New Early-Warning System Protects Historic Buildings from Rainstorm-Induced Waterlogging

By Nidhi DhullMay 13 2024Reviewed by Susha Cheriyedath, M.Sc.

A recent study published in Water explored different methods and techniques for extreme rainstorm warnings in cultural heritage areas. A risk warning model was developed for rainstorm-induced waterlogging, specifically tailored for the historical and cultural districts of Beijing, China.

Background

Historic and cultural districts are authentic reflections of traditional patterns and features. With the increasing severity of climate change, rainstorm-induced waterlogging has become a major threat to the safety of these rich cultural relics worldwide. Precise forecast and warning systems are essential non-engineering measures to prevent waterlogging in these districts and enhance emergency management capabilities.

Current early-warning models for waterlogging are imprecise and underestimate risks in small-scale areas such as historic and cultural districts because they rely on general classifications of warning levels. Additionally, the relationship between meteorological forecasts and urban waterlogging responses is not efficiently optimized. Therefore, efficient early warning systems are required to ensure prompt and effective implementation of emergency measures and minimize disaster losses.

Methods

The researchers in this study prepared a rainstorm-induced waterlogging risk warning model using InfoWorks ICM software (9.5.5.19020) for Beijing’s historical and cultural districts. The system consists of three early-warning models, one each for building waterlogging, road waterlogging, and public evacuation. As per the surface runoff time in the selected areas of Beijing, 2-hour short-duration design rainfall data with return periods of 100, 200, 500, and 1000 years were used as input parameters for simulating rainstorm events.

The researchers introduced two different concepts and determination methods during model construction. First is the “1-hour rainfall intensity water logging index”, which relates rainfall intensity and rainwater accumulation using modeling and clustering algorithms without relying on monitoring data. The 1-hour rainfall intensity is a frequently utilized parameter in meteorological forecasts and its link with rainstorm-induced waterlogging can help determine related risks on time.

The other concept of “the waterlogging risk index” comprises three sub-indices: building waterlogging risk index, road waterlogging risk index, and public evacuation index. These were negatively correlated with their corresponding flood resilience assessment levels (high, medium, and low) and encompassed physical, organizational, social, and economic resilience.

Furthermore, to be consistent with the rainstorm warning levels issued by the meteorological department and district-level flood warning systems in Beijing, the proposed waterlogging early-warning models for buildings, roads, and public evacuation used a standardized four-level warning system, represented by blue, yellow, orange, and red colors. 

Results

The constructed model was validated using actual rainfall data from 9 August 2020, with a rainfall time step of 5 minutes. In the accuracy analysis involving a comparison of the simulation results with actual waterlogging locations, it achieved an average likelihood of 84 %. Overall, the model demonstrated high accuracy, practical applicability, and reliability.

This hydrologic and hydrodynamic model can provide simulated data on the waterlogging depth, velocity, and duration, which can be used to determine the 1-hour rainfall intensity water logging index and evaluate flood resilience levels in historic and cultural districts. Furthermore, the critical thresholds established for low, medium, and high waterlogging risks in this study align seamlessly with the prevailing meteorological warning standards in Beijing,

The three early-warning models are logically interconnected and functionally complementary. They accommodate the correlation between rainfall intensity and rainwater accumulation and the flood resilience properties of buildings, roads, and the society in districts. This helps obtain precisely graded warning levels and formulate the corresponding warning response measures.

The refined early-warning model offers better pre-disaster positioning and control than traditional monitoring-based waterlogging early-warning models. It can accurately assess the warning levels (red, orange, yellow, and blue) for buildings, roads, and the general public within the historic and cultural district. Depending on the warning severity levels, appropriate emergency response measures can be promptly implemented, including evacuating residents, closing roads, activating drainage facilities, etc.

Conclusion

To summarize, the standardized warning system developed in this study exhibits high efficiency and accuracy in responding to rainstorms and waterlogging disasters. It helps emergency response departments, rescue personnel, and the general public understand and promptly act on the warning. Overall, science-backed emergency response planning and pre-disaster emergency preparations can be executed in historical and cultural districts, safeguarding people’s lives and property to the maximum possible extent.

The effective linking of early warning, emergency management, and meteorological forecasting in the proposed model allows for adjustments and optimizations based on actual conditions of various historic and cultural districts under different rainfall conditions. The authors recommend further research to refine the early-warning model in different flood disasters, other than rainstorm-induced waterlogging, deeply analyzing its warning function throughout the entire lifecycle of flood prevention and emergency management.

Journal Reference

Wu, J., Li, J., Wang, X., Xu, L., Li, Y., Li, J., Zhang, Y., & Xie, T. (2024). Methods for Constructing a Refined Early-Warning Model for Rainstorm-Induced Waterlogging in Historic and Cultural Districts. Water, 16(9), 1290. https://doi.org/10.3390/w16091290‌, https://www.mdpi.com/2073-4441/16/9/1290

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