中国东部沿海地区暴雨对植被活动的影响

doi:10.13249/j.cnki.sgs.2020.02.018

地理科学 ›› 2020, Vol. 40 ›› Issue (2): 324-334.doi: 10.13249/j.cnki.sgs.2020.02.018

• • 上一篇

中国东部沿海地区暴雨对植被活动的影响

李建国, 袁冯伟, 赵宴青, 刘丽丽()

江苏师范大学地理测绘与城乡规划学院,江苏徐州 221116

收稿日期:2019-01-11 修回日期:2019-05-20 出版日期:2020-02-10 发布日期:2020-04-09
通讯作者: 刘丽丽 E-mail:huanyin728@126.com
作者简介:李建国（1986-）,男,江苏泗阳人,博士,副教授,主要研究海岸带开发及其生态环境效应。E-mail: lijianguo531@126.com
基金资助:
国家自然科学基金项目(41701371);教育部人文社会科学研究一般项目(17YJCZH085);江苏省高校自然科学研究项目(17KJB170006);江苏高校优势学科建设工程项目资助

Effect of Rainstorms on Vegetation Activities in Eastern Coastal Area of China

Li Jianguo, Yuan Fengwei, Zhao Yanqing, Liu Lili()

School of Geography, Geomatics, and Planning, Jiangsu Normal University, Xuzhou 221116, Jiangsu, China

Received:2019-01-11 Revised:2019-05-20 Online:2020-02-10 Published:2020-04-09
Contact: Liu Lili E-mail:huanyin728@126.com
Supported by:
National Natural Science Foundation of China(41701371);The Ministry of Education of Humanities and Social Science Project(17YJCZH085);The Natural Science Foundation of the Jiangsu Higher education Institutions of China(17KJB170006);The Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要/Abstract

摘要：

构建1982~2015年的长时间序列NDVI（Normalized Difference Vegetation Index）数据集和收集190 个地面气象站点的降雨数据,运用空间数理统计方法在像元尺度上分析34 a来中国东部沿海地区暴雨灾害发生频率的变化与NDVI变化之间的关系。结果表明：1982~2015年中国东部沿海地区暴雨发生频率在0~13次/a,平均为2次/a。1982~2015年中国东部沿海地区总的暴雨发生次数在逐渐增加,主要集中在夏季。1982~2015年中国东部沿海地区NDVI均值为0.54,植被总体生长状态良好。NDVI分布呈明显的南高北低的空间特征,且总体表现为增加趋势,平均增加0.002/10a。1982~2015年中国东部沿海地区夏季暴雨灾害对NDVI具有明显的抑制作用。总体来看,暴雨灾害发生频率每增加1%,研究区植被NDVI将降低0.01%,但空间上表现为苏北和山东地区显著的抑制性,而河北与广东和广西北部表现为显著的促进作用。

关键词: 暴雨, NDVI, 定量关系, 沿海地区

Abstract:

Within the context of global climate change, rainstorm has become one of the important factors restricting plant growth. In this study, the NDVI and meteorological dataset (GIMMS-NDVI and daily precipitation, temperature and radiation) in eastern China from 1982 to 2015 have been obtained to explore the relationships between rainstorm frequency anomaly and NDVI changes. The results show that rainstorm frequency of the study area varies from 0 to 13 times per year during the study period, with a mean value of 2 times per year. In addition, summer rainstorm frequency tends to increase gradually. The mean of NDVI values in the study area is 0.54, which show a clear spatial pattern. Furthermore, the NDVI values in the southern part of study area are generally higher than that in the northern part. However, the slope of NDVI values of the whole region show a rising trend, with an increase of 0.002/10a. The mean of partial correlation coefficients and multiple regression coefficients between rainstorm frequency anomaly and the NDVI variations are -0.02 and -0.01 respectively, representing that rainstorms have a significant negative effect on vegetation activities. In general, the NDVI in the study area will decrease by 0.01% with an increase of 1% of rainstorm frequency. Spatially, the significantly negative effect of rainstorms has been found in northern Jiangsu and Shandong Provinces. However, in Hebei, Guangdong and northern Guangxi Provinces, a significantly positive effect of rainstorm has been observed.

Key words: rainstorm, NDVI, quantitative relation, coastal area

中图分类号:

Q948.112

李建国, 袁冯伟, 赵宴青, 刘丽丽. 中国东部沿海地区暴雨对植被活动的影响[J]. 地理科学, 2020, 40(2): 324-334.

Li Jianguo, Yuan Fengwei, Zhao Yanqing, Liu Lili. Effect of Rainstorms on Vegetation Activities in Eastern Coastal Area of China[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(2): 324-334.

图/表 6

表1

图1

图2

图3

图4

表2

参考文献 49

[1]	He Z, Lei L, Zeng Z C . Response of seasonal cycle of carbon dioxide concentration to vegetation activity from satellite observation[C]// 2016 international geoscience and remote sensing symposium (IGARSS). IEEE, 2016: 5350-5353.
[2]	李建国, 袁冯伟, 赵冬萍 , 等. 滨海滩涂土壤有机碳演变驱动因子框架[J]. 地理科学, 2018,38(4):580-589.
	[ Li Jianguo, Yuan Fengwei, Zhao Dongping et al. Driving factors framework of soil organic carbon evolution in coastal wetlands. Scientia Geographica Sinica, 2018,38(4):580-589.]
[3]	Pei F, Wu C, Liu X et al. Monitoring the vegetation activity in china using vegetation health indices[J]. Agricultural & Forest Meteorology, 2018,248:215-227.
[4]	王晓利 . 中国沿海极端气候变化及其对NDVI的影响特征研究[D]. 烟台: 中国科学院烟台海岸带研究所, 2017.
	[ Wang Xiaoli . Characteristics of extreme climate change and its impact on NDVI in China's Coastal Areas. Yantai: Yantai Institute of Coastal Insistities, Chinese Academy of Sciences, 2017.]
[5]	Quiring S M, Ganesh S . Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in texas[J]. Agricultural & Forest Meteorology, 2010,150(3):330-339.
[6]	Li X, Qu Y . Evaluation of vegetation responses to climatic factors and global vegetation trends using GLASS LAI from 1982 to 2010[J]. Canadian Journal of Remote Sensing, 2018,44(4):357-372.
[7]	Tong S, Zhang J, Bao Y et al. Analyzing vegetation dynamic trend on the mongolian plateau based on the hurst exponent and influencing factors from 1982-2013[J]. Journal of Geographical Sciences, 2018,28(5):595-610.
[8]	Pettorelli N, Ryan S, Mueller T et al. The normalized difference vegetation index (NDVI): Unforeseen successes in animal ecology[J]. Climate Research, 2011,46(1):15-27.
[9]	Zhang X, Ming W, Kai L et al. Using NDVI time series to diagnose vegetation recovery after major earthquake based on dynamic time warping and lower bound distance[J]. Ecological Indicators, 2018,94:52-61.
[10]	Valderrama-Landeros L, Flores-De-Santiago F, Kovacs J M et al. An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme[J]. Environmental Monitoring & Assessment, 2018,190(1):23.
[11]	Stoms D M, Hargrove W W . Potential ndvi as a baseline for monitoring ecosystem functioning[J]. International Journal of Remote Sensing, 2000,21(2):401-407.
[12]	Wang J, Rich P M, Price K P . Temporal responses of ndvi to precipitation and temperature in the central great plains, USA[J]. International Journal of Remote Sensing, 2003,24(11):2345-2364.
[13]	谭淑端, 朱明勇, 张克荣 , 等. 植物对水淹胁迫的响应与适应[J]. 生态学杂志, 2009,28(9):1871-1877.
	[ Tan Shuduan, Zhu Mingyong, Zhang Kerong et al. Response and adaptation of plants to flooding stress. Chinese Journal of Ecology, 2009,28(9):1871-1877.]
[14]	Pradhan C, Mohanty M . Submergence stress: Responses and adaptations in crop plants[M]// Molecular Stress Physiology of Plants. India:Springer, 2013: 331-357.
[15]	刘可, 杜灵通, 侯静 , 等. 近30年中国陆地生态系统NDVI时空变化特征[J]. 生态学报, 2017,38(6):1885-1896.
	[ Liu Ke, Du Lingtong, Hou Jing et al. Spatio-temporal changes of NDVI in Chinese terrestrial ecosystems in the past 30 years. Acta Ecologica Sinica, 2017,38(6):1885-1896.]
[16]	Zhao A, Zhang A, Liu X et al. Spatiotemporal changes of normalized difference vegetation index (NDVI) and response to climate extremes and ecological restoration in the Loess Plateau, China[J]. Theoretical and Applied Climatology, 2018,133(1-2):555-567.
[17]	Liu G, Liu H, Yin Y . Global patterns of ndvi-indicated vegetation extremes and their sensitivity to climate extremes[J]. Environmental Research Letters, 2013,8(2):279-288.
[18]	Yao J, Chen Y, Zhao Y et al. Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: A case study in Xinjiang, China[J]. Theoretical and Applied Climatology, 2018,131(3-4):1503-1515.
[19]	Pei F, Wu C, Liu X et al. Detection and attribution of extreme precipitation changes from 1961 to 2012 in the Yangtze River Delta in China[J]. Catena, 2018,169:183-194.
[20]	Deng Y, Jiang W, He B et al. Change in intensity and frequency of extreme precipitation and its possible teleconnection with large-scale climate index over the China from 1960 to 2015[J]. Journal of Geophysical Research: Atmospheres, 2018,123(4):2068-2081.
[21]	Cui L, Shi J . Temporal and spatial response of vegetation NDVI to temperature and precipitation in eastern China[J]. Journal of Geographical Sciences, 2010,20(2):163-176.
[22]	Archernicholls S, Lowe D, Schultz D M et al. Aerosol-radiation-cloud interactions in a regional coupled model: The effects of convective parameterisation and resolution[J]. Atmospheric Chemistry & Physics, 2015,15(19):27449-27499.
[23]	Zhao W, Khalil M A K . The relationship between precipitation and temperature over the contiguous United States[J]. Journal of Climate, 1993,6(6):1232-1236.
[24]	赵璐, 赵作权, 王伟 . 中国东部沿海地区经济空间格局变化[J]. 经济地理, 2014,34(2):14-18.
	[ Zhao Lu, Zhao Zuoquan, Wang Wei . Changes of economic spatial pattern in the eastern coastal regions of China. Economic Geography, 2014,34(2):14-18.]
[25]	廖建华, 李丹勋, 王兴奎 , 等. 长江上游植被覆盖的时空分异季节变化及其驱动因子研究[J]. 环境科学学报, 2009,29(5):1103-1112.
	[ Liao Jianhua, Li Danxun, Wang Xingkui et al. Seasonal variation of spatio-temporal distribution of vegetation coverage and its driving factors in the upper reaches of the Yangtze River. Acta Scientiae Circumstantiae, 2009,29(5):1103-1112.]
[26]	王杰, 张明军, 王圣杰 , 等. 基于高分辨率格点数据的1961~2013年青藏高原雪雨比变化[J]. 地理学报, 2016,71(1):142-152.
	[ Wang Jie, Zhang Mingjun, Wang Shengjie et al. Snow-to-rain ratio variation over Qinghai-Xizang Plateau from 1961 to 2013 based on high resolution grid data. Acta Geographica Sinica, 2016,71(1):142-152.]
[27]	张泽铭, 张翠翠, 侯红运 , 等. 《降水量等级》(GB/T 28592—2012) 应用实例[J]. 现代农业科技, 2016 ( 19):125.
	[ Zhang Zeming, Zhang Cuicui, Hou Hongyun et al. Application of grade of precipitation (GB/T 28592-2012). Modern Agricultural Science Technology, 2016,19:125.]
[28]	王海燕, 杨方廷, 刘鲁 . 标准化系数与偏相关系数的比较与应用[J]. 数量经济技术经济研究, 2006,23(9):150-155.
	[ Wang Haiyan, Yang Fangting, Liu Lu . Comparison and application of standardization coefficient and partial correlation coefficient. Quantitative Economics of Economics and Technology, 2006,23(9):150-155.]
[29]	杨思遥, 孟丹, 李小娟 , 等. 华北地区2001-2014年植被变化对SPEI气象干旱指数多尺度的响应[J]. 生态学报, 2018,38(3):1028-1039.
	[ Yang Siyao, Meng Dan, Li Xiaojuan et al. Multi-scale response of vegetation change to spei meteorological drought index in north China from 2001 to 2014. Acta Ecologica Sinica, 2018,38(3):1028-1039.]
[30]	魏军, 李婷, 胡会芳 , 等. 基于RClimDex模型的石家庄市极端降水时空变化特征[J]. 干旱气象, 2016,34(4):623-630.
	[ Wei Jun, Li Ting, Hu Huifang et al. Temporal and spatial variations of extreme precipitation in Shijiazhuang City based on rclimdex model. Journal of Arid Meteorology, 2016,34(4):623-630.]
[31]	Yao J, He X Y, Li X Y et al. Monitoring responses of forest to climate variations by modis NDVI: A case study of Hun River upstream, northeastern China[J]. European Journal of Forest Research, 2012,131(3):705-716.
[32]	Jia Y, Yu G, Gao Y et al. Global inorganic nitrogen dry deposition inferred from ground and space-based measurements[J]. Scientific Report, 2016,6(6):19810.
[33]	李建国, 濮励杰, 刘金萍 , 等. 2001年至2010年三峡库区重庆段植被活动时空特征及其影响因素[J]. 资源科学, 2012,34(8):1500-1507.
	[ Li Jianguo, Pu Lijie, Liu Jinping et al. Spatio-temporal characteristics and influencing factors of vegetation activities in chongqing region of the Three Gorges Reservoir Area from 2001 to 2010. Resource Science, 2012,34(8):1500-1507.]
[34]	李建国, 王欢, 王净 , 等. 基于线性拟合的城市化过程对陆地生态系统生产能力的影响——以江苏省为例[J]. 资源科学, 2018,40(1):32-43.
	[ Li Jianguo, Wang Huan, Wang Jing et al. The impact of urbanization process on terrestrial ecosystem productivity based on linear fitting: A case study of Jiangsu Province. Resource Science, 2018,40(1):32-43.]
[35]	尹海魁, 赵文廷, 李超 , 等. 基于生态分区的河北省植被覆盖时空变化特征研究[J]. 土壤通报, 2016,47(1):29-35.
	[ Yin Haikui, Zhao Wenting, Li Chao et al. Spatial-temporal changes of vegetation cover in Hebei Province based on ecological zoning. Chinese Journal of Soil Science, 2016,47(1):29-35.]
[36]	Zhao M, Running S W . Drought-induced reduction in global terrestrial net primary production from 2000 through 2009[J]. Science, 2010,329(5994):940-943.
[37]	Piao S, Fang J, Ciais P et al. The carbon balance of terrestrial ecosystems in China[J]. Nature, 2010,458(7241):1009-1013.
[38]	Dore M H I . Climate change and changes in global precipitation patterns: What do we know?[J]. Environment International, 2005,31(8):1167-1181.
[39]	Ying S, Ding Y H . A projection of future changes in summer precipitation and monsoon in East Asia[J]. Science China Earth Sciences, 2010,53(2):284-300.
[40]	Song S, Bai J . Increasing winter precipitation over arid central asia under global warming[J]. Atmosphere, 2016,7(10):139.
[41]	Gu G, Adler R F . Interdecadal variability/long-term changes in global precipitation patterns during the past three decades: Global warming and/or pacific decadal variability?[J]. Climate Dynamics, 2013,40(11-12):3009-3022.
[42]	Salzmann M . Global warming without global mean precipitation increase?[J]. Science Advances, 2016,2(6):e1501572.
[43]	Gaur A, Schardong A, Simonovic S . Effects of global warming on precipitation extremes: Dependence on storm characteristics[J]. Water Resources Management, 2018,32(8):1-10.
[44]	Vargo L J, Galewsky J, Rupper S et al. Sensitivity of glaciation in the arid subtropical andes to changes in temperature, precipitation, and solar radiation[J]. Global & Planetary Change, 2018,163:86-96.
[45]	Zhao C, Tie X, Lin Y . A possible positive feedback of reduction of precipitation and increase in aerosols over eastern central China[J]. Geophysical Research Letters, 2006,33(11):229-239.
[46]	Grant R F, Mekonnen Z A, Riley W J et al. I: Microtopography determines how active layer depths respond to changes in temperature and precipitation at an arctic polygonal tundra site: Mathematical modelling with ecosys[J]. Journal of Geophysical Research Biogeosciences, 2017,122(12):3161-3173.
[47]	Hu M, Xia B . A significant increase in the normalized difference vegetation index during the rapid economic development in the pearl river delta of china[J]. Land Degradation & Development, 2019,30(4):359-370.
[48]	Bastos A, Running S W, Gouveia C et al. The global NPP dependence on enso: La niña and the extraordinary year of 2011[J]. Journal of Geophysical Research Biogeosciences, 2013,118(3):1247-1255.
[49]	Ding J, Yang T, Zhao Y et al. Increasingly important role of atmospheric aridity on Tibetan alpine grasslands[J]. Geophysical Research Letters, 2018,45(6):2852-2859.

等级	时段降雨量
等级	12 h降雨量 24 h降雨量
微量降雨（零星小雨）	<0.1	<0.1
小雨	0.1~4.9	0.1~9.9
中雨	5.0~14.9	10.0~24.9
大雨	15.0~29.9	25.0~49.9
暴雨	30.0~69.9	50.0~99.9
大暴雨	70.0~139.9	100.0~249.9
特大暴雨	≥140.0	≥250.0

地区	偏相关系数					多元回归系数
地区	最小值	最大值	变化区间	均值	标准差	最小值	最大值	变化区间	均值	标准差
山东	-0.39	0.30	0.70	-0.20	0.12	-0.31	0.15	0.46	-0.04	0.05
江苏	-0.37	0.35	0.71	-0.19	0.16	-0.20	0.18	0.38	-0.04	0.04
浙江	-0.34	0.33	0.67	-0.05	0.22	-0.21	0.26	0.47	0.00	0.05
福建	-0.51	0.45	0.97	-0.09	0.23	-0.19	0.17	0.35	-0.01	0.04
广西	-0.37	0.44	0.81	-0.14	0.19	-0.10	0.08	0.18	-0.01	0.04
广东	-0.41	0.47	0.87	0.01	0.24	-0.10	0.20	0.30	0.02	0.03
海南	-0.43	0.48	0.90	0.10	0.23	-0.45	0.35	0.80	0.01	0.09
辽宁	-0.40	0.36	0.76	0.05	0.23	-0.16	0.16	0.33	0.01	0.05
天津	-0.19	-0.19	0.00	-0.19	0.00	0.03	0.03	0.00	-0.03	0.00
河北	-0.30	0.31	0.62	0.01	0.21	-0.07	0.11	0.18	0.02	0.05
上海	-0.29	0.40	0.69	0.21	0.16	-0.26	0.30	0.56	0.04	0.10
北京	-0.40	0.28	0.68	-0.19	0.15	-0.08	0.07	0.15	-0.03	0.04
全区	-0.52	0.50	1.02	-0.02	0.24	-0.45	0.35	0.80	-0.01	0.05

中国东部沿海地区暴雨对植被活动的影响

Effect of Rainstorms on Vegetation Activities in Eastern Coastal Area of China

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 49

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	王波, 甄峰, 孙鸿鹄. 基于社交媒体签到数据的城市居民暴雨洪涝响应时空分析[J]. 地理科学, 2020, 40(9): 1543-1552.
[2]	王晓辰, 韩增林, 彭飞, 蔡先哲. 中国海洋科技创新效率发展格局演变与类型划分[J]. 地理科学, 2020, 40(6): 890-899.
[3]	张华, 徐存刚, 王浩. 2001−2018年西北地区植被变化对气象干旱的响应[J]. 地理科学, 2020, 40(6): 1029-1038.
[4]	张野, 王康, 刘欣铭, 张苏辰, 周嘉, 王菜林. 基于Copula函数的暴雨要素三维联合分布——以宽甸县为例[J]. 地理科学, 2017, 37(4): 603-610.
[5]	李净, 刘红兵, 李彩云, 李龙. 基于GIMMS 3g NDVI的近30年中国北部植被生长季始期变化研究[J]. 地理科学, 2017, 37(4): 620-629.
[6]	李军, 朱慧. 重庆地区MODIS/NDVI时间序列数据重建研究[J]. 地理科学, 2017, 37(3): 437-444.
[7]	陆晴, 吴绍洪, 赵东升. 1982~2013年青藏高原高寒草地覆盖变化及与气候之间的关系[J]. 地理科学, 2017, 37(2): 292-300.
[8]	方利, 王文杰, 蒋卫国, 陈民, 王永, 贾凯, 李延森. 2000~2014年黑龙江流域（中国）植被覆盖时空变化及其对气候变化的响应[J]. 地理科学, 2017, 37(11): 1745-1754.
[9]	杨佩国, 靳京, 赵东升, 李静. 基于历史暴雨洪涝灾情数据的城市脆弱性定量研究——以北京市为例[J]. 地理科学, 2016, 36(5): 733-741.
[10]	陈洪全, 张云峰. 江苏沿海经济发展的区域差异及空间格局演变[J]. 地理科学, 2016, 36(2): 283-288.
[11]	史培军, 孔锋. 1951~2010年中国年代际累积暴雨时空格局变化的相关因素研究[J]. 地理科学, 2016, 36(10): 1457-1465.
[12]	杨林, 韩科技, 陈子扬. 沿海地区经济增长与海洋灾害损失的动态关系研究：1989~2011年[J]. 地理科学, 2015, 35(8): 969-975.
[13]	神祥金, 周道玮, 李飞, 张海艳. 中国草原区植被变化及其对气候变化的响应[J]. 地理科学, 2015, 35(5): 622-629.
[14]	许艳, 濮励杰, 朱明. 基于作物生长期的江苏省沿海地区气候生产潜力估算[J]. 地理科学, 2015, 35(5): 658-664.
[15]	权瑞松. 多情景视角的上海中心城区地铁暴雨内涝暴露性分析[J]. 地理科学, 2015, 35(4): 471-475.