Hydrological time series are shaped by complex, nonlinear dynamics that vary across different regimes of flow and weather conditions. Traditional linear models struggle to capture such regime-dependent behavior, motivating the use of flexible copula-based approaches. This thesis develops a regime-switching vine copula framework to analyze three hydrological variables measured at the Noce River’s Ott station: water level (height), temperature, and electrical conductivity. The data consist of over 20,000 hourly observations (2021–2023), supplemented by precipitation inputs from two relevant locations: the Malga reservoir, which regulates upstream flow, and the Mezzo station, a local meteorological gauge. The analysis proceeds in three steps. First, Markov-switching autoregressive models identify high- and low-regime states for each hydrological variable. Second, regime-specific copula models capture how dependencies among water level, temperature, and conductivity change across joint states. Third, vine copula regressions link each hydrological variable to lagged precipitation inputs from Malga and Mezzo, providing a flexible representation of conditional dependence. The results show that overall, pairwise associations are modest, but regime-dependent patterns are important. Internal dependencies among height, temperature, and conductivity are strongest when water levels and temperatures are high, but conductivity is low. Incorporating precipitation enhances the model fit, with vine regressions indicating that the Mezzo station, particularly at the two-hour lag, acts as a consistent external driver, whereas Malga precipitation shows a more limited, regime-dependent influence. Overall, the study demonstrates that regime-switching vine copulas offer a powerful tool for uncovering subtle dependent structures in hydrological systems. They provide new insights into how local precipitation and managed storage interact with river dynamics and offer a pathway for improved hydrological modeling and risk assessment under changing climatic conditions.      «

Hydrological time series are shaped by complex, nonlinear dynamics that vary across different regimes of flow and weather conditions. Traditional linear models struggle to capture such regime-dependent behavior, motivating the use of flexible copula-based approaches. This thesis develops a regime-switching vine copula framework to analyze three hydrological variables measured at the Noce River’s Ott station: water level (height), temperature, and electrical conductivity. The data consist of over...     »