Snow in mountainous regions is a key water resource from local to continental scales. However, the link between increasingly frequent snow droughts and socio-hydrologic impacts from headwaters to lowlands is not fully understood. To untangle this relationship, here we combine snow and climate reanalyses with a range of data of terrestrial water storage, streamflow, and emergency water-use restrictions for the archetypal 2022 snow drought in the Po basin (Italy). We find that a persistent high-pressure ridge during winter 2021–2022 translated into a −88% anomaly in peak snow water equivalent, intraseasonal snowmelt, and earlier melt-out dates. Compounded by reduced summer precipitation and importantly increased temperatures, this snow deficit led to the lowest terrestrial water storage on record in summer 2022. Emergency water-use restrictions were concurrent with the peak in snowmelt deficit in early summer, rather than the peak in precipitation deficit in mid-winter. This study highlights the contribution of snowmelt deficit in driving the 2022 socio-hydrologic drought in the Po Basin.

Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Numerous studies have focused on spatial patterns and mechanisms of this phenomenon, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We studied the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000–2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. The objectives of this study were to (i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. We show that 63% of vegetated areas experienced significant trends during the 2000–2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts (>1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps.

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.

Flood forecasting undergoes a constant evolution, becoming more and more demanding about the models used for hydrologic simulations. The advantages of developing distributed or semi-distributed models have currently been made clear. Now the importance of using continuous distributed modeling emerges. A proper schematization of the infiltration process is vital to these types of models. Many popular infiltration schemes, reliable and easy to implement, are too simplistic for the development of continuous hydrologic models. On the other hand, the unavailability of detailed and descriptive information on soil properties often limits the implementation of complete infiltration schemes. In this work, a combination between the Soil Conservation Service Curve Number method (SCS-CN) and a method derived from Horton equation is proposed in order to overcome the inherent limits of the two schemes. The SCS-CN method is easily applicable on large areas, but has structural limitations. The Horton-like methods present parameters that, though measurable to a point, are difficult to achieve a reliable estimate at catchment scale. The objective of this work is to overcome these limits by proposing a calibration procedure which maintains the large applicability of the SCS-CN method as well as the continuous description of the infiltration process given by the Horton's equation suitably modified. The estimation of the parameters of the modified Horton method is carried out using a formal analogy with the SCS-CN method under specific conditions. Some applications, at catchment scale within a distributed model, are presented.

The ever-increasing availability of new remote sensing and land surface model datasets opens new opportunities for hydrologists to improve flood forecasting systems. The current study investigates the performance of two operational soil moisture (SM) products provided by the “EUMETSATSatellite Application Facility in Support of Operational Hydrology and Water Management” (H-SAF, http://hsaf.meteoam.it/) within a recently-developed hydrological model called the “simplified continuous rainfall-runoff model” (SCRRM) and the possibility of using such a model at an operational level. The model uses SM datasets derived from external sources (i.e., remote sensing and land surface models) as input for calculating the initial wetness conditions of the catchment prior to the flood event. Hydro-meteorological data from 35 Italian catchments ranging from 800 to 7400 km2 were used for the analysis for a total of 593 flood events. The results show that H-SAF operational products used within SCRRM satisfactorily reproduce the selected flood events, providing a median Nash–Sutcliffe efficiency index equal to 0.64 (SM-OBS-1) and 0.60 (SM-DAS-2), respectively. Given the results obtained along with the parsimony, the simplicity and independence of the model from continuously-recorded rainfall and evapotranspiration data, the study suggests that: (i) SM-OBS-1 and SM-DAS-2 contain useful information for flood modelling, which can be exploited in flood forecasting; and (ii) SCRRM is expected to be beneficial as a component of real-time flood forecasting systems in regions characterized by low data availability, where a continuous modelling approach can be problematic.