Drought hazard: better indicator and projections

[matamadio]

The drought frequency analysis is currently based on FAO Agricultural Stress Index. It is based on satellite observations of crop health since 1984, meaning there is no probabilistic modelling, just empirical data.

The current representation of drought hazard:

• Combines cropland and pasture land
• Shows 2 separate seasons
• Measure hazard as frequency of impact over two threshold values of affected land: one third (30%) and half (50%).

Example:

This is aligned with the approach used by FAO website.
However, it is not the most intuitive metric to explain; either we simplify how it is expressed, or elaborate it into a new, easier to understand index.

@stufraser1 always interested in your suggestions if you have any

[matamadio]

Added SPEI and scPDSI in the docs. Would be good to develop an automation to source and aggregate those at subnational level.

[artessen]

Data sources for the implementation of automatic retrieval:

• scPDSI: https://crudata.uea.ac.uk/cru/data/drought/
• SPEI: https://spei.csic.es/database.html

[matamadio]

Thanks. These are both 55km resolution, which might not be good enough for subnational evaluation.

CEDA has released Hydro-JULES: Global high-resolution drought datasets from 1981-2022 at 5 km resolution.

These are global scale high-resolution drought indices developed from a combination of precipitation and potential evapotranspiration datasets for the Hydro-JULES project. Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation estimates, Global Land Evaporation Amsterdam Model (GLEAM) and Bristol Hourly potential evapotranspiration (hPET) estimates were used. The drought index is developed using the Standardized Precipitation-Evapotranspiration Index (SPEI). These high-resolution global scale drought indices are available from 1981-2022 at a monthly and 5km spatial resolution. The SPEI indices are available from 1-48 months. The datasets provide valuable information for the study and analysis of droughts at much higher resolution from global to local scale.

The data can be sub-setted directly from their repo via API, which could be leveraged by automation.