Local-scale monitoring of evapotranspiration based on downscaled GRACE observations and remotely sensed data: An application of terrestrial water balance approach

Evapotranspiration (ET), as one of the most central parameters to the climate systems, plays a predominant role in the water, energy, carbon cycles, and energy–moisture exchanges between the earth and the atmosphere. Since the in-situ estimation of ET is challenging, the remote sensing-based techniques are deemed a reliable surrogate for the traditional ET estimation methods. The Gravity Recovery And Climate Experiment (GRACE) mission turned out to perform well in detecting the variations of ET over large basins. However, the subbasin-wise variations of ET based on the GRACE mission remain challenging due to its coarse resolution. In this study, the GRACE/GRACE-FO observations were downscaled based on Random Forest Machine Learning (RFML) algorithm and were integrated with fine-resolution precipitation and runoff data within the framework of the terrestrial water balance approach to derive small-scale variations of ET over Kizilirmak Basin (KB) in Türkiye. The results were compared to the fine-resolution ET products of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and Noah model and revealed acceptable precision of the methodology for the subbasin-wise application. The findings suggest that excluding the Lower Kizlirmak Basin (LKB), the water balance-based ET manifests a very high agreement with the MODIS- and Noah-derived ET over all the subbasins of the KB. The impacts of the main ET driving parameters such as temperature, water availability, solar radiation, surface albedo, and vegetation status were further investigated in terms of linear correlations. It is found that there is a high association between the monthly variations of ET and its conditioning factors over the KB, especially between the water availability and ET