Drought is a prolonged period of abnormally low precipitation that results in water shortages affecting ecosystems, agriculture, and human populations across defined geographic regions. As a critical GIS use case, drought analysis leverages spatial data integration, remote sensing, and geospatial modeling to monitor, predict, and manage water scarcity at local, regional, and global scales.

GIS technology enables analysts to synthesize diverse datasets — including satellite-derived vegetation indices (NDVI), soil moisture readings, precipitation records, and hydrological boundaries — into dynamic spatial analysis workflows. Tools such as raster analysis, interpolation, and time-series mapping allow geospatial professionals to visualize drought progression, delineate affected areas, and quantify impacts on land use and infrastructure. Platforms like ArcGIS and QGIS support the creation of drought severity maps aligned with standardized indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI).

The practical benefits include enhanced early warning systems, optimized water resource allocation, informed agricultural planning, and evidence-based policy development. By transforming complex environmental datasets into actionable geospatial intelligence, GIS professionals play a pivotal role in building climate resilience and mitigating drought-related socioeconomic impacts.