Uncertainty analysis of model inputs in riverine water temperature simulations

Abstract Simulation models are often affected by uncertainties that impress the modeling results. One of the important types of uncertainties is associated with the model input data. The main objective of this study is to investigate the uncertainties of inputs of the Heat-Flux (HFLUX) model. To do...

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Autores principales:	Babak Abdi, Omid Bozorg-Haddad, Xuefeng Chu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2744ea8d0ec14b1eb5d45ba7f6de9f85
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spelling	oai:doaj.org-article:2744ea8d0ec14b1eb5d45ba7f6de9f852021-12-02T18:37:10ZUncertainty analysis of model inputs in riverine water temperature simulations10.1038/s41598-021-99371-02045-2322https://doaj.org/article/2744ea8d0ec14b1eb5d45ba7f6de9f852021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99371-0https://doaj.org/toc/2045-2322Abstract Simulation models are often affected by uncertainties that impress the modeling results. One of the important types of uncertainties is associated with the model input data. The main objective of this study is to investigate the uncertainties of inputs of the Heat-Flux (HFLUX) model. To do so, the Shuffled Complex Evolution Metropolis Uncertainty Algorithm (SCEM-UA), a Monte Carlo Markov Chain (MCMC) based method, is employed for the first time to assess the uncertainties of model inputs in riverine water temperature simulations. The performance of the SCEM-UA algorithm is further evaluated. In the application, the histograms of the selected inputs of the HFLUX model including the stream width, stream depth, percentage of shade, and streamflow were created and their uncertainties were analyzed. Comparison of the observed data and the simulations demonstrated the capability of the SCEM-UA algorithm in the assessment of the uncertainties associated with the model input data (the maximum relative error was 15%).Babak AbdiOmid Bozorg-HaddadXuefeng ChuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Babak Abdi Omid Bozorg-Haddad Xuefeng Chu Uncertainty analysis of model inputs in riverine water temperature simulations
description	Abstract Simulation models are often affected by uncertainties that impress the modeling results. One of the important types of uncertainties is associated with the model input data. The main objective of this study is to investigate the uncertainties of inputs of the Heat-Flux (HFLUX) model. To do so, the Shuffled Complex Evolution Metropolis Uncertainty Algorithm (SCEM-UA), a Monte Carlo Markov Chain (MCMC) based method, is employed for the first time to assess the uncertainties of model inputs in riverine water temperature simulations. The performance of the SCEM-UA algorithm is further evaluated. In the application, the histograms of the selected inputs of the HFLUX model including the stream width, stream depth, percentage of shade, and streamflow were created and their uncertainties were analyzed. Comparison of the observed data and the simulations demonstrated the capability of the SCEM-UA algorithm in the assessment of the uncertainties associated with the model input data (the maximum relative error was 15%).
format	article
author	Babak Abdi Omid Bozorg-Haddad Xuefeng Chu
author_facet	Babak Abdi Omid Bozorg-Haddad Xuefeng Chu
author_sort	Babak Abdi
title	Uncertainty analysis of model inputs in riverine water temperature simulations
title_short	Uncertainty analysis of model inputs in riverine water temperature simulations
title_full	Uncertainty analysis of model inputs in riverine water temperature simulations
title_fullStr	Uncertainty analysis of model inputs in riverine water temperature simulations
title_full_unstemmed	Uncertainty analysis of model inputs in riverine water temperature simulations
title_sort	uncertainty analysis of model inputs in riverine water temperature simulations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2744ea8d0ec14b1eb5d45ba7f6de9f85
work_keys_str_mv	AT babakabdi uncertaintyanalysisofmodelinputsinriverinewatertemperaturesimulations AT omidbozorghaddad uncertaintyanalysisofmodelinputsinriverinewatertemperaturesimulations AT xuefengchu uncertaintyanalysisofmodelinputsinriverinewatertemperaturesimulations
_version_	1718377787436302336

Uncertainty analysis of model inputs in riverine water temperature simulations

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