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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Suspended sediment dynamics in a large drainage basin: the River Rhine
TL;DR: In this paper, suspended sediment transport was studied in the River Rhine between Kaub and the German-German border and it was concluded that sediment depletion occurs during a hydrological year and during individual floods.
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Application of the coupled MIKE SHE/MIKE 11 modelling system to a lowland wet grassland in southeast England
TL;DR: In this article, a coupled MIKE SHE/MIKE 11 model was developed for a lowland wet grassland, the Elmley Marshes, in southeast England, and two consecutive 18-month periods were used for model calibration and validation which were based upon comparisons of observed and simulated groundwater depths and ditch water levels.
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Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique
TL;DR: In this article, the authors examined the potential of artificial neural networks (ANN) in estimating the actual crop evapotranspiration (ET) from limited climatic data, and employed radial-basis function (RBF) type ANN for computing the daily values of ET for rice crop.
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Estimation of rainfall elasticity of streamflow in Australia
TL;DR: In this paper, the rainfall elasticity of streamflow is defined as the proportional change in mean annual streamflow divided by the proportion of the change in the mean annual rainfall, which is a simple estimate of the sensitivity of long-term streamflow to changes in longterm rainfall and is particularly useful as an initial estimate of climate change impact.
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Suspended sediment load prediction of river systems: An artificial neural network approach
TL;DR: A multilayer perceptron (MLP) ANN with an error back propagation algorithm, using historical daily and weekly hydroclimatological data (precipitation P ( t ), current discharge Q ( t ), antecedent discharge Q( t −1), and antecient sediment load SL ( t − 1) ), is used to predict the suspended sediment load at the selected monitoring stations.