Vol. 129, No. 2 * Pages 107–240 * April - June 2025

In this study, the trend for a category of variables, that is, for the total average annual amount of precipitation for the vegetation period (P-VP) from April to October is presented. Moreover, with the help of Geographic Information System (GIS) numerical analysis, geospatial distribution of the obtained results on the territory of the Central Serbia is described.The main objective of this study is the possible changes in trends for the total average annual amount of precipitation for the vegetation period in the observed area.In terms of methodology, trend testing was conducted using the Mann-Kendall trend test (M-K), trend equation, and trend magnitude.The data used for the necessary analysis were taken from the Meteorological Yearbooks of the Republic Hydrometeorological Institute of Serbia, with a total of 24 meteorological stations, for the observed time period from 1949 to 2018.A total of 24 time series were analyzed. The average annual amount of precipitation for the vegetation period of the observed area is 427.6 mm. The values range from 362.3 mm to 625.5 mm. The lowest value was recorded in Nis, while the highest value was recorded in Zlatibor. Based on the obtained results, a statistically significant positive trend was recorded in 2 time series, whereas in the remaining 22 time series there was no trend. Furthermore, the results obtained by the trend equation, and trend magnitude indicate a slight increase in the total average annual precipitation in 21 time series and a decrease was recorded in the remaining 3 time series. A decrease in the average annual amount of precipitation for the vegetation period was recorded in three cities, namely: Jagodina (-15.9 mm), Bujanovac (-4.6 mm), and Zajecar (-0.5 mm). Comprehending the interaction between precipitation and vegetation period is crucial for implementing adaptation and mitigation measures in terrestrial ecosystems. The preliminary findings of this study can offer a technical foundation and valuable reference for water resource and sustainable ecological management strategies in the Republic of Serbia, benefiting policymakers and stakeholders involved.

The introduction of the LN5 and mLN5 distributions extends the commonly used three-parameter log-normal distribution (LN3) by enhancing tail modeling, which is critical for accurate representation of extreme values in hydrology and climatology. This paper details two methods for parameter estimation: the established local maximum likelihood method and the newly developed triangular method, an adaptation of the relative least squares approach. The effectiveness of these distributions is demonstrated through their application to datasets from the Czech Hydrometeorological Institute, encompassing average daily flow, precipitation, atmospheric pressure, and air temperature. Results show significant improvements in modeling extreme events with LN5 and mLN5 over LN3, as well as over other compared distributions such as generalized Gamma and generalized Weibull, particularly in tail behavior, underscoring their potential for advancing environmental studies. Appendices include comprehensive derivations of the functional characteristics of LN5 and mLN5 and introduce an alternative parametrization for LN5.

This study addresses the pressing issue of flood frequency analysis in Bosnia and Herzegovina (BH), focusing on major rivers—Una, Sana, Vrbas, and Bosna. In light of the global impact of floods on lives, property, and infrastructure, the research aims to understand and predict these events, particularly considering climate change and socioeconomic development. Employing goodness-of-fit tests such as Kolmogorov-Smirnov and Cramér-Von Mises, the study identifies the most suitable probability distributions for modeling river discharge data. Pearson 3, generalized extreme value (GEV), and Gumbel distributions emerge as best fits, demonstrating variations across rivers. The research emphasizes the importance of tailoring models to specific hydrological characteristics, with the Bosna River best modeled by the Pearson 3 distribution and the Sana River by the GEV distribution. Calculated return periods for extreme flood events provide valuable insights into potential discharge magnitudes, highlighting the crucial role of accurate probability distributions in informed risk management and infrastructure planning. This study fills a critical gap in flood frequency analysis for selected rivers in BH, offering essential information for water resource management and flood risk assessment in the context of ongoing climate change.

Validated hail observations are available from the hail suppression system operated by the National Chamber of Agriculture since 2019. Using this dataset, large hail (larger than a walnut) reports were collected from recent years. The environments of hail-producing thunderstorms were reconstructed upon ERA5 reanalysis data, and characteristics were sought between thermodynamic profiles and hodographs that could help recognize the environmental conditions for potentially large hail-producing thunderstorms. During the investigation, 52 cases over a total of 35 days were examined. Although the number of cases did not allow for statistical conclusions, the authors identified similarities among the cases through ensemble sounding and hodograph analysis, which could serve as useful operational tools for forecasters.

Accession to the European Union has resulted in a remarkable step forward in air quality protection in Hungary. At present, particulate matter means the most significant environmental health risk, and it is Hungary’s most problematic pollutant: its concentration exceeds the EU limit values for longer periods under certain conditions. It is presented how the rates and contributors of the PM₁₀ and PM_2.5 emissions varied in Hungary during the two decades of the 2000-2021 period. Special attention is paid to the residential combustion sector which is a key category in particulate matter emissions. Price elasticity of demand of natural gas and other fuels are investigated together with the latest comprehensive population census on conventional stoves and boilers used in the Hungarian households to determine the possible measures that could be taken to improve and change the residential heating habits.

One of the most significant impacts of climate change on domestic agriculture is an adverse change in rainfall patterns and an increased frequency of droughts. In our study, we analyzed the daily rainfall data at the automatic hydro-meteorological measuring station of the Lower Tisza Water Directorate (ATIVIZIG) in Szentes from 1981 to 2000 and 2001 to 2020. The focus of our study was the change in precipitation patterns caused by climate change and the phenology-dependent water demand and related yield of field cereal crops (wheat, maize). The yields of wheat and maize grown on the two largest arable land areas in the Szentes District and its wider region, the Southern Great Plain, between 2003 and 2020 showed a moderately strong correlation with changes in annual rainfall for the farms studied. This points to a strong dependence on rainfall for yields, also taking into account the risk of rainfall extremes. We found that in the second period under study, the dispersion of both annual and monthly rainfall totals increased strongly but in an insignificant way. The number of days with high rainfall increased by 19.3% and the number of days with extreme rainfall increased by 40.9%. Even larger increases were observed for the highest five-day rainfall totals (62.1%).

In Slovakia, the spatial distribution of precipitation is inhomogeneous, which is mainly caused by the prevailing airflow of individual synoptic types in combination with a diverse georelief. The article examines the precipitation conditions of Slovakia for all northern and southern types of cyclonic situations in the period 1991–2020. In the selected area we worked with precipitation totals from 798 stations provided by the Slovak Hydrometeorological Institute. We processed the separate dataset in Microsoft Excel and created distinct layers of precipitation totals for all the situations studied in ArcGIS software using a Python script. From the separate datasets, we created precipitation fields for the average annual precipitation total and fields for the percentage of the average annual precipitation fallen during all northern and all southern types of cyclonic situations out of the total average annual precipitation fallen during the whole period under study and for each decade, respectively. Based on the results obtained, we can observe changes in the frequency of occurrence and in the spatial distribution of precipitation, especially for southern, but also for northern cyclonic types of situations. These findings can be used in practice in several fields, especially in synoptic meteorology, climatology, and hydrology.