PDF Download

[1]HE Hongchang,MA Bingxin,JING Juanli,et al.Spatiotemporal Changes of NPP and Natural Factors in the Southwestern Karst Areas from 2000 to 2019[J].Research of Soil and Water Conservation,2022,29(03):172-178+188.

Copy

Spatiotemporal Changes of NPP and Natural Factors in the Southwestern Karst Areas from 2000 to 2019

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 29 Number of periods: 2022 03 Page number: 172-178+188 Column: Public date: 2022-04-20

Title:: Spatiotemporal Changes of NPP and Natural Factors in the Southwestern Karst Areas from 2000 to 2019

Author(s):: HE Hongchang¹, MA Bingxin¹, JING Juanli^1,2, XU Yong¹, DOU Shiqing¹, LIU Bing¹; (1.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, Guangxi 541006, China; 2.Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin, Guangxi 541006, China)

Keywords:: southwest karst area; NPP; trend analysis; Mann-Kendall test; R/S analysis; geographic detector

CLC:: P931.5

DOI:: -

Abstract:: Net primary production is an important variable that characterizes plant activities. Analyzing the spatiotemporal changes and driving forces of NPP is of great significance to ecological protection and vegetation restoration. Based on MOD17A3 NPP, site-based meteorological data, DEM and other datas, we used trend analysis, Mann-Kendall test, R/S analysis and geographic detector methods to analyze the temporal and spatial changes and future trends of NPP, and to further explore the driving effects of natural factors on NPP in southwest China. The results show that:(1)the NPP of the study area was higher in the south and lower in the north, with an overall average value of 751.37 gC/(m²·a);(2)from 2000—2019, the average annual NPP for the study period showed an overall upward trend, with a rising rate of 3.67 gC/(m²·a); the areas with an increasing trend accounted for 78.10% of the total study area, in which 42.14% of those areas showed a significant increase, mainly distributed in the Sichuan Basin and Wumeng Mountain area;(3)furthermore, the areas expected to maintain an increasing trend in the future, accounted for 76.97% of the total study area, in which the areas expected to show a strong, continuous and significant increase accounted for 30.67%, which mainly distributed in the Sichuan Basin and around Wumeng Mountain;(4)the humidity, biological warmth index, sunshine hours and temperature were the controlling factors affecting the change of NPP in the geographical exploration, the average q of above driving factors was greater than 0.3; the interaction between each two factors presented double-factor enhancement and non-linearity enhancement, among which the interaction q value of elevation and BWI was the highest with a magnitude of 0.498. It suggests that the combined effect of elevation and biological warmth index has the greatest influence on NPP in the southwestern karst area.

References:: [1] Nemani R R, Keeling C D, Hashimoto H, et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999[J]. Science, 2003,300(5625):1560-1563.
[2] 陶波,李克让,邵雪梅,等.中国陆地净初级生产力时空特征模拟[J].地理学报,2003,58(3):372-380.
[3] Field C B, Behrenfeld M J, Randerson J T, et al. Primary production of the biosphere:integrating terrestrial and oceanic components[J]. Science, 1998,281(5374):237-240.
[4] Piao S, 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.
[5] Liu Q, Cai H Y, Jin G Z. Effects of selective cutting on the carbon density and net primary productivity of a mixed broadleaved-Korean pine forest in Northeast China[J]. Chinese Journal of Applied Ecology, 2013,24(10):2709-2716.
[6] 何云玲,张一平.云南省自然植被净初级生产力的时空分布特征[J].山地学报,2006,24(2):193-201.
[7] 刘婧,汤峰,张贵军,等.2000—2015年滦河流域植被净初级生产力时空分布特征及其驱动因子分析[J].中国生态农业学报:中英文,2021,29(4):659-671.
[8] 兰小丽,孙慧兰,许玉凤.贵州植被NPP时空格局及其对气候变化的响应[J].安徽农学通报,2020,26(18):162-166.
[9] Peng C, Apps M J. Modelling the response of net primary productivity(NPP)of boreal forest ecosystems to changes in climate and fire disturbance regimes[J]. Ecological Modelling, 1999,122(3):175-193.
[10] 刘恒,汤弟伟,宋鄂平,等.2000—2015年武陵山区植被净初级生产力的时空动态特征及其驱动因子[J].水土保持研究,2020,27(6):218-225.
[11] 李镇清,富兰克.中国西南岩溶地区的植被资源[J].草业科学,2008,25(9):51-53.
[12] 宋同清,彭晚霞,杜虎,等.中国西南喀斯特石漠化时空演变特征、发生机制与调控对策[J].生态学报,2014,34(18):5328-5341.
[13] 谷晓平,黄玫,季劲钧,等.近20年气候变化对西南地区植被净初级生产力的影响[J].自然资源学报,2007,22(2):251-259.
[14] 李燕丽,潘贤章,王昌昆,等.2000—2011年广西植被净初级生产力时空分布特征及其驱动因素[J].生态学报,2014,34(18):5220-5228.
[15] 左丽媛,高江波.基于地理探测器的喀斯特植被NPP定量归因[J].生态环境学报,2020,29(4):686-694.
[16] Stow D, Daeschner S, Hope A, et al. Spatial-temporal trend of seasonally-integrated normalized difference vegetation index as an indicator of changes in Arctic Tundra vegetation in the early 1990s[C]//Igarss 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium(Cat. No.01 Ch37217). IEEE, 2001.
[17] Masroor M, Rehman S, Avtar R, et al. Exploring climate variability and its impact on drought occurrence:Evidence from Godavari Middle sub-basin, India[J]. Weather and Climate Extremes, 2020,30:doi.org/10,1016/j.wace.2020.100277.
[18] Du S, Gu H, Wen J, et al. Detecting flood variations in Shanghai over 1949—2009 with Mann-Kendall tests and a newspaper-based database[J]. Water, 2015,7(5):1808-1824.
[19] Xu C, Chen Y, Li W, et al. Climate change and hydrologic process response in the Tarim River Basin over the past 50 years[J]. Chinese Science Bulletin, 2006,51(A01):25-36.
[20] Vicente M, Begueria S, Lopez I. A multiscalar drought index sensitive to global warming:the standardized precipitation evapotranspiration index[J]. Journal of Climate, 2010,23(7):1696-1718.
[21] Rao A R, Bhattacharya D. Hypothesis testing for long-term memory in hydrologic series[J]. Journal of Hydrology, 1999,216(3):183-196.
[22] 江田汉,邓莲堂. Hurst指数估计中存在的若干问题:以在气候变化研究中的应用为例[J].地理科学,2004,24(2):177-182.
[23] 梅丽明,沈正,陈伯海,等.海浪波高序列的长期相关性及自仿射结构[J].海洋学报,1995,17(4):11-21.
[24] 王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.
[25] 王欢,高江波,侯文娟.基于地理探测器的喀斯特不同地貌形态类型区土壤侵蚀定量归因[J].地理学报,2018,73(9):1674-1686.
[26] Wang J F, Li X H, Christakos G, et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China[J]. International Journal of Geographical Information Science, 2010,24(1):107-127.
[27] 李佳洺,陆大道,徐成东,等.胡焕庸线两侧人口的空间分异性及其变化[J].地理学报,2017,72(1):148-160.
[28] 徐文铎.吉良的热量指数及其在中国植被中的应用[J].生态学杂志,1985,3(3):215-222.
[29] 刘春迎. KIRA指标在中国植被与气候关系研究中的应用[J].植物生态学报,1999,23(2):30-36.
[30] 倪健. KIRA指标的拓展及其在中国植被与气候关系研究中的应用[J].应用生态学报,1997,8(2):161-170.
[31] 赵志平,吴晓莆,李果,等.2009—2011年我国西南地区旱灾程度及其对植被净初级生产力的影响[J].生态学报,2015,35(2):350-360.
[32] 冯新灵,冯自立,罗隆诚,等.青藏高原冷暖气候变化趋势的R/S分析及Hurst指数试验研究[J].干旱区地理,2008,31(2):175-181.
[33] 李登科,王钊.基于MOD17A3的中国陆地植被NPP变化特征分析[J].生态环境学报,2018,27(3):397-405.
[34] 董丹,倪健.利用CASA模型模拟西南喀斯特植被净第一性生产力[J].生态学报,2011,31(7):1855-1866.
[35] 黄晓云,林德根,王静爱,等.气候变化背景下中国南方喀斯特地区NPP时空变化[J].林业科学,2013,49(5):10-16.
[36] 盛叶子,曾蒙秀,林德根,等.2000—2014年人类活动对贵州省植被净初级生产力的影响[J].中国岩溶,2020,39(1):62-70.
[37] 金芳芳.西南山区生态特征的变化及其与人类活动的关系[D]. 河南开封:河南大学,2018.

Similar References:

Memo

Last Update: 2022-04-20