Assessing geeSEBAL automated calibration and meteorological reanalysis uncertainties to estimate evapotranspiration in subtropical humid climates

Abstract

The application of energy balance models for estimation of evapotranspiration (ET) still has challenges to be addressed for large scale applications. The algorithm for automated calibration using inverse modeling at extreme conditions (CIMEC) is based on the definition of endmembers that represent the extreme conditions of the ET spectrum, between hot (dry and sparse vegetation) and cold (wet and dense vegetation) surfaces, with pre-defined quantiles for the endmember selection. The main goal was to assess geeSEBAL algorithm uncertainties related to the (i) automated calibration, including the use of additional filters (land cover, homogeneity, and domain area) and (ii) the use of a global climate grid as input data. Based on a sensitivity analysis, we defined new set of quantiles to increase the accuracy of ET estimates in subtropical humid climates, since the default quantiles were adjusted to semiarid climates with dry summers. To validate our ET estimates we used eddy covariance measurements from five flux towers located in the South of Brazil. Processing 132 Landsat cloud free images and using adjusted quantiles, we found a root mean square error (RMSE) of 0.91 mm d − 1 and a coefficient of determination (R²) of 0.82 with geeSEBAL driven by meteorological measurements. Using the pre-defined quantiles, we found an RMSE of 1.16 mm d − 1 (27% higher) and R² of 0.75. The upscaling instantaneous ET to daily ET resulted in an underestimation of the daily ET using the pre-defined quantiles, while the optimized quantiles corrected the daily estimates. Furthermore, our results suggested a low sensitivity of geeSEBAL to meteorological inputs, since RMSE slightly increased to 1.04 mm d − 1 (14.3% higher) and R² decreased to 0.76 (8.5% smaller) when driven by global climate data. For data scarce areas, geeSEBAL is a feasible alternative for cropland ET estimation and water resources management in subtropical humid climates.