Abstract
Hydrological conditions are changed continuously and these phenomenons generate errors on flood forecasting models and will lead to get unrealistic results. Therefore, to overcome these difficulties, a concept called model updating is proposed in hydrological studies. Real-time model updating is one of the challenging processes in hydrological sciences and has not been entirely solved due to lack of knowledge about the future state of the catchment under study. Basically, in terms of flood forecasting process, errors propagated from the rainfall-runoff model are enumerated as the main source of uncertainty in the forecasting model. Hence, to dominate the exciting errors, several methods have been proposed by researchers to update the rainfall-runoff models such as parameter updating, model state updating, and correction on input data. The current study focuses on investigations about the ability of rainfall-runoff model parameters to cope with three types of existing errors, timing, shape and volume as the common errors in hydrological modelling. The new lumped model, the ERM model, has been selected for this study to evaluate its parameters for its use in model updating to cope with the stated errors. Investigation about ten events proves that the ERM model parameters can be updated to cope with the errors without the need to recalibrate the model.
Highlights
Land use/land cover change and climate change have significant influences on catchment hydrological characteristics
The current study focuses on investigations about the ability of rainfall-runoff model parameters to cope with three types of existing errors, timing, shape and volume as the common errors in hydrological modelling
The previous sections described the potential errors in rainfall-runoff models and the simulated hydrograph linked to the hydrological changes for model updating
Summary
Land use/land cover change and climate change have significant influences on catchment hydrological characteristics The appearance of these phenomena has potential effects on generating unusual flood events and may lead to produce various types of flooding. The current study discusses about the second category to evaluate the ERM model’s adaptive performance in terms of real-time flood forecasting. Due to the reduced number of calibrated parameters in terms of model updating, the ERM model with one routing component (eight parameters) has been selected. The model updating is carried out just by changing these parameters and the rest of parameters should be kept on their optimum levels In this way, if the parameters update the model properly, there is no need to recalibrate the model which is not an easy process especially in real-time flood forecasting
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