Ocena pretokov Idrijce in Soče za modeliranje onesnaženosti z živim srebrom

River discharges play an important role in understanding mercury fate in contaminated catchments. While hydrological and hydraulic models are commonly used to calculate discharges, their complexity and computational costs often pose challenges. This study evaluates the fit of the statistical curve and one of the machine learning methods, namely model trees, and explores their performance in predicting downstream river discharges based on upstream discharge measurements. The model trees method performs better, particularly with high discharges, which transport the vast majority of mercury downstream. The resulting relationships can be used as an input to various models assessing the impact of mercury pollution from the former mine in Idrija and the climate change on mercury transport in the river systems of the Idrijca and Soča/Isonzo rivers. The application of such models will improve our understanding of mercury cycling in the contaminated catchment and in the Gulf of Trieste’s coastal environment.