RESEARCH ARTICLE Representativeness-based sampling network design for the State of Alaska

• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: Arctic; Alaska
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.588.7368; http://www.srs.fs.usda.gov/pubs/ja/2013/ja_2013_hoffman_001.pdf

Abstract Resource and logistical constraints limit the frequency and extent of environmental observa-tions, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent envi-ronmental variability at desired scales. A quantitative methodology for stratifying sampling domains, informing site selection, and determining the repre-sentativeness 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 charac-teristics are distributed and how they may shift in the future. Representative sampling locations are identi-fied on present and future ecoregion maps. A repre-sentativeness 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-scal-ing approach for integration of models and measure-ments. These techniques can be applied at different spatial and temporal scales to meet the needs of individual measurement campaigns.