Response of remote sensing-based climatic indicators to drought conditions during El Niño events and implications for water–food–energy nexus

Abstract

Hydrometeorological extremes, such as droughts, are a major threat to society and can have extensive damaging effects. In this study, daily rainfall estimates from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) quasi-global rainfall dataset were used to calculate the Standardised Precipitation Index (SPI) for the assessment of meteorological drought in Southern Province, Zambia. Normalised Difference Vegetation Index (NDVI) imagery (250 m resolution) from MODIS-Terra, for the period 2000–2021, were used to derive the Standardised Vegetation Index (SVI) in order to assess agricultural drought. The Mann–Kendall trend test and Sen’s slope were used to determine the spatial-temporal trends and their magnitudes. This study demonstrated that the droughts of the Southern Province of Zambia can be classified into two categories: regressive and aggressive droughts. Regressive droughts are associated with moderate to strong El Niño events. Although El Niño events undermine water security, regressive droughts tend to result in resilient vegetation owing to residue soil moisture. In contrast, aggressive droughts are characterised by an increase in drought intensity as the season progresses. Water security prospects in the region should focus on climate-smart approaches, such as managed aquifer recharge, to ensure water availability even under extreme drought conditions.