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Multi-model Approach for Reducing Uncertainties in Rainfall-Runoff Models
In our changing world, there is a need for preventing hydrological extremes, studying impacts of severe changes in climate, operating water resources planning and management, etc. In this context, it is important to surge our capacity in forecasting the water availability, by improving the hydrological models. The recognition of models imperfections leads to the integration of uncertainty analysis into the modelling procedures. The present study promotes the concept of combining the estimated outputs of different rainfall-runoff models to produce an overall combined output to be used as an alternative to that obtained from a single individual model. The conceptual rainfall-runoff models used are MEDOR, GR4J and HBV. They are applied on Nahr Ibrahim watershed, which is a typical Lebanese coastal Mediterranean watershed. Several methods of combination are considered, namely the weighted average (WA), the neural network (NN) and the genetic algorithm (GA). Better discharge outputs have been generated by these methods. This study leads to an improvement in the hydrological modelling by increasing the performances of the models and the reliability of the results.
Multi-model Approach for Reducing Uncertainties in Rainfall-Runoff Models
In our changing world, there is a need for preventing hydrological extremes, studying impacts of severe changes in climate, operating water resources planning and management, etc. In this context, it is important to surge our capacity in forecasting the water availability, by improving the hydrological models. The recognition of models imperfections leads to the integration of uncertainty analysis into the modelling procedures. The present study promotes the concept of combining the estimated outputs of different rainfall-runoff models to produce an overall combined output to be used as an alternative to that obtained from a single individual model. The conceptual rainfall-runoff models used are MEDOR, GR4J and HBV. They are applied on Nahr Ibrahim watershed, which is a typical Lebanese coastal Mediterranean watershed. Several methods of combination are considered, namely the weighted average (WA), the neural network (NN) and the genetic algorithm (GA). Better discharge outputs have been generated by these methods. This study leads to an improvement in the hydrological modelling by increasing the performances of the models and the reliability of the results.
Multi-model Approach for Reducing Uncertainties in Rainfall-Runoff Models
Springer Water
Gourbesville, Philippe (editor) / Caignaert, Guy (editor) / Andraos, Cynthia (author) / Najem, Wajdi (author)
2020-07-26
13 pages
Article/Chapter (Book)
Electronic Resource
English
Outputs combination , Hydrological models , Weighted average , Neural network , Genetic algorithm , Mediterranean coastal watershed Engineering , Geoengineering, Foundations, Hydraulics , Hydrology/Water Resources , Computational Science and Engineering , Simulation and Modeling , Mathematics and Statistics
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