Representativeness-based Sampling Network Design for the State of Alaska

• Main Authors: Hoffman, Forrest, Kumar, Jitendra, Mills, Richard, Hargrove, William
• Format: Dataset
• Language: English
• Published: Next Generation Ecosystems Experiment - Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); NGEE Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) 2013
• Subjects: 54 Environmental Sciences; Ecoregions;Ecoregions;Sampling network design;Multivariate spatiotemporal clustering;Alaska; Arctic; Alaska
• Online Access: https://dx.doi.org/10.5440/1108686; https://www.osti.gov/servlets/purl/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