Hydro-informatics for flood risk modelling and zonation

Abstract

he researchers used hydro-informatics methods to model and map flood risks in the Kafu River catchment area. To improve flood preparedness, mitigation planning, and sustainable watershed management, the study focused on identifying and analysing the main causes of flooding, developing a comprehensive flood risk model, and ensuring its accuracy and reliability. A multidisciplinary approach was adopted to accomplish these goals. Historical river flow and rainfall data spanning 30 years (1993–2023) were collected and analysed to assess rainfall trends and flood recurrence intervals. The research integrated modelling techniques, geographic information systems, community surveys, and field-based data collection in a mixed-method approach. Hydrological modelling was performed using HECHMS software, with rainfall-runoff relationships developed through the SCS Unit Hydrograph

method for direct runoff transformation and the SCS Curve Number method for loss estimation.

Peak flows and related flood depths for five return periods were simulated by calibrating both

the

hydraulic model (HEC-RAS) and the hydrological model (HEC-HMS). Estimated peak discharges for the 10-, 50-, 100-, 200-, and 500-year return periods were 203.26 m³/s, 281.02 m³/s, 313.90 m³/s, 346.66 m³/s, and 389.87 m³/s, respectively, based on the Gumbel distribution. The maximum flood depths at the Kimengo cross-section near Kafu Bridge were 5.13 m, 6.34 m, 6.67 m, 7.13 m, and 7.46 m when these discharges were input into the HECRAS model. Statistical analyses, including the Chi-square test of independence and flood depth

comparisons, confirmed the significant relationship between flood frequency and proximity to

river channels. The model's ability to predict flood depths was validated by a strong correlation

(R²

> 0.90) between field data and simulated results. The study identified key factors contributing to flooding as land use changes—particularly deforestation and wetland encroachment—as well as heavy rainfall events and insufficient drainage infrastructure. Flood risk maps highlighted the areas surrounding the main Kafu River channel and its primary tributaries as most vulnerable to flooding. The findings demonstrated that combining GIS tools with hydrological and hydraulic models offers a strong framework for flood risk assessment and zoning. Overall, the developed model proved to be a valuable and reliable tool for flood risk management within the Kafu catchment area. To reduce flood impacts, it was recommended that local authorities and water resource managers prioritise flood-prone zones in future land use and infrastructure planning, employ GIS-based flood risk frameworks, and enhance community awareness.