Journal ArticleDOI
River flow forecasting through conceptual models part I — A discussion of principles☆
J.E. Nash,J.V. Sutcliffe +1 more
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TLDR
In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.About:
This article is published in Journal of Hydrology.The article was published on 1970-04-01. It has received 19601 citations till now. The article focuses on the topics: Conceptual model & Flood forecasting.read more
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Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments
TL;DR: In this article, the authors examine the role of complexity in hydrological models by studying the relation between the number of optimised parameters and model performance and conclude that an inadequate complexity typically results in model over-parameterization and parameter uncertainty.
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Dynamically dimensioned search algorithm for computationally efficient watershed model calibration
TL;DR: DDS performance is compared to the shuffled complex evolution (SCE) algorithm for multiple optimization test functions as well as real and synthetic SWAT2000 model automatic calibration formulations and results show DDS to be more efficient and effective than SCE.
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Operational Validation and Intercomparison of Different Types of Hydrological Models
TL;DR: A theoretical framework for model validation, based on the methodology originally proposed by Klemes, is presented and it is concluded that all models performed equally well when at least 1 year's data were available for calibration, while the distributed models performed marginally better for cases where no calibration was allowed.
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Automated Base Flow Separation and Recession Analysis Techniques
TL;DR: In this paper, an automated base flow separation technique using a digital filter has been tested against three other automated techniques and manual separation methods, and the results of this method were compared to manual estimates with an efficiency of 74 percent.
A comparison of performance of several artificial intelligence
TL;DR: Lin et al. as discussed by the authors developed a hydrological forecasting model based on past records, which is crucial to developing a water forecasting model. But the model is not suitable for forecasting the future.