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Optimization of canopy resistance models for estimating evapotranspiration on summer maize in a semi-arid condition of China

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Abstract

Accurate estimation of evapotranspiration (ET) is crucial for improving crop water-use efficiency and facilitating precision irrigation. The Penman–Monteith (P-M) model is a widely used approach for ET calculation, with canopy resistance (rc) acting as a critical determinant of accuracy that is greatly affected by meteorological, crop, and soil factors. This study focused on the calibration of two rc models: Jarvis (JA) and coupled resistance (CO), utilizing ant colony optimization (ACO) and the least square method (LSM). Data from an eddy covariance system and a meteorological station in the Huailai summer maize field (115°47’E, 40°20’N) for the year 2013 were employed for model parameterization. Performance evaluation of these models was conducted using ET measurements obtained through the eddy covariance system in 2014. Path analysis was conducted to elucidate the impact of net radiation (Rn), temperature (T), vapor pressure deficit (VPD), soil water content (θ) and leaf area index (LAI) on rc. Results revealed a decrease in rc during growing season, with influencing factors ordered as Rn, LAI, VPD, T, and θ. The CO model optimized by ACO exhibited superior accuracy, with R2 of 0.89. Integration of the ACO-optimized the CO model into P-M model for ET estimation also demonstrated the highest accuracy with R2 of 0.72. JA model optimized by ACO provided satisfactory results in both parameters calibration and ET estimation. Therefore, the ACO-optimized CO model was recommended as the optimal approach for rc calculation of summer maize in semi-arid area. Additionally, the JA model optimized by ACO was proposed as a credible alternative to the CO model. These findings contribute to the refinement of ET estimation methodologies, offering valuable insights for precision agricultural water management practices.

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Data availability

This study uses publicly available dataset provided by the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).

References

  • Ali HB, Bournet P-E, Cannavo P, Chantoiseau E, Sourgnes M (2016) Stomatal resistance of New Guinea impatiens pot plants. Part 1: model development for well watered plants based on design of experiments. Biosyst Eng 149:112–124

    Article  Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrig Drainage 56:300

    Google Scholar 

  • Anapalli SS, Krutz JL, Pinnamaneni SR, Reddy KN, Fisher DK (2021) Eddy covariance quantification of soybean (Glycine max L.), crop coefficients in a farmer’s field in a humid climate. Irrig Sci 39:651–669

    Article  Google Scholar 

  • Azad A, Manoochehri M, Kashi H, Farzin S, Karami H, Nourani V, Shiri J (2019) Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling. J Hydrol 571:214–224

    Article  Google Scholar 

  • Bao Y, Liu T, Duan L, Tong X, Ji H, Zhang L, Singh V (2022) A comparative study of three stomatal conductance models for estimating evapotranspiration in a dune ecosystem in a semi-arid region. Sci Total Environ 802:149937

    Article  CAS  PubMed  Google Scholar 

  • Bastidas-Obando E, Bastiaanssen W, Jarmain C (2017) Estimation of transpiration fluxes from rainfed and irrigated sugarcane in South Africa using a canopy resistance and crop coefficient model. Agric Water Manage 181:94–107

    Article  Google Scholar 

  • Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2:353–373

    Article  Google Scholar 

  • Bowen IS (1926) The ratio of heat losses by conduction and by evaporation from any water surface. Phys Rev 27:779

    Article  CAS  Google Scholar 

  • Brenner A, Incoll L (1997) The effect of clumping and stomatal response on evaporation from sparsely vegetated shrublands. Agric for Meteorol 84:187–205

    Article  Google Scholar 

  • Cabral OM, Rocha HR, Gash JH, Ligo MA, Tatsch JD, Freitas HC, Brasilio E (2012) Water use in a sugarcane plantation. GCB Bioenergy 4:555–565

    Article  Google Scholar 

  • Chávez JL, Gowda P, Howell T, Neale C, Copeland K (2009) Estimating hourly crop ET using a two-source energy balance model and multispectral airborne imagery. Irrig Sci 28:79–91

    Article  Google Scholar 

  • Chen D, Hsu K, Duan X, Wang Y, Wei X, Muhammad S (2020) Bayesian analysis of jujube canopy transpiration models: does embedding the key environmental factor in Jarvis canopy resistance sub-model always associate with improving transpiration modeling? Agric Water Manage 234:106112

    Article  Google Scholar 

  • Chen X, Yu L, Cui N, Cai H, Jiang X, Liu C, Shu Z, Wu Z (2022) Modeling maize evapotranspiration using three types of canopy resistance models coupled with single-source and dual-source hypotheses—A comparative study in a semi-humid and drought-prone region. J Hydrol 614:128638

    Article  Google Scholar 

  • DeJonge K, Ascough Ii J, Andales A, Hansen N, Garcia L, Arabi M (2012) Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation. Agric Water Manage 115:92–103

    Article  Google Scholar 

  • Falge E, Baldocchi D, Olson R, Anthoni P, Aubinet M, Bernhofer C, Burba G, Ceulemans R, Clement R, Dolman H (2001) Gap filling strategies for defensible annual sums of net ecosystem exchange. Agric for Meteorol 107:43–69

    Article  Google Scholar 

  • Gentine P, Entekhabi D, Chehbouni A, Boulet G, Duchemin B (2007) Analysis of evaporative fraction diurnal behaviour. Agric for Meteorol 143:13–29

    Article  Google Scholar 

  • Guo A, Liu S, Zhu Z, Xu Z, Xiao Q, Ju Q, Zhang Y, Yang X (2020) Impact of lake/reservoir expansion and shrinkage on energy and water vapor fluxes in the surrounding area. J. Geophys. Res. Atmos 125:e2020JD032833

    Article  Google Scholar 

  • Harris PP, Huntingford C, Cox PM, Gash JH, Malhi Y (2004) Effect of soil moisture on canopy conductance of Amazonian rainforest. Agric for Meteorol 122:215–227

    Article  Google Scholar 

  • Holland S, Heitman J, Howard A, Sauer T, Giese W, Ben-Gal A, Agam N, Kool D, Havlin J (2013) Micro-Bowen ratio system for measuring evapotranspiration in a vineyard interrow. Agric for Meteorol 177:93–100

    Article  Google Scholar 

  • Hoshika Y, Fares S, Savi F, Gruening C, Goded I, De Marco A, Sicard P, Paoletti E (2017) Stomatal conductance models for ozone risk assessment at canopy level in two Mediterranean evergreen forests. Agric for Meteorol 234:212–221

    Article  Google Scholar 

  • Inman-Bamber N, McGlinchey M (2003) Crop coefficients and water-use estimates for sugarcane based on long-term Bowen ratio energy balance measurements. Field Crops Res 83:125–138

    Article  Google Scholar 

  • Iritz Z, Lindroth A, Heikinheimo M, Grelle A, Kellner E (1999) Test of a modified Shuttleworth-Wallace estimate of boreal forest evaporation. Agric for Meteorol 98:605–619

    Article  Google Scholar 

  • Jarvis P (1976) The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philosophical transactions of the royal society of London. B, Biol Sci 273:593–610

    CAS  Google Scholar 

  • Jiang X, Kang S, Tong L, Li F (2016) Modification of evapotranspiration model based on effective resistance to estimate evapotranspiration of maize for seed production in an arid region of northwest China. J Hydrol 538:194–207

    Article  Google Scholar 

  • Kempf–Leonard K, 2004. Encyclopedia of social measurement. Academic Pr.

  • Kimball BA, Boote KJ, Hatfield JL, Ahuja LR, Stockle C, Archontoulis S, Baron C, Basso B, Bertuzzi P, Constantin J (2019) Simulation of maize evapotranspiration: an inter-comparison among 29 maize models. Agric for Meteorol 271:264–284

    Article  Google Scholar 

  • Lhomme JP, Elguero E, Chehbouni A, Boulet G (1998) Stomatal control of transpiration: examination of Monteith’s formulation of canopy resistance. Water Resour Res 34:2301–2308

    Article  CAS  Google Scholar 

  • Li S, Kang S, Zhang L, Li F, Hao X, Ortega-Farias S, Guo W, Ji S, Wang J, Jiang X (2013) Quantifying the combined effects of climatic, crop and soil factors on surface resistance in a maize field. J Hydrol 489:124–134

    Article  Google Scholar 

  • Li S, Zhang L, Kang S, Tong L, Du T, Hao X, Zhao P (2015) Comparison of several surface resistance models for estimating crop evapotranspiration over the entire growing season in arid regions. Agric for Meteorol 208:1–15

    Article  Google Scholar 

  • Li X, Kang S, Li F, Jiang X, Tong L, Ding R, Li S, Du T (2016) Applying segmented Jarvis canopy resistance into Penman–Monteith model improves the accuracy of estimated evapotranspiration in maize for seed production with film-mulching in arid area. Agric Water Manage 178:314–324

    Article  Google Scholar 

  • Liu C, Zhang X, Zhang Y (2002) Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter. Agric for Meteorol 111:109–120

    Article  Google Scholar 

  • Liu X, Xu J, Wang W, Lv Y, Li Y (2020) Modeling rice evapotranspiration under water-saving irrigation condition: Improved canopy-resistance-based. J Hydrol 590:125435

    Article  Google Scholar 

  • Liu S, Xu Z (2013) Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-10m tower, 2013). In: Xu Z (ed) Liu S. National Tibetan Plateau Data Center

    Google Scholar 

  • Liu S, Xu Z (2014) Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-10m tower, 2014). In: Xu Z (ed) Liu S. National Tibetan Plateau Data Center

    Google Scholar 

  • Medlyn BE, Duursma RA, Eamus D, Ellsworth DS, Colin Prentice I, Barton CV, Crous KY, Angelis P, Freeman M, Wingate L (2012) Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biol 18:3476

    Article  Google Scholar 

  • Monteith JL (1965) Evaporation and environment. In: Monteith JL (ed) Symposia of the society for experimental biology, vol 19. Cambridge University Press (CUP), Cambridge, pp 205–234

    Google Scholar 

  • Moorhead JE, Marek GW, Colaizzi PD, Gowda PH, Evett SR, Brauer DK, Marek TH, Porter DO (2017) Evaluation of sensible heat flux and evapotranspiration estimates using a surface layer scintillometer and a large weighing lysimeter. Sensors 17:2350

    Article  PubMed  PubMed Central  Google Scholar 

  • Nyolei D, Diels J, Mbilinyi B, Mbungu W, van Griensven A (2021) Evapotranspiration simulation from a sparsely vegetated agricultural field in a semi-arid agro-ecosystem using Penman-Monteith models. Agric for Meteorol 303:108370

    Article  Google Scholar 

  • Ortega-Farias S, Poblete-Echeverría C, Brisson N (2010) Parameterization of a two-layer model for estimating vineyard evapotranspiration using meteorological measurements. Agric for Meteorol 150:276–286

    Article  Google Scholar 

  • Panda R, Behera S, Kashyap P (2004) Effective management of irrigation water for maize under stressed conditions. Agric Water Manage 66:181–203

    Article  Google Scholar 

  • Rana G, Ferrara RM, Cona F, De Lorenzi F (2021) Actual transpiration and canopy resistance in a Mediterranean vineyard irrigated with saline water. Irrig Sci 39:469–481

    Article  Google Scholar 

  • Shankar V, Ojha C, Prasad KH (2012) Irrigation scheduling for maize and Indian-mustard based on daily crop water requirement in a semi-arid region. Int J Agricul Biosys Eng 6:77–86

    Google Scholar 

  • Shuttleworth WJ, Gurney RJ (1990) The theoretical relationship between foliage temperature and canopy resistance in sparse crops. Q J r Meteorolog Soc 116:497–519

    Article  Google Scholar 

  • Shuttleworth WJ, Wallace J (1985) Evaporation from sparse crops-an energy combination theory. Q J r Meteorolog Soc 111:839–855

    Article  Google Scholar 

  • Srivastava R, Panda R, Chakraborty A, Halder D (2018) Comparison of actual evapotranspiration of irrigated maize in a sub-humid region using four different canopy resistance based approaches. Agric Water Manage 202:156–165

    Article  Google Scholar 

  • Thom A (1972) Momentum, mass and heat exchange of vegetation. Q J r Meteorolog Soc 98:124–134

    Article  Google Scholar 

  • Twine TE, Kustas W, Norman J, Cook D, Houser P, Meyers T, Prueger J, Starks P, Wesely M (2000) Correcting eddy-covariance flux underestimates over a grassland. Agric for Meteorol 103:279–300

    Article  Google Scholar 

  • Villalobos FJ, Testi L, Orgaz F, García-Tejera O, Lopez-Bernal A, González-Dugo MV, Ballester-Lurbe C, Castel JR, Alarcón-Cabañero JJ, Nicolás-Nicolás E (2013) Modelling canopy conductance and transpiration of fruit trees in Mediterranean areas: a simplified approach. Agric for Meteorol 171:93–103

    Article  Google Scholar 

  • Wang J, Wang J, Zhao C, McGiffen M, Liu J, Wang G (2018) Assessing the performance of two models on calculating maize actual evapotranspiration in a semi-humid and drought-prone region of China. Theor Appl Climatol 131:1147–1156

    Article  Google Scholar 

  • Wei Z, Liu Y, Xu D, Cai JB (2014) Evapotranspiration and ratio of soil evaporation to evapotranspiration of winter wheat and maize. Appl Mech Mater 539:832–837

    Article  Google Scholar 

  • Willmott CJ, Robeson SM, Matsuura K (2012) A refined index of model performance. Int J Climatol 32:2088–2094

    Article  Google Scholar 

  • Wilson KB, Hanson PJ, Mulholland PJ, Baldocchi DD, Wullschleger SD (2001) A comparison of methods for determining forest evapotranspiration and its components: sap-flow, soil water budget, eddy covariance and catchment water balance. Agric for Meteorol 106:153–168

    Article  Google Scholar 

  • Wu L, Zhou H, Ma X, Fan J, Zhang F (2019) Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: application in contrasting climates of China. J Hydrol 577:123960

    Article  Google Scholar 

  • Wu Z, Cui N, Zhao L, Han L, Hu X, Cai H, Gong D, Xing L, Chen X, Zhu B (2022) Estimation of maize evapotranspiration in semi-humid regions of northern China using Penman–Monteith model and segmentally optimized Jarvis model. J Hydrol 607:127483

    Article  Google Scholar 

  • Xu S, Yu Z, Zhang K, Ji X, Yang C, Sudicky EA (2018) Simulating canopy conductance of the Haloxylon ammodendron shrubland in an arid inland river basin of northwest China. Agric for Meteorol 249:22–34

    Article  Google Scholar 

  • Yan H, Zhang C, Hiroki O (2018) Parameterization of canopy resistance for modeling the energy partitioning of a paddy rice field. Paddy Water Environ 16:109–123

    Article  Google Scholar 

  • Yan H, Acquah SJ, Zhang C, Wang G, Huang S, Zhang H, Zhao B, Wu H (2019) Energy partitioning of greenhouse cucumber based on the application of Penman-Monteith and Bulk Transfer models. Agric Water Manage 217:201–211

    Article  Google Scholar 

  • Yang J, Li B, Shiping L (2000) A large weighing lysimeter for evapotranspiration and soil-water–groundwater exchange studies. Hydrol Processes 14:1887–1897

    Article  Google Scholar 

  • Yang C, Zhu Z, Tan L, Liu S, Xu Z, Bai J, Xiao Q (2015) Analysis on evapotranspiration of maize field measured by Lysimeters in Huailai. Plateau Meteorol 34:1095–1106

    Google Scholar 

  • Yu Q, Zhang Y, Liu Y, Shi P (2004) Simulation of the stomatal conductance of winter wheat in response to light, temperature and CO2 changes. Ann Bot 93:435–441

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang B, Kang S, Li F, Zhang L (2008) Comparison of three evapotranspiration models to Bowen ratio-energy balance method for a vineyard in an arid desert region of northwest China. Agric for Meteorol 148:1629–1640

    Article  Google Scholar 

  • Zhang X, Chen S, Sun H, Shao L, Wang Y (2011) Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades. Agric Water Manage 98:1097–1104

    Article  Google Scholar 

  • Zhang B, Xu D, Liu Y, Li F, Cai J, Du L (2016) Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in north China. Agric for Meteorol 216:1–12

    Article  Google Scholar 

  • Zhao W, Liu B, Zhang Z (2010) Water requirements of maize in the middle Heihe River basin. China Agric Water Manage 97:215–223

    Article  Google Scholar 

  • Zheng J, Fan J, Zhang F, Wu L, Zou Y, Zhuang Q (2021) Estimation of rainfed maize transpiration under various mulching methods using modified Jarvis–Stewart model and hybrid support vector machine model with whale optimization algorithm. Agric Water Manage 249:106799

    Article  Google Scholar 

  • Zhou M, Ishidaira H, Hapuarachchi H, Magome J, Kiem A, Takeuchi K (2006) Estimating potential evapotranspiration using Shuttleworth-Wallace model and NOAA-AVHRR NDVI data to feed a distributed hydrological model over the Mekong River basin. J Hydrol 327:151–173

    Article  Google Scholar 

  • Zhou H, Kang S, Tong L, Ding R, Li S, Du T (2019) Improved application of the Penman-Monteith model using an enhanced Jarvis model that considers the effects of nitrogen fertilization on canopy resistance. Environ Exp Bot 159:1–12

    Article  CAS  Google Scholar 

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Acknowledgements

The dataset was provided by the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn). This work was funded by the National Natural Science Foundation of China (52109060).

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Authors and Affiliations

  1. State Key Laboratory of Hydraulics and Mountain River Engineering and College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, China

    Cun Zhan, Lu Zhao, Yaling Zhang, Xinbei Lin, Gang Zhou & Zetao Zheng

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  1. Cun Zhan
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  2. Lu Zhao
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  3. Yaling Zhang
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  4. Xinbei Lin
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  5. Gang Zhou
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Contributions

CZ, LZ, YZ, XL contributed to the methodology, data curation, validation, visualization, and writing of the original manuscript text; LZ provided support through writing review and editing, as well as funding acquisition. GZ, ZZ data curation. All authors reviewed the manuscript.

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Correspondence to Lu Zhao.

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Zhan, C., Zhao, L., Zhang, Y. et al. Optimization of canopy resistance models for estimating evapotranspiration on summer maize in a semi-arid condition of China. Irrig Sci 42, 611–626 (2024). https://doi.org/10.1007/s00271-023-00903-5

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