Assessing risk-prone areas is the central topic of this scientific publication.

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.

Conclusions

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.

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.

How to cite

Aleksova, B., Milevski, I., Mijalov, R., Marković, S., Cvetković, V. & Lukić, T. (2024). Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index. Open Geosciences, 16(1), 20220684. https://doi.org/10.1515/geo-2022-0684