1961—2020年青海省饱和水汽压差变化特征及影响因子分析

doi:10.13866/j.azr.2023.02.02

摘要/Abstract

摘要：

饱和水汽压差（Vapor Pressure Deficit, VPD）作为蒸散的主要驱动因子之一，能够反映大气从地表获取水分的能力，厘清VPD时空变化特征有助于理解青藏高原地区大气干湿程度对气候变化的响应。利用Mann-Kendall检验、多元线性回归等方法分析了青海省1961—2020年VPD时空变化特征以及影响因子。结果表明：1961—2020年青海省平均VPD呈上升趋势，且在1998年发生突变；其季节平均值和对应的气候倾向率均表现为：夏季>春季>秋季>冬季；不同的功能区，VPD平均值表现为：柴达木盆地>东部农业区>青海湖地区>青南牧区，对应的气候倾向率表现为：东部农业区>柴达木盆地>青南牧区>青海湖地区。多年平均VPD在空间上呈“鞍形场”分布格局，且除了青南牧区东北部的贵南站呈减少趋势，其余地区均为增加趋势。VPD突变前后的气象主导因子有所差异，但总体主要为最高温度和相对湿度。春、夏、秋3个季节以及多年VPD变化过程中，海拔贡献率最高，其次为经度；冬季海拔贡献率依旧最高，其次为纬度。

关键词: 饱和水汽压差, 变化特征, 影响因子, 青海省

Abstract:

The vapor pressure deficit can reflect the ability of the atmosphere to obtain water from the surface,which is one of the main driving factors of evapotranspiration. Clarifying the spatial and temporal variation of vapor pressure deficit (VPD) can help to investigate the response of air dryness to climate change in Tibetan Plateau. Mann-Kendall test, multiple linear regression were used to analyze temporal and spatial variation characteristics and the influencing factors of VPD before and after the breakpoint from 1961 to 2020 in Qinghai Province. The results showed that VPD had an increasing trend in Qinghai Province from 1961 to 2020 and a mutation in 1998. The seasonal averages and corresponding climatic trend rates of VPD were the same as summer>spring>autumn>winter. In different functional areas, average VPDs showed as Qaidam Basin>Eastern Agricultural Area>Qinghai Lake Area>Qingnan Pastoral Area, the corresponding climatic trend rates were the Eastern Agricultural Area>Qaidam Basin>Qingnan Pastoral Area>Qinghai Lake Area. The multi-year average VPD showed a “saddle field” distribution in space, and had an increasing trend except the Guinan Station in the northeastern of Qingnan pastoral area, which had a decreasing trend. The predominant meteorological factors of VPD were different before and after mutation in Qinghai Province. However, the highest temperature and relative humidity in general were the main factors. During the variation of VPD in the spring, summer, autumn and multi-year, the contribution rate of altitude was the highest, followed by longitude; while in winter, the contribution rate of altitude is still the highest, followed by latitude.

Key words: vapor pressure deficit, variation characteristics, influencing factors, Qinghai Province

李素雲,祁栋林,温婷婷,史飞飞,乔斌,肖建设. 1961—2020年青海省饱和水汽压差变化特征及影响因子分析[J]. 干旱区研究, 2023, 40(2): 173-181.

LI Suyun,QI Donglin,WEN Tingting,SHI Feifei,QIAO Bin,XIAO Jianshe. The variation characteristics and influencing factors of vapor pressure deficit in Qinghai Province from 1961 to 2020[J]. Arid Zone Research, 2023, 40(2): 173-181.

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参考文献 30

[1]	McVicar Tim R, Roderick Michael L, Donohue R J, et al. Global review and synthesis of trends in observed terrestrial near-surface wind speeds: Implications for evaporation[J]. Journal of Hydrology, 2012, 416/417: 182-205. doi: 10.1016/j.jhydrol.2011.10.024
[2]	宁梓妤, 徐宪立, 杨东, 等. 中国西南地区饱和水汽压差的年际变化及其影响因素[J]. 农业现代化研究, 2022, 43(1): 172-179.
	[Ning Ziyu, Xu Xianli, Yang Dong, et al. Temporal variation of vapor pressure deficit and its influencing factors in Southwest China[J]. Research of Agricultural Modernization, 2022, 43(1): 172-179.]
[3]	赵晓涵, 张方敏, 韩典辰, 等. 内蒙古半干旱区蒸散特征及归因分析[J]. 干旱区研究, 2021, 38(6): 1614-1623.
	[Zhao Xiaohan, Zhang Fangmin, Han Dianchen, et al. Evapotranspiration changes and its attribution in semi-arid regions of Inner Mongolia[J]. Arid Zone Research, 2021, 38(6): 1614-1623.]
[4]	汪步惟, 张雪芹. 1971—2014年青藏高原参考蒸散变化及其归因[J]. 干旱区研究, 2019, 36(2): 269-279.
	[Wang Buwei, Zhang Xueqin. Change and attribution of reference evapotranspiration over the Tibetan Plateau during the period of 1971-2014[J]. Arid Zone Research, 2019, 36(2): 269-279.]
[5]	张红梅, 吴炳方, 闫娜娜. 饱和水汽压差的卫星遥感研究综述[J]. 地球科学进展, 2014, 29(5): 559-568. doi: 10.11867/j.issn.1001-8166.2014.05.0559
	[Zhang Hongmei, Wu Bingfang, Yan Nana. Remote sensing estimates of vapor pressure deficit: An overview[J]. Advances in Earth Science, 2014, 29(5): 559-568.] doi: 10.11867/j.issn.1001-8166.2014.05.0559
[6]	Lendzion, Leuschner. Growth of European beech (Fagus sylvatica L.) saplings is limited by elevated atmospheric vapour pressure deficits[J]. Forest Ecology and Management, 2008, 256: 648-655. doi: 10.1016/j.foreco.2008.05.008
[7]	郭飞, 吉喜斌, 金博文, 等. 干旱区荒漠-绿洲过渡带3种典型灌木气孔导度对环境变化的响应及其对蒸腾的调控[J]. 高原气象, 2021, 40(3): 632-643. doi: 10.7522/j.issn.1000-0534.2021.00011
	[Guo Fei, Ji Xibin, Jin Bowen, et al. The response of stomatal conductance of three typical shrubs to environmental change and stomatal regulation of transpiration in an arid desert-oasis ecotone[J]. Plateau Meteorology, 2021, 40(3): 632-643.] doi: 10.7522/j.issn.1000-0534.2021.00011
[8]	闫敏, 李增元, 田昕, 等. 黑河上游植被总初级生产力遥感估算及其对气候变化的响应[J]. 植物生态学报, 2016, 40(1): 1-12. doi: 10.17521/cjpe.2015.0253
	[Yan Min, Li Zengyuan, Tian Xin, et al. Remote sensing estimation of gross primary productivity and its response to climate change in the upstream of Heihe River basin[J]. Chinese Journal of Plant Ecology, 2016, 40(1): 1-12.] doi: 10.17521/cjpe.2015.0253
[9]	Wang Kaicun, Dickinson Robert. A review of global terrestrial evapotranspiration: observation, modeling, climatology and climatic variability[J]. Reviews of Geophysics, 2012, 50(2): 1-54.
[10]	Yuan Wenping, Zheng Yi, Piao Shilong, et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth[J]. Science Advances, 2019, 5(8): 1396-1407. doi: 10.1126/sciadv.aax1396 pmid: 31453338
[11]	Seager Richard, Hooks Allison, Williams A Park, et al. Climatology, variability, and trends in the U.S. vapor pressure deficit, an important fire-related meteorological quantity[J]. Journal of Applied Meteorology and Climatology, 2015, 54(6): 1121-1141. doi: 10.1175/JAMC-D-14-0321.1
[12]	Lobell David B, Hammer Graeme L, McLean Greg, et al. The critical role of extreme heat for maize production in the United States[J]. Nature Climate Change, 2013, 3(5): 497-501. doi: 10.1038/nclimate1832
[13]	Zhang Shuai, Tao Fulu, Zhang Zhao. Spatial and temporal changes in vapor pressure deficit and their impacts on crop yields in China during 1980-2008[J]. Journal of Meteorology Research, 2017, 31(4): 800-808. doi: 10.1007/s13351-017-6137-z
[14]	Novick Kimberly A, Ficklin Darren L, Stoy Paul C, et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes[J]. Nature Climate Change, 2016, 6(11): 1023-1027. doi: 10.1038/nclimate3114
[15]	Mao Kebiao, Chen Jingming, Li Zhaoliang, et al. Global water vapor content decreases from 2003 to 2012: An analysis based on MODIS data[J]. Chinese Geographical Science, 2017, 27(1): 1-7. doi: 10.1007/s11769-017-0841-6
[16]	韩永贵, 韩磊, 黄晓宇, 等. 基于指数平滑和ARIMA模型的西北地区饱和水汽压差预测[J]. 干旱区研究, 2021, 38(2): 303-311.
	[Han Yonggui, Han Lei, Huang Xiaoyu, et al. Prediction of vapor pressure deficit in Northwest China based on exponential and ARIMA models[J]. Arid Zone Research, 2021, 38(2): 303-311.]
[17]	李旭谱, 张福平, 胡猛, 等. 基于SPOT NDVI的植被覆盖时空演变规律分析——以西北五省为例[J]. 干旱地区农业研究, 2012, 35(5): 180-184, 199.
	[Li Xupu, Zhang Fuping, Hu Meng, et al. Analysis of regulation of spatial-temporal variation of the vegetation coverage based on SPOT NDVI data: A case study in Northwest China[J]. Agricultural Research in the Arid Areas, 2012, 35(5): 180-184, 199.]
[18]	余晓雨, 贾绍凤, 朱文彬. 青海省地表净辐射通量的遥感估算方法及时空特征分析[J]. 高原气象, 2022, 41(4): 921-933. doi: 10.7522/j.issn.1000-0534.2021.00033
	[Yu Xiaoyu, Jia Shaofeng, Zhu Wenbin. Estimation of land surface net radiation flux based on remote sensing and analysis of its spatial-temporal characteristics in Qinghai Province[J]. Plateau Meteorology, 2022, 41(4): 921-933.] doi: 10.7522/j.issn.1000-0534.2021.00033
[19]	刘义花, 刘蓉娜, 李红梅. 1961—2016年青海高原干湿变化与植被变化关系研究[J]. 中国农学通报, 2019, 35(28): 98-104. doi: 10.11924/j.issn.1000-6850.casb18070113
	[Liu Yihua, Liu Rongna, Li Hongmei. The relationship between dry-wet change and vegetation in Qinghai Plateau: 1961-2016[J]. Chinese Agricultural Science Bulletin, 2019, 35(28): 98-104.] doi: 10.11924/j.issn.1000-6850.casb18070113
[20]	袁瑞瑞, 黄萧霖, 郝璐. 近40年中国饱和水汽压差时空变化及影响因素分析[J]. 气候与环境研究, 2021, 26(4): 413-424.
	[Yuan Ruirui, Huang Xiaolin, Hao Lu. Spatio-temporal variation of vapor pressure deficit and impact factors in China in the past 40 years[J]. Climatic and Environmental Research, 2021, 26(4): 413-424.]
[21]	李得宴, 杨维芳, 高志钰, 等. 不同饱和水汽压模型对GNSS反演可降水量的影响分析[J]. 全球定位系统, 2020, 45(6): 55-63.
	[Li Deyan, Yang Weifang, Gao Zhiyu, et al. Analysis of influence of different saturated water vapor pressure models on GNSS inversion precipitable water[J]. GNSS World of China, 2020, 45(6): 55-63.]
[22]	Li Zongxing, Feng Qi, Liu Wei,et al Spatial and temporal trend of potential evapotranspiration and related driving forces in Southwestern China during 1961-2009[J]. Quaternary International, 2014, 336: 127-144. doi: 10.1016/j.quaint.2013.12.045
[23]	谢琰, 文军, 刘蓉, 等. 太阳辐射和水汽压差对黄河源区高寒湿地潜热通量的影响研究[J]. 高原气象, 2018, 37(3): 614-625. doi: 10.7522/j.issn.1000-0534.2017.00063
	[Xie Yan, Wen Jun, Liu Rong, et al. The role of solar radiation and water vapor pressure deficit on controlling latent heat flux density over the alpine wetland of the source region of the Yellow River[J]. Plateau Meteorology, 2018, 37(3): 614-625.] doi: 10.7522/j.issn.1000-0534.2017.00063
[24]	Mann H B. Nonparametric tests against trend[J]. Econometrica, 1945, 13(3): 245-259. doi: 10.2307/1907187
[25]	Kendall M G. Rank Correlation Methods[M]. London: Hodder Education, 1945: 38-76.
[26]	孙康慧, 曾晓东, 李芳. 1980—2014 年中国生态脆弱区气候变化特征分析[J]. 气候与环境研究, 2019, 24(4): 455-468.
	[Sun Kanghui, Zeng Xiaodong, Li Fang. Climate change characteristics in ecological fragile zones in China during 1980-2014[J]. Climatic and Environmental Research, 2019, 24(4): 455-468.]
[27]	曹晓云, 肖建设, 乔斌, 等. 1961—2019年柴达木盆地沙尘强度时空变化特征[J]. 干旱气象, 2021, 39(1): 46-53.
	[Cao Xiaoyun, Xiao Jianshe, Qiao Bin, et al. Temporal and spatial variation on characteristics of dust intensity in Qaidam basin from 1961 to 2019[J]. Journal of Meteorology, 2021, 39(1): 46-53.]
[28]	张雁飞. 临河区近57年平均地面水汽压变化特征分析[J]. 农业灾害研究, 2019, 9(2): 27-29.
	[Zhang Yanfei. Analysis on the variation characteristics of average surface water pressure in Linhe District in the past 57 years[J]. Journal of Agricultural Catastrophology, 2019, 9(2): 27-29.]
[29]	赵美亮, 曹广超, 曹生奎, 等. 1980—2017年青海省地表温度时空变化特征[J]. 干旱区研究, 2021, 38 (1): 178-187.
	[Zhao Meiliang, Cao Guangchao, Cao Shengkui, et al. Temporal and spatial variation on characteristics of surface temperature in Qinghai Province from 1961 to 2019[J]. Arid Zone Research, 2021, 38 (1): 178-187.]
[30]	魏凤英. 现代气候统计诊断与预测技术[M]. 北京: 气象出版社, 2007: 63-72.
	[Wei Fengying. Modern Climate Statistical Diagnosis and Prediction Techniques[M]. Beijing: China Meteorological Press, 2007: 63-72.]

分区	年份	平均气温(T_ave)	最高气温(T_max)	最低气温(T_min)	降水量(p)	降水日数(D_p)	相对湿度(rh)	绝对湿度 (arh)	风速 (wnd)	贡献率/%
青海省	1961—1997	0.67^**	0.85^**	0.25	-0.68^**	-0.84^**	-0.80^**	-0.31^*	0.09	T_max（47.9^**）> rh（26.9）
青海省	1998—2020	0.59^**	0.69^**	0.31	-0.23	-0.52^**	-0.91^**	-0.50^**	0.49^**	rh（71.1^）> T_max（28.9^）
柴达木盆地	1961—1989	0.71^**	0.77^**	0.48^**	-0.55^**	-0.68^**	-0.76^**	-0.53^**	-0.16	rh（49.9^）> T_max（48.5^）
柴达木盆地	1990—2020	0.66^**	0.80^**	0.46^**	-0.25	-0.45^**	-0.62^**	0.17	-0.21	T_max（60.8^）> rh（39.2^）
东部农业区	1961—2002	0.70^**	0.81^**	0.33^*	-0.69^**	-0.81^**	-0.82^**	-0.18	-0.07	T_max（40.2^）> rh（33.7^）> p（18.4^**）
东部农业区	2003—2020	0.70^**	0.76^**	0.48^**	-0.60^**	-0.76^**	-0.95^**	-0.57^**	0.39^**	rh（81.2^**）> T_max（15.2）
青海湖地区	1961—1996	0.52^**	0.84^**	0.01	-0.73^**	-0.80^**	-0.86^**	-0.47^**	0.46^**	T_max（37.7^）> rh（34.3^）> p（20.4^**）
青海湖地区	1997—2020	0.41^**	0.69^**	0.09	-0.29^*	-0.63^**	-0.92^**	-0.43^**	0.52^**	rh（57.3^）> T_max（29.8^）
青南牧区	1961—1997	0.69^**	0.85^**	0.38^**	-0.48^**	-0.78^**	-0.79^**	-0.04	0.38^**	T_max（53.5^）> rh（30.7^）> D_p（15.8）
青南牧区	1998—2020	0.61^**	0.85^**	0.17	-0.25	-0.56^**	-0.96^**	-0.52^**	0.54^**	rh（73.0^）> T_max（27.0^）

不同时间段	方程	相关系数	贡献率/%
1961—2020年	VPD = -0.204 × lat - 0.459 × lon - 0.003 × h + 66.342	0.93^**	h(53.4^*) > lon(38.0^) > lat(8.6^)
春季	VPD = -0.293 × lat - 0.413 × lon - 0.003 × h + 66.865	0.94^**	h (56.5^*)> lon(32.0^) > lat(11.5^*)
夏季	VPD = -0.141 × lat - 0.908 × lon - 0.005 × h + 116.884	0.92^**	h (52.9^*)> lon(43.7^) > lat*(3.4)
秋季	VPD = -0.127 × lat - 0.431 × lon - 0.002 × h + 58.279	0.90^**	h(50.9^*) > lon(42.7^) > lat*(6.4)
冬季	VPD = -0.256 × lat - 0.078× lon - 0.001 × h + 22.521	0.91^**	h(53.2^*) > lat(29.3^) > lon(17.5^*)