Long-Term Periodic Modeling in Hydrology: Role of Sunspot Cycles

Abstract

Solar irradiation is the dominant source of energy driving hydrologic phenomena on the Earth. This study is a step toward investigating the statistical significance of the relationship between hydrologic data and persistent solar cycles, thereby expanding our knowledge of developing long-term periodic hydrologic models. The task was performed by smoothing the historical monthly mean sunspot data from January 1749 to December 2014, using a discrete Fourier model with a logistic distribution filter capable of removing low amplitude harmonics in the frequency domain, in order to reveal the essential cycles and detect the fundamental solar period. The fundamental period is exploited to promote a Fourier model for generating sunspot forecasts that can be devoted to monitoring, on average, the variation pattern of hydrologic data beyond their records. The results showed that the sunspot pattern can reasonably be synthesized by considering a single fundamental period equal to 2,532 months corresponding to de Vries cycle. A case study of monthly rainfall data was used to develop a sinusoidal model triggering typical solar periodicities of Schwabe, Hale, Gleissberg, and de Vries cycles. The model was able to capture the current long-scale rainfall pattern resulted from hidden periodicities that would only be revealed if a wider range of historical rainfall data was available.