## 3 Results  
### Performance Analysis on Weather Data  
A performance analysis of weather variables was conducted to better explain the performance of the target variable of this study, i.e., the reference evapotranspiration. This analysis may be also relevant for other applications, since it can guide the choice of the most suitable data source for the different set of weather variables, based on the corresponding statistical performance indicators. Figure 3 shows the statistical performance indicators of the kriging estimator computed with Equations (2-3), where \(\chi^{i}_{\text{p}}\) are the spatially interpolated observed daily weather data obtained by cross-validation and \(\chi^{i}_{\text{b}}\) are the daily weather observations. Both indicators were computed over the reference period April-September of years 2008-2018 (2013 days). In the case of air temperature, the kriging performances at each of the 18 reference AWSs were assessed by cross validation, after conditioning the kriging estimates to the observations at the remaining 76 temperature stations, i.e., the remaining 17 reference AWSs plus the additional 59 air temperature sensors, distributed across the region as depicted in Figure 11. In this way, we explicitly considered the availability of air temperature data with a much higher density than the other weather variables involved in ET\({}_{0}\) computations: a common scenario to many regions of the world. Appendix B shows the sensitivity of kriging interpolator to the density of the network for what concerns air temperature data. For all other weather variables, the kriging estimates at each AWSs where conditioned to just the remaining 17 AWSs, for which the corresponding observations were available. In Figure 3a, the BIAS values are represented according to the color scale on the side: green corresponds to BIAS close to zero. In Figure 3b, the color scale ranges from blue to red, as RMSE runs from the minimum to maximum values. Summarizing the main results depicted in Figure 3, temperature BIAS varies over the region from \(-0.8\)\({}^{\circ}\)C to \(1.6\)\({}^{\circ}\)C, with a mean RMSE of \(1.2\)\({}^{\circ}\)C and a maximum RMSE of \(2.0\)\({}^{\circ}\)C. Wind speed BIAS varies from \(-1.66\) m s\({}^{-1}\) to \(2.19\) m s\({}^{-1}\), with mean RMSE equal to \(1.16\) m s\({}^{-1}\) and maximum RMSE equal to \(2.78\) m s\({}^{-1}\). Solar radiation BIAS ranges from \(-30\) W m\({}^{-2}\) to \(20\) W m\({}^{-2}\), with mean RMSE equal to \(30\) W m\({}^{-2}\) and a maximum RMSE at \(53\) W m\({}^{-2}\). VPD BIAS varies over the region from \(-0.19\) kPa to \(0.30\) kPa, with a mean RMSE of \(1.50\) kPa and a maximum RMSE of \(2.24\) kPa. The worst performances in terms of RMSE are observed at AWSs n. 1, 9 and 13 for T, at AWSs n. 5, 10, 12 for WS, at AWSs n. 1, 10, 12 for RS and at AWSs n. 1, 6, 13 for VPD. The performances considerably change across the region due to: (i) the irregularly spaced AWS network that may penalize some of the AWS sites, such n. 13; (ii) border effects that penalize the locations at the inland boundaries (AWSs n. 2, 5, 10, 12) of the region and along coastline (AWSs n. 1, 6, 9); (iii) complex topography, especially in proximity of the sea (AWS n. 1). Figure 3: Statistical performance of the kriging estimator by cross-validation: (**a**) BIAS; (**b**) RMSE. In relative terms, considering the statistics of weather observations over the irrigation seasons of years 2008-2018, kriging spatial interpolations provided the best performances with temperature, thanks to the strong correlation with the elevation taken as auxiliary variable. However, as illustrated in Appendix B, the density of the network in the case of temperature slightly influenced the kriging performances. Figure 4 shows the performances of the two considered reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). Temperature, vapor pressure deficit and solar radiation were bias-corrected as explained in Section 2.2.