ALBERT

Description: With the introduction of low-power wireless technologies, like Bluetooth Low Energy (BLE), new applications are approaching the home automation, healthcare, fitness, automotive and consumer electronics markets. BLE devices are designed to maximize the battery life, i.e., to run for long time on a single coin-cell battery. In typical application scenarios of home automation and Ambient Assisted Living (AAL), the sensors that monitor relatively unpredictable and rare events should coexist with other sensors that continuously communicate health or environmental parameter measurements. The former usually work in connectionless mode, acting as advertisers, while the latter need a persistent connection, acting as slave nodes. The coexistence of connectionless and connection-oriented networks, that share the same central node, can be required to reduce the number of handling devices, thus keeping the network complexity low and limiting the packet’s traffic congestion. In this paper, the medium access management, operated by the central node, has been modeled, focusing on the scheduling procedure in both connectionless and connection-oriented communication. The models have been merged to provide a tool supporting the configuration design of BLE devices, during the network design phase that precedes the real implementation. The results highlight the suitability of the proposed tool: the ability to set the device parameters to allow us to keep a practical discovery latency for event-driven sensors and avoid undesired overlaps between scheduled scanning and connection phases due to bad management performed by the central node.

Description: Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density.

Description: 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.

Description: 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. Copyright © 2006 John Wiley & Sons, Ltd.

Description: Characterizing the hydrometeorological extremes, both in terms of rainfall and streamflow, as well as the estimation of long term water balance indicators are essential issues for the flood alert and water management services which are in charged to provide environmental monitoring. In recent years simulations carried out with meteorological models are getting available at increasing spatial and temporal resolutions (both historical reanalysis and near real-time hindcast studies); these meteorological data sets can thus be used as input in distributed hydrological models to drive long-period hydrological reanalysis. In this work we adopted a high resolution meteorological reanalysis dataset that covers the whole Europe territory for the period between 1979 and 2008, with 4 km grid spacing and 3 hours of time resolution. This reanalysis dataset is used together with a rainfall downscaling algorithm and a rainfall bias correction technique in order to produce input to a continuous and distributed hydrological model; the resulting modelling chain allows to produce long time series of distributed hydrological variables, inter alia streamflows and evapotranspiration, in the Liguria Region of Italy territory, located in the Northern part of Italy, and among the western Mediterranean areas mostly impacted by severe hydro-meteorological events. The observations available from the local rain gauges network were compared with the rainfall estimated by the dataset, and then used to perform a bias correction with the aim of matching the observed climatology. An analysis of the annual maxima discharges derived by simulated streamflow timeseries was carried out, by comparing them with observed discharge where available and using as benchmark a regional statistical analyses elsewhere. Eventually an investigation of long term water balance was done by comparing simulated runoff coefficients with available estimations based on observations. The study highlights both limits and potentialities of the considered framework as a methodological approach to undertake hydrological studies in any point of a considered study area mainly characterized by a collection of small basins, thus allowing to overcome the limits of observations which are punctual and in some cases not fully reliable.

Description: The characterization of the hydro-meteorological extremes, in terms of both rainfall and streamflow, and the estimation of long-term water balance indicators are essential issues for flood alert and water management services. In recent years, simulations carried out with meteorological models are becoming available at increasing spatial and temporal resolutions (both historical reanalysis and near-real-time hindcast studies); thus, these meteorological datasets can be used as input for distributed hydrological models to drive a long-period hydrological reanalysis. In this work we adopted a high-resolution (4 km spaced grid, 3-hourly) meteorological reanalysis dataset that covers Europe as a whole for the period between 1979 and 2008. This reanalysis dataset was used together with a rainfall downscaling algorithm and a rainfall bias correction (BC) technique in order to feed a continuous and distributed hydrological model. The resulting modeling chain allowed us to produce long time series of distributed hydrological variables for the Liguria region (northwestern Italy), which has been impacted by severe hydro-meteorological events. The available rain gauges were compared with the rainfall estimated by the dataset and then used to perform a bias correction in order to match the observed climatology. An analysis of the annual maxima discharges derived by simulated streamflow time series was carried out by comparing the latter with the observations (where available) or a regional statistical analysis (elsewhere). Eventually, an investigation of the long-term water balance was performed by comparing simulated runoff ratios (RRs) with the available observations. The study highlights the limits and the potential of the considered methodological approach in order to undertake a hydrological analysis in study areas mainly featured by small basins, thus allowing us to overcome the limits of observations which refer to specific locations and in some cases are not fully reliable.

Description: The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, whose dynamics strongly affects 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 at investigating 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 parameters resampling on the filter updating of the snowpack state; (3) the system sensitivity to the frequency of the assimilated observations.