Sub-Grid Modeling of Coupled Hydrodynamic, Vegetative and Morphodynamic Processes in a Salt Marsh Environment

Abstract

Modeling hydrodynamic processes in a salt marsh system is a challenging task, due to the complexity of topographic features and the scale of such features relative to the overall system size. With growing availability of high-resolution topographic measurements, like LiDAR-derived DEM data, it is increasingly desirable to run a high-resolution model in a large domain and for a long period of time to get trends of sedimentation patterns, morphological change and marsh evolution. However, high spatial resolution poses a big challenge in both computational time and memory storage. Vegetation dynamics in a marsh system needs to be properly taken care of with respect to the vegetation zones and elevation from the mean sea level. The interaction between micro-scale topography and vegetation canopy dominates the overall morphological evolution.

Hydrodynamic modeling of a complex salt marsh system: Importance of channel shoreline and bathymetric resolution

“…Interestingly, it can easily mislead into believing other subsequent model interpretations of velocity and fux estimation. The velocity magnitude is completely out of order compared to the Grid-C, which is validated extensively with feld measurements shown inDeb et al (2022a) andDeb et al (2018a). With the higher resolution in channels and berm location in Grid-B, the phase difference remains almost unchanged close to 80 • during food shown in Figure13and 14 c-d, with a reduced magnitude in velocity and volume fux.…”

“…For the study area BHNWR, three high resolution LiDAR data sets of the years 2007, 2011 and 2014 are collected from different sources (Deb et al, 2018a), processed to a regular grid with 1 m resolution and referenced to the NAVD88 datum.…”

“…These data sets are compared with the ground truth survey at different locations to identify the most reliable one with minimum vegetation bias. Metadata, accuracy and extent of these data sets are described in Deb et al (2018a). The National Oceanic and Atmospheric Administration (NOAA) 2011 data set has the least bias compared to other data sets and therefore was used for the high-resolution model development of BHNWR (Figure 1c).…”

“…Artifcial ponding comes from the unresolved small marsh channels/creeks in the model grid that, in reality, drains the isolated low spots in DEM depressions formed by channel berms. A detailed description of the ponding problem and the ongoing work is provided in Deb et al (2018a).…”