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Adding to the toolbox for tidal-inundation mapping in estuarine areas.

Rebecca L FlitcroftPatrick ClintonKelly Christiansen
Published in: Journal of coastal conservation (2018)
In estuaries, land-surface and tidal elevation conspire to influence the amount of salt-water inundation in a specific location, ultimately affecting the distribution of estuary vegetation. Plants vary in their tolerances to salinity and inundation. Understanding even small changes in land-surface elevation at a site scale provides relevant information to managers seeking to design effective long-term restoration projects. Restoration of estuary habitats has been identified as a tool to mediate some anticipated effects of climate change, including flooding from sea-level rise, precipitation regimes, and storminess. Further, habitat restoration that is effective in the face of climate uncertainty is critical to the sustainable production of seafood and maintenance of ecosystem functions. We offer a simple method that links tidal elevations to upslope topography, allowing managers to determine where tidal inundation of upslope areas may occur. This method does not require complex modeling, rather we combine existing high- accuracy tide-gage information with LiDAR imagery. However, we found that if LiDAR is not flown at low tide, or at consistent tidal heights, it poses significant challenges in the interpretation of tidal elevations. Where LiDAR is consistently collected at low tide, this method of linking the tidal datum to upslope topography is not data-intensive, and does not require long-term data collection. Along with locally specific information, the types of map products that can be developed using this method should identify places that may be potentially vulnerable to salt-water inundation, along with places that may be effective migration corridors for marshes and other habitats.
Keyphrases
  • mental health
  • climate change
  • carbon dioxide
  • human health
  • health information
  • electronic health record
  • big data
  • healthcare
  • microbial community
  • mass spectrometry
  • deep learning
  • quality improvement