Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets

Text

Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets

Christopher G. Smith - Nama Orang; Julie Bernier - Nama Orang; Alisha M. Ellis - Nama Orang; Kathryn E.L. Smith - Nama Orang;

Coastal marsh wetlands experience variations in vertical gains and losses through time, which have allowed them to infill relict topography and record variations in drivers. The stratigraphic unit associated with the development of the marsh also reflects the long-term importance of key ecosystem services supplied by the marsh environment, including carbon storage and storm mitigation. Mapping these coastal wetland sediments and the marsh unit thickness is challenging as traditional coastal geophysical tools are not easily deployable (acoustic methods) or are unreliable in saline-soil environments (e.g., ground-penetrating radar), leaving core-based methods the most viable mapping method. In the present study, we utilized prior information on the geologic architecture of the region to select spatial and physical metrics that likely persisted throughout evolution of the marsh during the late Holocene. We then assessed the individual and collective power of these metrics to predict marsh thickness observed from cores. Employing regressive predictive models powered by these data, we improve the quantification of marsh thickness for a coastal fringing marsh within the Grand Bay estuary in Mississippi and Alabama (USA). The information gained from this approach yields improved estimates of the carbon stocks in this environment. Additionally, the stored sediment masses reflect the past, and potential future, persistence of the Grand Bay marsh under historical and present marsh-estuarine sediment exchange fluxes. Such improvements to both the sediment budget of recent marsh stratigraphic units and the spatial extent provide new resources for comparison with large-scale landscape models, the latter of which may be used, when validated, to predict future change and ecosystem transformations.

Ketersediaan

237 551.136 Perpustakaan BIG (Eksternal Harddisk) Tersedia

Informasi Detail

Judul Seri: Applied Computing and Geoscience - Open Access
No. Panggil: 551.136
Penerbit: Amsterdam : Elsevier., 2025
Deskripsi Fisik: 12 hlm PDF, 5.867 KB
Bahasa: Inggris
ISBN/ISSN: 2590-1974
Klasifikasi: 551.136
Tipe Isi: text
Tipe Media: -
Tipe Pembawa: -
Edisi: Vol.25, February 2025
Subjek: Coastal marsh stratigraphy
Predictive regressive models
Marsh unit thickness
Info Detail Spesifik: -
Pernyataan Tanggungjawab: -

Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas

Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets
Coastal marsh wetlands experience variations in vertical gains and losses through time, which have allowed them to infill relict topography and record variations in drivers. The stratigraphic unit associated with the development of the marsh also reflects the long-term importance of key ecosystem services supplied by the marsh environment, including carbon storage and storm mitigation. Mapping these coastal wetland sediments and the marsh unit thickness is challenging as traditional coastal geophysical tools are not easily deployable (acoustic methods) or are unreliable in saline-soil environments (e.g., ground-penetrating radar), leaving core-based methods the most viable mapping method. In the present study, we utilized prior information on the geologic architecture of the region to select spatial and physical metrics that likely persisted throughout evolution of the marsh during the late Holocene. We then assessed the individual and collective power of these metrics to predict marsh thickness observed from cores. Employing regressive predictive models powered by these data, we improve the quantification of marsh thickness for a coastal fringing marsh within the Grand Bay estuary in Mississippi and Alabama (USA). The information gained from this approach yields improved estimates of the carbon stocks in this environment. Additionally, the stored sediment masses reflect the past, and potential future, persistence of the Grand Bay marsh under historical and present marsh-estuarine sediment exchange fluxes. Such improvements to both the sediment budget of recent marsh stratigraphic units and the spatial extent provide new resources for comparison with large-scale landscape models, the latter of which may be used, when validated, to predict future change and ecosystem transformations.
Other Resource Link

Komentar

Anda harus masuk sebelum memberikan komentar