Publications in Scientific Journals:
L. Brocca, A. Tarpanelli, T. Moramarco, F. Melone, S.M. Ratto, M. Cauduro, S. Ferraris, N. Berni, F. Ponziani, W. Wagner, T. Melzer:
"Soil Moisture Estimation in Alpine Catchments through Modeling and Satellite Observations";
Vadose Zone Journal,
Soil moisture is widely recognized as a state variable governing the mass and energy balance between the land surface and the atmosphere. For that, its knowledge is of upmost importance for many applications including flood and landslide prediction. In alpine catchments, soil moisture estimation is a very difficult task, because of complex topography, high vegetation density, and presence of snow and outcrops. In this study, the possibility to estimate soil moisture for these areas by using modeled and satellite data is investigated. Specifically, an updated version of a soil water balance model, which takes the snowmelt process into account, is employed. Moreover, satellite-derived soil moisture observations obtained by the Advanced SCATterometer (ASCAT) sensor onboard the MetOp satellite are tested by considering two products: the Surface Soil Moisture (SSM) and the Soil Water Index (SWI). The latter is obtained through the application of an exponential filter and it is aimed to reduce the differences in the layer depth of in situ measurements (10 cm) and satellite data (∼2-3 cm). Quality-checked in situ soil moisture measurements collected at four continuous monitoring sites in Valle dŽAosta (North Italy) are used to test the accuracy of modeled and satellite estimates. Notwithstanding the above issues, results indicated the potential not only of modeling approaches but also, unexpectedly, of satellite data to retrieve soil moisture in high elevation regions (>1000 m a.s.l.). Indeed, by estimating correctly the snowmelt contribution, the agreement between modeled and observed data is quite good, with correlation coefficient values, r, in the range 0.795-0.940. In addition, the ASCAT-derived SWI product provides satisfactorily results with r = 0.635-0.869. Based on these findings, in situ, modeled, and satellite soil moisture data will be used for improving flood and landslide risk prediction at the Valle dŽAosta Functional center to improve the Civil Protection Alert System.
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