气候与水文

1982—2017年典型干旱年的中国GPP变化

  • 曹玉娟 ,
  • 司文洋 ,
  • 杜自强 ,
  • 梁寒雪 ,
  • 雷添杰 ,
  • 孙斌 ,
  • 武志涛
展开
  • 1.山西大学黄土高原研究所,山西 太原 030006
    2.山西省黄河实验室,山西 太原 030006
    3.中国农业科学院农业环境与可持续发展研究所,北京 100081
    4.中国林业科学研究院资源信息研究所,北京 100091
曹玉娟(1997-),女,硕士研究生,主要从事陆地生态系统与全球变化等方面的研究. E-mail: 2409951683@qq.com

收稿日期: 2023-02-23

  修回日期: 2023-03-06

  网络出版日期: 2023-11-10

基金资助

国家自然科学基金(41977412);山西省科技创新人才团队专项(202204051001010)

Changes in GPP of China during the typical drought years from 1982 to 2017

  • Yujuan CAO ,
  • Wenyang SI ,
  • Ziqiang DU ,
  • Hanxue LIANG ,
  • Tianjie LEI ,
  • Bin SUN ,
  • Zhitao WU
Expand
  • 1. Institute of Loess Plateau, Shanxi University, Taiyuan 030006, Shanxi, China
    2. Shanxi Yellow River Laboratory, Taiyuan 030006, Shanxi, China
    3. Institute of Environment and Sustainable Development in Agriculture, CAAS, Beijing 100081, China
    4. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Received date: 2023-02-23

  Revised date: 2023-03-06

  Online published: 2023-11-10

摘要

干旱导致的总初级生产力(GPP)的减少会对陆地碳汇产生重大影响。基于全国618个站点的月气象数据计算的标准降水蒸散指数(SPEI)和2套公开的GPP数据集(EC-LUE GPP和GLASS GPP),系统分析了中国1982—2017年典型干旱年的GPP在不同时间尺度下受不同程度干旱影响的变化。结果表明:(1) 基于SPEI的5个选取指标,选出1982—2017年典型干旱的年份为2001年和2011年。(2) 在年和季节尺度上,2001年的GPP受干旱影响严重区域主要在华北、东北和中东部地区的北部,2011年主要集中在西南地区的东南部和中东部地区。月尺度上,2001年5月的GPP受干旱影响最严重,主要集中在华北和东北的大部分区域。2011年1月GPP受干旱影响最为严重,主要集中在中东部地区的大部分区域。(3) 无论是年、季节还是月尺度,随着干旱程度的加重,导致GPP的下降率越大,极端干旱的影响最大。从季节尺度看,2001年夏季极端干旱造成GPP下降率分别为19.96%(EC-LUE GPP)和15.57%(GLASS GPP);2011年春季极端干旱造成GPP下降率分别为14.32%(EC-LUE GPP)和8.75%(GLASS GPP)。研究结果可进一步加深不同时间尺度下不同等级干旱对GPP影响的认识,对了解干旱条件下陆地与大气之间的碳交换具有重要意义。

本文引用格式

曹玉娟 , 司文洋 , 杜自强 , 梁寒雪 , 雷添杰 , 孙斌 , 武志涛 . 1982—2017年典型干旱年的中国GPP变化[J]. 干旱区地理, 2023 , 46(10) : 1577 -1590 . DOI: 10.12118/j.issn.1000-6060.2023.078

Abstract

The reduction in gross primary productivity (GPP) resulting from drought can significantly impact the terrestrial carbon sink. Based on the standard precipitation evapotranspiration index (SPEI) calculated from the monthly meteorological data of 618 sites from the entire country and two publicly available GPP datasets (i.e., EC-LUE GPP and GLASS GPP, respectively), changes in the GPP affected on different scales by different degrees of drought in a typical drought year during 1982—2017 (2001 and 2011) in China were analyzed systematically. The results revealed that: (1) Based on the five selected indicators of the SPEI, the typical drought years during 1982—2017 were selected as 2001 and 2011. (2) On the annual and seasonal scales, the drought-affected GPP in 2001 was observed mainly in north China, northeast China, and the northern part of middle east region of China, as well as in the southeast and middle east of the southwest region of China in 2011. On the monthly scale, the GPP in May 2001 was the most severely affected by drought, mainly concentrated in most of north China and northeast China; however, in January 2011, the GPP was mainly concentrated in majority of the middle east region of China. (3) Irrespective of the annual, seasonal, or monthly scale, with the increase in the degree of drought, the decline rate of GPP was higher, and the impact of extreme drought was the highest. For example, on the seasonal scale, the decline in the GPP during extreme drought in the summer of 2001 was 19.96% (EC-LUE GPP) and 15.57% (GLASS GPP), and the decline in the GPP during extreme drought in the spring of 2011 was 14.32% (EC-LUE GPP) and 8.75% (GLASS GPP). The results revealed can further deepen the understanding of the effect of different grades of drought on GPP, which is key for understanding the exchange of carbon between the land and atmosphere under drought conditions.

参考文献

[1] Du L T, Song N P, Liu K, et al. Comparison of two simulation methods of the temperature vegetation dryness index (TVDI) for drought monitoring in semi-arid regions of China[J]. Remote Sensing, 2017, 9(2): 177, doi: 10.3390/rs9020177.
[2] 姚玉璧, 张强, 李耀辉, 等. 干旱灾害风险评估技术及其科学问题与展望[J]. 资源科学, 2013, 35(9): 1884- 1897.
[2] [Yao Yubi, Zhang Qiang, Li Yaohui, et al. Drought risk assessment technological progresses and problems[J]. Resources Science, 2013, 35(9): 1884-1897. ]
[3] Dai A G. Increasing drought under global warming in observations and models[J]. Nature Climate Change, 2013, 3(2): 52-58.
[4] Leng G Y, Tang Q H, Rayburg S. Climate change impacts on meteorological, agricultural and hydrological droughts in China[J]. Global and Planetary Change, 2015, 126: 23-34.
[5] 杨涛, 陆桂华, 李会会, 等. 气候变化下水文极端事件变化预测研究进展[J]. 水科学进展, 2011, 22(2): 279-286.
[5] [Yang Tao, Lu Guihua, Li Huihui, et al. Advances in the study of projection of climate change impacts on hydrological extremes[J]. Advances in Water Science, 2011, 22(2): 279-286. ]
[6] 秦大河, 丁一汇, 王绍武, 等. 中国西部生态环境变化与对策建议[J]. 地球科学进展, 2002, 17(3): 314-319.
[6] [Qin Dahe, Ding Yihui, Wang Shaowu, et al. Ecological environment change in west China and its response strategy[J]. Advances in Earth Science, 2002, 17(3): 314-319. ]
[7] 刘洋洋, 章钊颖, 同琳静, 等. 中国草地净初级生产力时空格局及其影响因素[J]. 生态学杂志, 2020, 39(2): 349-363.
[7] [Liu Yangyang, Zhang Zhaoying, Tong Linjing, et al. Spatiotemporal dynamics of China’s grassland NPP and its driving factors[J]. Chinese Journal of Ecology, 2020, 39(2): 349-363. ]
[8] Sheffield J, Wood E F, Chaney N, et al. A drought monitoring and forecasting system for sub- Sahara African water resources and food security[J]. Bulletin of the American Meteorological Society, 2014, 95(6): 861-882.
[9] Reichstein M, Bahn M, Ciais P, et al. Climate extremes and the carbon cycle[J]. Nature, 2013, 500(7462): 287-295.
[10] Du L, Mikle N, Zou Z H, et al. Global patterns of extreme drought-induced loss in land primary production: Identifying ecological extremes from rain- use efficiency[J]. Science of the Total Environment, 2018, 628-629: 611-620.
[11] Reyer C, Leuzinger S, Rammig A, et al. A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability[J]. Global Change Biology, 2013, 19(1): 75-89.
[12] 周国逸, 李琳, 吴安驰. 气候变暖下干旱对森林生态系统的影响[J]. 南京信息工程大学学报(自然科学版), 2020, 12(1): 81-88.
[12] [Zhou Guoyi, Li Lin, Wu Anchi. Effect of drought on forest ecosystem under warming climate[J]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 2020, 12(1): 81-88. ]
[13] Corinne L Q, Michael R R, Josep G C, et al. Trends in the sources and sinks of carbon dioxide[J]. Nature Geoscience, 2009, 2(12): 831-836.
[14] Orinne L Q, Robbie M A, Josep G C, et al. Global carbon budget 2016[J]. Earth System Science Data, 2016, 8(2): 605-649.
[15] Yuan W P, Liu S, Zhou G S, et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes[J]. Agricultural and Forest Meteorology, 2007, 143(3-4): 189-207.
[16] Prentice I C, Heimann M, Sitch S. The carbon balance of the terrestrial biosphere: Ecosystem models and atmospheric observations[J]. Ecological Applications, 2000, 10(6): 1553-1573.
[17] Rachel T P, Zhao M S, Wang H M, et al. Impact of satellite based PAR on estimates of terrestrial net primary productivity[J]. International Journal of Remote Sensing, 2010, 31(19): 5221-5237.
[18] Xiao J F, Frederic C, Cecile G, et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years[J]. Remote Sensing of Environment, 2019, 233: 111383, doi: 10.1016/j.rse. 2019.111383.
[19] 张雪琪, 夏倩倩, 陈亚宁, 等. 近20 a塔里木河生态输水对植被总初级生产力变化的影响[J]. 干旱区地理, 2021, 44(3): 718-728.
[19] [Zhang Xueqi, Xia Qianqian, Chen Yaning, et al. Effects of ecological water conveyance on gross primary productivity of vegetation in Tarim River in recent 20 years[J]. Arid Land Geography, 2021, 44(3): 718-728. ]
[20] Du L T, Tian Q, Yu T, et al. A comprehensive drought monitoring method integrating MODIS and TRMM data[J]. International Journal of Applied Earth Observations and Geoinformation, 2012, 23: 245-253.
[21] He W, Ju W M, Jiang F, et al. Peak growing season patterns and climate extremes-driven responses of gross primary production estimated by satellite and process based models over North America[J]. Agricultural and Forest Meteorology, 2021, 298: 108292, doi:10.1016/j.agrformet.2020.108292.
[22] Vicca S, Balzarolo M, Filella I, et al. Remotely-sensed detection of effects of extreme droughts on gross primary production[J]. Scientific Reports, 2016, 6(1): 28269, doi: 10.1038/srep28269.
[23] Chonggang X, Nate G M, Rosie A F, et al. Increasing impacts of extreme droughts on vegetation productivity under climate change[J]. Nature Climate Change, 2019, 9(12): 948-953.
[24] Yu Z, Wang J X, Liu S, et al. Global gross primary productivity and water use efficiency changes under drought stress[J]. Environmental Research Letters, 2017, 12(1): 5258, doi: 10.1088/1748-9326/aa5258.
[25] Zhao M S, Steven W R. Response to comments on “drought-induced reduction in global terrestrial net primary production from 2000 through 2009”[J]. Science, 2011, 333(6046): 1039, doi:10.1126/science.1199169.
[26] Ciais P, Reichstein M, Viovy N, et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003[J]. Nature: International Weekly Journal of Science, 2005, 437(7058): 529-533.
[27] 杨洁, 王义民, 畅建霞, 等. PDSI与马尔科夫耦合的干旱预测[J]. 人民珠江, 2016, 37(8): 1-5.
[27] [Yang Jie, Wang Yimin, Chang Jianxia, et al. Drought prediction based on PDSI and Markov China Model[J]. Pearl River, 2016, 37(8): 1-5. ]
[28] 黄生志, 黄强, 王义民, 等. 基于SPI的渭河流域干旱特征演变研究[J]. 自然灾害学报, 2015, 24(1): 15-22.
[28] [Huang Shengzhi, Huang Qiang, Wang Yimin, et al. Evolution of drought characteristics in the Weihe River Basin based on standardized precipitation index[J]. Journal of Natural Disasters, 2015, 24(1): 15-22. ]
[29] Vicente-Serrano S M, Beguería S, López-Moreno J I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index[J]. Journal of Climate, 2010, 23(7): 1696-1718.
[30] Zhao H, Gao G, An W, et al. Timescale differences between SC-PDSI and SPEI for drought monitoring in China[J]. Physics and Chemistry of the Earth, 2015, 102: 48-58.
[31] Alessandro A, Pierre F, Christian B, et al. Spatiotemporal patterns of terrestrial gross primary production: A review[J]. Reviews of Geophysics, 2015, 53(3): 785-818.
[32] 高振翔, 叶剑, 丁仁惠, 等. 中国植被总初级生产力对气候变化的响应[J]. 水土保持研究, 2022, 29(4): 394-399.
[32] [Gao Zhenxiang, Ye Jian, Ding Renhui, et al. Response of vegetation gross primary productivity to climate change in China[J]. Research of Soil and Water Conservation, 2022, 29(4): 394-399. ]
[33] Yuan W P, Liu S G, Yu G R, et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data[J]. Remote Sensing of Environment, 2010, 114(7): 1416-1431.
[34] Martin J, Sujan K, Ulrich W, et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes[J]. Scientific Data, 2019, 6(1): 74, doi: 10.1038/s41597-019-0076-8.
[35] Chongya J, Youngryel R. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS)[J]. Remote Sensing of Environment, 2016, 186: 528-547.
[36] Piao S L, Sitch S, Ciais P, et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends[J]. Global Change Biology, 2013, 19(7): 2117-2132.
[37] Wang W L, Dungan J, Hashimoto H, et al. Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 1. Primary production[J]. Global Change Biology, 2011, 17(3): 1350-1366.
[38] 侯吉宇, 周艳莲, 刘洋. 不同叶面积指数遥感数据模拟中国总初级生产力的时空差异[J]. 遥感技术与应用, 2020, 35(5): 1015-1027.
[38] [Hou Jiyu, Zhou Yanlian, Liu Yang. Spatial and temporal differences of GPP simulated by different satellite-derived LAI in China[J]. Remote Sensing Technology and Application, 2020, 35(5): 1015-1027. ]
[39] 张心竹, 王鹤松, 延昊, 等. 2001—2018年中国总初级生产力时空变化的遥感研究[J]. 生态学报, 2021, 41(16): 6351-6362.
[39] [Zhang Xinzhu, Wang Hesong, Yan Hao, et al. Analysis of spatio-temporal changes of gross primary productivity in China from 2001 to 2018 based on remote sensing[J]. Acta Ecologica Sinica, 2021, 41(16): 6351-6362. ]
[40] Running S W, Thornton P E, Nemani R, et al. Global terrestrial gross and net primary productivity from the earth observing system[C]// Sala O E, Jackson R B, Mooney H A, et al. Methods in Ecosystem Science. New York: Springer, 2000.
[41] 杜文丽, 孙少波, 吴云涛, 等. 1980—2013年中国陆地生态系统总初级生产力对干旱的响应特征[J]. 生态学杂志, 2020, 39(1): 23-35.
[41] [Du Wenli, Sun Shaobo, Wu Yuntao, et al. The response of gross primary production to drought in terrestrial ecosystems of China during 1980—2013[J]. Chinese Journal of Ecology, 2020, 39(1): 23-35. ]
[42] 童志辉, 熊助国, 孙睿, 等. 利用多源数据估算黑河流域总初级生产力[J]. 干旱区地理, 2020, 43(2): 440-448.
[42] [Tong Zhihui, Xiong Zhuguo, Sun Rui, et al. Estimating gross primary production in the Heihe River Basin from multiple data sources[J]. Arid Land Geography, 2020, 43(2): 440-448. ]
[43] Zheng Y, Shen R Q, Wang Y W, et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982—2017[J]. Earth System Science Data, 2020, 12(4): 2725-2746.
[44] 梁顺林, 程洁, 贾坤, 等. 陆表定量遥感反演方法的发展新动态[J]. 遥感学报, 2016, 20(5): 875-898.
[44] [Liang Shunlin, Cheng Jie, Jia Kun, et al. Recent progress in land surface quantitative remote sensing[J]. National Remote Sensing Bulletin, 2016, 20(5): 875-898. ]
[45] 薛华柱, 李阳阳, 董国涛. 基于SPEI指数分析河西走廊气象干旱时空变化特征[J]. 中国农业气象, 2022, 43(11): 923-934.
[45] [Xue Huazhu, Li Yangyang, Dong Guotao. Analysis of spatial-temporal variation characteristics of meteorological drought in the Hexi Corridor based on SPEI index[J]. Chinese Journal of Agrmeteorology, 2022, 43(11): 923-934. ]
[46] 王佳瑞, 孙从建, 郑振婧, 等. 近57年来黄土高原干旱特征及其与大气环流的关系[J]. 生态学报, 2021, 41(13): 5340-5351.
[46] [Wang Jiarui, Sun Congjian, Zheng Zhenjing, et al. Drought characteristics of the Loess Plateau in the past 60 years and its relationship with changes in atmospheric circulation[J]. Acta Ecologica Sinica, 2021, 41(13): 5340-5351. ]
[47] Wu Z T, Yu L, Du Z Q, et al. Recent changes in the drought of China from 1960 to 2014[J]. International Journal of Climatology, 2020, 40(7): 3281-3296.
[48] 路金强, 甘容, 杨峰, 等. 基于SPEI指数的河南省干旱特征及与环流指数的相关性分析[J]. 中国农村水利水电, 2022, 474(4): 17-24.
[48] [Lu Jinqiang, Gan Rong, Yang Feng, et al. Drought characteristics and its correlation with circulation index Henan Province based on SPEI index[J]. China Rural Water and Hydropower, 2022, 474(4): 17-24. ]
[49] Lei T J, Wu J J, Li X H, et al. A new framework for evaluating the impacts of drought on net primary productivity of grassland[J]. Science of the Total Environment, 2015, 536: 161-172.
[50] 卫洁. 基于遥感的黄淮海冬小麦区干旱指数适应性研究[D]. 太原: 山西大学, 2019.
[50] [Wei Jie. Suitability of drought index in winter wheat area of Huang-Huai-Hai Plain based on remote sensing[D]. Taiyuan: Shanxi University, 2019. ]
[51] Yu T, Sun R, Xiao Z Q, et al. Estimation of global vegetation productivity from global land surface satellite data[J]. Remote Sensing, 2018, 10(2): 327, doi: 10.3390/rs10020327.
[52] 王小红, 刘宪锋, 孙高鹏, 等. 2001—2020年秦巴山区植被生产力对干旱的响应[J]. 应用生态学报, 2022, 33(8): 2105-2112.
[52] [Wang Xiaohong, Liu Xianfeng, Sun Gaopeng, et al. Response of vegetation productivity to drought in the Qinling-Daba Mountains, China from 2001 to 2020[J]. Chinese Journal of Applied Ecology, 2022, 33(8): 2105-2112. ]
[53] 马志婷. 1960—2014年中国干旱时空变化及其对植被的影响[D]. 太原: 山西大学, 2018.
[53] [Ma Zhiting. Spatial and temporal variation of drought and its impact on vegetation in China during 1960—2014[D]. Taiyuan: Shanxi University, 2018. ]
[54] 包春兰, 陈华根. 东北平原植被对气候变化的滞后响应研究[J]. 测绘标准化, 2020, 36(3): 14-20.
[54] [Bao Chunlan, Chen Huagen. On time-lag response of vegetation cover to climate change in Northeast Plain[J]. Standardization of Surveying and Mapping, 2020, 36(3): 14-20. ]
[55] 李小燕, 任志远, 张翀, 等. 中国植被覆盖气候限制性分区及时空变化[J]. 陕西师范大学学报(自然科学版), 2013, 41(3): 76-81.
[55] [Li Xiaoyan, Ren Zhiyuan, Zhang Chong, et al. Vegetation cover restrictive zoning and temporal and spatial change of China[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2013, 41(3): 76-81. ]
[56] 左冰洁, 孙玉军. 福建省几种气象干旱指数的对比分析[J]. 气象, 2019, 45(5): 685-694.
[56] [Zuo Bingjie, Sun Yujun. Comparative analysis of several drought indices to use in Fujian Province[J]. Meteorological Monthly, 2019, 45(5): 685-694. ]
[57] 张世喆, 朱秀芳, 刘婷婷, 等. 气候变化下中国不同植被区总初级生产力对干旱的响应[J]. 生态学报, 2022, 42(8): 3429-3440.
[57] [Zhang Shizhe, Zhu Xiufang, Liu Tingting, et al. Response of gross primary production to drought under climate change in different vegetation regions of China[J]. Acta Ecologica Sinica, 2022, 42(8): 3429-3440. ]
[58] Wang H Y, He B, Zhang Y F, et al. Response of ecosystem productivity to dry/wet conditions indicated by different drought indices[J]. Science of the Total Environment, 2018, 612: 347-357.
[59] 田汉勤, 徐小锋, 宋霞. 干旱对陆地生态系统生产力的影响[J]. 植物生态学报, 2007, 31(2): 231-241.
[59] [Tian Hanqin, Xu Xiaofeng Song Xia. Drought impacts on terrestrial ecosystem productivity[J]. Chinese Journal of Plant Ecology, 2007, 31(2): 231-241. ]
[60] Chen G S, Tian H Q, Zhang C, et al. Drought in the southern United States over the 20th century: Variability and its impacts on terrestrial ecosystem productivity and carbon storage[J]. Climatic Change, 2012, 114(2): 379-397.
[61] Priante N, Vourlitis G L, Hayashi M, et al. Comparison of the mass and energy exchange of a pasture and a mature transitional tropical forest of the southern Amazon Basin during a seasonal transition[J]. Global Change Biology, 2004, 10(5): 863-876.
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