{"title":"一个简化水质模型的概率评估及其与湖泊水文特征的联系。","authors":"Joana Postal Pasqualini, Fernando Mainardi Fan","doi":"10.1016/j.envsoft.2025.106593","DOIUrl":null,"url":null,"abstract":"<div><div>Simple models can be valuable for supporting the management of engineering applications across various fields, such as water resources and environmental sanitation. However, the inherent uncertainty associated with these approaches should be recognized and quantified to better support decision-making. This study assessed variability arising from two primary factors: the adoption of different numerical methods and parameter variability within a complete stirring tank reactor (CSTR) model applied for phosphorous predictions across four different hydrological conditions. An ensemble approach, integrating multiple numeric methods, was employed to evaluate numerical uncertainty. Parametric uncertainty was assessed through Monte Carlo simulations. The study focused solely on characterizing uncertainties without evaluating model performance or comparing the results from the applied models to observational data. The numerical and parametric uncertainty ranges were similar in reservoirs with low-volume fluctuations and differed in reservoirs where volume increased over time. The overall magnitude of observed uncertainty depended on the hydrodynamics during the analysis period. The choice of numerical approach is particularly important in assessing cases involving seasonal shifts in volumetric storage or potential impacts from climate change. Results supported the theory that a probabilistic approach supports decision-making by encompassing a wider range of possible results. Moreover, due to its simplified methodology, probabilistic analysis enhances the confidence in the modeling results, even with the inherent simplifications of zero-dimensional modeling. This added layer of analysis helps to account for uncertainties, improving the overall reliability of the model despite its simplicity.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106593"},"PeriodicalIF":4.6000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Probabilistic assessment of a simplified water quality model and its linkage to the hydrological characteristics of lakes\",\"authors\":\"Joana Postal Pasqualini, Fernando Mainardi Fan\",\"doi\":\"10.1016/j.envsoft.2025.106593\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Simple models can be valuable for supporting the management of engineering applications across various fields, such as water resources and environmental sanitation. However, the inherent uncertainty associated with these approaches should be recognized and quantified to better support decision-making. This study assessed variability arising from two primary factors: the adoption of different numerical methods and parameter variability within a complete stirring tank reactor (CSTR) model applied for phosphorous predictions across four different hydrological conditions. An ensemble approach, integrating multiple numeric methods, was employed to evaluate numerical uncertainty. Parametric uncertainty was assessed through Monte Carlo simulations. The study focused solely on characterizing uncertainties without evaluating model performance or comparing the results from the applied models to observational data. The numerical and parametric uncertainty ranges were similar in reservoirs with low-volume fluctuations and differed in reservoirs where volume increased over time. The overall magnitude of observed uncertainty depended on the hydrodynamics during the analysis period. The choice of numerical approach is particularly important in assessing cases involving seasonal shifts in volumetric storage or potential impacts from climate change. Results supported the theory that a probabilistic approach supports decision-making by encompassing a wider range of possible results. Moreover, due to its simplified methodology, probabilistic analysis enhances the confidence in the modeling results, even with the inherent simplifications of zero-dimensional modeling. This added layer of analysis helps to account for uncertainties, improving the overall reliability of the model despite its simplicity.</div></div>\",\"PeriodicalId\":310,\"journal\":{\"name\":\"Environmental Modelling & Software\",\"volume\":\"192 \",\"pages\":\"Article 106593\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Modelling & Software\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1364815225002774\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815225002774","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Probabilistic assessment of a simplified water quality model and its linkage to the hydrological characteristics of lakes
Simple models can be valuable for supporting the management of engineering applications across various fields, such as water resources and environmental sanitation. However, the inherent uncertainty associated with these approaches should be recognized and quantified to better support decision-making. This study assessed variability arising from two primary factors: the adoption of different numerical methods and parameter variability within a complete stirring tank reactor (CSTR) model applied for phosphorous predictions across four different hydrological conditions. An ensemble approach, integrating multiple numeric methods, was employed to evaluate numerical uncertainty. Parametric uncertainty was assessed through Monte Carlo simulations. The study focused solely on characterizing uncertainties without evaluating model performance or comparing the results from the applied models to observational data. The numerical and parametric uncertainty ranges were similar in reservoirs with low-volume fluctuations and differed in reservoirs where volume increased over time. The overall magnitude of observed uncertainty depended on the hydrodynamics during the analysis period. The choice of numerical approach is particularly important in assessing cases involving seasonal shifts in volumetric storage or potential impacts from climate change. Results supported the theory that a probabilistic approach supports decision-making by encompassing a wider range of possible results. Moreover, due to its simplified methodology, probabilistic analysis enhances the confidence in the modeling results, even with the inherent simplifications of zero-dimensional modeling. This added layer of analysis helps to account for uncertainties, improving the overall reliability of the model despite its simplicity.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.