Abstract:Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS-2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring-summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat-ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only.

The typical complex orography of the Mediterranean coastal areas support the formation of the so-called back-building mesoscale convective systems (MCS) producing torrential rainfall often resulting in flash floods. As these events are usually very small-scaled and localized, they are hardly predictable from a hydrometeorological standpoint, frequently causing a significant amount of fatalities and socioeconomic damage. Liguria, a northwestern Italian region, is characterized by small catchments with very short hydrological response time and is thus extremely prone to the impacts of back-building MCSs. Indeed, Liguria has been hit by three intense back-building MCSs between 2011 and 2014, causing a total death toll of 20 people and several hundred millions of euros of damages. Consequently, it is necessary to use hydrometeorological forecasting frameworks coupling the finescale numerical weather prediction (NWP) outputs with rainfall–runoff models to provide timely and accurate streamflow forecasts. Concerning the aforementioned back-building MCS episodes that recently occurred in Liguria, this work assesses the predictive capability of a hydrometeorological forecasting framework composed by a kilometer-scale cloud-resolving NWP model (WRF), including a 6-h cycling 3DVAR assimilation of radar reflectivity and conventional weather stations data, a rainfall downscaling model [Rainfall Filtered Autoregressive Model (RainFARM)], and a fully distributed hydrological model (Continuum). A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators has been explored to drive the meteorological component of the proposed forecasting framework. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving the quantitative precipitation forecast, and, subsequently, the flash flood prediction in cases of back-building MCS events.

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Abstract. The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling – particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size.The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application.

A reliable estimation of soil moisture conditions is fundamental for rivers’ discharge predictions, especially in small catchments where flash floods occur. In this context, microwave remote sensing can be exploited to estimate soil moisture at large scale. These estimates can be used to enhance the predictions of hydrological models using data assimilation techniques. Flash flood early warning systems can, thus, be improved. This study tested the effect of the assimilation of three different ASCAT-derived soil moisture products, processed and distributed within the EUMETSAT H-SAF framework (SM-OBS-1, SM-OBS-2, SM-DAS-2), into a distributed physically based hydrological model (Continuum). The study areas were three Italian catchments, representative of the typical Mediterranean small basins prone to flash floods. The products were first preprocessed in order to be comparable with the model soil moisture state estimate. Subsequently, they were assimilated using three Nudging-based techniques. Then, observed discharges were compared with the modeled one in order to understand the impact of the assimilation. The analysis was executed for a multiyear period ranging from July 2012 to June 2014 in order to test the assimilation algorithms for operational purposes in real-cases scenarios. Findings showed that the assimilation of H-SAF soil moisture products with simple preprocessing and assimilation techniques can enhance discharge predictions; the improvements significantly affect high flows. Although SM-OBS-2 and SM-DAS-1 are added-value products with respect to SM-OBS-1 (respectively, higher spatial and temporal resolution), they may not necessarily perform better. The impact of the assimilation strongly relies on the permanent catchment characteristics (e.g., topography, hydrography, land cover)