## 5 Discussion  
The capability of S-1 SAR imagery to improve the understanding of wet-snow dynamics over Mediterranean mountain areas was tested. We successfully connected melting dynamics with changes in the backscattering signal by applying the principles stated by [43]. On top of that, we applied this approach not only to the final spring melting but also along all melting cycles that commonly appear throughout the year in these types of environments. Despite the fact that SAR images have been used throughout the whole snow season for retrieving SWE [37] and snow depth [36], and for identifying rain on snow events [104], few studies are dedicated to relating melting-runoff dynamics to snowpack changes derived from the SAR signal during melting cycles [19; 35; 43; 44; 85; 105]. This may be a consequence of the limited availability of SAR images on a regular basis, before the launch of S-1, and the fact that having melting cycles throughout the season is a specific characteristic of snow in semi-arid environments. Therefore, this work can be considered a pilot application of S-1 SAR images in these types of environments. We advance in both the assessment of the applicability of S-1 on a regular basis and the characterization of wet-snow dynamics in semi-arid mountain regions, such as the Sierra Nevada. The characteristic of snow over these environments implied some technical challenges when adapting the general methodology to retrieve wet snow from SAR imagery. One of these challenges was the selection of a suitable reference image to remove common ground features that interfere with wet snow detection. The lack of a long-lasting winter accumulation made it difficult to find a full, dry snow image, as are commonly used in other studies [19; 90]. Therefore, the selection of a dry-summer image was chosen as reference. Several options were investigated following other authors [28; 106]. Among them, we observed that the soil dryness during the summer months over our target catchments made almost no differences in the use of these different approaches. Figure 10: Scatter plot between the number of wet pixels in the catchment (x-axis) and baseflow in the outlet after applying the corresponding delay for **(a)** Cycle C-3 2016–2017, 21 days delay; **(b)** Cycle C-3 2017–2018, 13 days delay; **(c)** Cycle C-3 2018–2019, 30 days delay. Another issue was the definition of the threshold in the \(\Delta\sigma\) signal to discriminate wet snow. Here, a value of \(-3.00\) dB was chosen based on a wet-dry histogram analysis in the target area. This value is lower than the originally stated by [34]. However, it agrees with other studies in alpine regions such as Austria or Argentina [80; 90]. Another example of the use of a similar threshold is found in the Argentinian Andes, a mountain range that shares more similarities with Mediterranean conditions than with Alpine snow [95]. These findings are also in line with the threshold defined by [107; 108; 109], where wet snow was successfully analyzed in very different zones such as Finland, Canada, or Pakistan. In any case, the definition of the threshold depends on several factors. Among these the land cover below the blanket and the snowpack conditions [83] and, therefore, it needs to be tailored for each specific application. This tailoring is especially relevant in our study area since; for instance, the reference images cannot be selected in winter due to the lack of long and stable conditions for the dry snowpack. Therefore, the general conditions of the algorithm changes and the selection of these thresholds need to be validated. In future steps, domain adaptation techniques could be used to transfer these threshold between scenes acquired under different conditions, thus supporting the generalization of the approach [110]. One limitation of our methodology is related to the presence of vegetation. The penetration of the C-band SAR signal is limited and can therefore hamper the detection of wet snow.