Effects of different reference temperature values on the accuracy of vegetation transpiration estimation by three-temperature model

doi:10.17521/cjpe.2021.0219

Abstract

Abstract:

Aims Parameterization of reference temperature has always been the key and difficult part in calculating evapotranspiration and its evaporation and transpiration components by using three-temperature model. In this paper, the best value of reference temperature was determined by quantifying and comparing the influence of different reference temperature values on the accuracy of transpiration estimation by three-temperature model.

Methods Based on the Bowen ratio and thermal infrared observation data of a typical urban lawn, sensitivity analysis and error analysis were carried out on the input variables involved in the sub-model of the three-temperature model to determine the most critical variables for the accuracy of transpiration estimation. Then the influence of input variables parameterization on the calculation of transpiration was quantified and compared to determine the best value of reference temperature.

Important findings When using the three-temperature model, the best estimation is to select the maximum temperature of the whole piece of paper as the reference leaf temperature (R² = 0.91, root mean square error (RMSE) = 0.078 mm·h^-1). When the maximum value of the vegetation canopy temperature was used as the reference temperature, it is directly assumed that the transpiration at the maximum temperature of the vegetation is zero (there is a certain transpiration rate in fact). Therefore, it is easy to underestimate the actual transpiration, resulting in that the estimation accuracy of the three-temperature model was slightly lower than the accuracy of using the maximum value of the reference leaf temperature, but the estimation effect is still good (R² = 0.87, RMSE = 0.080 mm·h^-1). Therefore, considering the limitations of the reference leaf settings, if the reference leaf temperature cannot be measured in practical applications, the maximum temperature of the vegetation canopy as the reference temperature can be used to achieve good estimate results.

Key words: reference temperature, vegetation transpiration, three-temperature model, vegetation temperature, parameterization process

XIONG Bo-Wen, LI Tong, HUANG Ying, YAN Chun-Hua, QIU Guo-Yu. Effects of different reference temperature values on the accuracy of vegetation transpiration estimation by three-temperature model[J]. Chin J Plant Ecol, 2022, 46(4): 383-393.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2021.0219

https://www.plant-ecology.com/EN/Y2022/V46/I4/383

Figures/Tables 12

References 32

[1]	Arnfield AJ (2003). Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23, 1-26. DOI URL
[2]	Beven K (1979). A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology, 44, 169-190. DOI URL
[3]	Bowen IS (1926). The ratio of heat losses by conduction and by evaporation from any water surface. Physical Review, 27, 779-787. DOI URL
[4]	Brutsaert W (1986). Catchment-scale evaporation and the atmospheric boundary layer. Water Resources Research, 22, 39-45.
[5]	Burman RD, Cuenca RH, Weiss A (1983). Techniques for estimating irrigation water requirements. Advances in Irrigation, 2, 335-394.
[6]	Dyer AJ (1974). A review of flux-profile relationships. Boundary- Layer Meteorology, 7, 363-372. DOI URL
[7]	Hatfield JL, Reginato RJ, Idso SB (1983). Comparison of long-wave radiation calculation methods over the United States. Water Resources Research, 19, 285-288. DOI URL
[8]	Jensen ME, Burman RD, Allen RG (1990). Evapotranspiration and Irrigation Water Requirements. American Society of Civil Engineers, New York. 25-41.
[9]	Lhomme JP, Monteny B, Amadou M (1994). Estimating sensible heat flux from radiometric temperature over sparse millet. Agricultural and Forest Meteorology, 68, 77-91. DOI URL
[10]	Litvak E, Manago KF, Hogue TS, Pataki DE (2017a). Evapotranspiration of urban landscapes in Los Angeles, California at the municipal scale. Water Resources Research, 53, 4236-4252. DOI URL
[11]	Litvak E, McCarthy HR, Pataki DE (2017b). A method for estimating transpiration of irrigated urban trees in California. Landscape and Urban Planning, 158, 48-61. DOI URL
[12]	Liu X, Li XX, Harshan S, Roth M, Velasco E (2017). Evaluation of an urban canopy model in a tropical city: the role of tree evapotranspiration. Environmental Research Letters, 12, 094008. DOI: 10.1088/1748-9326/aa7ee7. DOI URL
[13]	McCabe MF, Wood EF (2006). Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sensing of Environment, 105, 271-285. DOI URL
[14]	Monteith JL, Unsworth MH (2013). Principles of Environmental Physics: Plants, Animals and the Atmosphere. Academic Press, New York.
[15]	Nouri H, Beecham S, Kazemi F, Hassanli AM (2013). A review of ET measurement techniques for estimating the water requirements of urban landscape vegetation. Urban Water Journal, 10, 247-259. DOI URL
[16]	Qiu GY (1996). A New Method for Estimation of Evapotranspiration. PhD dissertation, United Graduate School of Agriculture Science, Tottori University, Tottori, Japan.
[17]	Qiu GY, Momii K, Yano T, Lascano RJ (1999). Experimental verification of a mechanistic model to partition evapotranspiration into soil water and plant evaporation. Agricultural and Forest Meteorology, 93(2), 79-93. DOI URL
[18]	Qiu GY, Sase S, Shi P, Ding G (2003). Theoretical analysis and experimental verification of a remotely measurable plant transpiration transfer coefficient. Japan Agricultural Research Quarterly, 37(3), 141-149. DOI URL
[19]	Qiu GY, Shi PJ, Wang LM (2006). Theoretical analysis of a remotely measurable soil evaporation transfer coefficient. Remote sensing of Environment, 101, 390-398. DOI URL
[20]	Qiu GY, Tan SL, Wang Y, Yu XH, Yan CH (2017). Characteristics of evapotranspiration of urban lawns in a sub-tropical megacity and its measurement by the “three temperature model + infrared remote sensing” method. Remote Sensing, 9, 502. DOI: 10.3390/rs9050502. DOI URL
[21]	Qiu GY, Wang B, Li T, Zhang XN, Zou ZD, Yan CH (2021). Estimation of the transpiration of urban shrubs using the modified three-dimensional three-temperature model and infrared remote sensing. Journal of Hydrology, 594, 125940. DOI: 10.1016/j.jhydrol.2020.125940. DOI URL
[22]	Qiu GY, Wu X, Wang S, Song XF (2006). Three temperature (3T) model—A method to estimate evapotranspiration and evaluate environmental quality based on surface temperature. IV. Plant transpiration transfer coefficient. Journal of Plant Ecology (Chinese Version), 30, 852-860.
	[ 邱国玉, 吴晓, 王帅, 宋献方 (2006). 三温模型——基于表面温度测算蒸散和评价环境质量的方法IV. 植被蒸腾扩散系数. 植物生态学报, 30, 852-860.] DOI
[23]	Qiu GY, Xiong YJ (2014). Water Versus Energy: Evapotranspiration, Thermal Environment and Energy Budget. Science Press, Beijing.
	[ 邱国玉, 熊育久 (2014). 水与能: 蒸散发, 热环境及其能量收支. 科学出版社, 北京.]
[24]	Qiu GY, Yano T, Momii K (1998). An improved methodology to measure evaporation from bare soil based on comparison of surface temperature with a dry soil surface. Journal of Hydrology, 210, 93-105. DOI URL
[25]	Qiu GY, Yu XH, Wen HY, Yan CH (2020). An advanced approach for measuring the transpiration rate of individual urban trees by the 3D three-temperature model and thermal infrared remote sensing. Journal of Hydrology, 587, 125034. DOI: 10.1016/j.jhydrol.2020.125034. DOI URL
[26]	Weiss A (1982). An experimental study of net radiation, its components and prediction. Agronomy Journal, 74, 871-874. DOI URL
[27]	Xia J, Shi W, Wang Q, Zou L (2017). Discussion of several hydrological issues regarding sponge city construction. Water Resources Protection, 33(1), 1-8.
	[ 夏军, 石卫, 王强, 邹磊 (2017). 海绵城市建设中若干水文学问题的研讨. 水资源保护, 33(1), 1-8.]
[28]	Xiong YJ, Qiu GY (2014). Simplifying the revised three- temperature model for remotely estimating regional evapotranspiration and its application to a semi-arid steppe. International Journal of Remote Sensing, 35, 2003-2027. DOI URL
[29]	Yan C, Qiu GY (2016). The three-temperature model to estimate evapotranspiration and its partitioning at multiple scales: a review. Transactions of the ASABE, 59, 661-670. DOI URL
[30]	Yu XH, Yang YJ, Tan SL, Li RL, Qin HP, Qiu GY (2017). Evapotranspiration and its cooling effect of urban green roof. Chinese Journal of Environmental Engineering, 11, 5333-5340.
	[ 于小惠, 杨雅君, 谭圣林, 李瑞利, 秦华鹏, 邱国玉 (2017). 绿色屋顶蒸散发及其降温效果. 环境工程学报, 11, 5333-5340.]
[31]	Zhang YY, Qin HP, Zhang JY, Hu YC (2020). An in situ measurement method of evapotranspiration from typical LID facilities based on the three-temperature model. Journal of Hydrology, 588. DOI: 10.1016/j.jhydrol.2020.125105. DOI
[32]	Zou ZD, Yang YJ, Qiu GY (2019). Quantifying the evapotranspiration rate and its cooling effects of urban hedges based on three-temperature model and infrared remote sensing. Remote Sensing, 11, 202. DOI: 10.3390/rs11020202. DOI URL

气象要素 Meteorological factor	仪器型号 Instrument model	安装高度 Height (m)	测量精度 Measuring accuracy
气温和相对湿度 Air temperature and relative humidity	225-050YA, Novalynx, Grass Valley, USA	2.0, 1.5	±3%, ±0.6 ℃
风速与风向 Wind speed and direction	200-WS-02, Novalynx, Grass Valley, USA	2.0	±0.2 m∙s^-1, ±3°
太阳辐射 Solar radiation	PYP-PA, Apogee, Santa Monica, USA	2.0	10-40 μV·W^-1·m^-2
有效光合辐射 Photosynthetic active radiation	QSOA-S, Apogee, Santa Monica, USA	2.0	<3%
太阳净辐射 Net solar radiation	240-100, Novalynx, Grass Valley, USA	2.0	<4%
土壤热通量 Soil heat flux	HFP01, Hukseflux, Center Moriche, USA	-0.05, -0.02	50 μV·W^-1·m^-2

气象要素 Meteorological factor	仪器型号 Instrument model	安装高度 Height (m)	测量精度 Measuring accuracy
气温和相对湿度 Air temperature and relative humidity	225-050YA, Novalynx, Grass Valley, USA	2.0, 1.5	±3%, ±0.6 ℃
风速与风向 Wind speed and direction	200-WS-02, Novalynx, Grass Valley, USA	2.0	±0.2 m∙s^-1, ±3°
太阳辐射 Solar radiation	PYP-PA, Apogee, Santa Monica, USA	2.0	10-40 μV·W^-1·m^-2
有效光合辐射 Photosynthetic active radiation	QSOA-S, Apogee, Santa Monica, USA	2.0	<3%
太阳净辐射 Net solar radiation	240-100, Novalynx, Grass Valley, USA	2.0	<4%
土壤热通量 Soil heat flux	HFP01, Hukseflux, Center Moriche, USA	-0.05, -0.02	50 μV·W^-1·m^-2

观测日期 Observation date	斜率 Slope					相关系数 Correlation coefficient					均方根误差 Root mean square error (mm·h^-1)
观测日期 Observation date	最大值 Max	前10% 平均值 First 10% mean	前20% 平均值 First 20% mean	前50% 平均值 First 50% mean	平均值 Mean	最大值 Max	前10% 平均值 First 10% mean	前20% 平均值 First 20% mean	前50% 平均值 First 50% mean	平均值 Mean	最大值 Max	前10% 平均值 First 10% mean	前20% 平均值 First 20% mean	前50% 平均值 First 50% mean	平均值 Mean
05-30	1.02	1.04	1.04	1.06	1.10	0.89	0.89	0.89	0.89	0.89	0.075	0.080	0.081	0.084	0.104
05-31	1.19	1.13	1.13	1.14	1.12	0.92	0.89	0.88	0.87	0.82	0.073	0.080	0.083	0.088	0.115
06-01	0.95	0.92	0.93	0.94	0.96	0.93	0.92	0.92	0.92	0.91	0.082	0.082	0.081	0.080	0.080
06-02	0.92	0.84	0.85	0.87	0.89	0.87	0.81	0.82	0.83	0.86	0.083	0.078	0.073	0.067	0.057
08-01	0.87	0.88	0.88	0.88	0.88	0.98	0.98	0.98	0.98	0.97	0.066	0.061	0.060	0.058	0.051
08-05	1.27	0.83	0.83	0.83	0.82	0.99	0.96	0.96	0.96	0.96	0.039	0.090	0.089	0.087	0.082
08-09	0.85	0.84	0.84	0.81	0.77	0.92	0.91	0.91	0.90	0.87	0.100	0.091	0.089	0.087	0.088
10-03	1.05	1.07	1.08	1.14	1.08	0.93	0.92	0.92	0.91	0.90	0.066	0.069	0.071	0.077	0.091
10-19	1.06	1.08	1.08	1.15	1.10	0.97	0.97	0.97	0.96	0.97	0.066	0.065	0.065	0.064	0.065
11-07	1.02	1.05	1.06	1.17	1.08	0.98	0.98	0.97	0.96	0.97	0.067	0.065	0.064	0.063	0.063

观测日期 Observation date	斜率 Slope					相关系数 Correlation coefficient					均方根误差 Root mean square error (mm·h^-1)
观测日期 Observation date	最大值 Max	前10% 平均值 First 10% mean	前20% 平均值 First 20% mean	前50% 平均值 First 50% mean	平均值 Mean	最大值 Max	前10% 平均值 First 10% mean	前20% 平均值 First 20% mean	前50% 平均值 First 50% mean	平均值 Mean	最大值 Max	前10% 平均值 First 10% mean	前20% 平均值 First 20% mean	前50% 平均值 First 50% mean	平均值 Mean
05-30	1.02	1.04	1.04	1.06	1.10	0.89	0.89	0.89	0.89	0.89	0.075	0.080	0.081	0.084	0.104
05-31	1.19	1.13	1.13	1.14	1.12	0.92	0.89	0.88	0.87	0.82	0.073	0.080	0.083	0.088	0.115
06-01	0.95	0.92	0.93	0.94	0.96	0.93	0.92	0.92	0.92	0.91	0.082	0.082	0.081	0.080	0.080
06-02	0.92	0.84	0.85	0.87	0.89	0.87	0.81	0.82	0.83	0.86	0.083	0.078	0.073	0.067	0.057
08-01	0.87	0.88	0.88	0.88	0.88	0.98	0.98	0.98	0.98	0.97	0.066	0.061	0.060	0.058	0.051
08-05	1.27	0.83	0.83	0.83	0.82	0.99	0.96	0.96	0.96	0.96	0.039	0.090	0.089	0.087	0.082
08-09	0.85	0.84	0.84	0.81	0.77	0.92	0.91	0.91	0.90	0.87	0.100	0.091	0.089	0.087	0.088
10-03	1.05	1.07	1.08	1.14	1.08	0.93	0.92	0.92	0.91	0.90	0.066	0.069	0.071	0.077	0.091
10-19	1.06	1.08	1.08	1.15	1.10	0.97	0.97	0.97	0.96	0.97	0.066	0.065	0.065	0.064	0.065
11-07	1.02	1.05	1.06	1.17	1.08	0.98	0.98	0.97	0.96	0.97	0.067	0.065	0.064	0.063	0.063

不同参考叶片温度取值 Different reference leaf temperature	相关系数 Correlation coefficient	斜率 Slope	截距 Intercept	均方根误差 Root mean square error (mm·h^-1)
最大值 Max	0.91	0.98	-0.03	0.078
前10%平均值 First 10% mean	0.90	0.98	-0.03	0.079
前20%平均值 First 20% mean	0.90	0.99	-0.02	0.078
前50%平均值 First 50% mean	0.89	1.00	-0.02	0.078
平均值 Mean	0.86	1.01	-0.01	0.083