水稻格点作物模型在中国区域的不确定性评估
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摘要
作物模型是评估气候变化对农业生产影响的主要手段之一,但中国对格点作物模型间的比较研究尚处于初始阶段。为全面评估不同作物模型在中国不同区域对水稻产量模拟的有效性,利用联合国粮农组织(FAO)和中国农业农村部种植业管理司(SYB)水稻年平均产量统计资料,对由2种气候资料(AgMERRA和WFDEI-GPCC)和3种不同种植管理情景(Default、Fullharm和Harmnon情景)驱动的7种全球格点作物模型(CGMS-WOFOST、CLM-CROP、EPIC-BOKU、GEPIC、LPJML、PDSSAT和PEPIC模型)模拟的中国水稻产量进行了对比分析。结果表明:不同格点作物模型之间的模拟结果差异较大,在不同区域不同格点作物模型的模拟效果差异显著,不同格点作物模型对气候变化和种植管理情景的响应和敏感性不同,大部分模拟结果低估了水稻产量。使用不同水稻统计产量数据会对评估结果产生一定的影响。格点作物模型能够一定程度上模拟出水稻产量的年际变化和气候变化对产量的影响,但对于统计水稻产量上升的趋势较难模拟。通过综合分析产量在时间和空间上的波动情况,并利用2种评分方法对模拟表现打分,发现LPJML和PDSSAT在7种格点作物模型中模拟效果最好,同时也对不同气候数据和种植管理情景的变化最敏感,CLM-CROP的模拟效果最差。对不同种植管理情景,Default情景下的模拟效果显著高于Fullharm和Harmnon情景。多种格点作物模型集合平均(MME)可以降低单个格点作物模型模拟的误差,但需对MME中的集合模型进行挑选。
Crop model is a widely-used strategy to evaluate the impact of climate change imposed on agriculture.The inter-comparison of gridded crop model is still at initial stage in China, thus making it crucial to comprehensively evaluate the performance of each global gridded crop model(GGCM). Using the statistics derived from Food and Agricultura Organization of the United Nations(FAO) and Ministry of Agriculture and Rural Affairs of the People's Republic of China(SYB) statistical rice yearly mean yield, this paper compared the simulated rice yields of 7 GGCMs(i.e. CGMS-WOFOST, CLM-CROP, EPIC-BOKU, GEPIC, LPJML, PDSSAT and PEPIC),which are driven by 2 climate datasets(AgMERRA and WFDEI-GPCC) and 3 management scenarios(Default,Fullharm and Harmnon) from 1980 to 2009 in China. The comparisons show that the simulated rice yields from different GGCMs have significant discrepancy in different regions of China. Each GGCM has different response and sensitiveness to climate datasets and management scenarios. The majority of simulations underestimate rice yield in China even though different statistical rice yield data will affect evaluation results. To some degree, GGCMs are able to simulate the inter-annual variation of rice yield and climate change effects, but hardly simulate the pattern of rice yield increase of statistics. The analyses of rice yield fluctuation on temporal and spatial aspect demonstrate LPJML and PDSSAT perform better among 7 GGCMs using 2 skill score approaches, and are most sensitive to different climate datasets and management scenarios, while CLM-CROP have lowest stimulation accuracy. In terms of management scenarios, the simulation on Default scenario performs dramatically better than that of Fullharm and Harmnon scenarios. In addition, Multi-gridded crop model ensemble(MME) could reduce simulation error compared to single GGCM, but requires suitable members to precisely perform MME.
Crop model is a widely-used strategy to evaluate the impact of climate change imposed on agriculture.The inter-comparison of gridded crop model is still at initial stage in China, thus making it crucial to comprehensively evaluate the performance of each global gridded crop model(GGCM). Using the statistics derived from Food and Agricultura Organization of the United Nations(FAO) and Ministry of Agriculture and Rural Affairs of the People's Republic of China(SYB) statistical rice yearly mean yield, this paper compared the simulated rice yields of 7 GGCMs(i.e. CGMS-WOFOST, CLM-CROP, EPIC-BOKU, GEPIC, LPJML, PDSSAT and PEPIC),which are driven by 2 climate datasets(AgMERRA and WFDEI-GPCC) and 3 management scenarios(Default,Fullharm and Harmnon) from 1980 to 2009 in China. The comparisons show that the simulated rice yields from different GGCMs have significant discrepancy in different regions of China. Each GGCM has different response and sensitiveness to climate datasets and management scenarios. The majority of simulations underestimate rice yield in China even though different statistical rice yield data will affect evaluation results. To some degree, GGCMs are able to simulate the inter-annual variation of rice yield and climate change effects, but hardly simulate the pattern of rice yield increase of statistics. The analyses of rice yield fluctuation on temporal and spatial aspect demonstrate LPJML and PDSSAT perform better among 7 GGCMs using 2 skill score approaches, and are most sensitive to different climate datasets and management scenarios, while CLM-CROP have lowest stimulation accuracy. In terms of management scenarios, the simulation on Default scenario performs dramatically better than that of Fullharm and Harmnon scenarios. In addition, Multi-gridded crop model ensemble(MME) could reduce simulation error compared to single GGCM, but requires suitable members to precisely perform MME.
引文
[1]Rosenzweig C,Elliott J,Deryng D,et al.Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison[J].Proceedings of the National Academy of Sciences of the United States of America,2014,111(9):3268-3273.
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[7]Frieler K,Schauberger B,Arneth A,et al.Understanding the weather signal in national crop-yield variability[J].Earth’s Future,2017,5(6):605-616.
[8]Iizumi T,Yokozawa M,Sakurai G,et al.Historical changes in global yields:major cereal and legume crops from 1982 to2006[J].Global Ecology and Biogeography,2014,23(3):346-357.
[9]Ray D K,Ramankutty N,Mueller N D,et al.Recent patterns of crop yield growth and stagnation[J].Nature Communications,Nature Publishing Group,2012,(3):1293-1297.
[10]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.
[11]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.
[12]Rosenzweig C,Ruane A C,Antle J,et al.Coordinating AgMIPdata and models across global and regional scales for 1.5℃and 2.0℃assessments[J].Phil.Trans.R.Soc.A.,2017,376(2199):20160455.
[13]张祎,赵艳霞.多模式集合模拟气候变化对玉米产量的影响[J].中国生态农业学报,2017,25(6):941-948.Zhang W,Zhao Y X.Multi-model emsemble for simulation of the impact of climate change on maize yield[J].Chinese Journal of Eco-Agriculture,2017,25(6):941-948.(in Chinese)
[14]黄健熙,黄海,马鸿元,等.基于MCMC方法的WOFOST模型参数标定与不确定性分析[J].农业工程学报,2018,34(16):113-119.Huang J X,Huang H,Ma H Y,et al.Markov chain monte carlo based WOFOST model parameters calibration and uncertainty analysis[J].Transactions of the CSAE,2018,34(16):113-119.(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]姚凤梅,秦鹏程,张佳华,等.基于模型模拟气候变化对农业影响评估的不确定性及处理方法[J].科学通报,2011,56(8):547-555.Yao F M,Qin P C,Zhang J H,et al.Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods[J].Chin Sci Bull,2011,56(8):547-555.(in Chinese)
[17]杨伟才,毛晓敏.气候变化影响下作物模型的不确定性[J].排灌机械工程学报,2018,36(9):874-879,902.Yang W C,Mao X M.Uncertainty of crop models under influence of climate change[J].Journal of Drainage and Irrigation Machinery Engineering,2018,36(9):874-879,902.(in Chinese)
[18]秦鹏程,姚凤梅,曹秀霞,等.利用作物模型研究气候变化对农业影响的发展过程[J].中国农业气象,2011,32(2):240-245.Qin P C,Yao F M,Cao X X,et al.Development process of modeling impacts of climate change on agricultural productivity based on crop models[J].Chinese Journal of Agrometeorololy,2011,32(2):240-245.(in Chinese)
[19]de Wit A J W,van Diepen C A.Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs[J].International Journal of Applied Earth Observation and Geoinformation,2008,10(4):414-425.
[20]Drewniak B,Song J,Prell J,et al.Modeling agriculture in the Community Land Model[J].Geoscientific Model Development,2013,6(2):495-515.
[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]Williams J R.The EPIC model in:computer models of watershed hydrology,singh,VP[M].Water Resources Publications,Highlands Ranch,Colorado,USA,1995.
[23]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.
[24]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.
[25]Liu J,Williams J R,Zehnder A J B,et al.GEPIC-modelling wheat yield and crop water productivity with high resolution on a global scale[J].Agricultural Systems,2007,94(2):478-493.
[26]Elliott J,Kelly D,Chryssanthacopoulos J,et al.The parallel system for integrating impact models and sectors(p SIMS)[J].Environmental Modelling and Software,Elsevier Ltd,2014,62(2014):509-516.
[27]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.
[28]Folberth C,SkalskyR,Moltchanova E,et al.Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations[J].Nature Communications,2016,7(May):11872.
[29]Liu W,Yang H,Folberth C,et al.Global investigation of impacts of PET methods on simulating crop-water relations for maize[J].Agricultural and Forest Meteorology,Elsevier,2016,221:164-175.
[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]Sacks W J,Deryng D,Foley J A,et al.Crop planting dates:an analysis of global patterns[J].Global Ecology and Biogeography,2010,19(5):607-620.
[32]Portmann F,Siebert S,Bauer C,et al.Global dataset of monthly growing areas of 26 irrigated crops[J].Frankfurt Hydrology Paper,2008,60:400.
[33]Portmann F T,Siebert S,D?ll P.MIRCA2000-Global monthly irrigated and rainfed crop areas around the year2000:A new high-resolution data set for agricultural and hydrological modeling[J].Global Biogeochemical Cycles,2010,24(1).
[34]Mueller N D,Gerber J S,Johnston M,et al.Closing yield gaps through nutrient and water management[J].Nature,2013,494(7437):390.
[35]Potter P,Ramankutty N,Bennett E M,et al.Characterizing the spatial patterns of global fertilizer application and manure production[J].Earth Interactions,2010,14(1):1-22.
[36]Foley J A,Ramankutty N,Brauman K A,et al.Solutions for a cultivated planet[J].Nature,2011,145(7369):337-342.
[37]Nelson A,Gumma M K.A map of lowland rice extent in the major rice growing countries of Asia[OL].2015,http://irri.org/our-work/research/policy-and-markets/mapping.
[38]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.
[39]Taylor K E.Summarizing multiple aspects of model performance in a single diagram[J].Journal of Geophysical Research Atmospheres,2001,106(D7):7183-7192.
[40]Iizumi T,Ramankutty N.How do weather and climate influence cropping area and intensity[J].Global Food Security,2015,4:46-50.
[2]Davidb L,Marshallb B.On the use of statistical models to predict crop yield responses to climate change[J].Agricultural&Forest Meteorology,2010,150(11):1443-1452.
[3]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.
[4]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.
[5]Folberth C,Elliott J,Müller C,et al.Uncertainties in global crop model frameworks:effects of cultivar distribution,crop management and soil handling on crop yield estimates[J].Biogeosciences Discussions,2016,(12):1-30.
[6]Asif S,Naqvi A,Ashfaq M,et al.Current agricultural production system of punjab is vulnerable to climate change[J].2017,55(1):125-135.
[7]Frieler K,Schauberger B,Arneth A,et al.Understanding the weather signal in national crop-yield variability[J].Earth’s Future,2017,5(6):605-616.
[8]Iizumi T,Yokozawa M,Sakurai G,et al.Historical changes in global yields:major cereal and legume crops from 1982 to2006[J].Global Ecology and Biogeography,2014,23(3):346-357.
[9]Ray D K,Ramankutty N,Mueller N D,et al.Recent patterns of crop yield growth and stagnation[J].Nature Communications,Nature Publishing Group,2012,(3):1293-1297.
[10]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.
[11]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.
[12]Rosenzweig C,Ruane A C,Antle J,et al.Coordinating AgMIPdata and models across global and regional scales for 1.5℃and 2.0℃assessments[J].Phil.Trans.R.Soc.A.,2017,376(2199):20160455.
[13]张祎,赵艳霞.多模式集合模拟气候变化对玉米产量的影响[J].中国生态农业学报,2017,25(6):941-948.Zhang W,Zhao Y X.Multi-model emsemble for simulation of the impact of climate change on maize yield[J].Chinese Journal of Eco-Agriculture,2017,25(6):941-948.(in Chinese)
[14]黄健熙,黄海,马鸿元,等.基于MCMC方法的WOFOST模型参数标定与不确定性分析[J].农业工程学报,2018,34(16):113-119.Huang J X,Huang H,Ma H Y,et al.Markov chain monte carlo based WOFOST model parameters calibration and uncertainty analysis[J].Transactions of the CSAE,2018,34(16):113-119.(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]姚凤梅,秦鹏程,张佳华,等.基于模型模拟气候变化对农业影响评估的不确定性及处理方法[J].科学通报,2011,56(8):547-555.Yao F M,Qin P C,Zhang J H,et al.Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods[J].Chin Sci Bull,2011,56(8):547-555.(in Chinese)
[17]杨伟才,毛晓敏.气候变化影响下作物模型的不确定性[J].排灌机械工程学报,2018,36(9):874-879,902.Yang W C,Mao X M.Uncertainty of crop models under influence of climate change[J].Journal of Drainage and Irrigation Machinery Engineering,2018,36(9):874-879,902.(in Chinese)
[18]秦鹏程,姚凤梅,曹秀霞,等.利用作物模型研究气候变化对农业影响的发展过程[J].中国农业气象,2011,32(2):240-245.Qin P C,Yao F M,Cao X X,et al.Development process of modeling impacts of climate change on agricultural productivity based on crop models[J].Chinese Journal of Agrometeorololy,2011,32(2):240-245.(in Chinese)
[19]de Wit A J W,van Diepen C A.Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs[J].International Journal of Applied Earth Observation and Geoinformation,2008,10(4):414-425.
[20]Drewniak B,Song J,Prell J,et al.Modeling agriculture in the Community Land Model[J].Geoscientific Model Development,2013,6(2):495-515.
[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]Williams J R.The EPIC model in:computer models of watershed hydrology,singh,VP[M].Water Resources Publications,Highlands Ranch,Colorado,USA,1995.
[23]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.
[24]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.
[25]Liu J,Williams J R,Zehnder A J B,et al.GEPIC-modelling wheat yield and crop water productivity with high resolution on a global scale[J].Agricultural Systems,2007,94(2):478-493.
[26]Elliott J,Kelly D,Chryssanthacopoulos J,et al.The parallel system for integrating impact models and sectors(p SIMS)[J].Environmental Modelling and Software,Elsevier Ltd,2014,62(2014):509-516.
[27]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.
[28]Folberth C,SkalskyR,Moltchanova E,et al.Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations[J].Nature Communications,2016,7(May):11872.
[29]Liu W,Yang H,Folberth C,et al.Global investigation of impacts of PET methods on simulating crop-water relations for maize[J].Agricultural and Forest Meteorology,Elsevier,2016,221:164-175.
[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]Sacks W J,Deryng D,Foley J A,et al.Crop planting dates:an analysis of global patterns[J].Global Ecology and Biogeography,2010,19(5):607-620.
[32]Portmann F,Siebert S,Bauer C,et al.Global dataset of monthly growing areas of 26 irrigated crops[J].Frankfurt Hydrology Paper,2008,60:400.
[33]Portmann F T,Siebert S,D?ll P.MIRCA2000-Global monthly irrigated and rainfed crop areas around the year2000:A new high-resolution data set for agricultural and hydrological modeling[J].Global Biogeochemical Cycles,2010,24(1).
[34]Mueller N D,Gerber J S,Johnston M,et al.Closing yield gaps through nutrient and water management[J].Nature,2013,494(7437):390.
[35]Potter P,Ramankutty N,Bennett E M,et al.Characterizing the spatial patterns of global fertilizer application and manure production[J].Earth Interactions,2010,14(1):1-22.
[36]Foley J A,Ramankutty N,Brauman K A,et al.Solutions for a cultivated planet[J].Nature,2011,145(7369):337-342.
[37]Nelson A,Gumma M K.A map of lowland rice extent in the major rice growing countries of Asia[OL].2015,http://irri.org/our-work/research/policy-and-markets/mapping.
[38]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.
[39]Taylor K E.Summarizing multiple aspects of model performance in a single diagram[J].Journal of Geophysical Research Atmospheres,2001,106(D7):7183-7192.
[40]Iizumi T,Ramankutty N.How do weather and climate influence cropping area and intensity[J].Global Food Security,2015,4:46-50.
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