Paper No. 05109 of the Journal of the American Water Resources Association (JAWRA) (Copyright © 2006). Discussions are open until June 1, 2007.
MULTIFRACTAL SCALING OF DAILY RUNOFF TIME SERIES IN AGRICULTURAL WATERSHEDS1
Article first published online: 10 AUG 2007
JAWRA Journal of the American Water Resources Association
Volume 42, Issue 6, pages 1659–1670, December 2006
How to Cite
Zhou, X., Persaud, N., Wang, H. and Lin, H. (2006), MULTIFRACTAL SCALING OF DAILY RUNOFF TIME SERIES IN AGRICULTURAL WATERSHEDS1. JAWRA Journal of the American Water Resources Association, 42: 1659–1670. doi: 10.1111/j.1752-1688.2006.tb06027.x
- Issue published online: 10 AUG 2007
- Article first published online: 10 AUG 2007
- spectral analysis;
- universal multifractal model;
- trace moment;
- exceedence probability distribution;
- time series analysis
Abstract: Multifractal scaling behavior of long-term records of daily runoff time series in 32 subwatersheds covering a wide range of sizes was examined. These subwatersheds were associated with four agricultural watersheds with different climates and topography. The empirical moment scaling curves obtained using the trace moment method showed that the runoff time series exhibited a multifractal behavior, which was valid over a time scale range from one day to about three years. The multi-fractal scaling of the runoff time series was well described by the Universal Multifractal Model. The spectral analysis (β < 1) and the order of fractional integration (H ⋍; 0) indicated that the runoff time series were conservative. The multifractal parameters, α (multifractal index) and C1 (co-dimension), were reasonably close to each other for subwatersheds within each of the watersheds and were generally similar among the four watersheds. The α values of the four watersheds were 1.10 ± 0.13, 1.61 ± 0.06,1.61 ± 0.24, and 1.63 ± 0.19. The C1 values of four watersheds were 0.19 ± 0.01, 0.17 ± 0.01, 0.17 ± 0.04, and 0.11 ± 0.02. The multifractal analyses provided useful insight into the runoff time series, especially the occurrence and distribution of extreme events.