Ricerc@Sapienza

On 16 August 2018, a Mw 5.1 earthquake occurred in the Molise region (Central Italy) during an intense rainfall event which cumulated up to 140 mm in 3 days. Within 5 days after the seismic event, 88 landslides were surveyed and classified in disrupted and coherent as well as in first-time failures and reactivation. As it resulted by the inventorying, most of the surveyed ground effects were represented by coherent landslides involving clays, marly clays, and cover deposits on low dipping slopes.

Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band.

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.

Hydrometric monitoring networks have a crucial role in water resources management, flood forecasting and hydrologic
modelling. They should be designed to provide as much information as possible and to reduce uncertainty
in the ungauged locations. Among the several methods proposed in literature for the optimal design of monitoring
networks, the one based on information theory is one of the most common. Recently, new methodologies based on
complex network theory have been employed to identify the most and the less influential stations in a monitoring

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.