Drought Intelligence Explorer: a 45-year district-level dataset of ten drought indicators for India and an open analytical platform

Publicly available drought records for India rarely combine sub-national detail, multi-decadal coverage, several indicator families and a documented processing chain in one citable product. Here we present a monthly, district-level dataset of ten drought indicators covering all of India from January 1981 to December 2025, together with the open, browser-based platform that serves it. Four of the indicators are standardized z-score indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), each at 1-, 3-, 6- and 12-month accumulation windows. The other two are percentage-based indicators: a root-zone Soil Moisture Anomaly (SMA) and the Vegetation Condition Index (VCI). The dataset covers 724 administrative districts in 36 States and Union Territories and holds 3{,}715{,}048 valid district-month values. The indicators come from CHIRPS v2.0 precipitation, ERA5-Land potential evaporation and root-zone soil moisture, and MODIS MOD13A3 NDVI, extracted and aggregated by a scripted Google Earth Engine pipeline. SPI, SPEI and SMA use a fixed 1981--2020 reference period (40 years); VCI uses 2000 to the release date. Missing observations are never imputed. As a scientific consistency check, the released record reproduces documented drought episodes with the expected geography and timing (for example, every Rajasthan district is flagged in July to September 2002, and the vegetation index collapses two to three months after the 2015 Marathwada rainfall deficit). Software quality is guarded by per-fit calibration gates, a checksummed release manifest and an 82-assertion offline self-test. Every value is reproducible from public inputs and documented commands, citable through a versioned DOI (10.5281/zenodo.24113364), and analysable and exportable through the platform without an account or backend. The product is a transparent research instrument, not an operational drought declaration.

Authors

Publication Details

Published
2026-11-11
DOI
https://doi.org/10.31223/x5f52x
Primary Topic
Hydrology and Drought Analysis
Type
preprint
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preprint

Drought Intelligence Explorer: a 45-year district-level dataset of ten drought indicators for India and an open analytical platform

Ankit Deshmukh
Hydrology and Drought Analysis
preprint

Drought Intelligence Explorer: a 45-year district-level dataset of ten drought indicators for India and an open analytical platform

Ankit Deshmukh
preprint en

Abstract

Publicly available drought records for India rarely combine sub-national detail, multi-decadal coverage, several indicator families and a documented processing chain in one citable product. Here we present a monthly, district-level dataset of ten drought indicators covering all of India from January 1981 to December 2025, together with the open, browser-based platform that serves it. Four of the indicators are standardized z-score indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), each at 1-, 3-, 6- and 12-month accumulation windows. The other two are percentage-based indicators: a root-zone Soil Moisture Anomaly (SMA) and the Vegetation Condition Index (VCI). The dataset covers 724 administrative districts in 36 States and Union Territories and holds 3{,}715{,}048 valid district-month values. The indicators come from CHIRPS v2.0 precipitation, ERA5-Land potential evaporation and root-zone soil moisture, and MODIS MOD13A3 NDVI, extracted and aggregated by a scripted Google Earth Engine pipeline. SPI, SPEI and SMA use a fixed 1981--2020 reference period (40 years); VCI uses 2000 to the release date. Missing observations are never imputed. As a scientific consistency check, the released record reproduces documented drought episodes with the expected geography and timing (for example, every Rajasthan district is flagged in July to September 2002, and the vegetation index collapses two to three months after the 2015 Marathwada rainfall deficit). Software quality is guarded by per-fit calibration gates, a checksummed release manifest and an 82-assertion offline self-test. Every value is reproducible from public inputs and documented commands, citable through a versioned DOI (10.5281/zenodo.24113364), and analysable and exportable through the platform without an account or backend. The product is a transparent research instrument, not an operational drought declaration.

Hydrology and Drought Analysis
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