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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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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Hydrological modelling using artificial neural networks
TL;DR: A template is proposed in order to assist the construction of future ANN rainfall-runoff models and it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
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Automated methods for estimating baseflow and ground water recharge from streamflow records
Jeffrey G. Arnold,Peter M. Allen +1 more
TL;DR: In this article, the authors compared six sites located in the midwest and eastern United States where previous water balance observations had been made to computerized techniques to estimate: (1) base flow and (2) ground water recharge.
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Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm
George Kuczera,Eric Parent +1 more
TL;DR: The Metropolis algorithm provides a quantum advance in the capability to deal with parameter uncertainty in hydrologic models by using a random walk that adapts to the true probability distribution describing parameter uncertainty.
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Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach
TL;DR: This paper addresses the problem of evaluating the predictive uncertainty of TOPMODEL using the Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Ringelbach research catchment in the Vosges, France.
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Parameterisation, calibration and validation of distributed hydrological models
TL;DR: In this article, a case study based on the MIKE SHE code and the 440 km 2 Karup catchment in Denmark is presented, where the importance of a rigorous and purposeful parameterisation is emphasized in order to get as few free parameters as possible for which assessments through calibration are required.