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Flood analysis comparison with probability density functions and a stochastic weather generator.
Israel García-Ledesma1, Jaime Madrigal1, Jesús Pardo-Loaiza1
1Faculty of Civil Engineering, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico.
Peerj
|May 9, 2025
Summary
Flood prediction in Morelia, Mexico, was improved by comparing theoretical distribution functions and stochastic weather generators. Advanced modeling and high-resolution data revealed unregulated urban growth significantly worsens flood impacts, necessitating strategic interventions.
Area of Science:
- Hydrology and Water Resource Management
- Climate Change Adaptation
- Urban Planning and Natural Disaster Management
Background:
- Increasing frequency and severity of extreme hydrological events due to climate change necessitate improved flood prediction.
- Effective flood risk management is crucial for urban areas, particularly in regions like Morelia, Mexico.
- Accurate flood inundation mapping is vital for urban planning and mitigating disaster impacts.
Purpose of the Study:
- To compare two distinct methodologies for flood event prediction in Morelia, Mexico: theoretical distribution functions and stochastic weather generators.
- To integrate runoff predictions into a hydraulic model for simulating flood inundation areas.
- To assess the impact of urban growth on flood risk and provide tools for decision-making.
Main Methods:
- Utilized Soil Conservation Service Curve Number (SCS-CN) method and a multivariate stochastic model (MASVC) for runoff estimation.
- Employed HEC-RAS for hydrodynamic modeling, simulating flood inundation using two-dimensional shallow water equations.
- Incorporated high-resolution digital elevation models (DEMs) and land use data to enhance hydraulic simulation accuracy.
Main Results:
- Both theoretical distribution functions and stochastic weather generators replicated system behavior similarly but yielded different water levels due to flow variations.
- The stochastic model tended to generate higher maximum water levels compared to theoretical distribution functions.
- High-resolution DEMs (5m urban, 0.5m drainage) and land use data significantly improved hydraulic simulation accuracy.
Conclusions:
- Unregulated urban growth in flood-prone areas substantially amplifies flood impacts, underscoring the need for strategic urban planning.
- Generated flood hazard maps and simulations serve as valuable tools for decision-making in flood risk management.
- Integrating advanced modeling techniques is essential for enhancing the precision and reliability of flood predictions in hydrology.
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