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  • Skill assessment  (2)
  • Chesapeake Bay  (1)
  • 1
    Publikationsdatum: 2022-05-26
    Beschreibung: Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Computational Geosciences 15 (2011): 627-636, doi:10.1007/s10596-011-9229-3.
    Beschreibung: An approach to analyze regime change in spatial time series data sets is followed and extended to jointly analyze a dynamical model depicting regime shift and observational data informing the same process. We analyze changes in the joint model-data regime and covariability within each regime. The method is applied to two observational data sets of equatorial sea surface temperature (TAO/TRITON array and satellite) and compared with the predicted data by the ECCO-JPL modeling system.
    Beschreibung: Funding for this work was provided by Spanish National Program on Space, under contract ESP2005-06823-C05. A. Aretxabaleta has been additionally supported by a Juan de la Cierva grant of the Spanish Government. K. Smith was supported by NSF Grant DMS-0934653.
    Schlagwort(e): Skill assessment ; Data clustering ; Gaussian Mixture Models ; ENSO
    Repository-Name: Woods Hole Open Access Server
    Materialart: Preprint
    Format: application/pdf
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    Publikationsdatum: 2022-10-27
    Beschreibung: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Molino, G. D., Defne, Z., Aretxabaleta, A. L., Ganju, N. K., & Carr, J. A. Quantifying slopes as a driver of forest to marsh conversion using geospatial techniques: application to Chesapeake Bay coastal-plain, United States. Frontiers in Environmental Science, 9, (2021): 616319, https://doi.org/10.3389/fenvs.2021.616319.
    Beschreibung: Coastal salt marshes, which provide valuable ecosystem services such as flood mitigation and carbon sequestration, are threatened by rising sea level. In response, these ecosystems migrate landward, converting available upland into salt marsh. In the coastal-plain surrounding Chesapeake Bay, United States, conversion of coastal forest to salt marsh is well-documented and may offset salt marsh loss due to sea level rise, sediment deficits, and wave erosion. Land slope at the marsh-forest boundary is an important factor determining migration likelihood, however, the standard method of using field measurements to assess slope across the marsh-forest boundary is impractical on the scale of an estuary. Therefore, we developed a general slope quantification method that uses high resolution elevation data and a repurposed shoreline analysis tool to determine slope along the marsh-forest boundary for the entire Chesapeake Bay coastal-plain and find that less than 3% of transects have a slope value less than 1%; these low slope environments offer more favorable conditions for forest to marsh conversion. Then, we combine the bay-wide slope and elevation data with inundation modeling from Hurricane Isabel to determine likelihood of coastal forest conversion to salt marsh. This method can be applied to local and estuary-scale research to support management decisions regarding which upland forested areas are more critical to preserve as available space for marsh migration.
    Beschreibung: Funding for this study was provided by the United States Geological Survey’s Coastal/Marine Hazards and Resources Program and Ecosystems Mission Area.
    Schlagwort(e): Salt marsh ; Coastal forest ; Sea level rise ; Chesapeake Bay ; Marsh migration
    Repository-Name: Woods Hole Open Access Server
    Materialart: Article
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
    Publikationsdatum: 2022-05-26
    Beschreibung: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Estuaries and Coasts 39 (2016): 311-332, doi:10.1007/s12237-015-0011-y.
    Beschreibung: Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review, we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a “theory of everything” for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.
    Beschreibung: NKG, ALA, and RPS acknowledge support from the USGS Coastal and Marine Geology Program. DKR gratefully acknowledges support from NSF (OCE-1314642) and NIEHS (1P50-ES021923-01). MJB and JMPV gratefully acknowledge support from NOAA NOS NCCOS (NA05NOS4781201 and NA11NOS4780043). MJB and SJL gratefully acknowledge support from the Strategic Environmental Research and Development Program—Defense Coastal/Estuarine Research Program (RC-1413 and RC-2245).
    Schlagwort(e): Numerical modeling ; Hydrodynamics ; Ecological modeling ; Ecosystem modeling ; Skill assessment
    Repository-Name: Woods Hole Open Access Server
    Materialart: Article
    Standort Signatur Einschränkungen Verfügbarkeit
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