多种格点作物模型对中国区域水稻产量模拟能力评估
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摘要
基于部门间影响模型比较计划(The Inter-Sectoral Impact Model Intercomparison Project,ISIMIP)FAST-TRACK轮模拟中由5种国际耦合模式比较计划第五阶段(CMIP5)全球气候资料驱动下的6种水稻格点作物模型模拟水稻产量的结果,评估了格点作物模型模拟中国区域水稻历史产量(1980-2004年)的时空分布模拟效果,并基于多种作物模型等权重集合平均(Multi-Crop Models Ensemble,MCME)对未来(2020-2099年)4种不同典型浓度路径(Recommended Concentration Pathways,RCPs)情景下的中国区域水稻产量进行预估。结果表明:相对于单种水稻模型模拟的结果,采用MCME可以有效提高水稻模型在中国区域的模拟能力。MCME模拟中国区域水稻历史年平均产量相关系数R和RMSE分别为0.798和1540.6kg·hm~(-2),在空间上对东北和西南地区模拟效果较好,其它地区模拟效果一般,模拟水稻产量的空间变率较大。未来随着气温和CO_2浓度的上升,水稻产量呈增加趋势,在RCP8.5情景下中国区域平均水稻产量在21世纪末增加最多,达到22%,RCP6.0情景下约增产15%,RCP2.6和RCP4.5情景下水稻产量在21世纪上半叶增产,21世纪下半叶产量保持稳定甚至略有下滑,在21世纪末分别增产约4%和10%,在空间上东北和西南地区水稻增产较多,可达40%以上,其它水稻主产区如长江中下游地区和华南地区增产较小。
Based on The Inter-Sectoral Impact Model Intercomparison Project(ISIMIP) FAST-TRACK round’s results, we evaluated rice yield simulations of 6 global gridded crop models(GGCMs) driven by 5 Coupled Model Intercomparison Project Phase 5(CMIP5) climate datasets from 1980 to 2004 as history period. Subsequently, the multi-crop model ensemble(MCME) was used to predict temporal-spatial distribution of future rice yield over China from the year 2020 to 2099 under different Recommended Concentration Pathways(RCPs). The results showed that MCME provided better performance for historic rice yield distribution with R of 0.798 and RMSE of 1540.6 kg·ha~(-1) compared to single crop model results. MCME results showed better simulations in the north-east and south-westregions of China, but had poor performance in other regions. Moreover, the MCME overestimated spatial variability. Furthermore, under the increasing of temperature and CO_2 concentration, rice yield had the largest growth of nearly 20% in the late 21~(st) century under RCP8.5 scenario compare to the early 21~(st) century and had a larger growth of 15% under RCP6.0 scenario approximately. For RCP2.6 and RCP4.5 scenarios, rice yield increased in the first half of 21~(st) century, and stayed stable or even slightly decreased in the second half of 21~(st) century, thus leading to a rise of only 4% and 10% respectively by the late 21~(st) century. Rice yield would increase significantly(>40%) in the north-east and south-west regions of China. While other main rice planting areas like middle and lower reaches of Yangtze River and South China only experienced little increase.
Based on The Inter-Sectoral Impact Model Intercomparison Project(ISIMIP) FAST-TRACK round’s results, we evaluated rice yield simulations of 6 global gridded crop models(GGCMs) driven by 5 Coupled Model Intercomparison Project Phase 5(CMIP5) climate datasets from 1980 to 2004 as history period. Subsequently, the multi-crop model ensemble(MCME) was used to predict temporal-spatial distribution of future rice yield over China from the year 2020 to 2099 under different Recommended Concentration Pathways(RCPs). The results showed that MCME provided better performance for historic rice yield distribution with R of 0.798 and RMSE of 1540.6 kg·ha~(-1) compared to single crop model results. MCME results showed better simulations in the north-east and south-westregions of China, but had poor performance in other regions. Moreover, the MCME overestimated spatial variability. Furthermore, under the increasing of temperature and CO_2 concentration, rice yield had the largest growth of nearly 20% in the late 21~(st) century under RCP8.5 scenario compare to the early 21~(st) century and had a larger growth of 15% under RCP6.0 scenario approximately. For RCP2.6 and RCP4.5 scenarios, rice yield increased in the first half of 21~(st) century, and stayed stable or even slightly decreased in the second half of 21~(st) century, thus leading to a rise of only 4% and 10% respectively by the late 21~(st) century. Rice yield would increase significantly(>40%) in the north-east and south-west regions of China. While other main rice planting areas like middle and lower reaches of Yangtze River and South China only experienced little increase.
引文
[1]Wheeler T,Von Braun J.Climate change impacts on global food security[J].Science,2013,341(6145):508-513.
[2]FAO.FAO-STAT[EB/OL].http://www.fao.org/faostat/en/#data,2017.
[3]IPCC.Climate change 2014:synthesis report.Contribution of working groups I,II and III to the Fifth assessment report of the intergovernmental panel on climate change[M].Core Writing Team,R.K.Pachauri and L.A.Meyer,2014.
[4]Zhao C,Piao S,Wang X,et al.Plausible rice yield losses under future climate warming[J].Nature Plants,2016,3(1):16202.
[5]Cai C,Li G,Yang H,et al.Do all leaf photosynthesis parameters of rice acclimate to elevated CO2,elevated temperature,and their combination,in FACE environments[J].Global Change Biology,2018,24(4):1685-1707.
[6]孙擎,杨再强,高丽娜,等.低温对早稻幼穗分化期叶片生理特性的影响及其与产量的关系[J].中国生态农业学报,2014,22(11):1326-1333.Sun Q,Yang Z Q,Gao L N,et al.Effect of low temperature stress on physiological characteristics of flag leaf and its relationship with grain yield during panicle primordium differentiation stage of early rice[J].Chinese Journal of Eco-Agriculture,2014,22(11):1326-1333.(in Chinese)
[7]Zhao C,Liu B,Piao S,et al.Temperature increase reduces global yields of major crops in four independent estimates[J].Proceedings of the National Academy of Sciences of the United States of America,National Academy of Sciences,2017,114(35):9326-9331.
[8]Dias H B,Sentelhas P C.Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields[J].Field Crops Research,Elsevier,2017,213:174-185.
[9]Maiorano A,Martre P,Asseng S,et al.Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles[J].Field Crops Research,2017,202:5-20.
[10]Schleussner C-F,Deryng D,Müller C,et al.Crop productivity changes in 1.5℃and 2℃worlds under climate sensitivity uncertainty[J].Environmental Research Letters,2018,13(6):064007.
[11]Li T,Hasegawa T,Yin X,et al.Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions[J].Global Change Biology,2015,21(3):1328-1341.
[12]Masutomi Y,Takahashi K,Harasawa H,et al.Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models[J].Agriculture,Ecosystems and Environment,2009,131(3-4):281-291.
[13]李阔,熊伟,潘婕,等.未来升温1.5℃与2.0℃背景下中国玉米产量变化趋势评估[J].中国农业气象,2018,39(12):765-777.Li K,Xiong W,Pan J,et al.Trend evaluation on changes of maize yield in China under global warming by 1.5℃and2.0℃[J].Chinese Journal of Agrometeorology,2018,39(12):765-777.(in Chinese)
[14]周桐宇,江敏,孙汪亮,等.RCPs情景下福建省水稻生产的适应性调整模拟研究[J].中国水稻科学,2018,32(3):265-276.Zhou T Y,Jiang M,Sun W L,et al.Simulation of rice adaptability adjustment in Fujian province under RCPs scenarios[J].Chin J Rice Sci,2018,32(3):265-276.(in Chinese)
[15]杨绚,汤绪,陈葆德,等.利用CMIP5多模式集合模拟气候变化对中国小麦产量的影响[J].中国农业科学,2014,47(15):3009-3024.Yang X,Tang X,Chen B D,et al.Impacts of climate change on wheat yield in China simulated by CMIP5 Multi-Model ensemble projections[J].Scientia Agricultura Sinica,2014,47(15):3009-3024.(in Chinese)
[16]Knutti R,Sedlá?ek J.Robustness and uncertainties in the new CMIP5 climate model projections[J].Nature Climate Change,2012,3(4):369-373.
[17]Taylor K E,Stouffer R J,Meehl G A.An overview of CMIP5and the experiment design[J].Bulletin of the American Meteorological Society,2012,93(4):485-498.
[18]Guo X,Huang J,Luo Y,et al.Projection of heat waves over China for eight different global warming targets using 12CMIP5 models[J].Theoretical and Applied Climatology,Springer Vienna,2017,128(3-4):507-522.
[19]Warszawski L,Frieler K,Huber V,et al.The Inter-Sectoral Impact Model Intercomparison Project(ISI-MIP):project framework[J].Proceedings of the National Academy of Sciences,2014,111(9):3228-3232.
[20]Williams J R.The EPIC model in:computer models of watershed hydrology,singh,VP[M].Highlands Ranch,Colorado,USA:Water Resources Publications,1995:909-1000.
[21]Izaurralde R C,Williams J R,McGill W B,et al.Simulating soil C dynamics with EPIC:model description and testing against long-term data[J].Ecological Modelling,2006,192(3-4):362-384.
[22]Bouwman A F,Kram T,Klein Goldewijk K.Integrated modelling of global environmental change.An overview of IMAGE[M].Netherlands:Netherlands Environmental Assessment Agency,Bilthoven,2006:225-228.
[23]Lindeskog M,Arneth A,Bondeau A,et al.Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa[J].Earth System Dynamics,2013,4(2):385-407.
[24]Smith B,Prentice I C,Sykes M T.Representation of vegetation dynamics in the modelling of terrestrial ecosystems:comparing two contrasting approaches within European climate space[J].ETEMA SPECIAL ISSUEGlobal Ecology&Biogeography,2001,10:621-637.
[25]Waha K,Van Bussel L G J,Müller C,et al.Climate‐driven simulation of global crop sowing dates[J].Global Ecology and Biogeography,Wiley Online Library,2012,21(2):247-259.
[26]Bondeau A,Smith P C,Zaehle S,et al.Modelling the role of agriculture for the 20th century global terrestrial carbon balance[J].Global Change Biology,2007,13(3):679-706.
[27]Elliott J,Kelly D,Chryssanthacopoulos J,et al.The parallel system for integrating impact models and sectors p SIMS[A].Proceedings of the conference on extreme science and engineering discovery environment gateway to discovery[C].New York:ACM Press,2013:1.
[28]Jones J W,Hoogenboom G,Porter C H,et al.The DSSATcropping system model[J].European Journal of Agronomy,2003,18(3-4):235-265.
[29]Fricko O,Havlik P,Rogelj J,et al.The marker quantification of the Shared Socioeconomic Pathway 2:a middle-of-the-road scenario for the 21st century[J].Global Environmental Change,Elsevier Ltd,2017,42:251-267.
[30]Weedon G P,Gomes S,Viterbo P,et al.Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century[J].Journal of Hydrometeorology,2011,12(5):823-848.
[31]Hempel S,Frieler K,Warszawski L,et al.A trend-preserving bias correction-the ISI-MIP approach[J].Earth System Dynamics,2013,4(2):219-236.
[32]Elliott J,Müller C,Deryng D,et al.The global gridded crop model intercomparison:data and modeling protocols for Phase 1(v1.0)[J].Geoscientific Model Development,2015,8(2):261-277.
[33]Müller C,Elliott J,Chryssanthacopoulos J,et al.Global gridded crop model evaluation:benchmarking,skills,deficiencies and implications[J].Geoscientific Model Development,2017,10(4):1403-1422.
[34]Nelson A,Gumma M K.A map of lowland rice extent in the major rice growing countries of Asia[OL].http://irri.org/ourwork/research/policy-and-markets/mapping,2015.
[35]Feng M,Huang C,Channan S,et al.Quality assessment of Landsat surface reflectance products using MODIS data[J].Computers and Geosciences,Elsevier,2012,38(1):9-22.
[36]Wang J,Wang E,Yang X,et al.Increased yield potential of wheat-maize cropping system in the North China Plain by climate change adaptation[J].Climatic Change,2012,113(3-4):825-840.
[37]Xu Y,Gao X,Giorgi F.Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections[J].Climate Research,2010,41(1):61-81.
[2]FAO.FAO-STAT[EB/OL].http://www.fao.org/faostat/en/#data,2017.
[3]IPCC.Climate change 2014:synthesis report.Contribution of working groups I,II and III to the Fifth assessment report of the intergovernmental panel on climate change[M].Core Writing Team,R.K.Pachauri and L.A.Meyer,2014.
[4]Zhao C,Piao S,Wang X,et al.Plausible rice yield losses under future climate warming[J].Nature Plants,2016,3(1):16202.
[5]Cai C,Li G,Yang H,et al.Do all leaf photosynthesis parameters of rice acclimate to elevated CO2,elevated temperature,and their combination,in FACE environments[J].Global Change Biology,2018,24(4):1685-1707.
[6]孙擎,杨再强,高丽娜,等.低温对早稻幼穗分化期叶片生理特性的影响及其与产量的关系[J].中国生态农业学报,2014,22(11):1326-1333.Sun Q,Yang Z Q,Gao L N,et al.Effect of low temperature stress on physiological characteristics of flag leaf and its relationship with grain yield during panicle primordium differentiation stage of early rice[J].Chinese Journal of Eco-Agriculture,2014,22(11):1326-1333.(in Chinese)
[7]Zhao C,Liu B,Piao S,et al.Temperature increase reduces global yields of major crops in four independent estimates[J].Proceedings of the National Academy of Sciences of the United States of America,National Academy of Sciences,2017,114(35):9326-9331.
[8]Dias H B,Sentelhas P C.Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields[J].Field Crops Research,Elsevier,2017,213:174-185.
[9]Maiorano A,Martre P,Asseng S,et al.Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles[J].Field Crops Research,2017,202:5-20.
[10]Schleussner C-F,Deryng D,Müller C,et al.Crop productivity changes in 1.5℃and 2℃worlds under climate sensitivity uncertainty[J].Environmental Research Letters,2018,13(6):064007.
[11]Li T,Hasegawa T,Yin X,et al.Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions[J].Global Change Biology,2015,21(3):1328-1341.
[12]Masutomi Y,Takahashi K,Harasawa H,et al.Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models[J].Agriculture,Ecosystems and Environment,2009,131(3-4):281-291.
[13]李阔,熊伟,潘婕,等.未来升温1.5℃与2.0℃背景下中国玉米产量变化趋势评估[J].中国农业气象,2018,39(12):765-777.Li K,Xiong W,Pan J,et al.Trend evaluation on changes of maize yield in China under global warming by 1.5℃and2.0℃[J].Chinese Journal of Agrometeorology,2018,39(12):765-777.(in Chinese)
[14]周桐宇,江敏,孙汪亮,等.RCPs情景下福建省水稻生产的适应性调整模拟研究[J].中国水稻科学,2018,32(3):265-276.Zhou T Y,Jiang M,Sun W L,et al.Simulation of rice adaptability adjustment in Fujian province under RCPs scenarios[J].Chin J Rice Sci,2018,32(3):265-276.(in Chinese)
[15]杨绚,汤绪,陈葆德,等.利用CMIP5多模式集合模拟气候变化对中国小麦产量的影响[J].中国农业科学,2014,47(15):3009-3024.Yang X,Tang X,Chen B D,et al.Impacts of climate change on wheat yield in China simulated by CMIP5 Multi-Model ensemble projections[J].Scientia Agricultura Sinica,2014,47(15):3009-3024.(in Chinese)
[16]Knutti R,Sedlá?ek J.Robustness and uncertainties in the new CMIP5 climate model projections[J].Nature Climate Change,2012,3(4):369-373.
[17]Taylor K E,Stouffer R J,Meehl G A.An overview of CMIP5and the experiment design[J].Bulletin of the American Meteorological Society,2012,93(4):485-498.
[18]Guo X,Huang J,Luo Y,et al.Projection of heat waves over China for eight different global warming targets using 12CMIP5 models[J].Theoretical and Applied Climatology,Springer Vienna,2017,128(3-4):507-522.
[19]Warszawski L,Frieler K,Huber V,et al.The Inter-Sectoral Impact Model Intercomparison Project(ISI-MIP):project framework[J].Proceedings of the National Academy of Sciences,2014,111(9):3228-3232.
[20]Williams J R.The EPIC model in:computer models of watershed hydrology,singh,VP[M].Highlands Ranch,Colorado,USA:Water Resources Publications,1995:909-1000.
[21]Izaurralde R C,Williams J R,McGill W B,et al.Simulating soil C dynamics with EPIC:model description and testing against long-term data[J].Ecological Modelling,2006,192(3-4):362-384.
[22]Bouwman A F,Kram T,Klein Goldewijk K.Integrated modelling of global environmental change.An overview of IMAGE[M].Netherlands:Netherlands Environmental Assessment Agency,Bilthoven,2006:225-228.
[23]Lindeskog M,Arneth A,Bondeau A,et al.Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa[J].Earth System Dynamics,2013,4(2):385-407.
[24]Smith B,Prentice I C,Sykes M T.Representation of vegetation dynamics in the modelling of terrestrial ecosystems:comparing two contrasting approaches within European climate space[J].ETEMA SPECIAL ISSUEGlobal Ecology&Biogeography,2001,10:621-637.
[25]Waha K,Van Bussel L G J,Müller C,et al.Climate‐driven simulation of global crop sowing dates[J].Global Ecology and Biogeography,Wiley Online Library,2012,21(2):247-259.
[26]Bondeau A,Smith P C,Zaehle S,et al.Modelling the role of agriculture for the 20th century global terrestrial carbon balance[J].Global Change Biology,2007,13(3):679-706.
[27]Elliott J,Kelly D,Chryssanthacopoulos J,et al.The parallel system for integrating impact models and sectors p SIMS[A].Proceedings of the conference on extreme science and engineering discovery environment gateway to discovery[C].New York:ACM Press,2013:1.
[28]Jones J W,Hoogenboom G,Porter C H,et al.The DSSATcropping system model[J].European Journal of Agronomy,2003,18(3-4):235-265.
[29]Fricko O,Havlik P,Rogelj J,et al.The marker quantification of the Shared Socioeconomic Pathway 2:a middle-of-the-road scenario for the 21st century[J].Global Environmental Change,Elsevier Ltd,2017,42:251-267.
[30]Weedon G P,Gomes S,Viterbo P,et al.Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century[J].Journal of Hydrometeorology,2011,12(5):823-848.
[31]Hempel S,Frieler K,Warszawski L,et al.A trend-preserving bias correction-the ISI-MIP approach[J].Earth System Dynamics,2013,4(2):219-236.
[32]Elliott J,Müller C,Deryng D,et al.The global gridded crop model intercomparison:data and modeling protocols for Phase 1(v1.0)[J].Geoscientific Model Development,2015,8(2):261-277.
[33]Müller C,Elliott J,Chryssanthacopoulos J,et al.Global gridded crop model evaluation:benchmarking,skills,deficiencies and implications[J].Geoscientific Model Development,2017,10(4):1403-1422.
[34]Nelson A,Gumma M K.A map of lowland rice extent in the major rice growing countries of Asia[OL].http://irri.org/ourwork/research/policy-and-markets/mapping,2015.
[35]Feng M,Huang C,Channan S,et al.Quality assessment of Landsat surface reflectance products using MODIS data[J].Computers and Geosciences,Elsevier,2012,38(1):9-22.
[36]Wang J,Wang E,Yang X,et al.Increased yield potential of wheat-maize cropping system in the North China Plain by climate change adaptation[J].Climatic Change,2012,113(3-4):825-840.
[37]Xu Y,Gao X,Giorgi F.Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections[J].Climate Research,2010,41(1):61-81.