EGU24-22495, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Monte Carlo Framework to Evaluate the Benefits of Flood Warnings in an Urban Flood-Prone Polder Area

Felipe Duque1, Greg O’Donnell2, Yanli Liu3, Mingming Song4, and Enda O’Connell2
Felipe Duque et al.
  • 1Carrera de Ingeniería Ambiental, Centro de Investigaciones Tropicales del Ambiente y Bio-diversidad (CITIAB), Universidad Nacional de Loja (UNL), Avenida Pio Jaramillo Alvarado, La Argelia, Loja 1101608, Ecuador (
  • 2School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK (g.m.o’
  • 3The National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Guangzhou Road, Nanjing 210029, China (
  • 4College of Geomatics & Municipal Engineering, Zhejiang University of Water Resources and Electric Power, Xuelin Road, Hangzhou 310020, China (

Polders, situated in delta regions and enclosed by dykes to avert flooding (from rivers or tides), depend on pumping mechanisms to transfer water from internal artificial rivers to external ones, particularly during storms. Urban polders are highly susceptible to pluvial flooding if their drainage, storage, and pumping capacities are insufficient. This study introduces a Monte Carlo (MC) framework to assess the effectiveness of rainfall threshold-based flood warnings in mitigating pluvial flooding in an urban flood-prone polder area based on 24-hour forecasts. The framework calculates metrics including the potential duration of waterlogging, the maximum area inundated, and the costs of pump operation, taking into account a wide range of possible storm scenarios. The benefits of flood warnings are evaluated by comparing these metrics across different scenarios: scenarios with no warnings, perfect forecasts, deterministic forecasts, and probabilistic forecasts. Probabilistic forecasts incorporate the idea of 'predictive uncertainty' (PU). A specific polder region in Nanjing was selected for this case study. Findings indicate a balance between waterlogging duration and pumping costs, and demonstrate that probabilistic rainfall predictions can significantly improve these metrics. These insights are valuable for designing and assessing the advantages of a rainfall threshold-based flood early warning system (FEWS) in a polder area.

How to cite: Duque, F., O’Donnell, G., Liu, Y., Song, M., and O’Connell, E.: A Monte Carlo Framework to Evaluate the Benefits of Flood Warnings in an Urban Flood-Prone Polder Area, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22495,, 2024.

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