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Abstract
ABSTRACT
River floods in the mountainous regions of the Ukrainian Carpathians are a natural hazard that often leads to significant destruction and substantial economic damage to the region. The key driver of flooding is typically heavy rainfall, which results from certain patterns in regional atmospheric circulation. We studied the atmospheric circulation regimes over Ukraine for the period 1948–2021 using the modified Jenkinson–Collison classification. Circulation types associated with airflows from the western quarter are the most frequent throughout the year. However, seasonality in circulation patterns related to the dynamics of regional atmospheric centers of action is also well expressed. The linear trends in the frequency of circulation types are found statistically significant for meridional processes associated with advection from the north or south. Circulation types according to the Jenkinson–Collison classification, as well as the Niedźwiedź regional synoptic classification, were applied to cases of extreme floods in the river basins of the Ukrainian Carpathians to identify features of the pressure field leading to the formation of heavy precipitation. During the study period, 10 flood events, characterized by extremely high or historically significant water levels, were selected. Both pluvial floods in summer and mixed floods in winter were considered. In cases of the warm period, the circulation types with airflows directed towards the mountain range from the east or north are observed, and floods formed in the Ciscarpathia. In the cold period, circulation types with airflows from the western quarter increased precipitation and river discharge in Transcarpathia. 45% of observed circulation types belonged to the cyclonic group; however, the relative position of baric systems in other types also ensured the convergence of atmospheric moisture into the flood area.
Full text
Introduction
As studies from different regions show, the classification of atmospheric processes helps to describe the connections between types of atmospheric circulation and weather phenomena, including extreme events. Knowledge of the characteristics of regional synoptic processes enables forecasters to lay the foundation for synoptic weather forecasts. Despite the advances in modern numerical forecasting, synoptic analysis remains a reliable basis for the forecaster to quickly assess the overall situation, as well as to be sure that the numerical model is accurately reproducing the synoptic situation and the associated meteorological parameters and phenomena. Automating this process using classifications of atmospheric circulation types (CT) accelerates forecasting by highlighting the potential for adverse weather phenomena. The links between large-scale atmospheric teleconnections and the occurrence of droughts (Kingston et al. 2015; Burgdorf et al. 2019) and wet periods (Vicente-Serrano et al. 2009; Lee et al. 2018) are well established. The study of the dynamics of regional synoptic processes has made it possible to identify connections between abnormally warm and cold periods in different regions and the development of stable (blocking) processes in the atmosphere (e.g., Namias 1982; D'Errico et al. 2022), while cyclonic activity is often suppressed due to the shifting of cyclone tracks (Lehmann and Coumou 2015).
Of particular interest is the application of synoptic classifications to compounded phenomena, such as heavy precipitation and subsequent floods in specific regions. Global trends over the past half-century indicate that the spatiotemporal extent of floods and their frequency are decreasing, despite an increase in extreme precipitation events (Ivancic and Shaw 2015; Sharma et al. 2018). However, in some regions, such as northwestern Europe, the trends are positive (Liu et al. 2022). Studies of flood modeling have shown that, although heavy rainfall is typically the dominant contributor to flood formation, other factors can also significantly increase peak flow, such as high soil moisture saturation from prior precipitation (Wasko and Nathan 2019; Yu et al. 2023) and snowmelt during the corresponding season (Berghuijs et al. 2019).
Existing studies show a strong relationship between cyclonic CT, particularly the position of the cyclone center relative to a river basin, and the occurrence of intense precipitation leading to floods. Thus, Prudhomme and Genevier (2011) demonstrated the effectiveness of using the automated version of the Hess–Brezowsky Grosswetterlagen classification (Hess and Brezowsky 1977) to identify flood situations in various European river basins, including mountainous areas (Petrow et al. 2007). Numerous studies have highlighted the benefits of using objective classifications to identify circulation patterns in heavy rainfall analysis and forecasting associated with flood-generation mechanisms (e.g., Schlef et al. 2019; Richardson et al. 2020).
When applying classifications, it is essential to additionally assess the structure of the identified pressure systems to determine their anomalies for the study area. For instance, the study by Tolika and Skoulikaris (2023) revealed that the CT identified during flood periods in Greece predominantly belonged to the cyclonic group, but the depth of these cyclones was greater compared to the average pressure for similar CT. Extremely low pressure in cyclones responsible for flash floods in Chinese provinces was also noted by Chen et al. (2024). Furthermore, cyclonic CT generally have higher efficiency in producing precipitation compared to other types, despite the relatively low frequency of cyclones (Cortesi et al. 2014). An analysis of synoptic situations during flood events in Catalonia showed that cyclonic fields contributing to precipitation in this region were enhanced by mesoscale factors, which triggered local flash floods, primarily during the warm season (Gilabert and Llasat 2018). On ocean coasts, there is a significant relationship between cyclonic CT and atmospheric rivers, which, when interacting with mountainous terrain, lead to heavy precipitation and flooding (Ralph et al. 2006; Kingston et al. 2016; Eiras-Barca et al. 2018). Complex orography, in turn, leads to large spatial heterogeneity in precipitation, which complicates the relationship between heavy precipitation and atmospheric CT (Seibert et al. 2007; Ustrnul et al. 2023).
According to the Organization for Economic Cooperation and Development (OECD 2019), floods are one of the most common natural disasters in the world, with annual losses amounting to about 40 billion US dollars. In Ukraine, catastrophic floods usually occur on the mountain rivers of the Carpathian region. Several pluvial floods can be observed here during the warm and transitional seasons. Due to the mountainous terrain, high drainage density, and significant precipitation, these floods often become catastrophic. The flood regime of the rivers in the Ukrainian Carpathians is influenced by both natural factors and the complex interaction between society and the environment. During severe and catastrophic floods, the water discharge exceeds the average runoff by tens of times, resulting in the inundation of residential buildings and farmlands, damage to protective dams, highways, bridges, and railways. Most importantly, such floods often lead to the loss of human lives.
The issue of flood classification has been studied by many researchers across different countries (Diakakis et al. 2020; National Weather Service n.d.). In the European Union, most countries classify historical floods into four or five categories, characterizing their significance as “very low,” “low,” “moderate,” “high,” or “very high.” The similar classification is currently used in Ukraine, according to which floods are divided by the magnitude and scale of damage into: “low” (“small”), “high,” “significant,” and “catastrophic” (Nezhikhovsky 1971). Catastrophic floods, according to this classification, cause extensive material damage and lead to loss of life, affecting large areas within one or more river systems. More than 70% of agricultural land, numerous settlements, industrial plants, and public utilities are inundated. Economic and production activities are completely paralyzed, and the lifestyle of the population is temporarily disrupted. The recurrence interval for these catastrophic floods is once every 100–200 years.
Considering the need to improve the scheme for assessing flood events based on synoptic situations for implementation in operational meteorological and hydrological practice, this study solves three objectives: (i) assessing the atmospheric circulation regimes and trends in CT; (ii) analysis of the spatiotemporal characteristics of extreme floods in the river basins of the Ukrainian Carpathians; and (iii) identifying distinctive CT during flood periods as indicators of heavy precipitation capable of causing floods.
Data and Methods
Classification of
In our study, the modified Jenkinson–Collison classification (Jenkinson and Collison 1977), which was proposed by Miró et al. (2020) for the western Mediterranean and northeastern Iberian Peninsula, was used to classify synoptic processes over the territory of Ukraine. This classification is based on Lamb's earlier manual classification (Lamb 1972), originally developed for the British Isles, which considers the structure of the pressure field and the direction of airflows only at the sea surface level. In the modified Jenkinson–Collison classification, pressure fields at both the surface and the 500 hPa pressure level are combined, allowing for the description of the vertical extent of high and low pressure systems and advection in the middle troposphere. The definition of CT in this classification is based on the calculation of three parameters using the pressure data at the grid points: flow strength, flow direction, and vorticity of the airflow. Then, a set of four rules is used to classify the current situation into one or another CT based on the combination of these parameters. In our study, a 16-point grid with a step of five degrees and the center at 50° N, 30° E was used.
A daily catalog of CT for the territory of Ukraine based on 14 synoptic patterns was obtained for the period 1948–2021. The area where the CTs were determined is bounded by coordinates 10°–45° E and 40°–60° N. Following Semenova (2023), a brief description of CT based on composite SLP and 500 hPa fields is presented in Table 1. An initial analysis of the CT frequency revealed that the “Thermal Anticyclone” type has a very low frequency (0.2%) that is likely due to the lack of conditions for the long-term persistence of a “cold pool” in the geographic conditions of Ukraine, where intense air mass exchange is typically observed throughout most of the year (Lipinskyy et al. 2003). Therefore, in this study, the anticyclone CT (“ANTICYC”) also includes low thermal anticyclones, which can generally be considered as anticyclones in the initial stage of their development.
TABLE 1 Circulation types according to the modified Jenkinson–Collison classification for the territory of Ukraine and their frequency in the period 1948–2021 (based on Semenova 2023).
| Name of CT (abbreviation) | Frequency (%) | Interpretation |
| Anticyclone (ANTICYC) | 7.1 | At the surface, the anticyclone is over eastern and central Europe, with its main center located within Ukraine. At upper levels, most of Ukraine is under the influence of a ridge. For low anticyclones, an upper-level trough oriented from the northeast is observed over Ukraine |
| West advection (W_AD) | 11.5 | Western transport of air masses is over Ukraine at both the surface and upper levels |
| Anticyclonic west advection (AW_AD) | 13.7 | At the surface, an anticyclone or ridge from the west is positioned over Ukraine. At upper levels, there is a quasi-western transport of air masses |
| Cyclone (CYCLONIC) | 5.2 | At the surface, an extensive cyclone is over Ukraine, the trough of which often extends to the Balkan or Apennine peninsula. The upper-level cyclone is shifted west of the surface center. |
| Low cyclone (LOW CYCLONE) | 2.6 | At the surface, a cyclone is located over Ukraine. This cyclone can be of thermal origin (mainly in summer) or a frontal cyclone. At upper levels, there is a western transport of air masses |
| Trough (TROUGH) | 1.0 | The trough oriented from the north is over Ukraine at both the surface and upper levels |
| East Advection (E_AD) | 12.9 | At the surface, the territory of Ukraine is in the area of eastern winds on the southern periphery of an anticyclone over the East European Plain. At upper levels, there is an anticyclonic ridge oriented from the south |
| East advection with cut-off low above (E_AD_CUT) | 4.0 | At the surface, the territory of Ukraine is in the area of eastern winds on the southern periphery of an anticyclone over the East European Plain. At upper levels, there is a cyclone, cut off from the north by a band of high pressure |
| North advection (N_AD) | 7.0 | At the surface, the territory of Ukraine is in the transition zone between an anticyclone in the west and a cyclone in the east, with northern winds prevailing. An upper-level trough oriented from the north or northeast is observed over Ukraine |
| North-eastern advection (NE_AD) | 7.4 | At the surface, the territory of Ukraine is located on the southeastern periphery of the anticyclone over Europe, in the zone of northeastern winds. An upper-level shallow trough oriented from the northeast is observed over Ukraine |
| North-western advection (NW_AD) | 10.5 | At the surface, most of Ukraine is situated under an anticyclonic ridge from the west, with northwestern winds prevailing. At upper levels, northwestern winds are observed in the eastern part of the ridge over Ukraine |
| South advection (S_AD) | 8.1 | At the surface, the territory of Ukraine is in a transition zone with southern winds, situated between a vast anticyclone to the east and a cyclone over western and central Europe. The western periphery of an upper-level ridge oriented from the south produces southern winds over Ukraine |
| South-western advection (SW_AD) | 9.0 | At the surface, the territory of Ukraine is in a transition zone with southwesterly winds, between an anticyclone over the Caspian region and a cyclone centered over the Baltic Sea and Scandinavia. At upper levels, western and southwestern winds are observed over Ukraine |
Rare processes include troughs (“TROUGH”) and low cyclones (“LOW CYCLONE”). However, combining these types with the main CT “CYCLONIC” may reduce the representativeness of the diversity of regional synoptic patterns, especially for precipitation events characterized by large spatial heterogeneity. Additionally, troughs are important elements of pressure fields associated with convergence zones and atmospheric fronts. Therefore, retaining this CT in the classification is justified for studying synoptic patterns related to specific weather phenomena. Similarly, preserving the “E_AD_CUT” type is advisable, as upper cut-off lows usually cause severe weather in their influence areas (Nieto et al. 2008; Awan and Formayer 2017; Ferreira 2021).
According to Table 1, CT can be grouped based on the direction of advection and/or the type of pressure field:
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Western group: W_AD, AW_AD (total CT frequency 25.2%);
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Northern group: N_AD, NE_AD, NW_AD (24.9%);
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Eastern group: E_AD, E_AD_CUT (16.9%);
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Southern group: S_AD, SW_AD (17.1%);
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Cyclonic group: CYCLONIC, LOW CYCLONE, TROUGHT (8.8%);
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Anticyclonic group: ANTICYC (7.1%).
These groups will be used in further analysis. To examine changes in the annual and monthly time series of CT frequency, we applied the nonparametric Mann–Kendall test (Chandler and Scott 2011) to assess linear trends. Additionally, we used Theil–Sen nonparametric regression analysis to determine the magnitude of these changes (Wilcox 2010).
In addition, to analyze the synoptic processes on flood days, we utilized CT from the Calendar of CT by Niedźwiedź (2017). This manual classification, developed for southern Poland (bounded by 49°–51° N and 18°–24° E), is based on the principles of the Lamb's classification. It includes 21 CT, distinguishing between cyclonic (index c) and anticyclonic (index a) situations, with coding for the main direction of air mass transport in the study area (e.g., north (Na), northeast (NEa), east (Ec), etc.). The classification also differentiates pressure systems into separate types: centers of cyclones (Cc) and anticyclones (Ca), ridges (Ka), and troughs (Bc). This classification has been successfully applied to identify synoptic patterns influencing heavy precipitation in southern Poland (Twardosz and Niedźwiedź 2001; Twardosz and Cebulska 2020) and the occurrence of floods in the Western Carpathians (Niedźwiedź et al. 2015; Niedźwiedź and Łupikasza 2016), making it a promising tool for testing its effectiveness in the Ukrainian Carpathians.
ERA5 reanalysis data from the Copernicus Climate Change Service—Climate Data Store (Hersbach et al. 2020) were used to analyze SLP and 500 hPa pressure fields, total precipitation, and the dynamics of vertically integrated moisture divergence in the area restricted by 48°–50° N and 22°–26° E.
River Basins and Floods Data Management
Information about notable floods in the Carpathians is extensive and has been systematized in several hydrological periodicals. It is important to note that water management infrastructure protects most residential settlements. However, due to constant fluctuations in water levels and considering the high-water levels in certain parts of mountain rivers, the bank fortifications damaged during previous floods, which were not restored, are collapsing.
According to an international audit on flood prevention and elimination, conducted in Ukraine, Georgia, Serbia, Poland, Belarus, and other countries, the amount of money invested in reducing disaster risks is the most cost-effective approach. Proper preventive measures can significantly reduce the negative impacts of natural disasters. In the specific case of Ukraine, for every 1 hryvnia invested in flood protection, 7 hryvnias are saved that would otherwise be spent on restoration efforts (Floods of 2008, 2010, and 2020: Consequences and Damages 2020). Therefore, a thorough study, systematization, and classification of each catastrophic and significant flood event are crucial tasks for scientists to effectively manage water resources in flood-prone regions.
The Ukrainian Carpathians have the densest river network, as well as an extensive network of water gauge stations (WGS), where river water regime observations are conducted. As shown in Figure 1, this study utilizes data from 56 WGS stations of the stationary network of the Hydrometeorological Service of Ukraine, with most observation periods beginning in the late 1950s and extending to 2020 (Table A1). Thus, severe flood events in this study were selected using hydrological data for the period 1955–2020.
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Observation data are preprocessed at regional hydrometeorological centers before being transferred to the Boris Sreznevsky Central Geophysical Observatory (CGO) of the State Emergency Service of Ukraine. The Department of Hydrology and State Water Cadaster, a structural unit of the CGO, is responsible for compiling hydrological yearbooks and reference materials, such as Long-term Data on the Regime and Resources of Land Surface Waters (LWD) and Observation Data on Evaporation from Water Surfaces. This study used data from the LWD for the periods 1981–2000, 2001–2010, 2011–2015, and 2016–2020, obtained under an agreement between the Department of Land Hydrology at Odesa National I.I. Mechnikov University and the CGO.
Additionally, earlier reference publications from the Hydrometeorological Service of Ukraine, collected in the Laboratory of Hydrometeorological Information and Calculations of the Department of Land Hydrology, were utilized. To analyze extreme flood events, we also used historical data of river discharges in the Ukrainian Carpathians in warm and cold periods, presented in a number of publications (Boyko and Petrenko 2006; Gopchenko et al. 2018; Ovcharuk and Goptsiy 2022; Zabolotnia et al. 2022; Snizhko et al. 2023).
The Ukrainian Carpathians are characterized by a moderately continental climate with significant orographic effects. At the study area, precipitation is abundant, particularly in higher elevations, ranging from 800 to 1400 mm annually, with maxima during late spring and summer, conditions favorable for frequent flash floods and high river discharge. The main hydrographic characteristics of river basins in the Ukrainian Carpathians are also represented in Table A1. As good as this table illustrates, hydrological gauging stations (WGSs) are situated in the mountainous watersheds of the Danube (Tisza) and Dniester River basins. Stations are primarily located in the Transcarpathian and Ciscarpathian regions, with catchment elevations reaching up to 1200 m above sea level. Distance from the river mouth ranges from just 1 km (Cheremosh—Usteriky) to 196 km (Prut—Chernivtsi). The largest rivers in the region are the Dniester, Tisza, and Prut. In general, most rivers in the study area are classified as small to medium, with watershed areas ranging from 18.10 km2 (the Kamenka-Dora River) to 6890 km2 (the Prut River in Chernivtsi). River slope varies significantly; high-gradient mountain streams (e.g., Kamenka—Dora: 111‰) show rapid runoff potential. Main rivers in lower stretches have gentler slopes (e.g., Latoritsa—Chop: 1.9‰, Stryi—Verkhne Syniovydne: 2.4‰). Afforestation levels range from 18% (Studeny—Nizhny Studeny) to 95% (Svicha—Myslivka), typically higher in smaller, high-altitude watersheds. Lake coverage is generally low (< 5%), but several catchments show notable percentages (e.g., Strv'yazh—Luky with 55%). Most stations have long-term data records beginning in the mid-20th century. A few have data from the early 20th century (e.g., Prut–Chernivtsi since 1895, Dniester–Strilky since 1914). Several stations exhibit gaps in observations, especially during wars and transitional periods. High-gradient, forested rivers (e.g., Bystrytsia Solotvynska, Svicha, Kamyanka) are particularly prone to flash floods. Major rivers like the Tisza, Prut, and Dniester show increasing catchment area and decreasing slope downstream, reflecting more stable hydrological regimes. Rivers with multiple stations (e.g., Uzh, Latoritsa, Stryi) allow analysis of longitudinal changes in hydrological and geomorphological properties.
Results
In accordance with the objectives of the study, the results are presented in three parts, which address: (i) an assessment of the regimes of regional atmospheric circulation, (ii) an overview of extreme flood events in the Ukrainian Carpathians, and (iii) an analysis of weather patterns in selected flood events.
Frequency of
Annual Frequency of
Analysis of the time series of the annual frequency of CT reveals that certain groups exhibit varying levels of activity over different periods (Figure 2). For instance, the Western group had its peak frequency between 1977 and 2001. During these years, the NW_AD type also showed the highest frequency, with the combined total frequency of western and north-western CT exceeding 51% in 1990. In contrast, a relatively low frequency of these CT was observed from the 1950s to the 1970s, with some years seeing frequencies below 8%–10%.
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The average frequency of northern (N_AD) and north-eastern (NE_AD) advection processes remained relatively stable throughout the study period. However, after 2010, the number of years with higher frequencies of these types increased by 10%–12%, with an average frequency of 7%. The E_AD processes exhibited their highest frequencies during 1965–1970 and 2010–2015. It is noteworthy that the positive and negative anomalies in the frequency of E_AD processes are inversely related to the anomalies observed in the Western group processes.
The highest frequency of the southern group CT was observed during the 1950s and 1960s, with totals reaching up to 20% in some years. In the second half of the study period, the frequency of these types exceeded 10%–12% in only a few years.
The frequency dynamics of the Cyclonic and Anticyclonic groups, as well as the E_AD_CUT type, do not exhibit pronounced periods of high or low frequency. In some years, the total frequency of occurrence for both the Cyclonic and Anticyclonic groups exceeded 10%–14%.
Seasonal Frequency of
The occurrence of certain CT has a seasonality, which generally corresponds to the regime of global atmospheric circulation (Figure 3). During the cold period (November–February), western advection (W_AD) predominates, accounting for 13% to 17% of all cases. Western CT associated with anticyclogenesis (ANT_W_AD) and northwestern advection (NW_AD) are also common. In November–December, Southern group processes rank second in frequency, likely due to the activation of cyclogenesis over the Mediterranean and Black Seas.
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In spring, eastern processes (E_AD) become most prominent, with their frequency reaching nearly 20% in May. In March, western advection remains the second most frequent, while in April, the frequency of southern advection processes (S_AD) increases to around 12%.
In summer and most of autumn (September–October), the ANT_W_AD type predominates, with its maximum frequency in August (23%). In June–July, northern group processes rank second in frequency, while in August and September, eastern and northeastern advection types become more frequent. In October, the contribution of westerly advection (W_AD) increases, and together with ANT_W_AD, these types account for over 30% of all cases.
Cyclones and anticyclones maintain consistently low frequencies throughout the year. The highest frequency of cyclones is observed between April and July, with a maximum in May (7.5%), while anticyclones are more frequent during the cold period (December–March), though their maximum occurs in October (11%). Troughs and eastern processes with upper cut-off lows have the lowest frequency across all seasons compared to other CT.
Trends in the Frequency of
As shown in Table 2, more than half of the CT have negative trends in their annual frequency. All CT in the Western and Northern groups, as well as the E_AD_CUT type, show positive trends. At the same time, the negative trends for both CT in the Southern group, as well as the positive trends for the two types in the Northern group (N_AD and NW_AD), are statistically significant. An analysis of trends in the CT frequency by months reveals that in January and February, all trends are statistically insignificant. In March, significant positive trends are observed for the Northern group processes (NW_AD and N_AD), while anticyclones display negative trends. In April, all trends remain statistically insignificant, but in May and July, a significant positive trend is noted for the NE_AD type. In June, statistically significant trends are observed for the NW_AD type (positive) and for the S_AD and Low cyclone types (both negative). In August, the S_AD, CYCLONIC, and LOW CYCLON types have significant negative trends. In September, the NE_AD and E_AD types show significant positive trends, while the SW_AD type has a negative trend. In October and November, trends for all CT are statistically insignificant. In December, only the NE_AD type demonstrates a statistically significant positive trend.
TABLE 2 Signs of linear trends in the frequency of circulation types (using the Mann–Kendall test) for months and years. The Theil–Sen's estimate of the magnitude of change per decade is shown in parentheses for the statistically significant trends at the 95% confidence level.
| Circulation type | ANT_W_AD | W_AD | NW_AD | N_AD | NE_AD | E_AD | E_AD_CUT | S_AD | SW_AD | TROUGH | CYCLONIC | LOW CYCLON | ANTICYC |
| January | − | + | + | + | + | − | + | − | − | − | − | + | − |
| February | + | + | + | + | − | + | − | − | + | − | − | + | − |
| March | + | + | +(0.05) | +(0.18) | + | − | − | − | + | + | − | + | −(0.36) |
| April | − | + | + | + | − | − | − | − | − | − | + | + | − |
| May | − | + | + | + | +(0.28) | − | + | − | + | −(0.06) | − | − | + |
| June | + | − | +(0.35) | + | + | + | + | −(0.29) | − | + | − | −(0.18) | − |
| July | − | − | − | + | +(0.38) | + | + | − | −(0.27) | + | − | − | + |
| August | + | − | − | + | + | − | + | −(0.15) | − | − | −(0.13) | −(0.11) | + |
| September | − | − | + | + | +(0.33) | +(0.35) | + | − | −0.37) | − | + | − | − |
| October | − | − | + | + | + | 0 | + | − | + | − | − | + | − |
| November | + | − | + | + | − | − | − | − | + | + | + | − | + |
| December | − | + | + | + | +(0.10) | − | − | − | − | + | − | − | + |
| Year | + | + | +(1.66) | + | +(1.49) | − | + | −(1.96) | −(0.88) | − | − | − | − |
It is worth noting that two CT exhibit trends with the same sign across all months but are opposite to each other: the N_AD type shows positive trends (statistically significant in March), while the S_AD type displays negative trends (statistically significant in June and August). Since both types represent meridional transport in the atmosphere, an increase in the frequency of northern flows, coupled with a simultaneous decrease in southern flows of nearly the same magnitude, may suggest a tendency towards changes in intensity and/or shifts in atmospheric centers of action within regional circulation patterns (Hasanean 2004; Jeong et al. 2011; Falarz 2019; Breton et al. 2022).
During the winter and spring months, positive trends prevail in the western and northern CT groups. In the warm period (May–September), positive trends are consistent throughout the season for the NE_AD and E_AD_CUT types, with statistically significant trends for NE_AD in May, July, and September. In the Cyclonic group, June–July sees a decrease in the frequency of cyclones, accompanied by an increase in the occurrence of Troughs. Positive but insignificant trends are observed for Low cyclones from January to April, while negative trends are noted for Anticyclones during the same period.
Cases of Extreme Floods in the Ukrainian Carpathians
Reference sources (Boyko and Petrenko 2006; Gopchenko et al. 2018; Ovcharuk and Goptsiy 2022; Zabolotnia et al. 2022; Snizhko et al. 2023) and observational data from the State Water Cadastre for 2012, 2017, and 2023 on flood discharges on the rivers of the Ukrainian Carpathians in warm and cold periods of the year were used for the analysis. We chose 10 outstanding cases for which the maximum discharge series were ranked, and probability curves were plotted for each of the 56 WGS. Figure 4 represents an example of the flow duration curve for
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While EFAS originally defines the categories “exceptionally high (low)”, “notably high (low)”, “above (below) normal” and “normal” through percentile ranges (> 90 (< 10), 75–90 (10–25), 60–75 (25–40) and 40–60), in our study the same categories were adapted and presented in terms of exceedance probabilities for the reference period 1955–2020, with more detailed probability intervals introduced to refine the classification (Table 3).
TABLE 3 Relationship between EFAS flow categories and the exceedance probabilities applied in this study.
| EFAS category | Percentile range | Exceedance probability |
| Exceptionally high flow | > 90 | 5%–10% |
| Extremely high flow (Additional in this study) | — | < 5% |
| Notably high flow | 75–90 | 10%–33.3% |
| Above normal | 60–75 | 33.3%–45% |
| Normal | 40–60 | 45%–55% |
| Below normal | 25–40 | 55%–66.6% |
| Notably low flow | 10–25 | 66.6%–90% |
| Exceptionally low flow | < 10 | > 90% |
Table 4 represents the results of a probabilistic analysis of significant floods in the Ukrainian Carpathians for the period from 1955 to 2020. The 2008 flood can be confidently classified as catastrophic; at 11 WGSs, historical maximums were exceeded. At the same time, at 13 WGSs the probability of exceeding the maximum discharges was less than 5%. Thus, at 41% of stations (23 out of 56) discharges can be classified as extremely high. It is also worth noting the 1980 flood, during which record high discharges were recorded at 7 stations, and extremely high at 11 WGSs. During the 1998 flood, record-high discharges were observed at 2 WGSs, and extremely high at 11 stations. In other cases, flood discharges can be referred to as exceptionally high or noticeably high.
TABLE 4 Assessment of the number of water gauge stations observing specific discharge exceedance probabilities during major floods in the rivers of the Ukrainian Carpathians.
| Flood event/probabilities | Record high flow (when circled) | Extremely high flow | Exceptionally high flow | Notably high flow | Above average | Near average | Below average | Notably low flow | Exceptionally low flow | The number of catchments in which flooding was observed |
| July 1964 | — | — | 5 | 9 | 1 | 3 | 1 | 6 | 31 | 56 |
| June 1969 | 12 | 14 | 7 | 7 | 3 | 0 | 1 | 1 | 9 | 54 |
| May 1970 | 2 | 1 | 4 | 24 | 2 | 5 | 3 | 8 | 7 | 56 |
| July 1980 | 7 | 11 | 10 | 13 | 6 | 3 | 0 | 3 | 3 | 56 |
| July 2008 | 11 | 13 | 5 | 7 | 4 | 2 | 6 | 7 | 1 | 56 |
| July 2010 | 1 | 3 | 5 | 15 | 5 | 4 | 3 | 4 | 16 | 56 |
| June 2020 | 1 | 5 | 8 | 10 | 4 | 2 | 3 | 6 | 16 | 55 |
| December 1957 | 6 | 3 | 5 | 8 | 0 | 1 | 3 | 5 | 6 | 37 |
| November 1998 | 2 | 11 | 11 | 5 | 5 | 0 | 5 | 6 | 8 | 53 |
| March 2001 | 3 | 4 | 3 | 20 | 8 | 3 | 2 | 8 | 5 | 56 |
Figure 5 shows the spatial distribution of local discharge maxima with different exceedance probabilities across the territory of the Ukrainian Carpathians during major river floods. The proposed maps make it possible to assess the flood scale, as well as the frequency of peak discharges within individual watersheds and throughout the region as a whole. Thus, each figure presents the discharge exceedance probabilities during a single flood event for each of the 56 WGS. In cases where a station was not operational on the flood date, it is marked in white under the category “Missing data.”
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In Table 5, the main information for the selected floods during the warm and cold periods, representing both pluvial and mixed floods, is summarized. In some cases, such as in July 1964, the spatial distribution shown on the map does not fully reflect the flood intensity as described in Table 3. The area where this flood occurred is characterized by the highest river network density, and a likely reason for this discrepancy may be the insufficient number of gauging stations that recorded the extreme values presented in Table 4.
TABLE 5 Floods in the Carpathian Region (by seasons).
| Date | Areas/rivers | Causes | Precipitation (max) | Impacts and consequences |
| Warm season floods | ||||
| July 1964 | Limnytsia, Bystrytsia Solotvynska and Nadvirnianska, Prut | Catastrophic rain and mud floods | Up to 168 mm (Khrypeliv) | All bridges on Bystrytsia destroyed; severe damage to agriculture, oil and gas sectors. |
| June 1969 | Dniester basin across Lviv, Ivano-Frankivsk, Chernivtsi | Continuous heavy rain over several days | 150–250 mm in 24 h | Water level rise of 4–7.5 m; historical maxima exceeded at 12 stations. |
| May 1970 | Tisza basin (Ukraine and Romania) | Prolonged rain over 50,000 km2 + snowmelt | 100–150 mm in 48 h | Widespread flood; 7 rivers reached extreme discharge; record highs at the 2 gauge stations. |
| July 1980 | Tisza and Dniester basins | Extreme rainfall over multiple days | Up to 340 mm (Novy Kropyvnyk) | Peak discharges with < 1% repeatability; multiple peaks observed across river basins. |
| July 2008 | Western Ukraine (6 regions) | Cyclonic activity + overlapping river peaks | Up to 351 mm (Yaremche) | 30 deaths, 41,000 buildings flooded, massive infrastructure losses, rivaled 1969 levels. |
| July 2010 | Dniester, Chernivtsi and Ivano-Frankivsk | Cyclones following wet June; extreme runoff | 2–4 times monthly norm | 3 deaths, 1 missing, 1402 buildings damaged, 105 settlements affected. |
| June 2020 | Carpathian region (Dniester, Prut, Siret, Tisza) | Persistent June rain, intense downpours | Up to 235 mm (in 2 days) | Rain floods in populated areas; some rivers high but dams held; localized severe flooding. |
| Cold season floods | ||||
| December 1957 | Uzh, Latoritsa, Rika, Tisza, Prut | Heavy rain + melting snow, warm air masses | Up to 135 mm (Podobovets) | Mixed floods formed; significant flow increases; exceeded thresholds at 13 stations. |
| November 1998 | Transcarpathian rivers (Latoritsa, Borzhava, Tisza) | Rainy autumn + intense rainfall in November | Up to 207 mm (Rika basin) | Record flood, severe damage; water levels rose by 4–6 m; affected nearly all rivers. |
| March 2001 | Tisza, Latoritsa, Borzhava, Uzh | Prolonged rain + rapid snowmelt + secondary rainfall peaks | Up to 296 mm (March 3–5) | Discharge exceeded 1998 flood; flood levels remained high longer due to renewed rains. |
Circulation Patterns During the Flood Periods According to the Jenkinson–Collison Classification
The study analyzed the frequency of CT during different periods before and during the flood. However, here we focus on the results for the most significant periods in terms of precipitation conditions and flood formation. The analysis examines the frequency of CT during two periods: (i) the period from the 3rd to the 6th day before the onset of the flood, and (ii) the period consisting of the 2 days preceding the flood and the first 2 days of the flood. The period of up to 6 days was chosen based on the understanding that floods develop against the background of preceding weather conditions and moisture accumulation in the catchment area. As shown by Gopchenko et al. (2018), for the Ukrainian Carpathians, the average duration of water flow from slopes into the channel network ranges from 30 to 150 h.
As shown in Figure 6a, during the pre-flood period from the 3rd to the 6th day before the event, most CT were observed, except for ANTICYCLONIC, LOW CYCLON, and NW_AD. Advection processes from the east and northeast predominated, with types E_AD, E_AD_CUT, and NE_AD accounting for a combined total of 40%. In second place were processes from the west (W_AD, AW_AD), which had a total frequency of 32.5%.
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The advective processes of the Southern Group have a rather low frequency (12.5%), while cyclones and troughs (7.5% in total) as well as northwestern advection (7.5%) are generally rare during this period.
The 4-day period, consisting of the 2 days before the flood and the first 2 days of the flood (Figure 6b), typically experiences the highest precipitation, which triggers the onset of the flood. During this period, circulation processes from the Cyclonic group have the greatest influence, accounting for 45% of the total. Eastern and northeastern advection processes come second, with a combined frequency of 22.5%. The types E_AD and E_AD_CUT occur with almost the same frequency as in the preceding period, indicating that the process of upper-level cyclone often contributes to the localization of the precipitation zone over the flooded area. Processes from the Southern and Western groups have a lower frequency (12.5% each), while advection from the north and northwest is among the least common.
Thus, comparing the two periods—before the flood and during the flood—makes it possible to identify two leading types of processes that contribute to the occurrence and intensification of precipitation: (1) advective processes of the eastern group, and (2) processes of the cyclonic group. In this case, cyclonic systems, which are generally mobile, tend to replace western CT during the flood period. The persistence of eastern CT highlights the importance of orographic impact on enhancing precipitation, as the eastern flows are quasi-perpendicular to the Ukrainian Carpathians.
The results of the frequency analysis of CT according to Niedźwiedź's classification are shown in Figure 7. During the four-day periods, all cyclonic types (types 11–20) were observed, totaling 77.5%. The remaining 22.5% comprised anticyclonic CT, of which only five types were observed. Among the cyclonic types, Nc (type 11) and Wc (type 17) were the most frequent, each accounting for 15%. Type Nc is associated with air mass transport from the north, while Wc is associated with transport from the west, under cyclonic circulation. This situation is typical for the rear part of a cyclone located east of the study area. Considering that the classification is centered on southern Poland, the formation of heavy precipitation in the Ukrainian Carpathians may be influenced by the position of the cyclone's surface center over Ukraine. The second most common types (10% each) are advective processes from the northeast (NEc, type 12) and troughs (Bc, type 20). Cyclonic processes associated with airflows from the southern quarter (Sec, Sc, SWc) total 15% and usually correspond to the warm sectors of cyclones. In the anticyclonic group, type Na (type 1) has the highest frequency. Overall, advective processes from the northern quarter (Na, Nea, NWa) account for 17.5%, which complements the high frequency of the same direction observed in the cyclonic group. Therefore, during floods in the Ukrainian Carpathians, the transport of air masses from the northern quarter predominates, comprising 45% of both the cyclonic and anticyclonic groups.
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A comparison of the CT according to the two classifications shows good agreement in the direction of air mass transport with the structure of the SLP fields (Table 6). In the cyclonic group of the Jenkinson–Collison (J–C) classification, all types predominantly correspond to cyclonic CT (c) in Niedźwiedź's classification, featuring winds from the north or west. This confirms that the Ukrainian Carpathians are influenced by the rear part of a cyclone centered over Ukraine. The processes of the J–C Eastern group correspond primarily to Niedźwiedź's CT with northern or northeastern directions, although these peripheral processes can be either cyclonic or anticyclonic. The J–C Southern group processes are associated with Niedźwiedź's CT that involve southern winds within a cyclone or trough, typical for the warm sector of a cyclone over the Carpathians. The CT from the J–C Western group align with the same airflow directions in Niedźwiedź's classification.
TABLE 6 Comparison of circulation types according to Jenkinson–Collison (J–C) and Niedźwiedź classifications (number of cases) and the presence of atmospheric fronts (/-f, number of fronts) during the period 2 days before the flood and the first 2 days of the flood.
| J–C/Niedźwiedź | Na | NEa | NWa | Ca | Ka | Nc | NEc | Ec | SEc | Sc | SWc | Wc | NWc | Cc | Bc |
| E_AD | 1 | 1/1f | |||||||||||||
| E_AD_C_L | 1 | 1 | 1/1f | 1/1f | 1 | ||||||||||
| NE_AD | 1 | 1 | 1 | ||||||||||||
| CYCLONIC | 2 | 5/3f | 1 | 1 | 2 | ||||||||||
| LOW CYCLONE | 1/1f | 1/1f | 1/1f | 1/1f | 1/1f | ||||||||||
| TROUGH | 2 | ||||||||||||||
| AW_AD | 1 | 1/1f | |||||||||||||
| W_AD | 1/1f | 2/1f | |||||||||||||
| NW_AD | 1/1f | ||||||||||||||
| N_AD | 1 | 1/1f | |||||||||||||
| S_AD | 2/1f | 1/1f | |||||||||||||
| SW_AD | 1 | 1 |
Using data from Niedźwiedź's catalog, we found that atmospheric fronts of various types were present in the region on nearly half of the days. Atmospheric fronts were observed in all five cases with the Low cyclone, but were not observed in any cases with the Trough, which was likely located outside the region, as indicated by the position of the trough in the J–C classification. For other CT, atmospheric fronts were present in approximately half of the cases, primarily in cyclonic types according to Niedźwiedź's classification.
These results suggest that both classifications can be used together to clarify synoptic processes directly over the Carpathians, including the presence of atmospheric fronts. However, it is important to consider the spatial shift of the areas for which the classifications were developed. The centers of these territories may be influenced by different parts of the same pressure system, which can lead to varying directions of airflows.
Pressure Fields
Consider the mean SLP fields and geopotential heights at the 500 hPa pressure level, averaged over the 2 days before the flood and the first 2 days of the flood (Figure 8). As noted earlier, during this period, CT associated with cyclones predominate at the surface level. In the warm season, synoptic patterns over the western regions of Ukraine are quite varied, featuring troughs of various orientations, central parts of cyclones, or peripheral areas of baric systems (either cyclones or anticyclones). At the same time, the primary airflow direction over the Ukrainian Carpathians tends to come from the northern or eastern quadrants. In the presence of atmospheric fronts, which can experience orographic intensification, these situations are considered potentially hazardous for heavy precipitation events (Semenova and Nazhmudinova 2019). At the 500 hPa pressure level, the pressure field structure during this period aligns with the prevailing CT a deep trough or a cut-off low was observed over the western regions of Ukraine.
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In the cold period of the year, CT associated with advection from the west or southwest predominated in all cases. This not only led to heavy precipitation in Transcarpathia but also caused warming, which resulted in snow melting and the formation of mixed floods. At the upper levels, quasi-zonal westerly transport was consistently observed over the entire region.
During the warm period, a notable feature of the mean SLP fields is occasionally observed: an orographic ridge or anticyclone over the Ukrainian Carpathians. This local structure was clearly evident with airflows from the north, northeast, or east, both across the entire Eastern Carpathians (e.g., in July 2008) and only over the Ukrainian Carpathians (e.g., in July 1964). The orographic anticyclone was not present in cases where a cyclone was located directly over the region or during the cold period under conditions of westerly advection. The influence of orography on increasing precipitation is well known, with the position of the windward side of mountains relative to the airflow being a key factor (Barry 2008). It is likely that the presence of a low anticyclone over the mountains, with its system of diverging downslope airflows, may enhance local convergent zones in the foothills, especially in the context of synoptic-scale low-pressure systems in the region. This could, in turn, strengthen upward motions and increase precipitation.
Precipitation Fields and Moisture Convergence
Figure 9 displays the composite precipitation fields and graphs of the time course of vertically integrated moisture divergence for the month in which the flood occurred. The spatial distribution of precipitation reveals that, in most cases, a local intensive maximum in precipitation amounts was observed over the study area. The highest precipitation totals over a four-day period were recorded in June 1969 (> 160 mm), as well as in July 1964 and July 2010 (> 140 mm).
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The time course of vertically integrated moisture divergence shows a noticeable peak in moisture convergence during or just before the flood period in all cases. In the instances of maximum precipitation totals mentioned, convergence also reached peak values (−0.9 to −1.1 kg m−2). Thus, the structure of the described pressure fields in most cases contributed to an increase in moisture content in the air column over the Ukrainian Carpathians, making it potentially favorable for heavy precipitation.
Although the presence of local moisture convergence is common in heavy precipitation events, the selection of this peak can be useful as a distinguishing feature between hazardous and non-hazardous weather patterns in the absence of a pronounced predominance of any CT.
Discussion
In this study, we analyzed the temporal variability of atmospheric circulation processes from 1948 to 2021, for the first time applying the modified Jenkinson–Collison classification directly to the territory of Ukraine. We found that the most common CT is westerly transport associated with anticyclogenesis, along with western and northwestern advection. This contrasts with other regions of Europe, including neighboring countries to Ukraine, where the traditional Jenkinson–Collison classification identifies the predominant anticyclonic CT (Donat et al. 2010; Grimalt-Gelabert et al. 2013; Spellman 2017; Nita et al. 2022). It should be noted that in most studies using only SLP data, the second most frequent type is the uncertain CT (U), including at the global level (Fernández-Granja et al. 2023). The modified classification, however, allowed classifying all days for the territory of Ukraine, demonstrating the advantage of incorporating upper-level data into the classification of synoptic processes. The maximum frequency of western anticyclonic CT occurs from July to October, while western advection types are more frequent in the winter months. This pattern is also observed in studies for central and northern Europe (Sepp and Jaagus 2002; Hoy et al. 2012).
The seasonal dynamics of the western, northern, and southern CT closely are in good agreement with the fluctuations of the principal centers of action in the North Atlantic Oscillation (NAO) pattern. In summer, the ridge of the Azores High moves onto the continent, causing northwest winds in its front part. In winter, strengthening pressure gradients between the Azores High and the Icelandic Low contribute to strong western and southwestern winds over most of Europe (Hurrell et al. 2003). The high frequency of CT associated with eastward transport, including upper cut-off lows, highlights the significance of peripheral processes in the atmospheric regime over Ukraine. Eastern processes are predominantly observed in spring and early summer (Bartoszek 2017). Persistent easterly winds at the surface often arise due to powerful anticyclogenesis northeast or north of the country. This situation frequently occurs when a blocking anticyclone is established, with spring being the most common period for blocking in Europe (Barriopedro et al. 2006; Brunner et al. 2017).
Analysis of trends in the frequency of CT showed that in most cases they are insignificant, but clear periods of dominance of one type or another were identified. The increase in the frequency of westward advection since the 1960s, observed in our study, has also been noted in other regions of Europe (Kyselý and Huth 2006; Huguenin et al. 2020). However, for Ukraine, after peaking in the 1990s, the number of western CTs remained high in the early 2000s, with positive trends mainly during the winter months. An increase in western circulation patterns associated with anticyclogenesis was also noted in a study for the western part of Ukraine, conducted using the Niedźwiedź classification (Kholiavchuk 2022).
Another important feature of CT dynamics is the positive trends in the frequency of CT within the Northern group, accompanied by simultaneous negative trends in the frequency within the Southern group. A spatial shift in the prevailing direction of meridional flows may indirectly signify a change in the position of the main centers of action in the region. For instance, the findings of Cresswell-Clay et al. (2022) indicate that during the 20th century, the Azores High tended to expand significantly in area during the winter season, influencing areas typically associated only with the summer period. Concurrently, there was a noticeable increase in the number of anticyclones during the warm season in the Scandinavia and western Russia regions (Pepler et al. 2019), which contribute to peripheral advective processes with northern and eastern winds in Ukraine. Regarding the Siberian High, several studies suggest a significant weakening of this semi-permanent center of action over Central Asia in the past century (Panagiotopoulos et al. 2005; Tubi and Dayan 2013). The suggestion that the spread of this anticyclone into Europe is linked to the extent of snow cover in Eurasia (Judah et al. 2001) underscores the potential decrease in the frequency of this event due to observed trends in the reduction of snow cover during winter and spring (Yeo et al. 2017).
Regarding the presented cases of extreme floods, it should be noted that such a spatiotemporal analysis of the magnitude and frequency of significant and catastrophic floods on the rivers of the Ukrainian Carpathians over a long period was carried out for the first time. For example, the article by Udovenko and Kovalets (2015) presents only the catastrophic flood of July 2008 in the Ukrainian Carpathians. The authors note that the precipitation was enhanced by mountainous conditions, resulting in an unusual precipitation distribution that was not adequately captured by the existing meteorological network. In contrast, a study by Hostiuk et al. (2022) examines the characteristics of floods in the Pokut Carpathians, focusing on the influence of precipitation, geomorphology, and hydrography. They also analyzed the largest floods recorded on the Cheremosh, Black Cheremosh, and Rybnitsa rivers in 2008, 2010, and 2020, but primarily to highlight the importance of flood risk management in small Carpathian catchments. In the Polish part of the Carpathians, Bryndal et al. (2017) studied the influence of extreme precipitation and flash floods on geomorphological changes in small Carpathian catchments. They showed that the most significant changes occurred in ungoverned parts of rivers, highlighting the need to improve flood risk management systems. Thus, the issue of improving flood risk management is relevant for the entire Carpathian region, and the classification of catastrophic floods and their causes is one of the ways to solve this task.
Application of the modified Jenkinson–Collison classification to selected extreme flood events in the Ukrainian Carpathians demonstrated good agreement between the regional position of synoptic systems and the observed distribution of precipitation maxima that led to floods in the study region. Studies indicate that since the mid-20th century, there have been no significant changes in the annual amounts of precipitation in the Carpathians (Vyshnevskyi and Donich 2021). However, some redistribution of seasonal precipitation totals has been observed. During the summer, there is a tendency towards a decrease in precipitation, especially in June, while in the cold period of the year, there is an increase in precipitation and river runoff, particularly in high-altitude areas (Spinoni et al. 2015; Kynal and Kholiavchuk 2016). Moreover, research indicates (Bartholy and Pongrácz 2010) that the number of days with heavy and extreme precipitation increased towards the end of the last century and maintained this trend into the early 2000s. Regarding the results of our study, these tendencies in precipitation may be associated with significant trends in the frequency of meridional CT (north and south groups) in the region.
Regional studies have shown that during months with extreme precipitation in the Carpathians, cyclonic CT predominated. Precipitation intensification typically occurs on windward slopes, particularly in the presence of atmospheric fronts that enhance forced convection (Twardosz et al. 2016; Kholiavchuk and Cebulska 2019). However, anticyclonic conditions are also observed, and during the summer, heavy showers can occur in the foothills and on the slopes when airflow is perpendicular to the mountain ridge (Twardosz 2009). In the case of the Ukrainian Carpathians, our results also show that the most frequent CT associated with heavy precipitation were characterized by airflow approaching the mountain slopes at a high angle. During the warm period, these were predominantly airflows from the north and east, inducing forced lifting of air on the northern slopes of the mountains and resulting in heavy precipitation, often accompanied by flooding in Ciscarpathia. Similar patterns, where near-surface airflows came from the north and northeast while the middle troposphere saw southern winds, were observed in cases of heavy precipitation in the Western Carpathians (Lupikasza 2020; Wypych et al. 2018). In the cold season, mixed flooding in the Transcarpathian region was driven by westward flows in the lower and middle troposphere, creating favorable conditions for both precipitation and rapid snowmelt.
Since orographic precipitation enhancement occurs under specific wind directions in the lower troposphere relative to mountain ridges, detailing the pressure field structure in this layer can help identify areas with the highest airflow convergence. An example of such a classification is the Schüepp scheme (Stefanicki et al. 1998), developed for the central Alps, which considers criteria such as the pressure field and wind direction and speed in the lower and middle troposphere. Furthermore, given that forced convection is often realized in mountainous areas, a comprehensive diagnosis and forecast of meteorological conditions for flood formation should include additional parameters that characterize the convective instability and moisture convergence into the region.
An important factor contributing to the formation of intense local precipitation is the presence of moisture convergence directly above the research area. Despite the Carpathians being distant from large moisture reservoirs, the interaction of airflow in the lower troposphere with mountain slopes can create conditions for moisture accumulation and the saturation of air masses through forced lifting (Zhao et al. 2020). As noted by Darand and Pazhoh (2019), the greatest contribution to precipitation comes from moisture convergence within the lower 3 km, particularly at the level of mountain slopes. In addition, in deep southern cyclones, horizontal transport of water vapor is so intense that it can reach the criteria of an atmospheric river, providing a large moisture flux into the region of precipitation formation (Farr et al. 2022). Clearly, parameters associated with three-dimensional moisture fluxes require further study for potential use as predictors of deep convection and heavy precipitation in the Ukrainian Carpathians (Banacos and Schultz 2005; van Zomeren and van Delden 2007).
Conclusions
Our study investigated the occurrence of extreme floods in the Ukrainian Carpathians as a complex phenomenon determined by atmospheric circulation. Floods in this region are formed under the influence of various interrelated factors, but intense and prolonged precipitation is the key factor triggering the process of flood formation. We studied the long-term atmospheric circulation regime using a modified Jenkinson–Collison automatic classification, which allowed us to identify the features of regional synoptic processes in different seasons of the year and the dynamics of atmospheric patterns over Ukraine since 1948. A comprehensive analysis of meteorological fields for 10 selected cases of the most intense (historical) floods in the Ukrainian Carpathians revealed the presence of specific conditions, such as CT potentially favorable for airflows approaching the mountain ridge at a large angle, local precipitation maxima, and integrated moisture transport into the region of flood formation. However, the analysis of CT during flood periods according to the modified Jenkinson–Collison classification, as well as the Niedźwiedź classification, did not allow us to identify a dominant weather pattern, which indicates the need for regional detailing of the classification and the search for local signs of heavy precipitation. This could involve subtyping or developing a local classification scheme specific to the Ukrainian Carpathians, which would establish a closer link between intense precipitation and atmospheric CT.
Finally, as the main idea of using classifications such as Jenkinson–Collison is to automate the assessment of synoptic situations, we believe that CT can provide a rapid initial assessment of flood hazard based on pressure fields both forecast and fact. Moreover, the significance of this indicator can be determined by a weighting coefficient, which will depend on the degree of danger of a particular type of circulation for the formation of heavy precipitation in the Carpathians. Combined with forecast precipitation, the CT indicator can form an effective unified factor (the meteorological hazard) in the risk assessment formula (Dung et al. 2022), which can be issued by operational meteorological departments and mapped. In the conditions of Ukraine, where flood risk assessment is carried out using qualitative methods based on expert assessments (Guidelines on operational hydrology 2012), the introduction of a quantitative meteorological risk factor can contribute to more substantiated assessments, and therefore more effective decision-making on the preparation and mitigation of the consequences of floods.
Author Contributions
Inna Semenova: conceptualization, methodology, software, investigation, writing – original draft, data curation, validation, visualization, formal analysis. Valeriya Ovcharuk: data curation, investigation, formal analysis, validation, writing – review and editing. Maryna Goptsiy: visualization, formal analysis, data curation.
Acknowledgements
The authors would like to thank Dr. Sergio M. Vicente-Serrano (IPE-CSIC) for providing the software to calculate circulation types using the modified Jenkinson–Collison method.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
Calendar of circulation types for Southern Poland. URL: (Accessed: 24.04.2024). ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S). Climate Data Store (CDS). URL: (Accessed: 12.02.2024). ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S). Climate Data Store (CDS). URL: (Accessed: 11.03.2024).
Appendix - A
TABLE A1 Main hydrographic characteristics of river basins at the Ukrainian Carpathians.
|
The rivers of Ukraine (mountain catchments of the Ukrainian Carpathians) Transcarpathian rivers (Danube basin) |
|||||||||
| WGS number on the map | River—post (WGS) | Distance from the mouth, km | River slope, ‰ | Main catchment characteristics | |||||
| Average from the farthest point | Weighted average from farthest point | Area, km2 | Average altitude, m a.s.l. | Afforestation, % | Lake, % | Period of observation | |||
| 1 | Tisza—Rakhiv | 4 | 15.3 | 9.1 | 1070 | 1100 | 68 | 5 | 1947–2020 |
| 2 | Tіsza—Vylok | 158 | 5.4 | 2.9 | 9140 | — | 55 | — | 1954–2020 |
| 3 | Chorna Tisza—Yasinya | 22 | 27.1 | 15.7 | 194 | 1000 | 75 | 5 | 1956–2020 |
| 4 | Bila Tisza—Luhy | 11 | 42.7 | 26.3 | 189 | 1200 | 77 | < 5 | 1955–2020 |
| 5 | Kosivska—Kosivska Polyana | 33 | 34.8 | 26 | 122 | 1060 | 83 | 15 | 1963–2020 |
| 6 | Teresva—Ust-Chorna | 2 | 20 | 17.2 | 572 | 1100 | 77 | < 5 | 1947–1976, 1978–2020 |
| 7 | Ryka—Verkhniy Bystryy | 15 | 40 | 31 | 165 | 920 | 64 | < 5 | 1954–1994, 1999–2020 |
| 8 | Ryka—Mizhhirya | 28 | 24.3 | 12.5 | 550 | 800 | 41 | < 5 | 1946–2020 |
| 9 | Holyatynka—Maydan | 18 | 23.4 | 23 | 86 | 790 | 40 | < 5 | 1956–1994, 1999–2020 |
| 10 | Pylypets—Pylypets | 6.2 | 41.1 | 30 | 44.2 | 820 | 29 | < 5 | 1956–2020 |
| 11 | Studeny—Nizhny Studeny | 7.5 | 31.6 | 22.7 | 25.4 | 800 | 18 | < 5 | 1954–1994, 1999–2020 |
| 12 | Borzhava—Dovhe | 37 | 35.9 | 12.6 | 408 | 620 | 71 | 10 | 1946–2020 |
| 13 | Latorytsia—Podpolozzia | 24 | 17.7 | 12.3 | 324 | 720 | 50 | 5 | 1946–2020 |
| 14 | Latoritsa—Svaliava | 53 | 11.4 | 7.4 | 680 | 700 | 61 | 5 | 1961–2020 |
| 15 | Latoritsa—Mukacheve | 85 | 8.1 | 4.5 | 1360 | 570 | 63 | 5 | 1946–2020 |
| 16 | Latoritsa—Chop | 135 | 5.2 | 1.9 | 2870 | 310 | 41 | 10 | 1956–2020 |
| 17 | Vicha—Nelipine | 36 | 20 | 14.5 | 241 | 760 | 72 | < 5 | 1958–2020 |
| 18 | Stara—Zniats'ovo | 28 | 19.6 | 6.0 | 224 | 300 | 42 | 10 | 1952–2020 |
| 19 | Uzh—Zhornava | 28 | 19.6 | 12.3 | 286 | 670 | 45 | < 5 | 1952–2020 |
| 20 | Uzh—Zarichovo | 68 | 10.6 | 6.3 | 1280 | 560 | 54 | 5 | 1946–2020 |
| 21 | Uzh—Uzhhorod | 95 | 8.1 | 4.1 | 1970 | 530 | 57 | 15 | 1946–2020 |
| 22 | Turya—Simer | 45 | 23.1 | 15.6 | 464 | 540 | 61 | 5 | 1958–2020 |
| 23 | Siret—Storozhynets | 65 | 9.3 | 4.7 | 672 | 590 | 51 | 25 | 1953–2020 |
| 24 | Prut—Tatariv | 36 | 27.5 | 11.9 | 366 | 1000 | 85 | < 5 | 1959–2020 |
| 25 | Prut—Yaremcha | 54 | 21.8 | 9.6 | 597 | 990 | 87 | < 5 | 1950–2020 |
| 26 | Prut—Chernivtsi | 196 | 7.8 | 3.6 | 6890 | 450 | 42 | — | 1895–1911, 1919–1935, 1945–2020 |
| 27 | Kamenka—Dora | 6.2 | 111 | 66.4 | 18.1 | 870 | 76 | < 5 | 1946–2020 |
| 28 | Cheremosh—Usteriky | 1 | 9.8 | 9.0 | 1500 | 1100 | 51 | 5 | 1958–2020 |
| 29 | Bilyy Cheremosh—Yablunytsya | 37 | 19 | 10.2 | 552 | 1200 | 56 | < 5 | 1958–2020 |
| 30 | Chornyy Cheremosh—Verkhovyna | 68 | 16.7 | 11.4 | 657 | 1200 | 57 | 5 | 1958–2020 |
| 31 | Iltsia—Iltsi | 13 | 40.2 | 30.5 | 86.1 | 1100 | 52 | 5 | 1959–2020 |
| 32 | Putila—Putila | 23 | 24.2 | 15.8 | 181 | 960 | 50 | < 5 | 1963–2020 |
| Ciscarpathian rivers (Dniester basin) | |||||||||
| 33 | Dnister—Strilky | 35 | 10.1 | 5.9 | 384 | 620 | 40 | < 5 | 1914, 1916–1918, 1920, 1925–1929, 1958–2020 |
| 34 | Dnister—Sambir | 74 | 6.5 | 3.9 | 850 | 570 | 51 | 30 | 1946–2020 |
| 35 | Strv'yazh—Khyriv | 31 | 9.1 | 7.3 | 355 | 500 | 35 | 55 | 1964–1988, 1996–2020 |
| 36 | Strv'yazh—Luky | 88 | 4 | 1.7 | 910 | 400 | 23 | 55 | 1957–2020 |
| 37 | Bystrytsya—Ozymyna | 38 | 9.1 | 6.4 | 206 | 520 | 37 | 30 | 1954–1965, 1967, 1968, 1970–2020 |
| 38 | Tys′menytsya—Drohobych | 25 | 20.9 | 9.1 | 250 | 390 | 36 | 25 | 1940–1943, 1945–2020 |
| 39 | Stryy—Matkiv | 29 | 15.3 | 7.2 | 106 | 860 | 56 | < 5 | 1955–2020 |
| 40 | Stryy—Zavadivka | 73 | 6 | 3.3 | 740 | 800 | 35 | 30 | 1962–2020 |
| 41 | Stryy—Verkhnye Syn'ovydne | 154 | 4.7 | 2.4 | 2400 | 760 | 48 | 25 | 1929–1940, 1951–2020 |
| 42 | Opir—Skole | 44 | 12.8 | 6.3 | 733 | 820 | 50 | 15 | 1924–1929, 1956–2020 |
| 43 | Slavs'ka—Slavs'ke | 13 | 38.1 | 15.9 | 76.3 | 860 | 53 | 15 | 1954–2020 |
| 44 | Holovchanka—Tukhlya | 12 | 18.4 | 10.9 | 130 | 810 | 41 | 10 | 1955–2020 |
| 45 | Orava—Svyatoslav | 25 | 17.0 | 16.0 | 204 | 830 | 77 | 15 | 1945–2020 |
| 46 | Svicha—Myslivka | 20 | 23.9 | 14.5 | 201 | 1000 | 95 | < 5 | 1955–2020 |
| 47 | Svicha—Zarichne | 81 | 10.2 | 7.8 | 1280 | 730 | 64 | 10 | 1953–2020 |
| 48 | Luzhanka—Hoshiv | 29 | 28.3 | 18.3 | 146 | 660 | 55 | 15 | 1949–1968, 1970–2020 |
| 49 | Sukil'—Tysiv | 30 | 26 | 16.8 | 138 | 770 | 80 | 5 | 1959–2020 |
| 50 | Limnytsya—Osmoloda | 27 | 23.7 | 15.5 | 203 | 1200 | 83 | < 5 | 1957–2020 |
| 51 | Limnytsya—Perevozets' | 106 | 10.6 | 8.0 | 1490 | 760 | 55 | 30 | 1954–2020 |
| 52 | Chechva—Spas | 23 | 12.6 | 10.9 | 269 | 820 | 72 | 5 | 1956–2020 |
| 53 | Lukva—Bodnariv | 49 | 8.4 | 6.4 | 185 | 480 | 62 | 10 | 1954–2020 |
| 54 | Bystrytsia Nadvirnyanska—Pasichna | 34 | 19.5 | 12.1 | 482 | 1000 | 72 | 5 | 1957–2020 |
| 55 | Vorona—Tysmenytsia | 67 | 4.6 | 3.4 | 657 | 330 | 24 | 30 | 1962–2020 |
| 56 | Bystrytsia Solotvynska—Huta | 17 | 44.6 | 30.3 | 112 | 1100 | 92 | < 5 | 1949–2020 |
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