Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies
Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qian, Budong [verfasserIn] De Jong, Reinder [verfasserIn] Huffman, Ted [verfasserIn] Wang, Hong [verfasserIn] Yang, Jingyi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Theoretical and applied climatology - Wien [u.a.] : Springer, 1948, 123(2015), 3-4 vom: 22. Jan., Seite 651-669 |
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| Übergeordnetes Werk: |
volume:123 ; year:2015 ; number:3-4 ; day:22 ; month:01 ; pages:651-669 |
| Links: |
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| DOI / URN: |
10.1007/s00704-015-1378-1 |
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| Katalog-ID: |
SPR007337353 |
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| 520 | |a Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. | ||
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| 700 | 1 | |a Yang, Jingyi |e verfasserin |4 aut | |
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10.1007/s00704-015-1378-1 doi (DE-627)SPR007337353 (SPR)s00704-015-1378-1-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Qian, Budong verfasserin aut Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. Regional Climate Model (dpeaa)DE-He213 Climate Scenario (dpeaa)DE-He213 Future Climate Scenario (dpeaa)DE-He213 Canadian Prairie (dpeaa)DE-He213 Canadian Regional Climate Model (dpeaa)DE-He213 De Jong, Reinder verfasserin aut Huffman, Ted verfasserin aut Wang, Hong verfasserin aut Yang, Jingyi verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 123(2015), 3-4 vom: 22. Jan., Seite 651-669 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:123 year:2015 number:3-4 day:22 month:01 pages:651-669 https://dx.doi.org/10.1007/s00704-015-1378-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 123 2015 3-4 22 01 651-669 |
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10.1007/s00704-015-1378-1 doi (DE-627)SPR007337353 (SPR)s00704-015-1378-1-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Qian, Budong verfasserin aut Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. Regional Climate Model (dpeaa)DE-He213 Climate Scenario (dpeaa)DE-He213 Future Climate Scenario (dpeaa)DE-He213 Canadian Prairie (dpeaa)DE-He213 Canadian Regional Climate Model (dpeaa)DE-He213 De Jong, Reinder verfasserin aut Huffman, Ted verfasserin aut Wang, Hong verfasserin aut Yang, Jingyi verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 123(2015), 3-4 vom: 22. Jan., Seite 651-669 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:123 year:2015 number:3-4 day:22 month:01 pages:651-669 https://dx.doi.org/10.1007/s00704-015-1378-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 123 2015 3-4 22 01 651-669 |
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10.1007/s00704-015-1378-1 doi (DE-627)SPR007337353 (SPR)s00704-015-1378-1-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Qian, Budong verfasserin aut Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. Regional Climate Model (dpeaa)DE-He213 Climate Scenario (dpeaa)DE-He213 Future Climate Scenario (dpeaa)DE-He213 Canadian Prairie (dpeaa)DE-He213 Canadian Regional Climate Model (dpeaa)DE-He213 De Jong, Reinder verfasserin aut Huffman, Ted verfasserin aut Wang, Hong verfasserin aut Yang, Jingyi verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 123(2015), 3-4 vom: 22. Jan., Seite 651-669 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:123 year:2015 number:3-4 day:22 month:01 pages:651-669 https://dx.doi.org/10.1007/s00704-015-1378-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 123 2015 3-4 22 01 651-669 |
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10.1007/s00704-015-1378-1 doi (DE-627)SPR007337353 (SPR)s00704-015-1378-1-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Qian, Budong verfasserin aut Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. Regional Climate Model (dpeaa)DE-He213 Climate Scenario (dpeaa)DE-He213 Future Climate Scenario (dpeaa)DE-He213 Canadian Prairie (dpeaa)DE-He213 Canadian Regional Climate Model (dpeaa)DE-He213 De Jong, Reinder verfasserin aut Huffman, Ted verfasserin aut Wang, Hong verfasserin aut Yang, Jingyi verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 123(2015), 3-4 vom: 22. Jan., Seite 651-669 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:123 year:2015 number:3-4 day:22 month:01 pages:651-669 https://dx.doi.org/10.1007/s00704-015-1378-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 123 2015 3-4 22 01 651-669 |
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10.1007/s00704-015-1378-1 doi (DE-627)SPR007337353 (SPR)s00704-015-1378-1-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Qian, Budong verfasserin aut Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. Regional Climate Model (dpeaa)DE-He213 Climate Scenario (dpeaa)DE-He213 Future Climate Scenario (dpeaa)DE-He213 Canadian Prairie (dpeaa)DE-He213 Canadian Regional Climate Model (dpeaa)DE-He213 De Jong, Reinder verfasserin aut Huffman, Ted verfasserin aut Wang, Hong verfasserin aut Yang, Jingyi verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 123(2015), 3-4 vom: 22. Jan., Seite 651-669 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:123 year:2015 number:3-4 day:22 month:01 pages:651-669 https://dx.doi.org/10.1007/s00704-015-1378-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 123 2015 3-4 22 01 651-669 |
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Enthalten in Theoretical and applied climatology 123(2015), 3-4 vom: 22. Jan., Seite 651-669 volume:123 year:2015 number:3-4 day:22 month:01 pages:651-669 |
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Qian, Budong @@aut@@ De Jong, Reinder @@aut@@ Huffman, Ted @@aut@@ Wang, Hong @@aut@@ Yang, Jingyi @@aut@@ |
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Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regional Climate Model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Climate Scenario</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Future Climate Scenario</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Canadian Prairie</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Canadian Regional Climate Model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">De Jong, Reinder</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huffman, Ted</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Hong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Jingyi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Theoretical and applied climatology</subfield><subfield code="d">Wien [u.a.] : Springer, 1948</subfield><subfield code="g">123(2015), 3-4 vom: 22. 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Qian, Budong |
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projecting yield changes of spring wheat under future climate scenarios on the canadian prairies |
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Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies |
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Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. |
| abstractGer |
Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. |
| abstract_unstemmed |
Abstract The potential impact of the rise in atmospheric $ CO_{2} $ concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated $ CO_{2} $ concentration were simulated, but only up to 15 % if the elevated $ CO_{2} $ effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages. |
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| score |
7.3994217 |