An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau

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 Rive...

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Published in:Earth system science data 2024-03, Vol.16 (3), p.1425-1446
Main Authors: Şerban, Raul-David, Jin, Huijun, Şerban, Mihaela, Bertoldi, Giacomo, Luo, Dongliang, Wang, Qingfeng, Ma, Qiang, He, Ruixia, Jin, Xiaoying, Li, Xinze, Tang, Jianjun, Wang, Hongwei
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Language:English
Subjects:
Atmospheric models
Climate change
Datasets
Distribution
Harsh environments
Headwaters
Highway construction
Land cover
Meadows
Permafrost
Permafrost distribution
Plateaus
Rivers
Steppes
Surface temperature
Swamps
Vegetation
Water conservation
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creator Şerban, Raul-David
Jin, Huijun
Şerban, Mihaela
Bertoldi, Giacomo
Luo, Dongliang
Wang, Qingfeng
Ma, Qiang
He, Ruixia
Jin, Xiaoying
Li, Xinze
Tang, Jianjun
Wang, Hongwei
description 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 (>0.96, p
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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 &lt;0.05 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. 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subjects Atmospheric models
Climate change
Datasets
Distribution
Harsh environments
Headwaters
Highway construction
Land cover
Meadows
Permafrost
Permafrost distribution
Plateaus
Rivers
Steppes
Surface temperature
Swamps
Vegetation
Water conservation
title An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau
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