Influence of forest ecosystem on regional temperature and precipitation variation in Saihanba region

XING Bo; WANG Yingnan; YIN Zhiqiang; SHAO Hai; HE Zekang

doi:10.12097/j.issn.1671-2552.2023.12.013

2023 Vol. 42, No. 12

Article Contents

Article navigation > Geological Bulletin of China > 2023 > 42(12): 2174-2184

Next Article Previous Article

XING Bo, WANG Yingnan, YIN Zhiqiang, SHAO Hai, HE Zekang. 2023. Influence of forest ecosystem on regional temperature and precipitation variation in Saihanba region. Geological Bulletin of China, 42(12): 2174-2184. doi: 10.12097/j.issn.1671-2552.2023.12.013

Citation:

XING Bo, WANG Yingnan, YIN Zhiqiang, SHAO Hai, HE Zekang. 2023. Influence of forest ecosystem on regional temperature and precipitation variation in Saihanba region. Geological Bulletin of China, 42(12): 2174-2184. doi: 10.12097/j.issn.1671-2552.2023.12.013

Influence of forest ecosystem on regional temperature and precipitation variation in Saihanba region

1.
Command Center of Comprehensive Survey for Natural Resources, China Geological Survey, Beijing 100055, China
2.
China Institute of Geo-Environment Monitoring, Beijing 100081, China
3.
Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing 100035, China

More Information

Corresponding author: YIN Zhiqiang, yinzhiqiang@mail.cgs.gov.cn

Received Date: 28 May 2022

Revised Date: 24 August 2022

Publish Date: 15 December 2023

Abstract

Abstract

Forest is the highly-efficient interface between earth and atmosphere for substance migration and energy transfer, which can influence local climate characteristics.In this study, Mann-Kendall trend test and break points recognization, wavelet analysis were applied to analyze temperature and precipitation data over 60 years from 1961-2020 to characterize temperatures and precipitations in Saihanba on annual and monthly scales through contrasting those in Weichang and Duolun.It was found that at the interannual scale, forest decreased local annual average temperature, maximum temperature and minimum temperature.Although it could not change the rising trends of temperature, it delayed the beginning time of warming, and enhanced average temperature variation of interannual in short period.At the seasonal and monthly scale, forest maintained stable temperature in late spring and early summer and reduced average temperature variation of interannual in July, meanwhile widened temperature differences between summer and winter and more perienced low temperature in winter.At the interannual scale, forest decreased interannual variation of annual precipitation, speeded up precipitation rising and broke the cycle period of interannual.At the seasonal and monthly scale, forest dispersed precipitation to different seasons, and slowed down the rate of precipitation variation in June and July.
- Saihanba /
- forest ecosystem /
- precipitation /
- temperature /
- MK trend test /
- wavelet transform /
- geological survey engineering

FullText(HTML)

References

[1]	Gocic M, Trajkovic S. Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimatior statistical tests in Serbia[J]. Global and Planetary Change, 2013, 100: 172-182. doi: 10.1016/j.gloplacha.2012.10.014 CrossRef Google Scholar
[2]	Grossmann A, Morlet J. Decomposition of Hardyfuncions into square integrable wavelets of constant shape[J]. SIAM.J. Math. Anal., 1984, 15(4): 723-736. doi: 10.1137/0515056 CrossRef Google Scholar
[3]	Gwate O, Ndou G. Exploring water use and production dynamics of an indigenous African dry forest in south-western Zimbabwe[J]. Journal of Arid Environments, 2022, 198: 104678. . doi: 10.1016/j.jaridenv.2021.104678 CrossRef Google Scholar
[4]	Kendall M. Rank Correlation Methods[M]. London: Charles Griffin, 1975. Google Scholar
[5]	Hermida L, López L, Merino A, et al. Hailfall in southwest France: Relationship with precipitation, trends and wavelet analysis[J]. Atmospheric Research, 2015, 156: 174-188. doi: 10.1016/j.atmosres.2015.01.005 CrossRef Google Scholar
[6]	Liu J R, Fu G B, Song X F, et al. Stable isotopic compositions in Australianprecipitation[J]. J. Geophys. Res., 2010, 115: D23307. Google Scholar
[7]	Mann H. Non-parametric tests against trend[J]. Econometrica, 1945, 13: 245-259. doi: 10.2307/1907187 CrossRef Google Scholar
[8]	Morlet J. Sampling theory and wave propagation[M]. NATO ASI Series, FI, Springer, 1983: 233-261. Google Scholar
[9]	Sang Y F, Wang Z, Liu C. Comparison of the MK test and EMD method for trend identification in hydrological time series[J]. Journal of Hydrology, 2014, 510: 293-298. doi: 10.1016/j.jhydrol.2013.12.039 CrossRef Google Scholar
[10]	Sen P K. Estimates of the Regression Coefficient Based on Kendall's Tau[J]. Publications of the American Statistical Association, 1968, 63(324): 1379-1389. . doi: 10.1080/01621459.1968.10480934 CrossRef Google Scholar
[11]	Yi H, Shu H. The improvement of the Morlet wavelet for multi-period analysis of climate data[J]. Compt. Rendus Geosci., 2012, 344(10): 483-497. doi: 10.1016/j.crte.2012.09.007 CrossRef Google Scholar
[12]	卞娟娟, 郝志新, 郑景云, 等. 1951—2010年中国主要气候区划界线的移动[J]. 地理研究, 2013, 32(7): 1179-1187. Google Scholar
[13]	陈军明, 赵平, 郭晓寅. 中国西部植被覆盖变化对北方夏季气候影响的数值模拟[J]. 气象学报, 2010, 68(2): 251-259. Google Scholar
[14]	丁一汇, 李巧萍, 董文杰. 植被变化对中国区域气候的数值模拟研究[J]. 气象学报, 2005, 63(5): 613-621. doi: 10.3321/j.issn:0577-6619.2005.05.007 CrossRef Google Scholar
[15]	樊宝敏, 李智勇. 过去4000年中国降水与森林变化的数量关系[J]. 生态学报, 2010, 30(20): 5666-5676. Google Scholar
[16]	范广州, 吕世华. 陆面植被类型对华北地区夏季降水影响的数值模拟研究[J]. 高原气象, 1999, 18(4): 649-658. Google Scholar
[17]	符淙斌, 王强. 气候突变的定义和监测方法[J]. 大气科学, 1992, 16(4): 482-493. Google Scholar
[18]	韩熠哲, 马伟强, 王炳赟, 等. 青藏高原近30年降水变化特征分析[J]. 高原气象, 2017, 36(6): 1477-1486. Google Scholar
[19]	姜大膀, 王娜. IPCC AR6报告解读: 水循环变化[J]. 气候变化研究进展, 2021, 17 (6): 699-704. Google Scholar
[20]	姜大膀, 王式功, 郎咸梅, 等. 沙区绿化对区域气候影响的数值模拟研究[J]. 中国沙漠, 2003, 23(1): 63-66. Google Scholar
[21]	况雪源, 韩跃超. 中全新世背景下中国区域气候对指标变化响应的降尺度模拟研究[J]. 地球科学进展, 2021, 36(12): 1301-1312. Google Scholar
[22]	刘琳, 徐宗学, 杨晓静. 西南地区旱涝演变与ENSO事件的关系[J]. 资源科学, 2019, 41(11): 2144-2153. Google Scholar
[23]	闵庆文, 袁嘉祖. 森林对于降水的可能影响: 几种分析方法所得结果的比较[J]. 自然资源学报, 2001, 16(5): 467-473. doi: 10.3321/j.issn:1000-3037.2001.05.013 CrossRef Google Scholar
[24]	任艳林. 1965—2011年河北塞罕坝地区降水量变化规律的小波分析[J]. 北京大学学报(自然科学版), 2012, 48(6): 918-914. Google Scholar
[25]	邵海, 殷志强, 王轶, 等. 河北坝上高原如意河流域风积沙厚度空间展布预测方法[J]. 地质通报, 2022, 41(12): 2138-2145. Google Scholar
[26]	孙敏茹, 郭玲玲, 闵学武, 等. 塞罕坝机械林场气候因子变化与当地林业发展的关系[J]. 河北林业科技, 2013, 5: 69-71. Google Scholar
[27]	田磊, 杨建玲, 翟涛, 等. 森林对降水的影响概述[J]. 宁夏农林科技, 2012, 53(4): 19-22. Google Scholar
[28]	王安琪, 高玉琴, 蔡涛. 大凌河流域朝阳地区1955~2014降水趋势变化及突变分析[J]. 水文, 2017, 37(5): 92-96. Google Scholar
[29]	王日明, 吴天亮, 曾庆鑫, 等. 区域森林覆盖率对其降水的影响[J]. 西部林业科学, 2020, 49(1): 72-80. Google Scholar
[30]	邢博, 金爱芳, 殷志强, 等. 基于多源数据的流域水平衡和水源涵养变化研究——以坝上高原小滦河流域为例[J], 地质通报, 2022, 41(12): 2114-2124. Google Scholar
[31]	徐宗学, 孟翠玲, 巩同梁, 等. 西藏自治区气温变化趋势分析[J]. 自然资源学报, 2009, 24(1): 162. Google Scholar
[32]	殷志强, 邢博, 邵海, 等. 支撑赛罕坝生态屏障建设水平衡和科学绿化研究取得新认识[R]. 中国地质调查局《地质调查专报》, 2022. Google Scholar
[33]	张井勇, 董文杰, 叶笃正, 等. 中国植被覆盖对夏季气候影响的新证据[J]. 科学通报, 2003, 48(1): 5. Google Scholar
[34]	中国气象局. 地面气象观测资料质量控制QX/T 118-2010[M]. 气象出版社, 2010. Google Scholar
[35]	中国气象局气候变化中心. 中国气候变化蓝皮书(2021)[M]. 北京: 科学出版社, 2021. Google Scholar
[36]	左斌斌, 徐宗学, 任梅芳, 等. 北京市通州区1966—2016年降水特性研究[J]. 北京师范大学学报(自然科学版), 2019, 55(5): 556-563. Google Scholar