Global climate change and rapid urbanization, mainly driven by anthropogenic activities, lead to urban flood vulnerability and uncertainty in sustainable stormwater management. This study projected the temporal and spatial variation in urban flood susceptibility during the period 2020-2050 on the basis of shared socioeconomic pathways (SSPs). A case study in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was conducted for verifying the feasibility and applicability of this approach. GBA is predicted to encounter the increase in extreme precipitation with high intensity and frequency, along with rapid expansion of constructed areas, resulting in exacerbating of urban flood susceptibility. The areas with medium and high flood susceptibility will be expected to increase continuously from 2020 to 2050, by 9.5 %, 12.0 %, and 14.4 % under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. In terms of the assessment of spatial-temporal flooding pattern, the areas with high flood susceptibility are overlapped with that in the populated urban center in GBA, surrounding the existing risk areas, which is consistent with the tendency of construction land expansion. The approach in the present study will provide comprehensive insights into the reliable and accurate assessment of urban flooding susceptibility in response to climate change and urbanization.

Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes.

Urban waterlogging can cause considerable economic damage, public inconvenience, and even mortality. Effective prediction of inundation probability on an urban agglomeration scale is an essential step in adaptation planning. This study proposes an urban waterlogging exposure assessment framework using the Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). WNB classifier delineates the risk distribution projections by assimilating risk factors and empirical data of urban waterlogging events. This projection was subsequently validated using an overarching accuracy coefficient of 0.85 and a Kappa coefficient of 0.75, these numerical metrics serving as critical evaluative thresholds for the verification of the WNB model's efficacy and precision. CNM is used to analyze the composition and correlation of system risk attributes according to its network topology. We applied the proposed framework to the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). We found that 20.0% of the study areas are exposed to waterlogging risk, of which 1.4% of the study areas are at high risk. There is a clear spatial concentration of urban waterlogging risks in the downtown area of populated cities. In addition, according to CNM, the urban waterlogging hazards of most townships are stressed by multiple factors such as fractional vegetation cover, impervious surface percentage, and soil water retention. The townships stressed by a single factor are attributed to the distance from the waterway or road density. The framework could provide in-depth insights into urban waterlogging preparedness and emergency response.