{"id":5556,"date":"2023-01-06T15:42:51","date_gmt":"2023-01-06T14:42:51","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=5556"},"modified":"2023-01-06T15:42:51","modified_gmt":"2023-01-06T14:42:51","slug":"interpolated-daily-temperature-and-precipitation-data-for-level-ii-icp-forests-plots-in-germany","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2023\/01\/06\/interpolated-daily-temperature-and-precipitation-data-for-level-ii-icp-forests-plots-in-germany\/","title":{"rendered":"Interpolated daily temperature and precipitation data for Level II ICP Forests plots in Germany"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p><strong><a href=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/01\/Rukh-et-al-2022.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-5561 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/01\/Rukh-et-al-2022-300x192.png\" alt=\"\" width=\"300\" height=\"192\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/01\/Rukh-et-al-2022-300x192.png 300w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/01\/Rukh-et-al-2022-768x491.png 768w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/01\/Rukh-et-al-2022-640x409.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/01\/Rukh-et-al-2022.png 825w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>Key message<\/strong><\/p>\n<p align=\"justify\">A harmonized, comprehensive meteorological time series for 78 German intensive forest monitoring plots (Level II) has been made available from 1961 to 2019. The used hybrid spatial interpolation routine using simple linear regression and inverse distance weighting allows for gap filling of missing data and also for extrapolation outside measurement period to analyze long-term effects of climate on forest ecosystems. The dataset is available at <a href=\"https:\/\/www.openagrar.de\/receive\/openagrar_mods_00079174\">https:\/\/www.openagrar.de\/receive\/openagrar_mods_00079174<\/a>. The associated metadata are available at: <a href=\"https:\/\/metadata-afs.nancy.inra.fr\/geonetwork\/srv\/fre\/catalog.search#\/metadata\/433a028f-dfc8-4a7c-82af-b8d7efafd724\">https:\/\/metadata-afs.nancy.inra.fr\/geonetwork\/srv\/fre\/catalog.search#\/metadata\/433a028f-dfc8-4a7c-82af-b8d7efafd724<\/a>.<\/p>\n<p><strong>Keywords<\/strong><br \/>\nClimate data; Intensive forest monitoring; Linear regression; Inverse distance\u00a0weighting; Spatial interpolation; ICP Forests Level II; Germany<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1186\/s13595-022-01167-3'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nRukh, S., Schad, T., Strer, M. et al. Interpolated daily temperature and precipitation data for Level II ICP Forests plots in Germany. Annals of Forest Science 79, 47 (2022). <a href=\"https:\/\/doi.org\/10.1186\/s13595-022-01167-3\">https:\/\/doi.org\/10.1186\/s13595-022-01167-3<\/a><\/p>\n<p><strong>Data and\/or Code availability<\/strong><br \/>\nThe provided dataset with this paper is available in OpenAgrar repository under the link <a href=\"https:\/\/www.openagrar.de\/receive\/openagrar_mods_00079174\">https:\/\/www.openagrar.de\/receive\/openagrar_mods_00079174<\/a>. According to ICP Forests and national guidelines: free for research, the associated metadata are available at <a href=\"https:\/\/metadata-afs.nancy.inra.fr\/geonetwork\/srv\/fre\/catalog.search#\/metadata\/433a028f-dfc8-4a7c-82af-b8d7efafd724\">https:\/\/metadata-afs.nancy.inra.fr\/geonetwork\/srv\/fre\/catalog.search#\/metadata\/433a028f-dfc8-4a7c-82af-b8d7efafd724<\/a><\/p>\n<p><strong>Handling Editor<\/strong><br \/>\nV\u00e9ronique Lesage<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key message A harmonized, comprehensive meteorological time series for 78 German intensive forest monitoring plots (Level II) has been made available from 1961 to 2019. The used hybrid spatial interpolation routine using simple linear regression and inverse distance weighting allows for gap filling of missing data and also for extrapolation outside measurement period to analyze [&hellip;]<\/p>\n","protected":false},"author":109,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,110,100,109],"tags":[],"class_list":["post-5556","post","type-post","status-publish","format-standard","hentry","category-article-type","category-data-in-repository","category-data-paper","category-open-access","cat-14-id","cat-110-id","cat-100-id","cat-109-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5556","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/users\/109"}],"replies":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/comments?post=5556"}],"version-history":[{"count":5,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5556\/revisions"}],"predecessor-version":[{"id":5562,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5556\/revisions\/5562"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=5556"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=5556"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=5556"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}