Search

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (300k+ km²) covering a transitional continental-to-Mediterranean region where snow plays an important, but still poorly constrained societal and ecological role. IT-SNOW provides ∼500-m, daily maps of Snow Water Equivalent (SWE), snow depth, bulk-snow density, and liquid water content for the 5 period 01/09/2010 - 31/08/2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 350+ on-the-ground snow-depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in-situ snow data from three focus regions (Aosta Valley, Lombardia, and Molise) shows little to none mean bias compared to the former, and Root Mean Square Errors on the order of 30 to 60 cm and 90 to 300 mm for in-situ, measured snow depth and Snow Water Equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak SWE and annual streamflow that are in line with expectations for this mixed rain-snow region (22% on average). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of SWE across the Italian landscape at peak accumulation, which on average occurs on the 4th of March. Nearly 52% of mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23%), and central Apennines (5%). IT-SNOW is freely available with the following DOI: https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources - a crucial endevor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this shifting mechanism to climate, as well as the role played by the cryosphere in the Earth energy budget, has motivated the development of a broad spectrum of predictive models. Such models rarely combine a high degree of physical realism in both the seasonal snow and glaciers, and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present S3M v5.1, a spatially explicit and hydrology-oriented cryospheric model that successfully reconstructs seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier energy and mass balances, snow rheology and hydraulics, and a data-assimilation protocol. Comparatively novel aspects of S3M with respect to the existing literature are an explicit representation of the spatial patterns of snow liquid-water content, an hybrid approach to snowmelt that decouples the radiation- and temperature-driven contributions, the implementation of the ∆h parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the Italian north-western Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises an user manual as well as resources to prepare input data and set up computational environments and libraries. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, the Liguria Region of Italy has been affected by several flash floods that have caused significant losses in terms of damage to infrastructure and human lives. The forecast of this kind of events is a critical issue for civil protection. The suitable approach to deal with this problem starts from the most accurate forecast of the rainfall field to be the input of the hydrological model. For this purpose, it is possible to take advantage of the output of the nowcasting, that is a forecast at short lead time (0 to 6 hours), but also of the forecast of the Numerical Weather Prediction Systems. The realization of an integrated hydrological nowcasting chain coupling the combination of different rainfall fields and the hydrological model can improve the discharge forecast. Within this study this coupling is performed joining in a unique tool the nowcasting technique PhaSt, a spectral-based nowcasting procedure, the NWPS MOLOCH corrected with data assimilation and the hydrological model Continuum, a continuous distributed hydrological model. The connection between the output of the forecasting models is performed through a technique called blending, that tries to linearly combine the rainfall fields according to their reliability function of the lead time. The blending has been approached in a novel way in this study exploiting the information retrieved from the NWPS both in terms of indication regarding the variation of total volume on the domain considered and in terms of location of the rainfall structures. An evaluation of the performances of the chain has been performed on some case events of autumn 2014 all over Liguria Region, northern Italy. A distributed analysis, executed on the entire domain on which the hydrological model is computed, has been possible obtaining representative results for all the basins involved in the events (more than 600 hydrological sections have been considered). According to the results obtained with the case studies it remains an open challenge the definition of a blending function that takes the best from every forecasting method. [ABSTRACT FROM AUTHOR]