Abstract
The devastating effects of drought on agriculture, water resources, and other socioeconomic activities have severe consequences on food security and water resource management. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. In this study, meteorological droughts over six coastal synoptic stations were investigated using three-month Standardized Precipitation Index (SPI). The dry seasons of November-December-January (NDJ), December-January-February (DJF), and January-February-March (JFM) were the focal seasons for the study. Trends of dry seasons SPIs were evaluated using seasonal Mann–Kendall test. The relationship between drought SPI and ocean-atmosphere climate indices and their predictive ability were assessed using Pearson correlation and Akaike Information Criterion (AIC) stepwise regression method to select best climate indices at lagged timestep that fit the SPI. The SPI exhibited moderate to severe drought during the dry seasons. Accra exhibited a significant increasing SPI trend in JFM, NDJ, and DJF seasons. Besides, Saltpond during DJF, Tema, and Axim in NDJ season showed significant increasing trend of SPI. In recent years, SPIs in dry seasons are increasing, an indication of weak drought intensity, and the catchment areas are becoming wetter in the traditional dry seasons. Direct (inverse) relationship was established between dry seasons SPIs and Atlantic (equatorial Pacific) ocean's climate indices. The significant climate indices modulating drought SPIs at different time lags are a combination of either Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, or AMO for a given station. The AIC stepwise regression model explained up to 48% of the variance in the drought SPI and indicates Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, and AMO have great potential for seasonal drought prediction over Coastal Ghana.
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