Effect of Land Use and Drainage System Changes on Urban Flood Spatial Distribution in Handan City: A Case Study

Abstract

This study simulated urban flooding under various land use and drainage system conditions and described the process of historical ground–underground construction and its influence on spatial variations in waterlogging, taking Handan City as an example. The obtained results can provide support for urban water security and sustainable urban water resource management. The land use change, represented by the expansion of sealed surfaces, has a positive impact on the distribution and the volume of flood in Handan City, while the drainage system has the opposite effect. The flooding distribution changes over decades reveal that flooding risk is reduced in most areas by improved drainage conditions but exacerbated in impervious areas and riversides due to increasing impermeable areas, the rapid draining of pipes, and poor outlet conditions. This study demonstrates how the dual changes in land use and drainage pipeline networks affect urban flooding distribution; we suggest considering land use and the extension of drainage pipelines in future construction.

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

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Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

“…Additionally, coastal urbanization has increased the number of impervious surfaces [22][23][24], altering hydrological patterns and affecting precipitation and flow processes. Coastal cities also face challenges in flood management due to inadequate urban drainage infrastructure [25][26][27][28] and significant ground subsidence [29,30]. In summary, while significant progress has been made in understanding and mapping flood characteristics, there remains a critical gap in comprehensively assessing the risk of compound flooding, particularly in analyzing the complex interactions and cumulative impacts of multiple flood-inducing factors in coastal areas [31].…”

A Comparison of Machine Learning Models for Predicting Flood Susceptibility Based on the Enhanced NHAND Method

“…This urban land expansion not only transforms the environment but also disrupts river flow [9]. In the context of heavy rainfall, inadequacies in urban surfaces, drainage systems, and the concentration of urban populations can contribute to severe flooding incidents [10]. Therefore, gaining a comprehensive understanding of floods is crucial for mitigating flood-induced losses and ensuring the safety of human lives and societal assets.…”