H51G-1455
Comparison of Crop Evapotranspiration Estimates from Reference Evapotranspiration Equations and a Variational Data Assimilation Approach
Comparison of Crop Evapotranspiration Estimates from Reference Evapotranspiration Equations and a Variational Data Assimilation Approach
Friday, 18 December 2015
Poster Hall (Moscone South)
Abstract:
Crop evapotranspiration (ETc) is a key component of water resources management in irrigation of farmlands as it determines the crop water consumption. Numerous methods have been used to estimate ETc for scheduling irrigation and evaluating the soil water balance. However, there is a significant difference in ETc estimates from various models, which leads to a large uncertainty in the soil water balance, crop water consumption, and irrigation scheduling. In this study, several commonly-used ETc equations (Turc, Priestley-Taylor, Hargreaves-Samani, Penman-Monteith) are compared with the variational data assimilation approach (VDA) of Bateni et al. (2013). The ETc equations initially estimate the reference evapotranspiration (ETo), which is the evapotranspiration from a healthy and actively-transpiring grass field with ample water in the soil. Thereafter, ETc is calculated by multiplying ETo by the crop coefficient (Kc), which accounts for the crop type and soil water stress. To properly apply the Kc to non-standard conditions, a daily water balance estimation for the root zone is required, which is done by two soil water budget models (Cropwat, Hydrus-1D) that compute incoming and outgoing water flows in the soil profile. In contrast to these methods that estimate ETc in two steps, the VDA approach directly predicts ETc by assimilating sequences of land surface temperature into the heat diffusion equation and thus it is expected to provide more accurate ETc estimates.All approaches are applied over three cropland sites namely, Bondville, Fermi, and Mead in the summer of 2006 and 2007. These sites are part of the AmeriFlux network and provide a wide variety of hydrological conditions. The results show that the variational data assimilation approach performs better compared to other equations.