Difference of rainfall-runoff models and effect on flood forecasting: A brief review

Abstract

Selecting a rainfall-runoff model for use in flood forecasting is not a direct decision and actually may contain the selection of more than one. There are a range of rainfall-runoff models for flow forecasting. They range in type from transfer function (empirical black box), through lumped conceptual to more physically-based distributed models. The rainfall-runoff models also are often accompanied by updating techniques for taking account of recent measurements of flow so as to improve the accuracy of model predictions in real-time. Against this variety of available modelling techniques, this study improved understanding of the most important and well known rainfall-runoff models for flood forecasting and highlighting their similarities and differences. Six models are selected in this study: the Probability Distributed Moisture (PDM) model, the Isolated Event Model (IEM), the US National Weather Service Sacramento model, the Grid Model, the Transfer Function (TF) model and the Physically Realisable Transfer Function (PRTF) model. The first three are conceptual soil moisture accounting models, with the Grid Model having a distributed formulation, whilst the TF and PRTF are “black box” time-series models. Also new model for the forecasting (e.g neural network (NN), fuzzy rule-based are reviewed. An important feature of the use of rainfall-runoff models in a real-time forecasting environment is the ability to integrate recent observations of flow in order to develop forecast performance. The available methods for forecast updating are reviewed with specific reference to state correction and error prediction techniques.