Improving Channel Hydrological Connectivity in Coastal Hydrodynamic Models With Remotely Sensed Channel Networks

Abstract

Coastal wetlands are nourished by rivers and periodical tidal flows through complex, interconnected channels.However, in hydrodynamic models, channel dimensions with respect to model grid size and uncertainties in topography preclude the correct propagation of tidal and riverine signals. It is therefore crucial to enhance channel geomorphic connectivity and simplify sub-channel features based on remotely-sensed networks for practical computational applications. Here, we utilize channel networks derived from diverse remote sensing imagery as a baseline to build a ~10 m resolution hydrodynamic model that covers the Wax Lake Delta and adjacent wetlands (~ 360 km 2 ) in coastal Louisiana, USA. In this richly-gauged system, intensive calibrations are conducted with 18 synchronous field-observations of water levels taken in 2016, and discharge data taken in 2021. We modify channel geometry, targeting realism in channel connectivity. The results show that a minimum channel depth of 2 m and a width of four grid elements (approximatively 40m) are required to enable a realistic tidal propagation in wetland channels. The optimal depth for tidal propagation can be determined by a simplified cost function method that evaluates the competition between flow travel time and alteration of the volume of the channels. The integration of high spatial-resolution models and remote sensing imagery provides a general framework to improve models performance in salt marshes, mangroves, deltaic wetlands, and tidal flats.

Spatial Variability in Salt Marsh Drainage Controlled by Small Scale Topography

“…(2022) and Zhang, Wright, et al. (2022); and (c) remove the effects related to errors in topography.…”

“…It is worth noting that the empirical/iterative approach of Zhang, Jones, et al (2022) and Zhang, Wright, et al (2022) could modify bed elevations even in regions where corrections are not needed. If the spatial resolution of the model is too coarse to capture the presence of small tidal creeks (note that tidal creeks facilitate water drainage on marsh surface and therefore increase WLC), a decrease in bed elevation and/or friction would be required.…”

“…As such, it is essential that topographic data have sufficient spatial resolution to identify small tidal creeks and levees on marsh platforms, for example, 1-m spatial resolution. This condition is important to (a) capture the enormous spatial variability in hydrodynamic conditions across marsh landscapes via numerical modeling; (b) minimize possible fictitious changes in bed elevation and/or friction introduced using the procedure proposed by Zhang, Jones, et al (2022) and Zhang, Wright, et al (2022); and (c) remove the effects related to errors in topography.…”

“…(2022) and Zhang, Wright, et al. (2022) proposed an iterative/empirical methodology which corrects bed elevation in wetlands by using the difference between observed and modeled water‐level change. They assumed that lowering (increasing) bed elevation allows for more (less) tidal propagation and therefore larger (smaller) water‐level changes.…”

“…In this paper, we employ UAVSAR water-level change products derived from data collected during Delta-X campaigns (Jones et al, 2022). Thus far, this data set has been mainly used to correct bathymetric information derived from lidar (e.g., Wright et al, 2022;Zhang, Jones, et al, 2022;Zhang, Wright, et al, 2022). Zhang, Jones, et al (2022) and Zhang, Wright, et al (2022) proposed an iterative/empirical methodology which corrects bed elevation in wetlands by using the difference between observed and modeled water-level change.…”