Bayesian algorithm for the estimation of the dielectric constant from active and passive remotely sensed data

An inversion technique based on the merging of microwave remotely sensed data is applied to ground-based radiometer and scatterometer data acquired for the same area. The purpose of this technique is to retrieve the dielectric constant of bare soils. The algorithm is based on a Bayesian approach and combines prior information on the dielectric constant and surface roughness with observed data, in order to obtain a marginal posterior probability density function. The function describes how the probability is distributed within the range of the dielectric constant values, given the measured values of emissivity and backscattering coefficient. The algorithm allows for the incorporation of all the available sources of information, such as multipolarization and multifrequency data. Several criteria, which have been used to compare the predicted and the observed values, show that for dielectric constant values higher than 10 the best performance is achieved when data with one polarization and one or two frequencies are exploited. For dielectric constant values of less than 10, the configuration with two polarizations produces the best estimates.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

Data-driven mapping of avalanche release areas: a case study in South Tyrol, Italy

Avalanche hazard and risk mapping is of utmost importance in mountain areas in Europe and elsewhere. Advanced methods have been developed to describe several aspects of avalanche hazard assessment, such as the dynamics of snow avalanches or the intensity of snowfall to assume as a reference meteorological forcing. However, relatively little research has been conducted on the identification of potential avalanche release areas. In this paper, we present a probabilistic assessment of potential avalanche release areas in the Italian Autonomous Province of Bolzano, eastern Alps, using the Weights of Evidence and Logistic Regression methods with commonly available GIS datasets. We show that a data-driven statistical model performs better than simple, although widely adopted, screening criteria that were proposed in the past, although the complexity of observed release areas is only partly captured by the model. In the best case, the model enables predicting about 70 % of avalanches in the 20 % of area classified at highest hazard. Based on our results, we suggest that probabilistic identification of potential release areas could provide a useful aid in the screening of sites for subsequent, more detailed hazard assessment.     

Numerical study of tides in Ontario Lacus,a hydrocarbon lake on the surface of the Saturnian moon Titan

In the context of the emergence of extra-terrestrial oceanography, we adapted an existing oceanographic model, SLIM (www.climate.be/slim), to the conditions of Titan, a moon of Saturn. The tidal response of the largest southern lake at Titan’s surface, namely Ontario Lacus, is simulated. SLIM solves the 2D, depth-averaged shallow water equations on an unstructured mesh using the discontinuous Galerkin finite element method, which allows for high spatial resolution wherever needed. The impact of the wind forcing, the bathymetry, and the bottom friction is also discussed. The predicted maximum tidal range is about 0.56 m in the southern part of the lake, which is more than twice as large as the previous estimates (see Tokano, Ocean Dyn 60:(4) 803–817 10.1007/s10236-010-0285-3 (Tokano 2010)). The patterns and magnitude of the current are also markedly different from those of previous studies: the tidal motion is not aligned with the major axis of the lake and the speed is larger nearshore. Indeed, the main tidal component rotates clockwise in an exact period of one Titan day and the tidal currents can reach 0.046 ms ^?1 close to the shores depending on the geometry and the bathymetry. Except for these specific nearshore regions, the current speed is less than 0.02 ms ^?1. Circular patterns can be observed offshore, their rotational direction and size varying along the day.