Prediction of spatial variability of phosphorous over the St-Esprit watershed
Spatial data analysis tools for predicting the variability of non-point source pollutants minimize the time, effort and cost involved in extensive and exhaustive real field data measurements. In this study, exploratory data analysis, fitting of semivariogram models, and kriging techniques of geostat...
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| Veröffentlicht in: | Water, air, and soil pollution air, and soil pollution, 2005-11, Vol.168 (1-4), p.267-288 |
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| creator | Sarangi, A Madramootoo, C.A Enright, P Chandrasekharan, H |
| description | Spatial data analysis tools for predicting the variability of non-point source pollutants minimize the time, effort and cost involved in extensive and exhaustive real field data measurements. In this study, exploratory data analysis, fitting of semivariogram models, and kriging techniques of geostatistics were used to develop the spatial variability map of soil phosphorous saturation (P sub(sat)) percentage over the St-Espirit watershed (2610 ha), located in Quebec, Canada. The P sub(sat) measured values for the 281 geo referenced land parcel units (LPU) within the watershed were interpreted and analyzed using the ArcGIS super(+) tool. The geostatistical extension module of ArcGIS super(+) was used for exploratory data analysis, semivariogram model fitting, and development of a P sub(sat) prediction map using the ordinary kriging technique. Using these geostatistical procedures and adjustment of lag sizes and lag intervals representing the data sets, it was estimated that the spherical semivariogram model fitted well to represent the P sub(sat) variability with residual sum square (RSS) of 0.0003 and coefficient of determination (R super(2)) of 0.98. Further, the developed model was used to predict the P sub(sat) variability over the St. Esprit watershed using the 1605 geo-referenced LPU locations. The generated spatial variability map was geo-spatially processed with the natural drainage network and land use feature classes of the watershed to ascertain the phosphorous loading and locate vulnerable LPUs for phosphorous management. It was observed that the P sub(sat) levels were higher at the up stream locations and near the drainage channels than the locations close to watershed outlet. Also, the land pockets with more than 60% agricultural land use resulted in supra-optimal P sub(sat) values (10% > P sub(sat) < 20%), out of which 8.5 to 16.3 ha agricultural land of the St. Esprit watershed exhibited critical agro-environmental threshold P sub(sat) values (P sub(sat) > 20%). It was also revealed that, around 23.5% of the watersheds cropped area has reached these threshold levels which necessitate judicious P input management. |
| doi_str_mv | 10.1007/s11270-005-1777-5 |
| format | Article |
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In this study, exploratory data analysis, fitting of semivariogram models, and kriging techniques of geostatistics were used to develop the spatial variability map of soil phosphorous saturation (P sub(sat)) percentage over the St-Espirit watershed (2610 ha), located in Quebec, Canada. The P sub(sat) measured values for the 281 geo referenced land parcel units (LPU) within the watershed were interpreted and analyzed using the ArcGIS super(+) tool. The geostatistical extension module of ArcGIS super(+) was used for exploratory data analysis, semivariogram model fitting, and development of a P sub(sat) prediction map using the ordinary kriging technique. Using these geostatistical procedures and adjustment of lag sizes and lag intervals representing the data sets, it was estimated that the spherical semivariogram model fitted well to represent the P sub(sat) variability with residual sum square (RSS) of 0.0003 and coefficient of determination (R super(2)) of 0.98. Further, the developed model was used to predict the P sub(sat) variability over the St. Esprit watershed using the 1605 geo-referenced LPU locations. The generated spatial variability map was geo-spatially processed with the natural drainage network and land use feature classes of the watershed to ascertain the phosphorous loading and locate vulnerable LPUs for phosphorous management. It was observed that the P sub(sat) levels were higher at the up stream locations and near the drainage channels than the locations close to watershed outlet. Also, the land pockets with more than 60% agricultural land use resulted in supra-optimal P sub(sat) values (10% > P sub(sat) < 20%), out of which 8.5 to 16.3 ha agricultural land of the St. Esprit watershed exhibited critical agro-environmental threshold P sub(sat) values (P sub(sat) > 20%). It was also revealed that, around 23.5% of the watersheds cropped area has reached these threshold levels which necessitate judicious P input management.</description><identifier>ISSN: 0049-6979</identifier><identifier>EISSN: 1573-2932</identifier><identifier>DOI: 10.1007/s11270-005-1777-5</identifier><identifier>CODEN: WAPLAC</identifier><language>eng</language><publisher>Dordrecht: Springer</publisher><subject>Agricultural land ; agricultural runoff ; Agronomy. Soil science and plant productions ; Applied sciences ; Biological and medical sciences ; Biological and physicochemical phenomena ; Data analysis ; Data processing ; Drainage ; Drainage patterns ; Earth sciences ; Earth, ocean, space ; Engineering and environment geology. Geothermics ; Environmental monitoring ; equations ; Exact sciences and technology ; Fittings ; Freshwater ; Fundamental and applied biological sciences. Psychology ; geographic information systems ; Geostatistics ; Kriging ; Land ; Land use ; losses from soil ; Mathematical models ; Natural water pollution ; Nonpoint source pollution ; phosphorus ; Point source pollution ; Pollution ; Pollution sources. Measurement results ; Pollution, environment geology ; Runoff ; Soil and sediments pollution ; Soil and water pollution ; Soil science ; Spatial analysis ; spatial data ; spatial variation ; statistical models ; Variability ; water pollution ; Water treatment and pollution ; Watersheds</subject><ispartof>Water, air, and soil pollution, 2005-11, Vol.168 (1-4), p.267-288</ispartof><rights>2006 INIST-CNRS</rights><rights>Springer Science + Business Media, Inc. 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-bcfce3dd7406bc8f064bc2c9b24fe85510e4b1b72f480963dfefe48639eb84ea3</citedby><cites>FETCH-LOGICAL-c453t-bcfce3dd7406bc8f064bc2c9b24fe85510e4b1b72f480963dfefe48639eb84ea3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17378880$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Sarangi, A</creatorcontrib><creatorcontrib>Madramootoo, C.A</creatorcontrib><creatorcontrib>Enright, P</creatorcontrib><creatorcontrib>Chandrasekharan, H</creatorcontrib><title>Prediction of spatial variability of phosphorous over the St-Esprit watershed</title><title>Water, air, and soil pollution</title><description>Spatial data analysis tools for predicting the variability of non-point source pollutants minimize the time, effort and cost involved in extensive and exhaustive real field data measurements. In this study, exploratory data analysis, fitting of semivariogram models, and kriging techniques of geostatistics were used to develop the spatial variability map of soil phosphorous saturation (P sub(sat)) percentage over the St-Espirit watershed (2610 ha), located in Quebec, Canada. The P sub(sat) measured values for the 281 geo referenced land parcel units (LPU) within the watershed were interpreted and analyzed using the ArcGIS super(+) tool. The geostatistical extension module of ArcGIS super(+) was used for exploratory data analysis, semivariogram model fitting, and development of a P sub(sat) prediction map using the ordinary kriging technique. Using these geostatistical procedures and adjustment of lag sizes and lag intervals representing the data sets, it was estimated that the spherical semivariogram model fitted well to represent the P sub(sat) variability with residual sum square (RSS) of 0.0003 and coefficient of determination (R super(2)) of 0.98. Further, the developed model was used to predict the P sub(sat) variability over the St. Esprit watershed using the 1605 geo-referenced LPU locations. The generated spatial variability map was geo-spatially processed with the natural drainage network and land use feature classes of the watershed to ascertain the phosphorous loading and locate vulnerable LPUs for phosphorous management. It was observed that the P sub(sat) levels were higher at the up stream locations and near the drainage channels than the locations close to watershed outlet. Also, the land pockets with more than 60% agricultural land use resulted in supra-optimal P sub(sat) values (10% > P sub(sat) < 20%), out of which 8.5 to 16.3 ha agricultural land of the St. Esprit watershed exhibited critical agro-environmental threshold P sub(sat) values (P sub(sat) > 20%). It was also revealed that, around 23.5% of the watersheds cropped area has reached these threshold levels which necessitate judicious P input management.</description><subject>Agricultural land</subject><subject>agricultural runoff</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Applied sciences</subject><subject>Biological and medical sciences</subject><subject>Biological and physicochemical phenomena</subject><subject>Data analysis</subject><subject>Data processing</subject><subject>Drainage</subject><subject>Drainage patterns</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Engineering and environment geology. Geothermics</subject><subject>Environmental monitoring</subject><subject>equations</subject><subject>Exact sciences and technology</subject><subject>Fittings</subject><subject>Freshwater</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>geographic information systems</subject><subject>Geostatistics</subject><subject>Kriging</subject><subject>Land</subject><subject>Land use</subject><subject>losses from soil</subject><subject>Mathematical models</subject><subject>Natural water pollution</subject><subject>Nonpoint source pollution</subject><subject>phosphorus</subject><subject>Point source pollution</subject><subject>Pollution</subject><subject>Pollution sources. Measurement results</subject><subject>Pollution, environment geology</subject><subject>Runoff</subject><subject>Soil and sediments pollution</subject><subject>Soil and water pollution</subject><subject>Soil science</subject><subject>Spatial analysis</subject><subject>spatial data</subject><subject>spatial variation</subject><subject>statistical models</subject><subject>Variability</subject><subject>water pollution</subject><subject>Water treatment and pollution</subject><subject>Watersheds</subject><issn>0049-6979</issn><issn>1573-2932</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</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>eNqFkU9LXDEUxYO00KntB-jKR0HtJvbmf7IUsbZgqWBdh7y8pBN5TsYko_jtm-kIhS7qhXAh_O7hcA5CHwicEAD1uRJCFWAAgYlSCos9tCBCMUwNo6_QAoAbLI0yb9DbWm-hj9Fqgb5flTAl31JeDTkOde1acvPw4EpyY5pTe9p-r5e59lfypg75IZShLcNw3fB5XZfUhkfXQqnLML1Dr6Oba3j_vPfRzZfzn2df8eWPi29np5fYc8EaHn30gU2T4iBHryNIPnrqzUh5DFoIAoGPZFQ0cg1GsimGGLiWzIRR8-DYPjre6a5Lvt-E2uxdqj7Ms1uF7tEqYRhRBHQnP_2X3IZFpNSUdfToBZSCopq_DHIlwQjZwY__gLd5U1Y9me4QmGIcoENkB_mSay0h2p7pnStPloDddmt33dre7R-7VvSbw2dhV72bY3Ern-rfQ8WU1nqrfbDjosvW_SqdubmmQBj0bCjVhP0GmuesiA</recordid><startdate>20051101</startdate><enddate>20051101</enddate><creator>Sarangi, A</creator><creator>Madramootoo, C.A</creator><creator>Enright, P</creator><creator>Chandrasekharan, H</creator><general>Springer</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7T7</scope><scope>7TV</scope><scope>7U7</scope><scope>7UA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H96</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>L.G</scope><scope>M0C</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>P64</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7ST</scope><scope>SOI</scope><scope>7U6</scope><scope>7SU</scope><scope>KR7</scope><scope>7TG</scope><scope>KL.</scope></search><sort><creationdate>20051101</creationdate><title>Prediction of spatial variability of phosphorous over the St-Esprit watershed</title><author>Sarangi, A ; Madramootoo, C.A ; Enright, P ; Chandrasekharan, H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c453t-bcfce3dd7406bc8f064bc2c9b24fe85510e4b1b72f480963dfefe48639eb84ea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Agricultural land</topic><topic>agricultural runoff</topic><topic>Agronomy. Soil science and plant productions</topic><topic>Applied sciences</topic><topic>Biological and medical sciences</topic><topic>Biological and physicochemical phenomena</topic><topic>Data analysis</topic><topic>Data processing</topic><topic>Drainage</topic><topic>Drainage patterns</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Engineering and environment geology. Geothermics</topic><topic>Environmental monitoring</topic><topic>equations</topic><topic>Exact sciences and technology</topic><topic>Fittings</topic><topic>Freshwater</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>geographic information systems</topic><topic>Geostatistics</topic><topic>Kriging</topic><topic>Land</topic><topic>Land use</topic><topic>losses from soil</topic><topic>Mathematical models</topic><topic>Natural water pollution</topic><topic>Nonpoint source pollution</topic><topic>phosphorus</topic><topic>Point source pollution</topic><topic>Pollution</topic><topic>Pollution sources. Measurement results</topic><topic>Pollution, environment geology</topic><topic>Runoff</topic><topic>Soil and sediments pollution</topic><topic>Soil and water pollution</topic><topic>Soil science</topic><topic>Spatial analysis</topic><topic>spatial data</topic><topic>spatial variation</topic><topic>statistical models</topic><topic>Variability</topic><topic>water pollution</topic><topic>Water treatment and pollution</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sarangi, A</creatorcontrib><creatorcontrib>Madramootoo, C.A</creatorcontrib><creatorcontrib>Enright, P</creatorcontrib><creatorcontrib>Chandrasekharan, H</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Pollution Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</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>Business Premium Collection</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>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ABI/INFORM Global</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</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>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Civil Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><jtitle>Water, air, and soil pollution</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sarangi, A</au><au>Madramootoo, C.A</au><au>Enright, P</au><au>Chandrasekharan, H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of spatial variability of phosphorous over the St-Esprit watershed</atitle><jtitle>Water, air, and soil pollution</jtitle><date>2005-11-01</date><risdate>2005</risdate><volume>168</volume><issue>1-4</issue><spage>267</spage><epage>288</epage><pages>267-288</pages><issn>0049-6979</issn><eissn>1573-2932</eissn><coden>WAPLAC</coden><abstract>Spatial data analysis tools for predicting the variability of non-point source pollutants minimize the time, effort and cost involved in extensive and exhaustive real field data measurements. In this study, exploratory data analysis, fitting of semivariogram models, and kriging techniques of geostatistics were used to develop the spatial variability map of soil phosphorous saturation (P sub(sat)) percentage over the St-Espirit watershed (2610 ha), located in Quebec, Canada. The P sub(sat) measured values for the 281 geo referenced land parcel units (LPU) within the watershed were interpreted and analyzed using the ArcGIS super(+) tool. The geostatistical extension module of ArcGIS super(+) was used for exploratory data analysis, semivariogram model fitting, and development of a P sub(sat) prediction map using the ordinary kriging technique. Using these geostatistical procedures and adjustment of lag sizes and lag intervals representing the data sets, it was estimated that the spherical semivariogram model fitted well to represent the P sub(sat) variability with residual sum square (RSS) of 0.0003 and coefficient of determination (R super(2)) of 0.98. Further, the developed model was used to predict the P sub(sat) variability over the St. Esprit watershed using the 1605 geo-referenced LPU locations. The generated spatial variability map was geo-spatially processed with the natural drainage network and land use feature classes of the watershed to ascertain the phosphorous loading and locate vulnerable LPUs for phosphorous management. It was observed that the P sub(sat) levels were higher at the up stream locations and near the drainage channels than the locations close to watershed outlet. Also, the land pockets with more than 60% agricultural land use resulted in supra-optimal P sub(sat) values (10% > P sub(sat) < 20%), out of which 8.5 to 16.3 ha agricultural land of the St. Esprit watershed exhibited critical agro-environmental threshold P sub(sat) values (P sub(sat) > 20%). It was also revealed that, around 23.5% of the watersheds cropped area has reached these threshold levels which necessitate judicious P input management.</abstract><cop>Dordrecht</cop><pub>Springer</pub><doi>10.1007/s11270-005-1777-5</doi><tpages>22</tpages></addata></record> |
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| subjects | Agricultural land agricultural runoff Agronomy. Soil science and plant productions Applied sciences Biological and medical sciences Biological and physicochemical phenomena Data analysis Data processing Drainage Drainage patterns Earth sciences Earth, ocean, space Engineering and environment geology. Geothermics Environmental monitoring equations Exact sciences and technology Fittings Freshwater Fundamental and applied biological sciences. Psychology geographic information systems Geostatistics Kriging Land Land use losses from soil Mathematical models Natural water pollution Nonpoint source pollution phosphorus Point source pollution Pollution Pollution sources. Measurement results Pollution, environment geology Runoff Soil and sediments pollution Soil and water pollution Soil science Spatial analysis spatial data spatial variation statistical models Variability water pollution Water treatment and pollution Watersheds |
| title | Prediction of spatial variability of phosphorous over the St-Esprit watershed |
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