Ricerc@Sapienza

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial (≥1 km) and temporal (≥12 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring.

The challenges that poses the hydrological sciences require a lot of efforts, especially in the development and improvement of mathematical models. The need to understand deeply the phenomena puts us in the condition of create models that are more accurate, but at the same time increase their simplicity and reliability. It is therefore important be provided with high-quality data, whether observed or simulated samples through appropriate software, without forgetting the protection capacity of vulnerable prone areas through geomorphological models.

Small- and medium-scale catchments (1-100 km2) are generally characterized by high spatial and temporal requirements, due to their rapid reactions to rainfall inputs at fine spatial and temporal scales. However, the natural variability of rainfall fields may lead to large uncertainty and bias in rain estimation at those scales. For this reason, flood risk assessment and management (e.g. in urban areas) could greatly benefit from stochastic spatial-temporal modelling of precipitation. The calibration of the Gaussian displacements spatial-temporal rainfall model (GDSTM), i.e.

For the study and modeling of hydrological phenomena both in urban and rural areas, a proper estimation of the areal reduction factor (ARF) is crucial. The ARF is defined as the ratio between the average rainfall occurring on a specific area and the point rainfall. In literature there exist several methodologies to estimate this ratio, as a consequence the corresponding ARFs have different properties. Spite the importance of the topic for the definition of design rainfall events, the ARF estimation has still some open issues.

The challenges that poses the hydrological sciences require a lot of efforts, especially in the development and improvement of mathematical models. The need to understand deeply the phenomena puts us in the condition of create models that are more accurate, but at the same time increase their simplicity and reliability. It is therefore important to be provided with high-quality data, whether observed or simulated samples through appropriate software.