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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Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.
TL;DR: EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting.
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Incorporating variable source area hydrology into a curve‐number‐based watershed model
Elliot M. Schneiderman,Tammo S. Steenhuis,Dominique Thongs,Zachary M. Easton,Mark S. Zion,Andrew L. Neal,Guillermo F. Mendoza,M. Todd Walter +7 more
TL;DR: In this paper, the Variable Source Loading Function (VSLF) model was proposed to simulate the watershed runoff response to rainfall using the standard SCS-CN equation, but spatially distributes the runoff response according to a soil wetness index.
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Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data
Kamel Soudani,Guerric Le Maire,Eric Dufrêne,C. François,Nicolas Delpierre,Erwin Ulrich,Sébastien Cecchini +6 more
TL;DR: In this paper, the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands.
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Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
TL;DR: Multiple regression and machine learning approaches are used to simulate monthly streamflow in five highly seasonal rivers in the highlands of Ethiopia and compare their performance in terms of predictive accuracy, error structure and bias, model interpretability, and uncertainty when faced with extreme climate conditions.
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Fuzzy computing based rainfall-runoff model for real time flood forecasting
TL;DR: In this paper, a model for forecasting the river flow of Narmada basin in India using fuzzy computing has been developed, and the most appropriate set of input variables was determined to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff.