An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau
<p>Ground surface temperature (GST), measured at approximately 5 cm in depth, is a key controlling parameter for subsurface biophysical processes at the land–atmosphere boundary. This work presents a valuable dataset of GST observations at various spatial scales in the Headwater Area of the Ye...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-03-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/1425/2024/essd-16-1425-2024.pdf |
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| author | R.-D. Şerban R.-D. Şerban R.-D. Şerban H. Jin H. Jin M. Şerban G. Bertoldi D. Luo Q. Wang Q. Ma R. He X. Jin X. Li X. Li X. Li J. Tang H. Wang H. Wang |
| author_facet | R.-D. Şerban R.-D. Şerban R.-D. Şerban H. Jin H. Jin M. Şerban G. Bertoldi D. Luo Q. Wang Q. Ma R. He X. Jin X. Li X. Li X. Li J. Tang H. Wang H. Wang |
| author_sort | R.-D. Şerban |
| collection | DOAJ |
| description | <p>Ground surface temperature (GST), measured at approximately 5 cm in depth, is a key controlling parameter for subsurface biophysical processes at the land–atmosphere boundary. This work presents a valuable dataset of GST observations at various spatial scales in the Headwater Area of the Yellow River (HAYR), a representative area of high-plateau permafrost on the northeastern Qinghai–Tibet Plateau (QTP). GST was measured every 3 h using 72 iButton temperature loggers (DS1922L) at 39 sites from 2019 to 2020. At each site, GST was recorded in two plots at distances from 2 to 16 m under similar and different land-cover conditions (steppe, meadow, swamp meadow, and bare ground). These sensors proved their reliability in harsh environments because there were only 165 biased measurements from a total of 210 816. A high significant correlation (<span class="inline-formula">>0.96</span>, <span class="inline-formula"><i>p</i><0.001</span>) was observed between plots, with a mean absolute error (MAE) of 0.2 to 1.2 °C. The daily intra-plot differences in GST were mainly <span class="inline-formula"><2</span> °C for sites with similar land cover in both plots and <span class="inline-formula">>2</span> °C when GST of bare ground was compared to that of sites with vegetation. From autumn to spring, the differences in GST could increase to 4–5 °C for up to 15 d. The values of the frost number (FN) were quite similar between the plots with differences in FN <span class="inline-formula"><0.05</span> for most of the sites. This dataset complements the sparse observations of GST on the QTP and helps to identify the permafrost distribution and degradation at high resolution as well as to validate and calibrate the permafrost distribution models. The datasets are openly available in the National Tibetan Plateau/Third Pole Environment Data Center (<a href="https://doi.org/10.11888/Cryos.tpdc.272945">https://doi.org/10.11888/Cryos.tpdc.272945</a>, Şerban and Jin, 2022).</p> |
| first_indexed | 2024-04-24T23:42:17Z |
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| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-24T23:42:17Z |
| publishDate | 2024-03-01 |
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| series | Earth System Science Data |
| spelling | doaj.art-67b3836841c54494b5808bbca2d91a742024-03-15T11:35:13ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162024-03-01161425144610.5194/essd-16-1425-2024An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet PlateauR.-D. Şerban0R.-D. Şerban1R.-D. Şerban2H. Jin3H. Jin4M. Şerban5G. Bertoldi6D. Luo7Q. Wang8Q. Ma9R. He10X. Jin11X. Li12X. Li13X. Li14J. Tang15H. Wang16H. Wang17Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bolzano 39100, ItalyInstitute for Alpine Environment, Eurac Research, Bolzano 39100, ItalyNational Key Laboratory of Cryosphere Science and Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNational Key Laboratory of Cryosphere Science and Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSchool of Civil Engineering and Transportation, Permafrost Institute, and China-Russia Joint Laboratory of Cold Regions Engineering and Environment, Northeast Forestry University, Harbin 150090, ChinaApplied Geomorphology and Interdisciplinary Research Centre, Department of Geography, West University of Timişoara, Timişoara 300223, RomaniaInstitute for Alpine Environment, Eurac Research, Bolzano 39100, ItalyNational Key Laboratory of Cryosphere Science and Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNational Key Laboratory of Cryosphere Science and Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNational Key Laboratory of Cryosphere Science and Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNational Key Laboratory of Cryosphere Science and Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSchool of Civil Engineering and Transportation, Permafrost Institute, and China-Russia Joint Laboratory of Cold Regions Engineering and Environment, Northeast Forestry University, Harbin 150090, ChinaNational Key Laboratory of Cryosphere Science and Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Engineering, China University of Petroleum-Beijing, Karamayi 834000, ChinaSchool of Civil Engineering and Transportation, Permafrost Institute, and China-Russia Joint Laboratory of Cold Regions Engineering and Environment, Northeast Forestry University, Harbin 150090, ChinaNational Key Laboratory of Cryosphere Science and Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSchool of Civil Engineering and Transportation, Permafrost Institute, and China-Russia Joint Laboratory of Cold Regions Engineering and Environment, Northeast Forestry University, Harbin 150090, China<p>Ground surface temperature (GST), measured at approximately 5 cm in depth, is a key controlling parameter for subsurface biophysical processes at the land–atmosphere boundary. This work presents a valuable dataset of GST observations at various spatial scales in the Headwater Area of the Yellow River (HAYR), a representative area of high-plateau permafrost on the northeastern Qinghai–Tibet Plateau (QTP). GST was measured every 3 h using 72 iButton temperature loggers (DS1922L) at 39 sites from 2019 to 2020. At each site, GST was recorded in two plots at distances from 2 to 16 m under similar and different land-cover conditions (steppe, meadow, swamp meadow, and bare ground). These sensors proved their reliability in harsh environments because there were only 165 biased measurements from a total of 210 816. A high significant correlation (<span class="inline-formula">>0.96</span>, <span class="inline-formula"><i>p</i><0.001</span>) was observed between plots, with a mean absolute error (MAE) of 0.2 to 1.2 °C. The daily intra-plot differences in GST were mainly <span class="inline-formula"><2</span> °C for sites with similar land cover in both plots and <span class="inline-formula">>2</span> °C when GST of bare ground was compared to that of sites with vegetation. From autumn to spring, the differences in GST could increase to 4–5 °C for up to 15 d. The values of the frost number (FN) were quite similar between the plots with differences in FN <span class="inline-formula"><0.05</span> for most of the sites. This dataset complements the sparse observations of GST on the QTP and helps to identify the permafrost distribution and degradation at high resolution as well as to validate and calibrate the permafrost distribution models. The datasets are openly available in the National Tibetan Plateau/Third Pole Environment Data Center (<a href="https://doi.org/10.11888/Cryos.tpdc.272945">https://doi.org/10.11888/Cryos.tpdc.272945</a>, Şerban and Jin, 2022).</p>https://essd.copernicus.org/articles/16/1425/2024/essd-16-1425-2024.pdf |
| spellingShingle | R.-D. Şerban R.-D. Şerban R.-D. Şerban H. Jin H. Jin M. Şerban G. Bertoldi D. Luo Q. Wang Q. Ma R. He X. Jin X. Li X. Li X. Li J. Tang H. Wang H. Wang An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau Earth System Science Data |
| title | An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau |
| title_full | An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau |
| title_fullStr | An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau |
| title_full_unstemmed | An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau |
| title_short | An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau |
| title_sort | observational network of ground surface temperature under different land cover types on the northeastern qinghai tibet plateau |
| url | https://essd.copernicus.org/articles/16/1425/2024/essd-16-1425-2024.pdf |
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