Exploring the multiscale structure of urban waterlogging resilience systems for adaptive policy optimisation

Urban waterlogging resilience systems show complex scale effects and cascading dynamics. Accurately identifying their scale structure and operational order strengthens urban waterlogging resilience and upgrades disaster adaptation efficiency. Most existing studies depend on a 'macro-meso-micro' three-tier scale structure. However, their scale divisions are often constrained by data availability or administrative boundaries, neglecting the multiscale, nested characteristics of resilience systems and the alignment between crucial indicators and scale structures. Using Xiamen Island (XMI), China, as a study area, this research creates a comprehensive analytical framework that integrates multi-source data fusion, network percolation models and machine learning techniques to analyse the scale structure of waterlogging resilience systems. The findings reveal that the robustness of the XMI waterlogging resilience system transitions substantially at three key scale nodes, namely 5 km², 15 km² and 30 km², diverging from conventional administrative scales. The impact mechanisms of several resilience indicators on waterlogging adaptation performance depict notable scale dependence and threshold effects, with smaller scales exhibiting more intricate and diverse mechanisms than larger ones. According to XMI's waterlogging resilience scale structure and the non-linear relationships between critical indicators and adaptation results, this study proposes a differentiated indicator management system and dynamic management mechanisms while offering scientific evidence to support the efficient integration of urban spatial management with waterlogging resilience strategies.