摘要：Water management in mountain regions is facing multiple pressures due to climate change and anthropogenic activities. This is particularly relevantfor mountain areas where water abundance in the past allowed for many anthropogenic activities, exposing them to future water scarcity. Herestochastic system dynamics modelling (SDM) was implemented to explore water scarcity conditions affecting the stored water and turbined outflows inthe Santa Giustina (S. Giustina) reservoir (Autonomous Province of Trento, Italy). The analysis relies on a model chain integrating outputs from climate change simulationsinto a hydrological model, the output of which was used to test and select statistical models in an SDM for replicating turbined water and stored volumewithin the S. Giustina dam reservoir. The study aims at simulating future conditions of the S. Giustina reservoir in terms of outflow andvolume as well as implementing a set of metrics to analyse volume extreme conditions. Average results on 30-year slices of simulations show that even under the short-term RCP4.5 scenario (2021–2050) future reductions for storedvolume and turbined outflow are expected to be severe compared to the 14-year baseline (1999–2004 and 2009–2016; −24.9 % of turbinedoutflow and −19.9 % of stored volume). Similar reductions are expected also for the long-term RCP8.5 scenario (2041–2070; −26.2 % ofturbined outflow and −20.8 % of stored volume), mainly driven by the projected precipitations having a similar but lower trend especially inthe last part of the 2041–2070 period. At a monthly level, stored volume and turbined outflow are expected to increase for December to March (outflowonly), January to April (volume only) depending on scenarios and up to +32.5 % of stored volume in March for RCP8.5 for 2021–2050. Reductions arepersistently occurring for the rest of the year from April to November for turbined outflows (down to −56.3 % in August) and from May toDecember for stored volume (down to −44.1 % in June). Metrics of frequency, duration and severity of future stored volume values suggest ageneral increase in terms of low volume below the 10th and 20th percentiles and a decrease of high-volume conditions above the 80th and 90thpercentiles. These results point at higher percentage increases in frequency and severity for values below the 10th percentile, while volumevalues below the 20th percentile are expected to last longer. Above the 90th percentile, values are expected to be less frequent than baselineconditions, while showing smaller severity reductions compared to values above the 80th percentile. These results call for the adoption ofadaptation strategies focusing on water demand reductions. Months of expected increases in water availability should be considered periods forwater accumulation while preparing for potential persistent reductions of stored water and turbined outflows. This study provides results andmethodological insights that can be used for future SDM upscaling to integrate different strategic mountain socio-economic sectors (e.g.hydropower, agriculture and tourism) and prepare for potential multi-risk conditions.
