It is generally observed that precipitation is gradually depleted in ¹⁸O and ²H isotopes as elevation increases (‘altitude’ effect) or when moving inland from seacoasts (‘continental’ effect); the regionally accurate estimation of these large-scale effects is important in isotope hydrological or paleoclimatological applications. Nevertheless, seasonal and spatial differences should be considered. Stable isotope composition of monthly precipitation fallen between January 2016 and December 2018 was studied for selected stations situated along an elevation transect and a continental transect in order to assess the isotopic ‘altitude’ and ‘continental’ effects in modern precipitation across the Adriatic–Pannonian region. Isotopic characteristics argue that the main driver of the apparent vertical depletion of precipitation in heavy stable isotopes is different in summer (raindrop evaporation) and winter (condensation), although, there is no significant difference in the resulting ‘altitude’ effect. Specifically, an ‘altitude’ effect of −1.2‰/km for δ¹⁸O and −7.9‰/km for δ²H can be used in modern precipitation across the Adriatic–Pannonian region. Isotopic characteristics of monthly precipitation showed seasonally different patterns and suggest different isotope hydrometeorological regimes along the continental transect. While no significant decrease was found in δ¹⁸O data moving inland from the Adriatic from May to August of the year, a clear decreasing trend was found in precipitation fallen during the colder season of the year (October to March) up to a break at ~400 km inland from the Adriatic coast. The estimated mean isotopic ‘continental’ effect for the colder season precipitation is −2.4‰/100 km in δ¹⁸O and −20‰/100 km in δ²H. A prevailing influence of the Mediterranean moisture in the colder season is detected up to this breakpoint, while the break in the δ¹⁸O data probably reflects the mixture of moisture sources with different isotopic characteristics. A sharp drop in the d-excess (>3‰) at the break in precipitation δ¹⁸O trend likely indicates a sudden switch from the Mediterranean moisture domain to additional (mainly Atlantic) influence, while a gradual change in the d-excess values might suggest a gradual increase of the non-Mediterranean moisture contribution along the transect.

The isotopic composition of precipitation provides insight into the origin of water vapor, and the conditions attained during condensation and precipitation. Thus, the spatial variation of oxygen and hydrogen stable isotope composition (δ_p) and d-excess of precipitation was explored across the Iberian Peninsula for October 2002–September 2003 with 24 monitoring stations of the Global Network of Isotopes in Precipitation (GNIP), and for October 2004–June 2006, in which 13 GNIP stations were merged with 21 monitoring stations from a regional network in NW Iberia. Spatial autocorrelation structure of monthly and amount weighted seasonal/annual mean δ_p values was modelled, and two isoscapes were derived for stable oxygen and hydrogen isotopes in precipitation with regression kriging. Only using the GNIP sampling network, no spatial autocorrelation structure of δ_p could have been determined due to the scarcity of the network. However, in the case of the merged GNIP and NW dataset, for δ_p a spatial sampling range of ~450 km in planar distance (corresponding to ~340 km in geodetic distance) was determined. The range of δ_p, which also broadly corresponds to the range of the d-excess, probably refers to the spatially variable moisture contribution of the western, Atlantic-dominated, and eastern, Mediterranean-dominated domain of the Iberian Peninsula. The estimation error of the presented Iberian precipitation isoscapes, both for oxygen and hydrogen, is smaller than the ones that were reported for the regional subset of one of the most widely used global model, suggesting that the current regional model provides a higher predictive power.

Stable isotopes of hydrogen and oxygen are important natural tracers with a wide variety of environmental applications (e.g., the exploration of the water cycle, ecology and food authenticity). The spatially explicit predictions of their variations are obtained through various interpolation techniques. In the present work, a classical random forest (RF) and two of its variants were applied. RF and a random forest version employing buffer distance (RF_sp) were applied to each month separately, while a random forest model was trained using all data employing month and year as categorical variables (RF_tg). Their performance in predicting the spatial variability of precipitation stable oxygen isotope values for 2008–2017 across Europe was compared. In addition, a comparison was made with a publicly available alternative machine learning model which employs extreme gradient boosting. Input data was retrieved from the Global Network of Isotopes in Precipitation (GNIP; no. of stations: 144) and other national datasets (no. of stations: 127). Comparisons were made on the basis of absolute differences, median, mean absolute error and Lin’s concordance correlation coefficient. All variants were capable of reproducing the overall trends and seasonal patterns over time of precipitation stable isotope variability measured at each chosen validation site across Europe. The most important predictors were latitude in the case of the RF, and meteorological variables (vapor pressure, saturation vapor pressure, and temperature) in the case of the RF_sp and RF_tg models. Diurnal temperature range had the weakest predictive power in every case. In conclusion, it may be stated that with the merged dataset, combining GNIP and other national datasets, RF_sp yielded the smallest mean absolute error 1.345‰) and highest Lin’s concordance correlation coefficient (0.987), while with extreme gradient boosting (based on only the GNIP data) the mean absolute error was 1.354‰, and Lin’s concordance correlation coefficient was 0.984, although it produced the lowers overall median value (1.113‰), while RF_sp produced 1.124‰. The most striking systematic bias was observed in the summer season in the northern validation stations; this, however, diminished from 2014 onward, the point after which stations beyond 55° N are available in the training set.

Tritium (³H) as a constituent of the water molecule is an important natural tracer in hydrological sciences. The anthropogenic tritium introduced into the atmosphere unintentionally became an excellent tracer of processes on a time scale of up to 100 years. A prerequisite for tritium applications is to know the distribution of tritium activity in precipitation. Here we present a database of isoscapes derived from 41 stations for amount-weighted annual mean tritium activity in precipitation for the period 1976 to 2017 on spatially continuous interpolated 1 km×1 km grids for the Adriatic–Pannonian region (called the AP³H_v1 database), with a special focus on post-2010 years, which are not represented by existing global models. Five stations were used for out-of-sample evaluation of the model performance, independently confirming its capability of reproducing the spatiotemporal tritium variability in the region. The AP³H database is capable of providing reliable spatiotemporal input for hydrogeological application at any place within Slovenia, Hungary, and their surroundings. Results also show a decrease in the average spatial representativity of the stations regarding tritium activity in precipitation from ∼440 km in 1970s, when bomb tritium still prevailed in precipitation, to ∼235 km in the 2010s. The post-2010 isoscapes can serve as benchmarks for background tritium activity for the region, helping to determine potential future local increases in technogenic tritium from these backgrounds. The gridded tritium isoscape is available in NetCDF-4 at https://doi.org/10.1594/PANGAEA.896938 (Kern et al., 2019).