ALBERT

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: 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.

Description: 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.