首页|An improved method to estimate the rate of change of streamflow recession and basin synthetic recession parameters from hydrographs

An improved method to estimate the rate of change of streamflow recession and basin synthetic recession parameters from hydrographs

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Recession analysis of -dQ/dt similar to Q is popularly used for determining catchment storage-discharge relationship, estimating the aquifer hydraulic properties, and predicting low flow processes. However, there are uncertainties arising from the numerical approximation of -dQ/dt by the finite difference (Delta Q/Delta t) for individual recession and the fitting cloud-like data of -dQ/dt similar to Q for many recession events in a catchment. In this study, we proposed an improved variable time step increment method (IVTS) to minimize the effects of the noisy observations on estimating -dQ/dt, and a fitting method in terms of the binning-average of -dQ/dt similar to Q data points to determine the basin synthetic recession parameters. The proposed methods are validated by using numerical generation of recessions with different noises and recession behaviors and the observed hydrographs from twenty catchments in Huai River Basin in China. The results from our proposed method are compared with those from the other popularly used methods. The IVTS method is proven to be robust for approximation of -dQ/dt, which can significantly reduce errors of the estimated -dQ/dt and the recession slope (b). Combining with the IVTS method, the fitting method in terms of the binning average by splitting range of -dQ/dt, instead of Q, could overcome the shortage of traditional recession analysis methods that underestimate the recession slope. Our study indicates that the more accurate and reliable calculation of -dQ/dt similar to Q data and estimation of basin synthetic recession parameters could increase the objectivity in interpretation of recession behaviors and accuracy in perdition of low flow processes.

Recession analysisNumerical derivative-dQ/dtObservation noiseParameter estimationBasin scaleFLOWSTORAGECLIMATESTAGE

Gao, Man、Chen, Xi、Singh, Shailesh Kumar、Wei, Lingna

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Tianjin Univ

Natl Inst Water & Atmospher Res

Nanjing Univ Informat Sci & Technol

2022

Journal of Hydrology

Journal of Hydrology

EISCI
ISSN:0022-1694
年,卷(期):2022.604
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