Zoning the Potential Flood Hazard and Its Relationship with Hydro-Geomorphological Indices Using the MFFPI Model in the Samian Watershed

Extended Abstract

Introduction

Flash floods are one of the most dangerous natural phenomena, causing significant loss of life and property due to their rapid and unpredictable nature. These floods claim thousands of lives each year and damage infrastructure, particularly in agricultural areas and along riverbanks. Key factors that exacerbate the damage include the lack of accurate flood hazard maps, insufficient preventive measures, dense drainage networks, and steep slopes. To mitigate the effects of floods and preserve ecosystems, it is essential to use flood models with spatial and watershed scales. Hydrological modeling, along with advanced technologies such as remote sensing and Geographic Information Systems (GIS), are powerful tools in this field. These methods provide precise spatial data and can simultaneously analyze factors such as slope, vegetation, and land use, enabling the production of more accurate flood hazard maps. Among these, numerical models like the FFPI model, based on physiographic parameters, are particularly useful in evaluating the risk of flash floods and managing crises. Numerous studies have shown that these models can effectively help identify high-risk areas and predict flood occurrences. In specific regions, such as the Samian watershed in Ardabil Province, due to its unique geographic features and climate change, accurate flood hazard assessment and mapping are crucial. This study, using the MFFPI model, analyzes factors such as slope, soil type, and vegetation, aiming to improve flood risk management and reduce potential damages.

Materials and Methods

This study's research methodology, designed to assess flood risk in the Samian watershed, is based on the analysis of geographical and hydrogeomorphological data. The Samian watershed, with an area of 4,200 square kilometers, is located in Ardabil province and includes units of the foothill plains. The data used in this study include 1:25,000 digital maps from the National Cartographic Center, a 30-meter resolution Digital Elevation Model (DEM), monthly and annual rainfall statistics from the Meteorological Organization, Landsat OLI 9 satellite images, 1:25000 soil texture maps, and 1:100,000 geological maps. The software used for data analysis includes ArcGIS 10.7, ENVI 5.3, Google Earth, SPSS, and Excel. In this study, hydrogeomorphological indices such as slope length, stream power, topographic moisture, slope curvature, and surface curvature were used to evaluate the topographical and geological features related to flood occurrence. Specifically, slope length and stream power indices were considered key factors influencing the hydrological response of the watershed. Additionally, the MFFPI model was employed to analyze the flash flood risk in the Samian watershed. This model uses parameters such as slope, stream density, soil texture, land cover, and land use. Pearson’s correlation test, applied in SPSS, was used to analyze the relationship between hydrogeomorphological indices and floods. This test examined the relationship between independent and dependent variables and analyzed the impact of each index on flood occurrence. Finally, these analyses were applied to assess the flood potential and identify flood-prone areas in the Samian watershed.

Results and Discussion

This study examined various physiographic factors influencing flash floods in the Samian watershed. The results show that land slope, flow accumulation, soil permeability, slope curvature, land use, lithological features, and vegetation significantly affect flood potential in this region. Land slope, especially in low-slope areas, is directly related to water accumulation and the likelihood of flood occurrence. Flow accumulation is also higher in areas with high stream density, such as main channels, and these areas have a higher flood potential. Soil permeability and lithological features play important roles in controlling runoff and flooding. Areas with less permeable soils and impermeable lithological features have the greatest potential for flooding. On the other hand, vegetation cover and land use, especially in residential and agricultural areas, increase runoff and decrease permeability, thereby intensifying flood risk. Correlation analysis shows that heavy rainfall (SPI) has the strongest positive relationship with flood risk. Finally, the MFFPI model was used to predict flood hazard zonation in the Samian watershed, producing a map with five flood risk classes, from low to high potential. This research emphasizes the importance of accurately analyzing physiographic factors in water resource management and flash flood prediction.

Conclusion

This study emphasizes the importance of hydrogeomorphological indices in flood hazard zoning for sudden floods in the Samian watershed using the MFFPI model. The model includes factors such as slope, stream density, soil permeability, land use, slope curvature, and soil texture, with flood hazard maps prepared in five risk categories. The results showed that areas with gentle slopes, poor vegetation cover, impermeable soils, and inappropriate land use are most prone to floods. Statistical analysis (Spearman and Pearson correlations) revealed that slope and soil texture had the highest significant positive impacts (0.78 and 0.70, respectively), and SPI, as the most influential index, with a coefficient of 0.64, determined flood severity. The TWI index was effective in areas with low slopes and water retention capacity, while NDVI and LSF showed no significant impact. The findings showed that SPI is useful for identifying high-risk areas in steep regions, while TWI is better suited for flatter areas. This study highlights the importance of advanced numerical models like MFFPI in flood risk management and damage reduction using GIS and remote sensing data and provides recommendations such as improving drainage infrastructure, restoring vegetation cover, and developing early warning systems.