Compound flooding frequently threatens life and assets of people who live in low‐lying coastal regions. Co‐occurrence or sequence of extremes (e.g., high river discharge and extreme coastal water level) is of paramount importance as it may result in flood hazards with potential impacts larger than each extreme in isolation. Here, we use a coupled approach, that is, bivariate statistical analysis linked to hydrodynamic modeling, to quantify compounding effects of flood drivers and generate flood hazard maps near Savannah, Georgia. Also, we integrate wetland elevation correction in digital elevation models to improve hydrodynamic simulations of compound events and hence the accuracy of flood hazard (inundation and velocity) maps. Using statistical measures, we analyze compounding effects of terrestrial/coastal flood drivers and wetland elevation correction on maximum floodwater height (MFH) and velocity (MFV) for 50‐year return period scenarios. In addition, we compare our results to MFH and MFV patterns of Hurricane Matthew that hit the West Atlantic Coasts on October 2016. The statistical measures indicate significant differences among the scenarios, partly explained by wetland elevation correction. Inundation and velocity maps suggest that a proposed composite, that is, synthesis of marginal Q, marginal H, and “AND” scenarios, can lead to the lowest average underestimation of MFH (−0.35 m) and overestimation of MFV (0.20 m/s) within wetland areas. We conclude that a thorough compound flooding assessment should leverage statistical analysis and hydrodynamic modeling of extremes including corrections of coastal digital elevation models.
Emergent herbaceous wetlands are characterized by complex salt marsh ecosystems that play a key role in diverse coastal processes including carbon storage, nutrient cycling, flood attenuation and shoreline protection. Surface elevation characterization and spatiotemporal distribution of these ecosystems are commonly obtained from LiDAR measurements as this low-cost airborne technique has a wide range of applicability and usefulness in coastal environments. LiDAR techniques, despite significant advantages, show poor performance in generation of digital elevation models (DEMs) in tidal salt marshes due to large vertical errors. In this study, we present a methodology to (i) update emergent herbaceous wetlands (i.e., the ones delineated in the 2016 National Land Cover Database) to present-day conditions; and (ii) automate salt marsh elevation correction in estuarine systems. We integrate object-based image analysis and random forest technique with surface reflectance Landsat imagery to map three emergent U.S. wetlands in Weeks Bay, Alabama, Savannah Estuary, Georgia and Fire Island, New York. Conducting a hyperparameter tuning of random forest and following a hierarchical approach with three nomenclature levels for land cover classification, we are able to better map wetlands and improve overall accuracies in Weeks Bay (0.91), Savannah Estuary (0.97) and Fire Island (0.95). We then develop a tool in ArcGIS to automate salt marsh elevation correction. We use this ‘DEM-correction’ tool to modify an existing DEM (model input) with the calculated elevation correction over salt marsh regions. Our method and tool are validated with real-time kinematic elevation data and helps correct overestimated salt marsh elevation up to 0.50 m in the studied estuaries. The proposed tool can be easily adapted to different vegetation species in wetlands, and thus help provide accurate DEMs for flood inundation mapping in estuarine systems.
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