Representativeness-based Sampling Network Design for the State of Alaska
Abstract
This data set collection consists of data products described in Hoffman et. al., 2013. 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 bemore »
- Authors:
- Publication Date:
- Other Number(s):
- https://doi.org/10.5440/1108686; NGA077
- DOE Contract Number:
- AC05-00OR22725
- Research Org.:
- Next Generation Ecosystems Experiment - Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US)
- Sponsoring Org.:
- U.S. DOE > Office of Science > Biological and Environmental Research (BER)
- Collaborations:
- PNL, BNL,ANL,ORNL
- Subject:
- 54 ENVIRONMENTAL SCIENCES
- Keywords:
- Sampling network design; Alaska; Multivariate spatiotemporal clustering; Ecoregions
- Geolocation:
- 71.4,-129.3|50.9,-129.3|50.9,-180.0|71.4,-180.0|71.4,-129.3
- OSTI Identifier:
- 1108686
- DOI:
- https://doi.org/10.5440/1108686
- Project Location:
-
Citation Formats
Hoffman, Forrest, Kumar, Jitendra, Mills, Richard, and Hargrove, William. Representativeness-based Sampling Network Design for the State of Alaska. United States: N. p., 2013.
Web. doi:10.5440/1108686.
Hoffman, Forrest, Kumar, Jitendra, Mills, Richard, & Hargrove, William. Representativeness-based Sampling Network Design for the State of Alaska. United States. doi:https://doi.org/10.5440/1108686
Hoffman, Forrest, Kumar, Jitendra, Mills, Richard, and Hargrove, William. 2013.
"Representativeness-based Sampling Network Design for the State of Alaska". United States. doi:https://doi.org/10.5440/1108686. https://www.osti.gov/servlets/purl/1108686. Pub date:Sat Jun 01 00:00:00 EDT 2013
@article{osti_1108686,
title = {Representativeness-based Sampling Network Design for the State of Alaska},
author = {Hoffman, Forrest and Kumar, Jitendra and Mills, Richard and Hargrove, William},
abstractNote = {This data set collection consists of data products described in Hoffman et. al., 2013. 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. This dataset contains one zipped file, one .txt file, and one .sh file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).},
doi = {10.5440/1108686},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2013},
month = {6}
}