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Regional variation of recession flow power‐law exponent
Author(s) -
Patnaik Swagat,
Biswal Basudev,
Nagesh Kumar Dasika,
Sivakumar Bellie
Publication year - 2018
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.11441
Subject(s) - structural basin , recession , exponent , power law , drainage basin , flow (mathematics) , geology , environmental science , hydrology (agriculture) , statistics , physics , mathematics , geography , geomorphology , mechanics , economics , geotechnical engineering , cartography , linguistics , philosophy , keynesian economics
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 = kQ α . 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.