Temporal upscaling of evapotranspiration for drip-irrigated grapevines in solar greenhouses: A comparative study in Northeast China’s cold region
Keywords:
agricultural engineering, and and water engineering, farm produce processingAbstract
Temporal upscaling of evapotranspiration (ET) is crucial for improving water use efficiency and water-saving irrigation management in crop production, playing a vital role in guiding farmland irrigation. This study investigated the water consumption patterns and evaluated different temporal upscaling methods for ET in drip irrigated grapevines in Northeast China’s cold region. Based on a three-year experiment (2018, 2020, 2021), four upscaling methods were examined: the evaporation fraction method (EF method), the improved evaporation fraction method (EF′ method), the crop coefficient method (Kc method), and the direct canopy resistance method (rc method), applied to both instantaneous-to-daily and daily-to-whole-growth-period timescales. The results demonstrated that all key parameters, including evaporation fraction (EF), improved evaporation fraction (EF′), crop coefficient (Kc), and canopy resistance (rc), exhibited stable values from 8:00-16:00 when upscaling ET from instantaneous to daily scales. Their mean standard deviations (SD) ranged from 0.08-0.21, 0.08-0.19, 0.10-0.18, and 41.43-137.72 s/m, respectively. Regarding the simulation of instantaneous to daily upscaling, the methods ranked as EF method>EF′ method>Kc method>rc method. The EF method achieved optimal performance at specific times: 11:30 (shoot growth and the flowering period), 12:00 (fruit expansion), and 12:30 (maturity). For daily to whole growth period upscaling, all four methods performed best during the fruit expansion stage, maintaining the same performance ranking. The EF method consistently exhibited the smallest errors, with MAE values of 35.31 mm, 33.00 mm, and 42.97 mm, and RRMSE values of 12.21%, 11.40%, and 14.62% in 2018, 2020, and 2021, respectively. Therefore, the EF method is recommended for both upscaling ET from instantaneous to daily timescales and from daily to the entire growth period for drip-irrigated grapevines in Northeast China’s cold region. The findings not only enrich the analytical framework for agricultural hydrological processes, but also hold significant implications for implementing “hourly precision water management” in greenhouse grape cultivation and enhancing water use efficiency in greenhouse grape systems.
Keywords: evapotranspiration, methods for temporal upscaling, improved evaporation fraction, crop coefficient, greenhouse grapevines
DOI: 10.25165/j.ijabe.20261903.9948
Citation: Liu Y Y N, Zhang K J, Sun Y. Net energy-oriented agitation optimization for cow manure–corn straw co-fermentation. Int J Agric & Biol Eng, 2026; 19(3): 281–293.
References
[1] Allen R G, Pereira L S, Raes D, Smith M. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Rome: FAO, 1998.
[2] Dai J J, Zhang X P, Luo Z D, Wang R, Liu Z L, et al. Variation of the stable isotopes of water in the soil-plant-atmosphere continuum of a cinnamomum camphora woodland in the East Asian monsoon region. Journal of Hydrology, 2020; 589: 125199.
[3] Nyolei D, Diels J, Mbilinyi B, Mbungu W, van Griensven A. Evapotranspiration simulation from a sparsely vegetated agricultural field in a semi-arid agro-ecosystem using Penman-Monteith models. Agricultural and Forest Meteorology, 2021; 303: 108370.
[4] Wang Z C, Pan X, Ding X, Guluzade R, Yang Z, Yuan J, et al. Intercomparison of temporal upscaling strategies for daily evapotranspiration estimation from the perspective of land surface temperature. Journal of Hydrology, 2025; 660（B）: 133461.
[5] Kumar U, Sahoo B, Chatterjee C, Raghuwanshi N S. Evaluation of simplified surface energy balance index （s-sebi） method for estimating actual evapotranspiration in Kangsabati Reservoir command using landsat 8 imagery. Journal of the Indian Society of Remote Sensing, 2020; 48: 1421–1432.
[6] Shirmohammadi-Aliakbarkhani Z, Saberali S F. Evaluating of eight evapotranspiration estimation methods in arid regions of Iran. Agricultural Water Management, 2020; 239: 106243.
[7] Yang Y, Chen R S, Song Y X, Han C T, Liu J F, Liu Z W. Sensitivity of potential evapotranspiration to meteorological factors and their elevational gradients in the Qilian Mountains, northwestern China. Journal of Hydrology, 2019; 568: 147–159.
[8] Wang C, Jiang Y C, Bai Y K, Wang T L. Numerical study on static properties and failure mechanisms of landing assembled Chinese solar greenhouses. Computers and Electronics in Agriculture, 2021; 188: 106347.
[9] Jackson R D, Hatfield J L, Reginato R J, Idso S B, Pinter Jr P J. Estimation of daily evapotranspiration from one time-of-day measurements. Agricultural Water Management, 1983; 7: 351–362.
[10] Zhang B Z, Xu D, Liu Y, Li F S, Cai J B, Du L J. Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in north China. Agricultural and Forest Meteorology, 2016; 216: 1–12.
[11] Gong X W, Liu H, Sun J S, Zhang H, Ma X J, Wang W N. Variation of evapotranspiration in different spatial scales for solar greenhouse tomato and its controlling meteorological factors. Transactions of the Chinese Society of Agricultural Engineering, 2017; 33（8）: 166–175. （in Chinese）
[12] Li Y, Liu X Y, Xu Z W, Peng Y H, Xu J Z. Comparative analysis of different temporal up-scaling methods for evapotranspiration applied to water-saving irrigated paddy fields. Transactions of the Chinese Society of Agricultural Engineering, 2021; 37（21）: 90–99. （in Chinese）
[13] Colaizzi P D, Evett S R, Howell T A, Tolk J A. Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements. Transactions of the ASABE, 2006; 49（5）: 1409–1417.
[14] Shuttleworth W, Gurney R, Hsu A, Ormsby J. Fife: the variation in energy partition at surface flux sites. IAHS Publications, 1989; 186: 523–534.
[15] Sugita M, Brutsaert W. Daily evaporation over a region from lower boundary layer profiles measured with radiosondes. Water Resources Research, 1991; 27（5）: 747–752.
[16] Chemin Y, Alexandridis T. Improving spatial resolution of ET seasonal for irrigated rice in Zhanghe, China. In: 22nd Asian Conference on Remote Sensing, Singapore: CRISP, 2001.
[17] Farah H O, Bastiaanssen W G M, Feddes R A. Evaluation of the temporal variability of the evaporation fraction in a tropical watershed. International Journal of Applied Earth Observation and Geoinformation, 2004; 5（2）: 129–140.
[18] Malek E, Bingham G E, McCurdy G D. Continuous measurement of aerodynamic and alfalfa canopy resistances using the Bowen ratio-energy balance and Penman-Monteith methods. Boundary-Layer Meteorology, 1992; 59: 187–194.
[19] Tang R, Li Z L, Sun X M, Bi Y Y. Temporal upscaling of instantaneous evapotranspiration on clear‐sky days using the constant reference evaporation fraction method with fixed or variable surface resistances at two cropland sites. Journal of Geophysical Research: Atmospheres, 2017; 122（2）: 784–801.
[20] Chen R N, Liu X J, Liu L Y. Improved GPP upscaling from instantaneous measurements to daily sums using the light-use-efficiency-based model. Agricultural and Forest Meteorology, 2025; 368: 110529.
[21] Katimbo A, Rudnick D R, Liang W Z, DeJonge K C, Lo T H, Franz T E, et al. Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions. Agricultural Water Management, 2022; 274: 107972.
[22] Yan H F, Li M, Zhang C, Zhang J Y, Wang G Q, Yu J J, et al. Comparison of evapotranspiration upscaling methods from instantaneous to daytime scale for tea and wheat in southeast China. Agricultural Water Management, 2022; 264: 107464.
[23] Yang D N, Yang S S, Huang J J, Zhang S Y, Zhang S, Zhang J H, et al. Improving time upscaling of instantaneous evapotranspiration based on machine learning models. Big Earth Data, 2025; 9（1）: 127–154.
[24] Chen H, Yang D W, Lyu H F. Comparison of temporal extrapolation methods for evapotranspiration over variant underlying croplands. Transactions of the Chinese Society of Agricultural Engineering, 2013; 29（6）: 73–81. （in Chinese）
[25] Jiang L, Zhang B Z, Han S J, Chen H, Wei Z. Upscaling evapotranspiration from the instantaneous to the daily time scale: assessing six methods including an optimized coefficient based on worldwide eddy covariance flux network. Journal of Hydrology, 2021; 596: 126135.
[26] Nassar A, Torres-Rua A, Kustas W, Alfieri J, Hipps L, Prueger J, et al. Assessing daily evapotranspiration methodologies from one-time-of-day sUAS and EC information in the GRAPEX project. Remote Sensing, 2021; 13（15）: 2887.
[27] Zhang B Z, Chen H, Xu D, Li F S. Methods to estimate daily evapotranspiration from hourly evapotranspiration. Biosystems Engineering, 2017; 153: 129–139.
[28] Liu G S, Liu Y, Xu D. Comparison of evapotranspiration temporal scaling methods based on lysimeter measurements. Journal of Remote Sensing, 2011; 15（2）: 270–280. （in Chinese）
[29] Gao X, Mei X R, Zhang J S, Cai J F, Gu F X, Hao W P, et al. Comparison of three modified models in evapotranspiration and its components over a rainfed spring maize cropland on the loess plateau, China. Agricultural and Forest Meteorology, 2023; 330: 109322.
[30] Wang J X, Cai Y H, Gao X D, Zhou Y Q, Wu P T, Zhao X N. Modeling the dynamics of evapotranspiration of wolfberry （Lycium barbarum L.） under different cultivation methods on the Tibetan Plateau. Journal of Hydrology （Amsterdam）, 2024; 639: 131537.
[31] Cui N B, He Z L, Jiang S Z, Wang M J, Yu X Y, Zhao L, et al. Inter-comparison of the Penman-Monteith type model in modeling evapotranspiration and its components in an orchard plantation of southwest China. Agricultural Water Management, 2023; 289: 108541.
[32] Wei X G, Zheng S Y, Chen D Y, Qiu L C. Estimation of evapotranspiration and its components of drip-irrigated grapevine under greenhouse conditions in cold Northeast China. Irrigation Science, 2025; 43: 755–772.
[33] Fu S N, Wei X G, Wang T L, Bai Y K. Effects of different water and fertilizer treatments on grapevine sap flow and water consumption characteristics. Scientia Horticulturae, 2022; 304: 111317.
[34] Zhao P, Li S E, Li F S, Du T S, Tong L, Kang S Z. Comparison of dual crop coefficient method and Shuttleworth–Wallace model in evapotranspiration partitioning in a vineyard of northwest China. Agricultural Water Management, 2015; 160: 41–56.
[35] Allen R G, Tasumi M, Trezza R. Satellite-based energy balance for mapping evapotranspiration with internalized calibration （metric）—method. Journal of Irrigation and Drainage Engineering, 2007; 133: 380–394.
[36] Zhang Y Q, Wang J D, Gong S H, Xu D, Sui J, Wu Z D. Analysis of water saving and yield increasing mechanism in maize field with drip irrigation under film mulching based on transpiration estimated by sap flow meter. Transactions of the Chinese Society of Agricultural Engineering, 2018; 34（21）: 89–97.
[37] Zheng S Y, Cui N B, Wei X G, Wang T L, Bai Y K, Pei D J, et al. Dynamics, plant physiological and environmental controls of energy exchange in a grapevine greenhouse in northeast China. Journal of Hydrology, 2024; 637: 131395.
[38] Pei D J, Wei X G, Bai Y K, Wang C, Liu Y, Jiang S Y. Path analysis of the main control factors of transpiration in greenhouse, drip-irrigated grapes in cold areas of northeast China. Water, 2022; 14（22）: 3764.
[39] Tasumi M, Allen R G, Trezza R, Wright J L. Satellite-based energy balance to assess within-population variance of crop coefficient curves. Journal of Irrigation and Drainage Engineering, 2005; 131: 94.
[40] McAdams W H. Heat transmission （3rd ed.）. 1954. doi: 10.1126/science.120.3128.984
[41] Zheng S Y, Wang T L, Wei X G. Estimating grapevine transpiration in greenhouse with three different methods in a Penman–Monteith model in northeast China. Irrigation Science, 2022; 40: 13–27.
[42] Wei X G, Wu L L, Ge D, Yao M Z, Bai Y K. Prediction of the maturity of greenhouse grapes based on imaging technology. Plant Phenomics, 2022; 2022: 9753427.
[43] Xia H M, Li A N, Zhao W, Bian J H. Review of temporal scale expansion for evapotranspiration retrieved by remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 2015; 31（24）: 162–173. （in Chinese）
[44] Yan H F, Zhang C, Gerrits M C, Acquah S J, Zhang H N, Wu H M, et al. Parametrization of aerodynamic and canopy resistances for methoding evapotranspiration of greenhouse cucumber. Agricultural and Forest Meteorology, 2018; 262: 370–378.
[45] Qiang X M, Cai H J, Zhou X G, Wang J. Experimental study on alfalfa’s canopy resistance and its variation under reference condition. Agricultural Research in the Arid Areas, 2009; 27: 107–111. （in Chinese）
[46] Yang D W, Chen H, Lei H M. Analysis of the diurnal pattern of evaporation fraction and its controlling factors over croplands in the northern China. Journal of Integrative Agriculture, 2013; 12（8）: 1316–1329.
[47] Hua J C, Zhu Y H, Wang Z L, Lyu H S. Spatiotemporal variation of crop coefficient of winter wheat in Huaibei Plain. Journal of Irrigation and Drainage, 2021; 40（2）: 118–124. （in Chinese）
[48] Chen Z J, Zhu Z C, Sun S J, Wang Q Y, Su T Y, Fu Y J. Estimation of daily evapotranspiration and crop coefficient of maize under mulched drip irrigation by stacking ensemble learning mode. Transactions of the Chinese Society of Agricultural Engineering, 2021; 37（5）: 95–104. （in Chinese）
[49] Higuchi A, Kondoh A, Kishi S. Relationship among the surface albedo, spectral reflectance of canopy, and evaporation fraction at grassland and paddy field. Advances in Space Research, 2000; 26（7）: 1043–1046.
[50] Kurc S A, Small E E. Dynamics of evapotranspiration in semiarid grassland and shrubland ecosystems during the summer monsoon season, central New Mexico. Water Resources Research, 2004; 40（9）: 2004WR003068.
[51] Liu X Y, Gao M Y, Zhou X Y, Xu J Z. Enforcing energy balance closure by evaporation ratio method and its effect on evapotranspiration estimation of paddy fields. Transactions of the Chinese Society of Agricultural Engineering, 2021; 37（11）: 121–130. （in Chinese）
[52] Xing Z, Qiao Z Y, Liu G. Measuring error of soil heat flux plate and applicability of Philip’s equation for modification. Acta Pedologica Sinica, 2020; 57（1）: 92–99. （in Chinese）
[53] Liu X Y, Xu J Z, Zhou X Y, Wang W G, Yang S H. Evaporation fraction and its application in estimating daily evapotranspiration of water-saving irrigated rice field. Journal of Hydrology, 2020; 584: 124317.
[54] Rong Y, Wang W S, Wu P J, Wang P, Zhang C L, Wang C Z, et al. A novel hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity. Water Resources Research, 2024; 60（9）: e2023WR036809.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Agricultural and Biological Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJABE is an international peer reviewed, open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
