Abstract. 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.
Abstract. During the autumn of 2011 two catastrophic, very intense rainfall events affected two different parts of the Liguria Region of Italy causing various flash floods. The first occurred in October and the second at the beginning of November. Both the events were characterized by very high rainfall intensities (> 100 mm h−1) that persisted on a small portion of territory causing local huge rainfall accumulations (> 400 mm 6 h−1). Two main considerations were made in order to set up this work. The first consideration is that various studies demonstrated that the two events had a similar genesis and similar triggering elements. The second very evident and coarse concern is that two main elements are needed to have a flash flood: a very intense and localized rainfall event and a catchment (or a group of catchments) to be affected. Starting from these assumptions we did the exercise of mixing the two flash flood ingredients by putting the rainfall field of the first event on the main catchment struck by the second event, which has its mouth in the biggest city of the Liguria Region: Genoa. A complete framework was set up to quantitatively carry out a “what if” experiment with the aim of evaluating the possible damages associated with this event. A probabilistic rainfall downscaling model was used to generate possible rainfall scenarios maintaining the main characteristics of the observed rainfall fields while a hydrological model transformed these rainfall scenarios in streamflow scenarios. A subset of streamflow scenarios is then used as input to a 2-D hydraulic model to estimate the hazard maps, and finally a proper methodology is applied for damage estimation. This leads to the estimation of the potential economic losses and of the risk level for the people that stay in the affected area. The results are interesting, surprising and in a way worrying: a rare but not impossible event (it occurred about 50 km away from Genoa) would have caused huge damages estimated between 120 and EUR 230 million for the affected part of the city of Genoa, Italy, and more than 17 000 potentially affected people.
Abstract. 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 the model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing 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 resolutions, obtained (i) by sampling the original Torgnon 30-minute time series at 3, 6, and 12 hours, (ii) by spatially interpolating neighboring in-situ station measurements and (iii) by extracting information from GLDAS, ERA5, ERA-Interim reanalyses at the gridpoint closest to the Torgnon station. 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. 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 as 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 as the control run while with 6- and 12-hourly temporal resolution forcings we generally observe a reduction in model performances, except for the SMASH model which shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighboring stations and reanalyses result to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. A simple bias-adjustment technique applied to ERA-Interim temperatures, however, allowed all models to achieve similar performances as in the control run. All models irrespectively of their complexity show weaknesses in the representation of the snow density.
Abstract. During the autumn of 2011 two catastrophic very intense rainfall events affected two different parts of the Liguria Region of Italy causing various flash floods. The first occurred in October and the second at the beginning of November. They became two "school cases" studied by many scientists around the world and they awaken the interest of the local authorities and of the civil protection actors regarding these type of calamities. Due to the large amount of damages and the numerous victims, they caused a general increase of the sensibleness of the citizens of the stricken areas regarding the natural hazards. Two main considerations were done in order to set up this work. The first consideration is that various studies demonstrated that the two events had a similar genesis and similar triggering elements. The second very evident and coarse concern is that two main elements are needed to have a flash flood: a very intense and localized rainfall event and a catchment (or a group of catchments) to be affected. Starting from these assumptions we did the exercise of mixing the two flash floods ingredients by putting the rainfall field of the first event on the main catchment stroke by the second event that has its mouth in correspondence of the biggest city of the Liguria Region: Genova. A complete framework was set up to quantitatively carry out a "what if" experiment with the aim of evaluating the possible damages associated to this event. The approachcombines a probabilistic rainfall downscaling model, a hydrological model, a 2D hydraulic model and a proper methodology for damages estimation. This leads to the estimation of the potential economic losses and of the risk level for the people that stays in the affected area. The results are interesting, surprising and in such a way worrying: a rare but not impossible event (it occurred about 50 km away from Genoa) would have caused huge damages estimated between 120 and 230 million of euros for the affected part of the city of Genova, Italy and more than 17 000 potentially affected people.
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