Meteorology and Atmospheric Physics

, Volume 130, Issue 2, pp 227–240 | Cite as

Rice evapotranspiration at the field and canopy scales under water-saving irrigation

  • Xiaoyin Liu
  • Junzeng Xu
  • Shihong Yang
  • Jiangang Zhang
Original Paper
  • 170 Downloads

Abstract

Evapotranspiration (ET) is an important process of land surface water and thermal cycling, with large temporal and spatial variability. To reveal the effect of water-saving irrigation (WSI) on rice ET at different spatial scales and understand the cross spatial scale difference, rice ET under WSI condition at canopy (ETCML) and field scale (ETEC) were measured simultaneously by mini-lysimeter and eddy covariance (EC) in the rice season of 2014. To overcome the shortage of energy balance deficit by EC system, and evaluate the influence of energy balance closure degree on ETEC, ETEC was corrected as \({\text{ET}}_{\text{EC}}^{*}\) by energy balance closure correction according to the evaporative fraction. Seasonal average daily ETEC, \({\text{ET}}_{\text{EC}}^{*}\) and ETCML of rice under WSI practice were estimated as 3.12, 4.03 and 4.35 mm day−1, smaller than the values reported in flooded paddy fields. Daily ETEC, \({\text{ET}}_{\text{EC}}^{*}\) and ETCML varied in a similar trends and reached the maximum in late tillering, then decreased along with the crop growth in late season. The value of ETEC was much lower than ETCML, and was frequently 1–2 h lagged behind ETCML. It indicated that the energy balance deficit resulted in underestimation of crop ET by EC system. The corrected value of \({\text{ET}}_{\text{EC}}^{*}\) matched ETCML much better than ETEC, with a smaller RMSE (0.086 mm h−1) and higher R 2 (0.843) and IOA (0.961). The time lapse between \({\text{ET}}_{\text{EC}}^{*}\) and ETCML was mostly shortened to less than 0.5 h. The multiple stepwise regression analysis indicated that net radiation (R n) is the dominant factor for rice ET, and soil moisture (θ) is another significant factor (p < 0.01) in WSI rice fields. The difference between ETCML and \({\text{ET}}_{\text{EC}}^{*}\) (\({\text{ET}}_{\text{CML}} - {\text{ET}}_{\text{EC}}^{*}\)) were significantly (p < 0.05) correlated with R n, air temperature (T a), and air vapor pressure deficit (D), and its partial correlation coefficients to R n and T a were slightly greater than D. Thus, R n, T a and D are important variables for understanding the spatial scale effect of rice ET in WSI fields, and for its cross scale conversion.

Notes

Acknowledgements

The research was financially supported by the National Natural Science Foundation of China (No. 51209066), the Fundamental Research Funds for the Central Universities (Nos. 2014B17114, 2015B34514), Innovative Young Scholar Project of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (No. 20145027912), the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Advanced Science and Technology Innovation Team in Colleges and Universities in Jiangsu Province.

References

  1. Abdullahi AS, Soom M, Amin M, Ahmad D, Shariff M, Rashid A (2013) Characterization of rice (Oryza sativa) evapotranspiration using micro paddy lysimeter and class “A” pan in tropical environments. Aust J Crop Sci 7:650–658Google Scholar
  2. Alberto MCR, Wassmann R, Hirano T, Miyata A, Hatano R, Kumar A, Padre A, Amante M (2011) Comparisons of energy balance and evapotranspiration between flooded and aerobic rice fields in the Philippines. Agric Water Manag 98(9):1417–1430CrossRefGoogle Scholar
  3. Alfieri JG, Kustas WP, Prueger JH, Hipps LE, Evett SR, Basara JB, Neale CM, French AN, Colaizzi P, Agam N (2012) On the discrepancy between eddy covariance and lysimetry-based surface flux measurements under strongly advective conditions. Adv Water Resour 50:62–78CrossRefGoogle Scholar
  4. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO, Rome, p 300Google Scholar
  5. Anderson R, Wang D (2014) Energy budget closure observed in paired Eddy covariance towers with increased and continuous daily turbulence. Agric For Meteorol 184:204–209CrossRefGoogle Scholar
  6. Anthoni PM, Freibauer A, Kolle O, Schulze E-D (2004) Winter wheat carbon exchange in Thuringia, Germany. Agric For Meteorol 121:55–67CrossRefGoogle Scholar
  7. Aubinet M, Vesala T, Papale D (2012) Eddy covariance: a practical guide to measurement and data analysis. Springer 2012:365–376Google Scholar
  8. Baldocchi DD (2003) Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob Change Biol 9(4):479–492CrossRefGoogle Scholar
  9. Baldocchi DD, Wilson KB (2001) Modelling CO2 and water vapor exchange of a temperate broadleaved forest across hourly to decadal time scales. Ecol Model 142(1/2):155–184CrossRefGoogle Scholar
  10. Belder P, Bouman B, Cabangon R, Guoan L, Quilang E, Yuanhua L, Spiertz J, Tuong T (2004) Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia. Agric Water Manag 65(3):193–210CrossRefGoogle Scholar
  11. Bormann H (2011) Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations. Clim Change 104(3–4):729–753CrossRefGoogle Scholar
  12. Cai JB, Xu D, Liu Y, Zhao NN (2010) Scaling effects and transformation of crop evapotranspiration for winter wheat after reviving. Chin J Hydraul Eng 41(7):862–869Google Scholar
  13. Chávez JL, Howell TA, Copeland KS (2009) Evaluating eddy covariance cotton ET measurements in an advective environment with large weighing lysimeters. Irrig Sci 28(1):35–50CrossRefGoogle Scholar
  14. Clark GA, Smajstrla AG, Zazueta FS (1993) Atmospheric parameters which affect evapotranspiration/IFAS extension. University of Florida CIR822, Florida, pp 1–6Google Scholar
  15. Deng H, Wang W, Cheng D (2011) Analysis on the change laws of evapotranspiration and its influencing factors in arid areas. Int Symp Water Resour Environ Prot 1:346–349Google Scholar
  16. Ding R, Kang S, Li F, Zhang Y, Tong L, Sun Q (2010) Evaluating eddy covariance method by large-scale weighing lysimeter in a maize field of northwest China. Agric Water Manag 98(1):87–95CrossRefGoogle Scholar
  17. Evett SR, Schwartz RC, Howell TA, Louis Baumhardt R, Copeland KS (2012) Can weighing lysimeter ET represent surrounding field ET well enough to test flux station measurements of daily and sub-daily ET? Adv Water Resour 50(6):79–90CrossRefGoogle Scholar
  18. 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 long term energy flux data sets. Agric For Meteorol 107(1):71–77CrossRefGoogle Scholar
  19. Foken T (2008) The energy balance closure problem: an overview. Ecol Appl 18(6):1351–1367CrossRefGoogle Scholar
  20. Foken T, Wichura B, Klemm O, Gerchau J, Winterhalter M, Weidinger T (2001) Micrometeorological measurements during the total solar eclipse of August 11, 1999. Meteorol Z 10(3):171–178CrossRefGoogle Scholar
  21. Foken T, Wimmer F, Mauder M, Thomas C, Liebethal C (2006) Some aspects of the energy balance closure problem. Atmos Chem Phys 6(2):4395–4402CrossRefGoogle Scholar
  22. Gebler S, Hendricks Franssen HJ, Pütz T, Post H, Schmidt M, Vereecken H (2014) Actual evapotranspiration and precipitation measured by lysimeters: a comparison with eddy covariance and tipping bucket. Hydrol Earth Syst Sci 19(5):2145–2161CrossRefGoogle Scholar
  23. Girona J, del Campo J, Mata M, Lopez G, Marsal J (2011) A comparative study of apple and pear tree water consumption measured with two weighing lysimeters. Irrig Sci 29(1):55–63CrossRefGoogle Scholar
  24. Gu J, Smith EA, Merritt JD (1999) Testing energy balance closure with GOES-retrieved net radiation and in situ measured eddy correlation fluxes in BOREAS. J Geophys Res (1984–2012) 104(D22):27881–27893CrossRefGoogle Scholar
  25. Hassan (2005) Estimation of rice evapotranspiration in paddy fields using remote sensing and field measurements. Universiti Putra Malaysia, MalaysiaGoogle Scholar
  26. Hatala JA, Detto M, Sonnentag O, Deverel SJ, Verfaillie J, Baldocchi DD (2012) Greenhouse gas (CO2, CH4, H2O) fluxes from drained and flooded agricultural peatlands in the sacramento-san joaquin delta. Agric Ecosyst Environ 150(6):1–18CrossRefGoogle Scholar
  27. Heitman JL, Horton R, Sauer TJ, Ren TS, Xiao X (2010) Latent heat in soil heat flux measurements. Agric For Meteorol 150(7–8):1147–1153CrossRefGoogle Scholar
  28. Heusinkveld BG, Jacobs AFG, Holtslag AAM, Berkowicz SM (2004) Surface energy balance closure in an arid region: role of soil heat flux. Agric For Meteorol 122(1–2):21–37CrossRefGoogle Scholar
  29. Hossen MS, Mano M, Miyata A, Baten MA, Hiyama T (2012) Surface energy partitioning and evapotranspiration over a double-cropping paddy field in bangladesh. Hydrol Process 26(26):1311–1320CrossRefGoogle Scholar
  30. JICA and DID (1998) The study on modernization of irrigation water management system in the Granary area of Peninsular Malaysia. Final Report, March 1998, Japan International CooperationGoogle Scholar
  31. Kato T, Kimura R, Kamichika M (2004a) Estimation of evapotranspiration, transpiration ratio and water-use efficiency from a sparse canopy using a compartment model. Agric Water Manag 65(3):173–191CrossRefGoogle Scholar
  32. Kato T, Tang Y, Gu S, Cui X, Hirota M, Du M, Li Y, Zhao X, Oikawa T (2004b) Carbon dioxide exchange between the atmosphere and an alpine meadow ecosystem on the Qinghai-Tibetan Plateau, China. Agric For Meteorol 124:121–134CrossRefGoogle Scholar
  33. Kato Y, Henry A, Fujita D, Katsura K, Kobayashi N, Serraj R (2011) Physiological characterization of introgression lines derived from an indica rice cultivar, IR64, adapted to drought and water-saving irrigation. Field Crop Res 123(2):130–138CrossRefGoogle Scholar
  34. Kessomkiat W, Hendricks Franssen HJ, Graf A, Vereecken H (2013) Estimating random errors of eddy covariance data: an extended two-tower approach. Agric For Meteorol 171–172:203–219CrossRefGoogle Scholar
  35. Kljun N, Calanca P, Rotach MP, Schmid HP (2004) A simple parameterisation for flux footprint predictions. Bound-Lay Meteorol 112:503–523CrossRefGoogle Scholar
  36. Kume T, Tanaka N, Kuraji K, Komatsu H, Yoshifuji N, Saitoh TM, Suzuki M, To Kumagai (2011) Ten-year evapotranspiration estimates in a Bornean tropical rainforest. Agric For Meteorol 151(9):1183–1192CrossRefGoogle Scholar
  37. Lage M, Bamouh A, Karrou M, El Mourid M (2003) Estimation of rice evapotranspiration using a microlysimeter technique and comparison with FAO Penman–Monteith and Pan evaporation methods under Moroccan conditions. Agronomie 23(7):625–631CrossRefGoogle Scholar
  38. Li Z, Yu G, Wen X, Zhang L, Ren C, Fu Y (2005) Energy balance closure at China FLUX sites. Sci China Ser D 48:51–62Google Scholar
  39. Li S, Kang S, Li F, Zhang L (2008) Evapotranspiration and crop coefficient of spring maize with plastic mulch using eddy covariance in northwest China. Agric Water Manag 95(11):1214–1222CrossRefGoogle Scholar
  40. Liu G, Liu Y, Cai J, Xu D (2011) Study on scale effect of farmland evapotranspiration and relationship with meteorological factors. Chin J Hydraul Eng 42(3):284–289Google Scholar
  41. Liu B, Hu JC, Zhang XS, Zhang FC (2014) Measurement simulation of hourly evapotranspiration in paddy field with different methods. Chin J Irrig Drain 33(4/5):369–373Google Scholar
  42. Mahrt L (2010) Computing turbulent fluxes near the surface: needed improvements. Agric For Meteorol 150(4):501–509CrossRefGoogle Scholar
  43. Masseroni D, Ravazzani G, Corbari C, Mancini M (2012) Turbulence integral length and footprint dimension with reference to experimental data measured over maize cultivation in Po Valley, Italy. Atmósfera 25(2):183–198Google Scholar
  44. Masseroni D, Ercolani G, Corbari C, Mancini M (2013) Accuracy of turbulent flux measurements through the use of high frequency data by eddy covariance tower: the case study of Landriano (PV), Italy. Ital J Agrometeorol 18(3):5–12Google Scholar
  45. Masseroni D, Corbari C, Mancini M (2014a) Limitations and improvements of the energy balance closure with reference to experimental data measured over a maize field. Atmósfera 27:335–352CrossRefGoogle Scholar
  46. Masseroni D, Facchi A, Romani M, Chiaradia EA, Gharsallah O, Gandolfi C (2014b) Surface energy flux measurements in a flooded and an aerobic rice field using a single eddy-covariance system. Paddy Water Environ 13(4):405–424CrossRefGoogle Scholar
  47. Massman W, Lee X (2002) Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agric For Meteorol 113(1):121–144CrossRefGoogle Scholar
  48. Matsoukas C, Benas N, Hatzianastassiou N, Pavlakis K, Kanakidou M, Vardavas I (2011) Potential evaporation trends over land between 1983–2008: driven by radiative fluxes or vapour-pressure deficit? Atmos Chem Phys 11(15):7601–7616CrossRefGoogle Scholar
  49. Mauder M, Liebethal C, Göckede M, Leps J-P, Beyrich F, Foken T (2006) Processing and quality control of flux data during LITFASS-2003. Bound-Lay Meteorol 121(1):67–88CrossRefGoogle Scholar
  50. Mauder M, Oncley SP, Vogt R, Weidinger T, Ribeiro L, Bernhofer C, Foken T, Kohsiek W, de Bruin HAR, Liu H (2007) The energy balance experiment EBEX-2000. Part II: intercomparison of eddy-covariance sensors and post-field data processing methods. Bound-Lay Meteorol 123(1):29–54CrossRefGoogle Scholar
  51. Mauder M, Cuntz M, Drüe C, Graf A (2013) A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric For Meteorol 169(4):122–135CrossRefGoogle Scholar
  52. Meyers T, Hollinger S (2004) An assessment of storage terms in the surface energy balance of maize soybean. Agric For Meteorol 125(1–2):105–115CrossRefGoogle Scholar
  53. Mikkelsen DS, De Datta SK (1991) Rice culture. Rice. Springer, Berlin, pp 103–186CrossRefGoogle Scholar
  54. Moore CJ (1986) Frequency response corrections for eddy correlation systems. Bound-Lay Meteorol 37(1–2):17–35CrossRefGoogle Scholar
  55. Ochsner TE, Sauer TJ, Horton R (2007) Soil heat storage measurements in energy balance studies. Agron J 99(1):311–319CrossRefGoogle Scholar
  56. Oncley SP, Delany AC, Horst TW, Tans PP (1993) Verification of flux measurement using relaxed eddy accumulation. Atmos Environ 27(15):2417–2426CrossRefGoogle Scholar
  57. Park H, Yamazaki T, Yamamoto K, Ohta T (2008) Tempo-spatial characteristics of energy budget and evapotranspiration in the eastern siberia. Agric For Meteorol 148(12):1990–2005CrossRefGoogle Scholar
  58. Peng SZ, Xu JZ (2009) Theory and model of high efficiency water saving irrigation in agriculture. Science Press, BeijingGoogle Scholar
  59. Peng SZ, Liu M, Yang SH, Xu JZ, Cai M, Wang YJ (2014) Analysis on evapotranspiration difference of paddy field under water-saving irrigation on field and plot scales. Trans CSAE 30:87–95Google Scholar
  60. Poblete-Echeverría C, Sepúlveda-Reyes D, Ortega-Farías S (2014) Effect of height and time lag on the estimation of sensible heat flux over a drip-irrigated vineyard using the surface renewal (SR) method across distinct phenological stages. Agric Water Manag 121:74–83CrossRefGoogle Scholar
  61. Rana G, Katerji N (2000) Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review. Eur J Agron 13(2):125–153CrossRefGoogle Scholar
  62. Reichstein M et al (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob Change Biol 11(9):1424–1439CrossRefGoogle Scholar
  63. Rothenberg SE, Feng X, Dong B, Shang L, Yin R, Yuan X (2011) Characterization of mercury species in brown and white rice (Oryza sativa L.) grown in water-saving paddies. Environ Pollut 159(5):1283–1289CrossRefGoogle Scholar
  64. Ruiz-Peñalver L, Vera-Repullo J, Jiménez-Buendía M, Guzmán I, Molina-Martínez J (2015) Development of an innovative low cost weighing lysimeter for potted plants: application in lysimetric stations. Agric Water Manag 151:103–113CrossRefGoogle Scholar
  65. Shuttleworth WJ (2007) Putting the “vap” into evaporation. Hydrol Earth Syst Sci 11(1):210–244CrossRefGoogle Scholar
  66. Timm AU, Roberti DR, Streck NA et al (2014) Energy partitioning and evapotranspiration over a rice paddy in southern brazil. J Hydrometeorol 15(5):1975–1988CrossRefGoogle Scholar
  67. Tomar VS, O’Toole JC (1980) Measurement of evapotranspiration in rice. In: World Meteorological Organization; International Rice Research Institute: Proceedings of a symposium on the agrometeorology of the rice crop. IRRI, pp 87–93Google Scholar
  68. Tsai JL, Tsuang BJ, Lu PS, Yao MH, Shen Y (2007) Surface energy components and land characteristics of a rice paddy. J Appl Meteorol Clim 46(11):1879–1900CrossRefGoogle Scholar
  69. Tyagi NK, Sharma DK, Luthra SK (2000) Determination of evapotranspiration and crop coefficients of rice and sunflower with lysimeter. Agric Water Manag 45(1):41–54CrossRefGoogle Scholar
  70. Ueyama M, Hirata R, Mano M, Hamotani K, Harazono Y, Hirano T, Miyata A, Takagi K, Takahashi Y (2012) Influences of various calculation options on heat, water and carbon fluxes determined by open-and closed-path eddy covariance methods. Tellus B 64(1):91–102CrossRefGoogle Scholar
  71. Uphoff N, Kassam A, Harwood R (2011) SRI as a methodology for raising crop and water productivity: productive adaptations in rice agronomy and irrigation water management. Paddy Water Environ 9(1):3–11CrossRefGoogle Scholar
  72. Vickers D, Mahrt L (1997) Quality control and flux sampling problems for tower and aircraft data. J Atmos Ocean Technol 14(3):512–526CrossRefGoogle Scholar
  73. Wang K, Dickinson RE (2012) A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Rev Geophys 50(2):RG2005CrossRefGoogle Scholar
  74. Wang X, Liu H, Zhang L, Zhang R (2014) Climate change trend and its effects on reference evapotranspiration at Linhe Station, Hetao Irrigation District. Water Sci Eng 7(3):250–266Google Scholar
  75. 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(2):153–168CrossRefGoogle Scholar
  76. Wilson K, Goldstein A, Falge E, Aubinet M, Baldocchi D, Berbigier P, Bernhofer C, Ceulemans R, Dolman H, Field C (2002) Energy balance closure at FLUXNET sites. Agric For Meteorol 113:223–243CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  • Xiaoyin Liu
    • 1
    • 2
  • Junzeng Xu
    • 1
    • 2
  • Shihong Yang
    • 1
    • 2
  • Jiangang Zhang
    • 3
  1. 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai UniversityNanjingPeople’s Republic of China
  2. 2.College of Water Conservancy and Hydropower EngineeringHohai UniversityNanjingPeople’s Republic of China
  3. 3.Kunshan Water Conservancy Engineering Quality and Safety Supervision and Water Technology Popularization StationKunshanPeople’s Republic of China

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