Droughts are complex natural phenomena with multifaceted impacts, and a thorough drought impact assessment should entail a suite of adequate modelling tools and also include observational data, thus hindering the feasibility of such studies at large scales. In this work we present a methodology that tackles this obstacle by narrowing down the study area to a smaller subset of potential drought hot-spots (i.e., areas where drought conditions are expected to be exacerbated, based on future climate projections). We achieve this by exploring a novel interpretation of a well-established meteorological drought index that we link to the hydrological drought status of a catchment by calibrating its use on the basis of streamflow observational data. We exemplify this methodology over 25 sub-catchments pertaining to the Adige catchment. At the regional level, our findings highlight how the response to meteorological drought in Alpine catchments is complex and influenced by both the hydrological properties of each catchment and the presence of water storage infrastructures. The proposed methodology provides an interpretation of the hydrologic behavior of the analyzed sub-catchments in line with other studies, suggesting that it can serve as a reliable tool for identifying potential drought hot-spots in large river basins.

High-resolution precipitation data is essential for hydrological modeling and climate risk assessment, particularly in Alpine regions where complex terrain and fragmented data sources present ongoing challenges. In this study, we introduce a new 1-km daily gridded precipitation dataset covering the extended Adige River catchment (~40,000 km²) for the period 1990–2023. The domain spans cross-border areas of northern Italy, Austria, and Switzerland and integrates approximately 700 rain gauge series provided by nine regional and national agencies.A key focus of the work is the comparison of multiple interpolation techniques, including kriging with external drift, local linear regression and machine learning, and their performance across diverse topographic settings. We tested several methods and identified those that offered the most reliable spatial estimates, with particular attention to areas of complex orography. The gridded outputs were evaluated against widely used precipitation products, including reanalysis datasets and lower-resolution observational grids such as SPARTACUS, ARCIS, E-OBS, EMO, CHIRPS, CHELSA and APGD to assess improvements in spatial accuracy and representation. Both the climatological conditions and local-scale variability of the precipitation regime were analysed.Results show that combining a dense observational network with carefully selected interpolation approaches significantly improves the spatial representation of precipitation, particularly in areas with complex topography. However, issues related to the underrepresentation of precipitation amounts at higher elevations due to rain-gauge undercatch and the data scarcity in mountain areas remained, and were further investigated to improve the quantification of the uncertainty of the final gridded fields and its implications for impact-oriented applications.The dataset is developed within the framework of the RETURN Extended Partnership and is intended to support research and operational use across multiple sectors, including water resources, natural hazards, and climate adaptation planning.

Glacier meltwater is critical in sustaining streamflow during low-flow periods in mountainous regions. Nevertheless, glacier melt is often poorly or statically assessed in hydrological simulations, leading to partial considerations for effectively managing water, especially during droughts.This study evaluates the contribution of glacier melt to summer flows and its capacity to mitigate hydrological droughts in the upper Adige River basin, located in the Italian Alps (6900 km2). To achieve this, a new dynamic glacier module was implemented into the ICHYMOD-TOPMELT hydrological model, enabling annual updates of glacier area and improved quantification of meltwater contributions under progressive glacier retreat (from 122 km2 in 1997 to 84 km2 in 2017).The hydrological model exhibited robust performance metrics, with Kling-Gupta Efficiency (KGE) values of 0.82 for the overall study period and 0.65 for summer low-flow months. The dynamic glacier module accurately captured observed glacier area, mass balance, and seasonal melt trends. Validation against ASTER-derived glacier mass balance data for 2000–2019 revealed an error of 11%, underscoring the model’s ability to effectively represent long-term glacier dynamics.Results indicate that glacier melt contributed an average of 5.86% to summer streamflow during the period 1997–2019, with significant spatial variability. In drier, more glacierized subbasins, melt contributions reached up to 30–40%, highlighting its importance in maintaining streamflow where precipitation is scarce. We analyzed also the interplay between snow water equivalent (SWE), temperature, and glacier melt during droughts, with SWE acting as a buffer to delay summer glacier melt under cooler conditions.Severe drought years (like 2003, 2005, and 2022) demonstrated considerable variability in glacier melt contributions. In 2003, high temperatures and limited SWE led to glacier melt accounting for 14.4% of summer flows. By contrast, colder temperatures in 2005 reduced contributions to 6.3%. In 2022, while high temperatures drove glacier melt, reduced glacier areas led to lower absolute contributions (8.2%) compared to earlier droughts. If we had the same glacier area in 2022 like in 1997, glacier contribution could have been up to 14.6 %.Findings highlight the declining capacity of glacier melt to buffer against hydrological droughts due to ongoing glacier retreat. With shrinking glaciers, future summer flows in the Adige River basin are expected to become increasingly dependent on precipitation and snowmelt, thereby heightening the vulnerability of water resources to climate variability.Additionally, simulations showed that models using a static glacier area tend to overestimate glacier melt contributions, emphasizing the necessity of integrating a dynamic glacier modeling framework in hydrological models. These frameworks are crucial for accurately projecting future water availability and informing adaptive water resource management strategies in glacier-fed catchments.