- No. 2(8) 2025
«Meteorology. Hydrology. Environmental monitoring»
- 5page
SPATIAL AND TEMPORAL TRENDS OF MAXIMUM SNOWMELT-RAINFALL RUNOFF IN THE RIVERS OF UKRAINIAN POLISSYA
“SPATIAL AND TEMPORAL TRENDS OF MAXIMUM SNOWMELT-RAINFALL RUNOFF IN THE RIVERS OF UKRAINIAN POLISSYA
The study is devoted to the investigation of long-term spatial and temporal trends of the characteristics of maximum snowmelt-rainfall runoff in the rivers of Ukrainian Polissya (right-bank part) using the hydro-genetic method of statistical analysis. The study of maximum river discharge has significant scientific and practical importance. Understanding the conditions of formation of extreme values of maximum river discharge, their frequency of occurrence, as well as the analysis of long-term trends, is particularly important for engineering design and hydrological forecasting activities. In recent years, floods in rivers caused by snowmelt and precipitation have begun to occur in earlier, almost winter periods of the year, forming the maximum snowmelt-rainfall runoff of the rivers of Ukrainian Polissya. At the same time, in a period of climate change the reduction of snow cover and its melting due to the increase of winter air temperatures has led to a decrease in floods in terms of magnitude and spatial extent, but the probability of catastrophic snow-rain floods has increased. Using the hydro-genetic method of statistical analysis, the study performed a verification of the statistical homogeneity of hydrological time series of maximum snowmelt-rainfall runoff in the rivers of Ukrainian Polissya (maximum water discharges, runoff depths), as well as an investigation of temporal trends, cyclic fluctuations of river runoff, and synchrony in their series. The analysis of the homogeneity of time series of maximum water discharges of snowmelt-rainfall runoff in the rivers of Ukrainian Polissya, based on cumulative curves, revealed that the observation series show a violation of homogeneity, in contrast to the time series of runoff depths. The main causes of this violation of homogeneity in the time series of maximum water discharges of snowmelt-rainfall runoff are the redistribution of runoff under the influence of meteorological conditions in the current climatological period and the anthropogenic regulation of river discharge. Summarizing the results of the analysis of fluctuations in the maximum snowmelt-rainfall runoff of the rivers of Ukrainian Polissya based on the integral curves of maximum water discharges and runoff depths, it can be noted that they are synchronous, and the inflection points of the integral curves almost coincide. At the same time, their overall shape may differ due to varying ratios of deviations, and as a result, the termination of identical hydrological phases according to these curves may not coincide in the multi-year context. Analyzing the chronological course of the time series of runoff values of the maximum snowmelt-rainfall runoff of the rivers of Ukrainian Polissya, it can be noted that all of them exhibit phases of cyclic fluctuations in water abundance, which determine the pronounced trends in the time series of maximum runoff. For the rivers of Ukrainian Polissya, the high-water phase was observed mainly until 1981-1982, while the low-water phase has persisted in the rivers until 2020. This provides the possibility of further application of statistical methods for the determination of the design parameters of maximum snowmelt-rainfall runoff of the rivers of Ukrainian Polissya, based on the available observational data on water runoff.
“MULTI-YEAR DYNAMICS OF THE CHANNEL PROCESSES IN THE DESNA RIVER NEAR CHERNIHIV CITY
Сhannel deformations of the Desna River near Chernihiv city were analyzed based on field studies and analysis of various temporal cartographic materials and space images. The hydrological regime of the river is characterized by intensive channel processes, which can be rather hazardous for the functioning of residential and economic infrastructure. Today, there is a rather worring situation on the northeastern outskirts of Chernihiv city, where the channel meander is at the final stage of its development and in the period of the nearest high floods, the formation of a straightening duct and the transformation of the meander into a flood channel is possible. As a result, the river, which flows directly along the city outskirts and widely used for recreational purposes, will become several kilometers away from the city. Based on the study of the hydrological regime of the river, quantitative assessment of the morphological parameters of the river, and the nature and intensity of the transformation of the meander over a long period as well as the forecast of the development of the meander for the coming years were made. Measures to stabilize the conditions of the river channel were proposed.
“RESULTS OF THE MODELING OF VARIABILITY OF OCEANOGRAPHIC PARAMETERS IN THE UKRAINIAN SECTOR OF THE BLACK SEA DURING STORMY PERIODS IN NOVEMBER 2023
Results of application of the complex of coupled numerical models Delft3D Flow Flexible Mesh (D-Flow FM) and D-Waves (spectral wave model SWAN) to the oceanographic conditions of the Black Sea and its north-western part during stormy periods in November 2023 are discussed. The modelling complex was set up in simulation mode to reproduce the spatial and temporal variability of oceanographic parameters in the water areas under consideration. Simulation was performed as a part of the modelling complex verification procedure. Meteorological data obtained from the forecast archive of the GFS (Global Forecasting System) global numerical weather prediction model was used as atmospheric forcing. Simulation results were compared against the observational data on oceanographic parameters at coastal hydrometeorological stations located in the ports of the Odessa region at the north-western coast of the Black Sea (Chornomorsk, Odesa, Pivdennyi). In addition, results of wind wave simulation, obtained from the modelling complex, were compared with the results of independent modelling via spectral wave model WAM Cycle 6 that assimilates wind reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The use of the modelling complex made it possible to reconstruct a complete picture of the spatial and temporal variability of oceanographic parameters during the stormy periods of November 2023 both in the entire Black Sea basin and in its north-western part. Good quantitative and qualitative consistency between the modelling results of sea level variability, wind wave heights, sea water temperature and observational data, collected at hydrometeorological stations in the area of seaports of the Odessa region, was obtained. The conclusion was made that the aforementioned complex of coupled numerical models D-Flow FM + D-Waves (SWAN) has good prospects for use in the system of hindcast and operative forecast of variability of oceanographic parameters of marine environment in the Black Sea and its distinct areas.
“SEASONAL AND LONG-TERM VARIABILITY OF SALINITY IN THE AREA OF RIVERINE AND MARINE WATERS INTERACTION BASED ON OBSERVATIONS AT COASTAL STATIONS OF UKRAINE
Statistical methods were used to study the seasonal variation and long-term variability of salinity as an indicator of the interaction between riverine and marine waters on the shelf of the Northwestern part of the Black Sea. A comparison of statistical indicators for two consecutive climatic periods, 1960-1990 and 1991-2020, showed a decrease of average salinity in the port of Odessa by more than 0.4 psu and an increase of salinity in Ochakiv due to a decrease in the Dnieper River runoff by almost 0.4 psu. These effects occurred on the background of a general linear trend of decreasing salinity in the Black Sea due to a climatic decrease in evaporation from the sea surface. These effects manifested themselves on the background of a general linear trend of decreasing salinity in the Black Sea due to a climatic decrease in evaporation from the sea surface. The transformation of the seasonal salinity cycle in Odessa during the period 1991-2020 compared to the previous 30 years consists of a general decrease of salinity in the summer-autumn season and a shift of the maximum from July to September. Salinity in Ochakiv increased significantly in September (by 0.5 psu). Correlation analysis of the series of average monthly salinity and river water discharge values showed that the maximum correlations between salinity and Dnieper discharge were obtained for zero delay, since the movement of desalinated water from the mouth of the Dnieper to the Odessa Gulf and back takes several days, but less than a month. A significant correlation also persists at delays of 1 and 2 months, indicating strong inertia of salinity and its interaction with the Dnieper River flow. Spectral analysis of the longest series of average monthly salinity in Odessa (1951-2020) revealed four significant harmonics corresponding to the main periods of variability: semi-annual, annual, 4-year, and 35-year. The first two periods correspond to seasonal variability, and the 4-year period corresponds to interannual variability. Long-term changes in salinity with a period of 35 years are associated with corresponding fluctuations in the components of the climate system, which contribute to changes in evaporation from the sea surface. Wavelet analysis has shown that the increase in the power of the 4-year harmonic interannual salinity fluctuations in the port of Odessa occurs during periods of El Ni?o (EN) influence, with maximum power occurring between adjacent events or directly during EN. Accordingly, the decrease in this value began after the La Ni?a (LN) phenomenon, with a minimum between the previous LN and the following EN, or between two consecutive LN events.
“CLIMATE CHARACTERISTICS OF THERMAL PERIODS IN UKRAINE UNTIL THE END OF THE 21ST CENTURY. PART IV: SUMMER SEASON
In the context of ongoing climate change and the rapid development of regional climate models, there is an increasing demand for detailed assessments of the duration, onset, and end dates of thermal periods, which are crucial for multiple sectors of the economy. Previous publications have examined changes in the warm period (mean daily air temperature t > 0°C), the vegetation period (t > 5°C), and the period of active vegetation (t > 10°C). This article concludes a series of studies on thermal periods in Ukraine and focuses on the characteristics of the climatic summer (t > 15°C), a critical indicator for human health, recreation, tourism, energy supply, and agricultural activities. The main goal of the presented research was to analyze the spatial and temporal patterns of the summer season and evaluate how these characteristics may change under future climate scenarios. Using E-OBS observational data, the dates of onset, termination, and duration of the summer period were calculated for the standard climatological period 1961–1990, along with observed changes during 1991–2010. Projections for future changes were conducted for three periods—2021–2040, 2041–2060, and 2081–2100—under moderate (RCP 4.5) and high (RCP 8.5) greenhouse gas representative concentration pathways, based on an ensemble of 34 Euro-CORDEX regional climate models with a high spatial resolution of 12?12 km, covering over 7,300 grid points across Ukraine. The analysis revealed that during 1961–1990, the summer season typically began between May 10 and 20 and ended between September 17 and 27. In 1991–2010, the season lengthened by 5–15 days, with the onset occurring 2–5 days earlier. Future projections suggest further extension of the summer season by 7–60 days depending on the region and scenario. Under RCP 8.5, the maximum summer duration could reach 180–200 days in Crimea and southern regions by the end of the century. In contrast, the Carpathians may experience a climatic summer lasting 80–120 days, similar to the Pre-Carpathian region at the end of the twentieth century, while in the Polissya region, the summer may extend to 140–160 days, resembling current conditions in Crimea. The results presented in this and previous parts of the study have substantial practical significance. They can support agricultural planning, risk assessment for food security, energy demand forecasting, and the design of climate change adaptation strategies. In addition, these findings are essential for evaluating the impacts of climate change on human health, planning recreational activities, promoting sustainable tourism, and managing territorial resources effectively.
“CLIMATE EDUCATION FOR GREEN RECONSTRUCTION: CHALLENGES, METHODS AND PRACTICAL CASES
The development of climate education is a crucial factor in shaping the competencies required for implementing Ukraine's green reconstruction, adapting to climate change, and achieving climate neutrality. It is substantiated that, in the context of increasing climate risks and transformations in global climate policy, education plays a system-forming role in ensuring the climate resilience of society and the economy. It is demonstrated that solving these tasks necessitates the systematic integration of educational, scientific, and practical approaches to foster interdisciplinary knowledge and skills. The results of implementing summer schools and workshops within the framework of the UniCities and New European Bauhaus Academy projects, which tested educational formats based on the challenge-based learning methodology and the living labs concept, are presented. These formats provide a combination of learning, research, and practice, contributing to the development of competencies provided by the European GreenComp system: systems thinking, cooperation, innovation, and the ability to act. It is demonstrated that interdisciplinary educational practices centered on real-world cases of green reconstruction enhance the readiness of future specialists to develop and implement adaptive, nature-oriented solutions in local communities. It is noted that climate education acts as a catalyst for the implementation of adaptive and nature-oriented solutions at the local level, enhancing the effectiveness of climate services and management practices. It has been proven that the development of climate education should become a strategic priority in the post-war green transformation of Ukraine and in increasing the state's climate resilience. The scientific novelty of the study lies in identifying climate education as a strategic tool for post-war green transformation in Ukraine, which ensures synergy between science, education, culture, and the practice of sustainable development.
“VERIFICATION OF THE ICON NUMERICAL WEATHER PREDICTION MODEL IN UKRAINE
This study presents a comprehensive verification of the ICON numerical weather prediction model over Ukraine for the year 2024. The evaluation covers key meteorological parameters - air temperature, wind speed, relative humidity, precipitation, and cloud cover - at 24-, 48-, and 72-hour forecast lead times. Both continuous metrics (correlation, mean absolute error, root mean square error, bias) and categorical metrics (POD, FAR, CSI) were applied, along with seasonal and spatial analyses. The model demonstrated high accuracy in forecasting mean temperature, with a correlation coefficient of r = 0.95 at 24 hours, low RMSE (?2.6?°C), and near-zero bias. Cloud cover forecasts also showed excellent performance, with POD > 0.94 and CSI up to 0.86 at a 10% threshold, maintaining stability across regions and seasons. By contrast, wind speed forecasts were less reliable, with lower correlations (r = 0.40 at 24 h), RMSE ~1.75?m/s, and consistent overestimation. Forecasts of relative humidity were moderately accurate (r = 0.88), although a persistent negative bias (~–4.2%) was observed. Precipitation forecasts exhibited the lowest skill, especially at longer lead times and higher thresholds. At a 0.1?mm threshold and 24-hour forecast, POD reached 0.61, but FAR remained high (>0.50), particularly in southern regions with frequent convective activity. Seasonal analysis indicated the best model performance in autumn and winter, with reduced accuracy in summer, especially for humidity and precipitation. Spatial verification at 24-hour lead time revealed regional differences: the lowest RMSE for mean temperature was found in Kherson (2.29?°C), while the highest wind speed error occurred in Donetsk (4.88?m/s). Overall, the ICON model provides robust forecasts for temperature and cloud cover, acceptable performance for humidity, and highlights the need for further refinement in wind and precipitation prediction. These findings offer valuable guidance for improving regional forecast applications and adjusting physical parameterizations under Ukrainian climate and topography conditions.
- 102page
FORECASTING OF MEDIUM AND LONG-TERM COMPONENTS OF GROUNDWATER LEVEL FLUCTUATIONS USING ARTIFICIAL NEURAL NETWORKS METHOD
Анотація Full version of the article DOI: http://doi.org/10.15407/Meteorology2025.08.102 pp. 102-113
“FORECASTING OF MEDIUM AND LONG-TERM COMPONENTS OF GROUNDWATER LEVEL FLUCTUATIONS USING ARTIFICIAL NEURAL NETWORKS METHOD
The cessation of regular monitoring of groundwater levels in Ukraine prompts the search for methods for reproducing and predicting the level, which will allow estimating groundwater flow rates, creating models of groundwater resource formation and moisture balance in watersheds. Artificial neural networks (ANN) of various architectures are considered as a data recovery tool for further modeling of water resources. In order to determine the optimal ANN architecture that can simulate the groundwater level (GWL) trend and provide forecasts, the effectiveness of different neural networks (RBF and MLP) in predicting the monthly average GWL was investigated. To select the optimal ANN configuration and assess the effectiveness of each network and its ability to make accurate predictions, the following methods and criteria were used: multiple correlation analysis, spectral analysis of Fourier transforms, wavelet analysis, and component separation by the duration of oscillation cycles. The forecast was made for the average monthly groundwater level from one of the few wells in the Western Bug River basin, for which observations were stopped back in June 2011. The most realistic results using ANN were obtained after isolating short-, medium-, and long-term components in the GWL fluctuations and performing forecasts for the last two components, which is a pioneering step for hydrogeological observations in Ukraine. If for the full (undivided) series of input data it is possible to obtain a forecast/recovery of data with low accuracy up to 4-5 years, then for the medium and long-term components - a more accurate forecast with a sufficiently probable trend up to 11-12 years. Wavelet analysis was used to determine the type of aquifer.
- 114page
CHANGES IN THE STATISTICAL STRUCTURE AND VARIABILITY OF POLLUTANTS TOTAL CONTENT IN THE ATMOSPHERIC AIR OVER URBANIZED AREAS AS A RESULT OF THE FULL-SCALE RUSSIAN INVASION
Анотація Full version of the article DOI: http://doi.org/10.15407/Meteorology2025.08.114 pp. 114-123
“CHANGES IN THE STATISTICAL STRUCTURE AND VARIABILITY OF POLLUTANTS TOTAL CONTENT IN THE ATMOSPHERIC AIR OVER URBANIZED AREAS AS A RESULT OF THE FULL-SCALE RUSSIAN INVASION
The full-scale Russian invasion has led to numerous changes in atmospheric air conditions. Estimates of average changes in pollutant content often do not allow for the identification of characteristic consequences of warfare due to the overlap of factors with opposing effects. In this study, based on Sentinel-5 Precursor satellite observations for 2019–2024, we analyzed the statistical structure and variability of the total content of nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and formaldehyde (HCHO) in the atmospheric air of urbanized areas. Over three years of war, we found a predominant decrease in NO2 and CO levels, an increase in SO2, and mixed changes in HCHO, reflecting both the consequences of industrial destruction and the emergence of additional emissions due to a shift to less ecological fuel types and the operation of diesel generators. Against the background of changes in mean values, the statistical distribution structure remained largely unchanged for NO2 and CO, though with a slight increase in the frequency of positive CO deviations within +1?. SO2 levels showed a decrease in the recurrence of both positive and negative deviations, clearly indicating reduced variability in the atmosphere. HCHO became less variable in the range of large deviations from the mean, with greater recurrence of minor variations close to average values. The obtained results complement the observed changes in the total pollutant content in cities during the full-scale Russian invasion, which is important for fixation the consequences of warfare under conditions of overlapping factors with opposing impacts.
- 124page
TREND ANALYSIS OF AIR TEMPERATURE AND ATMOSPHERIC PRECIPITATION IN UKRAINE BASED ON OBSERVATIONAL DATA AND CMIP6 CLIMATE PROJECTIONS
Анотація Full version of the article DOI: http://doi.org/10.15407/Meteorology2025.08.124 pp. 124-135
“TREND ANALYSIS OF AIR TEMPERATURE AND ATMOSPHERIC PRECIPITATION IN UKRAINE BASED ON OBSERVATIONAL DATA AND CMIP6 CLIMATE PROJECTIONS
This study presents an analysis of trends in annual and seasonal averages of daily minimum (TN), mean (TG), and maximum (TX) air temperature, as well as in annual and seasonal totals of atmospheric precipitation (RR), across both national and regional scales. Two time intervals were examined: 1946–2020, representing the period of observational data, and 2026–2100, representing future climate projections. All calculations were performed using high-resolution gridded datasets (~10 km × 10 km), which provide a sufficiently detailed spatial representation to support the development of national and regional climate-change adaptation strategies. For the historical period, the analysis relied on the ClimUAd dataset, constructed from homogenized observations of 178 meteorological stations across Ukraine and subsequently processed to ensure spatial consistency and representativeness of the climatic fields. For the future period, trends were estimated using a statistical ensemble of climate projections obtained using new-generation global climate models (CMIP6), based on updated scenarios of greenhouse gas emissions and socio-economic development of humanity (SSP2-4.5 and SSP5-8.5). Prior to trend analysis, bias correction was applied to all model outputs using the Quantile Delta Mapping (QDM) method, ensuring that systematic deviations from observed climate conditions were effectively removed. The results show statistically significant changes in temperature patterns throughout Ukraine for both the observation period and the future period for both considered scenarios (for all air temperature indicators considered, all seasons, and the year). Under SSP2-4.5, the intensity of warming remains broadly comparable to the trends observed in 1946–2020, whereas SSP5-8.5 indicates a much stronger and more rapid increase in temperature. Changes in precipitation are considerably less pronounced, vary in sign across regions and seasons, and are mostly statistically insignificant.
