We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼ 0.3 km²) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques within the field of remote sensing, but the assessment of their performance in Alpine areas to map snow depth distribution is still an open issue. Moreover, several existing attempts to map snow depth using UASs have used multi-rotor systems, since they guarantee higher stability than fixed-wing systems. We designed two field campaigns: during the first survey, performed at the beginning of the accumulation season, the digital elevation model of the ground was obtained. A second survey, at peak accumulation, enabled us to estimate the snow depth distribution as a difference with respect to the previous aerial survey. Moreover, the spatial integration of UAS snow depth measurements enabled us to estimate the snow volume accumulated over the area. On the same day, we collected 12 probe measurements of snow depth at random positions within the case study to perform a preliminary evaluation of UAS-based snow depth. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to −0.073 m and a RMSE equal to 0.14 m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5 cm up to ∼ 100 m): for this case study, snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m. This provides a possible indication of sampling resolution in similar conditions.

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.

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.

Multi-year droughts in Mediterranean climates may shift the water balance, that is, the partitioning rule of precipitation across runoff, evapotranspiration, and sub-surface storage. Mechanisms causing these shifts remain largely unknown and are not well represented in hydrologic models. Focusing on measurements from the headwaters of California's Feather River, we found that also in these mixed rain–snow Mediterranean basins a lower fraction of precipitation was partitioned to runoff during multi-year droughts compared to non-drought years. This shift in the precipitation–runoff relationship was larger in the surface-runoff-dominated than subsurface-flow-dominated headwaters (−39 % vs. −18 % decline of runoff, respectively, for a representative precipitation amount). The predictive skill of the Precipitation Runoff Modeling System (PRMS) hydrologic model in these basins decreased during droughts, with evapotranspiration (ET) being the only water-balance component besides runoff for which the drop in predictive skill during drought vs. non-drought years was statistically significant. In particular, the model underestimated the response time required by ET to adjust to interannual climate variability, which we define as climate elasticity of ET. Differences between simulated and data-driven estimates of ET were well correlated with accompanying data-driven estimates of changes in sub-surface storage (ΔS, r=0.78). This correlation points to shifts in precipitation–runoff relationships being evidence of a hysteretic response of the water budget to climate elasticity of ET during and after multi-year droughts. This hysteresis is caused by carryover storage offsetting precipitation deficit during the initial drought period, followed by vegetation mortality when storage is depleted and subsequent post-drought vegetation expansion. Our results point to a general improvement in hydrologic predictions across drought and recovery cycles by including the climate elasticity of ET and better accounting for actual subsurface water storage in not only soil, but also deeper regolith that stores water accessible to roots. This can be done by explicitly parametrizing carryover storage and feedback mechanisms capturing vegetation response to atmospheric demand for moisture.

Data of liquid water flow around a capillary barrier in snow are still limited. To gain insight into this process, we carried out observations of dyed water infiltration in layered snow at 0 °C during cold laboratory experiments. We considered three different finer-over-coarser textures and three different water input rates. By means of visual inspection, horizontal sectioning, and measurements of liquid water content (LWC), capillary barriers and associated preferential flow were characterized. The flow dynamics of each sample were also simulated solving the Richards equation within the 1-D multi-layer physically based snow cover model SNOWPACK. Results revealed that capillary barriers and preferential flow are relevant processes ruling the speed of water infiltration in stratified snow. Both are marked by a high degree of spatial variability at centimeter scale and complex 3-D patterns. During unsteady percolation of water, observed peaks in bulk volumetric LWC at the interface reached ∼ 33–36 vol % when the upper layer was composed by fine snow (grain size smaller than 0.5 mm). However, LWC might locally be greater due to the observed heterogeneity in the process. Spatial variability in water transmission increases with grain size, whereas we did not observe a systematic dependency on water input rate for samples containing fine snow. The comparison between observed and simulated LWC profiles revealed that the implementation of the Richards equation reproduces the existence of a capillary barrier for all observed cases and yields a good agreement with observed peaks in LWC at the interface between layers.