This paper is a review of physically-based rainfall interception modelling. Fifteen models were selected, representing distinct concepts of the interception process. Applications of these models to field data sets published before March 2008 are also analysed. We review the theoretical basis of the different models, and give an overview of the models’ characteristics. The review is designed to help with the decision on which model to apply to a specific data set. The most commonly applied models were found to be the original and sparse Gash models (69 cases) and the original and sparse Rutter models (42 cases). The remaining 11 models have received much less attention, but the contribution of the Mulder model should also be acknowledged. The review reveals the need for more modelling of deciduous forest, for progressively more sparse forest and for forest in regions with intensive storms and the consequent high rainfall rates. The present review also highlights drawbacks of previous model applications. Failure to validate models, the few comparative studies, and lack of consideration given to uncertainties in measurements and parameters are the most outstanding drawbacks. Finally, the uncertainties in model input data are rarely taken into account in rainfall interception modelling
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