## 3 Results
### Performance Analysis on Monthly Basis  
This section presents ET\({}_{0}\) estimate performances on a monthly basis. The statistical indicators of Equations (4-5) are averaged for each month and normalized by the corresponding monthly mean of the daily ET\({}_{0}\) observed at each AWSs (Table 3). Figure 6 shows the nBIAS and nRMSE of ET\({}_{0}\) computed by using weather data from kriging interpolation of ground sensor data. Figure 5: (**a**) BIAS and (**b**) RMSE of ET\({}_{0}\) computed with weather data from kriging interpolation, UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). Spread of the box plots refers to the variability of the statistical indices among the 18 AWS sites. Circles indicate the mean values. Central lines in the box indicate the median, the box edges are drawn at the 25 th (p\({}_{25}\)) and 75 th (p\({}_{75}\)) percentiles. Whiskers extend to the most extreme data values, excluding the outliers. Values are considered as outliers when larger than p\({}_{75}\) + 1.5(p\({}_{75}\)–p\({}_{25}\)) or smaller than p\({}_{25}\) - 1.5(p\({}_{75}\)–p\({}_{25}\)). The monthly statistical indicators are represented according to the color scale on the bottom. The markers' size describes to the expected maximum interannual variability of the statistics, defined by p\({}_{75}\) + 1.5(p\({}_{75}\)-p\({}_{25}\)), where p\({}_{25}\) and p\({}_{75}\) are, respectively the 25 th and 75 th percentiles of the monthly values in the years 2008-2018. The interannual variability of nRMSE is greater than that of the nBIAS. The months characterized by the worst performances in terms of nRMSE are April and September. June is the month that presents the best relative performances. However, the accuracy and precision of the estimates are very high: nBIAS ranges between \(-\)13% and 14% of the corresponding observed ET\({}_{0}\) monthly mean; nRMSE ranges between 8% and 21% of the corresponding observed ET\({}_{0}\) monthly mean, with an average nRMSE value of 14%. As for Figures 4 and 7 presents the differences between ET\({}_{0}\) estimate performances of each reanalysis dataset and the corresponding performances of the kriging estimator (given by Figure 6). Figure 7a shows the differences between absolute values of nBIAS, while Figure 7b between nRMSE: positive values (red color on the color bar) mean that the kriging estimator is performing better than reanalysis and negative values mean the opposite. In most cases, kriging estimator outperforms reanalysis data, but in the worst cases the increase of nBIAS and nRMSE is less than 15% for UMS and less than 10% for E5L. Figure 6: Statistical performance of the kriging estimator by cross-validation: **(a)** nBIAS; **(b)** nRMSE. The indicators are normalized by the observed monthly mean value of ET\({}_{0}\) at the automatic weather station (AWS) site of interest (Table 2) to allow comparison. Marker size is proportional to the value P\({}_{75}\) + 1.5(p\({}_{75}\)-p\({}_{25}\)) where the 25 th (p\({}_{25}\)) and 75 th (p\({}_{75}\)) percentiles are related to the interannual variability of the statistics. Figure 7: Differences between statistical indices of reanalysis data (UMS and E5L) and kriging (Figure 6): (a) absolute nBIAS; **(b)** nRMSE. Indices are normalized by the observed monthly mean value of ET\({}_{0}\) at the AWSs site of interest to allow comparison. E5L outperforms UMS in assessing reference evapotranspiration. However, both datasets show very high performances. For UMS, nBIAS ranges between \(-28\%\) and \(23\%\) of the corresponding observed ET\({}_{0}\) monthly mean and nRMSE ranges between \(11\%\) and \(32\%\) of the corresponding observed ET\({}_{0}\)monthly mean, with an average value of \(22\%\). For E5L, nBIAS ranges between \(-12\%\) and \(8\%\) of the corresponding observed ET\({}_{0}\) monthly mean and nRMSE ranges between \(7\%\) and \(27\%\) of the corresponding observed ET\({}_{0}\) monthly mean, with an average value of \(17\%\).