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- W358462613 abstract "Although the inherent uncertainty associated with rainfall-runoff processes is well known, most mathematical models of such systems are completely deterministic in nature. Stochastic modelling requires that the uncertainty, which is associated with both the model parameters and the stochastic inputs, should be quantified in some manner as an inherent part of the modelling analysis. To achieve these objectives, a Data-based mechanistic (DBM) modelling approach will be tested for the Jura lake system (Switzerland). In DBM modelling, the most parsimonious model structure is first inferred statistically from the available time series. State dependent non-linear dependencies can be identified objectively from the rainfall and runoff data and will be used as the bases for the estimation of non-linear transfer function models of the rainfall-runoff processes. After this the model will be accepted if it can be interpreted in a physically meaningful, mechanistic manner. Before this approach will be applied some preprocessing of the data has been done using Wavelet transformations. Furthermore a simplified snow-melt model has been applied in order to calculate equivalent rainfall. First results of this preprocessing and of the DBM modelling will be shown in this paper." @default.
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- W358462613 date "2002-01-01" @default.
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- W358462613 title "Data-based mechanistic modeling of rainfall-runoff processes and its application in a complex hydrological context" @default.
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