|HAN, MING - Colorado State University|
|CHAVEZ, JOSE - Colorado State University|
Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/23/2018
Publication Date: 4/4/2018
Citation: Han, M., Zhang, H., Chavez, J.L., Ma, L., Trout, T.J., Dejonge, K.C. 2018. Improved soil water deficit estimation through the integration of canopy temperature measurements into a soil water balance model. Irrigation Science. 36:187-201. https://doi.org/10.1007/s00271-018-0574-z.
Interpretive Summary: The FAO-56 (Food and Agricultural Organization) water balance model is a common approach for soil water deficit (SWD) prediction. However, the performance of the method depends on the accuracy in determining crop coefficient and total available water in the root zone (TAWr). In this paper, we propose a method to improve SWD estimation by optimizing TAWr using crop water stress index (CWSI) calculated from canopy temperature measurements. Field experiments were conducted for maize with different levels of full and regulated deficit irrigation during the 2012, 2013 and 2015 growing seasons. The optimized procedure significantly improved the performance of the soil water balance model when crops are under water stress.
Technical Abstract: Correct prediction of the dynamics of total available water in the root zone (TAWr) is critical for irrigation management as shown in the soil water balance model presented in FAO paper 56 (Allen et al., 1998). In this study, we propose a framework to improve TAWr estimation by incorporating the crop water stress index (CWSI) as calculated from remotely measured canopy temperature. This proposed method improved the performance of the FAO soil water balance model. Field experiments of irrigation management were conducted for maize during the 2012, 2013 and 2015 growing seasons near Greeley, Colorado, U.S.A. The performance of the FAO water balance model with CWSI determined TAWr was validated using soil water deficit measurement data and CWSI determined TAWr responses to irrigation treatments throughout the maize growing season. The statistical analyses between modeled and observed soil water deficit indicated that the CWSI determined TAWr significantly improved the performance of the soil water balance model, with reduction of the mean absolute error (MAE) and root mean squared error (RMSE) by 17% and 20%, respectively, compared with the standard FAO model with estimated TAWr. The proposed procedure may not work under well-watered conditions, because TAWr may not influence the crop transpiration or crop water stress in both daily and seasonal scales under such condition. The proposed procedure potentially could be applied in other ecosystems and with other crop water stress related measurements, such as surface evapotranspiration from remote sensing methodology.