Regional variation of recession flow power‐law exponent |
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Authors: | Swagat Patnaik Basudev Biswal Dasika Nagesh Kumar Bellie Sivakumar |
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Affiliation: | 1. Department of Civil Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India;2. Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India;3. Department of Civil Engineering, Indian Institute of Science, Bangalore, India;4. School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, Australia;5. Department of Land, Air and Water Resources, University of California, Davis, CA, USA |
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Abstract: | Recession flows of a basin provide valuable information about its storage–discharge relationship as during recession periods discharge occurs due to depletion of storage. Storage–discharge analysis is generally performed by plotting ?dQ/dt against Q , where Q is discharge at time t . For most real world catchments, ?dQ/dt versus Q show a power‐law relationship of the type: ?dQ/dt = kQα . Because the coefficient k varies across recession events significantly, the exponent α needs to be computed separately for individual recession events. The median α can then be considered as the representative α for the basin. The question that arises here is what are the basin characteristics that influence the value of α ? Studies based on a small number of basins (up to 50 basins) reveal that α has good relationship with several basin characteristics. However, whether such a relationship is universal remains an important question, because a universal relationship would allow prediction of the value of α for any ungauged basin. To test this hypothesis, here, we study data collected from a relatively large number of basins (358 basins) in USA and examine the influence of 35 different physio‐climatic characteristics on α . We divide the basins into 2 groups based on their longitudes and test the relationship between α and basin characteristics separately for the two groups. The results indicate that α is not identically influenced by different basin characteristics for the two datasets. This may suggest that the power‐law exponent α of a region is determined by the way local physio‐climatic forces have shaped the landscape. |
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Keywords: | basin characteristics Brutsaert– Nieber analysis recession flows step‐wise multiple linear regression storage– discharge relationship |
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