Representativeness-based Sampling Network Design for the State of Alaska

• Main Authors: Hoffman, Forrest, Kumar, Jitendra, Mills, Richard, Hargrove, William
• Language: unknown
• Published: 2019
• Subjects: 54 Environmental Sciences; Arctic; Alaska
• Online Access: http://www.osti.gov/servlets/purl/1108686; https://www.osti.gov/biblio/1108686; https://doi.org/10.5440/1108686

This data set collection consists of data products described in Hoffman et. al., 2013. Hoffman, Forrest M., Jitendra Kumar, Richard T. Mills, and William W. Hargrove. October 1, 2013. Representativeness-based Sampling Network Design for the State of Alaska. Landscape Ecology, 28(8):1567-1586. doi:10.1007/s10980-013-9902-0 Abstract: Resource and logistical constraints limit the frequency and extent of environmental observations, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent environmental variability at desired scales. A quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks is described here. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4 km2 resolution to define multiple sets of ecoregions across two decadal time periods. Maps of ecoregions for the present (2000-2009) and future (2090-2099) were produced, showing how combinations of 37 characteristics are distributed and how they may shift in the future. Representative sampling locations are identified on present and future ecoregion maps. A representativeness metric was developed, and representativeness maps for eight candidate sampling locations were produced. This metric was used to characterize the environmental similarity of each site. This analysis provides model-inspired insights into optimal sampling strategies, offers a framework for up-scaling measurements, and provides a down-scaling approach for integration of models and measurements. These techniques can be applied at different spatial and temporal scales to meet the needs of individual measurement campaigns. Dataset DOI:10.5440/1108686; https://doi.org/10.5440/1108686