Regional variation of recession flow power-law exponent

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 vs 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, since 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 two 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

Effect of catchment characteristics on the relationship between past discharge and the power law recession coefficient

This study concerns the relationship between the power law recession coefficient k (in −dQ/dt = kQα, Q being discharge at the basin outlet) and past average discharge QN (where N is the temporal distance from the center of the selected time span in the past to the recession peak), which serves as a proxy for past storage state of the basin. The strength of the k–QN relationship is characterized by the coefficient of determination R2N, which is expected to indicate the basin’s ability to hold water for N days. The main objective of this study is to examine how R2N value of a basin is related with its physical characteristics. For this purpose, we use streamflow data from 358 basins in the United States and selected 18 physical parameters for each basin. First, we transform the physical parameters into mutually independent principal components. Then we employ multiple linear regression method to construct a model of R2N in terms of the principal components. Furthermore, we employ step-wise multiple linear regression method to identify the dominant catchment characteristics that influence R2N and their directions of influence. Our results indicate that R2N is appreciably related to catchment characteristics. Particularly, it is noteworthy that the coefficient of determination of the relationship between R2N and the catchment characteristics is 0.643 for N = 45. We found that topographical characteristics of a basin are the most dominant factors in controlling the value of R2N. Our results may be suggesting that it is possible to tell about the water holding capacity of a basin by just knowing about a few of its physical characteristics