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Uncertainty in hydrograph separations based on geochemical mixing models
Institution:1. School of Animal Science, Yangtze University, Jingzhou 434020, China;2. Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou 515063, China;3. College of Marine and Biotechnology, Guangxi University for Nationalities, Nanning, Guangxi, China;4. Guangxi Colleges and Universities Key Laboratory of Utilization of Microbial and Botanical Resources, Guangxi University for Nationalities, China;1. Department of Geography, University of Zurich, Zurich, Switzerland;2. Université Grenoble-Alpes, CNRS, IRD, IGE, Grenoble INP, Grenoble, France;3. Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Abstract:A detailed uncertainty analysis of three-component mixing models based on the Haute–Mentue watershed (Switzerland) is presented. Two types of uncertainty are distinguished: the ‘model uncertainty’, which is affected by model assumptions, and the ‘statistical uncertainty’, which is due to temporal and spatial variability of chemical tracer concentrations of components. The statistical uncertainty is studied using a Monte Carlo procedure. The model uncertainty is investigated by the comparison of four different mixing models all based on the same tracers but considering for each component alternative hypotheses about their concentration and their spatio-temporal variability. This analysis indicates that despite the uncertainty, the flow sources, which generate the stream flow are clearly identified at the catchments scale by the application of the mixing model. However, the precision and the coherence of hydrograph separations can be improved by taking into account any available information about the temporal and spatial variability of component chemical concentrations.
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