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One-step approach for estimating maize actual water use: part II. Lysimeter evaluation of variable surface resistance models

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Abstract

This study evaluated maize variable bulk surface resistance (rs, s m−1) models developed using field/environmental data from non-irrigated fields in a humid climate. The different rs models were reported in the companion paper. Surface resistance values derived from the application of the new models were inserted in the 1965 Penman–Monteith ET equation to calculate actual maize evapotranspiration (ETa). This is the so-called one-step approach. The evaluation was performed with maize water use data measured with a large weighing lysimeter located in the middle of an irrigated maize field (semi-arid climate) near Bushland, Texas, USA. The evaluation was performed for different time scales (semi-hourly to daily) and the rs models bias (MBE) and root mean square errors (RMSE) were determined. Using daytime 30-min rs models in maize ETa (mm 30-min−1) estimation resulted with the lowest relative error of 15.4%. While daytime average rs models developed from daytime average explanatory variables resulted with the lowest relative error of 20.9% in 30-min maize ETa estimation. When rs models from 30-min and daylight average explanatory variables were used to obtain cumulative daytime maize ETa estimations, resulting errors were lower than for semi-hourly time step. In addition, when daytime 30-min and average rs models were applied in conjunction with a fixed rs nighttime value to obtain daily maize ETa, results were similar than those for the daytime time step. Furthermore, another evaluation incorporated rs values obtained adopted ranges of crop evaporative fraction (EF). When this EF-based rs values were used to estimate maize (ETa, mm 30-min−1) the error found was 5.9 ± 21.7%. This result seems high; however, when the EF range-based rs values were applied in the estimation of maize cumulative daytime ETa, results indicated an error of 5.9 ± 11.9%. In this last case, the relative error was much lower than for 30-min ETa estimations. Therefore, it is concluded that some maize rs models, reported in the companion paper, performed well when evaluated in a different climate and agronomic practices and are suitable for the estimation of ETa using the 1965 Penman–Monteith ET equation. In particular daytime 30-min rs models applied to 30-min intervals of ETa estimation and then accumulated over the day performed better than the others rs models and time steps studied.

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References

  • Allen RG, Jensen ME, Wright JL, Burman RD (1989) Operational estimates of reference evapotranspiration. Agron J 81:650–662

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Andales AA, Straw D, Marek TH, Simmons LH, Bartolo ME, Ley TW (2018) Design and construction of a precision weighing lysimeter in southeast Colorado. Trans ASABE 61(2):509–521

    Article  Google Scholar 

  • Anderson MC, Kustas WP, Alfieri JG, Gao F, Hain C, Prueger JH, Evett S, Colaizzi P, Howell T, Chávez JL (2012) Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign. Adv Water Resour 50:162–177

    Article  Google Scholar 

  • Berengena J, Gavilán P (2005) Reference evapotranspiration estimation in a highly advective semiarid environment. J Irrig Drain Eng 131(2):147–163

    Article  Google Scholar 

  • Bisquert M, Sánchez JM, López-Urrea R, Caselles V (2016) Estimating high resolution evapotranspiration from disaggregated thermal images. Remote Sens Environ 187:423–433

    Article  Google Scholar 

  • Blonquist JM Jr, Norman JM, Bugbee B (2009) Automated measurement of canopy stomatal conductance based on infrared temperature. Agric For Meteorol 149:1931–1945

    Article  Google Scholar 

  • Chávez JL, Howell TA, Copeland KS (2009) Evaluating eddy covariance cotton ET measurements in an advective environment with large weighing lysimeters. Irrig Sci 28:35–50

    Article  Google Scholar 

  • Chávez JL, López-Urrea R (2018) One-step Approach for Estimating Maize Actual Water Use: Part I. Modeling a Variable Surface Resistance, Irrig Sci (this issue)

  • Colaizzi PD, Kustas WP, Anderson MC, Agam N, Tolk JA, Evett SR, Howell TA, Gowda PH, O´Shaughnessy SA (2012) Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures. Adv Water Resour 50:134–151

    Article  Google Scholar 

  • Doorenbos J, Pruitt WO (1975) Crop water requirements. FAO Irrigation and Drainage, Paper 24. FAO, Rome

    Google Scholar 

  • Evett SR, Schwartz RC, Howell TA, Baumhardt RL, 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 50:79–90

    Article  Google Scholar 

  • FAOSTAT (2016) FAO Statistical Database (online), Consultation. http://www.fao.org/faostat/en/#data/QC. Accessed 14 May 2018

  • Gao Y, Duan A, Qiu X, Li X, Pauline U, Sun J, Wang H (2013) Modeling evapotranspiration in maize/soybean strip intercropping system with the evaporation and radiation interception by neighboring species model. Agric Water Manag 128:110–119

    Article  Google Scholar 

  • Gavilán P, Berengena J (2007) Accuracy of the Bowen ratio-energy balance method for measuring latent heat flux in a semiarid advective environment. Irrig Sci 25:127–140

    Article  Google Scholar 

  • Heng LK, Hsiao T, Evett S, Howell T, Steduto P (2009) Validating the FAO aquacrop model for irrigated and water deficient field maize. Agron J 101:488–498

    Article  Google Scholar 

  • Hirschi M, Michel D, Lehner I, Seneviratne SI (2017) Asite-level comparison of lysimeter and eddy covariance flux measurements of evapotranspiration. Hydrol Earth Syst Sci 21:1809–1825

    Article  Google Scholar 

  • Howell TA, Schneider AD, Jensen ME (1991) History of Lysimeter Design and Use for Evapotranspiration Measurements, in: Allen, R.G., Howell, T.A., Pruitt, W.O., Walter, I.A., Jensen, M.E. (Eds.), Lysimeters for Evapotranspiration and Environmental Measurements: Proc. Intl. Symp. Lysimetry, ASCE, Reston, VA, pp. 1–9

  • Howell TA, Schneider AD, Dusek DA, Marek TH, Steiner JL (1995) Calibration and scale performance of bushland weighing lysimeters. Trans ASAE 38(4):1019–1024

    Article  Google Scholar 

  • Irmak S, Mutiibwa D (2009) On the dynamics of stomatal resistance: Relationships between stomatal behavior and micrometeorological variables and performance of Jarvis-type parameterization. Trans ASABE 52(6):1923–1939

    Article  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 

  • Kullberg EG, DeJong KC, Chávez JL (2017) Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients. Agric Water Manag 179:64–73

    Article  Google Scholar 

  • Kustas WP, Hatfield JL, Prueger JH (2005) The Soil Moisture–Atmosphere Coupling Experiment (SMACEX): Background, hydrometeorological conditions, and preliminary findings. J Hydrometeorol 6:791–804

    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 Manag 178:314–324

    Article  Google Scholar 

  • Liengme B (2015) A Guide to Microsoft Excel 2013 for Scientists and Engineers, 1st edn. Academic Press, Cambridge, 382 pp

    Google Scholar 

  • López-Urrea R, Martín de Santa Olalla F, Fabeiro C, Moratalla A (2006) Testing evapotranspiration equations using lysimeter observations in a semiarid climate. Agric Water Manag 85:15–26

    Article  Google Scholar 

  • López-Urrea R, Montoro A, López-Fuster P, Fereres E (2009) Evapotranspiration and responses to irrigation of broccoli. Agric Water Manag 96:1155–1161

    Article  Google Scholar 

  • López-Urrea R, Montoro A, Trout TJ (2014) Consumptive water use and crop coefficients of irrigated sunflower. Irrig Sci 32:99–109

    Article  Google Scholar 

  • López-Urrea R, Martínez-Molina L, de la Cruz F, Montoro A, González-Piqueras J, Odi-Lara M, Sánchez JM (2016) Evapotranspiration and crop coefficients of irrigated biomass sorghum for energy production. Irrig Sci 34:287–296

    Article  Google Scholar 

  • Mcebisi M, Chávez JL, Allan A (2015) SEBAL-A: A remote sensing ET algorithm that accounts for advection with limited data. Part I: Development and validation. Remote Sens 7(11):15046–15067

    Article  Google Scholar 

  • Monteith JL (1965) Evaporation and environment. In: Fogg G. E. (Ed.), Symposium of the Society for Experimental Biology, The State and movement of water in living organisms. Academic Press, Inc., NY. 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(10):2316–2350

    Article  CAS  Google Scholar 

  • Papaioannou G, Papanikolaou N, Retails D (1993) Relationships of photosynthetically active radiation and shorwave irradiance. Theor Appl Climatol 48:23–27

    Article  Google Scholar 

  • Prueger JH, Hatfield JL, Aase JK, Pikul JL Jr (1997) Bowen-ratio comparisons with lysimeter evapotranspiration. Agron J 89:730–736

    Article  Google Scholar 

  • Pruitt WO, Angus DE (1960) Large weighing lysimeter for measuring evapotranspiration. Trans ASAE 3(2):13–15

    Article  Google Scholar 

  • Rambikur EH, Chávez JL (2014) Assessing inter-sensor variability and sensible heat flux derivation accuracy for a large aperture scintillometer. Sensors 14(2):2150–2170

    Article  PubMed  Google Scholar 

  • Ritchie JT, Burnett E (1968) A precision weighing lysimeter for row crop water use studies. Agron J 60(5):545–549

    Article  Google Scholar 

  • Sánchez JM, Kustas WP, Caselles V, Anderson M (2008) Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations. Remote Sens Environ 112:1130–1143

    Article  Google Scholar 

  • Sánchez JM, López-Urrea R, Doña C, Caselles V, González-Piqueras J, Niclòs R (2015) Modeling evapotranspiration in a spring wheat from thermal radiometry: crop coefficients and E/T partitioning. Irrig Sci 33:399–410

    Article  Google Scholar 

  • Shuttleworth WJ (2006) Towards one-step estimation of crop water requirement. Trans ASABE 49(4):925–935

    Article  Google Scholar 

  • Shuttleworth WJ, Wallace JS (2009) Calculating the water requirements of irrigated crops in Australia using the Matt-Shuttleworth approach. Trans ASABE 52(6):1895–1906

    Article  Google Scholar 

  • Srivastavaa RK, Panda RK, 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 Manag 202:156–165

    Article  Google Scholar 

  • Subedi A, Chávez JL, Andales AA (2017) ASCE-EWRI standardized Penman-Monteith evapotranspiration (ET) equation performance in southeastern Colorado. Agric Water Manag 179:74–80

    Article  Google Scholar 

  • Ventura F, Spano D, Duce P, Snyder RL (1999) An evaluation of common evapotranspiration equations. Irrig Sci 18:163–170

    Article  Google Scholar 

  • Williams LE, Phene CJ, Grimes DW, Trout TJ (2003) Water use of young Thompson seedless grapevines in California. Irrig Sci 22:1–9

    Google Scholar 

  • Wilmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63:1309–1313

    Article  Google Scholar 

  • Yang Y, Su H, Zhang R, Wu J, Qi J (2013) A new evapotranspiration model accounting for advection and its validation during SMEX02. Adv Meteorol 389568:13 p

    Google Scholar 

  • Zhao WZ, Ji XB, Kang ES, Zhang ZH, Jin BW (2010) Evaluation of Penman-Monteith model applied to a maize field in the arid area of northwest China. Hydrol Earth Syst Sci 14:1353–1364

    Article  Google Scholar 

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Acknowledgements

Lysimeter data from the USDA ARS CPRL in Bushland, Texas, USA is greatly appreciated. In particular we are thankful to USDA scientists Dr. Terry Howell (retired), Dr. Steve Evett, Dr. Prasanna Gowda, and Karen Copeland for sharing the lysimeter data. Financial support (Project # COL00688) received from Colorado Agricultural Experiment Station and the USDA National Institute for Food and Agriculture (NIFA) is greatly appreciated. R. López-Urrea acknowledges the financial support received from the Spanish Ministry of Education, Culture and Sports throughout the José Castillejo program (reference JC2015-00110) and from the Spanish Ministry of Economy and Competitiveness (Project AGL2014-54201-C4-4-R).

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Correspondence to J. L. Chávez.

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Communicated by R. G. Anderson.

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Appendices

Appendix

See Tables 6 and 7.

Table 6 List of acceptable “rs” models using the 30 min average diurnal data
Table 7 List of acceptable “rs” models using the average diurnal data

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López-Urrea, R., Chávez, J.L. One-step approach for estimating maize actual water use: part II. Lysimeter evaluation of variable surface resistance models. Irrig Sci 37, 139–150 (2019). https://doi.org/10.1007/s00271-018-0607-7

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