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State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 10 3 km2) – 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 initial period 1 September 2010–31 August 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 (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 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, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 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 water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the 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 at 10.5281/zenodo.7034956 and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolutions, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based EO data in hydrological modeling. In a set of six experiments, the distributed hydrological model Continuum is set up for the Po River basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture (SM) and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation, and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling–Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGE mean= 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite data on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications. [ABSTRACT FROM AUTHOR]

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]

Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products. In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm ( Brocca et al., 2014 ) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2R ASC , are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013. The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency ( NS ) less than 30% when using SM2R ASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2R ASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas. [ABSTRACT FROM AUTHOR]

Hydrological monitoring systems relying on radar data and distributed hydrological models are now feasible at large-scale and represent effective early warning systems for flash floods. Here we describe a system that allows hydrological occurrences in terms of streamflow at a national scale to be monitored. We then evaluate its operational application in Italy, a country characterized by various climatic conditions and topographic features. The proposed system exploits a modified conditional merging (MCM) algorithm to generate rainfall estimates by blending data from national radar and rain-gauge networks. Then, we use the merged rainfall fields as input for the distributed and continuous hydrological model, Continuum, to obtain real-time streamflow predictions. We assess its performance in terms of rainfall estimates from MCM, using cross-validation and comparison with a conditional merging technique at an event-scale. We also assess its performance against rainfall fields from ground-based data at catchment-scale. We further evaluate the performance of the hydrological system in terms of streamflow against observed data (relative error on high flows less than 25% and Nash–Sutcliffe Efficiency greater than 0.5 for 72% and 46% of the calibrated study sections, respectively). These results, therefore, confirm the suitability of such an approach, even at national scale, over a wide range of catchment types, climates, and hydrometeorological regimes, and for operational purposes. [ABSTRACT FROM AUTHOR]

Subsurface storage changes (Δ S) represent a key modulator of drought propagation through the hydrological cycle, but their contribution to the annual water balance, and to drought propagation and recovery has rarely been explicitly assessed across catchments and climates. To expand on previous work on this matter, here we performed a large-sample analysis of precipitation, discharge, actual evapotranspiration (ET), and Δ S for 10 hydrological years and 102 catchments across various hydro-climatological regimes in Italy. We found that Δ S cannot be neglected in the annual water balance. Storage depletion leads to the attenuation of hydrological drought compared to meteorological drought, meaning that subsurface storage actively supports discharge during drought. We also found that storage generally recovers from precipitation deficits over time scales similar to the discharge recovery time, while recovery times for ET are longer. These findings show that subsurface storage drives drought propagation and recovery, regardless of climatic and catchment characteristics, and are thus relevant to properly inform water managers about surface- and ground-water availability in a changing climate. • Large-sample analysis of water balance components to study drought propagation and recovery. • Subsurface storage changes account on average for the 11% of long-term mean annual precipitation across the catchments. • Subsurface storage depletion sustains discharge during drought and the following recovery period. • A water balance approach is needed to better understand drought propagation and recovery. [ABSTRACT FROM AUTHOR]

Hydrological models often do not properly simulate streamflow (Q) during extreme events, including droughts. Limited abilities in simulating Q during droughts may arise from a misrepresentation of Q generating processes during these periods, but little research has focused on distributed, process-based models over human-affected areas and extreme events. To shed more light into model consistency during these periods, we evaluated the ability of the hydrological model Continuum in simulating Q over the human-affected Po river basin in Italy during droughts of different severity over the last 13 years, including the severe 2022 event. To investigate the causes for potential model deterioration during severe droughts, we assessed the simulation of evapotranspiration (ET) and Terrestrial Water Storage (TWS) against independent remote sensing-based benchmarks, and possible inconsistencies in forcing and benchmark data. Finally, we included a moderate drought in the calibration period, as potential strategy to improve model performances during severe droughts. The model represented well Q (KGE = 0.81 for the outlet of the basin), ET (r = 0.94) and TWS (r = 0.76) over the whole study period. Focusing on Q and specific sub-periods, model performances were comparable during wet years (2014 and 2020) and moderate droughts (2012 and 2017), with KGE across the 38 study sub-catchments of 0.59±0.32 (mean ±standard deviation) during wet years and 0.55±0.25 during moderate droughts. The model simulated Q well for the outlet section of the basin also during the severe 2022 drought (KGE = 0.82). However, performances across the sub-catchments declined in 2022 (KGE = 0.18±0.69). For the severe drought, we detected a decrease in model performances for ET, in particular over human-affected croplands (mean decrease in r by 105% and mean increase in nRMSE by 86%). Furthermore, calibrating during a moderate drought did not improve model performances in 2022 (KGE = 0.18±0.63), pointing to the fairly unique conditions of this period in terms of hydrological processes and human interference on them. Our study highlighted decreased model skills specifically during a severe drought and identified the neglection of irrigation as the most plausible cause for this. Given projected increases in severe droughts and the frequent modelling simplification of human activities, despite their heavy interference in many regions, our findings are highly relevant to move towards more robust hydrological modelling in a changing climate and the Anthropogenic era, to support management and adaptation strategies. • Multivariate investigation of causes for model degradation during a severe drought. • Performance decrease linked to human influence and underestimation of evaporation. • Calibrating over a moderate drought did not improve simulation for a severe drought. [ABSTRACT FROM AUTHOR]