Extreme Rainfall Events (Coarse Field Level @5km) #231
Description
Ticket Contents
Description
Mapping extreme rainfall events at coarse field level (~5km resolution) allows identification of agricultural and forest stress patterns. Temporal analysis can highlight changes over the years, supporting disaster management, crop monitoring, and forest health assessment. Implementation on Google Earth Engine (GEE) enables computation of rasters, filtering, and vectorization for MWS-level reporting.
Goals
Goals
- Identify extreme rainfall events using satellite precipitation datasets (e.g., CHIRPS, GPM) over AoI/MWS.
- Compute raster layers of extreme rainfall occurrence at ~5km resolution.
- Vectorize rasters to generate MWS-level polygons summarizing extreme rainfall metrics.
- Publish raster and vector outputs as Earth Engine assets with metadata.
- Enable temporal analysis of extreme rainfall events and their impact on agriculture and forests.
Expected Outcome
- Raster datasets (~5km resolution) indicating extreme rainfall events:
- Annual or seasonal maxima
- Frequency of extreme events
- Vectorized MWS-level polygons with attributes:
- Total extreme rainfall (mm)
- Frequency of events
- Area (km²)
- Published Earth Engine assets (raster + vector) with metadata.
- GEE visualizations showing spatial distribution of extreme rainfall.
- MWS report summarizing trends and changes over the years.
- Validation report confirming coverage, spatial accuracy, and attribute completeness.
Acceptance Criteria
Acceptance Criteria
Data Acquisition
- Input precipitation datasets (CHIRPS, GPM) must be preprocessed and clipped to AoI/MWS boundaries.
- Resolution standardized to ~5km.
- Temporal range documented for annual or seasonal analysis.
Raster Computation
- Compute extreme rainfall metrics per pixel (annual maxima, frequency of extreme events).
- Entire AoI/MWS must be covered without gaps.
- Extreme rainfall threshold criteria must be documented (e.g., >95th percentile).
Vectorization
- Raster outputs converted to MWS-level polygons using
reduceToVectors()in GEE. - Each polygon must include:
- Total extreme rainfall
- Frequency of events
- Area (km²)
- Polygons must align with MWS boundaries.
Asset Publishing
- Raster and vector datasets must be published as Earth Engine assets.
- Metadata must include source datasets, resolution, processing date, and thresholds used.
Quality & Validation
- Coverage check: all AoI/MWS included.
- Accuracy check: raster values match reference datasets or historical records.
- Attribute check: all polygons include total rainfall, frequency, and area.
- Visualization in GEE confirms correct spatial distribution of extreme rainfall.
Implementation Details
Implementation Details
Data Sources
- CHIRPS or GPM precipitation datasets.
- AoI and MWS boundary shapefiles.
Processing
- Compute annual/seasonal extreme rainfall events using percentile thresholds.
- Generate raster layers at ~5km resolution.
- Clip outputs to AoI/MWS boundaries.
Vectorization & Publishing
- Convert raster outputs to MWS-level polygons using
reduceToVectors(). - Include attributes: total extreme rainfall, frequency, area.
- Upload raster and vector layers as EE assets with metadata.
Visualization
- GEE visualization with color-coded raster and polygons showing extreme rainfall distribution.
- Overlay with MWS boundaries for stress assessment.
Validation
- Compare outputs with historical rainfall records or station data.
- 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
High
Category
Backend