## 4 Results
### Time Series Analysis  
Figure 6 displays the time series obtained from FFSAR measurements during the 2-year study period, alongside the corresponding in situ values and the satellite's window position over each target. Most of the missing points in some of the series correspond to passes where the in situ WL\({}_{t}\) fell outside the satellite's measuring window. As some windows are not optimally set over some targets, this results in the loss of 15 passes for the Yesa Reservoir and 23 for the Itoiz Reservoir. For Itoiz, an additional 12 passes of UFSAR-derived measurements were lost due to the narrowness of the transect, though they were properly sampled with FFSAR. Furthermore, for the results of Mequinensa-Caspe, only the passes with in situ WL\({}_{t}\)s above 108 m were considered, as, otherwise, the water extent of the reservoir no longer reaches the satellite crossing, transforming the area into a narrow dry river with complex topology (see Figure 1, bottom panel). Consequently, there is a loss of 27 passes for Mequinensa-Caspe. The FFSAR-derived WL\({}_{a}\) errors from each of these passes are depicted in Figure 7, which also reveals the presence of four outliers in Mequinensa-North and one outlier in the Itoiz Reservoir. Figure 6: Time series obtained during the study period. The dots represent the WL\({}_{a}\)s obtained from FFSAR and UFSAR measurements, the dashed lines represent the values from the in situ station, and the gray shaded area represents the satellite’s window over the corresponding target. The horizontal dotted line in Mequinensa-Caspe represents the WL below which the reservoir extent is reduced enough so that the satellite track no longer crosses it. The FFSAR and UFSAR statistical results for all studied reservoirs are presented in Table 2. It can be appreciated that FFSAR WL\({}_{a}\)s exhibit a notable reduction of approximately one order of magnitude in almost all statistical indicators compared to UFSAR-derived WL\({}_{a}\)s. Among these indicators, RMSE, bias, and SDE are particularly sensitive to outliers. For instance, in Mequinensa-North, the presence of three outliers in FFSAR-derived WL\({}_{a}\)s causes the RMSE to increase nearly to the level of UFSAR-derived WL\({}_{a}\)s, and the SDE to reach an even higher level (see Figure 7, third panel). Notably, these errors may not be  
Figure 7: Time series of FFSAR and UFSAR aggregated water level errors (WL\({}_{a}\)Es) obtained during the study period. Only passes with a WL\({}_{t}\) inside the satellite’s window have been included. For Mequinensa-Caspe, only passes with transects within the reservoir have been included. discernible in the corresponding WL\({}_{a}\) time series depicted in Figure 6 due to differences in magnitude relative to the reservoir's WL\({}_{t}\) variation. Conversely, MAD is less sensitive to outliers. Table 2 illustrates that, in terms of MAD, FFSAR demonstrates an average accuracy of 4.2 cm and an average relative reduction in error of 85% compared to UFSAR measurements combined with the OCOG retracker, and 88% relative to the ocean retracker. If the aforementioned outliers in Mequinensa-North and Itoiz are disregarded, the resulting overall SDE is 4.4 cm, just around a factor 4 higher than expected precision determined over point targets as discussed in Section 3.6. In terms of the difference in error between the OCOG and the ocean retracker, the OCOG retracker consistently outperforms the ocean retracker across all targets. Specifically, the OCOG retracker yields an average MAD of 28.9 cm, whereas the ocean retracker resulted in an average MAD of 36.2 cm. Additionally, a notable observation was that the WL\({}_{a}\)s derived from UFSAR data generally exhibited a bias toward higher elevations compared to the in situ measurements.