# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
## 1 Introduction  
The accelerating pace of sea level rise poses significant threats to coastal urban areas, with immediate effects such as submergence, increased flooding, and saltwater intrusion [1]. These effects lead to severe impacts on various urban elements, including structural damage to buildings, disruption of transport mobility, and significant population displacement; they also induce challenges to future urban planning and development strategies [1]. In addition, sea level rise also threatens non-material aspects of urban life such as cultural sites with heritage value or historical significance, which are often irreplaceable [2]. Heritage sites often reside in coastal areas due to historical human settlement patterns [3]; for example, 77% of the 1092 Cultural World Heritage Sites designated by the United NationsEducational, Scientific and Cultural Organisation (UNESCO) in 2018 are situated in these coastal regions. Population-dense coastal areas face severe subsidence and are at risk of severe flooding, necessitating extensive flood defences [1]. Low-elevation regions like Southeast Asian deltas and island nations risk significant submergence and displacement [1]. Singapore, a Southeast Asian island state with a high population density of 8058 people per km\({}^{2}\) as of 2023 [4], has significant infrastructure along its 193 km coastline and faces heightened vulnerability to rising sea levels with issues such as shoreline erosion and coastal flooding [5]. Due to Singapore's nature as a small, compact, and highly urbanised island city-state, sea level rise poses a significant risk to its dense population, urban buildings, transport network, and historically significant sites. Therefore, the heightened vulnerability faced by Singapore highlights the urgent need for effective coastal management strategies. Hence, this study analyses the impact of sea level rise on Singapore to pinpoint areas and infrastructure at the most risk of damage. In conducting impact analyses, comprehensive tools are essential to assess the multifaceted consequences of sea level rise. These tools should account for factors such as population density, existing infrastructure, socio-economic factors, and demographic vulnerabilities to understand the broader implications on communities [6]. Regional vulnerability assessments use data from multiple sources (i.e., flood exposure, vulnerable assets, population at risk, damage) to evaluate sea level rise impacts on coastal communities and infrastructure, directing targeted preventive measures for vulnerable regions. Nevertheless, regional-scale vulnerability analysis often faces challenges such as inconsistencies between detailed ground-level data and broader regional data, which can lead to significant changes in risk estimates across spatial scales [7]. Furthermore, the complexity of integrating diverse, unstructured data sources can hinder precise and actionable assessments [8]. Other tools used for sea level rise vulnerability assessment, such as Geographic Information Systems (GISs), offer detailed insights into vulnerable elements in local areas [6; 9]. However, these GIS-based tools typically provide data for a narrow set of predefined categories, such as demographics or economics, and can create closed data silos that capture only one domain, such as economic or cultural perspective only [6]. Additionally, their centralised maintenance poses challenges for scalability, particularly when integrating more diverse data [10]. Issues such as the lack of data and inconsistencies between datasets further challenge the application of GIS-based tools [11]. Other GIS-based software used to analyse sea level rise impacts typically merge all data products into a single GIS layer [12], which may not be suitable for representing non-geospatial data. Mathematical modelling is another vital tool for evaluating the impacts of sea level rise on coastal areas, employing quantitative methods to project changes in populations and ecosystems [13]. For example, [PERSON] et al.