Abstract

Flash floods, driven largely by climate and land-use changes, are among the most destructive natural hazards globally, causing significant damage and loss of life. This study employs the Flash Flood Potential Index (FFPI) to assess flood-prone areas in North Macedonia’s catchments, utilizing GIS and advanced geospatial analytics. The average FFPI values for the main and subcatchments range from 5.34 to 6.46. Across the country’s territory, 24.7% of the subcatchments are identified as very highly vulnerable to flash floods (torrential catchments). Correspondingly, the FFPI model ranges from 2.1 to 15.1, with an average value of 5.9, revealing significant spatial variability in flash flood risks across the country. The model classifies vulnerability into five categories, from 1 (very low) to 5 (very high). The Treska, Kriva Reka, and Pčinja River catchments exhibit the highest flood-prone susceptibility, with average FFPI values of 3.36, 3.30, and 3.22, respectively. Nationwide, 25.6% of the area is categorized as highly vulnerable to flash floods. The FFPI model was validated using intense precipitation data and historical flash flood events, ensuring a robust assessment of flood susceptibility. This research addresses the challenges of flash flood forecasting and management in North Macedonia, particularly in regions with limited observational data. By integrating factors such as slope, lithology, land use, vegetation, and the Bare Soil Index (BSI), alongside rainfall, peak discharges, and response times, this study aims to inform improved flood risk management strategies.

Conclusions

This study aims to assess flash flood potential across North Macedonia by conducting advancedgeo-spatial analyses of key factors such as slope, lithology, rainfall, land cover and land use. Thiscontribution enhances the current understanding of flood risk in the region. Utilizing modern GIS and remote sensing tools, the study identifies areas most vulnerable to flash floods, offering valuableinsights for vulnerability assessment and flood risk reduction interventions. The FFPI model, due toits simplicity and ease of implementation, provides a robust framework for predicting and preventingflash floods, especially in areas lacking detailed hydrological data. The model’s regional applicationextends the relevance of flood management strategies, particularly in regions with steep slopes, lowvegetation cover, and high erosion potential. On a broader scale, this study’s results are valuable fordisaster risk management globally, offering a method that can be adapted to other regions facingsimilar challenges due to data scarcity.Nevertheless, the study recognizes certain limitations. To improve the model’s predictiveaccuracy, future research should incorporate more precise soil data, as well as real-time rainfallmonitoring. Establishing a more comprehensive historical record of flash flood events would alsostrengthen model validation and enhance forecasting capabilities. Additionally, integratinggeoanalytical data, such as the Stream Potential Index (SPI) and Terrain Wetness Index (TWI), alongside machine learning algorithms approaches (ML), could provide deeper insights into flooddynamics and refine flood risk management strategies.Additionally, the scientific contribution of this study is significant, offering a prototype fornationwide flood predictions that could be adapted to other regions with similar challenges. Themodel has also been successfully hindcasted for historical flood events, bolstering confidence in itsapplication for future hydrological research.Practically, the study’s findings are crucial for improving flood risk management in NorthMacedonia. Government agencies and local authorities can use the results to refine early warningsystems, develop sustainable fl ood protection measures, and inform urban planning, land usemanagement, and environmental protection policies. The adoption of this model will enable localand regional authorities to implement targeted preventive measures, increasing resilience forvulnerable populations. The findings underscore the importance of focusing on high-risk catchmentsfor immediate action, such as the implementation of flood barriers and early warning systems.Medium- and low-risk areas should also be prioritized to prevent risk escalation due to land-usechanges and climate variability. Policymakers can leverage these insights to allocate resourceseffectively and develop region-specific flood management plans. Given the significant variabilityacross the country, tailored flood risk management strategies that account for both natural andanthropogenic factors are essential.While the FFPI model provides valuable insights, future research should address its limitationsby incorporating temporal datasets and hydrodynamic modeling to better capture the dynamicnature of flood risks, particularly in the contex t of climate change and land-use transformations.Thus, exploring the role of socio-economic factors in influencing flood impacts is an importantavenue for further investigation.

How to cite

Aleksova, B., Milevski, I., Cvetković, V. M., & Nikolić, N. (2025). GIS-Based Assessment of Flash Flood Potential in North Macedonia: Insights from Advanced Geospatial Analytics. Preprints. https://doi.org/10.20944/preprints202501.0789.v1