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.
Highlights
Drought is a climate phenomenon on land where a given location experiences below normal precipitation. It can happen on a different timescale. e impacts of drought are visible in areas of agriculture, energy, and water resource management for both domestic and industrial use
In West Africa, the prolonged drought situation in the Sahel was connected to the influence of large ocean-atmosphere climate indices like El Niño Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), and Tropical Atlantic Oceanic indices [16]
The statistical analysis techniques were adopted. ey include computing three (3) months’ Standardized Precipitation Index (SPI) of rainfall, seasonal Mann-Kendall trend test, correlation analysis, and Akaike Information Criterion (AIC) [20] stepwise regression between the SPI and the climate indices. is paper is arranged as follows: Section 2 gives a brief description of the study area, Section 3 highlights the data and methods, Section 4 presents the results and discussions, and Section 5 is the conclusion
Summary
Drought is a climate phenomenon on land where a given location experiences below normal precipitation It can happen on a different timescale. Studies have shown the devastating impact of drought on West Africa most especially over the Sahel region [5, 6]. Ocean-atmosphere mechanism is a natural process that contributes to drought occurrence on spatiotemporal timescales [9]. In West Africa, the prolonged drought situation in the Sahel was connected to the influence of large ocean-atmosphere climate indices like ENSO, AMO, and Tropical Atlantic Oceanic indices [16]. Is hinders the effort of meteorologists or climate scientists in making projections on sub-seasonal to seasonal timescale To address this gap, this study seeks to investigate meteorological drought and its trends, ascertain the impact of remote global ocean climate indices on drought, and understand its predictability. The statistical analysis techniques were adopted. ey include computing three (3) months’ Standardized Precipitation Index (SPI) of rainfall, seasonal Mann-Kendall trend test, correlation analysis, and Akaike Information Criterion (AIC) [20] stepwise regression between the SPI and the climate indices. is paper is arranged as follows: Section 2 gives a brief description of the study area, Section 3 highlights the data and methods, Section 4 presents the results and discussions, and Section 5 is the conclusion
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