地球科学进展 ›› 2021, Vol. 36 ›› Issue (8): 773 -784. doi: 10.11867/j.issn.1001-8166.2021.054

青藏高原复杂地表蒸散发及其对水塔效应影响    下一篇

利用静止卫星估算青藏高原全域地表潜热通量
仲雷 1, 2, 3( ),葛楠 1,马耀明 4, 5, 6,傅云飞 1,马伟强 4, 5,韩存博 4,王显 1,程美琳 1   
  1. 1.中国科学技术大学地球和空间科学学院,安徽 合肥 230026
    2.中国科学院比较行星学卓越创新中心,安徽 合肥 230026
    3.江苏省气候变化协同创新中心,江苏 南京 210023
    4.中国科学院青藏高原 研究所,北京 100101
    5.中国科学院青藏高原地球科学卓越创新中心,北京 100101
    6.中国科学院大学地球与行星科学学院,北京 100049
  • 收稿日期:2021-03-17 修回日期:2021-05-11 出版日期:2021-08-10
  • 基金资助:
    国家自然科学基金项目“青藏高原全天空地表辐射收支与热状况的卫星遥感估算研究”(41875031);第二次青藏高原综合科学考察研究子专题“西风—季风协同作用区地气水热交换关键参数卫星遥感估算研究”(2019QZKK010305)

Estimation of Land Surface Latent Heat Flux over the Tibetan Plateau Using Geostationary Satellite Data

Lei ZHONG 1, 2, 3( ),Nan GE 1,Yaoming MA 4, 5, 6,Yunfei FU 1,Weiqiang MA 4, 5,Cunbo HAN 4,Xian WANG 1,Meilin CHENG 1   

  1. 1.School of Earth and Space Sciences,University of Science and Technology of China,Hefei 230026,China
    2.CAS Center for Excellence in Comparative Planetology,Hefei 230026,China
    3.Jiangsu Collaborative Innovation Center for Climate Change,Nanjing 210023,China
    4.Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing 100101,China
    5.CAS Center for Excellence in Tibetan Plateau Earth Sciences,Beijing 100101,China
    6.College of Earth and Planetary Sciences,University of Chinese Academy of Sciences,Beijing 100049,China
  • Received:2021-03-17 Revised:2021-05-11 Online:2021-08-10 Published:2021-09-22
  • About author:ZHONG Lei (1979-), male, Bengbu City, Anhui Province, Professor. Research areas include land-atmosphere interactions and satellite remote sensing. E-mail: zhonglei@ustc.edu.cn
  • Supported by:
    the National Natural Science Foundation of China "Remote sensing of surface radiation budgets and heating field under all-sky conditions"(41875031);The Second Tibetan Plateau Scientific Expedition and Research Program "Remote sensing of key land-atmosphere water and energy exchange parameters in westerly monsoon synergy zone"(2019QZKK010305)

青藏高原全域高时间分辨率潜热通量变化对定量理解高原能量和水分循环过程尤其是其日变化过程至关重要。为此,利用中国最新一代静止气象卫星Fengyun-4A上搭载的多通道扫描成像辐射计数据,结合中国区域高时空分辨率地表气象驱动数据集,基于陆面能量平衡系统模型估算得到青藏高原全域的地表潜热通量,卫星估算值与青藏高原观测研究平台站点实测值的均方根误差和平均偏差分别为76.05和17.33 W/m2。结果表明,青藏高原地表潜热通量呈现显著的季节变化、昼夜分野和区域差异:4月高原潜热整体上略低于感热,而7月高原西部、中部和东部的潜热均高于感热;潜热通量昼夜相差极大,4月的昼间、夜间和昼夜平均值分别为74.22、3.09和38.66 W/m2,而7月的相应值分别为122.75、6.49和64.62 W/m2。青藏高原地表热通量的空间分布具有经向区域差异,其中,净辐射通量与感热通量在高原西部和中部的数值明显高于高原东部,而潜热通量正好相反,在高原东部数值较高。研究结果可为青藏高原地表蒸散与大气热源的定量分析提供参考。

Variations of land surface latent heat flux with high temporal resolution are essential to the quantitative understanding of the energy and water transfer processes, especially their diurnal cycles over the Tibetan Plateau (TP). Therefore, the Advanced Geostationary Radiation Imager onboard the up-to-date Chinese geostationary meteorological satellite Fengyun-4A was utilized, with the China Meteorological Forcing Dataset in combination, for the estimation of land surface latent heat flux over the entire TP based on the SEBS model. The root mean square error and mean bias for the satellite estimations against the in situ measurements of the Tibetan Observation and Research Platform were 76.05 and 17.33 W/m2, respectively.

Results

showed that land surface latent heat flux over the TP exhibited distinct seasonal variation, day/night discrepancy and regional difference. In April, the plateau-scale latent heat flux was slightly lower than the sensible heat flux overall; while in July, the latent heat flux was higher than the sensible heat flux in all of the western, central, and eastern TP. The daytime, nighttime and daily-mean latent heat flux values in April were 74.22, 3.09, and 38.66 W/m2, while those in July were 122.75, 6.49 and 64.62 W/m2, respectively, depicting a clear diurnal variability. Additionally, the spatial distributions of land surface heat fluxes over the TP presented longitudinal regional differences: both the net radiation flux and sensible heat flux were stronger in the western and central TP, while conversely the latent heat flux was stronger in the eastern TP. The above results may provide information for the quantitative analysis on the surface evapotranspiration and the atmospheric heat source in future studies.

中图分类号: 

表1 FY-4A/AGRI通道列表
Table 1 Band list of FY-4A/AGRI
表2 CMFD大气驱动场数据集的气象参数信息
Table 2 Information of the meteorological parameters of CMFD atmospheric forcing dataset used in this study
图1 潜热通量FY-4A卫星估算值与TORP站点观测值的验证
Fig. 1 Validation of latent heat flux estimated via FY-4A against TORP in situ measurements
图2 青藏高原20184月和7月的月平均地表净辐射通量、感热通量和潜热通量的空间分布
Fig. 2 Spatial distribution of monthly mean land surface net radiation flux sensible heat flux and latent heat flux in April 2018 and July 2018 over the Tibetan Plateau
图3 青藏高原20184月和7月的地表净辐射通量、感热通量和潜热通量的频数分布
Fig. 3 Frequency distribution of land surface net radiation flux sensible heat flux and latent heat flux in April 2018 and July 2018 over the Tibetan Plateau
图4 青藏高原20184月的月平均地表潜热通量的逐3小时空间分布
Fig. 4 3-hour spatial distribution of monthly mean land surface latent heat flux in April 2018 over the Tibetan Plateau
图5 青藏高原20187月的月平均地表潜热通量的逐3小时空间分布
Fig. 5 3-hour spatial distribution of monthly mean land surface latent heat flux in July 2018 over the Tibetan Plateau
图6 青藏高原20184月和7月的月平均地表净辐射通量、感热通量和潜热通量的日变化
第1行:4月,第2行:7月;第1列:高原西部,第2列:高原中部,第3列:高原东部,第4列:高原全域
Fig. 6 Diurnal variation of monthly mean land surface net radiation flux sensible heat flux and latent heat flux in April 2018 and July 2018 over the Tibetan Plateau
Row 1: April, Row 2: July; Column 1: Western Tibetan Plateau, Column 2: Central Tibetan Plateau, Column 3: Eastern Tibetan Plateau, Column 4: the whole Tibetan Plateau
图7 青藏高原地表净辐射通量、感热通量和潜热通量的季节变化、昼夜分野和区域差异
第1行:昼间,第2行:夜间,第3行:昼夜平均;第1列:高原西部,第2列:高原中部,第3列:高原东部,第4列:高原全域
Fig. 7 Seasonal variation day/night discrepancy and regional difference of land surface net radiation flux sensible heat flux and latent heat flux over the Tibetan Plateau
Row 1: Daytime, Row 2: Nighttime, Row 3: Daily mean; Column 1: Western Tibetan Plateau, Column 2: Central Tibetan Plateau, Column 3: Eastern Tibetan Plateau, Column 4: The whole Tibetan Plateau
1 ZOU M J, ZHONG L, MA Y M, et al. Estimation of actual evapotranspiration in the Nagqu River Basin of the Tibetan Plateau [J]. Theoretical and Applied Climatology, 2018, 132(3/4): 1 039-1 047.
2 GUO Xiaoyin, CHENG Guodong. Advances in the application of remote sensing to evapotranspiration research [J]. Advances in Earth Science, 2004, 19(1): 107-114.
郭晓寅, 程国栋. 遥感技术应用于地表面蒸散发的研究进展[J]. 地球科学进展, 2004, 19(1): 107-114.
3 GAO Yanchun, LONG Di. Progress in models for evapotranspiration estimation using remotely sensed data [J]. Journal of Remote Sensing, 2008, 12(3): 515-528.
高彦春, 龙笛. 遥感蒸散发模型研究进展[J]. 遥感学报, 2008, 12(3): 515-528.
4 JACKSON R D, REGINATO R J, IDSO S B. Wheat canopy temperature: a practical tool for evaluating water requirements [J]. Water Resources Research, 1977, 13(3): 651-656.
5 CARLSON T N, GILLIES R R, PERRY E M. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover [J]. Remote Sensing Reviews, 1994, 9(1/2): 161-173.
6 PENMAN H L. Natural evaporation from open water, bare soil and grass [J]. Proceedings of the Royal Society of London, 1948, 193(1 032): 120-145.
7 MONTEITH J L. Evaporation and environment [J]. Symposia of the Society for Experimental Biology, 1965, 19: 205-234.
8 BASTIAANSSEN W G M, MENENTI M, FEDDES R A, et al. A remote sensing surface energy balance algorithm for land (SEBAL)-1. formulation [J]. Journal of Hydrology, 1998, 212(1/4): 198-212.
9 BASTIAANSSEN W G M, PELGRUMH, WANG J, et al. A remote sensing Surface Energy Balance Algorithm for Land (SEBAL)-2. validation [J]. Journal of Hydrology, 1998, 212(1/4): 213-229.
10 SU Z B. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes [J]. Hydrology and Earth System Sciences, 2002, 6(1): 85-100.
11 SHUTTLEWORTH J W, WALLACE J S. Evaporation from sparse crops—an energy combination theory [J]. Quarterly Journal of the Royal Meteorological Society, 1985, 111(469): 839-855.
12 NORMAN J M, KUSTAS W P, HUMES K S. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature [J]. Agricultural and Forest Meteorology, 1995, 77(3/4): 263-293.
13 CHEN X L, SU Z B, MA Y M, et al. An improvement of roughness height parameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau [J]. Journal of Applied Meteorology and Climatology, 2013, 52(3): 607-622.
14 CHEN X L, MASSMAN W J, SU Z B. A column canopy‐air turbulent diffusion method for different canopy structures [J]. Journal of Geophysical Research Atmospheres, 2019, 124: 488-506.
15 MA Y M, WANG J M, HUANG R H, et al. Remote sensing parameterization of land surface heat fluxes over arid and semi-arid areas [J]. Advances in Atmospheric Sciences, 2003, 20(4): 530-539.
16 TANG R L, LI Z L, JIA Y Y, et al. An intercomparison of three remote sensing-based energy balance models using Large Aperture Scintillometer measurements over a wheat-corn production region [J]. Remote Sensing of Environment, 2011, 115(12): 3 187-3 202.
17 MA Y M, HAN C B, ZHONG L, et al. Using MODIS and AVHRR data to determine regional surface heating field and heat flux distributions over the heterogeneous landscape of the Tibetan Plateau [J]. Theoretical and Applied Climatology, 2014, 117: 643-652.
18 HAN C B, MA Y M, CHEN X L, et al. Estimates of land surface heat fluxes of the Mt. Everest region over the Tibetan Plateau utilizing ASTER data [J]. Atmospheric Research, 2016, 168: 180-190.
19 ZOU M J, ZHONG L, MA Y M, et al. Comparison of two satellite-based evapotranspiration models of the Nagqu River Basin of the Tibetan Plateau [J]. Journal of Geophysical Research Atmospheres, 2018, 123(8): 3 961-3 975.
20 GE N, ZHONG L, MA Y M, et al. Estimation of land surface heat fluxes based on Landsat 7 ETM+ data and field measurements over the northern Tibetan Plateau [J]. Remote Sensing, 2019, 11(24): 2 899.
21 OKU Y, ISHIKAWA H, SU Z B. Estimation of land surface heat fluxes over the Tibetan Plateau using GMS data [J]. Journal of Applied Meteorology and Climatology, 2007, 46: 183-195.
22 ZHONG L, MA Y M, HU Z Y, et al. Estimation of hourly land surface heat fluxes over the Tibetan Plateau by the combined use of geostationary and polar-orbiting satellites [J]. Atmospheric Chemistry and Physics, 2019, 19(8): 5 529-5 541.
23 YANG J, ZHANG Z Q, WEI C Y, et al. Introducing the new generation of chinese geostationary weather satellites, Fengyun-4 [J]. Bulletin of the American Meteorological Society, 2017, 98(8): 1 637-1 658.
24 MIN M, WU C Q, LI C, et al. Developing the science product algorithm testbed for chinese next-generation geostationary meteorological satellites: Fengyun-4 series [J]. Journal of Meteorological Research, 2017, 31(4): 708-719.
25 YANG Kun. China meteorological forcing data (1979-2018)[J]. Big Data System for Pan-Third Pole, 2018. DOI: 10.11888/AtmosphericPhysics.tpe.249369.file.
doi: 10.11888/AtmosphericPhysics.tpe.249369.file    
26 YANG K, HE J, TANG W J, et al. On downward shortwave and longwave radiations over high altitude regions: observation and modeling in the Tibetan Plateau [J]. Agricultural and Forest Meteorology, 2010, 150: 38-46.
27 MA Y M, KANG S C, ZHU L P, et al. Tibetan observation and research platform atmosphere-land interaction over a heterogeneous landscape [J]. Bulletin of the American Meteorological Society, 2008, 89(10): 1 487-1 492.
28 VALOR E, CASELLES V. Mapping land surface emissivity from NDVI: application to European, African, and South American areas [J]. Remote Sensing of Environment, 1996, 57(3): 167-184.
29 CARLSON T N, RIPLEY D A. On the relation between NDVI, fractional vegetation cover, and leaf area index [J]. Remote Sensing of Environment, 1997, 62: 241-252.
30 HU Y Y, ZHONG L, MA Y M, et al. Estimation of the land surface temperature over the Tibetan Plateau by using chinese FY-2C geostationary satellite data [J]. Sensors, 2018, 18(2): 376.
31 SOBRINO J A, RAISSOUNI N. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco [J]. International Journal of Remote Sensing, 2000, 21(2): 353-366.
32 YANG K, KOIKE T, YE B S. Improving estimation of hourly, daily, and monthly solar radiation by importing global data sets [J]. Agricultural and Forest Meteorology, 2006, 137: 43-55.
33 YU Tengfei, FENG Qi, SI Jianhua, et al. Estimating terrestrial ecosystems evapotranspiration: a review on methods of integrating remote sensing and ground observations [J]. Advances in Earth Science, 2011, 26(12): 1 260-1 268.
鱼腾飞, 冯起, 司建华, 等. 遥感结合地面观测估算陆地生态系统蒸散发研究综述[J]. 地球科学进展, 2011, 26(12): 1 260-1 268.
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