Seasonal and interannual variations in energy balance closure over arid and semi-arid grasslands in northern China

doi:10.17521/cjpe.2021.0428

Abstract

Abstract:

Aims Eddy covariance (EC) systems are widely used for measuring the fluxes of carbon, water, and energy, as well as meteorological factors. As one important reference of independently evaluating scalar flux by EC technique, energy balance closure is widely used for evaluating data quality of carbon, water, and energy fluxes.

Methods Using the data of energy fluxes and meteorological variables retrieved from 56 site-year, the energy balance closure of six sites across three ecosystems (i.e. desert steppe, typical steppe, and meadow steppe) was analyzed by two widely used methods: linear regression from the ordinary least squares (OLS) and the energy balance ratio (EBR). The overall evaluation of energy balance closure, the seasonal and interannual variations and the related influencing factors were investigated.

Important findings The results show that: 1) the multiple-year EBR and OLS slope over the six sites had a mean value of 0.89 ± 0.11 and 0.96 ± 0.04, respectively, which are better than the results of the FLUXNET and ChinaFLUX. 2) There were significant differences over different sites and grassland types, with EBR of desert steppe (1.01 ± 0.09) and typical steppe (0.90 ± 0.11) both higher than meadow steppe (0.83 ± 0.05). There were seasonal variations of EBR over the six studied sites, and with better and stable results in growing season than non-growing season. The air temperature (T_a), vapor pressure deficit (VPD), soil moisture (SWC), and Albedo regulated the seasonal variation of EBR, with the low T_a and high Albedo remarkably reducing EBR during the non-growing season. 3) There were significant interannual variations of EBR across different sites and grassland types. The latent heat fraction (the ratio of latent heat flux to net radiation, LE/R_n), mean annual air temperature (MAT) and growing season Albedo significantly influenced interannual variation of EBR. The LE/R_n showed the strongest impact and explained 44% of the interannual variation of EBR. The significantly increasing in leaf area index (LAI) strongly regulated the upward of the available energy (net radiation minus ground heat flux, R_n- G₀), which contributes to the significant downward of EBR during observed years. It should be noted that EBR and OLS slope should be combined to better evaluate the energy balance closure. In conclusion, this study help improve our understanding of the potential linkage between energy balance closure and environmental factors, evaluate the quality of scalar flux estimates from EC technique, as well as improve the data processing protocol of flux data in the semi-arid and arid grassland region.

Key words: energy balance closure, eddy covariance, seasonal variation, interannual variation, grassland

WANG Yan-Bing, YOU Cui-Hai, TAN Xing-Ru, CHEN Bo-Yu, XU Meng-Zhen, CHEN Shi-Ping. Seasonal and interannual variations in energy balance closure over arid and semi-arid grasslands in northern China[J]. Chin J Plant Ecol, 2022, 46(12): 1448-1460.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2021.0428

https://www.plant-ecology.com/EN/Y2022/V46/I12/1448

Figures/Tables 9

Table 1 Site information of different sites in Nei Mongol grasslands

站点 Site	地理位置 Geo-location	海拔 Altitude (m)	年平均气温 Mean annual air temperature (°C)	年降水量 Mean annual precipitation (mm)	土壤类型 Soil type	草地类型 Grassland type	优势种 Dominant species	数据时段 Data period
四子王旗站 Siziwangqi station (SZ)	111.90° E 41.78° N	1 438	3.4	310	栗钙土 Kastanozem	荒漠草原 Desert steppe	短花针茅 Stipa breviflora 冷蒿 Artemisia frigida	2011-2018
锡林浩特割草站 Xilinhot grazed station (XL1)	116.67° E 43.56° N	1 250	2.0	350	栗钙土 Kastanozem	典型草原 Typical steppe	大针茅 Stipa grandis 羊草 Leymus chinensis 冷蒿 Artemisia frigida	2006-2018
锡林浩特围封站 Xilinhot fenced station (XL2)	116.67° E 43.55° N	1 250	2.0	350	栗钙土 Kastanozem	典型草原 Typical steppe	大针茅 Stipa grandis 羊草 Leymus chinensis 羽茅 Achnatherum sibiricum	2006-2018
西乌珠穆沁旗站 Xi Ujimqin Qi station (XW)	117.58° E 44.36° N	1 148	1.5	330	栗钙土 Kastanozem	典型草原 Typical steppe	大针茅 Stipa grandis 羊草 Leymus chinensis	2012-2018
多伦站 Duolun station (DL)	116.28° E 42.05° N	1 350	1.6	385	栗钙土 Kastanozem	典型草原 Typical steppe	西北针茅 Stipa sareptana var. krylovii 冷蒿 Artemisia frigida	2005-2018
额尔古纳站 Ergun station (EG)	119.39 °E 50.19° N	521	-2.5	355	黑钙土 Chernozem	草甸草原 Meadow steppe	狼针草 Stipa baicalensis 寸草 Carex duriuscula	2012-2018

Table 2 Energy balance closure of the different sites in Nei Mongol grasslands (mean ± SD)

时间 Time	站点 Station	最小二乘法线性回归 Linear regression from the ordinary least squares			能量平衡比率 Energy balance ratio
时间 Time	站点 Station	斜率 Slope	截距 Intercept	决定系数 R²	能量平衡比率 Energy balance ratio
全年 (1-12月) Whole year (January-December)	SZ	0.97 ± 0.13	0.30 ± 0.41	0.90 ± 0.03	1.01 ± 0.07
	XL1	0.94 ± 0.10	-0.05 ± 0.44	0.88 ± 0.02	0.92 ± 0.09
	XL2	1.01 ± 0.07	-1.15 ± 0.72	0.89 ± 0.04	0.86 ± 0.07
	XW	0.96 ± 0.05	-1.08 ± 0.13	0.94 ± 0.01	0.78 ± 0.06
	DL	1.00 ± 0.07	0.09 ± 0.25	0.94 ± 0.02	1.02 ± 0.07
	EG	0.88 ± 0.04	-0.69 ± 0.52	0.91 ± 0.04	0.74 ± 0.04
	平均值 Average	0.96 ± 0.04	-0.43 ± 0.57	0.91 ± 0.02	0.89 ± 0.11
非生长季 (10-次年3月) Non-growing season (October-March of next year)	SZ	0.88 ± 0.21	0.42 ± 0.64	0.78 ± 0.08	1.01 ± 0.13
	XL1	0.82 ± 0.14	-0.02 ± 0.48	0.76 ± 0.10	0.80 ± 0.17
	XL2	0.90 ± 0.12	-0.98 ± 0.78	0.77 ± 0.10	0.68 ± 0.15
	XW	0.95 ± 0.09	-1.10 ± 0.12	0.87 ± 0.04	0.58 ± 0.09
	DL	0.98 ± 0.06	0.17 ± 0.24	0.91 ± 0.02	1.04 ± 0.10
	EG	0.52 ± 0.21	-0.69 ± 0.38	0.43 ± 0.22	0.35 ± 0.17
	平均值 Average	0.84 ± 0.15	-0.37 ± 0.58	0.75 ± 0.15	0.74 ± 0.24
生长季 (4-9月) Growing season (April-September)	SZ	0.94 ± 0.12	0.61 ± 1.11	0.76 ± 0.13	1.01 ± 0.09
	XL1	0.86 ± 0.08	0.92 ± 0.52	0.71 ± 0.07	0.96 ± 0.09
	XL2	0.96 ± 0.07	-0.13 ± 0.50	0.87 ± 0.06	0.95 ± 0.06
	XW	0.94 ± 0.06	-0.79 ± 0.24	0.89 ± 0.05	0.86 ± 0.06
	DL	1.04 ± 0.08	-0.26 ± 0.23	0.85 ± 0.03	1.01 ± 0.07
	EG	0.81 ± 0.05	0.13 ± 0.66	0.79 ± 0.12	0.83 ± 0.05
	平均值 Average	0.93 ± 0.07	0.08 ± 0.56	0.81 ± 0.06	0.94 ± 0.07

Fig. 1 Energy balance closure across different grassland types and sites in Nei Mongol based on energy balance ratio (EBR)(A) and ordinary least squares (OLS) slope (B) method. Different uppercase and lowercase letters indicate significant differences among different grassland types and sites, respectively (p < 0.05); ns, p > 0.05. DL, EG, SZ, XL1, XL2 and XW represent Duolun site, Ergun site, Siziwangqi site, Xilinhot mowed site, Xilinhot fenced site and Xi Ujimqin Qi site, respectively.

Fig. 2 Relationships between ordinary least squares (OLS) slope and friction velocity (u*) across different sites in Nei Mongol grasslands. A, Daytime. B, Nighttime. DL, EG, SZ, XL1, XL2 and XW represent Duolun site, Ergun site, Siziwangqi site, Xilinhot mowed site, Xilinhot fenced site and Xi Ujimqin Qi site, respectively. ***, p < 0.001.

Fig. 3 Seasonal variations in energy balance ratio (EBR)(A), mean month air temperature (Ta)(B), vapor press deficit (VPD)(C), soil water content (SWC)(D) at 0-10 cm soil depth and Albedo (E) across different sites in Nei Mongol grasslands. The shallow green shaded area in the graph represents the growing season period (April to September). DL, EG, SZ, XL1, XL2 and XW represent Duolun site, Ergun site, Siziwangqi site, Xilinhot mowed site, Xiwuqi fenced site and Xi Ujimqin Qi site, respectively.

Fig. 4 Relationships among seasonal variations of energy balance ratio (EBR) with air temperature (Ta)(A), vapor pressure deficit (VPD)(B), soil water content (SWC)(C), and Albedo (D) across different sites in Nei Mongol grasslands. DL, EG, SZ, XL1, XL2 and XW represent Duolun site, Ergun site, Siziwangqi site, Xilinhot mowed site, Xilinhot fenced site and Xi Ujimqin Qi site, respectively. Each point represents the monthly mean value during observation multiple years in each site. The color lines represent the linear or nonlinear regression model results of each site, the thick black line represents the nonlinear mixed-effects model results for all sites when the site was considered as a random factor. The solid line represents significant relation (p < 0.05) and the dash line represents insignificant relation (p > 0.05). ns, p > 0.1; **, p < 0.01; ***, p < 0.001.

Fig. 5 Interannual variations in energy balance ratio (EBR)(A), turbulent energy fluxes (LE + H)(B) available energy fluxes (Rn - G0)(C) and Albedo (D) across different sites in Nei Mongol grasslands. The black lines represent the linear regression model results for all sites. DL, EG, SZ, XL1, XL2 and XW represent Duolun site, Ergun site, Siziwangqi site, Xilinhot mowed site, Xilinhot fenced site and Xi Ujimqin Qi site, respectively.

Fig. 6 Relationships among interannual variations of energy balance ration (EBR) with mean annual temperature (MAT)(A), latent heat fraction (LE/Rn)(B) and growing season Albedo (C) across different sites in Nei Mongol grasslands. DL, EG, SZ, XL1, XL2 and XW represent Duolun site, Ergun site, Siziwangqi site, Xilinhot mowed site, Xilinhot fenced site and Xi Ujimqin Qi site, respectively. The color lines represent the linear regression model results of each site, the thick black line represents the nonlinear mixed-effects model results for all sites when the site was considered as a random factor. The solid line represents significant relation (p < 0.05) and the dash line represents insignificant relation (p > 0.05). ns, p > 0.1; #, p < 0.1; *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Fig. 7 Relative importance of individual environmental variable in explaining seasonal (A) and interannual (B) variations of energy balance ratio (EBR) in Nei Mongol grasslands. #, p < 0.1; *, p < 0.05; **, p < 0.01; ***, p < 0.001. Albedo, MAT, LE, Rn, SWC, Ta, VPD represent growing season albedo, mean annual temperature, latent heat flux, net radiation, soil water content, mean monthly air temperature and vapor pressure deficit.

References 37

[1]	Aubinet M, Grelle A, Ibrom A, Rannik Ü, Moncrieff J, Foken T, Kowalski AS, Martin PH, Berbigier P, Bernhofer Ch, Clement R, Elbers J, Granier A, Grünwald T, Morgenstern K, et al. (2000). Estimates of the annual net carbon and water exchange of European forests: the EUROFLUX methodology. Advances in Ecological Research, 30, 114-175.
[2]	Blanken PD, Black TA, Neumann HH, Den Hartog G, Yang PC, Nesic Z, Staebler R, Chen W, Novak MD (1998). Turbulent flux measurements above and below the overstory of a boreal aspen forest. Boundary-Layer Meteorology, 89, 109-140. DOI URL
[3]	Blonquist JM Jr, Tanner BD, Bugbee B (2009). Evaluation of measurement accuracy and comparison of two new and three traditional net radiometers. Agricultural and Forest Meteorology, 149, 1709-1721. DOI URL
[4]	Burnham KP, Anderson DR (2002). Model Selection Multimodel Inference: a Practical Information-Theoretic Approach. 2nd ed. Springer, New York.
[5]	Chen SP, Chen JQ, Lin GH, Zhang WL, Miao HX, Wei L, Huang JH, Han XG (2009). Energy balance and partition in Inner Mongolia steppe ecosystems with different land use types. Agricultural and Forest Meteorology, 149, 1800-1809. DOI URL
[6]	Chen SP, You CH, Hu ZM, Chen Z, Zhang LM, Wang QF (2020). Eddy covariance technique and its applications in flux observations of terrestrial ecosystems. Chinese Journal of Plant Ecology, 44, 291-304. DOI URL
	[ 陈世苹, 游翠海, 胡中民, 陈智, 张雷明, 王秋凤 (2020). 涡度相关技术及其在陆地生态系统通量研究中的应用. 植物生态学报, 44, 291-304.] DOI
[7]	Cheng Y, Sayde C, Li Q, Basara J, Selker J, Tanner E, Gentine P (2017). Failure of Taylor’s hypothesis in the atmospheric surface layer and its correction for eddy-covariance measurements. Geophysical Research Letters, 44, 4287-4295. DOI URL
[8]	Cui WH, Chui TFM (2017). Subsurface lateral heat flux within the heterogeneous surface of a subtropical wetland and its potential contribution to energy imbalance. Journal of Hydrometeorology, 18, 3125-3144. DOI URL
[9]	Cui WH, Chui TFM (2019). Temporal and spatial variations of energy balance closure across FLUXNET research sites. Agricultural and Forest Meteorology, 271, 12-21. DOI URL
[10]	Duveiller G, Hooker J, Cescatti A (2018). The mark of vegetation change on Earth’s surface energy balance. Nature Communications, 9, 679. DOI: 10.1038/s41467-017-02810-8. DOI PMID
[11]	Falge E, Baldocchi D, Olson R, Anthoni P, Aubinet M, Bernhofer C, Burba G, Ceulemans R, Clement R, Dolman H, Granier A, Gross P, Grünwald T, Hollinger D, Jensen NO, et al. (2001). Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, 107, 43-69. DOI URL
[12]	Foken T (2008). The energy balance closure problem: an overview. Ecological Applications, 18, 1351-1367. DOI URL
[13]	Foken T, Aubinet M, Finnigan JJ, Leclerc MY, Mauder M, Paw UKT (2011). Results of a panel discussion about the energy balance closure correction for trace gases. Bulletin of the American Meteorological Society, 92, ES13-ES18. DOI URL
[14]	Franssen HJH, Stockli R, Lehner I, Rotenberg E, Seneviratne SI (2010). Energy balance closure of eddy-covariance data: A multisite analysis for European FLUXNET stations. Agricultural and Forest Meteorology, 150, 1553-1567. DOI URL
[15]	Hasi M, Zhang XY, Niu GX, Wang YL, Geng QQ, Quan Q, Chen SP, Han XG, Huang JH (2021). Soil moisture, temperature and nitrogen availability interactively regulate carbon exchange in a meadow steppe ecosystem. Agricultural and Forest Meteorology, 304-305, 108389. DOI: 10.1016/j.agrformet.2021.108389. DOI URL
[16]	Kochendorfer J, Paw UKT (2011). Field estimates of scalar advection across a canopy edge. Agricultural and Forest Meteorology, 151, 585-594. DOI URL
[17]	Leuning R, van Gorsel E, Massman WJ, Isaac PR (2012). Reflections on the surface energy imbalance problem. Agricultural and Forest Meteorology, 156, 65-74. DOI URL
[18]	Li ZQ, Yu GR, Wen XF, Zhang LM, Ren CY, Fu YL (2005). Energy balance closure at ChinaFLUX sites. Science in China Series D: Earth Sciences, 48(Supp. I),51-62.
[19]	Liu B, Cui YL, Luo YF, Shi YZ, Liu M, Liu FP (2019). Energy partitioning and evapotranspiration over a rotated paddy field in Southern China. Agricultural and Forest Meteorology, 276-277, 107626. DOI: 10.1016/j.agrformet.2019.107626. DOI URL
[20]	Majozi NP, Mannaerts CM, Ramoelo A, Mathieu R, Nickless A, Verhoef W (2017). Analysing surface energy balance closure and partitioning over a semi-arid savanna FLUXNET site in Skukuza, Kruger National Park, South Africa. Hydrology and Earth System Sciences, 21, 3401-3415. DOI URL
[21]	McGloin R, Šigut L, Havránková K, Dušek J, Pavelka M, Sedlák P (2018). Energy balance closure at a variety of ecosystems in Central Europe with contrasting topographies. Agricultural and Forest Meteorology, 248, 418-431. DOI URL
[22]	Michel D, Philipona R, Ruckstuhl C, Vogt R, Vuilleumier L (2008). Performance and uncertainty of CNR1 net radiometers during a one-year field comparison. Journal of Atmospheric and Oceanic Technology, 25, 442-451. DOI URL
[23]	Oliphant AJ, Grimmond CSB, Zutter HN, Schmid HP, Su HB, Scott SL, Offerle B, Randolph JC, Ehman J (2004). Heat storage and energy balance fluxes for a temperate deciduous forest. Agricultural and Forest Meteorology, 126, 185-201. DOI URL
[24]	Oncley SP, Foken T, Vogt R, Kohsiek W, Debruin HAR, Bernhofer C, Christen A, van Gorsel E, Grantz D, Feigenwinter C, Lehner I, Liebethal C, Liu H, Mauder M, Pitacco A, Ribeiro L, Weidinger T (2007). The Energy Balance Experiment EBEX-2000. Part I: overview and energy balance. Boundary-Layer Meteorology, 123, 1-28. DOI URL
[25]	Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, et al. (2005). On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11, 1424-1439. DOI URL
[26]	Shao CL, Chen JQ, Li LH, Xu WT, Chen SP, Gwen T, Xu JY, Zhang WL (2008). Spatial variability in soil heat flux at three Inner Mongolia steppe ecosystems. Agricultural and Forest Meteorology, 148, 1433-1443. DOI URL
[27]	Stoy PC, Mauder M, Foken T, Marcolla B, Boegh E, Ibrom A, Arain MA, Arneth A, Aurela M, Bernhofer C, Cescatti A, Dellwik E, Duce P, Gianelle D, van Gorsel E, et al. (2013). A data-driven analysis of energy balance closure across FLUXNET research sites: the role of landscape scale heterogeneity. Agricultural and Forest Meteorology, 171-172, 137-152. DOI URL
[28]	Teng DX, He XM, Qin L, Lv GH (2021). Energy balance closure in the Tugai forest in Ebinur Lake basin, northwest China. Forests, 12, 243. DOI: 10.3390/f1202043. DOI URL
[29]	Webb EK, Pearman GI, Leuning R (1980). Correction of flux measurements for density effects due to heat and water- vapour transfer. Quarterly Journal of the Royal Meteorological Society, 106, 85-100. DOI URL
[30]	Wilson K, Goldstein A, Falge E, Aubinet M, Baldocchi D, Berbigier P, Bernhofer C, Ceulemans R, Dolman H, Field C, Grelle A, Ibrom A, Law BE, Kowalski A, Meyers T, et al. (2002). Energy balance closure at FLUXNET sites. Agricultural and Forest Meteorology, 113, 223-243. DOI URL
[31]	Wu ZT, Dijkstra P, Koch GW, Peñuelas J, Hungate BA (2011). Responses of terrestrial ecosystems to temperature and precipitation change: a meta-analysis of experimental manipulation. Global Change Biology, 17, 927-942. DOI URL
[32]	Yue P, Zhang Q, Zhang L, Li HY, Yang Y, Zeng J, Wang S (2019). Long-term variations in energy partitioning and evapotranspiration in a semiarid grassland in the Loess Plateau of China. Agricultural and Forest Meteorology, 278, 107671. DOI: 10.1016/j.agrformet.2019.107671. DOI URL
[33]	Yu GR, Sun XM (2017). Principles of Flux Measurement in Terrestrial Ecosystems. 2nd ed. Higher Education Press, Beijing.
	[ 于贵瑞, 孙晓敏 (2017). 陆地生态系统通量观测的原理与方法. 2版. 高等教育出版社, 北京.]
[34]	Zhang LM, Luo YW, Liu M, Chen Z, Su W, He HL, Zhu ZL, Sun XM, Wang YF, Zhou GY, Zhao XQ, Han SJ, Ouyang Z, Zhang XZ, Zhang YP, et al. (2019). Carbon and water fluxes observed by the Chinese Flux Observation and Research Network (2003-2005). China Scientific Data, 4, 18-34.
	[ 张雷明, 罗艺伟, 刘敏, 陈智, 苏文, 何洪林, 朱治林, 孙晓敏, 王艳芬, 周国逸, 赵新全, 韩士杰, 欧阳竹, 张宪洲, 张一平, 等 (2019). 2003-2005年中国通量观测研究联盟(China FLUX)碳水通量观测数据集. 中国科学数据, 4, 18-34.]
[35]	Zhang Y, Peng CH, Li WZ, Tian LX, Zhu QA, Chen H, Fang XQ, Zhang GL, Liu GB, Mu XM, Li ZB, Li SQ, Yang YZ, Wang J, Xiao XM (2016). Multiple afforestation programs accelerate the greenness in the “Three North” region of China from 1982 to 2013. Ecological Indicators, 61, 404-412.
[36]	Zhang YS, Kadota T, Ohata T, Oyunbaatar D (2007). Environmental controls on evapotranspiration from sparse grassland in Mongolia. Hydrological Processes, 21, 2016-2027. DOI URL
[37]	Zuo JQ, Wang JM, Huang JP, Li WJ, Wang GY, Ren HL (2010). Estimation of ground heat flux for a semi-arid grassland and its impacts on the surface energy budget. Plateau Meteorology, 29, 840-848.
	[ 左金清, 王介民, 黄建平, 李维京, 王国印, 任宏利 (2010). 半干旱草地地表土壤热通量的计算及其对能量平衡的影响. 高原气象, 29, 840-848.]