Short answer
A drought dataset should be chosen for the drought process being studied, the region, time scale, variables, spatial resolution, temporal resolution, uncertainty, and reproducibility requirements. Precipitation-only SPI analysis needs reliable precipitation; water-balance and impact analysis may require temperature, evapotranspiration, soil moisture, streamflow, vegetation, or projection data.
Major dataset types
| Dataset type | Examples | Best use | Main caution |
|---|---|---|---|
| Station observations | Rain gauges and weather stations | Local SPI and validation | Missing data and station changes |
| Gridded observations | CHIRPS, PRISM, Daymet, GridMET | Spatial drought monitoring | Resolution and interpolation uncertainty |
| Reanalysis | ERA5, ERA5-Land | Multi-variable climate diagnostics | Model-data blending and bias |
| Water-balance products | TerraClimate and derived PET datasets | SPEI-like or ecological screening | PET method sensitivity |
| Climate projections | CMIP6, CORDEX | Future drought scenarios | Not observations; uncertainty is large |
How to choose a drought dataset
Start with the decision or research question, then select data. A farm-level seasonal SPI analysis needs different inputs than a regional climate-change drought projection. Report the dataset name, version, download date, spatial method, temporal aggregation, units, baseline period, and any corrections.
How datasets relate to DMAP-AI
The current DMAP-AI Research Version browser workflow is designed around SPI-based drought analysis, severity and event summaries, wavelet diagnostics, JSON export, and AI-assisted interpretation. Broader multi-index workflows may require desktop software, external processing, or additional datasets before interpretation.
Frequently asked questions
What is the best drought dataset?
There is no universal best dataset. The best choice depends on the location, variable, drought type, time scale, and uncertainty tolerance.
Can I compare drought results from different datasets?
Yes, and it is often useful. Differences can reveal sensitivity to precipitation estimates, gridding, bias correction, or model assumptions.
Do projection datasets replace observations?
No. Projection datasets explore possible futures, while observations and reanalysis describe historical or current climate conditions.
Selected references
- World Meteorological Organization. Standardized Precipitation Index User Guide.
- Funk et al. CHIRPS precipitation dataset documentation.
- Hersbach et al. ERA5 reanalysis documentation.
- World Climate Research Programme. CMIP6 documentation.