Nonlinear Trend and Multiscale Variability of Dry Spells in Senegal (1951–2010)

Abstract

Dry spells occurring during the rainy season have significant implications for agricultural productivity and socioeconomic development, particularly in rainfed agricultural countries such as Senegal. This study employs various chaos-theory-based tools, including the lacunarity method, rescaled analysis, and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, to investigate the distribution, predictability, and multiscale properties of the annual series of maximum dry spell length (AMDSL) in Senegal during the rainy season. The analysis focuses on 29 stations across Senegal, spanning the period from 1951 to 2010. The findings reveal persistent behavior in the AMDSL across nearly all stations, indicating that predictive models based on extrapolating past time trends could enhance AMDSL forecasting. Furthermore, a well-defined spatial distribution of the lacunarity exponent β is observed, which exhibits a discernible relationship with rainfall patterns in Senegal. Notably, the lacunarity exponent displays a south-to-north gradient for all thresholds, suggesting its potential for distinguishing between different drought regimes and zones while aiding in the understanding of spatiotemporal rainfall variability patterns. Moreover, the analysis identifies five significant intrinsic mode functions (IMFs) characterized by different periods, including interannual, interdecadal, and multidecadal oscillations. These IMFs, along with a nonlinear trend, are identified as the driving forces behind AMDSL variations in Senegal. Among the inter-annual oscillations, a 3-year quasi-period emerges as the primary contributor and main component influencing AMDSL variability. Additionally, four distinct morphological types of nonlinear trends in AMDSL variations are identified, with increasing–decreasing and increasing trends being the most prevalent. These findings contribute to a better understanding of the variability in annual maximum dry spell lengths, particularly in the context of climate change, and provide valuable insights for improving AMDSL forecasting. Overall, this study enhances our comprehension of the complex dynamics underlying dry spell occurrences during the rainy season and presents potential avenues for predicting and managing the AMDSL in Senegal.