Extreme flood events as a neglected natural hazard for marginalized Roma communities on the periphery and outside the municipalities of Slovakia

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With the incentive to reduce their flood exposure, this article sets out the following objectives: I) creation of a geodatabase of Slovak MRCs; ii) morphometric assessment of MRCs iii) flood hazard index calculation for individual MRCs iv) flood management recommendation for the most threatened MRCs. The presented study is based on an analysis of remote sensing and hydrological data, digital terrain model and Atlas of Roma communities (ARC). We used elevation percentile and elevation amplitude in terms of topographic analysis and landform classification. The distance of the MRC from the nearest watercourse, the occurrence of floods in the municipality and the barriers between the MRC and the nearest watercourse were used to estimate the flood hazard index. An analysis was carried out on a spatial geodatabase of 576 MRCs. We identified Roma communities on different landforms with 60.13% of them were in the first quartile of the elevation percentile. Among the regions with the highest number of most exposed MRCs, we listed the Topľa, Hornád 1, Torysa and Ondava 1 river basins sorted descending by the amount of MRCs with very high flood hazard. marginalized Roma community flood hazard index segregation topography Slovakia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Natural hazards, especially flood events resulting from extreme natural processes amplified by climate change, cause considerable pressure on our environment and communities (Ding et al., 2021 ; Kellogg, 2019 ). Human society seeks to reduce this effect, and therefore, the issue of flood hazard is receiving increasing attention. Based on the National Strategy for Security Risk Management of the Slovak Republic from 2015, the natural hazards are divided into four categories according to the degree of identified hazard. The riskiest for Slovakia are hazards caused by the weather, where floods occur almost annually, which is considered a very high risk for society. Floods are temporary inundations of land that is dry in normal conditions (Smith, 2013 ) and, therefore, are characterised as exogenous geomorphological hazards based on the instability of the earth's surface (Alcántara-Ayala, 2002 ). In addition, except for the geomorphological characteristics of the terrain, the geological composition of the subsoil and other factors such as engineering geological properties, land cover or technical conditions of the existing barriers have a significant impact on the occurrence of floods (Bezák et al., 2023 ). Flood hazard identification aims to assess “the probability that a flood of a particular intensity will occur over an extended period of time” (Wright, 2015 ). This paper is focused on fluvial floods - a complex, recurring natural process that has a significant ecological, geomorphological and hydrological effect on the surrounding river landscape, in which the exchange of energy and material flows is ensured (Smith & Ward, 1998 ). Approximately half of the flood events in Slovakia between 1996–2006 were of fluvial origin (Solín, 2007 ). While technological advancements have reduced the extent of property damage compared to the past, this does not hold for areas at the highest risk of flooding (Kawasaki et al., 2020 ). These high-risk areas, often offered at lower prices, are predominantly occupied by low-income populations and are typically associated with higher prices of property insurance coverage (Madajová et al., 2015 ). They are available at a lower price precisely because of the increased flood risk. It links us towards marginalized Roma communities (MRC), which are the prime example of residential segregation in the Slovak Republic. More than half of the Roma in Slovakia live in concentrated, residentially segregated communities, and one out of every fifth Roma lives in a community spatially segregated from the majority population (Mušinka et al., 2014 ). According to Rochovská & Rusnáková ( 2018 ), spatial segregation is also one of the key factors related to poverty and the inability to cope with it. An important aspect of society's resilience to natural hazards relates to a stable environmental system. When the environment is an important part of prosperity, health and well-being for a certain social group, while for another part of the population, it is a source of threat, risk and access to resources such as energy, water and greenspace is limited, we speak of environmental (in)justice (Walker, 2012 ). Environmental justice is conditioned by, for example, socio-economic situation, ethnicity or belonging to an excluded minority. In the Central European context, environmental injustice is significant concerning segregated Roma communities. Worse access to basic resources and needs is observed in several studies (Filčák, 2012a , 2012b ; Harper et al., 2009 ; Matoušek & Sýkora, 2016 ). Roma ethnicity was also one of the factors in assessing the vulnerability of Slovak municipalities to flood risk (Solín, 2012 ). Although the analysis of the current state of the Roma community in Slovakia is presented in detail in the Atlases of Roma Communities (ARC) from 2004, 2013, and 2019, systematic research of the Roma community concerning natural hazards is still missing. However, the most tragic consequences of flooding in Slovakia are connected with the Roma community. During the flooding in July 1998 in the Roma settlement in the basin of Malá Svinka, hundreds of Roma households have been destroyed and 50 people died. Most of the evacuations due to floods in Slovakia were also directed towards Roma people and their settlements. During the 20th century, several works were related to research of spatial inequalities, especially their conditionality and parameters (Barro, 2000 ; Kuznets, 1955 ; Sen, 1973 ). The increase in inequality in recent years on a global scale and in Slovakia too has caused growing interest in the issue of inequality. Reduction of the inequality growth rate, which is socially demanded, require a range of broad knowledge about its various aspects and dimensions (Výbošťok & Michálek, 2020 ). Examining the link between flood-prone areas and MRCs in conjunction with the growth of these settlements represents an unexplored area of research. In this context, our study applies more complex approaches focusing on monitoring MRC with topography consideration aimed at flood hazard identification on the national level. Then, we cope with the following sub-goals: i) creation of a spatial geodatabase of the MRC; ii) morphometric assessment of MRC based on the topography index; iii) flood hazard identification of MRCs within basic basin division of Slovakia; and iv) formulate recommendations for flood management in MRCs with the highest flood hazard. 2 Methodology 2.1 Source data The source data include remote sensing imagery of varying resolutions, vector and raster layers, as well as socio-economic and hydrological information. To identify MRC areas, we used orthophotos 2020-22 with 0.25 m spatial resolution provided by the Geodesy, Cartography and Cadastre Authority of the Slovak Republic (GCCA). Digital terrain model (DTM) DMR 5.0 with 1 m resolution derived from LiDAR point cloud was used to identify barriers, height and distance between MRC and the watercourse. Vector data represented by the Basic Database for Geographic Information System of Slovakia (ZBGIS) provided by GCCA support our spatial analysis related to MRC identification. The main sources of related data for Roma communities were ARC (2019) and EU SILC – MRC data provided by the Office of the Plenipotentiary of the Government of the Slovak Republic for Roma Communities. Hydrological data on the national level consists of data analyses about the occurrence of flood events in municipalities of Slovakia based on reports from news and government since 1996. 2.2 Spatial geodatabase of MRC The basis for the spatial representation of MRC in Slovakia consisted of publicly available data from the ARC 2019. ARC focused mainly on the part of the Roma population that does not live dispersed within the majority but is rather grouped in ethnically homogeneous concentrations (ARC also uses the term settlement as a synonym for concentration). We were particularly interested in the settlements of the Roma population, which ARC labels with the type On the periphery of the municipality and Outside the municipality. We did not include concentrations of the Roma population labelled as Inside the municipality in the analysis because they experience lower levels of marginalization (Ravasz et al., 2019 ) and are also less spatially distinguishable from the houses of the surrounding majority population. We focused on 696 MRCs overall, with 502 of them On the periphery of the municipality and 194 MRCs Outside the municipality . The ARC does not provide accurate coordinates for the municipality in which the Roma settlement is located. However, a more precise location within the municipality is required to evaluate flood hazard. We used orthophotos from 2020 to 2022, ZBGIS and Google Street View as described in the approach of Jančovič & Kidová ( 2024 ). It resulted in a database of 576 (out of 696) MRCs spatially identified within the municipality. 2.3 Topographic assessment of MRC areas LiDAR-derived DMR 5.0 with spatial resolution of 1 m was used to obtain information about the specific terrain attributes that represent the physical geographical conditions of MRC. For this purpose, we used a topographic index based on the Elevation percentile (EP) [1]. EP is a measure of local topographic position. It expresses the vertical position for a DTM grid cell (z 0 ) as the percentile of the elevation distribution within the filter window (Newman et al., 2018 ), such that: EP = count i∈C (z i > z 0 ) x (100 / n C ) [1] where z 0 is the elevation of the window's centre grid cell, z i is the elevation of cell i contained within the neighbouring set C , and n C is the number of grid cells contained within the window. Topographic indices inform about the relative vertical position of a place concerning its neighbourhood. A separate parameter defines the neighbourhood size. EP describes the relative vertical position of the calculated pixel within the neighbourhood as a percentile, which is computationally more demanding, the result is normalised, and its only parameter is the size of the neighbourhood. The value of this parameter determines whether the forms of microrelief (with a small neighbourhood) or macrorelief (with a large neighbourhood) will be highlighted. We were interested in the macrorelief and worked with a neighbourhood of 1x1 km. In the neighbourhood of such a scale, we can assume differences in elevation amplitude on the territory of the Slovak Republic. It is an important interpretative element of this topographic index. For example, the location at the highest point of a neighbourhood on the plain cannot be interpreted as the same as the highest point in the mountains, despite having the same percentile value. For this reason, we classified the concentrations of the Roma population according to the elevation amplitude in a 1 km wide buffer around their polygons (Fig. 1 a). Our classification was inspired by the work of Mazúr & Mazúrová ( 1965 ) which classified terrain into five basic classes: plain (up to 30 m), hilly (from 31 m to 100 m), upland (from 101 m to 310 m), mountainous (from 311 m to 640 m, where lower mountains interval is from 311 m to 480 m and higher mountains interval is from 471 m to 640 m) and high-mountainous (above 641 m). In the study, the authors suggested a radius of 1 km as being particularly suitable for surfaces with lower elevation amplitude, which corresponds to the location of most MRCs. Similarly to their work, we also set the thresholds according to the local minima of the frequency curve of the elevation amplitude (Fig. 2 ; 0–30m, 31–80m, 81–140m, 141–200m, 201–402m). In our case, we used the mapped MRCs' buffers to plot the frequency curve. In addition to the local minimum, we also considered the balance of representation between landform classes. Based on the local minimum, the plane class would contain the most significant proportion of MRCs. We thus reduced its interval according to the values in the work of Mazúr and Mazúrová. 2.4 Flood hazard index calculation The degree of flood hazard determination was based on physical geographical parameters. Identification of the MRC regions with the highest exposure to floods represented by flood hazard index was demonstrated based on four monitored parameters, i.e. the height of the MRC above the nearest low flow channel, horizontal distance of the MRC from the nearest low flow channel, barrier between MRC and low flow channel, and amount of flood events for individual municipalities with mapped MRC. The low flow channel, respectively water flow vector was obtained from the ZBGIS dataset. The distance of the MRC from the nearest low-flow channel was determined according to the place that is closest to the polygon of the MRC in Euclidean terms. Further, the ClosestPoint tool, which finds the closest point on the reference path to each specified x,y position, was used for both barrier identification and height above the nearest low-flow channel calculation. Then, barriers set by roads, railways, embankments and fences were obtained from the ZBGIS dataset based on the profile intersection with Euclidian distance from the low flow channel. Terrain barriers were identified only in MRC with low flow channels flowing through MRC. The barriers were categorized, normalized and aggregated. The largest barrier effect was assigned to levees, then highway, railway, embankment and least barrier effect was assigned to fence. All barriers were normalized by linear transformation based on maximal values (Malczewski, 1999 ) [2]. \(\:{w}_{ij}^{´}={w}_{ij}/{w}_{j}^{max}\) [2] The hydrological data obtained from the passportization of news as a frequency of floods for the period 1996–2021 allowed us to clarify the information on the degree of flood hazard in each municipality with the occurrence of MRC. All parameters used were normalized to the range of values 0–1. Based on the mentioned measurable parameters, the flood hazard index (FHI) [3] was calculated: FHI = nChH + nChD + nF – nB [3] where nCh is the normalised height above the nearest low-flow channel, nChD is the normalised distance from the nearest low-flow channel, nF is the normalised frequency of floods in the municipality, and nB represents normalised aggregated barriers between the low-flow channel and MRC. Thus, FHI values potential ranged from the minimum of -1 with the lowest flood hazard to 3 with the maximum flood hazard. We classified these values into 5 classes on an ordinal scale (very low, low, medium, high, very high) according to Jenks' natural break algorithm (Jenks, 1967 ). The ARC also records binary information on the previous occurrence of flooding in each MRC. According to this data, 185 of the 576 studied MRCs have been flooded in the past. We used this dataset for verification purposes, focusing on their distribution in each flood hazard class. We compared this distribution with the entire MRC database. Because ARC does not distinguish between flood type and our methodology was designed primarily for fluvial floods, the verification dataset may also include false positive values. 3 Results Overall, 696 MRC areas identified on the periphery and outside of the municipality were analysed through topography and flood hazard assessment. 3.1 Topography index The MRC polygons were divided into five classes based on the elevation amplitude of their buffers (Fig. 2 ). 118 MRCs were located on the plain (0–30 m), 110 MRCs on the hilly terrain (31–80 m), 164 on the upland (81–140 m), 108 on the mountainous (141–200 m), and 76 on the high-mountain terrain (201–402 m). Buffers mean elevation range was 107.23 m, median 96.97 m, the first quartile 41.66 m and the third quartile 153.29 m. When interpreting the results of the spatial distribution of EP values (Fig. 3 a), the elevation amplitude of individual localities was also relevant (Fig. 3 b). In its context, we could see that the highest values of mean EP were achieved in the areas of the plains, specifically on the Danube Plain and the Východoslovenská rovina Plain. The highest concentrations of low values of mean EP were found mainly in the northeast of the country in the Ondavská and Laborecká vrchovina highlands but also in the mountainous parts of Gemer or Spiš. EP statistics of individual MRCs showed the following trends: a) decrease in mean EP values with increasing elevation amplitude (Fig. 3 c), b) decrease in range of EP with increasing elevation amplitude (Fig. 3 d). In the case of the class with the lowest elevation amplitude (plain), we could observe a range of EP values within a single MRC polygon from the lowest decile ( 90) in 17 examples. Similar instances do not occur in any of the other classes. Setting aside the plain class with the greatest variability in its values, the average percentile value of the other classes for MRC was below 25 in 163 communities (out of 459), and only 4 out-of-plain polygons had this value in the highest quartile (> 75). At the same time, in at least part of their territory, individual MRCs had percentile values in the lowest quartile in 347 (60.13%) cases. Of these, 64 were located in the plain, 62 in the hilly terrain, 95 in the upland, 80 in the mountainous terrain and 46 in the high-mountain. 3.2 Flood hazard index For the flood hazard index calculation, the height and distance of MRC from the nearest low-flow channel, the barrier between MRC and the low-flow channel, and flood event occurrence were evaluated. In the set of 576 mapped MRCs, the mean height above the nearest low-flow channel was 3.4 m, the median was 0.5 m, the first quartile was 0 m, the third quartile was 3.1 m, and the maximum height above the nearest low-flow channel was 104 m. The 0 m value was reached by 43.1% (249) of all MRCs. Regarding the spatial distribution of these values (Fig. 4 a), most MRCs with zero height above the nearest low-flow channel were located in the Prešov (110), Košice (79) and Banská Bystrica (43) regions. There were 9 MRCs with zero value in Nitra, 3 in Bratislava, 2 in Trnava and Žilina and only one in the Trenčín region. The differences in height above the nearest low-flow channel values based on ARC attribute Concentration type were not statistically significant. The mean horizontal distance to the nearest low-flow channel was 95.7 m, the median was 24.1 m, the first quartile was 0.7 m, the third quartile was 113.1 m, and the maximum distance was 1371.2 m. The difference between separate concentration types based on ARC was not statistically significant. We have not observed any spatial patterns in its distribution (Fig. 4 b), but 39.5% (228) of the MRCs were located within the 10 m distance of a low-flow channel. Of these, 113 were in the Prešov, 63 in the Košice, 38 in the Banská Bystrica, 4 in the Nitra, 3 in the Bratislava and Žilina, and 2 in the Trenčín and Trnava regions. The quartile of the most distant MRCs (horizontal distance higher than 113.1 m) was most frequently located in Košice (60), Prešov and Banská Bystrica regions (both 27). The distribution of the most distant MRCs continued in other regions with Nitra (15), Bratislava (7), Trnava (6) and Trenčín with Žilina (both with one such case). Barriers between MRC and low-flow channels were represented as roads, railways, embankments and fences (Fig. 4 c). Their effect was determined by expert assessment. Most of the barriers were roads with 144 cases. The railroad as a barrier was near 8 MRC and dike in 2 cases. Embankments were close to 16 MRC and fences or walls near 19 MRC. In cases, where the low flow channel is flowing through MRC, we have identified only five MRCs with terrain barriers. The number of flood events for individual municipalities within the MRC area of the Slovak Republic (Fig. 4 d) is based on the GIS database of the occurrence of flood situations for 1996–2022. This database has been released from the Report on the progress and consequences of floods in the territory of the Slovak Republic from the negotiations of the Government of the Slovak Republic. The report contains the declaration and revocation of levels of flood activity, in which the date and time the municipality of the declaration of level 2nd or 3rd-degree of flood activity. For our paper's purpose, a flood situation is considered the situation in the municipality when a 3rd-degree flood activity occurs. Table 1 River basins of Slovakia with amounts of MRCs in their area divided into flood hazard index classes. Numbers in the basin names column represent subbasins of the adjacent river and are sorted in ascending order according to the flow directions of watercourses. Basin ID Basin name Flood hazard index Very low Low Medium High Very high Total 1 Bodrog 2 4 3 1 1 11 2 Dolny Dudvah 0 1 0 0 0 1 3 Danube 1 1 0 0 0 0 1 4 Danube 2 0 1 0 0 0 1 5 Dunajec 0 0 0 0 0 0 6 Hnilec 2 5 0 0 4 11 7 Hornad 1 6 13 6 16 15 56 8 Hornad 2 5 3 4 1 8 21 9 Hornad 3 6 8 13 2 4 33 10 Hron 1 0 1 6 1 3 11 11 Hron 2 0 5 1 0 1 7 12 Hron 3 1 3 1 2 0 7 13 Hron 4 4 0 2 0 1 7 14 Ipel 1 2 2 3 1 2 10 15 Ipel 2 1 1 1 0 1 4 16 Ipel 3 6 5 7 2 0 20 17 Laborec 1 1 1 1 4 4 11 18 Laborec 2 2 2 4 2 1 11 19 Latorica 1 0 4 2 1 1 8 20 Latorica 2 0 4 4 0 0 8 21 Maly Dunaj 1 0 1 1 0 0 2 22 Maly Dunaj 2 2 3 1 0 0 6 23 Morava 1 0 0 0 0 0 0 24 Morava 2 0 0 1 0 0 1 25 Morava 3 0 5 1 2 2 10 26 Myjava 0 1 1 0 0 2 27 Nitra 1 0 3 0 0 0 3 28 Nitra 2 2 2 2 0 1 7 29 Ondava 1 2 5 15 8 11 41 30 Ondava 2 4 2 4 6 2 18 31 Orava 1 0 0 0 0 0 0 32 Orava 2 0 0 0 0 0 0 33 Poprad 1 3 1 4 3 3 14 34 Poprad 2 1 2 2 2 10 17 35 Slana 1 2 6 6 2 5 21 36 Slana 2 3 4 3 8 4 22 37 Slana 3 2 9 3 6 0 20 38 Slatina 0 1 3 1 2 7 39 Bodva 3 0 1 4 4 12 40 Dolny Uh 3 6 2 3 1 15 41 Horny Uh 0 0 0 0 0 0 42 Tisa 0 0 0 0 0 0 43 Topla 2 9 6 13 18 48 44 Torysa 6 7 10 7 13 43 45 Vah 1 0 2 0 2 0 4 46 Vah 2 0 4 0 0 0 4 47 Vah 3 1 0 0 0 0 1 48 Vah 4 0 0 0 1 1 2 49 Vah 5 0 0 0 0 0 0 50 Vah 6 0 1 0 0 0 1 51 Vah 7 0 1 0 0 1 2 52 Vah 8 1 0 3 0 0 4 53 Vah 9 0 0 1 0 0 1 54 Vah 10 3 1 2 0 1 7 55 Zitava 2 0 0 0 0 2 Total 81 139 130 101 125 576 Very high hazard is in 125 municipalities with MRC, and 101 have high hazard. Medium flood hazard can be seen in 130 cases and low and very low in 139 and 81 cases, respectively. From a spatial aspect (Fig. 5 ), most of the very high and high municipalities are in the eastern part of Slovakia in the basins of Topľa (31), Hornád 1 (31), Torysa (20), Ondava 1 (19), Poprad and Slaná rivers (12 MRCs both). The first four mentioned are also the basins with the highest number of MRCs overall (Table 1 ). Conversely, there were only 6 MRCs with high and very high FHI in the Hornád 3 basin, even though it is placed 5th in the total number of Roma communities. Linking the flood hazard analysis with the results of the topographic analysis (Section 3.1), we found that the most exposed Roma settlements are located in areas with higher elevation amplitude corresponding to Upland, Mountainous and High-mountainous levels (Table 2 ). In contrast, low flood hazard was in turn more frequently associated with the plain. We also observed a negative correlation between the EP and flood hazard index (Fig. 6 ), with the value of -0.49. Table 2 MRCs distribution in flood hazard and landform classes. Very Low Low Medium High Very High Total Plain 16 (13.56%) 45 (38.14%) 35 (29.66%) 11 (9.32%) 11 (9.32%) 118 Hilly 16 (14.55%) 19 (17.27%) 30 (27.27%) 25 (22.73%) 20 (18.18%) 110 Upland 28 (17.07%) 33 (20.12%) 32 (19.51%) 24 (14.63%) 47 (28.66%) 164 Mountainous 9 (8.33%) 22 (20.37%) 24 (22.22%) 24 (22.22%) 29 (26.85%) 108 High-mountainous 12 (15.79%) 20 (26.32%) 9 (11.84%) 17 (22.37%) 18 (23.68%) 76 Total 81 (14.06%) 139 (24.13%) 130 (22.57%) 101 (17.53%) 125 (21.70%) 576 3.3 Verification In the verification layer of the MRCs with previous flood experience, we can observe its growing representation with increasing flood hazard classes (Table 3 ). Although this layer likely includes flood types not targeted by our methodology, up to 75% of the verification dataset was in the three highest flood hazard classes. Across all studied MRCs this was only 60%, with flood hazard classes more equally represented. The datasets differed most in the two extreme classes and were almost identical in the medium class. Table 3 MRCs distribution in different flood hazard classes. Comparison of results between MRCs with previous flooding experience (according to ARC) and the whole dataset. Very Low Low Medium High Very High Total # MRCs with recorded flooding 12 34 42 40 57 185 All MRCs 81 139 130 101 125 576 % MRCs with recorded flooding 6.49 18.38 22.70 21.62 30.81 100.00 All MRCs 14.06 24.13 22.57 17.53 21.70 100.00 Difference -7.58 -5.75 0.13 4.09 9.11 - 4 Discussion 4.1 Input data uncertainty We have worked extensively with ARC 2019 in our research, particularly in the case of spatial identification of MRCs. However, there are several methodological specificities associated with the production of ARC that we had to take into account when using these data. The Atlas data were obtained from a questionnaire survey by qualified estimation in 810 municipalities of the Slovak Republic (Mušinka, 2019 ). As several questions had ambiguous definitions, respondents often interpreted them differently. For example, the definition of the crucial concept of concentration itself reads as follows: " A concentration, as understood by the Atlas, is any housing development within which multiple households are perceived through Roma ethnicity " (Ravasz et al., 2019 ). Yet, in applying such a definition, it is questionable what "households perceived through Roma ethnicity" actually are or what "multiple" specifically means. Another of the ARC's core attributes that was defined ambiguously was Type of Concentration . The categorisation of Inside the municipality , On the periphery of the municipality and Outside the municipality was only vaguely defined, with a complete lack of specific instructions. Although the Inside the municipality concentrations are supposed to be the “central part of the built-up area of the village” and the On the periphery of the municipality MRCs are supposed to be “seamlessly connected to the houses in the main part of the village”, there is no indication of what the central part of the village is. Settlements Outside the municipality were defined as "a location at some distance from the continuous built-up area of the respective municipality, mostly separated by undeveloped land, a railway line, a river/creek, a road, etc." (Ravasz et al., 2019 ). From this definition, it is not clear whether every MRC that is separated from part of the municipality by any of the mentioned barriers should automatically fall Outside the municipality , even if it was situated in the main part of it. As a result, we can see the inconsistency in the responses in different municipalities, which is captured in Fig. 7 . Potential sources for spatial identification of Roma population concentrations were also ZBGIS databases. Specifically, in the building and shack vector layers, the TXT annotation attribute sometimes specifies that it is a Roma object. However, the openness of this attribute is problematic, as it does not have a precise definition of what information it should be filled with. As a result, it is not clear by what methodology the objects were marked as Roma, nor whether each of the operators considered it worthy of being recorded, or what the motivation of those who did so was. Therefore, the MRC spatial identification was based on a subjective assessment of the orthophoto images. Overall, 696 MRC areas on the periphery and outside of the municipality arose to identify from the ARC. However, according to building type uncertainty and remote image readability, we did not identify a few dozen of them. We chose support based on local environment knowledge for the most objective identification. The problematic MRC areas were discussed with experts from the Office of the Plenipotentiary of the Government of the Slovak Republic for Roma Communities. They provided us with up to twenty localities, and for this study, a set of 576 mapped MRC areas were considered for the performed analyses. 4.2 Specificity of flood hazard methodology This study presents a specific methodological approach to assessing flood hazard. The area of interest was dispersed across the entire country in the form of 576 discrete polygons of varying shapes and sizes. Some Roma settlements were almost the size of a village, while others consisted of only a few houses. Each polygon required the assignment of a representative flood hazard value for its entire area. More sophisticated methods, such as hydrodynamic or hydrologic modelling (Di Baldassarre et al., 2010), were unsuitable because the region of interest was too fragmented, and the associated data requirements exceeded our capabilities. Similarly, the use of empirical models (Mudashiru et al., 2021 ) would have required comprehensive data covering the whole of Slovakia, which were not publicly available at a sufficiently high spatial resolution to capture the smallest polygons. Our approach was based on flood hazard index, which we conceived by a thoughtful selection of sub-indices. We associated fluvial flood hazard with active floodplains, defined in terms of distance and elevation relative to the watercourse. Floodplain activity was characterized based on the frequency of previous flood events and the presence of topographic barriers. All sub-indices were normalized by their maximum value (Malczewski, 1999 ) and were given equal weight. The territory of Slovakia is very diverse and empirical weighting such as analytic hierarchy process (Saaty, 1988) would require nuanced complexity with no assurance of the outcomes. Topographic parameters are quite often used in flood hazard modelling (Ali et al., 2020 ; Al-Omari et al., 2024 ; Chakrabortty et al., 2023 ; Kittipongvises et al., 2020 ; M Amen et al., 2023 ; Madi et al., 2023 ). Stream distance or height above the nearest drainage (Rennó et al., 2008) are widely used both in empirical or physically-based models (Mudashiru et al., 2021 ). Historical flood events could be used both as a model parameter or reference layer (Chen, 2022; Kittipongvises et al., 2020 ; Kourgialas & Karatzas, 2017 ). In a recent study of flood risk in Slovak municipalities with MRC flood event frequency was used as a parameter of FHI too (Solín, 2025 ). However, the author only worked with ARC data at the municipality level, without precise topographic information on the coordinates of individual MRCs. This was also reflected in the FHI values, as they are visibly different from our results (Fig. 5 a). The use of barriers similar to our approach is not as widespread, but we can find suggestions for their use (Jančovič & Kidová, 2024 ). The specificity of the situation was also reflected in the verification phase. Instead of using independent control areas—which would have needed to be sufficiently varied to represent the diversity of the Slovak landscape—we employed existing data directly related to our units of interest: the MRC polygons. These polygons were subject to the same uncertainties described in Section 4.1 and likely included flood types other than fluvial floods, which account for only about half of all flood events in Slovakia (Solín, 2007 ). Nevertheless, they proved useful. Although our flood hazard classification was designed specifically for fluvial floods, the previous binary MRC flood occurrence data corresponded well with our classification. Areas classified as having low flood hazard were the least represented, while those classified as having high flood hazard were the most prevalent. 4.3 Flood hazard and river basin importance In the works targeting the analysis of floods in Slovakia, some basins appear repeatedly notable for their flood hazard. The basins of eastern Slovak rivers such as the Topľa, Torysa, Hornád and Ondava are in this context the most referred to. It is confirmed by the results of a multicriteria analysis of all municipalities in Slovakia (Vojtek et al., 2022 ) using the following factors: flood frequency, lithology, river density, maximum 5-day rainfall, slope, curvature, CLC 2018 and soil texture. The region was similarly significant in Solín ( 2011 ), where flood hazard was calculated for individual headwater basins. Each of the eastern Slovakian basins mentioned above contained a range of headwater basins with the highest flood hazard and almost no basins with the lowest hazard. The author cited soil permeability as the factor that caused this the most. In those basins, flysch sedimentary deposits with impermeable clay layers are often found. This is also indicated by the research of Bezák et al. ( 2023 ) dealing with the occurrence of flash floods based on hydrological online data. The Torysa, Ondava, and Hornád river basins are among the regions with the highest occurrence of flash floods. In addition to the flysch-related geology, he also cites land use or the geomorphology of the long, narrow valleys in which all the water from the basin is concentrated as other factors for the higher incidence of floods. According to our results the Topľa, Torysa, Hornád and Ondava river basins contain the largest number of MRCs with high and very high flood hazard (Fig. 5 b), but they also have the highest number of MRCs in general (Table 1 , Fig. 8 ). This prompts a closer examination of these basins also in the context of the flood exposure of Roma communities. The Topľa River catchment, which contains the most MRCs with very high FHI, has been examined more closely in terms of flood hazard (Ali et al., 2020 ) but without indicating the flood exposure of individual settlements. Such a scale would allow us to examine the flood exposure of MRCs as well as adjacent villages. Comparing them could be a solid case study of environmental justice focusing on Slovak marginalized Roma communities. 4.4 Flood management recommendation in threatened MRC Our results showed important information for river managers, mayors of municipalities, and government legislators. The importance of cooperation between local governments and stakeholders in the multi-management of the affected MRCs could be crucial for the bottom-up approach. To this day, the situation with the most threatened MRC areas is solved only locally by occasional channel regulation without any systematic approach. According to legislation and standards, flood protection in the Slovak Republic is built for a flood with a medium probability of recurrence for a design flow rate Q of 100-year water. In the year 1998, 50 Roma people became victims of catastrophic floods with a 10 000-year recurrence in the municipality of Jarovnice. This tragedy was followed by a modification of the Malá Svinka streams by the Slovak Water Management Enterprise. Within five years of the tragedy, over 5 million euros were invested in flood control measures in the municipalities of Jarovnice, Uzovské Pekľany and Renčišov, which were hit by the catastrophic flood in 1998. According to the statement of the mayor of Jarovnice, the most suitable solution was not found. They put gabions there, and the wires did not hold up. They were washed away and overturned. In twenty years, the riverbed has widened, the bottom level lowered, and about two meters of sediment had been deposited there. A recent statement by water managers revealed there is no money for the river channel sediment clean-up. The municipality is on a waiting list for measures. A plan with specific measures and an order of urgency have been adopted up to 2019. However, it is unclear what the effect is because people are still living on the active floodplain, and during the rains, entire families flee to a nearby hill. Given that no people should be subjected to fear for their livelihood whenever it rains, options and mechanisms which support the relocation of affected households should exist. The described situation in the Jarovnice settlement from 2018 (Ivan, 2018 ) is a very suitable example to point out where we can identify legislative and management gaps in solving flood protection issues from the environmental injustice point of view (Škobla & Filčák, 2024 ). The top-down approach promises the legislative anchoring of the entire problem related to functioning solution of MRC social status and flood control measures themselves. If we go further from flood hazard to flood risk assessment (Solín, 2025 ), some demographic and economic determinants of the MRC's ability to cope with flood event consequences can be identified. The recovery phase for financially weak, low-income, or poor households is disproportionately long and complicated (Michálek & Madajová, 2019 ). In many cases, without state aid, mainly due to a lack of financial resources or deep poverty, a flood is unmanageable and, for many households from MRC, an unsolvable task. Additionally, the number of children, age, level of education and income play a crucial role in the phase of coping and recovery after a flood event. Therefore, an exchange of scientific knowledge, stakeholders’ engagement, and active cooperation with crucial government representatives responsible for the decision-making process should be a priority for future planning of strategic documents related to urgency and alarming status of MRC. 5 Conclusion In the context of natural hazards and issue of flood exposure of Roma settlements an interdisciplinary approach is required. In this paper, the information about morphometric parameters of these areas provides the initial knowledge needed for predictions related to the extent of inundation and its impact on property and human lives. It brought a completely new perspective to the studies of segregation and environmental injustice of Slovak MRCs. For the presented research, we used mainly classical (orthophoto images) and also very precise (Airborne Laser-Scanning) remote sensing data combined with hydrological datasets. On a sample of up to 576 MRCs across Slovakia, we analysed their topography and calculated a flood hazard index. Only 228 MRCs were located in areas with lower elevation amplitude, such as plain and hilly areas, and we identified the remaining 348 in localities with higher elevation amplitude. Overall, up to 60.13% of all MRCs were located at least partly in the lowest quartile of elevation within a 1 km radius. A similar value was achieved in each defined landform. This is consistent with the values that can be reached by floodplains and is indicative of the flood potential in Roma communities. The highest flood hazard class was generally represented more in the terrain with higher elevation amplitude, such as Upland, Mountains and High-mountains. At the same time, of the 125 MRCs with the highest FHI value, most were located in the Topľa (18), Hornád 1 (15), Torysa (13) and Ondava 1 (11) river basins. The application potential of the paper should be highlighted. One of the priorities of the National Strategy for Equality, Inclusion and Participation of Roma for 2020–2030 is the property rights of land under settlements. Property rights can be considered one of the main conditions for the implementation of programmes aimed at the active inclusion of people from marginalized Roma communities and the overall development of the villages and territories listed in the Atlas of Roma Communities. The outputs of our paper should serve as an essential source of information in the process of land settlement in municipalities with the presence of the MRC, which can lead to the possible relocation of people from the most hazardous localities into safer alternatives. We believe that the paper's outcomes will be important for river managers, stakeholders and policymakers as well as other authorities working with MRC internationally. During paper preparation, we actively cooperated with the Operations Centre of Emergency Medical Service of the Slovak Republic, which requested our results of a unique spatial dataset to help them provide emergency intervention in MRC areas faster. The findings of our paper had practical relevance for public policymaking, especially in developing an actual preparation of the Action Plan for the priority area Housing and Health and are made fully available to the Office of the Government Plenipotentiary for Roma Communities in the Slovak Republic, where we stay in active cooperation. Finally, the results represent an accurate basis for continuing with detailed research on regional and local levels and provide a new approach to MRC flood hazard assessment applicable in other countries too. Declarations Acknowledgement This research was supported by the Slovak Research and Development Agency under the project Marginalized Roma concentrations in the context of natural hazards and social inequality (APVV-22-0428) and by the Science Grant Agency (VEGA) of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences (02/0086/21). Funding This research was supported by the Slovak Research and Development Agency under the project Marginalized Roma concentrations in the context of natural hazards and social inequality (APVV-22-0428) and by the Science Grant Agency (VEGA) of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences (02/0086/21). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conceptualization, methodology, resources and writing. Data curation and formal analysis were performed by Marián Jančovič and Lukáš Michaleje. Funding acquisition, project management and supervision were performed by Anna Kidová. Validation, visualization a investigation were performed by Marián Jančovič. References Alcántara-Ayala, I. (2002). Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphology , 47 (2), 107–124. https://doi.org/10.1016/S0169-555X(02)00083-1 Ali, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojteková, J., Costache, R., Linh, N. T. T., Nguyen, H. Q., Ahmad, A., & Ghorbani, M. A. (2020). GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia. Ecological Indicators , 117 . Scopus. https://doi.org/10.1016/j.ecolind.2020.106620 Al-Omari, A. A., Shatnawi, N. N., Shbeeb, N. I., Istrati, D., Lagaros, N. 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AUC GEOGRAPHICA , 46 (2), Article 2. https://doi.org/10.14712/23361980.2015.33 Mazúr, E., & Mazúrová, V. (1965). Mapa relatívnych výšok Slovenska a možnosť ich použitia pre geografickú rajonizáciu. Geografický časopis / Geographical Journal , 17 (1), 3–18. Michálek, A., & Madajová, M. S. (2019). Identifying regional poverty types in Slovakia. GeoJournal , 84 (1), 85–99. https://doi.org/10.1007/s10708-018-9852-9 Mudashiru, R. B., Sabtu, N., & Abustan, I. (2021). Quantitative and semi-quantitative methods in flood hazard/susceptibility mapping: A review. Arabian Journal of Geosciences , 14 (11), 941. https://doi.org/10.1007/s12517-021-07263-4 Mušinka, A. (2019). Metodika troch atlasov rómskych komunít na Slovensku a politické zastúpenie Rómov v mestách a obciach Prešovského samosprávneho kraja. Annales Scientia Politica , 8 (2), Article 2. Mušinka, A., Škobla, D., Hurrle, J., Matlovičová, K., & Kling, J. (2014). Atlas rómskych komunít na Slovensku [Atlas of Roma Communities in Slovakia] 2013. Newman, D. R., Lindsay, J. B., & Cockburn, J. M. H. (2018). Evaluating metrics of local topographic position for multiscale geomorphometric analysis. Geomorphology , 312 , 40–50. https://doi.org/10.1016/j.geomorph.2018.04.003 Ravasz, Á., Kovács, Ľ., & Markovič, F. (2019). Atlas rómskych komunít . https://www.minv.sk/?atlas-romskych-komunit-2019 Rochovská, A., & Rusnáková, J. (2018). Poverty, segregation and social exclusion of Roma communities in Slovakia. Bulletin of Geography. Socio-Economic Series , 42 (42), Article 42. https://doi.org/10.2478/bog-2018-0039 Sen, A. (1973). On Economic Inequality . Oxford Academic. https://doi.org/10.1093/0198281935.001.0001.002.003 Škobla, D., & Filčák, R. (2024). Life next to a landfill: Urban marginality, environmental injustice and the Roma. Race & Class , 65 (4), 74–91. https://doi.org/10.1177/03063968231203488 Smith, K. (2013). Environmental hazards: Assessing risk and reducing disaster (6th ed). Routledge. Smith, K., & Ward, R. (1998). Floods: Physical Processes and Human Impacts . Wiley. Solín, Ľ. (2007). Analysis of floods occurrence in Slovakia in the period 1996 – 2006. Journal of Hydrology and Hydromechanics, 56, 95-115. Solín, Ľ. (2011). Regionálna variabilita povodňovej hrozby malých povodí na Slovensku. Geografický Časopis / Geographical Journal , 63 (1), 29–52. Solín, Ľ. (2012). Spatial variability in the flood vulnerability of urban areas in the headwater basins of S lovakia. Journal of Flood Risk Management , 5 (4), 303–320. https://doi.org/10.1111/j.1753-318X.2012.01153.x Solín, Ľ. (2025). Hodnotenie povodňového rizika rómskych osád v SR: Flood risk assessment of Roma settlements in the Slovak Republic. Geografický Časopis / Geographical Journal , 77 (1), Article 1. https://doi.org/10.31577/geogrcas.2025.77.1.01 Vojtek, M., Janizadeh, S., & Vojteková, J. (2022). Riverine flood potential assessment at municipal level in Slovakia. Journal of Hydrology: Regional Studies , 42 , 101170. https://doi.org/10.1016/j.ejrh.2022.101170 Výbošťok, J., & Michálek, A. (2020). PRIESTOROVÁ DIMENZIA PRÍJMOVÝCH NEROVNOSTÍ: TEÓRIA, KONCEPTY A METÓDY. Geografický časopis / Geographical Journal , 72 (2), 107–129. https://doi.org/10.31577/geogrcas.2020.72.2.06 Walker, G. (2012). Environmental Justice: Concepts, Evidence and Politics . Taylor & Francis Group. http://ebookcentral.proquest.com/lib/uniba-ebooks/detail.action?docID=958746 Wright, D. B. (2015). Methods in Flood Hazard and Risk Assessment (p. 20). World Bank. https://doi.org/10/25136493/methods-flood-hazard-risk-assessment Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 May, 2025 Reviewers invited by journal 28 May, 2025 Editor invited by journal 26 May, 2025 Editor assigned by journal 02 May, 2025 First submitted to journal 01 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6574450","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463068069,"identity":"bc2f506d-5a5b-49af-a061-1554f7a388a2","order_by":0,"name":"Marián Jančovič","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYLACCSDmB2JmGIc4LZINJGkBAYMDxGqRd28+/MKi5o688fnDz6QLahjsQdbhBYZnjqVZSBx7ZrjtRpqZ9IxjDImzCdliOCPHzECC7XCC2Q0GY2MeNoYEOYJa5r8Bavl3OMG4//hnY55/DPYEtchL8Bg/kGw7nGDAkGP4mLeNgZGgwwx40tIYJPsOG864kVP4eGafROLMBkK2tB8+/Fni22F5/v7jGw4XfLOxlzhAyJYDDGzSSFFBRETKNzAwf/xAWN0oGAWjYBSMZAAAKgg+FCVzD3oAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1164-1079","institution":"Institute of Geography Slovak Academy of Sciences: Geograficky ustav Slovenskej akademie vied","correspondingAuthor":true,"prefix":"","firstName":"Marián","middleName":"","lastName":"Jančovič","suffix":""},{"id":463068070,"identity":"9b62db0b-9c25-418a-bf05-01b1ed91e0f9","order_by":1,"name":"Anna Kidová","email":"","orcid":"","institution":"Institute of Geography SAS: Geograficky ustav Slovenskej akademie vied","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Kidová","suffix":""},{"id":463068071,"identity":"19244588-cc81-4726-bdb5-5f75b20f7fb0","order_by":2,"name":"Lukáš Michaleje","email":"","orcid":"","institution":"Institute of Geography SAS: Geograficky ustav Slovenskej akademie vied","correspondingAuthor":false,"prefix":"","firstName":"Lukáš","middleName":"","lastName":"Michaleje","suffix":""}],"badges":[],"createdAt":"2025-05-01 22:46:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6574450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6574450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83746086,"identity":"89e86212-bedd-4441-bcbb-1a76836d8758","added_by":"auto","created_at":"2025-06-02 04:24:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":910710,"visible":true,"origin":"","legend":"\u003cp\u003eSpatially identified MRC with 1 km buffer zone (\u003cstrong\u003ea\u003c/strong\u003e) to obtain representative terrain classification according to Mazúr and Mazúrová (1965) division and Elevation percentile of the MRC (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/1daf0cd3ed43f7a604724913.png"},{"id":83746083,"identity":"3b3a86a1-1765-4c21-8179-3262a58b2c5d","added_by":"auto","created_at":"2025-06-02 04:24:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58424,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency curve of the elevation amplitude per MRC (in its 1 km buffer).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/e549fdd902ad5bb4957b36f6.png"},{"id":83746084,"identity":"c83c9822-981e-424d-84a1-2892d43467d2","added_by":"auto","created_at":"2025-06-02 04:24:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":303232,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of mean elevation percentile values of MRCs visualised in respective municipalities (\u003cstrong\u003ea\u003c/strong\u003e), Spatial distribution of MRC classification based on their elevation amplitude visualised in respective municipalities (\u003cstrong\u003eb\u003c/strong\u003e), Mean elevation percentile per MRC (\u003cstrong\u003ec\u003c/strong\u003e), Range of elevation percentile per MRC (\u003cstrong\u003ed\u003c/strong\u003e). Whisker’s length in boxplots is computed as 1.5 X (Q75 - Q25).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/24aadac49d8410dca0709415.png"},{"id":83746205,"identity":"40ecfe95-3910-415c-a01d-b0aa75b85635","added_by":"auto","created_at":"2025-06-02 04:32:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":468360,"visible":true,"origin":"","legend":"\u003cp\u003eMRC’s flood hazard sub-indices per respective municipality. Height above the nearest low-flow channel (a); Horizontal distance from the nearest low-flow channel (b); Aggregated standardised barriers between the nearest low-flow channel and MRC (c); \u003cstrong\u003eF\u003c/strong\u003elood frequency in municipalities with MRC since 1996 (d).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/b06c3c81cb6fab6377a721ba.png"},{"id":83746207,"identity":"aee42538-237e-46bb-b240-6ed597ab1fc0","added_by":"auto","created_at":"2025-06-02 04:32:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":236202,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of MRC’s flood hazard index per respective municipality. The base layer consists of the basic division of the river basin boundaries of Slovakia (\u003cstrong\u003ea\u003c/strong\u003e). Spatial distribution of very high flood hazard index MRCs per river basins of Slovakia (\u003cstrong\u003eb\u003c/strong\u003e). Amount of MRCs by every flood hazard class per river basin of Slovakia. River basins are labelled by Basin ID from Table 1. Hashed river basins are without any MRC.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/962b4e8e720ef440bd78942f.png"},{"id":83746094,"identity":"356a4877-4600-458f-9fd6-af9dec9976cd","added_by":"auto","created_at":"2025-06-02 04:24:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66022,"visible":true,"origin":"","legend":"\u003cp\u003eRelation between Flood hazard index (x-axis) and Elevation percentile (y-axis) values per MRC. Red dots visualised the trendline.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/b05bed610947ddb8380178e2.png"},{"id":83746210,"identity":"e783780c-2cca-45e1-9b7a-74f7dea1ac72","added_by":"auto","created_at":"2025-06-02 04:32:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":705253,"visible":true,"origin":"","legend":"\u003cp\u003eInconsistency in ARC concentration type classification. MRC is classified as\u003cem\u003e Inside the municipality\u003c/em\u003e (a), MRC is classified as \u003cem\u003eOn the periphery of the municipality\u003c/em\u003e (b), and MRC is classified as \u003cem\u003eOutside the municipality\u003c/em\u003e (c).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/910e3d66a2f971ac1ca452e7.png"},{"id":83746206,"identity":"774ea44e-316e-47b5-9eda-ee6c3646037f","added_by":"auto","created_at":"2025-06-02 04:32:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":22935,"visible":true,"origin":"","legend":"\u003cp\u003eAmount of MRC with five identified classes of Flood Hazard Index (FHI) within the basic river basin of Slovakia (1-55). River basins are labelled by Basin ID from Table 1.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/e18a85a946e1fe8fba3be996.png"},{"id":83746527,"identity":"9403f9da-1616-40cf-95cc-091da9017161","added_by":"auto","created_at":"2025-06-02 04:48:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3814106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6574450/v1/11bea21c-f7e3-4022-a3d3-6f69a94a6128.pdf"}],"financialInterests":"","formattedTitle":"Extreme flood events as a neglected natural hazard for marginalized Roma communities on the periphery and outside the municipalities of Slovakia","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eNatural hazards, especially flood events resulting from extreme natural processes amplified by climate change, cause considerable pressure on our environment and communities (Ding et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kellogg, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Human society seeks to reduce this effect, and therefore, the issue of flood hazard is receiving increasing attention. Based on the National Strategy for Security Risk Management of the Slovak Republic from 2015, the natural hazards are divided into four categories according to the degree of identified hazard. The riskiest for Slovakia are hazards caused by the weather, where floods occur almost annually, which is considered a very high risk for society. Floods are temporary inundations of land that is dry in normal conditions (Smith, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and, therefore, are characterised as exogenous geomorphological hazards based on the instability of the earth's surface (Alc\u0026aacute;ntara-Ayala, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In addition, except for the geomorphological characteristics of the terrain, the geological composition of the subsoil and other factors such as engineering geological properties, land cover or technical conditions of the existing barriers have a significant impact on the occurrence of floods (Bez\u0026aacute;k et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Flood hazard identification aims to assess \u0026ldquo;the probability that a flood of a particular intensity will occur over an extended period of time\u0026rdquo; (Wright, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This paper is focused on fluvial floods - a complex, recurring natural process that has a significant ecological, geomorphological and hydrological effect on the surrounding river landscape, in which the exchange of energy and material flows is ensured (Smith \u0026amp; Ward, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Approximately half of the flood events in Slovakia between 1996\u0026ndash;2006 were of fluvial origin (Sol\u0026iacute;n, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). While technological advancements have reduced the extent of property damage compared to the past, this does not hold for areas at the highest risk of flooding (Kawasaki et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These high-risk areas, often offered at lower prices, are predominantly occupied by low-income populations and are typically associated with higher prices of property insurance coverage (Madajov\u0026aacute; et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). They are available at a lower price precisely because of the increased flood risk. It links us towards marginalized Roma communities (MRC), which are the prime example of residential segregation in the Slovak Republic. More than half of the Roma in Slovakia live in concentrated, residentially segregated communities, and one out of every fifth Roma lives in a community spatially segregated from the majority population (Mušinka et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). According to Rochovsk\u0026aacute; \u0026amp; Rusn\u0026aacute;kov\u0026aacute; (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), spatial segregation is also one of the key factors related to poverty and the inability to cope with it.\u003c/p\u003e \u003cp\u003eAn important aspect of society's resilience to natural hazards relates to a stable environmental system. When the environment is an important part of prosperity, health and well-being for a certain social group, while for another part of the population, it is a source of threat, risk and access to resources such as energy, water and greenspace is limited, we speak of environmental (in)justice (Walker, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Environmental justice is conditioned by, for example, socio-economic situation, ethnicity or belonging to an excluded minority. In the Central European context, environmental injustice is significant concerning segregated Roma communities. Worse access to basic resources and needs is observed in several studies (Filč\u0026aacute;k, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; Harper et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Matoušek \u0026amp; S\u0026yacute;kora, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Roma ethnicity was also one of the factors in assessing the vulnerability of Slovak municipalities to flood risk (Sol\u0026iacute;n, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although the analysis of the current state of the Roma community in Slovakia is presented in detail in the Atlases of Roma Communities (ARC) from 2004, 2013, and 2019, systematic research of the Roma community concerning natural hazards is still missing. However, the most tragic consequences of flooding in Slovakia are connected with the Roma community. During the flooding in July 1998 in the Roma settlement in the basin of Mal\u0026aacute; Svinka, hundreds of Roma households have been destroyed and 50 people died. Most of the evacuations due to floods in Slovakia were also directed towards Roma people and their settlements.\u003c/p\u003e \u003cp\u003eDuring the 20th century, several works were related to research of spatial inequalities, especially their conditionality and parameters (Barro, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kuznets, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1955\u003c/span\u003e; Sen, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). The increase in inequality in recent years on a global scale and in Slovakia too has caused growing interest in the issue of inequality. Reduction of the inequality growth rate, which is socially demanded, require a range of broad knowledge about its various aspects and dimensions (V\u0026yacute;bošťok \u0026amp; Mich\u0026aacute;lek, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Examining the link between flood-prone areas and MRCs in conjunction with the growth of these settlements represents an unexplored area of research. In this context, our study applies more complex approaches focusing on monitoring MRC with topography consideration aimed at flood hazard identification on the national level. Then, we cope with the following sub-goals: i) creation of a spatial geodatabase of the MRC; ii) morphometric assessment of MRC based on the topography index; iii) flood hazard identification of MRCs within basic basin division of Slovakia; and iv) formulate recommendations for flood management in MRCs with the highest flood hazard.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Source data\u003c/h2\u003e \u003cp\u003eThe source data include remote sensing imagery of varying resolutions, vector and raster layers, as well as socio-economic and hydrological information. To identify MRC areas, we used orthophotos 2020-22 with 0.25 m spatial resolution provided by the Geodesy, Cartography and Cadastre Authority of the Slovak Republic (GCCA). Digital terrain model (DTM) DMR 5.0 with 1 m resolution derived from LiDAR point cloud was used to identify barriers, height and distance between MRC and the watercourse. Vector data represented by the Basic Database for Geographic Information System of Slovakia (ZBGIS) provided by GCCA support our spatial analysis related to MRC identification. The main sources of related data for Roma communities were ARC (2019) and EU SILC \u0026ndash; MRC data provided by the Office of the Plenipotentiary of the Government of the Slovak Republic for Roma Communities. Hydrological data on the national level consists of data analyses about the occurrence of flood events in municipalities of Slovakia based on reports from news and government since 1996.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Spatial geodatabase of MRC\u003c/h2\u003e \u003cp\u003eThe basis for the spatial representation of MRC in Slovakia consisted of publicly available data from the ARC 2019. ARC focused mainly on the part of the Roma population that does not live dispersed within the majority but is rather grouped in ethnically homogeneous concentrations (ARC also uses the term settlement as a synonym for concentration). We were particularly interested in the settlements of the Roma population, which ARC labels with the type \u003cem\u003eOn the periphery of the municipality\u003c/em\u003e and \u003cem\u003eOutside the municipality.\u003c/em\u003e We did not include concentrations of the Roma population labelled as \u003cem\u003eInside the municipality\u003c/em\u003e in the analysis because they experience lower levels of marginalization (Ravasz et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and are also less spatially distinguishable from the houses of the surrounding majority population. We focused on 696 MRCs overall, with 502 of them \u003cem\u003eOn the periphery of the municipality\u003c/em\u003e and 194 MRCs \u003cem\u003eOutside the municipality\u003c/em\u003e. The ARC does not provide accurate coordinates for the municipality in which the Roma settlement is located. However, a more precise location within the municipality is required to evaluate flood hazard. We used orthophotos from 2020 to 2022, ZBGIS and Google Street View as described in the approach of Jančovič \u0026amp; Kidov\u0026aacute; (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It resulted in a database of 576 (out of 696) MRCs spatially identified within the municipality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Topographic assessment of MRC areas\u003c/h2\u003e \u003cp\u003eLiDAR-derived DMR 5.0 with spatial resolution of 1 m was used to obtain information about the specific terrain attributes that represent the physical geographical conditions of MRC. For this purpose, we used a topographic index based on the Elevation percentile (EP) [1]. EP is a measure of local topographic position. It expresses the vertical position for a DTM grid cell (z\u003csub\u003e0\u003c/sub\u003e) as the percentile of the elevation distribution within the filter window (Newman et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), such that:\u003c/p\u003e \u003cp\u003e \u003cem\u003eEP\u0026thinsp;=\u0026thinsp;count\u003c/em\u003e \u003csub\u003e \u003cem\u003ei\u0026isin;C\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(z\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026gt; z\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) x (100 / n\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e [1]\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the elevation of the window's centre grid cell, \u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the elevation of cell \u003cem\u003ei\u003c/em\u003e contained within the neighbouring set \u003cem\u003eC\u003c/em\u003e, and \u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e is the number of grid cells contained within the window.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTopographic indices inform about the relative vertical position of a place concerning its neighbourhood. A separate parameter defines the neighbourhood size. EP describes the relative vertical position of the calculated pixel within the neighbourhood as a percentile, which is computationally more demanding, the result is normalised, and its only parameter is the size of the neighbourhood. The value of this parameter determines whether the forms of microrelief (with a small neighbourhood) or macrorelief (with a large neighbourhood) will be highlighted. We were interested in the macrorelief and worked with a neighbourhood of 1x1 km. In the neighbourhood of such a scale, we can assume differences in elevation amplitude on the territory of the Slovak Republic. It is an important interpretative element of this topographic index. For example, the location at the highest point of a neighbourhood on the plain cannot be interpreted as the same as the highest point in the mountains, despite having the same percentile value. For this reason, we classified the concentrations of the Roma population according to the elevation amplitude in a 1 km wide buffer around their polygons (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Our classification was inspired by the work of Maz\u0026uacute;r \u0026amp; Maz\u0026uacute;rov\u0026aacute; (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1965\u003c/span\u003e) which classified terrain into five basic classes: plain (up to 30 m), hilly (from 31 m to 100 m), upland (from 101 m to 310 m), mountainous (from 311 m to 640 m, where lower mountains interval is from 311 m to 480 m and higher mountains interval is from 471 m to 640 m) and high-mountainous (above 641 m). In the study, the authors suggested a radius of 1 km as being particularly suitable for surfaces with lower elevation amplitude, which corresponds to the location of most MRCs. Similarly to their work, we also set the thresholds according to the local minima of the frequency curve of the elevation amplitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; 0\u0026ndash;30m, 31\u0026ndash;80m, 81\u0026ndash;140m, 141\u0026ndash;200m, 201\u0026ndash;402m). In our case, we used the mapped MRCs' buffers to plot the frequency curve. In addition to the local minimum, we also considered the balance of representation between landform classes. Based on the local minimum, the plane class would contain the most significant proportion of MRCs. We thus reduced its interval according to the values in the work of Maz\u0026uacute;r and Maz\u0026uacute;rov\u0026aacute;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Flood hazard index calculation\u003c/h2\u003e \u003cp\u003eThe degree of flood hazard determination was based on physical geographical parameters. Identification of the MRC regions with the highest exposure to floods represented by flood hazard index was demonstrated based on four monitored parameters, i.e. the height of the MRC above the nearest low flow channel, horizontal distance of the MRC from the nearest low flow channel, barrier between MRC and low flow channel, and amount of flood events for individual municipalities with mapped MRC. The low flow channel, respectively water flow vector was obtained from the ZBGIS dataset. The distance of the MRC from the nearest low-flow channel was determined according to the place that is closest to the polygon of the MRC in Euclidean terms. Further, the ClosestPoint tool, which finds the closest point on the reference path to each specified x,y position, was used for both barrier identification and height above the nearest low-flow channel calculation. Then, barriers set by roads, railways, embankments and fences were obtained from the ZBGIS dataset based on the profile intersection with Euclidian distance from the low flow channel. Terrain barriers were identified only in MRC with low flow channels flowing through MRC. The barriers were categorized, normalized and aggregated. The largest barrier effect was assigned to levees, then highway, railway, embankment and least barrier effect was assigned to fence. All barriers were normalized by linear transformation based on maximal values (Malczewski, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) [2].\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{ij}^{\u0026acute;}={w}_{ij}/{w}_{j}^{max}\\)\u003c/span\u003e \u003c/span\u003e [2]\u003c/p\u003e \u003cp\u003eThe hydrological data obtained from the passportization of news as a frequency of floods for the period 1996\u0026ndash;2021 allowed us to clarify the information on the degree of flood hazard in each municipality with the occurrence of MRC. All parameters used were normalized to the range of values 0\u0026ndash;1. Based on the mentioned measurable parameters, the flood hazard index (FHI) [3] was calculated:\u003c/p\u003e \u003cp\u003e \u003cem\u003eFHI\u0026thinsp;=\u0026thinsp;nChH\u0026thinsp;+\u0026thinsp;nChD\u0026thinsp;+\u0026thinsp;nF \u0026ndash; nB\u003c/em\u003e [3]\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003enCh\u003c/em\u003e is the normalised height above the nearest low-flow channel, \u003cem\u003enChD\u003c/em\u003e is the normalised distance from the nearest low-flow channel, \u003cem\u003enF\u003c/em\u003e is the normalised frequency of floods in the municipality, and \u003cem\u003enB\u003c/em\u003e represents normalised aggregated barriers between the low-flow channel and MRC. Thus, FHI values potential ranged from the minimum of -1 with the lowest flood hazard to 3 with the maximum flood hazard. We classified these values into 5 classes on an ordinal scale (very low, low, medium, high, very high) according to Jenks' natural break algorithm (Jenks, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1967\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ARC also records binary information on the previous occurrence of flooding in each MRC. According to this data, 185 of the 576 studied MRCs have been flooded in the past. We used this dataset for verification purposes, focusing on their distribution in each flood hazard class. We compared this distribution with the entire MRC database. Because ARC does not distinguish between flood type and our methodology was designed primarily for fluvial floods, the verification dataset may also include false positive values.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eOverall, 696 MRC areas identified on the periphery and outside of the municipality were analysed through topography and flood hazard assessment.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Topography index\u003c/h2\u003e \u003cp\u003eThe MRC polygons were divided into five classes based on the elevation amplitude of their buffers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). 118 MRCs were located on the plain (0\u0026ndash;30 m), 110 MRCs on the hilly terrain (31\u0026ndash;80 m), 164 on the upland (81\u0026ndash;140 m), 108 on the mountainous (141\u0026ndash;200 m), and 76 on the high-mountain terrain (201\u0026ndash;402 m). Buffers mean elevation range was 107.23 m, median 96.97 m, the first quartile 41.66 m and the third quartile 153.29 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen interpreting the results of the spatial distribution of EP values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), the elevation amplitude of individual localities was also relevant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In its context, we could see that the highest values of mean EP were achieved in the areas of the plains, specifically on the Danube Plain and the V\u0026yacute;chodoslovensk\u0026aacute; rovina Plain. The highest concentrations of low values of mean EP were found mainly in the northeast of the country in the Ondavsk\u0026aacute; and Laboreck\u0026aacute; vrchovina highlands but also in the mountainous parts of Gemer or Spiš. EP statistics of individual MRCs showed the following trends: a) decrease in mean EP values with increasing elevation amplitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), b) decrease in range of EP with increasing elevation amplitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). In the case of the class with the lowest elevation amplitude (plain), we could observe a range of EP values within a single MRC polygon from the lowest decile (\u0026lt;\u0026thinsp;10) to the highest (\u0026gt;\u0026thinsp;90) in 17 examples. Similar instances do not occur in any of the other classes. Setting aside the plain class with the greatest variability in its values, the average percentile value of the other classes for MRC was below 25 in 163 communities (out of 459), and only 4 out-of-plain polygons had this value in the highest quartile (\u0026gt;\u0026thinsp;75). At the same time, in at least part of their territory, individual MRCs had percentile values in the lowest quartile in 347 (60.13%) cases. Of these, 64 were located in the plain, 62 in the hilly terrain, 95 in the upland, 80 in the mountainous terrain and 46 in the high-mountain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Flood hazard index\u003c/h2\u003e \u003cp\u003eFor the flood hazard index calculation, the height and distance of MRC from the nearest low-flow channel, the barrier between MRC and the low-flow channel, and flood event occurrence were evaluated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the set of 576 mapped MRCs, the mean height above the nearest low-flow channel was 3.4 m, the median was 0.5 m, the first quartile was 0 m, the third quartile was 3.1 m, and the maximum height above the nearest low-flow channel was 104 m. The 0 m value was reached by 43.1% (249) of all MRCs. Regarding the spatial distribution of these values (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), most MRCs with zero height above the nearest low-flow channel were located in the Prešov (110), Košice (79) and Bansk\u0026aacute; Bystrica (43) regions. There were 9 MRCs with zero value in Nitra, 3 in Bratislava, 2 in Trnava and Žilina and only one in the Trenč\u0026iacute;n region. The differences in height above the nearest low-flow channel values based on ARC attribute Concentration type were not statistically significant.\u003c/p\u003e \u003cp\u003eThe mean horizontal distance to the nearest low-flow channel was 95.7 m, the median was 24.1 m, the first quartile was 0.7 m, the third quartile was 113.1 m, and the maximum distance was 1371.2 m. The difference between separate concentration types based on ARC was not statistically significant. We have not observed any spatial patterns in its distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), but 39.5% (228) of the MRCs were located within the 10 m distance of a low-flow channel. Of these, 113 were in the Prešov, 63 in the Košice, 38 in the Bansk\u0026aacute; Bystrica, 4 in the Nitra, 3 in the Bratislava and Žilina, and 2 in the Trenč\u0026iacute;n and Trnava regions. The quartile of the most distant MRCs (horizontal distance higher than 113.1 m) was most frequently located in Košice (60), Prešov and Bansk\u0026aacute; Bystrica regions (both 27). The distribution of the most distant MRCs continued in other regions with Nitra (15), Bratislava (7), Trnava (6) and Trenč\u0026iacute;n with Žilina (both with one such case).\u003c/p\u003e \u003cp\u003eBarriers between MRC and low-flow channels were represented as roads, railways, embankments and fences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Their effect was determined by expert assessment. Most of the barriers were roads with 144 cases. The railroad as a barrier was near 8 MRC and dike in 2 cases. Embankments were close to 16 MRC and fences or walls near 19 MRC. In cases, where the low flow channel is flowing through MRC, we have identified only five MRCs with terrain barriers.\u003c/p\u003e \u003cp\u003eThe number of flood events for individual municipalities within the MRC area of the Slovak Republic (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) is based on the GIS database of the occurrence of flood situations for 1996\u0026ndash;2022. This database has been released from the Report on the progress and consequences of floods in the territory of the Slovak Republic from the negotiations of the Government of the Slovak Republic. The report contains the declaration and revocation of levels of flood activity, in which the date and time the municipality of the declaration of level 2nd or 3rd-degree of flood activity. For our paper's purpose, a flood situation is considered the situation in the municipality when a 3rd-degree flood activity occurs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRiver basins of Slovakia with amounts of MRCs in their area divided into flood hazard index classes. Numbers in the basin names column represent subbasins of the adjacent river and are sorted in ascending order according to the flow directions of watercourses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBasin ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBasin name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eFlood hazard index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBodrog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDolny Dudvah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDanube 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDanube 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDunajec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHnilec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHornad 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHornad 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHornad 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHron 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHron 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHron 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHron 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIpel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIpel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIpel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaborec 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaborec 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatorica 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatorica 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaly Dunaj 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaly Dunaj 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMorava 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMorava 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMorava 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMyjava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitra 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitra 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOndava 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOndava 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrava 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrava 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoprad 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoprad 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlana 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlana 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlana 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlatina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBodva\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDolny Uh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHorny Uh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTisa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTorysa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVah 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZitava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e139\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e130\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e101\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e125\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e576\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVery high hazard is in 125 municipalities with MRC, and 101 have high hazard. Medium flood hazard can be seen in 130 cases and low and very low in 139 and 81 cases, respectively. From a spatial aspect (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), most of the very high and high municipalities are in the eastern part of Slovakia in the basins of Topľa (31), Horn\u0026aacute;d 1 (31), Torysa (20), Ondava 1 (19), Poprad and Slan\u0026aacute; rivers (12 MRCs both). The first four mentioned are also the basins with the highest number of MRCs overall (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Conversely, there were only 6 MRCs with high and very high FHI in the Horn\u0026aacute;d 3 basin, even though it is placed 5th in the total number of Roma communities.\u003c/p\u003e \u003cp\u003eLinking the flood hazard analysis with the results of the topographic analysis (Section 3.1), we found that the most exposed Roma settlements are located in areas with higher elevation amplitude corresponding to Upland, Mountainous and High-mountainous levels (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, low flood hazard was in turn more frequently associated with the plain. We also observed a negative correlation between the EP and flood hazard index (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), with the value of -0.49.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMRCs distribution in flood hazard and landform classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (13.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (38.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (29.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (9.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (9.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e118\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHilly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (14.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (17.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (27.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (22.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (18.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e110\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (17.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (20.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (19.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (14.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (28.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e164\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMountainous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (8.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (20.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (22.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (22.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (26.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e108\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-mountainous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (15.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (26.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (11.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (22.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (23.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e81 (14.06%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e139 (24.13%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e130 (22.57%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e101 (17.53%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e125 (21.70%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e576\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Verification\u003c/h2\u003e \u003cp\u003eIn the verification layer of the MRCs with previous flood experience, we can observe its growing representation with increasing flood hazard classes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although this layer likely includes flood types not targeted by our methodology, up to 75% of the verification dataset was in the three highest flood hazard classes. Across all studied MRCs this was only 60%, with flood hazard classes more equally represented. The datasets differed most in the two extreme classes and were almost identical in the medium class.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMRCs distribution in different flood hazard classes. Comparison of results between MRCs with previous flooding experience (according to ARC) and the whole dataset.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRCs with recorded flooding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll MRCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRCs with recorded flooding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll MRCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Input data uncertainty\u003c/h2\u003e \u003cp\u003eWe have worked extensively with ARC 2019 in our research, particularly in the case of spatial identification of MRCs. However, there are several methodological specificities associated with the production of ARC that we had to take into account when using these data. The Atlas data were obtained from a questionnaire survey by qualified estimation in 810 municipalities of the Slovak Republic (Mušinka, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As several questions had ambiguous definitions, respondents often interpreted them differently. For example, the definition of the crucial concept of concentration itself reads as follows: \"\u003cem\u003eA concentration, as understood by the Atlas, is any housing development within which multiple households are perceived through Roma ethnicity\u003c/em\u003e\" (Ravasz et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Yet, in applying such a definition, it is questionable what \"households perceived through Roma ethnicity\" actually are or what \"multiple\" specifically means. Another of the ARC's core attributes that was defined ambiguously was \u003cem\u003eType of Concentration\u003c/em\u003e. The categorisation of \u003cem\u003eInside the municipality\u003c/em\u003e, \u003cem\u003eOn the periphery of the municipality\u003c/em\u003e and \u003cem\u003eOutside the municipality\u003c/em\u003e was only vaguely defined, with a complete lack of specific instructions. Although the \u003cem\u003eInside the municipality\u003c/em\u003e concentrations are supposed to be the \u0026ldquo;central part of the built-up area of the village\u0026rdquo; and the \u003cem\u003eOn the periphery of the municipality\u003c/em\u003e MRCs are supposed to be \u0026ldquo;seamlessly connected to the houses in the main part of the village\u0026rdquo;, there is no indication of what the central part of the village is. Settlements \u003cem\u003eOutside the municipality\u003c/em\u003e were defined as \"a location at some distance from the continuous built-up area of the respective municipality, mostly separated by undeveloped land, a railway line, a river/creek, a road, etc.\" (Ravasz et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). From this definition, it is not clear whether every MRC that is separated from part of the municipality by any of the mentioned barriers should automatically fall \u003cem\u003eOutside the municipality\u003c/em\u003e, even if it was situated in the main part of it. As a result, we can see the inconsistency in the responses in different municipalities, which is captured in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePotential sources for spatial identification of Roma population concentrations were also ZBGIS databases. Specifically, in the building and shack vector layers, the TXT annotation attribute sometimes specifies that it is a Roma object. However, the openness of this attribute is problematic, as it does not have a precise definition of what information it should be filled with. As a result, it is not clear by what methodology the objects were marked as Roma, nor whether each of the operators considered it worthy of being recorded, or what the motivation of those who did so was. Therefore, the MRC spatial identification was based on a subjective assessment of the orthophoto images. Overall, 696 MRC areas on the periphery and outside of the municipality arose to identify from the ARC. However, according to building type uncertainty and remote image readability, we did not identify a few dozen of them. We chose support based on local environment knowledge for the most objective identification. The problematic MRC areas were discussed with experts from the Office of the Plenipotentiary of the Government of the Slovak Republic for Roma Communities. They provided us with up to twenty localities, and for this study, a set of 576 mapped MRC areas were considered for the performed analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Specificity of flood hazard methodology\u003c/h2\u003e \u003cp\u003eThis study presents a specific methodological approach to assessing flood hazard. The area of interest was dispersed across the entire country in the form of 576 discrete polygons of varying shapes and sizes. Some Roma settlements were almost the size of a village, while others consisted of only a few houses. Each polygon required the assignment of a representative flood hazard value for its entire area. More sophisticated methods, such as hydrodynamic or hydrologic modelling (Di Baldassarre et al., 2010), were unsuitable because the region of interest was too fragmented, and the associated data requirements exceeded our capabilities. Similarly, the use of empirical models (Mudashiru et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) would have required comprehensive data covering the whole of Slovakia, which were not publicly available at a sufficiently high spatial resolution to capture the smallest polygons. Our approach was based on flood hazard index, which we conceived by a thoughtful selection of sub-indices. We associated fluvial flood hazard with active floodplains, defined in terms of distance and elevation relative to the watercourse. Floodplain activity was characterized based on the frequency of previous flood events and the presence of topographic barriers. All sub-indices were normalized by their maximum value (Malczewski, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and were given equal weight. The territory of Slovakia is very diverse and empirical weighting such as analytic hierarchy process (Saaty, 1988) would require nuanced complexity with no assurance of the outcomes.\u003c/p\u003e \u003cp\u003eTopographic parameters are quite often used in flood hazard modelling (Ali et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Al-Omari et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chakrabortty et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kittipongvises et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; M Amen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Madi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Stream distance or height above the nearest drainage (Renn\u0026oacute; et al., 2008) are widely used both in empirical or physically-based models (Mudashiru et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Historical flood events could be used both as a model parameter or reference layer (Chen, 2022; Kittipongvises et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kourgialas \u0026amp; Karatzas, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In a recent study of flood risk in Slovak municipalities with MRC flood event frequency was used as a parameter of FHI too (Sol\u0026iacute;n, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the author only worked with ARC data at the municipality level, without precise topographic information on the coordinates of individual MRCs. This was also reflected in the FHI values, as they are visibly different from our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The use of barriers similar to our approach is not as widespread, but we can find suggestions for their use (Jančovič \u0026amp; Kidov\u0026aacute;, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe specificity of the situation was also reflected in the verification phase. Instead of using independent control areas\u0026mdash;which would have needed to be sufficiently varied to represent the diversity of the Slovak landscape\u0026mdash;we employed existing data directly related to our units of interest: the MRC polygons. These polygons were subject to the same uncertainties described in Section 4.1 and likely included flood types other than fluvial floods, which account for only about half of all flood events in Slovakia (Sol\u0026iacute;n, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Nevertheless, they proved useful. Although our flood hazard classification was designed specifically for fluvial floods, the previous binary MRC flood occurrence data corresponded well with our classification. Areas classified as having low flood hazard were the least represented, while those classified as having high flood hazard were the most prevalent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Flood hazard and river basin importance\u003c/h2\u003e \u003cp\u003eIn the works targeting the analysis of floods in Slovakia, some basins appear repeatedly notable for their flood hazard. The basins of eastern Slovak rivers such as the Topľa, Torysa, Horn\u0026aacute;d and Ondava are in this context the most referred to. It is confirmed by the results of a multicriteria analysis of all municipalities in Slovakia (Vojtek et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) using the following factors: flood frequency, lithology, river density, maximum 5-day rainfall, slope, curvature, CLC 2018 and soil texture. The region was similarly significant in Sol\u0026iacute;n (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), where flood hazard was calculated for individual headwater basins. Each of the eastern Slovakian basins mentioned above contained a range of headwater basins with the highest flood hazard and almost no basins with the lowest hazard. The author cited soil permeability as the factor that caused this the most. In those basins, flysch sedimentary deposits with impermeable clay layers are often found. This is also indicated by the research of Bez\u0026aacute;k et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) dealing with the occurrence of flash floods based on hydrological online data. The Torysa, Ondava, and Horn\u0026aacute;d river basins are among the regions with the highest occurrence of flash floods. In addition to the flysch-related geology, he also cites land use or the geomorphology of the long, narrow valleys in which all the water from the basin is concentrated as other factors for the higher incidence of floods. According to our results the Topľa, Torysa, Horn\u0026aacute;d and Ondava river basins contain the largest number of MRCs with high and very high flood hazard (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), but they also have the highest number of MRCs in general (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This prompts a closer examination of these basins also in the context of the flood exposure of Roma communities. The Topľa River catchment, which contains the most MRCs with very high FHI, has been examined more closely in terms of flood hazard (Ali et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but without indicating the flood exposure of individual settlements. Such a scale would allow us to examine the flood exposure of MRCs as well as adjacent villages. Comparing them could be a solid case study of environmental justice focusing on Slovak marginalized Roma communities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Flood management recommendation in threatened MRC\u003c/h2\u003e \u003cp\u003eOur results showed important information for river managers, mayors of municipalities, and government legislators. The importance of cooperation between local governments and stakeholders in the multi-management of the affected MRCs could be crucial for the bottom-up approach. To this day, the situation with the most threatened MRC areas is solved only locally by occasional channel regulation without any systematic approach. According to legislation and standards, flood protection in the Slovak Republic is built for a flood with a medium probability of recurrence for a design flow rate Q of 100-year water. In the year 1998, 50 Roma people became victims of catastrophic floods with a 10 000-year recurrence in the municipality of Jarovnice. This tragedy was followed by a modification of the Mal\u0026aacute; Svinka streams by the Slovak Water Management Enterprise. Within five years of the tragedy, over 5\u0026nbsp;million euros were invested in flood control measures in the municipalities of Jarovnice, Uzovsk\u0026eacute; Pekľany and Renčišov, which were hit by the catastrophic flood in 1998. According to the statement of the mayor of Jarovnice, the most suitable solution was not found. They put gabions there, and the wires did not hold up. They were washed away and overturned. In twenty years, the riverbed has widened, the bottom level lowered, and about two meters of sediment had been deposited there. A recent statement by water managers revealed there is no money for the river channel sediment clean-up. The municipality is on a waiting list for measures. A plan with specific measures and an order of urgency have been adopted up to 2019. However, it is unclear what the effect is because people are still living on the active floodplain, and during the rains, entire families flee to a nearby hill. Given that no people should be subjected to fear for their livelihood whenever it rains, options and mechanisms which support the relocation of affected households should exist.\u003c/p\u003e \u003cp\u003eThe described situation in the Jarovnice settlement from 2018 (Ivan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) is a very suitable example to point out where we can identify legislative and management gaps in solving flood protection issues from the environmental injustice point of view (Škobla \u0026amp; Filč\u0026aacute;k, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The top-down approach promises the legislative anchoring of the entire problem related to functioning solution of MRC social status and flood control measures themselves. If we go further from flood hazard to flood risk assessment (Sol\u0026iacute;n, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), some demographic and economic determinants of the MRC's ability to cope with flood event consequences can be identified. The recovery phase for financially weak, low-income, or poor households is disproportionately long and complicated (Mich\u0026aacute;lek \u0026amp; Madajov\u0026aacute;, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In many cases, without state aid, mainly due to a lack of financial resources or deep poverty, a flood is unmanageable and, for many households from MRC, an unsolvable task. Additionally, the number of children, age, level of education and income play a crucial role in the phase of coping and recovery after a flood event. Therefore, an exchange of scientific knowledge, stakeholders\u0026rsquo; engagement, and active cooperation with crucial government representatives responsible for the decision-making process should be a priority for future planning of strategic documents related to urgency and alarming status of MRC.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn the context of natural hazards and issue of flood exposure of Roma settlements an interdisciplinary approach is required. In this paper, the information about morphometric parameters of these areas provides the initial knowledge needed for predictions related to the extent of inundation and its impact on property and human lives. It brought a completely new perspective to the studies of segregation and environmental injustice of Slovak MRCs. For the presented research, we used mainly classical (orthophoto images) and also very precise (Airborne Laser-Scanning) remote sensing data combined with hydrological datasets.\u003c/p\u003e \u003cp\u003eOn a sample of up to 576 MRCs across Slovakia, we analysed their topography and calculated a flood hazard index. Only 228 MRCs were located in areas with lower elevation amplitude, such as plain and hilly areas, and we identified the remaining 348 in localities with higher elevation amplitude. Overall, up to 60.13% of all MRCs were located at least partly in the lowest quartile of elevation within a 1 km radius. A similar value was achieved in each defined landform. This is consistent with the values that can be reached by floodplains and is indicative of the flood potential in Roma communities. The highest flood hazard class was generally represented more in the terrain with higher elevation amplitude, such as Upland, Mountains and High-mountains. At the same time, of the 125 MRCs with the highest FHI value, most were located in the Topľa (18), Horn\u0026aacute;d 1 (15), Torysa (13) and Ondava 1 (11) river basins.\u003c/p\u003e \u003cp\u003eThe application potential of the paper should be highlighted. One of the priorities of the National Strategy for Equality, Inclusion and Participation of Roma for 2020\u0026ndash;2030 is the property rights of land under settlements. Property rights can be considered one of the main conditions for the implementation of programmes aimed at the active inclusion of people from marginalized Roma communities and the overall development of the villages and territories listed in the Atlas of Roma Communities. The outputs of our paper should serve as an essential source of information in the process of land settlement in municipalities with the presence of the MRC, which can lead to the possible relocation of people from the most hazardous localities into safer alternatives. We believe that the paper's outcomes will be important for river managers, stakeholders and policymakers as well as other authorities working with MRC internationally. During paper preparation, we actively cooperated with the Operations Centre of Emergency Medical Service of the Slovak Republic, which requested our results of a unique spatial dataset to help them provide emergency intervention in MRC areas faster. The findings of our paper had practical relevance for public policymaking, especially in developing an actual preparation of the Action Plan for the priority area Housing and Health and are made fully available to the Office of the Government Plenipotentiary for Roma Communities in the Slovak Republic, where we stay in active cooperation. Finally, the results represent an accurate basis for continuing with detailed research on regional and local levels and provide a new approach to MRC flood hazard assessment applicable in other countries too.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Slovak Research and Development Agency under the project Marginalized Roma concentrations in the context of natural hazards and social inequality (APVV-22-0428) and by the Science Grant Agency (VEGA) of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences (02/0086/21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Slovak Research and Development Agency under the project Marginalized Roma concentrations in the context of natural hazards and social inequality (APVV-22-0428) and by the Science Grant Agency (VEGA) of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences (02/0086/21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conceptualization, methodology, resources and writing. Data curation and formal analysis were performed by Mari\u0026aacute;n Jančovič and Luk\u0026aacute;\u0026scaron; Michaleje. Funding acquisition, project management and supervision were performed by Anna Kidov\u0026aacute;. Validation, visualization a investigation were performed by Mari\u0026aacute;n Jančovič.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlc\u0026aacute;ntara-Ayala, I. (2002). Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. \u003cem\u003eGeomorphology\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(2), 107\u0026ndash;124. https://doi.org/10.1016/S0169-555X(02)00083-1\u003c/li\u003e\n\u003cli\u003eAli, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojtekov\u0026aacute;, J., Costache, R., Linh, N. T. T., Nguyen, H. Q., Ahmad, A., \u0026amp; Ghorbani, M. A. (2020). 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PRIESTOROV\u0026Aacute; DIMENZIA PR\u0026Iacute;JMOV\u0026Yacute;CH NEROVNOST\u0026Iacute;: TE\u0026Oacute;RIA, KONCEPTY A MET\u0026Oacute;DY. \u003cem\u003eGeografick\u0026yacute; časopis / Geographical Journal\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(2), 107\u0026ndash;129. https://doi.org/10.31577/geogrcas.2020.72.2.06\u003c/li\u003e\n\u003cli\u003eWalker, G. (2012). \u003cem\u003eEnvironmental Justice: Concepts, Evidence and Politics\u003c/em\u003e. Taylor \u0026amp; Francis Group. http://ebookcentral.proquest.com/lib/uniba-ebooks/detail.action?docID=958746\u003c/li\u003e\n\u003cli\u003eWright, D. B. (2015). \u003cem\u003eMethods in Flood Hazard and Risk Assessment\u003c/em\u003e (p. 20). World Bank. https://doi.org/10/25136493/methods-flood-hazard-risk-assessment\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"marginalized Roma community, flood hazard index, segregation, topography, Slovakia","lastPublishedDoi":"10.21203/rs.3.rs-6574450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6574450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMarginalized Roma communities (MRC) located in flood prone areas represent a very current research challenge. With the incentive to reduce their flood exposure, this article sets out the following objectives: I) creation of a geodatabase of Slovak MRCs; ii) morphometric assessment of MRCs iii) flood hazard index calculation for individual MRCs iv) flood management recommendation for the most threatened MRCs. The presented study is based on an analysis of remote sensing and hydrological data, digital terrain model and Atlas of Roma communities (ARC). We used elevation percentile and elevation amplitude in terms of topographic analysis and landform classification. The distance of the MRC from the nearest watercourse, the occurrence of floods in the municipality and the barriers between the MRC and the nearest watercourse were used to estimate the flood hazard index. An analysis was carried out on a spatial geodatabase of 576 MRCs. We identified Roma communities on different landforms with 60.13% of them were in the first quartile of the elevation percentile. Among the regions with the highest number of most exposed MRCs, we listed the Topľa, Hornád 1, Torysa and Ondava 1 river basins sorted descending by the amount of MRCs with very high flood hazard.\u003c/p\u003e","manuscriptTitle":"Extreme flood events as a neglected natural hazard for marginalized Roma communities on the periphery and outside the municipalities of Slovakia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 04:24:34","doi":"10.21203/rs.3.rs-6574450/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-28T12:07:50+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T11:58:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2025-05-26T11:26:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-02T13:30:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2025-05-01T18:46:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fbc1dc5a-c1b0-44d5-a703-568838f103e4","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T11:21:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 04:24:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6574450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6574450","identity":"rs-6574450","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}