Flood modeling is highly influenced by topography data set. Lower resolution digital elevation models (DEMs) are usually used because of their availability and less computational burden when they are used in modeling applications. However, low accuracy of these DEMs yields to even lower accuracy in flood risk analysis through spatial modeling. This study aims to explore the DEM resolution effects on coastal flood risk assessments. For this purpose, deterministic and probabilistic approaches are employed for flood inundation modeling utilizing hydrologically connected bathtub method. High‐resolution light detection and ranging (LiDAR) DEM is considered as the most accurate data from which different resolution maps are obtained using resampling techniques. This is incorporated into an error analysis framework along with U.S. Geological Survey (USGS) National Elevation Dataset (NED) DEMs. The probabilistic framework is developed by simulating the spatial variability of elevation errors compared to LiDAR DEM through a Monte Carlo‐based method called sequential Gaussian simulation. The proposed methodology is applied to the lower Manhattan in New York City. By integrating the flood model into the developed framework, flood inundation probability at each grid cells is obtained. Furthermore, using the concept of accuracy‐efficiency tradeoffs, a framework for selecting a suitable spatial resolution for probabilistic flood risk assessment has been suggested. The results show that by exercising a range of options presented in this paper, a broader insight into mapping resolution can be provided, improving flood assessment, evacuation zones, and mitigation plans depending upon the data availability in a region.

Climate change and rapid urbanization have intensified the frequency and severity of flooding, resulting in substantial damage to communities and infrastructure. Existing research on flood risk addresses a wide range of dimensions, ranging from physical to managerial aspects, which adds complexity to the assessment process. This paper introduces the Integrated Risk Linkages (IRL) Framework to provide a systematic approach to flood risk assessment. The IRL Framework defines risk as the intersection of hazard and vulnerability, where vulnerability is shaped by exposure and susceptibility. Resilience, including coping and adaptive capacities, serves as a counterbalance to vulnerability, offering pathways to mitigate flood impacts. Guided by the IRL framework, this study conducts a comprehensive review of the literature to identify and organize a detailed set of 99 criteria and sub-criteria into three overarching hierarchical structures: hazard, susceptibility, and resilience. Furthermore, the paper evaluates existing flood risk assessment methods, emphasizing their characteristics and practical applicability. The IRL framework presented in this study offers essential insights for navigating the complexities of flood risk management, serving as a valuable reference for researchers, policymakers, and practitioners. Its flexibility empowers users to adapt the framework by utilizing specific components or its entire hierarchical structure, depending on data availability and research objectives, thereby enhancing its applicability across diverse contexts.

Digital elevation model (DEM) as an essential input to flood risk analyzers is subject to a certain level of uncertainty, which increases as the resolution becomes coarser. To quantify this uncertainty, a probabilistic framework incorporating 2‐D hydrodynamic flood mapping by LISFLOOD‐FP along with a sequential Gaussian simulation (SGS) model is presented in this paper. Based on ordinary kriging (OK) interpolation techniques, spatial uncertainty is modeled through SGS by generating several equiprobable realizations. Furthermore, regression kriging (RK) is used in the SGS algorithm utilizing its ability to explore additional information such as terrain characteristics to estimate the elevation error. A new technique called nonstationary sequential Gaussian simulation is also proposed by introducing the spatial nonstationarity of DEM error into SGS framework based on two approaches of uniform and nonuniform moving window. Different DEM resolutions resampled from a 1‐m light detection and ranging‐derived DEM are used in the hydrodynamic model to derive the accuracy‐efficiency trade‐offs and select the most suitable spatial resolution for probabilistic analysis. A detailed comparison among OK, RK, nonstationary OK, and nonstationary RK models is made considering probabilistic floodplain characteristics. The subsequent risk curves are then derived under floods of different return periods. The methodology is implemented in the coastal areas of Lower Manhattan and Brooklyn in New York City. Results show that the proposed methodology could reduce the uncertainty in risk estimation imposed by the stationarity assumption. The methodology could help decision‐makers to more accurately utilize the existing tools and data for flood risk analysis and preparedness.

Floods are the most devastating natural hazards in the United States, and these events lead to significant economic consequences. Estimation of damages and losses at different parts of the floodplain can be used to identify the vulnerable areas susceptible to flood. Floodplain maps combined with damage assessment could identify high-flood-risk areas and present a platform to develop guidelines and zoning resolutions for proper land use, building new infrastructures, and placing and retracting residential areas. In this study, a risk-based methodology is proposed to quantitatively assess damages. For this purpose, a geographic information system (GIS)–based approach is used as an alternative to more complex software packages. The system is called a flood-damage estimator (FDE) and utilizes empirical synthetic depth-damage functions, land-use data, digital elevation model (DEM), raster maps (for featuring the characteristics of the properties at flood risk areas), and floodplain extent for the regio...