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遥感技术与应用  2021, Vol. 36 Issue (4): 887-897    DOI: 10.11873/j.issn.1004-0323.2021.4.0887
遥感应用     
黑河流域中上游水热通量足迹模型的对比分析
孙赛钰1,2(),王维真1,3(),徐菲楠1
1.中国科学院西北生态环境资源研究院,甘肃省遥感重点实验室 黑河遥感试验研究站,甘肃 兰州 730000
2.中国科学院大学,北京 100049
3.中国科学院寒旱区陆面过程与气候变化重点实验室,甘肃 兰州 730000
Comparison of Footprint Models of Surface Heat and Water Vapor Fluxes in the Middle and Upper Reaches of Heihe River Basin
Saiyu Sun1,2(),Weizhen Wang1,3(),Feinan Xu1
1.Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Key Laboratory of Remote Sensing of Gansu Province,Heihe Remote Sensing Experimental Research Station,Lanzhou 730000,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions,Chinese Academy of Sciences,Lanzhou 730000,China
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摘要:

遥感技术是获取区域地表水热通量的重要手段,利用地面观测值对遥感估算水热通量进行验证时,存在空间尺度不匹配的问题,结合足迹分析可以较好地解决这一问题,为遥感蒸散发模型提供空间尺度匹配的验证数据。利用黑河流域上游阿柔超级站和中游大满超级站的涡动相关仪观测数据,对常用的3种水热通量足迹模型Kormann&Meixner (KM) 模型、Kljun模型和Hsieh模型的输入参数进行了敏感性分析,并比较和分析了3个模型单时次和日尺度的足迹结果差异,为足迹模型的合理选用提供参考依据,以服务于数据质量判别和遥感模型的验证。结果表明:①奥布霍夫长度(L)是KM模型和Hsieh模型的敏感因子, L值变化时,Hsieh的足迹结果变化大于KM,而Kljun模型对L的敏感程度不高;观测高度(zm)和侧向风速标准差(σv)也是3个模型的敏感因子。②单时次30 min尺度上,KM和Hsieh的通量贡献源区大小和形状吻合较好,但与Kljun足迹结果存在显著差异;Kljun的源区范围明显较小,上风向通量贡献峰值明显大于KM和Hsieh,且上风向通量贡献峰值的位置明显小于另外两个模型。③日尺度上,3种足迹模型的水热通量源区形状相似,Kljun模型的源区范围最小。实验结果为足迹模型的合理选用提供参考依据,以服务于碳、水热通量数据质量判别和遥感模型的验证。

关键词: 涡动相关仪水热通量足迹模型通量贡献源区黑河流域    
Abstract:

Remote sensing is an important method to obtain regional surface heat and water vapor fluxes, however there is a mismatch of spatial scale between the observation data and remotely-sensed data when the remotely-sensed data is verified. Combined with footprint analysis, this problem can be better solved, providing a reference basis for the verification of remote sensing models. Based on the eddy covariance data from the Arou station in the upper reaches of the Heihe River basin and the Daman Superstation in the middle reaches, the sensitivity analysis of the input parameters of three commonly used flux footprint models, namely Kormann&Meixner model (hereafter referred as KM), Kljun model and Hsieh model was performed, and the difference in the footprint results of the three models at single time and daily scales is compared and analyzed. The objective of this study is not only to provide a reference basis for the reasonable selection of footprint model, but also to serve for the discrimination of data quality and the verification of relevant remote sensing models. The results showed that: (1) The KM and Hsieh models are very sensitive to the Obukhov length (L). When L changes, the footprint result of Hsieh model varies much more than that of KM model, while Kljun model is less sensitive to L. Observation height (zm) and standard deviation of lateral wind fluctuations (σv) are also sensitive factors of the three models. (2) On the every 30 min time scale, the footprint results between KM and Hsieh models are in good agreement in extent and shape, but there are significant differences with Kljun model. The footprint extent of Kljun model is obviously smaller, while the estimated position of maximum flux contribution is much larger as compared to KM and Hsieh models. The peak distance of the footprint to the tower is obviously smaller than that of the other two models. (3) On a daily time scale, the flux contribution source area of the three models are similar in shape, but the source region of the Kljun model is the smallest. The results of this study provide important information for the selection of proper footprint models, which is used for data quality control and remotely-sensed products evaluation.

Key words: Eddy covariance    Heat and water vapor fluxes    Footprint model    Flux contribution source area    Heihe River basin
收稿日期: 2020-05-24 出版日期: 2021-09-26
ZTFLH:  TP79  
基金资助: 高分辨率对地观测系统国家重大专项(21?Y20B01?9001?19/22);国家自然科学基金项目(41671373);中国科学院寒旱区陆面过程与气候变化重点实验室自主研究课题
通讯作者: 王维真     E-mail: sunsaiyu18@mails.ucas.ac.cn;weizhen@lzb.ac.cn
作者简介: 孙赛钰(1997-),女,河南南阳人,硕士研究生,主要从事水热通量研究。E?mail: sunsaiyu18@mails.ucas.ac.cn
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引用本文:

孙赛钰,王维真,徐菲楠. 黑河流域中上游水热通量足迹模型的对比分析[J]. 遥感技术与应用, 2021, 36(4): 887-897.

Saiyu Sun,Weizhen Wang,Feinan Xu. Comparison of Footprint Models of Surface Heat and Water Vapor Fluxes in the Middle and Upper Reaches of Heihe River Basin. Remote Sensing Technology and Application, 2021, 36(4): 887-897.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.4.0887        http://www.rsta.ac.cn/CN/Y2021/V36/I4/887

输入参数KM模型Kljun模型Hsieh模型
zm
u*
L
z0
ha
σv
表1  3种足迹模型的输入参数
图1  奥布霍夫长度(L)不同取值条件下得到的源区上风向最远距离(xmax)和源区面积(A)与参考值的比较
图2  观测高度(zm)不同取值条件下得到的源区上风向最远距离(xmax)和源区面积(A)与参考值的比较
图3  侧向风速标准差(σv)不同取值条件下得到的源区面积(A)与参考值的比较
L/mwind_dir/°U/ms-1u*/ms-1σv/ms-1zm/m时间
阿柔超级站-55.4106.6984.470.350.883.3320190728,17:00-17:30
-161.9131.3972.870.260.6420190713,7:00-7:30
56.7125.5582.260.190.6820190727,7:00-7:30
165.0113.0613.790.310.5320190727,20:00-20:30
大满超级站-58.8350.9691.710.200.723.51220120710,10:00-11:00
-189.312.71033.050.401.3320120705,11:00-11:30
50.8252.5422.060.260.4820120707,16:30-17:00
159.3248.0333.930.451.0120120709,1:30-2:00
表2  阿柔站和大满站不同时次的参数
图4  阿柔超级站和大满超级站在不同大气条件下3个模型的90%通量贡献源区
L/mxmax/mxp/mfmax/m-1A/104 m2
KMKljunHsiehKMKljunHsiehKMKljunHsiehKMKljunHsieh
阿柔超级站-55.456725845339.612.819.10.0080.0260.0148.31.65.4
-161.95902667053213300.0090.0250.0099.71.613.4
56.71089298122030.114.851.40.0080.0220.00541.43.752.4
165.093228185732.913.936.10.0080.0240.00714.61.712.8
大满超级站-58.835516533323.88.213.90.0140.0400.0196.51.56.1
-189.337717446719.48.619.50.0150.0380.0149.31.511
50.864220859417.610.324.80.0140.0320.01110.51.49.9
159.362218940621.89.417.00.0120.0350.01611.41.65.7
表3  阿柔站和大满站不同大气稳定度条件下90%通量贡献源区的xmax、xp、fmax和A值
图5  日尺度3个模型估算的90%观测通量贡献源区
1 Wang Weizhen, Xu Ziwei, Liu Shaomin, et al. The characteristics of heat and water vapor fluxes over different surfaces in the Heihe river basin [J].Advance in Earth Science, 2009,24(7):714-723.
1 王维真, 徐自为, 刘绍民, 等. 黑河流域不同下垫面水热通量特征分析[J]. 地球科学进展, 2009, 24(7): 714-723.
2 Baldocchi D D, Falge E, Gu L, et al. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities[J]. Bulletin of the American Meteorological Society, 2001, 82(11): 2415-2434.
3 Aubinet M, Grelle A, Ibrom A, et al. Estimates of the annual net carbon and water exchange of forests: The EUROFLUX methodology[J]. Advances in Ecological Research, 2000, 30(1): 113-175.
4 Yu Guirui, Zhang Leiming, Sun Xiaomin, et al. Advances in the observation of carbon flux in terrestrial ecosystems in Asia[J]. Chinese Science (Series D: Earth Sciences), 2004,34(S2): 15-29.
4 于贵瑞, 张雷明, 孙晓敏, 等. 亚洲区域陆地生态系统碳通量观测研究进展[J]. 中国科学(D辑:地球科学), 2004,34(): 15-29.
5 De Bruin H A, Den Hurk B J, Kohsiek W, et al. The scintillation method tested over a dry vineyard area[J]. Boundary-Layer Meteorology, 1995,76(1-2): 25-40.
6 Zhan Zhiming. Study on regional evapotranspiration model based on remote sensing method[J]. Remote Sensing Technology and Application, 2002,17(6): 364-369.詹志明. 区域遥感蒸散发模型方法研究[J]. 遥感技术与应用, 2002(6): 364-369.
7 Li Z, Tang R, Wan Z, et al. A Review of current methodologies for fegional evapotranspiration estimation from remotely sensed data[J]. Sensors, 2009, 9(5): 3801-3853.
8 Wang Wei, Lu Hui. Progress in application of remote sensing data in Hydrological simulation[J]. Remote Sensing Technology and Application, 2015,30(6): 1042-1050.
8 汪伟, 卢麾. 遥感数据在水文模拟中的应用研究进展[J]. 遥感技术与应用, 2015,30(6): 1042-1050.
9 Wang Jiemin, Gao Feng, Liu Shaomin. Remote sensing retrieval of evapotranspiration over the scale of drainage basin [J].Remote Sensing Technology and Application,2003,18(5):332-338.
9 王介民, 高峰, 刘绍民. 流域尺度ET的遥感反演[J]. 遥感技术与应用, 2003,18(5): 332-338..
10 Jia Zhenzhen, Liu Shaomin, Mao Defa, et al. A study of the validation method of remotely sensed evapotranspiration based on observation data[J] Advance in Earth Science, 2010, 25(11): 1248-1260.
10 贾贞贞, 刘绍民, 毛德发, 等. 基于地面观测的遥感监测蒸散量验证方法研究[J]. 地球科学进展, 2010,25(11): 1248-1260.
11 Liu S M, Xu Z W, Song L S, et al. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces[J]. Agricultural and Forest Meteorology ,2016,230: 97-113.
12 Hu Yonghong, Jia Gensuo. Evaluation of atmosphere land exchange inversion model in regionl scale based on Landsat imagery[J]. Remote Sensing Technology and Application, 2003,28(2): 191-199.
12 胡永红, 贾根锁. 基于Landsat影像的ALEXI模型小流域验证[J]. 遥感技术与应用, 2003,28(2): 191-199.
13 Zhang Ronghua, Du Junping, Sun Rui. Review of estimation and validation of regional evapotranspiration based on remote sensing[J]. Advance in Earth Science, 2002,27(12): 1295-1307.
13 张荣华, 杜君平, 孙睿. 区域蒸散发遥感估算方法及验证综述[J]. 地球科学进展, 2012,27(12): 1295-1307.
14 Baldocchi D D. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future[J]. Global Change Biology, 2003, 9(4): 479-492.
15 Wang Jiemin, Wang Weizhen, Ao Yinhuan, et al. Turbulence flux measurements under complicated conditions[J]. Advance in Earth Science, 2007,22(8): 791-797.
15 王介民, 王维真, 奥银焕, 等. 复杂条件下湍流通量的观测与分析[J]. 地球科学进展, 2007,22(8): 791-797.
16 Baldocchi D. TURNER REVIEW No. 15. 'Breathing' of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems[J]. Australian Journal of Botany, 2008,56(1): 1-26.
17 Schmid H P. Footprint modeling for vegetation atmosphere exchange studies: a review and perspective[J]. Agricultural and Forest Meteorology, 2002, 113(1): 159-183..
18 Cai Xuhui. Footprint analysis in micrometeorology and its extended applications[J] Chinese Journal of Atmospheric Sciences, 2008, 32(1): 123-132.
18 蔡旭晖. 湍流微气象观测的印痕分析方法及其应用拓展[J]. 大气科学, 2008,32(1): 123-132.
19 Heidbach K, Schmid H P, Mauder M, et al. Experimental evaluation of flux footprint models[J]. Agricultural and Forest Meteorology, 2017, 246: 142-153.
20 Kljun N, Calanca P, Rotach M W, et al. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)[J]. Geoentific Model Development, 2015, 8(11): 3695-3713.
21 Wang J, Zhuang J, Wang W, et al. Assessment of uncertainties in eddy covariance flux measurement based on intensive flux matrix of HiWATER-MUSOEXE[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(2): 259-263.
22 Leclerc M, Foken T. Footprints in micrometeorology and ecology[M]. Berlin: Springer, 2014.
23 Schmid H P. Source areas for scalars and scalar fluxes[J]. Boundary Layer Meteorology, 1994, 67(3): 293-318.
24 Kormann R, Meixner F X. An analytical footprint model for non-neutral stratification[J]. Boundary Layer Meteorology, 2001, 99(2): 207-224.
25 Hsieh C, Katul G G, Chi T, et al. An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows[J]. Advances in Water Resources, 2000, 23(7): 765-772.
26 Kljun N, Rotach M W, Schmid H P, et al. A three-dimensional backward lagrangian footprint model for a wide range of boundary-layer stratifications[J]. Boundary Layer Meteorology, 2002, 103(2): 205-226.
27 Kljun N, Calanca P, Rotach M W, et al. A simple parameterisation for flux footprint predictions[J]. Boundary Layer Meteorology, 2004, 112(3): 503-523.
28 Mi Na, Yu Guirui, Wen Xuefa, et al. A preliminary study on the representativeness of the flux observation of china[J]. Chinese Science (Series D: Earth Sciences), 2006, 36(S1): 22-33.
28 米娜, 于贵瑞, 温学发, 等.中国通量观测网络(ChinaFLUX)通量观测空间代表性初步研究[J]. 中国科学.D辑:地球科学, 2006, 36(): 22-33.
29 Liu S M , Xu Z W , Wang W Z , et al. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem[J]. Hydrology & Earth System Sciences, 2011, 15(4): 1291-1306.
30 Kim J, Hwang T, Schaaf C L, et al. Seasonal variation of source contributions to eddy-covariance CO2 measurements in a mixed Hardwood-conifer forest[J]. Agricultural and Forest Meteorology, 2018,253-254: 71-83.
31 Xu F, Wang W, Wang J, et al. Aggregation of area-averaged evapotranspiration over the Ejina Oasis based on a flux matrix and footprint analysis[J].Journal of Hydrology,2019,575:17-30.
32 Hutjes R W, Vellinga O S, Gioli B, et al. Disaggregation of airborne flux measurements using footprint analysis[J]. Agricultural and Forest Meteorology, 2010, 150(7): 966-983.
33 Ward H C, Evans J G, Grimmond C S, et al. Multi-scale sensible heat fluxes in the suburban environment from large-aperture scintillometry and eddy covariance[J]. Boundary Layer Meteorology, 2014, 152(1): 65-89.
34 Sun Genhou, Hu Zeyong, Wang Jiemin, et al. Comparison analysis of sensible heat fluxes at two spatial scales in Naqu area[J]. Plateau Meteorology, 2016,35(2): 285-296.
34 孙根厚, 胡泽勇, 王介民, 等. 那曲地区两种空间尺度感热通量的对比分析[J]. 高原气象, 2016,35(2): 285-296.
35 Zhu Mingjia, Zhao Qianyi, Liu Shaomin, et al. Analysis of the characteristics of turbulent flux and its footprint climatology at an agricultural site[J]. Advances in Eatrh Science, 2013,28(12): 1313-1325.
35 朱明佳, 赵谦益, 刘绍民, 等. 农田下垫面观测通量的变化特征及其气候学足迹分析 [J]. 地球科学进展, 2013, 28(12): 1313-1325.
36 Chen B, Black T A, Coops N C, et al. Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements[J]. Boundary Layer Meteorology, 2009, 130(2): 137-167.
37 Shuang Xi, Liu Shaomin, Xu Ziwei, et al. Investigation of spatial representativeness for surface flux measurements in the Heihe river basin[J]. Advances in Eatrh Science, 2009,24(7): 724-733.
37 双喜, 刘绍民, 徐自为, 等. 黑河流域观测通量的空间代表性研究[J]. 地球科学进展, 2009,24(7): 724-733.
38 Zhang H, Wen X F. Flux footprint climatology estimated by three analytical models over a subtropical coniferous plantation in Southeast China[J]. The Chinese Meteorological Society, 2015, 29(4): 654-666.
39 Van de Boer A, Moene A F, Schüttemeyer D, et al. Sensitivity and uncertainty of analytical footprint models according to a combined natural tracer and ensemble approach[J]. Agricultural and Forest Meteorology, 2013,169: 1-11.
40 Detto M, Montaldo N, Albertson J D, et al. Soil moisture and vegetation controls on evapotranspiration in a heterogeneous Mediterranean ecosystem on Sardinia, Italy[J]. Water Resources Research, 2006,42(8): 1-16.
41 Zhang Zhihui, Wang Weizhen, Ma Mingguo, et al. Data Processing and product analysis of eddy covariance flux data for WATER[J] Remote Sensing Technology and Application, 2010,25(6): 788-796.
41 张智慧, 王维真, 马明国, 等. 黑河综合遥感联合试验涡动相关通量数据处理及产品分析[J]. 遥感技术与应用, 2010,25(6): 788-796.
42 Li Xin, Liu Shaomin, Ma Mingguo, et al. HiWATER: An integrated remote sensing experiment on hydrological and ecological processes in the Heihe river basin[J]. Advance in Earth Science, 2012,27(5): 481-498.
42 李新, 刘绍民, 马明国, 等. 黑河流域生态—水文过程综合遥感观测联合试验总体设计[J]. 地球科学进展, 2012,27(5): 481-498.
43 Li X, Li X, Li Z, et al. Watershed allied telemetry experimental research[J]. Journal of Geophysical Research, 2009, 114(19): 2191-2196.
44 Liu S, Li X, Xu Z, et al. The heihe integrated observatory network: a basin-scale land surface processes observatory in China[J]. Vadose Zone Journal, 2018, 17(1): 180072.
45 Xu Ziwei, Liu Shaomin, Gong Lijuan, et al. A Study on the data processingand quality assessment of the eddy covariance system[J]. Advance in Earth Science, 2008,23(4): 357-370.
45 徐自为, 刘绍民, 宫丽娟, 等. 涡动相关仪观测数据的处理与质量评价研究[J]. 地球科学进展, 2008,23(4): 357-370.
46 Xu Z, Ma Y, Liu S, et al. Assessment of the energy balance closure under advective conditions and its impact using remote sensing data[J]. Journal of Applied Meteorology and Climatology, 2017, 56(1): 127-140.
47 Schmid H P. Experimental design for flux measurements: matching scales of observations and fluxes[J]. Agricultural and Forest Meteorology, 1997,87(2): 179-200.
48 Kljun N, Kastnerklein P, Fedorovich E, et al. Evaluation of lagrangian footprint model using data from wind tunnel convective boundary layer[J]. Agricultural and Forest Meteorology, 2004, 127(3): 189-201.
49 Heidbach K, Schmid H P, Mauder M, et al. Experimental evaluation of Flux Footprint Models[J]. Agricultural and Forest Meteorology, 2017,246: 142-153.
50 Rannik U, Aubinet M, Kurbanmuradov O, et al. Footprint analysis for measurements over a heterogeneous forest[J]. Boundary-Layer Meteorology, 2000, 97(1): 137-166.
51 Schmid H P. Footprint modeling for vegetation atmosphere exchange studies: a review and perspective[J]. Agricultural and Forest Meteorology, 2002, 113(1): 159-183.
52 Kljun N, Kormann R, Rotach M W, et al. Comparison of the langrangian footprint model LPDM-B with an analytical footprint model[J]. Boundary Layer Meteorology, 2003, 106(2): 349-355.
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[10] 吴阿丹,郭建文,李建轩,尚庆生,常海龙,刘丰. 基于Web的黑河流域生态水文WSN自动观测数据可视化系统应用研究[J]. 遥感技术与应用, 2013, 28(3): 416-422.
[11] 李 新,李小文,李增元,王 建,马明国,刘 强,肖 青. 黑河综合遥感联合试验研究进展:概述[J]. 遥感技术与应用, 2012, 27(5): 637-649.
[12] 李 新,刘 强,柳钦火,王 建,马明国,肖 青,车 涛,晋 锐,冉有华. 黑河综合遥感联合试验研究进展:水文与生态参量遥感反演与估算[J]. 遥感技术与应用, 2012, 27(5): 650-662.
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[14] 于文凭,马明国. MODIS地表温度产品的验证研究—以黑河流域为例[J]. 遥感技术与应用, 2011, 26(6): 705-712.
[15] 曹艳萍,南卓铜 . 利用GRACE重力卫星监测黑河流域水储量变化[J]. 遥感技术与应用, 2011, 26(6): 719-727.