西北旱区潜在蒸散发的气候敏感性及其干旱特征研究

阴晓伟; 吴一平; 赵文智; 赵富波; 孙彭成; 宋燕妮; 邱临静

doi:10.16030/j.cnki.issn.1000-3665.202012012

西北旱区潜在蒸散发的气候敏感性及其干旱特征研究

1. 西安交通大学人居环境与建筑工程学院，陕西西安　710049

2. 中国科学院西北生态环境资源研究院/临泽内陆河流域研究站，甘肃兰州　730000

基金项目: 国家重点研发计划项目（2019YFC0507403）；中科院战略先导专项B项目（XDB40020205）

详细信息

作者简介: 阴晓伟（1997-），男，硕士研究生，主要从事生态水文方向的研究。E-mail： yxw0228@stu.xjtu.edu.cn

通讯作者: 吴一平（1979-），男，博士，教授，主要从事环境变化与生态水文方向的研究。E-mail： yipingwu@xjtu.edu.cn

中图分类号: P641.131

收稿日期: 2020-12-05

修回日期: 2021-01-17

刊出日期: 2021-05-15

Drought characteristics and sensitivity of potential evapotranspiration to climatic factors in the arid and semi-arid areas of northwest China

1. School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi　710049, China

2. Linze Inland River Basin Research Station/Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu　730000, China

More Information

Corresponding author: WU Yiping, yipingwu@xjtu.edu.cn

Received Date: 05 December 2020

Revised Date: 17 January 2021

Publish Date: 15 May 2021

摘要

摘要:
潜在蒸散量（PET）是干旱监测评价的重要指标，分析影响潜在蒸散发的气候敏感因子对揭示气候变化的水文响应机理尤为重要。常采用的局部敏感性方法不适用于非线性模型且难以评估各气象因子间的相互作用。对此，基于1964—2018年西北旱区内163个气象站的监测数据，通过Penman-Monteith公式，采用Sobol全局敏感性方法分析了西北旱区潜在蒸散发的气候敏感因子，计算得到了自校准帕默尔干旱指数（scPDSI），进而分析了区域干旱的时空演变特征。结果表明：1964—2018年西北旱区年均潜在蒸散量为1157.8 mm，高值出现在新疆东部与内蒙古西部地区，低值出现在青海南部地区。1993年为转折点，西北旱区潜在蒸散发受气温、日照时数、风速、相对湿度等多种因素综合影响由显著下降的趋势转变为显著上升，且在夏季最为明显。在1964—1993年，净辐射、风速与相对湿度的变化对潜在蒸散发的影响较大；在1994—2018年，风速与相对湿度的变化对潜在蒸散发的影响较大。scPDSI的时空分布表明新疆北部、青海中部以及甘肃境内的干旱有缓解的趋势；而黄河流域西南部干旱呈现加重趋势，将加剧区域水资源紧张，威胁生态安全。
- 西北旱区 /
- 潜在蒸散发 /
- 干旱 /
- Penman-Monteith /
- scPDSI /
- Sobol全局敏感性分析
Abstract:
Potential evapotranspiration (PET) is an important part of drought monitoring and evaluation. It is very important to analyze the climatic sensitive factors of PET to reveal the mechanism of hydrological responses to climate change. However, the local sensitivity method commonly used in previous studies is not suitable for nonlinear models and it is difficult to evaluate the interaction among meteorological factors. In this study, based on the monitoring data of 163 meteorological stations in the arid and semi-arid areas of northwest China from 1964 to 2018, we use the Penman-Monteith equation and Sobol global sensitivity to analyze the climatic sensitive factors of PET in the study area and discuss the climatic causes of the PET change. We also calculate the scPDSI and analyze the evolution characteristics of drought in northwest China. The results show that the annual average PET of the study area is 1157.8 mm with substantial spatial variations. The high-value areas are located in the eastern part of Xinjiang and the western part of Inner Mongolia, and the low-value areas are located in the southern part of Qinghai Province. The year 1993 was identified as a turning point, and the PET changed from a significant downward trend to a significant upward trend, which was more apparent in the summer in that year. From 1964 to 1993, the net radiation, wind speed, and relative humidity had a relatively greater impact on PET, while in the period 1994-2018, wind speed and relative humidity had a relatively greater impact on PET. The spatio-temporal distribution of scPDSI show that the drought in northern Xinjiang, central Qinghai and Gansu is becoming less severe, while the drought severity in the southwest part of the Yellow River Basin is becoming more severe, exacerbating regional water resources shortage and threatening ecological security.
- arid and semi-arid areas of northwest China /
- potential evapotranspiration /
- drought /
- Penman-Monteith /
- scPDSI /
- Sobol global sensitivity analysis

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图 1 研究区概况

Figure 1.

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图 2 1964—2018年西北旱区潜在蒸散量空间分布

Figure 2.

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图 3 1964—2018年西北旱区潜在蒸散量变化趋势

Figure 3.

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图 4 西北旱区气象站点潜在蒸散量的Mann-Kendall趋势检验空间分布

Figure 4.

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图 5 气候因子敏感系数空间分布

Figure 5.

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图 6 气候因子敏感系数年内变化

Figure 6.

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图 7 1964—2018年西北旱区scPDSI及不同等级干旱站次比变化趋势

Figure 7.

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图 8 西北旱区1964—2018年scPDSI空间分布

Figure 8.

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图 9 西北旱区气象站点scPDSI的Mann-Kendall趋势检验空间分布

Figure 9.

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图 10 1964—2018年西北旱区不同季节潜在蒸散量变化趋势

Figure 10.

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图 11 夏季气候因子敏感系数年际变化

Figure 11.

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图 12 1964—2018年西北旱区夏季气候因子变化趋势

Figure 12.

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表 1 scPDSI干旱等级

Table 1. Classification of scPDSI

干旱等级	scPDSI
无旱	−0.99 ~ 0.99
轻旱	−1.99 ~ −1.00
中旱	−2.99 ~ −2.00
重旱	−3.99 ~ −3.00
特旱	≤ −4.00

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表 2 敏感区间范围^[22]

Table 2. Ranges of sensitivity coefficients^[22]

敏感系数	S<0.01	0.01≤S<0.1	S≥0.1
是否敏感	不敏感	敏感	很敏感

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表 3 不同等级干旱站次比Mann-Kendall趋势检验

Table 3. The Mann-Kendall trend test on the proportion of stations with different levels of drought

干旱等级	Z值	P值
中旱	−11.25	P<0.0001
重旱	−7.87	P<0.0001
特旱	−0.43	P=0.6683

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表 4 气候因子对潜在蒸散量变化的贡献

Table 4. Contribution of climate factors to PET

气候因子变化	净辐射		风速		相对湿度
气候因子变化	−1.05%	−1.89%	−29.21%	33.78%	7.15%	−8.17%
潜在蒸散量变化	−0.05%	−0.09%	−24.41%	26.68%	−7.25%	9.57%

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参考文献(30)

[1]	刘珂, 姜大膀. 基于两种潜在蒸散发算法的SPEI对中国干湿变化的分析[J]. 大气科学,2015,39(1):23 − 36. [LIU Ke, JIANG Dabang. Analysis of dryness/wetness over China using standardized precipitation evapotranspiration index based on two evapotranspiration algorithms[J]. Chinese Journal of Atmospheric Sciences,2015,39(1):23 − 36. (in Chinese with English abstract)
[2]	CHAHINE M T. The hydrological cycle and its influence on climate[J]. Nature,1992,359(6394):373 − 380. doi: 10.1038/359373a0
[3]	LIU C M, ZENG Y. Changes of pan evaporation in the recent 40 years in the Yellow River Basin[J]. Water International,2004,29(4):510 − 516. doi: 10.1080/02508060408691814
[4]	JHAJHARIA D, DINPASHOH Y, KAHYA E, et al. Trends in reference evapotranspiration in the humid region of northeast India[J]. Hydrological Processes,2012,26(3):421 − 435. doi: 10.1002/hyp.8140
[5]	CONG Z T, ZHAO J J, YANG D W, et al. Understanding the hydrological trends of river basins in China[J]. Journal of Hydrology,2010,388(3/4):350 − 356.
[6]	刘昌明, 张丹. 中国地表潜在蒸散发敏感性的时空变化特征分析[J]. 地理学报,2011,66(5):579 − 588. [LIU Changming, ZHANG Dan. Temporal and spatial change analysis of the sensitivity of potential evapotranspiration to meteorological influencing factors in China[J]. Acta Geographica Sinica,2011,66(5):579 − 588. (in Chinese with English abstract)
[7]	GOYAL R K. Sensitivity of evapotranspiration to global warming: a case study of arid zone of Rajasthan (India)[J]. Agricultural Water Management,2004,69(1):1 − 11. doi: 10.1016/j.agwat.2004.03.014
[8]	DINPASHOH Y, JHAJHARIA D, FAKHERI-FARD A, et al. Trends in reference crop evapotranspiration over Iran[J]. Journal of Hydrology,2011,399(3/4):422 − 433.
[9]	杨林山, 李常斌, 王帅兵, 等. 洮河流域潜在蒸散发的气候敏感性分析[J]. 农业工程学报,2014,30(11):102 − 109. [YANG Linshan, LI Changbin, WANG Shuaibing, et al. Sensitive analysis of potential evapotranspiration to key climatic factors in Taohe River Basin[J]. Transactions of the Chinese Society of Agricultural Engineering,2014,30(11):102 − 109. (in Chinese with English abstract)
[10]	张永生, 陈喜, 高满, 等. 不同气候区潜在蒸散发全局敏感性分析[J]. 河海大学学报(自然科学版),2017,45(2):137 − 144. [ZHANG Yongsheng, CHEN Xi, GAO Man, et al. Global sensitivity analysis of potential evapotranspiration in different climatic regions[J]. Journal of Hohai University (Natural Sciences),2017,45(2):137 − 144. (in Chinese with English abstract)
[11]	GUO D L, WESTRA S, MAIER H R. Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones[J]. Hydrology and Earth System Sciences,2017,21(4):2107 − 2126. doi: 10.5194/hess-21-2107-2017
[12]	XU Y P, PAN S L, FU G T, et al. Future potential evapotranspiration changes and contribution analysis in Zhejiang Province, East China[J]. Journal of Geophysical Research: Atmospheres,2014,119(5):2174 − 2192. doi: 10.1002/2013JD021245
[13]	ZHANG S N, WU Y P, SIVAKUMAR B, et al. Climate change-induced drought evolution over the past 50 years in the southern Chinese Loess Plateau[J]. Environmental Modelling & Software,2019,122:104519.
[14]	PALMER W C. Meteorological drought[M]. Washington, DC: Bureau, 1965.
[15]	WELLS N, GODDARD S, HAYES M J. A self-calibrating palmer drought severity index[J]. Journal of Climate,2004,17(12):2335 − 2351. doi: 10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2
[16]	王文, 许志丽, 蔡晓军, 等. 基于PDSI的长江中下游地区干旱分布特征[J]. 高原气象,2016,35(3):693 − 707. [WANG Wen, XU Zhili, CAI Xiaojun, et al. Aridity characteristic in middle and lower reaches of Yangtze River area based on palmer drought severity index analysis[J]. Plateau Meteorology,2016,35(3):693 − 707. (in Chinese with English abstract)
[17]	陈亚宁, 杨青, 罗毅, 等. 西北干旱区水资源问题研究思考[J]. 干旱区地理,2012,35(1):1 − 9. [CHEN Yaning, YANG Qing, LUO Yi, et al. Ponder on the issues of water resources in the arid region of northwest China[J]. Arid Land Geography,2012,35(1):1 − 9. (in Chinese with English abstract)
[18]	ALLEN R G, PEREIRA L S, RAES D, et al. Crop evapotranspiration. guidelines for computing crop water requirements[EB/OL]. 1998.
[19]	SOBOL I M. Sensitivity estimates for nonlinear mathematical models[J]. Math Model Comput Exp,1993,1(1):112118.
[20]	SOBOL I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates[J]. Mathematics & Computers in Simulation,2001,55(1/2/3):271 − 280.
[21]	DAI Y J, SHANGGUAN W, DUAN Q Y, et al. Development of a China dataset of soil hydraulic parameters using pedotransfer functions for land surface modeling[J]. Journal of Hydrometeorology,2013,14(3):869 − 887. doi: 10.1175/JHM-D-12-0149.1
[22]	TANG Y, REED P, WAGENER T, et al. Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation[J]. Hydrology and Earth System Sciences,2007,11(2):793 − 817. doi: 10.5194/hess-11-793-2007
[23]	陈亚宁, 徐长春, 杨余辉, 等. 新疆水文水资源变化及对区域气候变化的响应[J]. 地理学报,2009,64(11):1331 − 1341. [CHEN Yaning, XU Changchun, YANG Yuhui, et al. Hydrology and water resources variation and its responses to regional climate change in Xinjiang[J]. Acta Geographica Sinica,2009,64(11):1331 − 1341. (in Chinese with English abstract)
[24]	倪广恒, 李新红, 丛振涛, 等. 中国参考作物腾发量时空变化特性分析[J]. 农业工程学报,2006,22(5):1 − 4. [NI Guangheng, LI Xinhong, CONG Zhentao, et al. Temporal and spatial characteristics of reference evapotranspiration in China[J]. Transactions of the Chinese Society of Agricultural Engineering,2006,22(5):1 − 4. (in Chinese with English abstract)
[25]	尹云鹤, 吴绍洪, 戴尔阜. 1971—2008年我国潜在蒸散时空演变的归因[J]. 科学通报,2010,55(22):2226 − 2234. [YIN Yunhe, WU Shaohong, DAI Erfu. Attribution of temporal and spatial evolution of potential evapotranspiration in China from 1971 to 2008[J]. Chinese Science Bulletin,2010,55(22):2226 − 2234. (in Chinese) doi: 10.1360/csb2010-55-22-2226
[26]	曹雯, 申双和, 段春锋. 西北地区生长季参考作物蒸散变化成因的定量分析[J]. 地理学报,2011,66(3):407 − 415. [CAO Wen, SHEN Shuanghe, DUAN Chunfeng. Quantification of the causes for reference crop eapotranspiration changes in growing season in northwest China[J]. Acta Geographica Sinica,2011,66(3):407 − 415. (in Chinese with English abstract)
[27]	王小静, 李志, 赵姹, 等. 西北旱区1961—2011年参考作物蒸散量的时空分异[J]. 生态学报,2014,34(19):5609 − 5616. [WANG Xiaojing, LI Zhi, ZHAO Cha, et al. Spatiotemporal variations of the reference crop evapotranspiration in the arid region of northwest China during 1961—2011[J]. Acta Ecologica Sinica,2014,34(19):5609 − 5616. (in Chinese with English abstract)
[28]	HE IM, RICHARD R. A review of twentieth-century drought indices used in the United States[J]. Bulletin of the American Meteorological Society,2002,83(8):1149 − 1165. doi: 10.1175/1520-0477(2002)083<1149:AROTDI>2.3.CO;2
[29]	杨庆, 李明星, 郑子彦, 等. 7种气象干旱指数的中国区域适应性[J]. 中国科学: 地球科学,2017,47(3):337 − 353. [YANG Qing, LI Mingxing, ZHENG Ziyan, et al. Regional adaptability of 7 meteorological drought indices in China[J]. Scientia Sinica(Terrae),2017,47(3):337 − 353. (in Chinese)
[30]	DAI A G. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008[J]. Journal of Geophysical Research Atmospheres,2011,116(D12):D12115. doi: 10.1029/2010JD015541