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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-259
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
12 May 2017
Review status
This discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
Multiple Causes of Nonstationarity in the Weihe Annual Low Flow Series
Bin Xiong1, Lihua Xiong1, Jie Chen1, Chong-Yu Xu1,2, and Lingqi Li1 1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P.R. China
2Department of Geosciences, University of Oslo, P.O. Box 1022 Blindern, N-0315 Oslo, Norway
Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced a variety of non-stationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, a nonstationary framework of low-flow frequency analysis has been developed on basis of the Generalized Linear Model (GLM) to consider time-varying distribution parameters. In GLMs, the candidate explanatory variables to explain the time-varying parameters are comprised of the eight measuring indices of the climate and catchment conditions in low flow generation, i.e., total precipitation (P), mean frequency of precipitation events (λ), temperature (T), potential evapotranspiration (ET), climate aridity index (AIET), base-flow index (BFI), recession constant (K) and the recession-related aridity index (AIK). This framework was applied to the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China. Stepwise regression analysis was performed to obtain the best subset of those candidate explanatory variables for the final optimum model. The results show that the inter-annual variability in the variables of those selected best subsets plays an important role in modeling annual low flow series. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that AIK is of the highest relative importance among the best subset of eight candidates, followed by BFI and AIET. The incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to predict future occurrences of low-flow extremes in similar areas.

Citation: Xiong, B., Xiong, L., Chen, J., Xu, C.-Y., and Li, L.: Multiple Causes of Nonstationarity in the Weihe Annual Low Flow Series, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-259, in review, 2017.
Bin Xiong et al.
Bin Xiong et al.
Bin Xiong et al.

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Short summary
In changing environments, extreme low-flow events are expected to increase. Frequency analysis of low flow events considering the impacts of changing environments has attracted increasing attentions. This study developed a frequency analysis framework by applying eight indices to trace main causes of the change in the annual extreme low-flow events of the Weihe River. We showed that the fluctuation in annual low-flow series was affected by precipitation, air temperature and streamflow recession.
In changing environments, extreme low-flow events are expected to increase. Frequency analysis...
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