Short answer
CHIRPS is useful for drought analysis when precipitation is the main variable of interest, especially in data-sparse regions. It provides quasi-global rainfall estimates suitable for SPI, rainfall anomaly, and seasonal drought monitoring. Because it is rainfall-only, it should be combined with temperature, evapotranspiration, soil moisture, or impact data when water demand is important.
Dataset overview
CHIRPS stands for Climate Hazards Group InfraRed Precipitation with Station data. It is designed to support climate monitoring in regions where rain gauges may be limited or unevenly distributed. Its long record and precipitation focus make it attractive for drought monitoring, food-security analysis, and early warning.
| Question | Practical answer |
|---|---|
| Best use | Precipitation-based drought monitoring in data-sparse regions |
| Main strength | Rainfall-focused, long-record, satellite-gauge product |
| Main caution | Rainfall only; no direct temperature or evaporative-demand variables |
Drought-relevant variables
CHIRPS provides precipitation estimates. This makes it well suited for precipitation anomalies, SPI, seasonal rainfall deficits, and dry-spell analysis. It does not directly provide temperature, radiation, wind, evapotranspiration, or soil moisture, so those variables must come from other datasets if needed.
Strengths for drought analysis
The main strengths are rainfall focus, long record, quasi-global coverage in lower and middle latitudes, and usefulness in data-sparse regions. CHIRPS is often a strong choice where precipitation monitoring is central and local gauges are limited.
Limitations and cautions
CHIRPS is not a complete meteorological dataset and does not directly represent temperature-driven evaporative demand. Satellite-based rainfall estimates can have difficulty with complex terrain, snow, coastal zones, and convective extremes. Validation with local gauges is recommended when possible.
For scientific reporting, document the data version, extraction date, variable names, units, temporal aggregation, spatial method, baseline period, and any quality-control or bias-correction steps.
Recommended workflow
- Define the drought question and required time scale.
- Select variables and confirm units before calculation.
- Aggregate data to the required daily, monthly, seasonal, or annual period.
- Choose a baseline period and calculate the drought index or anomaly.
- Validate against local observations or an independent dataset when possible.
- Report uncertainty and avoid over-interpreting one data source.
How DMAP-AI can use this dataset
DMAP-AI can use CHIRPS as a precipitation-focused data option for SPI analysis and rainfall anomaly analysis, especially for regions where station data are limited. It is particularly useful when the objective is historical precipitation drought rather than a full water-balance assessment.
Frequently asked questions
Can this dataset replace local station observations?
It can support analysis where stations are unavailable or incomplete, but local observations remain valuable for validation. For high-stakes local decisions, compare gridded results with station records whenever possible.
Can it be used for SPI?
Yes, if the dataset provides precipitation over a sufficiently long and consistent period. The SPI result depends on the data source, time scale, baseline period, and fitting method.
Should I use only one dataset?
For screening, one dataset may be acceptable. For research or decision support, comparing multiple datasets helps identify sensitivity to data choice.
Selected references
- Climate Hazards Center. CHIRPS documentation.
- Funk et al. (2015). The climate hazards infrared precipitation with stations dataset. Scientific Data.
- World Meteorological Organization. Standardized Precipitation Index User Guide.
- FEWS NET and climate hazards monitoring resources.