###### Abstract  
Monitoring snowmelt dynamics in mountains is crucial to understand water releases downstream. Sentinel-1 (S-1) synthetic-aperture radar (SAR) has become one of the most widely used techniques to achieve this aim due to its high frequency of acquisitions and all-weather capability. This work aims to understand the possibilities of S-1 SAR imagery to capture snowmelt dynamics and related changes in streamflow response in semi-arid mountains. The results proved that S-1 SAR imagery was able not only to capture the final spring melting but also all melting cycles that commonly appear throughout the year in these types of environments. The general change detection approach to identify wet snow was adapted for these regions using as reference the average S-1 SAR image from the previous summer, and a threshold of \(-3.00\) dB, which has been assessed using Landsat images as reference dataset obtaining a general accuracy of 0.79. In addition, four different types of melting-runoff onsets depending on physical snow condition were identified. When translating that at the catchment scale, distributed melting-runoff onset maps were defined to better understand the spatiotemporal evolution of melting dynamics. Finally, a linear connection between melting dynamics and streamflow was found for long-lasting melting cycles, with a determination coefficient (R\({}^{2}\)) ranging from 0.62 to 0.83 and an average delay between the melting onset and streamflow peak of about 21 days. wet snow; Sentinel-1; C-band; synthetic-aperture radar (SAR); Mediterranean mountains; semi-arid regions; streamflow dynamics Article  
Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, 1 Fluvial Dynamics and Hydrology Research Group, Andalusian Institute for Earth System Research, University of Cordoba, Campus Rabanales, Edificio Leonardo da Vinci, Area de Ingenieria Hidraulica, 14071 Cordoba, Spain; [EMAIL_ADDRESS]
changes in downstream streamflow to be foreseen, and consequently, changes in the water resources availability [7; 8]. The physics of snow dynamics have been widely studied [9; 10; 11]. In snowpack, during the accumulation phase, snow water equivalent (SWE) increases, net energy inputs keep mainly negative, and the average snowpack temperature decreases [12; 13]. When these energy inputs become positive, due to external forcing, for instance radiation, the snow turns into wet snow and the melting phase begins [14; 15]. This melting period can be generally separated in three phases [11]: (i) moistening, (ii) ripening, and (iii) melting runoff. Traditionally, in situ monitoring has been the principal tool used for observing and, therefore, understanding snow dynamics [16]. However, the intrinsic limitations of accessibility, measurement representativeness, and area coverage have made remote sensing one of the techniques extensively used for snow monitoring [10; 11; 17; 18; 19; 20]. Optical sensors have been widely exploited to derive snow cover maps [8; 13; 18; 20; 21; 22; 23], as well as to compute snow albedo [24; 25; 26] and grain size [27]. The revisiting time of some days in the case of high resolution imagery, and the presence of clouds are the main limitation for these sensors in the case of snow monitoring [28; 29]. Terrestrial photography, which also works in the optical range of the electromagnetic spectrum, has been widely used to solve some of these issues, for instance, for retrieving snow cover fraction [30; 31], snow depth [3], and snow roughness [32; 33]. Active microwave sensors, which due to the fact that longer wavelengths are able to penetrate the cloud cover, have been exploited to gain information on the snowpack status, for example, wet/dry conditions [34; 35], snow depth [36], and SWE [37]. In these cases, some drawbacks remain related to the limited interaction of the SAR signal with snowpack, especially in dry conditions [34].