Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/249632 |
Resumo: | Monitoring the ecological status of water bodies is crucial to guarantee human health and economic development. However, monitoring is often deficient in developing regions due to high installation and maintenance costs, thus it is frequently supported by water quality models, whose results are themselves affected by the lack of detailed input data. A possible solution is to use probabilistic models that consider the inherent uncertainty of the different inputs. In this research, we extended a simple water quality model (QUAL-UFMG, based on Qual2E) through Monte Carlo simulations to generate probabilistic results and applied it to a representative case study in Brazil. Results showed that, depending on the distribution of probabilities and variability of parameters adopted, the outcome of a non-deterministic modelling approach may differ significantly from a deterministic one regarding compliance with water quality standards. Moreover, the probabilistic strategy is more scientifically transparent and robust, as it explicitly communicates the uncertainty in both the measured data and modelling results. We conclude that a probabilistic approach is particularly useful in regions with a low data availability such as developing countries, as uncertainties are high due to insufficient monitoring, and the risk to human health is elevated due to a low prevalence of sanitation. |
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Brum, Marianne Bueno dos PassosFan, Fernando MainardiSalla, Marcio RicardoSperling, Marcos von2022-10-03T04:49:08Z20221464-7141http://hdl.handle.net/10183/249632001149884Monitoring the ecological status of water bodies is crucial to guarantee human health and economic development. However, monitoring is often deficient in developing regions due to high installation and maintenance costs, thus it is frequently supported by water quality models, whose results are themselves affected by the lack of detailed input data. A possible solution is to use probabilistic models that consider the inherent uncertainty of the different inputs. In this research, we extended a simple water quality model (QUAL-UFMG, based on Qual2E) through Monte Carlo simulations to generate probabilistic results and applied it to a representative case study in Brazil. Results showed that, depending on the distribution of probabilities and variability of parameters adopted, the outcome of a non-deterministic modelling approach may differ significantly from a deterministic one regarding compliance with water quality standards. Moreover, the probabilistic strategy is more scientifically transparent and robust, as it explicitly communicates the uncertainty in both the measured data and modelling results. We conclude that a probabilistic approach is particularly useful in regions with a low data availability such as developing countries, as uncertainties are high due to insufficient monitoring, and the risk to human health is elevated due to a low prevalence of sanitation.application/pdfengJournal of Htdroinformatics. London. Vol. 24, n. 4 (July 2022), p. 783-797Qualidade da águaModelos matemáticosRiosModelos probabilísticosMétodo de Monte CarloJordão, Rio (MG)Modelling uncertaintyMonte Carlo simulationsProbabilistic analysisRiver modellingWater qualityAnalysis of a probabilistic approach for modelling and assessment of the water quality of riversEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001149884.pdf.txt001149884.pdf.txtExtracted Texttext/plain58513http://www.lume.ufrgs.br/bitstream/10183/249632/2/001149884.pdf.txt6e68edabd0701ee1c1f173d6bc19e990MD52ORIGINAL001149884.pdfTexto completoapplication/pdf1041474http://www.lume.ufrgs.br/bitstream/10183/249632/1/001149884.pdfbfda1220d7bedcea3065fa02d66b7b17MD5110183/2496322022-10-04 05:00:53.661289oai:www.lume.ufrgs.br:10183/249632Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2022-10-04T08:00:53Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers |
title |
Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers |
spellingShingle |
Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers Brum, Marianne Bueno dos Passos Qualidade da água Modelos matemáticos Rios Modelos probabilísticos Método de Monte Carlo Jordão, Rio (MG) Modelling uncertainty Monte Carlo simulations Probabilistic analysis River modelling Water quality |
title_short |
Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers |
title_full |
Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers |
title_fullStr |
Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers |
title_full_unstemmed |
Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers |
title_sort |
Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers |
author |
Brum, Marianne Bueno dos Passos |
author_facet |
Brum, Marianne Bueno dos Passos Fan, Fernando Mainardi Salla, Marcio Ricardo Sperling, Marcos von |
author_role |
author |
author2 |
Fan, Fernando Mainardi Salla, Marcio Ricardo Sperling, Marcos von |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Brum, Marianne Bueno dos Passos Fan, Fernando Mainardi Salla, Marcio Ricardo Sperling, Marcos von |
dc.subject.por.fl_str_mv |
Qualidade da água Modelos matemáticos Rios Modelos probabilísticos Método de Monte Carlo Jordão, Rio (MG) |
topic |
Qualidade da água Modelos matemáticos Rios Modelos probabilísticos Método de Monte Carlo Jordão, Rio (MG) Modelling uncertainty Monte Carlo simulations Probabilistic analysis River modelling Water quality |
dc.subject.eng.fl_str_mv |
Modelling uncertainty Monte Carlo simulations Probabilistic analysis River modelling Water quality |
description |
Monitoring the ecological status of water bodies is crucial to guarantee human health and economic development. However, monitoring is often deficient in developing regions due to high installation and maintenance costs, thus it is frequently supported by water quality models, whose results are themselves affected by the lack of detailed input data. A possible solution is to use probabilistic models that consider the inherent uncertainty of the different inputs. In this research, we extended a simple water quality model (QUAL-UFMG, based on Qual2E) through Monte Carlo simulations to generate probabilistic results and applied it to a representative case study in Brazil. Results showed that, depending on the distribution of probabilities and variability of parameters adopted, the outcome of a non-deterministic modelling approach may differ significantly from a deterministic one regarding compliance with water quality standards. Moreover, the probabilistic strategy is more scientifically transparent and robust, as it explicitly communicates the uncertainty in both the measured data and modelling results. We conclude that a probabilistic approach is particularly useful in regions with a low data availability such as developing countries, as uncertainties are high due to insufficient monitoring, and the risk to human health is elevated due to a low prevalence of sanitation. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-10-03T04:49:08Z |
dc.date.issued.fl_str_mv |
2022 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/249632 |
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1464-7141 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001149884 |
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1464-7141 001149884 |
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http://hdl.handle.net/10183/249632 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Journal of Htdroinformatics. London. Vol. 24, n. 4 (July 2022), p. 783-797 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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