Abstract

Evapotranspiration (ET) is a critical component of the hydrological cycle, and it links water and energy budgets in the form of latent heat (LvE) released into the atmosphere. However, ET is difficult to measure and is not always well described in arid regions. Thus, novel techniques are required for its accurate measurement. Scintillometers are an interesting alternative for traditional methods, such as Eddy Covariance systems (EC). Scintillometer studies have reported good results, but their signals can present unwanted contributions that result in incorrect heat fluxes. In this study, scintillometer data showed unrealistic heat flux values, and thus, the data were reprocessed through spectral analysis to eliminate unwanted contributions from electronic noise, absorption, and tripod vibrations using a new proposed data cleaning method. After performing the spectral cleaning method, scintillometer-based heat fluxes were calculated using several methods: (i) the standalone LAS method, (ii) Hill model, (iii) Ludi et al. model, and (iv) a hybrid model between Hill and Ludi et al. Furthermore, a Monin-Obukhov similarity theory (MOST) analysis was performed to evaluate the fluxes' sensitivity to the choice of the similarity functions. Corrected sensible heat flux (H) estimations agreed well with those obtained with an EC system. However, considerable differences were found for LvE (and, consequently, ET). The Ludi et al. model LvE estimates were closer to those obtained with the EC system, overestimating it by 14%, with a correlation slope of 1.07, R-2 = 0.91, and a Nash-Sutcliffe efficiency of 0.90. Furthermore, it was found that using different Monin-Obukhov similarity functions resulted in more than +/- 12% of difference on the estimated LvE. For future works, it is strongly recommended to apply the proposed spectral cleaning method as it greatly improves scintillometer data.