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

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}
引用次数: 0

摘要

调查河流水质趋势对保护生态系统和公众健康至关重要。以前的研究已经确定,水文气候驱动因素,如流量、温度和季节性,可能是水质随时间变化的关键驱动因素。这些驱动因素的重要性在空间上可能有所不同,不同的河流有不同的影响水质时间趋势的关键驱动因素。本研究的目的是利用来自维多利亚州(澳大利亚)136个河流监测点的27年(1995-2022)水质监测记录,评估河流水质时间变化的关键驱动因素。我们研究了河流水质时间变化的关键水文气候驱动因素。在这项研究中,我们研究了六个关键的水质参数:溶解氧(DO)、电导率(EC)、pH、浊度、总磷(TP)和总氮(TN)。我们使用多元线性回归模型(公式1)研究了水质的趋势,该模型适用于136个站点和六个组成部分中的每一个。该多元线性回归模型预测站点t (C t)的浓度为:流量(Q t)、季节性(seasonality)和长期潜在趋势(t)的函数。β t、β Q、β seasonality分别为趋势回归系数、流量回归系数和季节性回归系数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Modelling of the activated sludge process with a stratified settling unit Recent changes in the water and ecological condition at the arid Tarim River Basin A study on internal observation of vertical protective nets of temporary structures using image processing techniques Developing synthetic datasets for reef modelling Modelling hydrological impact of remotely sensed vegetation change
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1