Liu et al. (2025) A long-term Areal Flooding Risk Calculation Method Based on the Historical Rainfall Records

Identification

• Journal: Water Resources Management
• Year: 2025
• Date: 2025-12-23
• Authors: X.D. Liu, Wen Xu, Hongyuan Guo, Minhao Chen
• DOI: 10.1007/s11269-025-04345-w

Research Groups

• College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, P.R. China
• Shanghai Marine Meteorological Center, Shanghai, P.R. China

Short Summary

This study proposes a novel long-term areal flooding risk calculation method to quantify the cumulative impacts of multi-year extreme rainfall events, supporting long-term decision-making for flood risk management by integrating empirical rainfall frequencies with simulated inundation consequences that account for variable catchment areas.

Objective

• To propose a long-term areal flooding risk calculation method to quantify the comprehensive effects of extreme rainfall events over years, supporting long-term lifecycle decision-making for flood-prone areas.

Study Configuration

• Spatial Scale: A core district within a major city, covering 1.9618 square kilometers, including 277 sewer nodes, 276 links, 84 sub-catchments, and 4 outlets.
• Temporal Scale: Historical rainfall records from April 19, 2016, to September 30, 2024 (approximately 8.5 years).

Methodology and Data

• Models used:
  • Risk-SWMM (developed based on EPA SWMM engine) for one-dimensional sewer flow and two-dimensional surface overflow and inundation simulation.
  • Binary search method for flood diffusion and inundation.
  • Isovolumetric method for flood diffusion and inundation.
  • Weibull formula for empirical frequency calculation.
  • Annual Maxima Sampling (AMS) method for rainfall event selection.
• Data sources:
  • Recorded rainfall series (from Shanghai Marine Meteorological Observatory) with a temporal resolution of one hour.
  • Sewer network data (node elevations, sewer lengths, roughness coefficients, cross-sections, sub-catchment areas, slopes, impervious areas).
  • Digital Elevation Model (DEM) data (Gaofen-7 DEM with 1 meter resolution).
  • Remote sensing images (for estimating actual runoff coefficient).

Main Results

• A new computational method was proposed for calculating long-term areal flooding risk, providing decision-making support for flood risk management in important or flood-prone areas.
• The proposed method successfully considered the variation relationship between the upstream catchment area of the target region and the different return periods of rainfall events.
• The annual maxima sampling (AMS) method was effectively utilized to sample target rainfall events for flood risk calculation.
• Calculated long-term areal flooding risks (defined as empirical frequency × inundation volume/area) for flood-prone areas were found to increase with increasing Minimum Inter-Event Time (MIET).
• Annual total risks derived from the annual multiple sampling method were observed to be higher than those from the annual maximum method.

Contributions

• Introduces a novel long-term areal flooding risk calculation method that quantifies the cumulative impacts of multi-year extreme rainfall events, addressing limitations of traditional single-event flood models.
• Develops an algorithm for flood diffusion and inundation that dynamically accounts for variations in upstream catchment areas based on rainfall return periods.
• Presents Risk-SWMM, a new software tool built upon the EPA SWMM engine, capable of simulating two-dimensional overland flow and inundation.
• Provides a practical framework for long-term decision-making in urban planning, infrastructure development, and flood insurance rate adjustments, shifting the focus to the long-term resilience of flood-prone areas.

Funding

The research was not funding supported.

Citation

@article{Liu2025longterm,
  author = {Liu, X.D. and Xu, Wen and Guo, Hongyuan and Chen, Minhao},
  title = {A long-term Areal Flooding Risk Calculation Method Based on the Historical Rainfall Records},
  journal = {Water Resources Management},
  year = {2025},
  doi = {10.1007/s11269-025-04345-w},
  url = {https://doi.org/10.1007/s11269-025-04345-w}
}