scholarly journals Atmospheric CO2 Consumption by Chemical Weathering in the Main Tributaries of the Yellow River: Tao He, Huang Shui, and Datong He, Originating From the Northeastern Qinghai-Tibet Plateau

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuoma Yongji ◽  
Liuqian Zhao ◽  
Yunxiao Li ◽  
Longjun Zhang
Keyword(s):  
Transition Zone ◽  
Chemical Weathering ◽  
Yellow River ◽  
Tibet Plateau ◽  
The Yellow River ◽  
Silicate Weathering ◽  
Carbonate Weathering ◽  
Three Rivers ◽  
Qinghai Tibet Plateau ◽  
Relief Ratio

The Tao He, Huang Shui, and Datong He originate from the northeastern margin of the Qinghai-Tibet Plateau (QTP) and flow into the Yellow River on the Loess Plateau (LP), all with a basin area exceeding 15,000 km2, and are the three largest tributaries of the Yellow River QTP sub-basin. Water samples were collected at the river outlets, the QTP section, the transition zone between the QTP and the LP, and the LP section of each river. These water samples were used to explore CO2 consumption by chemical weathering and its control mechanisms. Runoff and physical erosion are the main factors controlling chemical weathering in the three rivers. The increase of relief ratio in the transition zone between the QTP and the LP makes the chemical weathering particularly intense in this area. The total CO2 consumption rates by chemical weathering in the Tao He and Huang Shui transition zones are 1.4 times and 1.7 times greater than in their QTP sections, and 1.7 times and 2.3 times greater than in their LP sections, respectively. In contrast, due to the high relief ratio of 8‰ in the Datong He transition zone, the residence time of the water is extremely short, and unweathered fine-grained materials are delivered downstream to continue weathering. The influence of differential lithology distribution on chemical weathering includes that the Datong He QTP section with carbonate exposure presents the most intense carbonate weathering in that basin, and the Tao He transition zone has low silicate weathering resulting from the distribution of early Permian strata. In addition, groundwater recharge most likely influenced the silicate weathering of Huang Shui significantly. The total area of the three rivers accounts for 25% of the Yellow River QTP sub-basin, while their contribution to the total CO2 consumption flux by chemical weathering approximates 36%. The silicate weathering of the northern QTP rivers is lower than the global average and significantly lower than those of the southern QTP rivers. However, carbonate weathering of the QTP rivers exhibit no north-south regional differences.

Application of Rn-222 isotope for the interaction between surface water and groundwater in the Source Area of the Yellow River

2016 ◽  
Vol 47 (6) ◽  
pp. 1253-1262 ◽  
Author(s):  
M. J. Zheng ◽  
C. W. Wan ◽  
M. D. Du ◽  
X. D. Zhou ◽  
P. Yi ◽  
...  
Keyword(s):  
Surface Water ◽  
Yellow River ◽  
Reference Value ◽  
Source Area ◽  
Tibet Plateau ◽  
The Yellow River ◽  
The North ◽  
Surface Water And Groundwater ◽  
Average Fraction ◽  
Qinghai Tibet Plateau

A pioneering rapid and direct measurement of dissolved 222Rn in the field has been used here to explore interaction between surface and groundwater in the source area of the Yellow River (SAYR). The results indicate average 222Rn activity of 2,371 Bq/m3 in surface water and 27,835 Bq/m3 in groundwater. The high 222Rn activity (up to 9,133 Bq/m3) found in the southeast part of the SAYR suggests possible influence of permafrost on the exchange between surface water and groundwater. The remarkable contrast among the different samples of a stream in the Shuangchagou basin, a typical basin in the SAYR, clearly indicates groundwater infiltration along the north tributary and occurrence of groundwater end-member in the south tributary. Considering no 222Rn decay and atmospheric evasion, the daily average fraction of groundwater input to the surface water through the end-member in a location (S1) is estimated at 19%. Despite the up to 40% uncertainty, this is the first estimate of a reference value for groundwater input in this basin and which can be improved in the future with more samples and measurements. 222Rn can be a rapid and easily measured tracer of surface water–groundwater interaction for future investigation in the Qinghai-Tibet Plateau.

Landform-related permafrost characteristics in the source area of the Yellow River, eastern Qinghai-Tibet Plateau

Geomorphology ◽  
2016 ◽  
Vol 269 ◽  
pp. 104-111 ◽  
Author(s):  
Jing Li ◽  
Yu Sheng ◽  
Jichun Wu ◽  
Ziliang Feng ◽  
Zuojun Ning ◽  
...  
Keyword(s):  
Yellow River ◽  
Source Area ◽  
Tibet Plateau ◽  
The Yellow River ◽  
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.

scholarly journals Streamflow Changes in the Headwater Area of Yellow River, NE Qinghai-Tibet Plateau during 1955–2040 and Their Implications

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1360
Author(s):  
Qiang Ma ◽  
Changlei Dai ◽  
Huijun Jin ◽  
Sihai Liang ◽  
Victor F. Bense ◽  
...  
Keyword(s):  
Neural Network ◽  
Yellow River ◽  
Tibet Plateau ◽  
Hydropower Station ◽  
The Yellow River ◽  
Permafrost Degradation ◽  
Annual Streamflow ◽  
Elman Neural Network ◽  
Monthly Streamflow ◽  
Qinghai Tibet Plateau

Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe’yan Hydrological Station, with a catchment area of 21,000 km2 and an areal extent of alpine permafrost at ~86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998–2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe’yan is reconstructed for the period of 1955–2019 using the double mass curve method, and then the streamflow at Huagnhe’yan is forecasted for the next 20 years (2020–2040) using the Elman neural network time-series method. The dam construction (1998–2000) has caused a reduction of annual streamflow by 53.5–68.4%, and a more substantial reduction of 71.8–94.4% in the drier years (2003–2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123–210% (2018–2019). Post-correction trends of annual maximum (QMax) and minimum (QMin) streamflow rates and the ratio of the QMax/QMin of the Yellow River in the HAYR (0.18 and 0.03 m3·s−1·yr−1 and −0.04 yr−1, respectively), in comparison with those of precorrection values (−0.11 and −0.004 m3·s−1·yr−1 and 0.001 yr−1, respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m3·s−1·yr−1. Rising rates of spring (0.57 m3·s−1·yr−1) and autumn (0.18 m3·s−1·yr−1) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins.

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