An improved multifractal detrended fluctuation analysis method for estimating the dynamic complexity of electrical conductivity of karst springs

Abstract

Abstract Due to the influences of geological structures, many confined ascending springs occur in North China's karst area. The electrical conductivity (EC) of karst spring flow is a fundamental variable in characterizing karst systems. However, deeply exploring hidden nonlinear dynamic characteristics is challenging. To avoid overreliance on the fitting polynomial order in the detrending process via classic multifractal detrended fluctuation analysis (MFDFA), the intrinsic time-scale decomposition (ITD) method was applied to identify the external data trend term by decomposing the original data into different frequency modes, and the ITD-MFFA method was proposed to reveal the formation mechanism of spring EC complex characteristics in Jinan's spring area. The results showed that the EC sequences of North China's karst springs are characterized by multifractal behavior and anti-persistence and exhibit multiyear complexity with an overall decreasing trend. Different recharge sources, formation conditions, and seasonal precipitation could be the primary factors driving spring EC complexity. Compared to those of the traditional MFDFA method, the ITD-MFFA method achieved improved anti-interference ability and stability. Timely determination of spring EC data dynamic complexity and monitoring future spring water quality trends can provide guiding significance for protecting karst springs.