## 5 Discussion  
### Validation of the PT-JPL-Thermal ET  
The \(ET\) derived from the PT-JPL-thermal showed, for all the estimators proposed (Table 3), a better performance than the estimated \(ET\) from MOD16 in which the high bias obtained indicates a significant underestimation of \(ET\) rates. Moreover, significant differences were observed in the \(ET\) dynamics between models with non-coincident timing of the peak annual values.  
The PT-JPL-thermal \(ET\) dynamics at a monthly scale (Table 3) showed satisfactory agreement with WATEN \(ET\) and better results with the WATEN one-month lag. WATEN is a finite differences model run in one-month discrete time intervals whose inputs data may induce a lag in its calculation. Additionally, due to in-field irrigation data not available for modeling, WATEN uses water transfer data to the reservoir that stores the water to be applied as irrigation in the BXII [65]. All this introduces uncertainty regarding the time scale. The amount of variance and the \(e_{2}\) coefficient indicate that the PT-JPL-thermal model is able to accurately capture the \(ET\) dynamics. However, MOD16 \(e_{2}\) negative values suggest a high variance in the residuals which, together with the high values of the calculated errors, indicate a low capability for reproducing the WATEN \(ET\) dynamics. In addition, the Theil's distribution components of the \(ET\) estimations (Table 4) obtained with the PT-JPL-thermal were close to ideal. With a low bias, there is well-represented variance and non-systematic residual errors in the \(ET\) estimations. For MOD16, the Theil's inequality components distribution showed a poor model performance under our Mediterranean conditions.  
When considering the average seasonality in the \(ET\) dynamics (Table 3), the PT-JPL-thermal estimates showed better results than the \(ET\) dynamics at a monthly scale with a higher amount of variance explained and smaller errors. On the other hand, the WATEN \(ET\) seasonality was not accurately represented by MOD16, which showed a similar \(e_{2}\) coefficient and error values than those obtained at a monthly scale over the whole study period.  
Since potential \(ET\) values estimated by Penman-Monteith (used in MOD16) and Priestley-Taylor (used in the PT-JPL-thermal) were similar (Figure 4), it could be assumed that differences between meteorological data sets used in the two RS models were negligible. This points out to the downscaling process from potential to actual \(ET\) as the source of disagreement, i.e., the use of the \(VPD\) (by MOD16) instead of the thermal approach (by the PT-JPL-thermal) to downscale to \(ET\). With regard to semiarid irrigated areas, high values of \(VPD\) may reflect the surrounding arid conditions but do not take into account the real values of water availability [42]. Similar conclusions were achieved by [PERSON] et al. [71] when comparing MODIS \(GPP\) with EC-derived \(GPP\) showing the importance of water availability apart from \(VPD\). In addition, MOD16 considers the surface of the soil covered by water to be zero when \(RH\) is less than 70%. \(RH\) records were below 70% over the irrigation season from 2003 to 2012 at the agro-climatic station Lebrija I and, in consequence, MOD16 assumed the wet surface fraction as non-existent, which led to an underestimation of \(ET\). Likewise, [PERSON] et al. [38] found the evaporative fraction from the irrigated cotton (the major cultivated crop in the mixed-irrigation BXII) to be underestimated in 67%. The significant correlation between the PT-JPL-thermal and WATEN \(ET\) estimations proved that the use of the thermal inertia concept as a proxy indicator of moisture status together with the re-formulation of the outgoing longwave radiation makes it possible to reproduce the dynamics of \(ET\) under our Mediterranean conditions in a more accurate manner than using the \(VPD\) alone.