{"title":"贝叶斯网络在淡水生态系统水质风险评价中的应用","authors":"V. Wepener, G. O’Brien","doi":"10.2989/16085914.2022.2130866","DOIUrl":null,"url":null,"abstract":"It is difficult to predict and manage the ecological consequences of multiple water quality stressors on our freshwater systems. This is due to the dynamism of the source-stressor-response relationships and multiple factors including lack of data, complex impact pathways and risks, and uncertainties that are difficult to parameterise. We present a risk-based probability modelling approach using a Bayesian network (BN), to manage multiple water quality stressors at multiple spatial scales. We illustrate the use of this approach, by evaluating the probable ecological effects of altered water quality associated with multiple sources in three case study rivers in South Africa. Water quality and land use activity were used to describe conceptual risk pathways, parameterise the BNs and model the probable consequences of multiple water quality stressors. The BN model demonstrated that the endpoints that were selected for the study reflected the risks associated with the levels of the input water quality variables. The model further demonstrated that the electrical conductivity BN was just as sensitive as the more complex salt model. The BN model was further able to accurately represent risks to all systems irrespective the water quality data base size. This approach can contribute towards more sustainable water resource management.","PeriodicalId":7864,"journal":{"name":"African Journal of Aquatic Science","volume":"47 1","pages":"231 - 244"},"PeriodicalIF":1.1000,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The application of Bayesian networks to evaluate risks from multiple stressors to water quality of freshwater ecosystems\",\"authors\":\"V. Wepener, G. O’Brien\",\"doi\":\"10.2989/16085914.2022.2130866\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is difficult to predict and manage the ecological consequences of multiple water quality stressors on our freshwater systems. This is due to the dynamism of the source-stressor-response relationships and multiple factors including lack of data, complex impact pathways and risks, and uncertainties that are difficult to parameterise. We present a risk-based probability modelling approach using a Bayesian network (BN), to manage multiple water quality stressors at multiple spatial scales. We illustrate the use of this approach, by evaluating the probable ecological effects of altered water quality associated with multiple sources in three case study rivers in South Africa. Water quality and land use activity were used to describe conceptual risk pathways, parameterise the BNs and model the probable consequences of multiple water quality stressors. The BN model demonstrated that the endpoints that were selected for the study reflected the risks associated with the levels of the input water quality variables. The model further demonstrated that the electrical conductivity BN was just as sensitive as the more complex salt model. The BN model was further able to accurately represent risks to all systems irrespective the water quality data base size. This approach can contribute towards more sustainable water resource management.\",\"PeriodicalId\":7864,\"journal\":{\"name\":\"African Journal of Aquatic Science\",\"volume\":\"47 1\",\"pages\":\"231 - 244\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"African Journal of Aquatic Science\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.2989/16085914.2022.2130866\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MARINE & FRESHWATER BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"African Journal of Aquatic Science","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.2989/16085914.2022.2130866","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MARINE & FRESHWATER BIOLOGY","Score":null,"Total":0}
The application of Bayesian networks to evaluate risks from multiple stressors to water quality of freshwater ecosystems
It is difficult to predict and manage the ecological consequences of multiple water quality stressors on our freshwater systems. This is due to the dynamism of the source-stressor-response relationships and multiple factors including lack of data, complex impact pathways and risks, and uncertainties that are difficult to parameterise. We present a risk-based probability modelling approach using a Bayesian network (BN), to manage multiple water quality stressors at multiple spatial scales. We illustrate the use of this approach, by evaluating the probable ecological effects of altered water quality associated with multiple sources in three case study rivers in South Africa. Water quality and land use activity were used to describe conceptual risk pathways, parameterise the BNs and model the probable consequences of multiple water quality stressors. The BN model demonstrated that the endpoints that were selected for the study reflected the risks associated with the levels of the input water quality variables. The model further demonstrated that the electrical conductivity BN was just as sensitive as the more complex salt model. The BN model was further able to accurately represent risks to all systems irrespective the water quality data base size. This approach can contribute towards more sustainable water resource management.
期刊介绍:
The African Journal of Aquatic Science is an international journal devoted to the study of the aquatic sciences, covering all African inland and estuarine waters. The Journal publishes peer-reviewed original scientific papers and short articles in all the aquatic science fields including limnology, hydrobiology, ecology, conservation, biomonitoring, management, water quality, ecotoxicology, biological interactions, physical properties and human impacts on African aquatic systems.