Extended Abstract

Introduction

Among the most destructive natural disasters worldwide, floods cause greater loss of life and economic damage than any other natural hazard. Floods are considered one of the most significant natural hazards, and their investigation at local, regional, and global scales is essential. The increasing risk of flooding is largely attributed to climate change, a factor that can lead to increases in the frequency, intensity, and temporal patterns of flood events. However, flash flooding is a rapid-onset type of flood that occurs as a result of intense rainfall or the sudden release of water from any impounded area, during which water behaves primarily as surface runoff. Runoff generation is mainly the outcome of interactions between natural and anthropogenic factors and is highly region-specific. This phenomenon affects both rural and urban areas. For example, in agricultural regions, floods often lead to the destruction of equipment, farm buildings, and infrastructure. Conversely, population growth in poorly planned urban areas exacerbates problems such as flooding, erosion, and water pollution, as natural lands are replaced by impervious surfaces and informal settlements lacking adequate drainage networks. The increasing flood risk in watersheds and downstream areas is largely driven by enhanced surface runoff resulting from land-use change, population density growth, and human interventions within watershed systems.

Methodology

The present study aims to assess flood susceptibility in the Karganroud watershed, located in Gilan Province, Iran, and to identify areas prone to flash flooding. This research is based on the analysis of geographic, hydrological, and geomorphological data and seeks to examine flood hazard patterns at the watershed scale through the integration of environmental information and spatial modeling. The datasets used in this study include a Digital Elevation Model (DEM) with a spatial resolution of 12.5 m derived from ALOS PALSAR data obtained from the United States Geological Survey (USGS), Landsat 8 OLI satellite imagery, the soil texture map of Iran at a scale of 1:250,000, and a geological map at a scale of 1:100,000. These datasets were employed as model inputs and for the extraction of flood-influencing indices. To analyze flood susceptibility, hydro-geomorphological parameters including slope, flow accumulation, profile curvature, lithology, soil texture, and land use were calculated. These factors were selected as the main criteria in the Modified Flash Flood Potential Index (MFFPI) model due to their direct influence on surface runoff generation, flow velocity, and flood concentration. This comprehensive approach enables multi-criteria analysis and the determination of the relative weight of each factor in shaping flood hazard patterns. Given that the environmental datasets were not normally distributed and were predominantly ordinal in nature, Spearman’s rank correlation coefficient was applied to evaluate the relationships between each independent parameter and the flash flood potential layer as the dependent variable. For this purpose, 500 random points were generated across the watershed, and the values of the selected parameters—including slope, flow accumulation, profile curvature, lithology, soil texture, and land use—were extracted at these locations. The results of this analysis facilitated the identification of the most influential factors contributing to flood occurrence and their prioritization for inclusion in the final model. Following the identification of key variables, GIS layers representing slope, profile curvature, and soil texture were selected as the primary parameters and utilized to generate the final flood susceptibility map. The MFFPI model integrated these layers to classify flood hazard into five categories ranging from very low to very high. This methodology enables the identification of high-risk flood-prone areas and provides a scientific basis for flood management and risk mitigation strategies.



Results and discussion

The results of this analysis indicated that slope, with a correlation coefficient of 0.768, exerted the strongest influence on the spatial pattern of flooding. This was followed by profile curvature (0.295) and soil texture (0.239), which ranked second and third, respectively. Accordingly, a secondary flood susceptibility map was generated using these three key factors. The final results revealed that approximately 26.62% of the watershed area falls within the high and very high flood hazard classes, which are mainly concentrated in the eastern and northwestern parts of the basin. The findings of this study can serve as a scientific basis for flood management, land-use planning, and risk reduction strategies in the Karganroud watershed.



Conclusion

This study aimed to analyze the spatial distribution of flood hazard in the Karganroud watershed, Gilan Province, using the MFFPI model and Geographic Information System (GIS) techniques. The integration of topographic, hydro-geomorphological, and environmental layers revealed a heterogeneous spatial pattern of flood risk across the basin, strongly influenced by slope, soil characteristics, and land-use patterns. The MFFPI output classified flood hazard into five categories ranging from very low to very high, indicating that approximately 26.62% of the watershed area—mainly in the eastern and northwestern sectors—falls within the high and very high risk classes. Spearman’s correlation analysis identified slope, profile curvature, and soil texture as the most influential factors controlling flood susceptibility. Accordingly, a secondary flood hazard map was produced based on these three key variables, showing good agreement with the final MFFPI results. Overall, the findings confirm the effectiveness of the MFFPI model for identifying flash flood–prone areas and supporting flood risk management and mitigation planning in the Karganroud watershed.