陕西黄河流域植被碳利用率时空特征及对气候的敏感性研究

doi:10.13866/j.azr.2023.12.09

Abstract

Abstract:

Vegetation carbon use efficiency (CUE) can objectively reflect the efficiency of vegetation in sequestering atmospheric carbon and the response of vegetation to climate change. Using MOD17, land use, and meteorological data, this study applied methods, such as the Hurst exponent, correlation analysis, and sensitivity analysis to explore the spatiotemporal variability of vegetation CUE and its sensitivity to climate factors in the Shaanxi section of the Yellow River Basin from 2001 to 2021. The results showed that (1) From 2001 to 2021, the gross primary productivity, net primary productivity (NPP), and vegetation CUE in the Shaanxi section of the Yellow River Basin exhibited an increasing trend, with an average CUE value of 0.51. (2) The study area was only 14.21% of the region, exhibiting a decreasing trend. The high-value areas of vegetation CUE are primarily concentrated in the windbreak and sand-fixation areas and the Grain for Green Project areas of northern Shaanxi. The areas where vegetation CUE indicated a decreasing trend accounted for 59.96%, most of which transitioned from an increasing trend to a decreasing trend. (3) Overall, temperature and precipitation correlated negatively with vegetation CUE, but the relationship with precipitation is more significant. Regions with positive correlations with temperature and precipitation are distributed in northern Shaanxi’s windbreak and sand-fixation areas. Sensitivity analysis of temperature and precipitation showed that the threshold values were 10 °C and 500 mm, respectively. When the temperature is below 10 °C and the precipitation is below 500 mm, the vegetation CUE increases with increasing temperature and precipitation. The relationship between vegetation CUE and climate factors is more significant and sensitive in arid areas, such as the conversion of farmland to forests and windbreak and sand-fixation areas in northern Shaanxi.

Key words: vegetation CUE, spatiotemporal characteristics, Hurst, climate factors, sensitivity, Yellow River Basin

WANG Juan, WANG Zhao, GUO Bin, HE Huijuan, DONG Jinfang. Spatiotemporal characteristics of vegetation carbon use efficiency and its sensitivity to climate in the Yellow River Basin in Shaanxi Province[J].Arid Zone Research, 2023, 40(12): 1959-1968.

Figures/Tables 8

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References 33

[1]	郑飞鸽, 易桂花, 张廷斌, 等. 三江源植被碳利用率动态变化及其对气候响应[J]. 中国环境科学, 2020, 40(1): 401-413.
	[Zheng Feige, Yi Guihua, Zhang Tingbin, et al. Study on spatio-temporal dynamics of vegetation carbon use efficiency and its response to climate factors in Three-River Headwaters Region[J]. China Environmental Science, 2020, 40(1): 401-413. ]
[2]	卢雅焱, 徐晓亮, 李基才, 等. 基于InVEST模型的新疆天山碳储量时空演变研究[J]. 干旱区研究, 2022, 39(6): 1896-1906.
	[Lu Yayan, Xu Xiaoliang, Li Jicai, et al. Research on the spatio-temporal variation of carbon storage in the Xinjiang Tianshan Mountains based on the InVEST model[J]. Arid Zone Research, 2022, 39(6): 1896-1906. ]
[3]	Choudhury B J. Modeling radiation and carbon use efficiencies of maize, sorghum, and rice[J]. Agricultural and Forest Meteorology, 2001, 106(4): 317-330. doi: 10.1016/S0168-1923(00)00217-3
[4]	底阳平, 曾辉, 张扬建, 等. 多尺度碳利用效率研究进展[J]. 生态学杂志, 2021, 40(6): 1849-1860.
	[Di Yangping, Zeng Hui, Zhang Yangjian, et al. Research advances in carbon use efficiency at multiple scales[J]. Chinese Journal of Ecology, 2021, 40(6): 1849-1860. ]
[5]	Delucia E H, Drake J E, Thomas R B. Forest carbon use efficiency: Is respiration a constant fraction of gross primary production[J]. Global Change Biology, 2007, 13(6): 1157-1167. doi: 10.1111/gcb.2007.13.issue-6
[6]	Zhang Y, Xu M, Chen H, et al. Global pattern of NPP to GPP ratio derived from MODIS data: Effects of ecosystem type, geographical location and climate[J]. Global Ecology and Biogeography, 2009, 18(3): 280-290. doi: 10.1111/geb.2009.18.issue-3
[7]	Zhang Y J, Yu G R, Yang J, et al. Climate-driven global changes in carbon use efficiency[J]. Global Ecology and Biogeography, 2014, 23(2): 144-155. doi: 10.1111/geb.2014.23.issue-2
[8]	安相, 陈云明, 唐亚坤. 东亚森林草地碳利用效率及碳通量空间变化的影响因素分析[J]. 水土保持研究, 2017, 24(5): 79-87, 92.
	[An Xiang, Chen Yunming, Tang Yakun, et al. Factors affecting the spatial variation of carbon use efficiency and carbon fluxes in East Asian forest and grassland[J]. Research of Soil and Water Conservation, 2017, 24(5): 79-87, 92. ]
[9]	刘洋洋, 王倩, 杨悦, 等. 2000—2013年中国植被碳利用效率(植被CUE)时空变化及其与气象因素的关系[J]. 水土保持研究, 2019, 26(5): 278-286, 2.
	[Liu Yangyang, Wang Qian, Yang Yue, et al. Spatiotemporal dynamic of vegetation carbon use efficiency and its relationship with climate factors in China during the period 2000-2013[J]. Research of Soil and Water Conservation, 2019, 26(5) : 278-286, 2. ]
[10]	陈智. 2000—2015年中国东北森林生产力和碳素利用率的时空变异[J]. 应用生态学报, 2019, 30(5): 1625-1632. doi: 10.13287/j.1001-9332.201905.033
	[Chen Zhi. Spatiotemporal variation of productivity and carbon use efficiency of forests in Northeast China from 2000 to 2015[J]. Chinese Journal of Applied Ecology, 2019, 30(5) : 1625-1632. ] doi: 10.13287/j.1001-9332.201905.033
[11]	Gang C, Wang Z, Zhou W, et al. Assessing the spatiotemporal dynamic of global grassland water use efficiency in response to climate change from 2000 to 2013[J]. Journal of Agronomy and Crop Science, 2015, 202(5): 343-354. doi: 10.1111/jac.2016.202.issue-5
[12]	罗赵慧, 朱璐平, 张晓君, 等. 粤港澳大湾区植植被CUE变化及与气候变化的关系[J]. 中国环境科学, 2021, 41(12): 5793-5805.
	[Luo Zhaohui, Zhu Luping, Zhang Xiaojun, et al. Spatiotemporal variation of CUE andits correlation with climate change in Guangdong-Hong Kong-Macao Greater Bay Area[J]. China Environmental Science, 2021, 41(12): 5793-5805. ]
[13]	王娟, 何慧娟, 董金芳, 等. 黄河流域植被净初级生产力时空特征及自然驱动因子[J] .中国沙漠, 2021, 41(6): 213-222. doi: 10.7522/j.issn.1000-694X.2021.00116
	[Wang Juan, He Huijuan, Dong Jinfang, et al. Spatio-temporal distribution of vegetation net primary productivity in the Yellow River Basin in 2000-2019 and its natural driving factors[J]. Journal of Desert Research, 2021, 41(6): 213-222. ] doi: 10.7522/j.issn.1000-694X.2021.00116
[14]	吕锦心, 梁康, 刘昌明, 等. 无定河流域土地覆被空间分异机制及相关水碳变量变化[J]. 干旱区研究, 2023, 40(4): 563-572.
	[Lv Jinxin, Liang Kang, Liu Changming, et al. Spatial differentiation mechanism of land cover and related changes in water-carbon variables in Wuding River Basin[J]. Arid Zone Research, 2023, 40(4): 563-572. ]
[15]	李登科, 王钊. 退耕还林后陕西省植被覆盖度变化及其对气候的响应[J]. 生态学杂志, 2020, 39(1): 1-10.
	[Li Dengke, Wang Zhao. Changes of fractional vegetation coverage after returning farmland to forests and its response to climate in Shaanxi[J]. Chinese Journal of Ecology, 2020, 39(1): 1-10. ]
[16]	陈雅如. 三峡库区森林生产力与碳储量对景观格局变化的响应[D]. 北京: 中国林业科学研究院, 2017.
	[Chen Yaru. The Response of Forest Productivity and Carbon Storage to Landscape Pattern Change in Three Gorges Reservoir Area[D]. Beijing: Chinese Academyof Forestry, 2017. ]
[17]	Jiang C, Wu Z F, Cheng J. Impacts of urbanization on net primary productivity in the Pearl River Delta[J]. International Journal of Plant Production, 2015, 9(4): 1735-6814.
[18]	吴宗洋, 蔡卓雅, 郭英, 等. 黄河流域多源遥感土地覆被数据精度评价与一致性分析[J] .中国生态农业学报, 2023, 31(6): 917-927.
	[Wu Zongyang, Cai Zhuoya, Guo Ying, et al. Accuracy evaluation and consistency analysis on multi-source remote sensing land cover data in the Yellow River Basin[J]. Chinese Journal of Eco-Agriculture, 2023, 31(6): 917-927. ]
[19]	Shi Xun, Andrew E, Xia L. Using spatial information technologies to select sites for biomass power plants: A case study in Guangdong Province[J]. Biomass and Bioenergy, 2008, 32(1): 35-43. doi: 10.1016/j.biombioe.2007.06.008
[20]	文妙霞, 何学高, 刘欢, 等. 基于地理探测器的宁夏草地植被覆被时空分异及驱动因子[J]. 干旱区研究, 2023, 40(8): 1322-1332.
	[Wen Miaoxia, He Xuegao, Liu Huan, et al. Analysis of the spatiotemporal variation characteristics and driving factors of grassland vegetation cover in Ningxia based on geographical detectors[J]. Arid Zone Research, 2023, 40(8): 1322-1332. ]
[21]	崔茜琳, 何云玲, 李宗善. 青藏高原植被水分利用效率时空变化及与气候因子的关系[J]. 应用生态学报, 2022, 33(6): 1525-1532. doi: 10.13287/j.1001-9332.202206.024
	[Cui Xilin, He Yunling, Li Zengshan. Spatial-temporal variation of vegetation water use efficiency and its relationship with climate factors over the Qinghai-Tibet Plateau, China[J]. Chinese Journal of Applied Ecology, 2022, 33(6): 1525-1532. ] doi: 10.13287/j.1001-9332.202206.024
[22]	Zheng H, Zhang L, Zhu R, et al. Responses of streamflow to climate and land surface change in the headwaters of the Yellow River Basin[J] .Water Resources Research, 2009, 45: 641-648.
[23]	袁甲, 沈非, 聂兵, 等. 皖江城市带植被NPP时空变化及其气候响应[J]. 测绘科学, 2017, 42(11): 62-67.
	[Yuan Jia, Shen Fei, Nie Bing, et al. Temporal-spatial patterns of vegetation net primary productivity and its response to climate factors in Wanjiang City belt based on CASA model[J]. Science of Surveying and Mapping, 2017, 42(11): 62-67. ]
[24]	姬盼盼, 高敏华, 杨晓东. 中国西北部干旱区 NPP 驱动力分析: 以新疆伊犁河谷和天山山脉部分区域为例[J]. 生态学报, 2019, 39(8): 2995-3006.
	[Ji Panpan, Gao Minhua, Yang Xiaodong, et al. Analysis of NPP driving force in an arid region of Northwest China: A case study in Yili Valley and parts of Tianshan Mountains, Xinjiang, China[J]. Acta Ecologica Sinica, 2019, 39(8): 2995-3006. ]
[25]	王金杰, 赵安周, 胡小枫. 京津冀植被净初级生产力时空分布及自然驱动因子分析[J]. 生态环境学报 2021, 30(6): 1158-1167. doi: 10.16258/j.cnki.1674-5906.2021.06.006
	[Wang Jinjie, Zhao Anzhou, Hu Xiaofeng. Spatiotemporal distribution of vegetation net primary productivity in Beijing-Tianjin-Hebei and natural driving factors[J]. Ecology and Environmental Sciences, 2021, 30(6): 1158-1167. ] doi: 10.16258/j.cnki.1674-5906.2021.06.006
[26]	张继平, 刘春兰, 郝海广, 等. 基于 MODIS GPP/NPP 数据的三江源地区草地生态系统碳储量及碳汇量时空变化研究[J]. 生态环境学报, 2015, 24(1): 8-13. doi: 10.16258/j.cnki.1674-5906.2015.01.002
	[Zhang Jiping, Liu Chunlan, Hao Haiguang, et al. Spatial-temporal change of carbon storage and carbon sink of grassland ecosystem in the Three-River Headwaters Region based on MODIS GPP/NPP data[J]. Ecology and Environmental Sciences, 2015, 24(1): 8-13. ] doi: 10.16258/j.cnki.1674-5906.2015.01.002
[27]	洪辛茜, 黄勇, 孙涛. 我国西南喀斯特地区2001—2018年植被净初级生产力时空演变研究[J]. 生态学报, 2021, 41(24): 1-11. doi: 10.1016/j.chnaes.2020.10.007
	[Hong Xinqian, Huang Yong, Sun Tao. Spatiotemporal evolution of vegetation net primary productivity in the karst region of southwest China from 2001 to 2018[J]. Acta Ecologica Sinica, 2021, 41(24): 1-11. ] doi: 10.1016/j.chnaes.2020.10.007
[28]	国志兴, 王宗明, 张柏, 等. 2000—2006年东北地区植被NPP的时空特征及影响因素分析[J]. 资源科学, 2008, 30(8): 1226-1235.
	[Guo Zhixing, Wang Zongming, Zhang Bo, et al. Analysis of temporal-spatial characteristics and factors influencing vegetation NPP in Northeast China from 2000 to 2006[J]. Resources Science, 2008, 30(8): 1226-1235. ]
[29]	Goudie A. Encyclopedia of Global Change[M]. Oxford: Oxford University Press, 2002: 365-369.
[30]	Yu G R, Zhu X J, Fu Y L, et al. Spatial pattern and climate drivers of carbon fluxes in terrestrial ecosystems of China[J] .Global Change Biology, 2013, 19(3): 798-810. doi: 10.1111/gcb.2013.19.issue-3
[31]	Lieth H. Primary production: Terrestrial ecosystems[J]. Human Ecology, 1973, 1: 303-332. doi: 10.1007/BF01536729
[32]	Ryan M G, Hubbard R M, Pongracic S, et al. Foliage, fine-root, woody-tissue and stand respiration in Pinus radiata in relation to nitrogen status[J]. Tree Physiology, 1996, 16: 333-343. pmid: 14871734
[33]	Allison S D, Wallenstein M D, Bradford M A. Soil-carbon response to warming dependent on microbial physiology[J]. NatureGeoscience, 2010, 3(5): 336-340.

气温/℃	气温敏感性系数εtem	降水量/mm	降水量敏感性系数εpre
<9	0.108	<400	0.002
9~9.5	0.276	400~450	0.065
9.5~10	0.049	450~500	0.055
10~10.5	-0.233	500~550	-0.025
10.5~11	-0.371	550~600	-0.065
11~11.5	-0.408	600~650	-0.095
11.5~12	-0.425	650~700	-0.021
12~12.5	-0.441	700~750	-0.013
12.5~13	-0.479	750~800	-0.002
>13	-0.493	>800	-0.039

Spatiotemporal characteristics of vegetation carbon use efficiency and its sensitivity to climate in the Yellow River Basin in Shaanxi Province

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