This paper describes a new empirical watershed model, the prime feature of which is its parsimony. It involves only three free parameters, a characteristic unparalleled by continuous process models able to work on a wide array of catchments. In spite of its crude simplicity, it achieved, on average, worthwhile results on a set of 140 French catchments and overwhelmingly outperformed a linear model involving 16 parameters. It performed roughly as well as a conceptual model with five free parameters, derived from the well-known TOPMODEL.
GR3J: un modèle pluie-débit journalier à trois paramètres testé en FranceRésumé Cet article décrit un nouveau modèle dont la principale caractéristique est le faible nombre de paramètres, trois seulement, ce qui n'a jamais été réalisé dans le domaine des modèles capables de travailler en continu sur un large éventail de bassins. En dépit d'une très grande simplicité, ce modèle a donné en moyenne de bons résultats sur 140 bassins versants situés en France et s'est révélé beaucoup plus efficace qu'un modèle pseudo-linéaire comportant 16 paramètres. Il a donné des résultats peu différents de ceux obtenus avec un modèle conceptuel à cinq paramètres, version simplifiée de TOPMODEL.
Land use/cover change (LUCC) is one of the crucial factors influencing the hydrological process, thus the flood characteristics in time and space. Therefore the evaluation of the change of flood characteristics implies an integrated analysis of LUCC and hydraulic simulation. In this study, the effect of LUCC on flood is examined based on an approach composed of three parts: (1)
Given the substantial impacts that are expected due to climate change, it is crucial that accurate rainfall-runoff results are provided for various decision-making purposes. However, these modeling results often generate uncertainty or bias due to the imperfect character of individual models. In this paper, a genetic algorithm together with a Bayesian model averaging method are employed to provide a multi-model ensemble (MME) and combined runoff prediction under climate change scenarios produced from eight rainfall-runoff models for the Yellow River Basin. The results show that the multi-model ensemble method, especially the genetic algorithm method, can produce more reliable predictions than the other considered rainfall-runoff models. These results show that it is possible to reduce the uncertainty and thus improve the accuracy for future projections using different models because an MME approach evens out the bias involved in the individual model. For the study area, the final combined predictions reveal that less runoff is expected under most climatic scenarios, which will threaten water security of the basin.
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