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

Abstract. 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.

[1] Many Earth system science and environmental applications require knowledge of mapped evaporation. Satellite remote sensing can indirectly provide these measurements with a spatial coverage that is logistically and economically impossible to obtain through ground-based observation networks. Here a model for surface energy fluxes estimation based on the assimilation of land surface temperature from satellite is presented. The data assimilation scheme provides a useful framework that allows us to combine measurements and models to produce an optimal and dynamically consistent estimate of the evolving state of the system. The assimilation scheme can take advantage of the synergy of multisensor-multiplatform observations in order to obtain estimations of surface fluxes, flux partitioning, and surface characteristics. The model is based on the surface energy balance and bulk transfer formulation. A simplified soil wetness model, which is a filter of antecedent precipitation, is introduced in order to develop a more robust estimation scheme. This approach is implemented and tested over the Southern Great Plain field experiment domain. Comparisons with observed surface energy fluxes and soil moisture maps have shown that this assimilation system can estimate, when compared with the ground truth observations, the surface energy balance and its partitioning among turbulent heat fluxes. The introduction of the simplified soil wetness model forced by precipitation data improved evaporative fraction estimation. Further research is still required to analyze the reliability of retrieved fluxes in periods where radiation is the limiting factor for latent heat flux.

Abstract. 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.

An objective identification and ranking of extraordinary rainfall events for Northwest Italy is established using time series of annual precipitation maxima for 1938–2002 at over 200 stations. Rainfall annual maxima are considered for five reference durations (1, 3, 6, 12, and 24 h). In a first step, a day is classified as an extraordinary rainfall day when a regional threshold calculated on the basis of a two‐components extreme value distribution is exceeded for at least one of the stations. Second, a clustering procedure taking into account the different rainfall durations is applied to the identified 163 events. Third, a division into six clusters is chosen using Ward's distance criteria. It is found that two of these clusters include the seven strongest events as quantified from a newly developed measure of intensity which combines rainfall intensities and spatial extension. Two other clusters include the weakest 72% historical events. The obtained clusters are analyzed in terms of typical synoptic characteristics. The two top clusters are characterized by strong and persistent upper air troughs inducing not only moisture advection from the North Atlantic into the Western Mediterranean but also strong northward flow towards the southern Alpine ranges. Humidity transports from the North Atlantic are less important for the weaker clusters. We conclude that moisture advection from the North Atlantic plays a relevant role in the magnitude of the extraordinary events over Northwest Italy.

Vegetation in arid and semi-arid regions is affected by intermittent water availability. We discuss a simple stochastic model describing the coupled dynamics of soil moisture and vegetation, and study the effects of rainfall intermittency. Soil moisture dynamics is described by a ecohydrological box model, while vegetation is represented by site occupancy dynamics in a spatially-implicit model. We show that temporal rainfall intermittency allows for vegetation persistence at low values of annual rainfall volume, where it would go extinct if rainfall were constant. Rainfall intermittency also generates long-term fluctuations in vegetation cover, even in the absence of significant inter-annual variations in the statistical properties of precipitation.

Abstract. A variational land data assimilation system is used to estimate latent heat flux and surface control on evaporation. The dynamic equation for surface temperature with energy balance is used as a constraint on the estimation using the adjoint technique. Measurements of land surface temperature from satellite remote sensing are assimilated over two subregions within the Southern Great Plains 1997 hydrology field experiment. The performance of the estimation is linked to the timing of the satellite overpass. During days when the measurements close to the time of peak ground temperature are available, the estimation is adequate. The approach shows that satellite remote sensing of land temperature may be used to provide estimates of components of the surface energy balance and land surface control on evaporation. The latter parameter is related to surface soil moisture, and here they are compared with independent values derived from ground measurements.

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