Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010
Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source...
Ausführliche Beschreibung
Autor*in: |
Sullivan, Timothy P. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Schlagwörter: |
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Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2016 |
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Übergeordnetes Werk: |
Enthalten in: Environmental earth sciences - Berlin : Springer, 2009, 75(2016), 9 vom: 25. Apr. |
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Übergeordnetes Werk: |
volume:75 ; year:2016 ; number:9 ; day:25 ; month:04 |
Links: |
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DOI / URN: |
10.1007/s12665-016-5546-0 |
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Katalog-ID: |
SPR026730960 |
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520 | |a Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. | ||
650 | 4 | |a Nitrogen |7 (dpeaa)DE-He213 | |
650 | 4 | |a Nitrate |7 (dpeaa)DE-He213 | |
650 | 4 | |a Surface water contamination |7 (dpeaa)DE-He213 | |
650 | 4 | |a Surface water modeling |7 (dpeaa)DE-He213 | |
650 | 4 | |a SWAT |7 (dpeaa)DE-He213 | |
700 | 1 | |a Gao, Yongli |4 aut | |
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10.1007/s12665-016-5546-0 doi (DE-627)SPR026730960 (SPR)s12665-016-5546-0-e DE-627 ger DE-627 rakwb eng Sullivan, Timothy P. verfasserin aut Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. Nitrogen (dpeaa)DE-He213 Nitrate (dpeaa)DE-He213 Surface water contamination (dpeaa)DE-He213 Surface water modeling (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Gao, Yongli aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 9 vom: 25. Apr. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:9 day:25 month:04 https://dx.doi.org/10.1007/s12665-016-5546-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 9 25 04 |
spelling |
10.1007/s12665-016-5546-0 doi (DE-627)SPR026730960 (SPR)s12665-016-5546-0-e DE-627 ger DE-627 rakwb eng Sullivan, Timothy P. verfasserin aut Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. Nitrogen (dpeaa)DE-He213 Nitrate (dpeaa)DE-He213 Surface water contamination (dpeaa)DE-He213 Surface water modeling (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Gao, Yongli aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 9 vom: 25. Apr. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:9 day:25 month:04 https://dx.doi.org/10.1007/s12665-016-5546-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 9 25 04 |
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10.1007/s12665-016-5546-0 doi (DE-627)SPR026730960 (SPR)s12665-016-5546-0-e DE-627 ger DE-627 rakwb eng Sullivan, Timothy P. verfasserin aut Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. Nitrogen (dpeaa)DE-He213 Nitrate (dpeaa)DE-He213 Surface water contamination (dpeaa)DE-He213 Surface water modeling (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Gao, Yongli aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 9 vom: 25. Apr. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:9 day:25 month:04 https://dx.doi.org/10.1007/s12665-016-5546-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 9 25 04 |
allfieldsGer |
10.1007/s12665-016-5546-0 doi (DE-627)SPR026730960 (SPR)s12665-016-5546-0-e DE-627 ger DE-627 rakwb eng Sullivan, Timothy P. verfasserin aut Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. Nitrogen (dpeaa)DE-He213 Nitrate (dpeaa)DE-He213 Surface water contamination (dpeaa)DE-He213 Surface water modeling (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Gao, Yongli aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 9 vom: 25. Apr. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:9 day:25 month:04 https://dx.doi.org/10.1007/s12665-016-5546-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 9 25 04 |
allfieldsSound |
10.1007/s12665-016-5546-0 doi (DE-627)SPR026730960 (SPR)s12665-016-5546-0-e DE-627 ger DE-627 rakwb eng Sullivan, Timothy P. verfasserin aut Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. Nitrogen (dpeaa)DE-He213 Nitrate (dpeaa)DE-He213 Surface water contamination (dpeaa)DE-He213 Surface water modeling (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Gao, Yongli aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 9 vom: 25. Apr. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:9 day:25 month:04 https://dx.doi.org/10.1007/s12665-016-5546-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 9 25 04 |
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Enthalten in Environmental earth sciences 75(2016), 9 vom: 25. Apr. volume:75 year:2016 number:9 day:25 month:04 |
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Sullivan, Timothy P. @@aut@@ Gao, Yongli @@aut@@ |
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A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). 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Sullivan, Timothy P. |
spellingShingle |
Sullivan, Timothy P. misc Nitrogen misc Nitrate misc Surface water contamination misc Surface water modeling misc SWAT Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 |
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Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 Nitrogen (dpeaa)DE-He213 Nitrate (dpeaa)DE-He213 Surface water contamination (dpeaa)DE-He213 Surface water modeling (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 |
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Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 |
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Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 |
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10.1007/s12665-016-5546-0 |
title_sort |
assessment of nitrogen inputs and yields in the cibolo and dry comal creek watersheds using the swat model, texas, usa 1996–2010 |
title_auth |
Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 |
abstract |
Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. © Springer-Verlag Berlin Heidelberg 2016 |
abstractGer |
Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. © Springer-Verlag Berlin Heidelberg 2016 |
abstract_unstemmed |
Abstract Identifying sources of nitrogen input and nitrogen yield to waterways are important aspects of watershed management. A Soil and Water Assessment Tool (SWAT) model was developed for the Cibolo and Dry Comal Creek watersheds in south central Texas, USA to assess how individual nitrogen source categories contribute to nitrogen yield. The model includes nitrogen inputs from atmospheric deposition, fertilizer, manure, a wastewater treatment plant (WWTP), and on-site sewage facilities (OSSFs). Model calibration was successful with all model performance parameters rated satisfactory or better except for percent bias (PBIAS) during the streamflow calibration period in Cibolo Creek. The high PBIAS value likely resulted from the model underestimating streamflow during two flood events coupled with potential measurement errors for the observed streamflow values during the floods. The largest contributors to nitrogen input were livestock (42.0 %), atmospheric deposition (26.8 %), deer (9.9 %), and farm fertilizer use (9.7 %). The largest contributors to nitrogen yield were atmospheric deposition (56.3 % of nitrogen yield), livestock (25.9 %), and the WWTP (7.1 %). The difference in ranking between nitrogen inputs and nitrogen yields occurred because the percentage of nitrogen input that becomes nitrogen yield varied with source. The WWTP (100 % of the WWTP nitrogen input became nitrogen yield) was highest because it directly discharged to the waterway. The WWTP was followed by feral hogs (8.0 %), atmospheric deposition (5.8 %), and waterfowl (2.1 %). These results provide a source-by-source assessment of nitrogen inputs and their corresponding yields allowing watershed managers to better assess nitrogen management strategies. © Springer-Verlag Berlin Heidelberg 2016 |
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9 |
title_short |
Assessment of nitrogen inputs and yields in the Cibolo and Dry Comal Creek watersheds using the SWAT model, Texas, USA 1996–2010 |
url |
https://dx.doi.org/10.1007/s12665-016-5546-0 |
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up_date |
2024-07-03T22:26:29.354Z |
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|
score |
7.4018364 |