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Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index

  • Bojana Aleksova EMAIL logo , Ivica Milevski , Risto Mijalov , Slobodan B. Marković , Vladimir M. Cvetković and Tin Lukić
Published/Copyright: August 30, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

This study presents a comprehensive analysis of flash flood susceptibility in the Kratovska Reka catchment area of Northeastern North Macedonia, integrating Geographic Information System, remote sensing, and field survey data. Key factors influencing flash flood dynamics, including Slope, Lithology, Land use, and Vegetation index, were investigated to develop the Flash Flood Potential Index (FFPI). Mapping slope variation using a 5-m Digital Elevation Model (DEM) revealed higher slopes in eastern tributaries compared to western counterparts. Lithological units were classified based on susceptibility to erosion processes, with clastic sediments identified as most prone to flash floods. Land use analysis highlighted non-irrigated agricultural surfaces and areas with sparse vegetation as highly susceptible. Integration of these factors into the FFPI model provided insights into flash flood susceptibility, with results indicating a medium risk across the catchment. The average value of the FFPI is 1.9, considering that the values range from 1 to 5. Also, terrains susceptible to flash floods were found to be 49.34%, classified as medium risk. Field survey data validated the model, revealing a significant overlap between hotspot areas for flash floods and high-risk regions identified by the FFPI. An average FFPI coefficient was calculated for each tributary (sub-catchment) of the Kratovska Reka. According to the model, Latišnica had the highest average coefficient of susceptibility to potential flash floods, with a value of 2.16. These findings offer valuable insights for spatial planning and flood risk management, with implications for both local and national-scale applications. Future research directions include incorporating machine learning techniques to enhance modeling accuracy and reduce subjectivity in assigning weighting factors.

Keywords: FFPI; flash floods risk; Kratovska Reka; GIS; geospatial analytics; Remote Sensing; natural hazards

1 Introduction

As mentioned in the study [1], South-Eastern Europe (SEE) has emerged as a global hotspot due to the increasing occurrence of weather-related hazards. Recent studies underscore Europe’s vulnerability to hydro-meteorological hazards, particularly flash floods, which pose significant risks to environmental stability, population centers, infrastructure, and socio-economic facets [2,3,4,5]. In the SEE region, heavy precipitation-induced flash floods constitute a major hazard [6,7]. Given their unpredictable nature and potential for catastrophic consequences, there is an urgent need to prioritize enhancing early warning systems [8]. Strengthening these systems empowers societies to manage flash flood threats effectively, enabling timely interventions to safeguard vulnerable populations [4].

Flash floods are among the most devastating natural hazards affecting North Macedonia, where various methods, including the Flash Flood Potential Index (FFPI), are used for modeling flash floods in catchment areas [9,10,11,12,13]. Flash floods are characterized by sudden, intense water volumes and sediment transport in torrents [14,15,16,17]. Predicting flash floods is challenging, but early signs such as heavy rain, thunderstorms, and rapid snowmelt are critical [18]. These events are influenced by factors such as intense rainfall, soil and basin characteristics, and human activities, exacerbated by climate change and land-use changes [19]. Improved indices like FFPI enhance flash flood warning accuracy [20], supported by Geographic Information System (GIS) for integrated hazard susceptibility mapping [21]. Hydrological responses vary based on physical geography, with deforestation impacting water propagation on slopes [22].

Additional FFPI studies have been conducted [22,23,24,25,26,27,28,29,30,31,32,33,34]. North Macedonia faces multiple natural hazards, including geohazards like erosion, landslides, and earthquakes [35,36,37,38,39]. Historically considered natural, these disasters are increasingly influenced by human activities. European flash flood events are extensively studied through international projects like HYDRATE, IFRAMM, and EFFS [27].

With the support of open-source software QGIS 3.34.2 and SAGA GIS 9.3.0, we collected satellite images and developed a comprehensive database for this study. Our primary objective is to assess flood risk areas using a statistical GIS-based approach. Beyond mapping hazards, it is crucial to underscore the importance of preventive measures and evacuation protocols to mitigate and ideally prevent the impacts of such hazards. This study employs an integrated approach combining GIS techniques with the FFPI. Our methodology focuses on identifying flash flood drivers, pinpointing high-risk areas, categorizing flood risk levels, and validating these assessments using historical data and remote sensing inputs in the Kratovska Reka catchment (North Macedonia). This initiative represents an essential first step towards implementing effective mitigation strategies for sustainable management of flash flood risks across local and national levels in North Macedonia.

2 Data and methods

2.1 Overview of the study area

According to the research, the main water artery in the Kratovska Reka catchment (68.5 km²) with a total of 20 tributaries, i.e., sub-catchments, is located in the NE part of North Macedonia (Figure 1). The tributaries of Kratovska Reka are characterized by their usually branched catchments, formed by several sources. Most of them are mountainous, rapid with gorge valleys, and have a typical V profile in the upper course. Throughout most of the year, these tributaries have a large flow of water, which reduces during the summer period [7]. For this work, a digitization and analysis of the sub-basins of the main watercourse Kratovska Reka was carried out. Tables 1 and 2 show the basic hydrographic features of the Kratovska Reka and its more significant tributaries.

Figure 1 (a) Sub-catchments of Kratovska Reka, (b) Kratovska Reka catchment in the North Macedonia territory, and (c) Kratovska Reka catchment location in the Balkan peninsula (South-East Europe).
Figure 1

(a) Sub-catchments of Kratovska Reka, (b) Kratovska Reka catchment in the North Macedonia territory, and (c) Kratovska Reka catchment location in the Balkan peninsula (South-East Europe).

Table 1

Basic characteristics of the river network of Kratovska Reka and its most important tributaries

Nu. Sub-catchment T P km2 AC H-a m Lw km Lc km a˚av H mm
1. Lisečki Potok / 3.8 2.5 1194.7 8.5 3.4 171.4 800.9
2. Nežilovski Potok L 4.5 1.3 1179.1 9.8 3.5 212.5 801.0
3. Drumski Potok L 2.1 1.3 1131.4 8.0 3.0 235.6 789.3
4. Bela Reka L 2.3 1.5 1046.9 7.9 3.2 236.5 773.7
5. Latišnica L 5.3 1.5 1077.5 11.9 4.4 196.9 774.6
6. Tabački Dol R 2.0 1.1 1068.6 9.7 4.4 183.8 776.3
7. Babakarina and Manceva Reka L 6.4 2.0 941.8 11.7 4.2 195.9 748.1
8. Mlački Dol R 2.3 1.1 900.3 8.4 3.9 204.3 747.8
9. Boškovačka Reka L 3.7 1.5 856.4 9.8 3.8 196.2 732.4
10. Raškovec R 3.8 1.2 964.9 11.4 4.8 197.2 759.9
11. Vrbički Potok R 2.2 1.7 909.6 9.1 3.9 186.6 748.9
12. Železnički Dol 1 R 1.5 1.6 819 6.9 2.9 194.9 732.7
13. Železnički Dol 2 R 1.2 2.4 785.6 6.0 3.1 205.7 725.9
14. Železnički Dol 3 R 1.4 1.3 739.7 7.5 3.5 205.9 719.0
15. Šlegovski Dol L 5.5 1.8 829.9 12.1 5.2 190.2 725.6
16. Manev Dol L 2.6 1.4 699.9 9.3 4.1 202.9 701.9
17. Živalevski Dol R 1.6 1.4 604.5 5.9 2.0 233.0 691.4
18. Talašmanski Dol / 3.0 1.0 548.4 8.7 2.4 195.8 680.4
19. Topolovički Dol L 2.0 1.3 577.2 7.6 2.5 166.7 681.4
20. Ketenovski Dol / 1.2 3.9 472.8 5.2 1.6 171.6 665.2
Kratovska Reka / 68.5 1.52 881 44.7 16.9 195.8 740.6

T = side of the tributary, P km2 = area, AC = coefficient of asymmetry of the watershed, H-a m = mean height of the catchment, Lw km = watershed length of the catchment, Lc km = maximum catchment length (per watercourse), a˚av = mean value of azimuth, H mm = mean amount of precipitation (according to WorldClim 2 data [40].

Table 2

Basic characteristics of the river network of Kratovska Reka and its most important tributaries

Nu. Sub-catchment L km Hs m Hvl m ∆m ∆‰ ΣN ΣL km D
1. Lisečki Potok 2.9 1338.4 803.4 535.0 184.5 11 7.9 2.1
2. Nežilovski Potok 3.2 1325.8 803.4 522.4 163.3 20 9.7 2.2
.3. Drumski Potok 2.9 1279.5 760.5 519.0 179.0 8 4.6 2.2
4. Bela Reka 2.6 1240.4 738.9 501.5 192.9 9 6.4 2.8
5. Latišnica 3.8 1203.5 702.7 500.8 131.8 20 11.4 2.2
6. Tabački Dol 4.1 1251.1 640.0 611.1 149.0 2 4.3 2.2
7. Babakarina and Manceva Reka 3.6 1145.6 601.8 543.8 151.1 28 18.3 2.9
8. Mlački Dol 3.6 1049.2 583.8 465.4 129.3 6 5.7 2.5
9. Boškovačka Reka 3.3 1149.6 573.2 576.4 174.7 12 9.8 2.6
10. Raškovec 4.4 1136.9 573.2 563.7 128.1 11 8.2 2.2
11. Vrbički Dol 3.6 1079.2 551.7 527.5 146.5 4 5.1 2.3
12. Železnički Dol 1 2.7 985.7 532.6 453.1 167.8 4 3.7 2.5
13. Železnički Dol 2 2.6 954.4 521.8 432.6 166.4 3 4.1 3.4
14. Železnički Dol 3 3.1 950.4 515.4 435.0 140.3 4 4.1 2.9
15. Šlegovski Dol 4.8 1075.4 504.7 570.7 118.9 19 13.5 2.5
16. Manev Dol 3.9 923.6 489.0 434.6 111.4 8 5.9 2.3
17. Živalevski Dol 1.7 607.7 457.5 150.2 88.4 7 4.0 2.5
18. Talašmanski Dol 1.5 627.7 443.8 183.9 122.6 17 6.1 2.0
19. Topolovički Dol 2.2 596.1 432.3 163.8 74.5 8 5.1 2.6
20. Ketenovski Dol 0.8 482.3 417.2 65.1 81.4 6 1.7 1.4
Kratovska Reka 16.9 1338.4 411.9 926.5 54.8 207 139.6 2.0

L, length of the watercourse in km; Hs, height of the source of the watercourse in m; Hvl, elevation of the mouth of the watercourse in m; ∆m, total fall of the watercourse in m; ∆‰, average drop of the watercourse in ‰; ΣN, total number of watercourses-tributaries; ΣL, total length of all tributaries in km; D, density of river network in km/km2.

Following the performed analyses, it can be observed that Kratovska Reka has 9 left and 8 right tributaries. By area, the largest Sub-catchments are Babakarina and Manceva Reka (6.4 km²), while the smallest are Ketenovski and Železnički Dol (1.2 km²). The Ketenovski Dol sub-catchment has the largest coefficient of asymmetry (3.9), while the Talašmanski Dol Sub-catchment has the lowest (1). The Sub-catchment of Lisečki Potok has the highest mean altitude (1194.7 m), whereas the Ketenovski Dol Sub-catchment has the lowest (472.8 m). According to the calculations of the length of the watersheds of the Kratovska Reka catchment, the sub-catchment of Šlegovski Dol has the largest length (12.1 km), and Ketenovski Dol has the shortest length (5.2 km). Additionally, the sub-catchment of Šlegovski Dol has the longest basin length per watercourse (5.2 km), while Ketenovski Dol has the smallest (1.6 km). The results are slightly different regarding azimuth. The sub-catchment of Bela Reka has the highest azimuth value (236.5°), and Topolovički Dol has the lowest value (166.7°). The results also show that the eastern (higher) part of the Kratovska Reka catchment receives the highest precipitation, while the western (lower) part receives the least. According to our calculations, the largest average amount of precipitation is in the sub-catchment of the Nežilovski Potok (801 mm), while the smallest is in the sub-catchment of the Ketenovski Dol (665.2 mm).

Kratovska Reka has a developed hydrographic network. According to our analysis and measurements (based on topographic map (TM) 100k scale and 5-m DEM), the river network consists of 207 permanent, periodic, and occasional watercourses with a total length of 139.6 km. Of these, 30 watercourses (14.5%) are up to 2 km long (total length, 11.8 km), and 177 watercourses (85.5%) are 2–5 km long (total length, 127.8 km).

Among the listed 177 watercourses, 20 are direct tributaries of the Kratovska Reka. These include Nežilovski Potok, Drumski Potok, Bela Reka, Latišnica, Babakarina and Manceva Reka, Boškovačka Reka, Šlegovski Dol, Manev Dol, and Topolovički Dol as left tributaries, and Tabački Dol, Mlački Dol, Raškovec (Figure 2), Vrbički Dol, Železnički 1, 2, and 3 Potok, and Živalevski Dol as right tributaries. Among these, the longest watercourse is Šlegovski Dol (left tributary) with a length of 4.8 km. This indicates that these are relatively short watercourses, with an average length of only 3.1 km.

Figure 2 Raškovec watercourse cut into tuffs, with excessive erosion. Photo: Aleksova B. 2023.
Figure 2

Raškovec watercourse cut into tuffs, with excessive erosion. Photo: Aleksova B. 2023.

The density of the river network of Kratovska Reka is relatively uniform and amounts to 2.0 km/km². According to the data obtained, within the sub-basin areas, it is somewhat higher in the western and southeastern parts of the catchment, specifically in the sub-basins of Železnički Potok 2, Babakarina and Manceva Reka, Železnički Potok 3, Bela Reka, Boškovačka Reka, and Topolovički Potok (3.4–2.6 km/km²). It should be noted that the eastern part of the watershed, particularly the tributaries of the Kratovska Reka that descend from the Osogovo Mountains (Lisec, 1,526 m), is characterized by a higher mean altitude and greater precipitation. Therefore, their watercourses usually have water throughout the year, with maximum flows during the spring (May and June) and autumn periods (November). These include the sub-basins of Lisečki, Drumski, Nežilovski Potok, Bela Reka, Latišnica (Figure 3), and Tabački Dol (1046.9–1194.7 m).

Figure 3 Estuary of Latišnica in Kratovska Reka. Photo: Aleksova B. 2023.
Figure 3

Estuary of Latišnica in Kratovska Reka. Photo: Aleksova B. 2023.

In general, the left tributaries descend from areas composed of volcanic rocks (andesites, dacites, tuffs, and breccias), while the right tributaries descend from the northwestern part of the Osogovo Mountain Massif (Figure 4). These tributaries have a lower average height (604.5–1068.6 m) with an average height above sea level of 960.9 m and a lower average annual amount of precipitation. Consequently, they have a variable flow and usually dry up during the summer period.

Figure 4 Meander of Kratovska Reka in the upstream part of the catchment. Photo: Aleksova B. 2023.
Figure 4

Meander of Kratovska Reka in the upstream part of the catchment. Photo: Aleksova B. 2023.

2.2 FFPI method

The FFPI is a statistical method used to identify areas susceptible to flash flooding, commonly applied in both global and regional frameworks [29,41,42]. This method provides a quantitative analysis of areas prone to flash floods based on weighting factors such as slope, vegetation cover, land use, and soil type. Given the scale of the available Soil Map of Macedonia (200k scale), which lacks detailed soil data, the lithological type of the researched area was considered instead. Flash floods in catchment areas are common when soil infiltration rates are reduced due to bare, deforested, and/or steep terrain. Factors like intensive precipitation (combined with snowmelt) [43], terrain slopes, soils, and vegetation density (forest, shrubs, and grasses) contribute to flash flood potential, with less vegetated terrain having a greater risk [44,45].

The FFPI, a widely used method, was first applied at the Colorado River Basin Forecast Center and is based on the US National Weather Service model [46,47]. It is determined using GIS software tools through a statistical approach that correlates various factors with the spatial distribution of watershed drainage [41,42,48,49,50,51,52,53]. Model values range from 1 to 10 (1 = lowest potential and 10 = highest potential). This model is preferred due to the limitations of meteorological parameters in predicting floods and the lack of a defined connection between this natural hazard and specific physical geographical characteristics.

The structure and texture of soils are key in defining water retention and infiltration. Terrain slopes influence runoff rate and concentration. Vegetation, which affects the retention of atmospheric water on the surface, varies seasonally (e.g., deciduous forests) and can be impacted by wildfires, which reduce soil infiltration due to burned organic matter. Land use, particularly urbanization, significantly impacts water infiltration, concentration, and outflow from the watershed. These natural conditions collectively provide information on the flash flood potential of a given area [46].

2.3 Data collection for FFPI

To identify flash floods in the Kratovska Reka catchment, an analysis of terrain susceptible to flash floods was conducted using GIS. The FFPI method was used for this analysis. According to the available Soil Map of Macedonia (200k scale), which lacks detailed soil data, the lithological type of the researched area was considered instead of soil types. The following weighting factors were used in developing the model: slope, vegetation cover, lithology, and land use. The FFPI is derived from the formula [47]:

FFPI = ( M + S + L + V ) / 4

where M represents the slope of the terrain, S – lithology, L – land use, and V – vegetation index.

The terrain slope (M) was obtained using QGIS 3.32.2-Lima and SAGA GIS 9.0.0 software packages, based on a 15-m DEM (Digital Elevation Model) derived from a 5-m DEM provided by the Ministry for Agriculture, Forestry, and Water Management of North Macedonia. The slope, expressed in percent, is calculated using the formula:

M = 10 n / 30

where n represents the average slope of the terrain expressed in %. If n is equal to or greater than 30%, then the M value is always 10. In this case, the average slope (n) in the catchment area of Kratovska Reka is 39.3%. Therefore, M (the slope of the terrain) has a value of 10.

The lithology (S) was analyzed using a digital lithological map based on the Geological Map of Macedonia (100k scale) [54]. Rocks were classified with values from 1 to 9, depending on their susceptibility to erosion.

The land use index (L) was calculated using data from the Corine Land Cover database (2018) [55], with classes assigned values from 1 to 10 based on their impact on flash flooding.

The vegetation index (V) was determined using the Bare Soil Index (BSI) from multispectral satellite imagery obtained through the Landsat 8 program via Earth Explorer, United States Geological Survey [56]. Remote sensing, particularly using the BSI, provides an efficient means to calculate erosion rates correlated with flash floods [57]. The BSI was calculated using the formula [58]:

BSI = [ ( B 6 + B 4 ) − ( B 5 + B 2 ) / ( B 6 + B 4 ) + ( B 5 + B 2 ) ]

where B6 (band 6) represents the short infrared spectral channel (SWIR 1), B4 (band 4) is the red spectral channel, B5 (band 5) is the near-infrared spectral channel (NIR), and B2 (band 2) is the blue spectral channel. After the bands were selected, the images were processed using the Atmospheric correction tool, and then, they were cut in the domain of the watershed boundary. The obtained values of the BSI index range from −1.9 to 0.7, while the average value is −0.9. To avoid negative values in the vegetation index (V) formula, a value of 1 has been added. Therefore, it reads:

V = 7.68 × ln ( BSI + 1 ) + 8

2.4 Calculation of FFPI

The FFPI is created by gathering raster datasets of certain characteristics within the specified area and employing GIS technology to adjust, categorize, and merge the data. Figure 5 demonstrates the stages of data processing using the FFPI method. The outcome is a numerical gauge representing a region’s susceptibility to flash flooding, which remains relatively consistent over time.

Figure 5 Flow chart with all the procedures and FFPI-based methodology used in this research.
Figure 5

Flow chart with all the procedures and FFPI-based methodology used in this research.

2.5 Verification of results using historical flood data

A field survey in Kratovo, North Macedonia, corroborated GIS and remote sensing findings by gathering firsthand data on flood events. Through interviews with residents and site visits, insights on flood frequency, impacts, and vulnerable areas were documented. This complemented the remote sensing analysis for a comprehensive flood risk assessment.

In order to validate the FFPI classification results, the methodology proposed by Yassin et al. [32] was followed, comparing the FFPI outcomes with historical flood data. This process is crucial for assessing the accuracy and reliability of the FFPI results. Historical flood data were obtained from field research and authoritative sources to ensure the credibility of the findings.

Landsat 8 satellite imagery was employed to observe flood events corresponding to historical data. The ENVI software, provided by Landsat, was utilized for processing and analyzing the imagery, including radiometric and atmospheric corrections to enhance quality [59]. ENVI’s advanced tools for image classification, feature extraction, and change detection enabled accurate identification and delineation of flood-affected areas. This validation against historical data enhances the understanding of flash flood dynamics and aids in developing robust flood risk assessment and management strategies.

Additionally, WorldClim version 2 data [40] provided average precipitation data for 1970–2000 at a spatial resolution of 1 km. This high-resolution climate data, including total monthly precipitation and seasonality, was correlated with hotspot areas identified from historical flood observations. This correlation enhances flood risk assessment by providing detailed insights into precipitation patterns and their relationship with flood occurrences, aiding in targeted flood management strategies.

3 Results

3.1 Slope index

The slope map is interpolated from the 5-m Digital Elevation Model (DEM). Variation of the slope is one of the important factors affecting the timing of runoff and the amount of infiltration. The infiltration rate decreases with increasing slope angle [60]. The average slope value in the whole area is 21.1° (Table 3). The tributaries that are located in the eastern part of the Kratovska Reka catchment have a higher slope value, opposite to the tributaries that are managed in the western (lower) part of the catchment. Thus, the Sub-catchment of the river Latišnica has the highest average slope value (24.6°), and the Sub-catchment of Ketenovski Dol has the lowest average value (11°). The slope map in percent is calculated and classified. After this procedure, the model is classified into an FFPI value with the range from 1 to 10, and any slope of 30 degrees or higher is an FFPI of 10 (Figure 6).

Table 3

Average slope in degrees (s˚av)

Nu. Sub-catchment s˚av
1. Lisečki Potok 23.5
2. Nežilovski Potok 23.6
.3. Drumski Potok 22.4
4. Bela Reka 22.5
5. Latišnica 24.6
6. Tabački Dol 23.7
7. Babakarina and Manceva Reka 23.4
8. Mlački Dol 23.0
9. Boškovačka Reka 19.9
10. Raškovec 22.7
11. Vrbički Dol 23.1
12. Železnički Dol 1 22.7
13. Železnički Dol 2 21.1
14. Železnički Dol 3 16.2
15. Šlegovski Dol 20.7
16. Manev Dol 15.9
17. Živalevski Dol 15.7
18. Talašmanski Dol 15.2
19. Topolovički Dol 15.3
20. Ketenovski Dol 11.0
Kratovska Reka 21.0
Figure 6 Slope index component of the FFPI for Kratovska Reka catchment.
Figure 6

Slope index component of the FFPI for Kratovska Reka catchment.

3.2 Lithology index

In this study, the lithology index is generated from the lithological map [54], based on the five main lithological units, including clastic sediments, tuffs, solid volcanic (andesites), solid volcanic (dacitic ignimbrites), and schists. The classification of the lithological units was analyzed according to their susceptibility to torrential floods (Table 4).

Table 4

Coefficient of lithological units (S)

Lithological units Geological period Susceptibility to erosion processes Coefficient, S
Clastic sediments Pliocene 1 9
Tuffs Pliocene 0.95 8
Solid volcanics (andesites) Pliocene 0.2 5
Solid volcanics (dacitic ignimbrites) Miocene 0.25 4
Schists Riphean-Cambrian 0.8 3

The highest coefficient is assigned to river clastic sediments (9), which are most susceptible to flash floods, and the lowest to shale (3) with the least susceptibility to this geohazard. This index is very important because the percentage of these components in the units will affect infiltration rates and runoff during intense rainfall. Andesites and schists are less likely to contribute to flash floods because they are resistant to erosion. On the other hand, clastic sediments, tuffs, and dacitic ignimbrites are more susceptible to being eroded and transported during flash flood events, increasing the potential for flooding. The lithology index map is valued into an FFPI and illustrated in Figure 7.

Figure 7 Lithology index component of the FFPI for Kratovska Reka catchment.
Figure 7

Lithology index component of the FFPI for Kratovska Reka catchment.

3.3 Land use index

The types of land use in the study area are shown in Table 5. Thus, the most susceptible terrains to FFPI are the non-irrigated agricultural surface, bare rocks, and areas with sparse vegetation, and the least susceptible are the areas under broad-leaved and mixed forests.

Table 5

Types of land use (L)

Classes of land Value Area (%)
Discontinuous urban environment 4 0.89
Non-irrigated farmland 9 0.06
Pastures 6 14.14
Complex land use 8 4.68
Agricultural land with significant areas under natural vegetation 7 13.51
Broadleaved forest 3 32.61
Mixed forest 3 1.17
Transitional forest shrubland 5 30.62
Bare rocks 9 1.33
Surfaces with sparse vegetation 9 1.01

Generally, the broadleaved forest and transitional forest shrubland are the two main types of land use structure. Based on the land use map, the land use index is generated and classified into the FFPI, and it is shown in Figure 8.

Figure 8 Land use index component of the FFPI for Kratovska Reka catchment.
Figure 8

Land use index component of the FFPI for Kratovska Reka catchment.

3.4 Vegetation index

The methodology described above provided thorough insights into the likelihood of flash floods and the intensity of erosion across the study area. By analyzing Landsat 8 satellite imagery and utilizing the Bare Soil Index (BSI), we were able to pinpoint areas at higher risk of flash floods with increased accuracy. Moreover, the correlation between vegetation density and erosion rate yielded valuable data for devising efficient land management strategies and implementing measures to mitigate the impact of flash flood events. The integration of remote sensing techniques and BSI computation significantly contributed to advancing our comprehension of erosion dynamics and the susceptibility of the study area to flash floods. The calculated value of the coefficient V ranges from 0 to 9.4, and the average is 3.7. A vegetation index is generated with a value from 1 to 9 (Figure 9).

Figure 9 Vegetation index component of the FFPI for Kratovska Reka catchment.
Figure 9

Vegetation index component of the FFPI for Kratovska Reka catchment.

3.5 FFPI

The GIS database has enabled the FFPI index to be run and to obtain information on the risk of flash floods for the catchment and sub-catchments (Table 6). According to the obtained calculations (Table 7), most of the catchment area belongs to the class with a medium probability of flash floods (72.04% or 49.34 km2). High and very high flood susceptibility is 11.21% or 7.68 km2. According to the model, the highest average coefficient of susceptibility to potential flash floods: Latišnica (2.16), Babakarina and Manceva Reka (2.11), Tabački Dol (2.06), Železnički Dol 2 (2.03), and Dol Potok 1 (2.01). There is a low probability of flash floods, especially at the confluence of Kriva Reka (16.75% or 11.48 km2). The average value of the Flash Potential Index (FFPI) is 1.9, considering that the values range from 1 to 5.

Table 6

Potential flash flood index (FFPI) in the Kratovska Reka sub-catchments

Nu. Sub-catchment FFPI coefficient
1. Lisečki Potok 1.98
2. Nežilovski Potok 1.93
3. Drumski Potok 1.85
4. Bela Reka 1.93
5. Latišnica 2.16
6. Tabački Dol 2.06
7. Babakarina and Manceva Reka 2.11
8. Mlački Dol 1.91
9. Boškovačka Reka 1.92
10. Raškovec 1.96
11. Vrbički Dol 1.93
12. Železnički Dol 1 2.01
13. Železnički Dol 2 2.03
14. Železnički Dol 3 1.83
15. Šlegovski Dol 1.97
16. Manev Dol 1.82
17. Živalevski Dol 1.96
18. Talašmanski Dol 1.82
19. Topolovički Dol 1.88
20. Ketenovski Dol 1.52
Kratovska Reka 1.93
Table 7

Terrains susceptible to flash floods

Probability of flash flooding Area
In km2 In %
Weak 11.48 16.74
Moderate 49.34 71.95
High 6.48 9.45
Very high 1.28 1.87
Total 68.58 100.00

By processing and analyzing terrain slope, lithology, land types, land use, and BSI, a model of terrain susceptibility to flash floods was obtained (Figure 10).

Figure 10 Susceptibility map to flash floods in the Kratovska Reka catchment.
Figure 10

Susceptibility map to flash floods in the Kratovska Reka catchment.

3.6 Comparison of FFPI risk level with hotspot area of historical data of flash flood occurrence

In addition to GIS and remote sensing analysis, a field survey was conducted to validate the findings. The survey focused on Kratovo town within the municipality of Kratovo, located in northeastern North Macedonia. It aimed to gather firsthand data on historical flood events through structured interviews with residents and site visits. Insights were obtained on flood frequency, duration, and local impacts. Key aspects included documenting past flood occurrences, identifying vulnerable areas and infrastructure, and capturing community perceptions on flood dynamics and resilience measures. This field data complemented the remote sensing analysis, providing crucial ground truth for validating the Landsat 8 imagery processed using ENVI software. Integrating field observations with remote sensing results enhances the comprehensiveness of flood risk assessment and management strategies tailored to the local context.

Thus, an examination of historical data has been undertaken to discern the areas prone to recurrent flash flood incidents within the specified time frame. The hotspot region for flash floods within the Kratovska Reka catchment is visually represented in Figure 11. Upon comparing with Figure 10, a notable observation emerges: the hotspot area for flash flood occurrences predominantly overlaps with the high-risk region susceptible to such events. It is noteworthy that this extreme-risk area has recorded numerous flooding events based on historical data analysis. This detailed comparison provides valuable insights into the distribution and severity of flash flood occurrences within the study area, thus informing future risk mitigation strategies and management efforts.

Figure 11 Hotspot Area based on historical data observations.
Figure 11

Hotspot Area based on historical data observations.

By combining GIS analysis, remote sensing techniques, and field surveys, we provided valuable insights into the factors influencing flash flood occurrences in the Kratovska Reka catchment area. This multi-faceted approach strengthens the credibility of the study findings and supports effective disaster preparedness and risk management strategies.

4 Discussion and concluding remarks

As mentioned earlier, it was observed that in an area identified as highly prone to flash flooding by FFPI, actual occurrences of flash floods were infrequent, which aligns with expectations based on the gathered data. This suggests that the findings from this section may not accurately represent the real situation, where extreme flash flooding is anticipated. This FFPI model incorporates additional variables such as Slope, Lithology, Land Use, and Vegetation Index. Previous studies have mainly focused on areas within river catchments where natural flash floods occur, which differs from the focus of this study. The study area is situated in a region where floods are predominantly caused by human activities, termed as urban flash floods. Infrastructure like drainage systems significantly influences urban flash floods. The absence of critical factors in this study may have impacted the outcomes. Precipitation is also vital to consider since prolonged and heavy rainfall typically triggers flash floods by elevating storm water levels.

Because of its intensity and spatial distribution, precipitation represents one of the most essential flash flood conditioning validation factors. Integration of WorldClim 2 precipitation data [40] with historical flood observations was conducted to enhance flood risk assessment. Thus, according to Aleksova et al. [7], intense and heavy rainfall often leads to the overflowing of the Kratovska Reka from its riverbed and the occurrence of flash floods. In summary, precipitation, especially heavy and intense precipitation, plays a central role in the validation of flash flood hotspot areas. The yearly precipitation in the catchment averages around 728.4 mm, peaking in May and November and dropping to a low in August. Approximately 57% of the annual precipitation occurs during the vegetation period, with spring and autumn receiving the most rainfall. Only about 9% of days see heavy rainfall exceeding 20.0 mm, with the possibility of reaching up to 110 mm per day. In winter, snow blankets the upper catchment, melting quickly in spring. To validate flash flood risks, intense rainfall data are crucial. However, lacking a pluviometry station in the area, we employed GIS Remote Sensing modeling and geospatial analysis. By overlaying average precipitation data with hotspot zones of historical data, we found that high-risk areas typically receive 700–750 mm of precipitation. The studies [38,61] on hazard areas in the catchment also validate this result. Although average rainfall alone isn’t sufficient to forecast flash floods, it often coincides with areas experiencing lower average rainfall. This implies that regions with reduced rainfall typically have less vegetation and a slower recuperation from geohazards. Furthermore, factors such as geological composition, and historical deforestation over the past century increase erosion risks. The sub-catchment Latišnica (marked by a dashed line; Figure 12) has most of the hotspot areas in the whole catchment.

Figure 12 Integration of average precipitation data (WorldClim 2)[40] with hotspot areas based on historical data observations.
Figure 12

Integration of average precipitation data (WorldClim 2)[40] with hotspot areas based on historical data observations.

The FFPI is a model that provides an index ranging from 1 to 10, and given that the Kratovska Reka catchment’s FFPI is at 49.34% for an index value of 5, which is the median, it can be classified as being at medium risk for flash floods. While the results are not entirely satisfactory, they do demonstrate the potential given that the primary factors have been considered. By incorporating an additional factor, such as the Stream Power Index (SPI), Topographic Wetness Index (TWI), Topographic Position Index (TPI), and Soil Index, more reasonable results may be obtained [32]. Furthermore, future research could integrate the Flood Vulnerability Index (FVI) Method to enhance the robustness of the analysis [62]. Given changes in both natural and human-influenced factors, it’s advisable to establish a monitoring and control system to oversee ground conditions [63]. New technologies enable detailed surface data collection, which, when processed through GIS, can be used to create predictive models. Such analysis is crucial for hazard prevention or mitigation, forming a vital part of spatial analysis [18]. However, the FFPI index overlooks factors like riverbed debris, landslides, and climate change impacts, necessitating comprehensive analyses with high-quality datasets and multiple methods to compare results. Despite each method having its pros and cons, employing various approaches can enhance effectiveness in assessing flash flood risks. It’s essential to select the most suitable method for each situation to optimize solutions for the problem at hand.

Obtaining and analyzing the databases is straightforward, facilitated by both proprietary and open-source software accessible to spatial analysis professionals. The model introduced in this research could serve as a valuable approach for spatial planning endeavors, offering practical insights for land management and aiding local authorities in flash flood risk reduction efforts [34]. The methodology devised in this study is applicable across various contexts and can be implemented on a national scale within any river catchment. This is particularly relevant given the dynamic nature of land use and the rising occurrence of extreme weather events [22]. To mitigate the detrimental impacts of flash floods, it’s crucial to identify and enact protective measures using a blend of GIS technology and on-site investigation. Collaborative efforts between local government bodies, alongside provincial and national services, can allocate resources for the deployment of biological and biotechnical interventions. These measures aim to substantially decrease the risk of severe torrential flooding in affected areas [10].

The combination of GIS and remote sensing has resulted in a potent tool for investigating and evaluating the potential for flash floods. The findings obtained through the FFPI method accurately reflect the risk of flash floods in the study area. This serves as a scientific foundation for managing natural resources at the local level [33]. Higher resolution historical data are needed for ROC curve analysis to quantitatively evaluate the flash flood model’s accuracy. Standardizing and implementing other methodologies would enhance the monitoring and identification of natural hazards in North Macedonia on local and regional levels. This underscores the need for developing vulnerability assessments and management programs in southeastern Europe [24]. Also, understanding the barriers restraining the effective operation of flood early warning systems is crucial for improving disaster preparedness, minimizing loss of life and property, and enhancing community resilience to flood events [64].

For future research is relevant to incorporate machine learning methods to determine individual parameters’ influence on flash flood occurrences more precisely [65]. These enhancements will enable more accurate susceptibility modeling, reducing subjectivity in assigning weighting factors and increasing the relevance of results for the specific regional area.

Acknowledgments

Slobodan B. Marković and Tin Lukić gratefully acknowledge the support of the Ministry of Science, Technological Development and Innovation of the Republic of Serbia (Grants No. 451-03-66/2024-03/200125 & 451-03-65/2024-03/200125) and the Provincial Secretariat for Higher Education and Scientific Research of Vojvodina (Serbia), No. 000871816 2024 09418 003 000 000 001 04 002 (GLOMERO), under Program 0201 and Program Activity 1012. The authors are grateful to the reviewers whose comments and suggestions greatly improved the manuscript.

  1. Author contributions: Conceptualization, B.A.; methodology, B.A.; software, B.A.; validation, B.A., T.L., and I.M.; formal analysis, B.A.; investigation, B.A.; resources, B.A., I.M., and T.L.; data curation, B.A., T.L., and I.M.; writing – original draft preparation, B.A. and T.L.; writing – review and editing, B.A., I.M., R.M., S.B.M., V.M.C., T.L.; visualization, B.A.; supervision, I.M., R.M., and T.L.; project administration, B.A.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.

  2. Conflict of interest: The authors state no conflict of interest.

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Received: 2024-03-27
Revised: 2024-06-29
Accepted: 2024-07-08
Published Online: 2024-08-30

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0684
Keywords for this article
FFPI; flash floods risk; Kratovska Reka; GIS; geospatial analytics; Remote Sensing; natural hazards
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BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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