Abstract
Snow plays a key role in the hydrological cycle. Especially in mountain regions such as the Alps, snowmelt is the main responsible of run-off regimes, strongly influencing groundwater storage, flooding, avalanches, and contaminant release. Hence, understanding and predicting snowmelt dynamics is essential for a better planning and accurate monitoring of our resources.
Snowmelt is a non-linear process affected by the strong variability of several climate factors. We can observe three main phases: i) moistening, ii) ripening and iii) run-off. The moistening is the initial phase. The air temperature increases and due to heat exchanges and/or rain the superficial layers of the snowpack start melting during the day and refreeze during the night. During the ripening phase, the wetting front penetrates through the snowpack but the meltwater is not yet released. During this phase, the snowpack becomes isothermal and when no more liquid water can be retained, the run-off phase starts. The sequence of these three phases can vary depending on temperatures with alternatively warming and refreezing cycles (Dingman, 2015).
Several non-invasive field measurements such as air temperature, relative humidity, wind speed, precipitation, solar radiation are used to extract information on snow dynamics. However, the key state variables to properly identify the three main melting phases are the snow water equivalent (SWE), i.e. the amount of water stored in form of snow, and the liquid water content (LWC), i.e. the amount of free liquid water inside the snowpack. Unfortunately observations of SWE and especially of LWC are rarely available. In detail, diurnal fluctuations of LWC happen during the moistening. The ripening is characterized by a relevant increment of LWC. Afterwards SWE decreases monotonically during the run-off phase.
To obtain information on these parameters, an alternative to ground-based measurements is represented by simulations from physically based models such as SNOWPACK (Bartelt & Lehning, 2002). Such models make use of the most common meteorological parameters and provide as output SWE, LWC and snow structure details. This allows the monitoring of snowmelt dynamics, but these parameters are not directly measured.
On the other side, active microwave sensors such as the Synthetic Aperture Radar (SAR), which are sensitive to the presence of LWC in the snowpack, may represent a valid source of information for monitoring the snowmelt (Shi & Dozier, 2000; Longepe et al. 2009), at high spatial and temporal resolution (i.e. with the Sentinel-1 constellation with a 20 m nominal resolution and 5 days of overpassing time at the equator). In this work, we aim at studying the relationship between the multi-temporal SAR signal acquired from Sentinel-1 and the measurements of LWC and SWE taken in different European alpine sites.
Characteristic backscattering mechanisms at the different melting phases are identifiable from the multi-temporal SAR signal. In detail, we recognize a decrease of the backscattering during the first phase which can be directly correlated with an increase of LWC. The decrease can initially be observed only during the afternoon when the snow melts because positive temperatures and solar radiation, and not in the early morning when the snow is refrozen. During the ripening phase, the backscattering decreases, reaching its minimum value. It can oscillate but it remains however low. Then, a rapid increase of the signal is observed when the run-off starts. In Figure 1 an example of such behavior observed at the Zugspitze site (Germany) is reported. The example shows the complexity and non-linearity of the melting process. The ripening is limited to a short phase and the run-off starts very early with a first increase of the backscattering. It decreases again in correspondence of a snow fall (probably wet) highlighted by an increase of LWC and SWE followed by a relatively colder period which lasted some days and probably interrupted the run-off process. Afterwards the backscattering increases again when the run-off restarts and lasts till the end of the season.
Fig. 1. Temporal evolution of the backscattering coefficient values acquired over Zugspitze (Germany) during the hydrological season 2017/2018. It is compared with LWC and SWE measured in situ and/or modeled with SNOWPACK. The three phases of the melting process are identified as follows: moistening is highlighted in light yellow, ripening in light pink and run-off in light green.
Based on this analysis, the variation in the backscattering can be exploited to automatically identify the timing of the snowmelt. Further research is needed to extend a single point observation to a spatially distributed information. Given their availability, high spatial resolution and frequent overpassing time, Sentinel SAR observations are worth being used to monitor snow melt dynamic over large regions, especially in remote areas and complex terrain such as mountain regions. This approach can represent an important support for hydrological monitoring.
References:
Bartelt, P., & Lehning, M. (2002). A physical SNOWPACK model for the Swiss avalanche warning: Part I: numerical model. Cold Regions Science and Technology, 35(3), 123-145.
Dingman, S. L. (2015). Physical hydrology. Waveland press.
Longepe, N., Allain, S., Ferro-Famil, L., Pottier, E., & Durand, Y. (2009). Snowpack characterization in mountainous regions using C-band SAR data and a meteorological model. IEEE Transactions on Geoscience and Remote Sensing, 47(2), 406-418.
Shi, J., & Dozier, J. (2000). Estimation of snow water equivalence using SIR-C/X-SAR. I. Inferring snow density and subsurface properties. IEEE Transactions on Geoscience and Remote Sensing, 38(6), 2465-2474.