indicator.
• Vectorize rasters to produce MWS-level polygons summarizing metrics.
• Publish raster and vector outputs as Earth Engine assets with metadata.
• Enable temporal and spatial analysis of climate patterns over multiple years.
• Produce MWS-level reports highlighting trends, changes, and hazard zones.

Expected Outcome

Expected Output

• Raster datasets (~5–10km resolution) for all climate indicators listed.
• Vectorized MWS-level polygons with attributes:
  • Indicator name
  • Metric value (e.g., total rainfall, max temperature, drought frequency)
  • Area (km²)
• Published Earth Engine assets (raster + vector) with metadata (source, resolution, processing date).
• GEE visualizations showing spatial distribution of climate indicators.
• Annual/seasonal reports summarizing trends and hazards.
• Validation report confirming coverage, accuracy, and consistency over years.

Acceptance Criteria

Acceptance Criteria

Data Acquisition

• Downscaled climate datasets (e.g., CORDEX, CHIRPS, ERA5-Land) must be preprocessed and clipped to AoI/MWS boundaries.
• Resolution standardized to ~5–10km.
• Temporal range documented for annual/seasonal projections.

Raster Computation

• Compute all climate indicators per pixel.
• Entire AoI/MWS must be covered without gaps.
• Thresholds and methodologies (e.g., drought index, heatwave criteria) must be documented.

Vectorization

• Raster outputs converted to MWS-level polygons using reduceToVectors() in GEE.
• Each polygon must include:
  • Indicator
  • Metric value
  • Area (km²)
• Polygons must align with MWS boundaries.

Asset Publishing

• Raster and vector datasets published as Earth Engine assets.
• Metadata includes source datasets, resolution, processing date, and methodology for each indicator.

Quality & Validation

• Coverage check: all AoI/MWS included.
• Accuracy check: raster outputs validated against historical datasets or reference models.
• Attribute check: all polygons include indicator name, metric value, and area.
• Visualization in GEE confirms correct spatial distribution.
• Annual/seasonal trends verified for consistency and plausibility.

Implementation Details

Implementation Details

Data Sources

• Downscaled climate projections (CORDEX, CHIRPS, ERA5-Land)
• AoI and MWS boundary shapefiles

Processing

• Compute raster layers for each indicator at ~5–10km resolution.
• Apply relevant indices for drought, heatwave, and flood detection.
• Clip outputs to AoI/MWS boundaries.

Vectorization & Publishing

• Convert raster outputs into MWS-level polygons using reduceToVectors().
• Include attributes: indicator, metric value, area.
• Upload raster and vector layers as EE assets with metadata.

Visualization

• Color-coded raster and vector layers in GEE for each climate indicator.
• Overlay with MWS boundaries for hazard and stress assessment.

Validation

• Compare raster outputs with historical data and reference datasets.
• Spot-check vector polygons for correct attribute values.
• Generate validation report documenting coverage, accuracy, and attribute completeness.

Mockups/Wireframes

No response

Product Name

KYL

Organisation Name

C4GT

Domain

No response

Tech Skills Needed

Python

Organizational Mentor

@ankit-work7 @amanodt @kapildadheech

Angel Mentor

No response

Complexity

Medium

Category

Backend