Modeling DEM Errors in Coastal Flood Inundation and Damages: A Spatial Nonstationary Approach

Abstract

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.

Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

“…Flash floods were ranked as first in terms of damages, exceeding those caused by earthquakes, volcanoes and landslides (Ali et al, 2020;Bui et al, 2020;Costache, Hong, & Pham, 2019). The number of flash floods has increased where globally more than 78 million people have been affected (more than 5,000 deaths annually) and losses in properties of about US$ 56 billion have been estimated (Grabs, 2010;Guha-Sapir, Hoyois, Wallemacq, & Below, 2017;Karamouz & Fereshtehpour, 2019;Modrick & Georgakakos, 2015;WMO, 2016). Moreover, it is predicted that floods in conjunction with other hazards, could produce by 2030, annual losses up to US$415 billion at the global level (UNISDR, 2015).…”

Simulation Performance Evaluation and Uncertainty Analysis on a Coupled Inundation Model Combining SWMM and WCA2D

“…The fit statistic (F) and root mean square error (RMSE) are commonly employed in sensitivity and uncertainty analysis due to their ability to describe the goodness-of-fit and difference between the simulated and comparison results (Yu et al 2016;Yin et al 2016;Karamouz and Fereshtehpour 2019), respectively. The fit statistic can be expressed as:…”

Storm surges during IANOS Medicane

“…The bathtub technique is too simplistic in terms of hydraulic processes and may lead to unphysical overestimations of coastal ood extents (Didier et al, 2015;Ramirez et al, 2016;Vousdoukas et al, 2016). Thus, an enhanced bathtub approach with hydraulic connectivity (Bathtub HC), i.e., allowed water ow in adjacent cardinal and diagonal directions ('eight-side rule'), is also available to constrict implausible overestimation of possibly inundated areas by coastal seawater masses (Karamouz and Fereshtehpour, 2019;West et al, 2018;Williams and Lück-Vogel, 2020). This method neglects bottom friction due to oodplain terrain roughness and permeability, time integration for the entire duration of the storm surge event, and water ow height and velocity that affect the overland ood extension from the coastline.…”