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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A nonlinear regression model for weekly stream temperatures
TL;DR: In this paper, a regression model was developed for stream temperatures recorded over a 3-year period (1978-1980) at 584 U.S. Geological Survey (USGS) gaging stations in the contiguous United States.
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Application of a neural network technique to rainfall-runoff modelling
TL;DR: In this paper, a neural network technique was used for rainfall runoff modeling. But, the results suggest that the neural network shows considerable promise in the context of rainfall-runoff modelling but, like all such models, has variable results.
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Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives
TL;DR: A consistent framework for parameter estimation in distributed hydrological catchment modelling using automatic calibration is formulated, and the balanced Pareto optimum solution corresponding to a proposed balanced aggregated objective function is seen to provide a proper balance between the two objectives.
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Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
Gianluca Tramontana,Martin Jung,Christopher R. Schwalm,Kazuhito Ichii,Gustau Camps-Valls,Botond Ráduly,Markus Reichstein,M. Altaf Arain,Alessandro Cescatti,Gerard Kiely,Lutz Merbold,Penélope Serrano-Ortiz,Sven Sickert,Sebastian Wolf,Dario Papale +14 more
TL;DR: In this article, the authors performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines).
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Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model
TL;DR: In this paper, a conceptual rainfall-runoff model was applied to 95 catchments in the Rhine basin for the purpose of modeling the effect of land use change on the runoff, and an approach to calibrate the model by associating the model parameters with the physical catchment characteristics was implemented.