Keito IIZAWA

Methods for Analyzing Flood and Sediment Dynamics in Mountainous Areas

Tokuzou HOSOYAMADA

This Study Aims to Develop a Numerical Model to Analyze Flood and Sediment Transport Dynamics in Mountainous Areas, Focusing on the Tsukadagawa River in Ishikawa Prefecture. Mountainous Regions in Japan Are Vulnerable to Complex Disasters Due to Increased Heavy Rainfall and Earthquakes Caused by Climate Change. Model Explores Bed Load Transport, Where Shear Stress Exceeds a Critical Value, Causing Morphological Changes in Riverbeds.　Using Finite Difference Methods and Nonlinear Long-Wave Equations, the Study Simulates Sediment Transport and Flood Wave Dynamics. Detailed Terrain Data from 10-Meter Mesh DEMs Were Processed Through QGIS to Reflect Geographic Features Accurately. One-Dimensional Simulations Confirmed Basic Sediment Transport Patterns, While Two-Dimensional Models Captured Erosion and Deposition, Particularly in Meandering Sections of the Tsukadagawa River. These Results Align with Observations from the 2025 Noto Peninsula Heavy Rainfall Disaster, Demonstrating the Model’s Applicability to Real-World Events.  Although the Two-Day Simulation Period Restricted Quantitative Assessments, the Model Effectively Identified Areas Prone to Topographic Changes. These Findings Provide Valuable Insights for Disaster Risk Mitigation, Highlighting the Model’s Potential to Improve Hazard Maps and Support River Management Practices.
This Research Shows that Sediment Transport Models Are Useful for Qualitative Disaster Risk Analysis, Allowing Planners to Anticipate Areas at Risk of Erosion and Deposition. It Also Emphasizes the Need for Long-Term Simulations Using Higher-Resolution Terrain Data and the Incorporation of Structural Elements, Such as Levees and Buildings, to Enhance the Model’s Realism and Practical Utility.  By Integrating Hydrological and Geomorphological Processes, This Study Offers a Framework for Better Understanding the Dynamics of Mountainous Rivers and Their Response to Natural Disasters. This Approach Could Help Minimize the Impact of Future Complex Disasters Through More Accurate Risk Assessments and Improved Preventative Measures.