Presenter/Author Information

K. Bogner
B. Hingray
A. Musy

Keywords

data-based mechanistic modelling, transfer functions, snowmelt modelling, wavelet transformation

Start Date

1-7-2002 12:00 AM

Description

Although the inherent uncertainty associated with rainfall-runoff processes is well known, mostmathematical models of such systems are completely deterministic in nature. Stochastic modelling requiresthat the uncertainty, which is associated with both the model parameters and the stochastic inputs, shouldbe quantified in some manner as an inherent part of the modelling analysis. To achieve these objectives, aData-based mechanistic (DBM) modelling approach will be tested for the Jura lake system (Switzerland). InDBM modelling, the most parsimonious model structure is first inferred statistically from the available timeseries. State dependent non-linear dependencies can be identified objectively from the rainfall and runoff dataand will be used as the bases for the estimation of non-linear transfer function models of the rainfall-runoffprocesses. After this the model will be accepted if it can be interpreted in a physically meaningful, mechanisticmanner. Before this approach will be applied some preprocessing of the data has been done using Wavelettransformations. Furthermore a simplified snow-melt model has been applied in order to calculate equivalentrainfall. First results of this preprocessing and of the DBM modelling will be shown in this paper.

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Jul 1st, 12:00 AM

Data-based Mechanistic Modelling of Rainfall-Runoff Processes and Its Application in a Complex Hydrological Context

Although the inherent uncertainty associated with rainfall-runoff processes is well known, mostmathematical models of such systems are completely deterministic in nature. Stochastic modelling requiresthat the uncertainty, which is associated with both the model parameters and the stochastic inputs, shouldbe quantified in some manner as an inherent part of the modelling analysis. To achieve these objectives, aData-based mechanistic (DBM) modelling approach will be tested for the Jura lake system (Switzerland). InDBM modelling, the most parsimonious model structure is first inferred statistically from the available timeseries. State dependent non-linear dependencies can be identified objectively from the rainfall and runoff dataand will be used as the bases for the estimation of non-linear transfer function models of the rainfall-runoffprocesses. After this the model will be accepted if it can be interpreted in a physically meaningful, mechanisticmanner. Before this approach will be applied some preprocessing of the data has been done using Wavelettransformations. Furthermore a simplified snow-melt model has been applied in order to calculate equivalentrainfall. First results of this preprocessing and of the DBM modelling will be shown in this paper.