The use of synthetic aperture radar (SAR) data is presently well established in operational services for flood management. Nevertheless, detecting inundated vegetation and urban areas still represents a critical issue, because the radar signatures of these targets are often ambiguous. This paper analyzes the role of the interferometric coherence in complementing intensity SAR data for mapping floods in agricultural and urban environments. The advantages of the joint use of intensity and coherence are first discussed in a theoretical way and then verified on a case study, namely, the flood that hit the Emilia-Romagna region (Northern Italy) in January 2014. The short revisit time of the COSMO-SkyMed images, as well as a dedicated acquisition plan tailored to the requirements of the Italian Civil Protection Department, has allowed us to build a data set of radar interferometric observations of the event. Results show that the analysis of the multitemporal trend of the coherence is useful for the interpretation of SAR data since it enables a considerable reduction of classification errors that could be committed considering intensity data only. Interferometric data have permitted us to distinguish zones where water receded from areas where it persisted for a longer time and, in one case, to measure changes of water level.

During the last decade the opportunity and usefulness of using remote-sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite-based products often allow for the advantage of observing hydrologic variables in a distributed way, offering a different view with respect to traditional observations that can help with understanding and modeling the hydrological cycle. Moreover, remote-sensing data are fundamental in scarce data environments. The use of satellite-derived digital elevation models (DEMs), which are now globally available at 30 m resolution (e.g., from Shuttle Radar Topographic Mission, SRTM), have become standard practice in hydrologic model implementation, but other types of satellite-derived data are still underutilized. As a consequence there is the need for developing and testing techniques that allow the opportunities given by remote-sensing data to be exploited, parameterizing hydrological models and improving their calibration. In this work, Meteosat Second Generation land-surface temperature (LST) estimates and surface soil moisture (SSM), available from European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) H-SAF, are used together with streamflow observations (S. N.) to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. The first part of the work aims at proving that satellite observations can be exploited to reduce uncertainties in parameter calibration by reducing the parameter equifinality that can become an issue in forecast mode. In the second part, four parameter estimation strategies are implemented and tested in a comparative mode: (i) a multi-objective approach that includes both satellite and ground observations which is an attempt to use different sources of data to add constraints to the parameters; (ii and iii) two approaches solely based on remotely sensed data that reproduce the case of a scarce data environment where streamflow observation are not available; (iv) a standard calibration based on streamflow observations used as a benchmark for the others. Two Italian catchments are used as a test bed to verify the model capability in reproducing long-term (multi-year) simulations. The results of the analysis evidence that, as a result of the model structure and the nature itself of the catchment hydrologic processes, some model parameters are only weakly dependent on discharge observations, and prove the usefulness of using data from both ground stations and satellites to additionally constrain the parameters in the calibration process and reduce the number of equifinal solutions.

Full process description and distributed hydrological models are very useful tools in hydrology as they can be applied in different contexts and for a wide range of aims such as flood and drought forecasting, water management, and prediction of impact on the hydrologic cycle due to natural and human-induced changes. Since they must mimic a variety of physical processes, they can be very complex and with a high degree of parameterization. This complexity can be increased by necessity of augmenting the number of observable state variables in order to improve model validation or to allow data assimilation. In this work a model, aiming at balancing the need to reproduce the physical processes with the practical goal of avoiding over-parameterization, is presented. The model is designed to be implemented in different contexts with a special focus on data-scarce environments, e.g. with no streamflow data. All the main hydrological phenomena are modelled in a distributed way. Mass and energy balance are solved explicitly. Land surface temperature (LST), which is particularly suited to being extensively observed and assimilated, is an explicit state variable. A performance evaluation, based on both traditional and satellite derived data, is presented with a specific reference to the application in an Italian catchment. The model has been firstly calibrated and validated following a standard approach based on streamflow data. The capability of the model in reproducing both the streamflow measurements and the land surface temperature from satellites has been investigated. The model has been then calibrated using satellite data and geomorphologic characteristics of the basin in order to test its application on a basin where standard hydrologic observations (e.g. streamflow data) are not available. The results have been compared with those obtained by the standard calibration strategy based on streamflow data.

The use of synthetic aperture radar (SAR) data is presently well established in operational services for flood management. However, some events might be missed because of the limited area that can be observed through a SAR image and the need of programming SAR acquisitions in advance. To tackle these problems, it is possible to setup a system that is able to trigger the SAR acquisitions based on flood forecasts and to take advantage of the various satellite SAR sensors that are presently operating. On behalf of the Italian Civil Protection Department (DPC), a prototype of this kind of system has been setup and preliminary tested, using COSMO-SkyMed (CSK) and Sentinel-1 (S-1) data, to monitor the Po River (Northern Italy) flood occurred in November 2014. This paper presents the prototype system and describes in detail the near real-time flood mapping algorithm implemented in the system. The algorithm was previously developed to classify CSK images, and is modified here in order to be applied to S-1 data too. The major outcomes of the monitoring of the Po River flood are also analyzed in this paper, highlighting the importance of the in advance programming of the radar acquisitions. Results demonstrate the reliability of the flood predictions provided by the model and the accuracy of the flood mapping algorithm. It is also shown that, when CSK and S-1 data are simultaneously acquired, their joint use allows for an interpretation of some ambiguous radar signatures in agricultural areas.