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Drought, a devastating disaster, significantly impacts tropical regions, particularly the Sahel and arid zones. This study focuses on drought occurrences in North-Central Nigeria, aiming to evaluate rainfall variability and drought events using a geospatial approach. The study’s objectives include analysing the spatiotemporal patterns of rainfall variability with remote sensing and GIS techniques, detecting spatiotemporal drought events, examining the relationship between rainfall variability and drought using statistical and geospatial modelling, and mapping drought-prone areas through remote sensing and GIS technologies. Data utilized spans a 30-year period (2002, 2012, and 2022) and includes meteorological records (monthly mean temperature, precipitation, evaporation, and evapotranspiration) sourced from the Nigerian Meteorological Agency (NiMET), NASA, and USGS. Additional inputs include ASTER DEM, Landsat 8 (OLI) imagery, land use/cover, and Normalized Difference Vegetation Index (NDVI) derived from Landsat 8. Analytical methods employed include descriptive statistics, correlation, and Mann–Kendall trend tests. To detect drought incidences, the Standardized Precipitation Index (SPI) was applied, which involves transforming rainfall data into a normalized distribution using a gamma function. This comprehensive geospatial approach enables a better understanding of drought patterns, supporting improved management and mitigation strategies in North-Central Nigeria.

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Geospatial Assessment of Rainfall Variability and Drought Occurrences in North-Central Nigeria

  • 章节

  • 发表日期：

    2026年

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  • Usman Salisu Lay,
  • Joy Orevaoghene Nwaezeigwe,
  • Vivian Aaron-Ibrahim

摘要

Drought, a devastating disaster, significantly impacts tropical regions, particularly the Sahel and arid zones. This study focuses on drought occurrences in North-Central Nigeria, aiming to evaluate rainfall variability and drought events using a geospatial approach. The study’s objectives include analysing the spatiotemporal patterns of rainfall variability with remote sensing and GIS techniques, detecting spatiotemporal drought events, examining the relationship between rainfall variability and drought using statistical and geospatial modelling, and mapping drought-prone areas through remote sensing and GIS technologies. Data utilized spans a 30-year period (2002, 2012, and 2022) and includes meteorological records (monthly mean temperature, precipitation, evaporation, and evapotranspiration) sourced from the Nigerian Meteorological Agency (NiMET), NASA, and USGS. Additional inputs include ASTER DEM, Landsat 8 (OLI) imagery, land use/cover, and Normalized Difference Vegetation Index (NDVI) derived from Landsat 8. Analytical methods employed include descriptive statistics, correlation, and Mann–Kendall trend tests. To detect drought incidences, the Standardized Precipitation Index (SPI) was applied, which involves transforming rainfall data into a normalized distribution using a gamma function. This comprehensive geospatial approach enables a better understanding of drought patterns, supporting improved management and mitigation strategies in North-Central Nigeria.