Hydro-Climate Symposium

Oral

396491 - Temporal Fractal Scaling Analysis of Groundwater Level Fluctuation in Confined Aquifers

Wednesday, June 6
4:00 PM - 5:30 PM
Location: Lake Superior B
Co-Authors: Ali Ercan, Davis,CA – University of California, Davis; M.Levent Kavvas, Davis, CA – University of California, Davis

Temporal fractal properties of groundwater level fluctuations of two confined aquifer wells (Well1 and Well2) with 70 and 80 years of daily data are analyzed. Detrended fluctuation analysis (DFA), Multifractal detrended fluctuation analysis (MF-DFA) and Multiscale Multifractal Analysis (MMA) were used to evaluate the Hurst exponent, monofractality, multifractality and multifractal behavior at different temporal scales. The DFA results showed that fractals exist in groundwater level fluctuation time series of both wells and that the Hurst exponent is closely dependent on the length and specific time interval of the time series. Persistent scaling pattern was found for all investigated time periods of Well1 and the scaling pattern fluctuates between anti-persistent and persistent regimes for Well2. Although the MF-DFA results showed that relatively high level of multifractality exists for both wells, a stronger multifractality was observed for the dataset of Well1 compared to Well2. Moreover, the MMA results confirmed the existence of multifractality and diverse correlations over different time scales. Furthermore, the underlying distribution of groundwater level fluctuations for Well1 represent mainly non-Gaussian long memory characteristics, which were fitted reasonably well by the Lévy stable distributions for various time periods. On the other hand, those of Well2 indicate mainly Gaussian characteristics, which sometimes fail to capture the peak values of the skewed distribution. Time series analysis of groundwater level fluctuations demonstrated that the observed fractal behavior is site-specific, and may further imply the need for generalized confined groundwater flow governing equations that can model both long-memory and Brownian finite-memory behavior.

Tongbi Tu

Graduate Student Researcher
University of California, Davis

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