A major goal in hydrological modelling is to identify and quantify different sources of uncertainty in the modelling process. This paper analyses the structural uncertainty in a streamflow modelling system by investigating a set of models with increasing model structure complexity. The models are applied to two basins: Kielstau in Germany and XitaoXi in China. The results show that the model st...

The application of artificial neural network (ANN) methodology for modelling daily flows during monsoon flood events for a large size catchment of the Narmada River in Madhya Pradesh (India) is presented. The spatial variation of rainfall is accounted for by subdividing the catchment and treating the average rainfall of each subcatchment as a parallel and separate lumped input to the model. A l...

Modelling rainfall-runoff processes in urban catchments has become an increasingly relevant issue, for instance to estimate the risk associated with urban flooding in densely populated areas. This needs hydrological models which, besides rainfall information, require good input data of detailed surface characteristics, such as imperviousness to accurately predict rainfall-runoff. One way to obt...

Current soil erosion and reservoir sedimentation modelling at the meso-scale is still faced with intrinsic problems with regard to open scaling questions, data demand, computational efficiency and deficient implementations of retention and re-mobilisation processes for the river and reservoir networks. To overcome some limitations of current modelling approaches, the semi-process-based, spatial...

Over the last decades or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall-runoff processes. In most studies, ANNs have been demonstrated to show superior result compared to the traditional modelling approaches. They are able to map underlying relationships between input and output data without detailed knowle...

Many existing hydrological modelling procedures do not make best use of available information, resulting in non-minimal uncertainties in model structure and parameters, and a lack of detailed information regarding model behaviour. A framework is required that balances the level of model complexity supported by the available data with the level of performance suitable for the desired application...