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<p>This study presents an integrated flood risk assessment for the Tosya district in Kastamonu, Türkiye, combining statistical extreme value analysis, GIS-based spatial modeling, and hydraulic simulations. Annual maximum daily rainfall data covering the period 1985–2024 were analyzed using the Generalized Extreme Value (GEV), Gumbel, and Log-Pearson Type III (LP3) distributions to estimate design rainfall values for selected return periods. The GEV model exhibited the best overall fit, with results closely matching those of the Gumbel distribution, indicating a near-zero shape parameter and confirming Gumbel-type statistical behavior in the dataset. A high-resolution SAR-based Digital Elevation Model (12.5&#xa0;m) and QGIS flow accumulation analysis were employed to identify topographically vulnerable areas, while HEC-RAS hydraulic modeling simulated potential flood extents under various design rainfall scenarios. The results highlight that low-lying downstream zones along the Devrez Stream are particularly prone to inundation due to gentle slopes and limited drainage capacity. This integrated approach provides practical insights for municipal flood management, infrastructure design, and disaster preparedness, offering a replicable framework for regional flood risk assessment applicable to similar catchments across Türkiye.</p>

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Integrated flood risk assessment in Tosya district (Kastamonu, Türkiye) using GEV–Gumbel–LP3 modeling, QGIS, and HEC-RAS simulations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Tayfun Kurt

摘要

This study presents an integrated flood risk assessment for the Tosya district in Kastamonu, Türkiye, combining statistical extreme value analysis, GIS-based spatial modeling, and hydraulic simulations. Annual maximum daily rainfall data covering the period 1985–2024 were analyzed using the Generalized Extreme Value (GEV), Gumbel, and Log-Pearson Type III (LP3) distributions to estimate design rainfall values for selected return periods. The GEV model exhibited the best overall fit, with results closely matching those of the Gumbel distribution, indicating a near-zero shape parameter and confirming Gumbel-type statistical behavior in the dataset. A high-resolution SAR-based Digital Elevation Model (12.5 m) and QGIS flow accumulation analysis were employed to identify topographically vulnerable areas, while HEC-RAS hydraulic modeling simulated potential flood extents under various design rainfall scenarios. The results highlight that low-lying downstream zones along the Devrez Stream are particularly prone to inundation due to gentle slopes and limited drainage capacity. This integrated approach provides practical insights for municipal flood management, infrastructure design, and disaster preparedness, offering a replicable framework for regional flood risk assessment applicable to similar catchments across Türkiye.