Abstract
River network patterns are important components of basin geomorphology. The geomorphological complexity of river networks has an impact on various natural processes and hazardous events. In order to find those correlations and establish quantitative relationships, a comprehensive textural examination on river network patterns is crucial. In this study, the fractal geometry approach is applied to analyse river network by the means of their geometric distinctiveness. Since river networks are fractal objects formed by repetition of certain natural processes over a long period of time, fractal geometry provides the most sensitive method to analyse their complex branching structure. Five river networks from Kelani river basin (1. Ambalanpiti oya, 2. Gurugoda oya, 3. Pugoda oya, 4. Pusweli oya, 5. Wakoya) are subjected to fractal analysis. Fixed-size box counting algorithm is applied to obtain fractal measures. The recursive algorithm is applied to the same river network twice; allocating weighted-lines for different orders and then allocating non-weighted equal width line for all tributaries, to test the best suitable option to model their geomorphological complexity. Non-plane-filling behaviour of river networks is confirmed by present values which are greater than 1 and lesser than 2. The largest fractal dimension value is for Gurugodaoya tributary, confirming that it has the most complex geomorphologic pattern. Smallest value is from Pugodaoya concluding the least geomorphological complexity. Multifractal spectra f(α) are constructed for each river network and detailed investigation is required (considering lithological features of the basin) to link f(α) to the physical characteristics of river network.
Highlights
Computation of spatial patterns and their detailed analysis using nonlinear approach is growing rapidly in many fields including landscape ecology
The box-counting method was applied to obtain the fractal dimension, considering each river network as a mono-fractal (Eq.s (1) and (2)). ln N(ε) Vs. ln (ε) graphs were plotted for all fractal sets and the fitted lines were drawn on the same graph (Figure 8. / Figure 9. (A, B, C, D, E))
All the fitted lines were plotted in the same graph (Figure 8. (F)/ Figure 9. (F)) for the sake of clarify the fractality variation of river networks comparing to other networks
Summary
Computation of spatial patterns and their detailed analysis using nonlinear approach is growing rapidly in many fields including landscape ecology. Mandelbrot (1983) used empirical length-area power law relationship to imply that rivers are fractal objects. The major objective of this approach is to spot the diversities in natural patterns and study their causes. The linear analyses essentially focus on the secondary parameters (contributing drainage area, channel length, channel slope, elevation, and etc.) and completely overlook the fractal behaviour of drainage network. Typical linear approach uses the slopearea correlation which generates the same results for dissimilar causative effects. A fractal measure is a powerful tool for patterns with dissimilar space filling characteristics. Fractal analysis probes the linearization, heterogeneity and connectivity of the drainage patterns (Mahmood et al, 2011)
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