2019 CSDMS meeting-050: Difference between revisions
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|CSDMS meeting abstract title=‘Seamless over the Strandline’ | |CSDMS meeting abstract title=‘Seamless over the Strandline’ Marine and Terrestrial Substrates Data to support Morphodynamic Modelling | ||
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{{CSDMS meeting abstract template 2019 | {{CSDMS meeting abstract template 2019 | ||
|CSDMS meeting abstract=The shoreline is a boundary where survey methods change dramatically, where the time dimension is extremely important, where sediment fluxes are very large, where flotsam is trapped, and where numerical/physical singularities occur as the water depth goes to zero. The shoreline boundary oscillates; and as sea-levels rise what is now shore will become sea. | |CSDMS meeting abstract=The shoreline is a boundary where survey methods change dramatically, where the time dimension is extremely important, where sediment fluxes are very large, where flotsam is trapped, and where numerical/physical singularities occur as the water depth goes to zero. The shoreline boundary oscillates; and as sea-levels rise what is now shore will become sea. Models of likely response of shorelines require detailed data on the sediment/beach/soil substrates. | ||
We investigated how to obtain the best supporting data using the Louisiana area as example. We investigated to what extent the marine and terrestrial data were already in harmony, and what challenges remain in trying to make one seamless dataset. Of course, technologies like LIDAR carry out highly detailed imaging that achieves this to an extent. But we are focused on direct samplings of the ground-truthing type on which physical properties, fabrics, chemical compositions, grain types, genesis, can be directly determined. | |||
Difficulties: | Difficulties: Terrestrial surveys have a different data topology, more focused on soil polygons and boreholes; offshore mappings focus on point-samplings, for instance grabs and cores. Soil descriptions focus on layer-profile identities such as “Mollisol”; offshore datasets focus on bulk textures and compositions. Strong semantic differences exist. Terrestrial areas are greatly modified by agriculture and construction. | ||
Positives: | Positives: We discovered several information-integration pathways for merging the data from the two realms. Exhaustive searching uncovered data on the onshore soils and riverbed sediments to match the marine data (e.g. dbSEABED). Named geographical locations are linkable with coordinates through gazetteers. Computational methods exist to merge polygon and point data sets. In semantics, glossaries provide some information to link onshore and offshore descriptions. Seamless mappings are demonstrated, useful in support of cross-shore morphodynamic models. | ||
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Latest revision as of 17:59, 4 April 2019
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‘Seamless over the Strandline’ Marine and Terrestrial Substrates Data to support Morphodynamic Modelling
The shoreline is a boundary where survey methods change dramatically, where the time dimension is extremely important, where sediment fluxes are very large, where flotsam is trapped, and where numerical/physical singularities occur as the water depth goes to zero. The shoreline boundary oscillates; and as sea-levels rise what is now shore will become sea. Models of likely response of shorelines require detailed data on the sediment/beach/soil substrates.
We investigated how to obtain the best supporting data using the Louisiana area as example. We investigated to what extent the marine and terrestrial data were already in harmony, and what challenges remain in trying to make one seamless dataset. Of course, technologies like LIDAR carry out highly detailed imaging that achieves this to an extent. But we are focused on direct samplings of the ground-truthing type on which physical properties, fabrics, chemical compositions, grain types, genesis, can be directly determined.
Difficulties: Terrestrial surveys have a different data topology, more focused on soil polygons and boreholes; offshore mappings focus on point-samplings, for instance grabs and cores. Soil descriptions focus on layer-profile identities such as “Mollisol”; offshore datasets focus on bulk textures and compositions. Strong semantic differences exist. Terrestrial areas are greatly modified by agriculture and construction.
Positives: We discovered several information-integration pathways for merging the data from the two realms. Exhaustive searching uncovered data on the onshore soils and riverbed sediments to match the marine data (e.g. dbSEABED). Named geographical locations are linkable with coordinates through gazetteers. Computational methods exist to merge polygon and point data sets. In semantics, glossaries provide some information to link onshore and offshore descriptions. Seamless mappings are demonstrated, useful in support of cross-shore morphodynamic models.
