| Abstract: |
The timely monitoring of melting cycles of snow is important for many applications as the forecast of flash-flood and avalanche risk, and the management of water resources, also in view of hydroelectric power planning. Water resource management and climate modeling require great accuracy in estimating the beginning of snow melting and in obtaining estimates of snow wetness on a daily basis.
Microwave emission at Ku and Ka bands (19 and 37 GHz, respectively) showed a significant sensitivity to snow melting and refreezing cycles, as it has already been demonstrated in [1] by using ground based sensors in an experiment on the Italian Alps. Although the use of satellite microwave data for this purpose is challenging due to the coarse ground resolution, an analysis of the snow conditions and their impact on the water stream was carried out in Mendoza river basin in Argentina by using the high-frequency channels of AMSR2.
The melting of snow accumulated in the upper Mendoza river basin (Mendoza Province, Argentina), during winter is the main water supply for agriculture, industry and human consumption in the area. More than one million of persons depends almost exclusively of this water resource. Mendoza River Basin is located in the West-center of Argentina, in Central Andes. The basin presents large altitude variations, ranging from 1000 m a.s.l., to almost 7000 m a.s.l. at the top of Aconcagua mount. Despite its importance, few studies have assessed the spatial and temporal variability of snow and its impact on the stream water of main rivers in this part of Central Andes.
A series of AMSR2 orbits has been acquired on the Mendoza basin from July 2012 to the end of May 2017, together with non-cloud-covered MODIS images from AQUA [2]. A few field stations are currently working in this area, providing daily average river discharge and the daily maximum and minimum air temperature. The long time series of data from these stations were used to compare meteorological information with remote sensing data during the period of time selected for this research.
By analyzing the temporal trends of brightness temperatures at Ka and Ku bands and their related indices (Frequency and Spectral Polarization Difference), together with MODIS acquisition for identifying the snow cover area, the snow depth has been estimated through the HydroAlgo algorithm [3]. Sentinel-1 SAR images were also considered in order to appraise and confirm the dry/wet status of the snow cover. The snow melting was clearly identified and related to the subsequent river discharge.
REFERENCES
[1] G. Macelloni, S. Paloscia, P. Pampaloni, M. Brogioni, R. Ranzi, and A. Crepaz, 2005, “Monitoring of melting refreezing cycles of snow with microwave radiometers: The Microwave Alpine Snow Melting Experiment (MASMEx 2002-2003)”, IEEE Trans. Geosci. Remote Sensing, 43, 11, pp. 2431-2441, 2005
[2] L. Cara, M. Masiokas, M. Viale, and R. Villalba, “ANÁLISIS DE LA COBERTURA NIVAL DE LA CUENCA SUPERIOR DEL RÍO MENDOZA A PARTIR DE IMÁGENES MODIS,” Meteorológica, vol. 41, no. 1, pp. 21–36, 2016.
[3] Santi E., S. Pettinato, S. Paloscia, P. Pampaloni, G. Macelloni, and M. Brogioni (2012), “An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo”, Hydrology and Earth System Sciences, 16, pp. 3659-3676, doi:10.5194/hess-16-3659-2012.
|
