Tracer methods are useful for investigating groundwater travel times and recharge rates and analysing impacts on groundwater quality. The most frequently used tracers are stable isotopes and tritium. Stable isotopes of oxygen (δ¹⁸O) and hydrogen (δ²H) are mainly used as indicators of the recharge condition. Tritium (³H) is used to estimate an approximate mean groundwater age. This paper presents the results of an analysis of stable isotope data and tritium activity in Croatian groundwater samples that were collected between 1997 and 2014 at approximately 100 sites. The composition of the stable isotopes of groundwater in Croatia originates from recent precipitation and is described using two regional groundwater lines. One of them is applied to groundwater accumulated in the aquifers in the Pannonian part of Croatia and the other is for groundwater accumulated in the Dinaric karst of Croatia. The isotope content shows that the studied groundwater is mainly modern water. A mix of sub-modern and modern water is mostly accumulated in semi-confined porous aquifers in northern Croatia, deep carbonate aquifers, and (sub)thermal springs.

Vrana Lake on the karst island of Cres (Croatia) is the largest freshwater lake in the Mediterranean islands. The lake cryptodepression, filled with 220 million m³ of fresh drinking water, represents a specific karst phenomenon. To better understand the impact of water level change drivers, the occurrence of meteorological and hydrological droughts was analysed. Basic machine learning methods (ML) such as the multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial neural network (ANN) were used to simulate water levels. Modelling was carried out considering annual inputs of precipitation, air temperature, and abstraction rate as well as their influential lags which were determined by auto-correlation and cross-correlation techniques. Hydrological droughts have been recorded since 1986, and after 2006 a series of mostly mild hot to moderate hot years was recorded. All three ML models have been trained to recognize extreme conditions in the form of less precipitation, high abstraction rate, and, consequently, low water levels in the testing (predicting) period. The best statistical indicators were achieved with the MNLR model. The methodologies applied in the study were found to be useful tools for the analysis of changes in water levels. Extended monitoring of water balance elements should precede any future increase in the abstraction rate.