ASSESSING THE EFFECTIVENESS OF SPI AND PDBM INDICES IN METEOROLOGICAL DROUGHTS DETECTION; A COMPARATIVE APPROACH.
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Copyright (c) 2023 International Journal of Advanced Academic Research
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This study conducts a detailed analysis of the comparative performance of two meteorological drought detection indices, namely the Standardized Precipitation Index (SPI) and the Percentage Deviation Below Mean Index (PDBM). The research focuses on a 60-year dataset (1956-2015) of annual rainfall records from meteorological stations in North-east Nigeria, provided by the Nigerian Meteorological Agency (NIMET) Oshodi. The stations utilized for this analysis include Nguru, Maiduguri, Bauchi, Yola, Gombe, and Jalingo. Rainfall data from these stations were aggregated and averaged to create a unified rainfall series for the sub-region. This combined dataset was subjected to SPI and PDBM analyses to comprehensively assess three key meteorological drought properties: frequency, duration, and magnitude, over a 12-month time scale. To illustrate the variation in drought magnitudes, SPI anomaly graphs were generated, and a bar graph was constructed to compare the performance of the two indices. The results revealed significant disparities between the two indices. Notably, SPI detected a larger number of drought events compared to PDBM, with 30 drought events identified by SPI as opposed to 17 by PDBM. SPI effectively detected various intensities of drought, including mild, moderate, severe, and extreme droughts, while PDBM primarily identified slight-intensity droughts. Furthermore, SPI captured drought events of higher magnitude, detecting four severe and three extreme droughts, while PDBM failed to identify any high-intensity droughts. These findings contribute to the existing body of knowledge on drought research, offering valuable insights for decision-makers and researchers. The study's conclusion highlights the superior performance of the SPI model in meteorological drought detection and recommends a review and enhancement of the PDBM model.