RCP 8.5气候变化情景下21世纪印度粮食单产变化的多模式集合模拟

doi:10.11821/dlxb201911009

地理学报 ›› 2019, Vol. 74 ›› Issue (11): 2314-2328.doi: 10.11821/dlxb201911009

RCP 8.5气候变化情景下21世纪印度粮食单产变化的多模式集合模拟

张学珍^1,²(),李侠祥^1,²,张丽娟³,席建超¹,戴尔阜¹

1. 中国科学院地理科学与资源研究所中国科学院陆地表层格局与模拟重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 哈尔滨师范大学寒区地理环境监测与空间信息服务黑龙江省重点实验室,哈尔滨 150025

收稿日期:2018-05-28 修回日期:2019-08-22 出版日期:2019-11-25 发布日期:2019-11-01
基金资助:
国家重点研发计划(2016YFA0600401);中国科学院重点部署项目(ZDRW-ZS-2016-6);中国科学院重点部署项目(QYZDB-SSW-DQC 005);中国科学院地理科学与资源研究所杰出青年人才基金项目(2015RC101);中国科学院青年创新促进会(2015038)

Multi-model ensemble projection of crop yield of India under RCP 8.5 climate change scenario during the 21st century

ZHANG Xuezhen^1,²(),LI Xiaxiang^1,²,ZHANG Lijuan³,XI Jianchao¹,DAI Erfu¹

1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China

Received:2018-05-28 Revised:2019-08-22 Published:2019-11-25 Online:2019-11-01
Supported by:
The National Key Research and Development Program of China(2016YFA0600401);Key Program from CAS, No.ZDRW-ZS-2016-6(ZDRW-ZS-2016-6);Key Program from CAS(QYZDB-SSW-DQC 005);Outstanding Young Scholars from IGSNRR(2015RC101);Youth Innovation Promotion Association CAS(2015038)

摘要/Abstract

摘要：

基于跨部门影响模型比较计划（ISI-MIP）中20种气候模式与作物模型组合的模拟结果,预估了RCP 8.5排放情景下21世纪印度小麦和水稻单产变化。研究发现：① 多模式集合模拟结果基本再现了印度小麦和水稻单产的空间差异;同时,再现了小麦和水稻单产对温度和降水变化的响应特征：与温度呈负相关,与降水呈正相关。② RCP 8.5情景下,水稻和小麦生长季温度和降水均呈增加趋势,小麦生长季的温度、降水增加幅度大于水稻。空间上,温度增加幅度自北向南逐渐减小,降水增幅则逐渐增加,并且小麦种植区升温幅度大于非种植区,降水增幅则少于非种植区,水稻种植区升温幅度小于非种植区,降水增幅则多于非种植区。③ RCP 8.5情景下,小麦和水稻单产均呈下降趋势,21世纪后半叶尤为明显。小麦单产的下降速度明显大于水稻,其中21世纪前半叶小麦和水稻单产下降速度约分别为1.3%/10a （P < 0.001）和0.7%/10a （P < 0.05）,后半叶分别增至4.9%/10a （P < 0.001）和4.4%/10a （P < 0.001）。小麦和水稻单产变化存在明显的空间异质性,小麦单产的最大下降幅度出现在德干高原西南部,降幅约60%,水稻单产最大下降幅度出现在印度河平原北部,降幅约50%。这意味着未来气候变化情景下印度粮食供给将面临较大的挑战。

关键词: 印度, 粮食单产, 多模式集合预估, RCP 8.5情景, ISI-MIP

Abstract:

Using the multi-model ensemble projections of wheat and rice in India during the 21st century from the Inter-Sectoral Impact Model Intercomparison Project, this study assessed the future changes in crop yield under the RCP 8.5 emission scenario. The results show that the multi-model ensemble simulations generally reproduce the spatial variability in crop yield that is represented by ground measurements. Furthermore, the simulations reproduce the response of crop yield to climate changes, which is characterized by negative correlations between crop yield and temperature and positive correlations between crop yield and precipitation. Under the RCP8.5 emission scenario, temperature and precipitation during the growing season of wheat and rice will increase. Generally, temperature will increase at a higher rate than precipitation; the increases in temperature and precipitation during the wheat growing season will be stronger than that during the rice growing season. In terms of spatial dimension, the increase in temperature will be weakened gradually from the north to the south, while that of precipitation will be intensified gradually from the north to the south. Temperature increase in the wheat producing areas will be stronger than that in the wheat non-producing area, while precipitation increase in the wheat producing areas will be weaker than that in the wheat non-producing areas. However, the scenario is predicted to be reversed for the rice producing areas. In response to the climate changes, wheat and rice yields will decrease in the 21st century, particularly in the second half. The decrease in wheat yield will be greater than that of rice yield. In the first half of the 21st century, wheat and rice yields are predicted to decrease at the rates of 1.3%/10 a (P < 0.001) and 0.7%/10 a (P < 0.05), respectively. In the second half of the 21st century, they are predicted to decrease at the rates of 4.9%/10 a (P < 0.001) and 4.4%/10 a (P < 0.05), respectively. The drought stress resulting from climate warming might be the main reason for this projected yield reduction. The greatest decrease in wheat yield (as high as 60%) will occur in the southwest part of the Deccan Plateau, and the greatest decrease in rice yield (as high as 50%) will occur in the northern part of the Gangetic Plain. These findings suggest that food supply in India will face extreme challenges under the future climate change scenarios.

Key words: India, crop yield per unit area, multi-model ensemble projection, RCP8.5 scenario, ISI-MIP

张学珍, 李侠祥, 张丽娟, 席建超, 戴尔阜. RCP 8.5气候变化情景下21世纪印度粮食单产变化的多模式集合模拟[J]. 地理学报, 2019, 74(11): 2314-2328.

ZHANG Xuezhen, LI Xiaxiang, ZHANG Lijuan, XI Jianchao, DAI Erfu. Multi-model ensemble projection of crop yield of India under RCP 8.5 climate change scenario during the 21st century[J]. Acta Geographica Sinica, 2019, 74(11): 2314-2328.

图/表 10

表1

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 40

[1]	Godfray H C J, Beddington J R, Crute I R , et al. Food security: The challenge of feeding 9 billion people. Science, 2010,327(5967):812-818.
[2]	Ministry of Agriculture . Agricultural Statistics at a Glance 2016. Oxford: Oxford University Press, 2017: 78-88.
[3]	Birthal P S, Khan T, Negi D S , et al. Impact of climate change on yields of major food crops in India: Implications for food security. Agricultural Economics Research Review, 2014,27(2):145-155.
[4]	Challinor A J, Wheeler T R . Crop yield reduction in the tropics under climate change: Processes and uncertainties. Agricultural and Forest Meteorology, 2008,148(3):343-356.
[5]	Guiteras R . The Impact of Climate Change on Indian Agriculture. Massachusetts: Massachusetts Institute of Technology, 2007: 3-4.
[6]	Aggarwal P K . Applications of Systems Simulation for Understanding and Increasing Yield Potential of Wheat and Rice. Netherlands: Wageningen University, 2000: 87-88.
[7]	Annette F . Regionalised inventory of biogenic greenhouse gas emissions from European agriculture. European Journal of Agronomy, 2003,19(2):135-160.
[8]	Lobell D B, Sibley A, Ortizmonasterio J I . Extreme heat effects on wheat senescence in India. Nature Climate Change, 2012,2(3):186-189.
[9]	Mall R K, Singh R, Gupta A , et al. Impact of climate change on Indian agriculture: A review. Climatic Change, 2006,78(2-4):445-478.
[10]	Krishnan P, Swain D K, Bhaskar B C , et al. Impact of elevated CO₂ and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies. Agriculture, Ecosystems and Environment, 2007,122(2):233-242.
[11]	Mohanty M, Sinha N K, Reddy K S . Elevated temperature and carbon dioxide concentration effects on wheat productivity in Madhya Pradesh: A simulation study. Journal of Agrometeorology, 2015,17(2):185-189.
[12]	Kumar M . Impact of climate change on crop yield and role of model for achieving food security. Environmental Monitoring and Assessment, 2016,188(8):1-14.
[13]	Rathore L S, Singh K K, Saseendran S A , et al. Modelling the impact of climate change on rice production in India. Mausam, 2001,52(1):263-274.
[14]	Levis S, Badger A, Drewniak B , et al. CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over RCP 8.5. Climatic Change, 2016,146:1-15.
[15]	Rosenzweig C, Elliott J, Deryng D , et al. Assessing agricultural risks of climate change in the 21 ^st century in a global gridded crop model intercomparison . Proceedings of the National Academy of Sciences, 2014,111(9):3268-3273.
[16]	Elguindi N, Grundstein A, Bernardes S , et al. Assessment of CMIP5 global model simulations and climate change projections for the 21st century using a modified Thornthwaite climate classification. Climatic Change, 2014,122(4):523-538.
[17]	Chen L, Frauenfeld O W . Surface air temperature changes over the twentieth and twenty-first centuries in China simulated by 20 CMIP5 models. Journal of Climate, 2014,27(11):3920-3937.
[18]	Liu Y, Feng J, Zhuguo M A . An analysis of historical and future temperature fluctuations over China based on CMIP5 simulations. Advances in Atmospheric Sciences, 2014,31(2):457-467.
[19]	Warszawski L, Frieler K, Huber V , et al. The inter-sectoral impact model intercomparison project (ISI-MIP): Project framework. Proceedings of the National Academy of Sciences, 2014,111(9):3228-3232.
[20]	Elliott J, Deryng D, Müller C , et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proceedings of the National Academy of Sciences, 2014,111(9):3239-3244.
[21]	Abedi T, Alemzadeh A, Kazemeyni S A . Wheat yield and grain protein response to nitrogen amount and timing. Australian Journal of Crop Science, 2011,5(3):330-336.
[22]	Stocker T F, Qin D, Plattner G K , et al. IPCC, 2013: Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, USA: Cambridge University Press, 2013: 1031.
[23]	Chaturvedi R, Joshi J, Jayaraman M , et al. Multi-model climate change projections for India under representative concentration pathways. Current Science, 2012,103(7):791-802.
[24]	Mukherjee S, Aadhar S, Stone D , et al. Increase in extreme precipitation events under anthropogenic warming in India. Weather and Climate Extremes, 2018,20(C):45-53.
[25]	Murari K K, Ghosh S, Patwardhan A , et al. Intensification of future severe heat waves in India and their effect on heat stress and mortality. Regional Environmental Change, 2015,15(4):569-579.
[26]	Yin Y, Tang Q, Liu X . A multi-model analysis of change in potential yield of major crops in China under climate change. Earth System Dynamics, 2015,6(1):45-59.
[27]	Mitchell T D, Jones P D . An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology, 2005,25(6):693-712.
[28]	Fang Xiuqi, Wang Yuan, Xu Tan , et al. Contribution of climate warming to rice yield in Heilongjiang province. Acta Geographica Sinica, 2004,59(6):820-828.
	[ 方修琦, 王媛, 徐锬 , 等. 近20年气候变暖对黑龙江水稻增产的贡献. 地理学报, 2004,59(6):820-828.]
[29]	Challinor A J, Wheeler T R, Craufurd P Q , et al. Adaptation of crops to climate change through genotypic responses to mean and extreme temperatures. Agriculture Ecosystems and Environment, 2007,119(1):190-204.
[30]	Challinor A J, Wheeler T R . Use of a crop model ensemble to quantify CO₂ stimulation of water-stressed and well-watered crops. Agricultural and Forest Meteorology, 2008,148(6/7):1062-1077.
[31]	Koehler A K, Challinor A J, Hawkins E , et al. Influences of increasing temperature on Indian wheat: Quantifying limits to predictability. Environmental Research Letters, 2013,8(3):034016.
[32]	Hempel S, Frieler K, Warszawski L , et al. A trend-preserving bias correction the ISI-MIP approach. Earth System Dynamics, 2013,4(2):219-236.
[33]	Krishna K K, Rupa K K, Ashrit R G , et al. Climate impacts on Indian agriculture. International Journal of Climatology, 2004,24(11):1375-1393.
[34]	Webster P J, Magana V O, Palmer T N , et al. Monsoons: Processes, predictability, and the prospects for prediction. Journal of Geophysical Research: Oceans, 1998,103(C7):14451-14510.
[35]	Selvaraju R . Impact of El Niño-Southern Oscillation on Indian food grain production. International Journal of Climatology, 2003,23(2):187-206.
[36]	Zhang Xuezhen, Li Xiaxiang, Xu Xinchuang , et al. Ensemble projection of climate change scenarios of China in the 21st century based on the preferred climate models. Acta Geographica Sinica, 2017,72(9):1555-1568.
	[ 张学珍, 李侠祥, 徐新创 , 等. 基于模式优选的21世纪中国气候变化情景集合预估. 地理学报, 2017,72(9):1555-1568.]
[37]	Gu Guoda, Yin Jinghua . Analysis on mid and long-term global food supply and demand. Journal of Huazhong Agricultural University, 2014,33(6):6-16.
	[ 顾国达, 尹靖华 . 全球中长期粮食供需趋势分析. 华中农业大学学报, 2014,33(6):6-16.]
[38]	Ortiz R, Sayre K D, Govaerts B , et al. Climate change: can wheat beat the heat. Agriculture, Ecosystems and Environment, 2008,126(1):46-58.
[39]	Kang Y, Khan S, Ma X . Climate change impacts on crop yield, crop water productivity and food security a review. Progress in Natural Science, 2009,19(12):1665-1674.
[40]	Chaturvedi R K, Joshi J, Jayaraman M , et al. Multi-model climate change projections for India under representative concentration pathways. Current Science, 2012,103(7):791-802.

气候系统模式		作物模型
模式名称	研发机构	模型名称	研发机构	胁迫项	模拟时间及作物
HadGEM2-ES	哈德利气候中心	EPIC	维也纳(BOKU)自然资源与生命科学大学	W, T, H, A, N, P, BD, AL	1980-2099年; 小麦、水稻
IPSL-CM5A-LR	法国Pierre-Simon物理学研究所	GEPIC	瑞士联邦水质科学技术研究所	W, T, H, A, N, P, BD, AL	1971-2099年; 小麦、水稻
MIROC-ESM-CHEM	日本海洋地球科学与技术局、大气海洋研究所和国家环境变化研究所	PEGASUS	英国东英吉利大学延德尔气候变化研究中心,加拿大麦吉尔大学	W, T, H, N, P, K	1971-2099小麦
GFDL-ESM2M	美国地球物理流体动力学实验室	pDSSAT	芝加哥大学计算机学院	W, T, H, A, N	1971-2099年小麦、水稻
NorESM1-M	挪威气候中心

RCP 8.5气候变化情景下21世纪印度粮食单产变化的多模式集合模拟

Multi-model ensemble projection of crop yield of India under RCP 8.5 climate change scenario during the 21st century

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 40

相关文章 7

Metrics

本文评价

[1]	袁丽华, 陈小强, 宋长青, 程昌秀, 沈石. 环印度洋区域国家地缘经济格局分析[J]. 地理学报, 2021, 76(4): 955-972.
[2]	刘佳, 梁一行, 李鹏, 肖池伟. 2001—2018年印度尼西亚MODIS活跃火的发生特征与响应[J]. 地理学报, 2020, 75(9): 1907-1920.
[3]	李佳洺, 杨宇, 樊杰, 金凤君, 张文忠, 刘盛和, 傅伯杰. 中印城镇化区域差异及城镇体系空间演化比较[J]. 地理学报, 2017, 72(6): 986-1000.
[4]	殷培红; 方修琦; 张学珍; 戚发全. 中国粮食单产对气候变化的敏感性评价[J]. 地理学报, 2010, 65(5): 515-524.
[5]	姜德华. 作物布局中的一些定量分析问题[J]. 地理学报, 1983, 38(2): 188-196.
[6]	杨逸畴, 李炳元, 尹泽生, 张青松. 西藏高原地貌的形成和演化[J]. 地理学报, 1982, 37(1): 76-87.
[7]	汤懋苍, 沈志宝, 陈有虞. 高原季风的平均气候特征[J]. 地理学报, 1979, 34(1): 33-42.