Data Based Mechanistic rainfall-flow models for climate change simulations
2006
Romanowicz, R.J.,Lancaster University (United Kingdom). Environmental Centre
This paper presents the application of a Data Based Mechanistic (DBM) approach and Stochastic Transfer Function (STF) methods to rainfall-flow modelling, with special emphasis on low flows. We present two different models, one with nonlinearity applied to the input, and the second with nonlinearity on the output. In both cases, the application of stochastic methods of identification and estimation of model parameters allows for the evaluation of predictive uncertainty of the estimated flow. The first model introduces input nonlinearity in the form of effective rainfall. The second model applies the STF approach to log - transformed flow acting as a surrogate of water storage in the catchment. Each of the models has two modules: a groundwater storage module and a surface water module. Logarithmic transformation of the output introduces a bias towards low flows. In order to model also high flows, the fast flow component is transformed using the linear STF model. Both models are applied to a karstic catchment, The River Thet in the United Kingdom
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