Recent research published in ‘Open Geosciences’ has shed light on flash flood susceptibility in the Kratovska Reka catchment area of Northeastern North Macedonia, an area that is particularly relevant for local agricultural practices. By employing advanced geospatial analytics, including Geographic Information Systems (GIS) and remote sensing, the study developed a Flash Flood Potential Index (FFPI) that assesses the risk of flash floods based on various environmental factors.
The study identified key contributors to flash flood dynamics such as slope, lithology, land use, and vegetation index. Notably, the analysis revealed that eastern tributaries of the catchment area exhibited steeper slopes compared to those in the west, which could influence water runoff patterns during heavy rainfall events. The lithological classification indicated that clastic sediments are particularly vulnerable to erosion and thus more prone to flash flooding. This understanding is crucial for farmers in the region, as areas with these geological characteristics may require additional flood mitigation strategies.
Land use patterns also played a significant role in determining flood risk. The research found that non-irrigated agricultural lands and regions with sparse vegetation were classified as highly susceptible to flash floods. This insight is particularly valuable for farmers and agricultural planners, as it highlights the need for sustainable land management practices to reduce vulnerability. Implementing strategies such as reforestation or the establishment of vegetation buffers could mitigate risks and protect crops from potential flood damage.
The FFPI model produced a medium risk classification for approximately 49.34% of the catchment area, with an average FFPI score of 1.9 on a scale of 1 to 5. The study also pinpointed specific tributaries at higher risk, with Latišnica identified as the most susceptible to flash floods, having an average FFPI coefficient of 2.16. For local farmers, this information can guide decision-making regarding crop selection, irrigation practices, and infrastructure investment to safeguard against flooding.
Furthermore, the validation of the model through field surveys demonstrated a significant correlation between identified flash flood hotspots and regions classified as high risk by the FFPI. This empirical support enhances the credibility of the findings and provides a robust framework for future flood risk management strategies.
The implications of this research extend beyond immediate flood risk assessments. By integrating machine learning techniques into future studies, researchers aim to refine the FFPI model, enhancing its accuracy and reducing the subjectivity in assigning risk factors. Such advancements could lead to more precise flood forecasting and risk assessments, ultimately benefiting the agricultural sector by enabling better preparedness and response strategies.
In summary, the findings from this study not only contribute to the understanding of flash flood dynamics in the Kratovska Reka catchment but also present significant opportunities for the agriculture sector. By adopting informed land management practices and utilizing advanced risk assessment tools, farmers can better navigate the challenges posed by natural hazards, ensuring the sustainability and resilience of their agricultural operations.