Mapping Permafrost Features that Influence the Hydrological Processes of a Thermokarst Lake on the Qinghai-Tibet Plateau, China

2014 ◽  
Vol 25 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Xicai Pan ◽  
Yanhui You ◽  
Kurt Roth ◽  
Lei Guo ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Hydrological Processes ◽  
Tibet Plateau ◽  
Thermokarst Lake ◽  
Qinghai Tibet Plateau

Using stable isotopes to illuminate thermokarst lake hydrology in permafrost regions on the Qinghai-Tibet plateau, China

2019 ◽  
Vol 30 (1) ◽  
pp. 58-71 ◽  
Author(s):  
Yuzhong Yang ◽  
Qingbai Wu ◽  
Yandong Hou ◽  
Peng Zhang ◽  
Hanbo Yun ◽  
...  
Keyword(s):  
Stable Isotopes ◽  
Tibet Plateau ◽  
Lake Hydrology ◽  
Thermokarst Lake ◽  
Qinghai Tibet Plateau ◽  
Permafrost Regions

Thermal effects of lateral supra-permafrost water flow around a thermokarst lake on the Qinghai-Tibet Plateau

2017 ◽  
Vol 31 (13) ◽  
pp. 2429-2437 ◽  
Author(s):  
Yanhui You ◽  
Qihao Yu ◽  
Xicai Pan ◽  
Xinbin Wang ◽  
Lei Guo ◽  
...  
Keyword(s):  
Water Flow ◽  
Thermal Effects ◽  
Tibet Plateau ◽  
Thermokarst Lake ◽  
Qinghai Tibet Plateau

scholarly journals Contribution of supra-permafrost discharge to thermokarst lake water balances on the northeastern Qinghai-Tibet Plateau

2017 ◽  
Vol 555 ◽  
pp. 621-630 ◽  
Author(s):  
Xicai Pan ◽  
Qihao Yu ◽  
Yanhui You ◽  
Kwok Pan Chun ◽  
Xiaogang Shi ◽  
...  
Keyword(s):  
Lake Water ◽  
Tibet Plateau ◽  
Thermokarst Lake ◽  
Water Balances ◽  
Qinghai Tibet Plateau

scholarly journals Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the Qinghai-Tibet Plateau

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 513 ◽  
Author(s):  
Jun Liu ◽  
Donghui Shanguan ◽  
Shiyin Liu ◽  
Yongjian Ding
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Yellow River ◽  
Hydrological Processes ◽  
Tibet Plateau ◽  
Maximum Temperature ◽  
The Yellow River ◽  
Observation Data ◽  
Yellow River Source ◽  
Qinghai Tibet Plateau

Multisource reanalysis datasets provide an effective way to help us understand hydrological processes in inland alpine regions with sparsely distributed weather stations. The accuracy and quality of two widely used datasets, the China Meteorological Assimilation Driving Datasets to force the SWAT model (CMADS), and the Climate Forecast System Reanalysis (CFSR) in the Qinghai-Tibet Plateau (TP), were evaluated in this paper. The accuracy of daily precipitation, max/min temperature, relative humidity and wind speed from CMADS and CFSR are firstly evaluated by comparing them with results obtained from 131 meteorological stations in the TP. Statistical results show that most elements of CMADS are superior to those of CFSR. The average correlation coefficient (R) between the maximum temperature and the minimum temperature of CMADS and CFSR ranged from 0.93 to 0.97. The root mean square error (RMSE) for CMADS and CFSR ranged from 3.16 to 3.18 °C, and ranged from 5.19 °C to 8.14 °C respectively. The average R of precipitation, relative humidity, and wind speed for CMADS are 0.46; 0.88 and 0.64 respectively, while they are 0.43, 0.52, and 0.37 for CFSR. Gridded observation data is obtained using the professional interpolation software, ANUSPLIN. Meteorological elements from three gridded data have a similar overall distribution but have a different partial distribution. The Soil and Water Assessment Tool (SWAT) is used to simulate hydrological processes in the Yellow River Source Basin of the TP. The Nash Sutcliffe coefficients (NSE) of CMADS+SWAT in calibration and validation period are 0.78 and 0.68 for the monthly scale respectively, which are better than those of CFSR+SWAT and OBS+SWAT in the Yellow River Source Basin. The relationship between snowmelt and other variables is measured by GeoDetector. Air temperature, soil moisture, and soil temperature at 1.038 m has a greater influence on snowmelt than others.

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