Driver-based classification of hydrological droughts in a large alpine catchment

Despite being traditionally regarded as water-rich, alpine regions are increasingly vulnerable to droughts due to the compounding effects of extreme climate events and conflicting water uses. This study focuses on the Upper Adige catchment, where shifts in its traditionally snow-driven hydrological regime are intensifying, calling for systematic adaptation to meet diverse demands across agriculture, ecosystems, and hydropower.

In this study, we investigate the formation mechanisms and leading causes of hydrological drought in this area analyzing 27 historical drought events related to the 1997-2022 time window. We apply the conceptual hydrological model ICHYMOD to assess key drought formation mechanisms in the region. The model is initially validated against observed streamflow time series and demonstrates reliable performance in capturing both dry and wet day patterns and in identifying severe drought events, with accuracy exceeding 75% across several validation sites. The analysis then focuses on a model-based evaluation of hydrological drought formation with reference to the entire Upper Adige basin, assessing how drought propagates through the hydrological cycle and identifying recurrent patterns. A tree-based classification framework aimed at classifying the droughts according to their driving mechanism is developed, deriving threshold and classification criteria informed by expert knowledge of the region. 

The automated classification subdivides the historical events into six categories, and the results closely mirror the outcomes of visual classification, affirming the robustness of the approach and its alignment with domain expertise. 25% of droughts originating from two or more leading mechanisms are classified as composite, constituting one additional category. Our results reveal that the longest droughts are typically driven by early snowmelt, which depletes summer water reserves, or by precipitation deficits heading into winter, leading to prolonged recessions of water resources. These drought categories also record the highest deficits in terms of streamflow volume, partially due to their extended durations. The lowest streamflows typically occur in spring, driven by either rainfall deficits or delayed snowmelt at the end of the winter recession. Temperature emerges as a key driver with contrasting effects: while high temperatures accelerate snowmelt and exacerbate summer droughts, excessively low temperatures prolong winter recessions, intensifying spring water conflicts when demands are most critical.

This framework provides a systematic approach to understanding drought formation in alpine regions and can be leveraged in conjunction with hydrometeorological monitoring to support the development of an operational drought warning system. Integrating real-time observations with the classification logic enables actionable early warnings, enhancing preparedness and guiding response strategies for future drought events.