## 3 Results
### Daily Time-Series: Modeled ET vs. Eddy Covariance ET  
To assess the accuracy of the models on the ground, daily time series data for 2019 were compared with eddy covariance observations from 17 sites across southern Europe and 1 site in Israel (IL-Yat). These sites encompass a diverse range of ecosystems, including evergreen needle forests, deciduous boreal forests, evergreen boreal forests, croplands, grasslands, shrublands, and savannahs covering mostly ecosystems in southern Europe. The results have shown daily ET varies across land cover types. Forests in France and Italy exhibited the highest daily ET (>6 mm/day), followed by croplands (<6 mm/day), and shrublands/savannahs (around 4 mm/day). This trend in higher ET in Mediterranean forests was consistent across some models (mainly STEPS) (Figure 3b). However, seasonal ET patterns differed among models across sites. For instance, STEPS and SCOPE overestimated summer ET in Spanish shrublands, while GLEAM and TSEB captured the right ET pattern observed there. In contrast, all models agreed on seasonal patterns in some Italian and French croplands and forests. Discrepancies in boreal forests and savannahs highlight the need for improvements in capturing the phenological response of these ecosystems (e.g., STEPS, SCOPE, TSEB). Forest and grassland sites generally showed the strongest agreement between modeled and eddy covariance (EC) ET observations (r\({}^{2}\) = 0.6-0.8), with low root mean square error (RMSE) and mean bias error (MBE) (Figure 6). IT_MBo exhibited the highest coefficient of determination (r\({}^{2}\)) scores, indicating strong agreement between modeled and ET observations in the range of 0.6-0.8. Their corresponding root mean square error (RMSE) and mean bias error (MBE) were also low (Figure 6), showing the high accuracy of these models in these two ecosystems (forests and grasslands). SCOPE and STEPS also showed good agreement in some forest, grassland, and cropland sites, while GLEAM performed best in shrublands. GLEAM, along with other sites, showed the highest congruence in shrubland sites (ES_LM1, ES_LM2, and ES_LMa), whereas only TSEB showed high congruence afterward. Five out of eighteen sites (mostly in France and Italy) showed significant agreement with both STEPS and SCOPE models (FR_HES, IT_Ren, FR_LGt, IT_MBo, and IT_Tor) (Figure 7). However, several sites exhibited lower accuracies. The Israeli forest (IL_Yat), Italian cropland (IT_BCi), and Spanish shrublands (ES_LJu and ES_Agu) showed lower agreement with modeled ET. Similarly, all models showed poor performance in evergreen boreal forest sites (ES_Cnd, FR_Pue, and IT_CP2), suggesting a need for improved model parameterization for boreal ecosystems, which are prone to wildfire in the Mediterranean region in the context of heat waves and water stress. Figure 6: Daily time series of ET derived from models [STEPS, SCOPE, TSEB, GLEAM] and their comparison with daily observations from 18 eddy covariance sites presenting four ecosystems (forest, savannah, shrubland, and cropland) of the Mediterranean countries. In summary, SCOPE demonstrates good performance with grasslands, GLEAM shows better in savannahs, and STEPS shows good performance in certain forest and crop sites. The performance of these products was checked with both unadjusted fluxes' original ET and corrected fluxes for energy balance closure (ETebc); accuracy results were the same. [PERSON] et al., 2022, also noticed, when evaluating with the original unadjusted ET flux, that GLEAM and ALEXI showed the highest coefficient of determination (\(\mathbf{r}^{2}\)), smallest percent bias (PBIAS), and best root mean square error (RMSE) with unadjusted ET [50]. Weak agreement with other EC sites may stem from seasonal fluctuations in meteorological, soil, and land conditions, as well as the limitations of some models in capturing heterogeneity in ecosystems. Some models cannot perform well in heterogeneous ecosystems. This limitation was noticed by [PERSON] et al.