Prediction of physico-chemical variables and chlorophyll a criteria for ecoregion lakes using the ratios of land use to lake depth
Establishing nutrient criteria for regional lakes is necessary to assess human impact on lake aquatic ecosystems and protect water quality and biotic integrity. Multiple linear regression models, in which the ratios of land use to mean lake depth (DEP) are the predictor variables, and the logarithms...
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| Veröffentlicht in: | Environmental earth sciences 2015-09, Vol.74 (5), p.3709-3719 |
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| creator | Huo, Shouliang Ma, Chunzi He, Zhuoshi Xi, Beidou Su, Jing Zhang, Li Wang, Ji |
| description | Establishing nutrient criteria for regional lakes is necessary to assess human impact on lake aquatic ecosystems and protect water quality and biotic integrity. Multiple linear regression models, in which the ratios of land use to mean lake depth (DEP) are the predictor variables, and the logarithms of physico-chemical variables and Chl a concentrations are the dependent variables, were developed to predict physico-chemical variables and chlorophyll a criteria for Yungui Plateau Ecoregion lakes. The contemporary land use data of 22 lake watersheds were analyzed and employed to develop the spatial relationship with the regression models. The data of five lake watersheds in four periods were used to verify the accuracy of the regression models, and to test their applicability in time scale. The intercept of these models (i.e., expected physico-chemical variables and Chl a concentrations in the absence of human activities) represents the criterion concentrations. Results suggested that the percentages of other construction land/DEP had strong positive influences on the concentrations of all variables (except electrical conductivity). The multiple linear regression models offered a potential method for regions with heavy anthropogenic disturbances to develop the physico-chemical variables and Chl a criteria. |
| doi_str_mv | 10.1007/s12665-015-4020-8 |
| format | Article |
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Multiple linear regression models, in which the ratios of land use to mean lake depth (DEP) are the predictor variables, and the logarithms of physico-chemical variables and Chl a concentrations are the dependent variables, were developed to predict physico-chemical variables and chlorophyll a criteria for Yungui Plateau Ecoregion lakes. The contemporary land use data of 22 lake watersheds were analyzed and employed to develop the spatial relationship with the regression models. The data of five lake watersheds in four periods were used to verify the accuracy of the regression models, and to test their applicability in time scale. The intercept of these models (i.e., expected physico-chemical variables and Chl a concentrations in the absence of human activities) represents the criterion concentrations. Results suggested that the percentages of other construction land/DEP had strong positive influences on the concentrations of all variables (except electrical conductivity). 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Multiple linear regression models, in which the ratios of land use to mean lake depth (DEP) are the predictor variables, and the logarithms of physico-chemical variables and Chl a concentrations are the dependent variables, were developed to predict physico-chemical variables and chlorophyll a criteria for Yungui Plateau Ecoregion lakes. The contemporary land use data of 22 lake watersheds were analyzed and employed to develop the spatial relationship with the regression models. The data of five lake watersheds in four periods were used to verify the accuracy of the regression models, and to test their applicability in time scale. The intercept of these models (i.e., expected physico-chemical variables and Chl a concentrations in the absence of human activities) represents the criterion concentrations. Results suggested that the percentages of other construction land/DEP had strong positive influences on the concentrations of all variables (except electrical conductivity). The multiple linear regression models offered a potential method for regions with heavy anthropogenic disturbances to develop the physico-chemical variables and Chl a criteria.</description><subject>anthropogenic activities</subject><subject>Anthropogenic factors</subject><subject>Aquatic ecosystems</subject><subject>Biogeosciences</subject><subject>Chlorophyll</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>ecoregions</subject><subject>electrical conductivity</subject><subject>Environmental Science and Engineering</subject><subject>Eutrophication</subject><subject>Freshwater</subject><subject>Geochemistry</subject><subject>Geology</subject><subject>humans</subject><subject>Hydrology/Water Resources</subject><subject>Lakes</subject><subject>Land use</subject><subject>linear models</subject><subject>Nutrients</subject><subject>plateaus</subject><subject>prediction</subject><subject>regression analysis</subject><subject>Terrestrial Pollution</subject><subject>Thematic Issue</subject><subject>Water depth</subject><subject>Water quality</subject><subject>Watersheds</subject><issn>1866-6280</issn><issn>1866-6299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU1rFTEUhoMoWG77A1wZcONmar4nWUrxo1Co0LoOuZmTO6m5k2syI3TbX27GEREXZpNAnvfhHF6EXlFySQnp31XKlJIdobIThJFOP0NnVCvVKWbM8z9vTV6ii1ofSDucckPUGXr6UmCIfo55wjng0_hYo8-dH-EYvUv4hyvR7RNU7KYB-zHlkhuUEnbYlzhD-8YhFww-FzismuS-NXypcTrgeQRcXLPX1Z5Wx1IBz_kXhQc4zeM5ehFcqnDx-96h-48f7q8-dze3n66v3t90Xgg-dxwMcCY1MyJIKSgE5oIwQfSKhmB6pwalKNf7vSTDEAauBh2CENoor0LgO_R2055K_r5Ane0xVg-pDQV5qZb2nGkupNINffMP-pCXMrXhGkX6XkrT6B2iG-VLrrVAsKcSj648Wkrs2ovderGtF7v2Ylcz2zK1sdMByl_m_4Reb6HgsnWHEqv9escaQAijvG3IfwKBnJpL</recordid><startdate>20150901</startdate><enddate>20150901</enddate><creator>Huo, Shouliang</creator><creator>Ma, Chunzi</creator><creator>He, Zhuoshi</creator><creator>Xi, Beidou</creator><creator>Su, Jing</creator><creator>Zhang, Li</creator><creator>Wang, Ji</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><scope>7QH</scope><scope>7U6</scope><scope>H97</scope></search><sort><creationdate>20150901</creationdate><title>Prediction of physico-chemical variables and chlorophyll a criteria for ecoregion lakes using the ratios of land use to lake depth</title><author>Huo, Shouliang ; Ma, Chunzi ; He, Zhuoshi ; Xi, Beidou ; Su, Jing ; Zhang, Li ; Wang, Ji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c443t-3e9e3258294f5541ef2af49f4761ff97a6d66138bb50ddfd36d8ff44896c6ff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>anthropogenic activities</topic><topic>Anthropogenic factors</topic><topic>Aquatic ecosystems</topic><topic>Biogeosciences</topic><topic>Chlorophyll</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>ecoregions</topic><topic>electrical conductivity</topic><topic>Environmental Science and Engineering</topic><topic>Eutrophication</topic><topic>Freshwater</topic><topic>Geochemistry</topic><topic>Geology</topic><topic>humans</topic><topic>Hydrology/Water Resources</topic><topic>Lakes</topic><topic>Land use</topic><topic>linear models</topic><topic>Nutrients</topic><topic>plateaus</topic><topic>prediction</topic><topic>regression analysis</topic><topic>Terrestrial Pollution</topic><topic>Thematic Issue</topic><topic>Water depth</topic><topic>Water quality</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huo, Shouliang</creatorcontrib><creatorcontrib>Ma, Chunzi</creatorcontrib><creatorcontrib>He, Zhuoshi</creatorcontrib><creatorcontrib>Xi, Beidou</creatorcontrib><creatorcontrib>Su, Jing</creatorcontrib><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Wang, Ji</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Science Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><collection>Aqualine</collection><collection>Sustainability Science Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><jtitle>Environmental earth sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huo, Shouliang</au><au>Ma, Chunzi</au><au>He, Zhuoshi</au><au>Xi, Beidou</au><au>Su, Jing</au><au>Zhang, Li</au><au>Wang, Ji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of physico-chemical variables and chlorophyll a criteria for ecoregion lakes using the ratios of land use to lake depth</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2015-09-01</date><risdate>2015</risdate><volume>74</volume><issue>5</issue><spage>3709</spage><epage>3719</epage><pages>3709-3719</pages><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>Establishing nutrient criteria for regional lakes is necessary to assess human impact on lake aquatic ecosystems and protect water quality and biotic integrity. Multiple linear regression models, in which the ratios of land use to mean lake depth (DEP) are the predictor variables, and the logarithms of physico-chemical variables and Chl a concentrations are the dependent variables, were developed to predict physico-chemical variables and chlorophyll a criteria for Yungui Plateau Ecoregion lakes. The contemporary land use data of 22 lake watersheds were analyzed and employed to develop the spatial relationship with the regression models. The data of five lake watersheds in four periods were used to verify the accuracy of the regression models, and to test their applicability in time scale. The intercept of these models (i.e., expected physico-chemical variables and Chl a concentrations in the absence of human activities) represents the criterion concentrations. Results suggested that the percentages of other construction land/DEP had strong positive influences on the concentrations of all variables (except electrical conductivity). The multiple linear regression models offered a potential method for regions with heavy anthropogenic disturbances to develop the physico-chemical variables and Chl a criteria.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12665-015-4020-8</doi><tpages>11</tpages></addata></record> |
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| subjects | anthropogenic activities Anthropogenic factors Aquatic ecosystems Biogeosciences Chlorophyll Earth and Environmental Science Earth Sciences ecoregions electrical conductivity Environmental Science and Engineering Eutrophication Freshwater Geochemistry Geology humans Hydrology/Water Resources Lakes Land use linear models Nutrients plateaus prediction regression analysis Terrestrial Pollution Thematic Issue Water depth Water quality Watersheds |
| title | Prediction of physico-chemical variables and chlorophyll a criteria for ecoregion lakes using the ratios of land use to lake depth |
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