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Journal ArticleDOI

River flow forecasting through conceptual models part I — A discussion of principles☆

J.E. Nash, +1 more
- 01 Apr 1970 - 
- Vol. 10, Iss: 3, pp 282-290
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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.
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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.

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Glacier melt: a review of processes and their modelling

TL;DR: In this article, the relevant processes at the surface-atmosphere interface, and their representation in melt models are discussed, and a recent trend towards modelling with both high temporal and spatial resolution, the latter accomplished by fully distributed models.
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Regionalisation of catchment model parameters

TL;DR: In this article, the authors simulate the water balance dynamics of 308 catchments in Austria using a lumped conceptual model involving 11 calibration parameters for two non-overlapping 11-year periods of daily runoff data and compare regionalisation methods for estimating the model parameters in ungauged catchments, in terms of the model performance.
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The role of topography on catchment-scale water residence time

TL;DR: In this article, the authors determine the dominant physical controls on catchment-scale water residence time and specifically test the hypothesis that residence time is related to the size of the basin Residence times were estimated by simple convolution models that described the transfer of precipitation isotopic composition to the stream network.
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Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments

TL;DR: In this paper, the authors proposed to use a combination of graphical results, absolute value error statistics (i.e. root mean square error), and normalized goodness-of-fit statistics (e.g. Nash-Sutcliffe Efficiency coefficient, NSE) for quantifying the goodness of observations against model-calculated values.
Journal ArticleDOI

Fitting and interpretation of sediment rating curves

TL;DR: In this paper, the authors derived sediment rating curves in the form of a power function for several locations along the river Rhine and its main tributaries, using different fitting procedures.
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