提孜那甫河流域融雪径流模拟及不确定性分析

doi:10.13278/j.cnki.jjuese.20180031

Abstract

Abstract: In order to improve the accuracy of the simulated daily streamflow by the snowmelt runoff model (SRM) and focus on the snowmelt runoff process in the typical watershed of cold and arid mountainous areas, the authors selected the Tizinafu River basin in Xinjiang as the study area, and conducted uncertainty analysis on the basis of streamflow calculated by SRM through adding base flow-data. Based on the Bayesian method, combined with the four commonly used base flow separation methods (digital filter,Kalinlin,BFI, and HYSEP(hydrograph separation program)methods), the parameter uncertainty analysis was carried out by using Markov chain Monte Carlo (MCMC) simulation, and the model performance was evaluated comprehensively. According to the results of the model evaluation, the model using Kalinlin base-flow data (SRM_K) has higher accuracy in both calibration and validation periods (the values of NSE (Nash-Sutcliffe efficiency coefficient)during model calibration and prediction periods are 0.866 and 0.721, respectively). MCMC method can identify model parameters very well, and can accurately obtain the posterior probability distribution of parameters, while TRMM data can describe the characteristics of precipitation in the study area when the related data is lacking or poorly representative.

Key words: snowmelt runoff model, base flow separation, Markov chain Monte Carlo, uncertainty

CLC Number:

TV121.6

Zhang Shuang, Zeng Xiankui, Wu Jichun. Snowmelt Runoff Simulation and Uncertainty Analysis in Tizinafu River Basin[J].Journal of Jilin University(Earth Science Edition), 2019, 49(5): 1415-1424.

References

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Snowmelt Runoff Simulation and Uncertainty Analysis in Tizinafu River Basin

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