Representativeness-Based Sampling Network Design for the Arctic
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. Described is a qua...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.422.4257 2023-05-15T14:57:15+02:00 Representativeness-Based Sampling Network Design for the Arctic Forrest M. Hoffman Jitendra Kumar Richard T. Mills William W. Hargrove The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.4257 http://www.geobabble.org/~hnw/Hoffman_AdvMeteorology_2012.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.4257 http://www.geobabble.org/~hnw/Hoffman_AdvMeteorology_2012.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.geobabble.org/~hnw/Hoffman_AdvMeteorology_2012.pdf text ftciteseerx 2016-01-08T04:07:02Z 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. Described is a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4 km 2 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 environemntal 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. Text Arctic Alaska Unknown Arctic |
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description |
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. Described is a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4 km 2 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 environemntal 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. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Forrest M. Hoffman Jitendra Kumar Richard T. Mills William W. Hargrove |
spellingShingle |
Forrest M. Hoffman Jitendra Kumar Richard T. Mills William W. Hargrove Representativeness-Based Sampling Network Design for the Arctic |
author_facet |
Forrest M. Hoffman Jitendra Kumar Richard T. Mills William W. Hargrove |
author_sort |
Forrest M. Hoffman |
title |
Representativeness-Based Sampling Network Design for the Arctic |
title_short |
Representativeness-Based Sampling Network Design for the Arctic |
title_full |
Representativeness-Based Sampling Network Design for the Arctic |
title_fullStr |
Representativeness-Based Sampling Network Design for the Arctic |
title_full_unstemmed |
Representativeness-Based Sampling Network Design for the Arctic |
title_sort |
representativeness-based sampling network design for the arctic |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.4257 http://www.geobabble.org/~hnw/Hoffman_AdvMeteorology_2012.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Alaska |
genre_facet |
Arctic Alaska |
op_source |
http://www.geobabble.org/~hnw/Hoffman_AdvMeteorology_2012.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.4257 http://www.geobabble.org/~hnw/Hoffman_AdvMeteorology_2012.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766329343831703552 |