## 5 Discussion
Previous studies conducted over the European Alps with the presence of dense vegetation related to forest coverage indicate that these areas need to be masked out in the processing of the SAR images to detect wet snow [34; 43]. In the case study of [28], a methodology based on machine learning was developed to detect wet snow by including not only backscattering features but also coherence and polarimetric values. In this case, the approach was extended to all types of land cover including dense forest. However, the authors showed that the results improved when dense forest was excluded from the evaluation, thus indicating that this land cover class represents a strong limitation to SAR signal [28]. In the case study in the Sierra Nevada, the presence of vegetation is limited to sparse shrubs that can provide only a negligible effect on the evaluation of the wet snow in the overall basin. In addition, other drawbacks could come from conditions such as the angle of incidence or the roughness of the snow; in our case, angles of incidence of 35\({}^{\circ}\)-36\({}^{\circ}\) are in close proximity to the 30\({}^{\circ}\) analyzed by [111], where the presence of snowpacks with high surface roughness was defined as an optimal range of values for retrieving the presence of wet snow. Likewise, in our case, the shallow snowpack and the high snow roughness after some wind episodes visible in the terrestrial imagery support the selection of this threshold. Regarding the presence of shadow, the use of VH polarization with a moderate angular dependence can support the detection of wet snow on slopes [34]. Instead, the use of ascending and descending images needs to be considered carefully as the detection of wet snow during the descending acquisitions in the early morning. In this area, such detection can lead to a reduced wet snow presence due to the refreezing process during the night. However, it could be interesting to evaluate the two available orbits, which take place during the same day at different times. In future analyses, a combination of ascending and descending can be addressed to capture differences between morning and evening snowpack. These daily changes are frequent in a Mediterranean mountain range like the Sierra Nevada, especially during spring melting [43]. The use of terrestrial photography also helped for a deep interpretation of the \(\Delta\sigma\) signal at a local scale. We were able to identify four types of LMs that triggered snowmelt onset based on the classification proposed by [3]. These LMs had different values depending on their timing throughout the year. This confirms the heterogeneity of the area, which can have different snowfalls events throughout the year, which ends up implying that we do not always have the minimum \(\Delta\sigma\) values in the long-lasting spring melting cycles. In addition, this terrestrial imagery also constitutes an independent source of information to indirectly validate the methodology proposed. The use of alternative microwave datasets could also be evaluated, even though actually there are some limitations. The datasets based on passive microwaves such as ESA CCI-SWE are of a coarse resolution (around 10 km) and cannot represent well the peculiarity of mountain areas. Indeed, these areas are masked in this type of dataset. Other datasets based on SAR Sentinel-1 are the recent snow depth product produced by [36]. Even though the approach is quite promising, these data are still under evaluation and cannot currently represent a robust reference for our case study. This understanding of wet-snow dynamics helps to comprehend the melting runoff onset maps at the catchment scale, which constitute a useful tool for water managers to know the distribution of the melting dynamics. One limitation of these maps is that they are computed at the end of the snow season. They can be used to understand snow behavior but not to foresee changes in advance. On the contrary, the relationships found in Section 4.2 go a step further, as they able to be computed in quasi real-time.