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 |
| Published: |
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 |
| Summary: | <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> |
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| ISSN: | 1866-3508 1866-3516 |