Coupling numerical models of deltaic wetlands with AirSWOT, UAVSAR, and AVIRIS-NG remote sensing data
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Published:2024-01-16
Issue:1
Volume:21
Page:241-260
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ISSN:1726-4189
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Container-title:Biogeosciences
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language:en
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Short-container-title:Biogeosciences
Author:
Cortese LucaORCID, Donatelli Carmine, Zhang Xiaohe, Nghiem Justin A.ORCID, Simard Marc, Jones Cathleen E.ORCID, Denbina Michael, Fichot Cédric G., Harringmeyer Joshua P.ORCID, Fagherazzi SergioORCID
Abstract
Abstract. Coastal marsh survival relies on the ability to increase elevation and offset sea level rise. It is therefore important to realistically model sediment fluxes between marshes, tidal channels, and bays as sediment availability controls accretion. Traditionally, numerical models have been calibrated and validated using in situ measurements at a few locations within the domain of interest. These datasets typically provide temporal information but lack spatial variability. This paper explores the potential of coupling numerical models with high-resolution remote sensing imagery. Products from three sensors from the NASA Delta-X airborne mission are used. Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provides vertical water level change on the marshland and was used to adjust the bathymetry and calibrate water fluxes over the marsh. AirSWOT yields water surface elevation within bays, lakes, and channels, and was used to calibrate the Chezy bottom friction coefficient. Finally, imagery from AVIRIS-NG provides maps of total suspended solids (TSS) concentration that were used to calibrate sediment parameters of settling velocity and critical shear stress for erosion. Three numerical models were developed at different locations along coastal Louisiana using Delft3D. The coupling enabled a spatial evaluation of model performance that was not possible using simple point measurements. Overall, the study shows that calibration of numerical models and their general performance will greatly benefit from remote sensing.
Funder
National Aeronautics and Space Administration Directorate for Biological Sciences Directorate for Geosciences
Publisher
Copernicus GmbH
Reference128 articles.
1. Allen, J., Somerfield, P., and Gilbert, F.: Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models, J. Mar. Syst., 64, 3–14, https://doi.org/10.1016/j.jmarsys.2006.02.010, 2007. a 2. Allison, M. A., Kineke, G. C., Gordon, E. S., and Goni, M. A.: Development and reworking of a seasonal flood deposit on the inner continental shelf off the Atchafalaya River, Cont. Shelf Res., 20, 2267–2294, https://doi.org/10.1016/S0278-4343(00)00070-4, 2000. a 3. Balogun, A.-L., Yekeen, S. T., Pradhan, B., and Althuwaynee, O. F.: Spatio-temporal analysis of oil spill impact and recovery pattern of coastal vegetation and wetland using multispectral satellite landsat 8-OLI imagery and machine learning models, Remote Sens., 12, 1225, https://doi.org/10.3390/rs12071225, 2020. a 4. Bates, P. D.: Flood inundation prediction, Annu. Rev. Fluid Mech., 54, 287–315, https://doi.org/10.1146/annurev-fluid-030121-113138, 2022. a 5. Bevington, A. E. and Twilley, R. R.: Island edge morphodynamics along a chronosequence in a prograding deltaic floodplain wetland, J. Coast. Res., 34, 806–817, https://doi.org/10.2112/JCOASTRES-D-17-00074.1, 2018. a
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