Assessment of reference evapotranspiration using remote sensing and forecasting tools under semi-arid conditions

Abstract

Reference evapotranspiration (ETo) assessment at regional scale is a challenge to agricultural and hydrological research requiring the characterization of vast areas to obtain accurate meteorological variables. To account for this, a new approach considering a modified version of the Makkink equation and based on a combination of remotely sensed solar radiation (Rs) considering the MSG satellite, and numerical weather forecast of near surface air temperature (T2m), was developed. The new approach is referred as Makkink-Advection equation (MAK-Adv).Once Rs and T2m were validated for the semiarid conditions registered in Southern Spain with very satisfactory results (RMSE=1.6MJm−2d−1 and 1.4°C respectively, with linear regressions with slope=0.99 and R2=0.97), the MAK-Adv approach was calibrated and validated with ETo measurements from a weighing lysimeter under near-reference conditions in Southern Spain. The new approach has provided accurate daily ETo estimations compared with lysimeter data (RMSE=0.50mmd−1, RE=12.3% and MAE=0.39mmd−1), and remains accurate during summer time (RMSE=0.62mmd−1 and RE=9%), the critical period when accurate ETo data are essential for a correct agricultural water management.Once calibrated and validated, and thanks to the use of remotely sensed data, the MAK-Adv approach allowed the ETo assessment at regional scale. For a semi-arid region located in southern Spain with an area of 87,300km2, a detailed spatial ETo assessment was performed, determining ETo values for the totality of the area with a resolution of about 9km2 allowing to evaluate the spatial and temporal variability at basin/region scale. This new approach gets over the serious limitations for a correct ETo assessment when traditional methodologies are not able to provide these data for vast areas, even with the modern weather station networks. Thus, MAK-Adv approach is especially useful for those areas with missing or inaccurate meteorological in situ observations.