## 4 Discussion  
The presented study highlights the complexities and uncertainties surrounding ET estimation in the Mediterranean region. Different models rely on distinct approaches. Some, like MOD16A2 and TSEB, focus solely on the energy balance approach, while others like STEPS and GLEAM couple energy and water balance approaches. This inherent bias can lead to contrasting ET patterns even with identical meteorological data, as we used for our models. This study delves deeper into how each model's underlying assumptions influence ET calculations across diverse landscapes in the Mediterranean region. The lesson learned is that the choice of model should be driven by the specific objective. Short-term crop water requirement estimations benefit from process-based models that incorporate a wider range of factors beyond just climate and vegetation cover. Conversely, long-term and seasonal assessments might find energy balance models more suitable [52; 53]. Traditional approaches that do not consider topographic and radiative influences on ET also surpass water or energy limits due to differences in the available energy \(--\)should not be used for arid regions. As observed in the case of the TerraClimate and GLEAM estimates over Egypt, it is highly recommended to use near-surface air temperature with precipitation in dry ecosystems to derive patterns of ET [54]. ET can have a lot of uncertainties due to the misinterpretation of the models, types of input, differences in scale and temporal/spatial resolution, and data coverage. This study rightly emphasizes the need for the careful evaluation of models before applying their output in critical areas like budget estimation, water productivity assessment, water use assessments, etc. [55]. A rigorous comparison of various models' accuracy with established ground-truth data or other reliable sources is crucial. We identified the strengths and weaknesses of these models across diverse Mediterranean ecosystems to guide researchers in selecting the most appropriate tool for each specific scenario. Revising STEPS and SCOPE model parameterization is recommended for dry ecosystems, i.e., rainfed, shrulands, savannahs, etc. The comparison showed a contrasting difference in ET patterns: the annual and seasonal patterns showed that SCOPE's response was closer to potential ET across the region, and STEPS is more aligned with MOD16A2-GLEAM compared to SCOPE-TSEB. A fundamental difference in the modeled ET estimates stems from their underlying approach: some are driven solely by the energy balance approach (such as MOD16A2 and TSEB) [33; 43], radiative forcing and energy balancing (SCOPE), while others couple energy and water balance approaches (like STEPS and GLEAM). This disparity might have led to different patterns over southern Europe and MENA, and even the use of common meteorological forcings has no effect. Surface condition parameters (e.g., LST, LAI, LULC) have significantly influenced ET calculations. LST and LAI, being major components of these models, are not temporally consistent with climatic parameters, and this is another limitation. This might have also influenced the accuracy of our daily ET. Similar behavior is observed in the comparison of Landflux, GLDAS, GLEAM, and ERA-Interim, which all failed to capture evaporative flux over irrigated croplands, so high-resolution LST and LAI forcings were suggested [56]. In addition, some recent products, i.e., CSIRO MODIS ReScaled EvapoTranspiration (CMRSET), SSEBop, and WaPOR are found as less biased than MOD16A2 and GLEAM with an improved land surface parameterization for dry ecosystems. Integrating fractional vegetation cover and climatic water deficit into algorithms can also reduce model uncertainties [57; 58]. The choice of variables in each model affects how they capture and represent the complex processes involved in ET, which affects the accuracy of ET estimates [59].