Non-Stationary Amplification of Inter-Annual Discharge Deficits in Low-Memory Mountain Catchments: A Stochastic Diagnostic Framework

Non-stationarity is increasingly recognized as a defining feature of contemporary hydroclimatic regimes, challenging the statistical assumptions that underpin inter-annual discharge deficits analysis and water-resources design. This study investigates how shifts in first- and second-order statistical moments (mean and variance, respectively) alter the perceived rarity and persistence of inter-annual drought events in low-memory, rapid-response mountain systems. I develop a stochastic Monte Carlo framework to explore changes in inter-annual discharge deficit frequency and multi-year drought clustering across successive climatic regimes, using the Northern Apennines (Italy) as a representative case study. The model is explicitly exploratory: it does not aim to reproduce observed discharge distributions, but to quantify how regime shifts in mean and variability propagate into tail exceedances and drought spells under stationarity-based metrics. Results show a pronounced amplification of annual hydrological drought exceedances and the emergence of persistent multi-year drought spells under contemporary conditions, which are strongly underestimated when historical baselines are assumed stationary. A comparison with long-term regional discharge trends—while acknowledging the distinct hydro-climatic response of high-memory versus low-memory basins—serves to contextualize the systemic nature of the observed drought amplification. The findings highlight the structural vulnerability of low-memory catchments to non-stationary forcing and underscore the limitations of traditional design thresholds for drought-risk assessment under the evolving climate.