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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An integrated modelling framework for regulated river systems
Wendy D. Welsh,Jai Vaze,Dushmanta Dutta,David Rassam,Joel Rahman,I. D. Jolly,Peter Wallbrink,Geoffrey M. Podger,Matthew Bethune,Matthew J. Hardy,Jin Teng,Julien Lerat +11 more
TL;DR: The Source IMS is an integrated modelling environment containing algorithms and approaches that allow defensible predictions of water flow and constituents from catchment sources to river outlets at the sea, designed and developed to underpin a wide range of water planning and management purposes.
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A blueprint for process‐based modeling of uncertain hydrological systems
TL;DR: In this article, a probability-based theoretical scheme for building process-based models of uncertain hydrological systems is presented, where uncertainty for the model output is assessed by estimating the related probability distribution via simulation.
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Distributed hydrological modelling in California semi-arid shrublands: MIKE SHE model calibration and uncertainty estimation
TL;DR: In this article, Monte Carlo simulation is used to randomly generate one thousand parameter sets for a 20-year calibration period encompassing variable climatic and wildfire conditions, from which behavioural (acceptable) MIKE SHE parameter sets are identified and 5% and 95% uncertainty bounds for monthly streamflow are calculated.
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Evapotranspiration evaluation models based on machine learning algorithms—A comparative study
TL;DR: In this paper, three different evapotranspiration models have been compared in an experimental site in Central Florida, characterized by humid subtropical climate, and four variants of each model were applied, varying the machine learning algorithm: M5P Regression Tree, Bagging, Random Forest and Support Vector Regression.
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Improving real time flood forecasting using fuzzy inference system
TL;DR: It has been concluded from the study that the TSC-T–S fuzzy model provide reasonably accurate forecast with sufficient lead-time and a new model performance criterion termed as peak percent threshold statistics (PPTS) is proposed to evaluate the performance of a flood forecasting model.