A. Lintern, R. Sargent, Judy Hagan, P. Wilson, A. Western, Cami Plum, D. Guo
{"title":"27年来河流水质的水文气候驱动因素:河流流量、温度和季节性的作用","authors":"A. Lintern, R. Sargent, Judy Hagan, P. Wilson, A. Western, Cami Plum, D. Guo","doi":"10.36334/modsim.2023.lintern","DOIUrl":null,"url":null,"abstract":": Investigating trends in stream water quality is vital for protecting ecosystems and public health. Previous studies have identified that hydro-climatic drivers such as streamflow, temperature and seasonality can be crucial drivers of water quality changes over time. The importance of each of these drivers can vary spatially, with different streams having different key drivers that affect temporal trends in water quality. The aim of this study is to assess the key drivers of temporal variability in stream water quality, using a 27-year (1995–2022) water quality monitoring record from 136 stream monitoring sites across the state of Victoria (Australia). We investigate the key hydro-climatic drivers of temporal change in stream water quality. In this study, we address six key water quality parameters: dissolved oxygen (DO), electrical conductivity (EC), pH, turbidity, total phosphorus (TP) and total nitrogen (TN). We investigated the trends in water quality using a multiple linear regression model (Equation 1), fitted for each of the 136 sites and for each of the six constituents. This multiple linear regression model predicts concentration at site t (C t ) as a function of: streamflow (Q t ), seasonality ( seasonality ), and a long-term underlying trend ( t ). β t , β Q , β seasonality are regression coefficients for trend, streamflow and seasonality (respectively).","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hydroclimatic drivers of stream water quality over 27 years: The role of streamflow, temperature and seasonality\",\"authors\":\"A. Lintern, R. Sargent, Judy Hagan, P. Wilson, A. Western, Cami Plum, D. Guo\",\"doi\":\"10.36334/modsim.2023.lintern\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": Investigating trends in stream water quality is vital for protecting ecosystems and public health. Previous studies have identified that hydro-climatic drivers such as streamflow, temperature and seasonality can be crucial drivers of water quality changes over time. The importance of each of these drivers can vary spatially, with different streams having different key drivers that affect temporal trends in water quality. The aim of this study is to assess the key drivers of temporal variability in stream water quality, using a 27-year (1995–2022) water quality monitoring record from 136 stream monitoring sites across the state of Victoria (Australia). We investigate the key hydro-climatic drivers of temporal change in stream water quality. In this study, we address six key water quality parameters: dissolved oxygen (DO), electrical conductivity (EC), pH, turbidity, total phosphorus (TP) and total nitrogen (TN). We investigated the trends in water quality using a multiple linear regression model (Equation 1), fitted for each of the 136 sites and for each of the six constituents. This multiple linear regression model predicts concentration at site t (C t ) as a function of: streamflow (Q t ), seasonality ( seasonality ), and a long-term underlying trend ( t ). β t , β Q , β seasonality are regression coefficients for trend, streamflow and seasonality (respectively).\",\"PeriodicalId\":390064,\"journal\":{\"name\":\"MODSIM2023, 25th International Congress on Modelling and Simulation.\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MODSIM2023, 25th International Congress on Modelling and Simulation.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36334/modsim.2023.lintern\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MODSIM2023, 25th International Congress on Modelling and Simulation.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36334/modsim.2023.lintern","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hydroclimatic drivers of stream water quality over 27 years: The role of streamflow, temperature and seasonality
: Investigating trends in stream water quality is vital for protecting ecosystems and public health. Previous studies have identified that hydro-climatic drivers such as streamflow, temperature and seasonality can be crucial drivers of water quality changes over time. The importance of each of these drivers can vary spatially, with different streams having different key drivers that affect temporal trends in water quality. The aim of this study is to assess the key drivers of temporal variability in stream water quality, using a 27-year (1995–2022) water quality monitoring record from 136 stream monitoring sites across the state of Victoria (Australia). We investigate the key hydro-climatic drivers of temporal change in stream water quality. In this study, we address six key water quality parameters: dissolved oxygen (DO), electrical conductivity (EC), pH, turbidity, total phosphorus (TP) and total nitrogen (TN). We investigated the trends in water quality using a multiple linear regression model (Equation 1), fitted for each of the 136 sites and for each of the six constituents. This multiple linear regression model predicts concentration at site t (C t ) as a function of: streamflow (Q t ), seasonality ( seasonality ), and a long-term underlying trend ( t ). β t , β Q , β seasonality are regression coefficients for trend, streamflow and seasonality (respectively).