基于SWH模型的青藏高原高寒草甸蒸散发及其组分变化分析

doi:10.12118/j.issn.1000-6060.2022.086

Abstract

Abstract:

Based on the SWH dual source model, the evapotranspiration of the Nagqu Station of Plateau Climate and Environment, the Nam Co Monitoring and Research Station for Multisphere Interactions, and the Southeast Tibet Observation and Research Station for the Alpine Environment was estimated. Based on the validation of the results, the characteristics of evapotranspiration changes on the Qinghai-Tibet Plateau, China and the main influencing factors of each station were analyzed. The main conclusions are as follows: the SWH model is well applicable to the three stations on the Tibetan Plateau. The annual evapotranspiration ranges from 388 mm to 732 mm, with an increase in interannual distribution, followed by a decrease. The differences in evapotranspiration components between the three stations are significant, the contribution of evaporation to evapotranspiration at the Nagqu and Nam Co stations is 53% and 56%, respectively, and the evapotranspiration at the Southeast Tibet Station is almost entirely contributed by transpiration, accounting for 95%. The leaf area index is the most important factor affecting evapotranspiration at the three stations. The vapor pressure deficit also has a significant effect on evapotranspiration at the Southeast Tibet Station. The findings of this study can provide a scientific basis for studying the temporal and spatial patterns of evapotranspiration and its components and the water cycle process over the Tibetan Plateau.

Key words: evapotranspiration, components of evapotranspiration, SWH model, Qinghai-Tibet Plateau

MEI Jing, SUN Meiping, LI Lin. Variations of evapotranspiration and its components in alpine meadow on the Tibetan Plateau based on SWH model[J].Arid Land Geography, 2022, 45(6): 1740-1751.

Figures/Tables 9

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References 38

[1]	Qiu J. China: The third pole[J]. Nature News, 2008, 454(7203): 393-396. doi: 10.1038/454393a
[2]	Ma N, Zhang Y S, Guo Y H, et al. Environmental and biophysical controls on the evapotranspiration over the highest alpine steppe[J]. Journal of Hydrology, 2015, 529: 980-992. doi: 10.1016/j.jhydrol.2015.09.013
[3]	Roderick M L, Hobbins M T, Farquhar G D. Pan evaporation trends and the terrestrial water balance: I. Principles and observations[J]. Geography Compass, 2009, 3(2): 746-760. doi: 10.1111/j.1749-8198.2008.00213.x
[4]	Yang K, Wu H, Qin J, et al. Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review[J]. Global and Planetary Change, 2014, 112: 79-91. doi: 10.1016/j.gloplacha.2013.12.001
[5]	蓝永超, 丁永建, 沈永平, 等. 气候变化对黄河上游水资源系统影响的研究进展[J]. 气候变化研究进展, 2005, 1(3): 122-125.
	[Lan Yongchao, Ding Yongjian, Shen Yongping, et al. Review on impact of climate change on water resources system in the upper reaches of Yellow River[J]. Advances in Climate Change Research, 2005, 1(3): 122-125.]
[6]	Oki T, Kanae S. Global hydrological cycles and world water resources[J]. Science, 2006, 313(5790): 1068-1072. doi: 10.1126/science.1128845 pmid: 16931749
[7]	Yang Y T, Long D, Shang S H. Remote estimation of terrestrial evapotranspiration without using meteorological data[J]. Geophysical Research Letters, 2013, 40(12): 3026-3030. doi: 10.1002/grl.50450
[8]	Stannard D I. Comparison of Penman-Monteith, Shuttleworth-Wallace, and modified Priestley-Taylor evapotranspiration models for wildland vegetation in semiarid rangeland[J]. Water Resources Research, 1993, 29(5): 1379-1392. doi: 10.1029/93WR00333
[9]	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. doi: 10.11821/xb201105001
	[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.] doi: 10.11821/xb201105001
[10]	李红霞, 张永强, 张新华, 等. 遥感Penman-Monteith模型对区域蒸散发的估算[J]. 武汉大学学报, 2011, 44(4): 457-461.
	[Li Hongxia, Zhang Yongqiang, Zhang Xinhua, et al. Estimation of regional transpiration and evaporation using Penman-Monteith equation[J]. Engineering Journal of Wuhan University, 2011, 44(4): 457-461.]
[11]	杨文峰, 李星敏, 卢玲. 基于能量平衡的蒸散遥感估算模型的应用研究[J]. 西北农林科技大学学报(自然科学版), 2013, 41(2): 46-52.
	[Yang Wenfeng, Li Xingmin, Lu Ling. Application of remote sensing model based on energy balance to estimate evapotranspiration[J]. Journal of Northwest A & F University (Natural Science Edition), 2013, 41(2): 46-52.]
[12]	宁亚洲, 张福平, 冯起, 等. 基于SEBAL模型的疏勒河流域蒸散发估算与灌溉效率评价[J]. 干旱区地理, 2020, 43(4): 928-938.
	[Ning Yazhou, Zhang Fuping, Feng Qi, et al. Estimation of evapotranspiration in Shule River Basin based on SEBAL model and evaluation on irrigation efficiency[J]. Arid Land Geography, 2020, 43(4): 928-938.]
[13]	史继清, 边多, 杨霏云, 等. 西藏地区潜在蒸散量变化特征及灰色模型预测初探[J]. 干旱区地理, 2021, 44(6): 1570-1579.
	[Shi Jiqing, Bian Duo, Yang Feiyun, et al. Variation characteristics of potential evapotranspiration and the forecast of grey model in Tibet[J]. Arid Land Geography, 2021, 44(6): 1570-1579.]
[14]	Martens B, Miralles D G, Lievens H, et al. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture[J]. Geoscientific Model Development, 2017, 10(5): 1903-1925. doi: 10.5194/gmd-10-1903-2017
[15]	Mu Q Z, Zhao M S, Running S W. Improvements to a MODIS global terrestrial evapotranspiration algorithm[J]. Remote Sensing of Environment, 2011, 115(8): 1781-1800. doi: 10.1016/j.rse.2011.02.019
[16]	Velpuri N M, Senay G B, Singh R K, et al. A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET[J]. Remote Sensing of Environment, 2013, 139: 35-49. doi: 10.1016/j.rse.2013.07.013
[17]	尹剑, 欧照凡, 付强, 等. 区域尺度蒸散发遥感估算——反演与数据同化研究进展[J]. 地理科学, 2018, 38(3): 448-456. doi: 10.13249/j.cnki.sgs.2018.03.015
	[Yin Jian, Ou Zhaofan, Fu Qiang, et al. Review of current methodologies for regional evapotranspiration estimation: Inversion and data assimilation[J]. Scientia Geographica Sinica, 2018, 38(3): 448-456.] doi: 10.13249/j.cnki.sgs.2018.03.015
[18]	李晴, 杨鹏年, 彭亮, 等. 基于MOD16数据的焉耆盆地蒸散量变化研究[J]. 干旱区研究, 2021, 38(2): 351-358.
	[Li Qing, Yang Pengnian, Peng Liang, et al. Study of the variation trend of evapotranspiration in the Yanqi Basin based on MOD16 data[J]. Arid Zone Resarch, 2021, 38(2): 351-358.]
[19]	赵燊, 陈少辉. 基于台站和MOD16数据的山东省蒸散及潜在蒸散时空变化[J]. 地理科学进展, 2017, 36(8): 1040-1047. doi: 10.18306/dlkxjz.2017.08.013
	[Zhao Shen, Chen Shaohui. Spatiotemporal variations of evapotranspiration and potential evapotranspiration in Shandong Province based on station observations and MOD16[J]. Progress in Geography, 2017, 36(8): 1040-1047.] doi: 10.18306/dlkxjz.2017.08.013
[20]	Shuttleworth W J, Wallace J S. Evaporation from sparse crops: An energy combination theory[J]. Quarterly Journal of the Royal Meteorological Society, 1985, 111(469): 839-855. doi: 10.1002/qj.49711146910
[21]	Ortega-Farias S, Poblete-Echeverría C, Brisson N. Parameterization of a two-layer model for estimating vineyard evapotranspiration using meteorological measurements[J]. Agricultural & Forest Meteorology, 2010, 150(2): 276-286.
[22]	Zhang B Z, Kang S Z, Li F S, et al. Comparison of three evapotranspiration models to Bowen ratio-energy balance method for a vineyard in an arid desert region of northwest China[J]. Agricultural & Forest Meteorology, 2008, 148(10): 1629-1640.
[23]	Kato T, Kimura R, Kamichika M. Estimation of evapotranspiration, transpiration ratio and water-use efficiency from a sparse canopy using a compartment model[J]. Agricultural Water Management, 2004, 65(3): 173-191. doi: 10.1016/j.agwat.2003.10.001
[24]	Brisson N, Itier B, L’Hotel J C, et al. Parameterisation of the Shuttleworth-Wallace model to estimate daily maximum transpiration for use in crop models[J]. Ecological Modelling, 1998, 107(2-3): 159-169. doi: 10.1016/S0304-3800(97)00215-9
[25]	Hu Z M, Li S G, Yu G R, et al. Modeling evapotranspiration by combing a two-source model, a leaf stomatal model, and a light-use efficiency model[J]. Journal of Hydrology, 2013, 501: 186-192. doi: 10.1016/j.jhydrol.2013.08.006
[26]	Hu Z M, Yu G R, Zhou Y L, et al. Partitioning of evapotranspiration and its controls in four grassland ecosystems: Application of a two-source model[J]. Agricultural & Forest Meteorology, 2009, 149(9): 1410-1420.
[27]	吴戈男, 胡中民, 李胜功, 等. SWH双源蒸散模型模拟效果验证及不确定性分析[J]. 地理学报, 2016, 71(11): 1886-1897. doi: 10.11821/dlxb201611002
	[Wu Genan, Hu Zhongmin, Li Shenggong, et al. Evaluation and uncertainty analysis of a two-source evapotranspiration model[J]. Acta Geographica Sinica, 2016, 71(11): 1886-1897.] doi: 10.11821/dlxb201611002
[28]	Jiang Z Y, Yang Z G, Zhang S Y, et al. Revealing the spatio-temporal variability of evapotranspiration and its components based on an improved Shuttleworth-Wallace model in the Yellow River Basin[J]. Journal of Environmental Management, 2020, 262: 110310, doi: 10.1016/j.jenvman.2020.110310. doi: 10.1016/j.jenvman.2020.110310
[29]	马耀明. 青藏高原地气相互作用过程高分辨率(逐小时)综合观测数据集(2005—2016)[DB/OL].[2022-04-18]. 国家青藏高原科学数据中心.
	[Ma Yaoming. A long-term dataset of integrated land-atmosphere interaction observations on the Tibetan Plateau (2005—2016)[DB/OL].[2022-04-18]. National Tibetan Plateau Data Center.]
[30]	Hu Z M, Wu G N, Zhang L X, et al. Modeling and partitioning of regional evapotranspiration using a satellite-driven water-carbon coupling model[J]. Remote Sensing, 2017, 9(1): 54, doi: 10.3390/rs9010054. doi: 10.3390/rs9010054
[31]	Li M S, Babel W, Chen X L, et al. A 3-year dataset of sensible and latent heat fluxes from the Tibetan Plateau, derived using eddy covariance measurements[J]. Theoretical and Applied Climatology, 2015, 122(3-4): 457-469. doi: 10.1007/s00704-014-1302-0
[32]	Dai A. Increasing drought under global warming in observations and models[J]. Nature Climate Change, 2013, 3(1): 52-58. doi: 10.1038/nclimate1633
[33]	Grossiord C, Buckley T N, Cernusak L A, et al. Plant responses to rising vapor pressure deficit[J]. New Phytologist, 2020, 226(6): 1550-1566. doi: 10.1111/nph.16485 pmid: 32064613
[34]	张亚春, 马耀明, 马伟强, 等. 青藏高原不同下垫面蒸散量及其与气象因子的相关性[J]. 干旱气象, 2021, 39(3): 366-373.
	[Zhang Yachun, Ma Yaoming, Ma Weiqiang, et al. Evapotranspiration variation and its correlation with meteorological factors on different underlying surfaces of the Tibetan Plateau[J]. Journal of Arid Meteorology, 2021, 39(3): 366-373.]
[35]	Ma N, Zhang Y Q. Increasing Tibetan Plateau terrestrial evapotranspiration primarily driven by precipitation[J]. Agricultural & Forest Meteorology, 2022, 317: 108887, doi: 10.1016/j.agrformet.2022.108887. doi: 10.1016/j.agrformet.2022.108887
[36]	Wang W G, Li J X, Yu Z B, et al. Satellite retrieval of actual evapotranspiration in the Tibetan Plateau: Components partitioning, multidecadal trends and dominated factors identifying[J]. Journal of Hydrology, 2018, 559: 471-485. doi: 10.1016/j.jhydrol.2018.02.065
[37]	Kool D, Agam N, Lazarovitch N, et al. A review of approaches for evapotranspiration partitioning[J]. Agricultural & Forest Meteorology, 2014, 184: 56-70.
[38]	Zhao J F, Li C, Yang T Y, et al. Estimation of high spatiotemporal resolution actual evapotranspiration by combining the SWH model with the METRIC model[J]. Journal of Hydrology, 2020, 586: 124883, doi: 10.1016/j.jhydrol.2020.124883. doi: 10.1016/j.jhydrol.2020.124883

站点	要素	模型1	模型2	模型3	模型4	模型5	模型6
那曲站	PAR	0.085^***（0.726）	0.002（0.019）	-0.000（0.000）	-0.017^**（-0.146）	-0.011（-0.096）	-0.005（-0.042）
	T_a	-	0.106^***（0.920）	0.105^***（0.909）	0.075^***（0.655）	0.073^***（0.636）	0.005（0.044）
	VPD	-	-	0.229（0.035）	0.648^*（0.097）	0.905^**（0.136）	1.719^***（0.259）
	R_n	-	-	-	0.082^***（0.348）	0.092^***（0.390）	0.085^***（0.359）
	G	-	-	-	-	-0.272^*（-0.115）	0.068（0.029）
	LAI	-	-	-	-	-	0.190^***（0.485）
	Constant	-1.369	1.158	1.153	0.965	0.634	-0.237
	N	230	230	230	230	230	230
	R²	0.528	0.875	0.875	0.888	0.891	0.952
	ΔR²	0.528	0.347	0.000	0.013	0.002	0.061
纳木错站	T_a	0.092^***（0.835）	0.066^***（0.599）	0.035^***（0.320）	-	-	-
	R_n	-	0.054^***（0.328）	0.047^***（0.289）	-	-	-
	LAI	-	-	0.590^***（0.405）	-	-	-
	Constant	1.495	1.123	0.597	-	-	-
	N	230	230	230	-	-	-
	R²	0.697	0.748	0.818	-	-	-
	ΔR²	0.697	0.052	0.069	-	-	-
藏东南站	PAR	0.117^***（0.787）	0.074^***（0.497）	0.048^***（0.320）	0.028^**（0.190）	-	-
	T_a	-	0.066^***（0.440）	0.054^***（0.359）	-0.008（-0.056）	-	-
	VPD	-	-	4.196^***（0.326）	5.092^***（0.396）	-	-
	LAI	-	-	-	0.043^***（0.496）	-	-
	Constant	-1.177	-0.388	-0.589	-1.275	-	-
	N	138	138	138	138	-	-
	R²	0.619	0.729	0.779	0.807	-	-
	ΔR²	0.619	0.110	0.050	0.029	-	-

Variations of evapotranspiration and its components in alpine meadow on the Tibetan Plateau based on SWH model

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