Atmospheric water vapor and soil moisture jointly determine the spatiotemporal variations of CO2 fluxes and evapotranspiration across the Qinghai-Tibetan Plateau grasslands

2021 ◽  
pp. 148379
Author(s):  
Hongqin Li ◽  
Chunyu Wang ◽  
Fawei Zhang ◽  
Yongtao He ◽  
Peili Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Vapor ◽  
Tibetan Plateau ◽  
Atmospheric Water Vapor ◽  
Co2 Fluxes ◽  
Spatiotemporal Variations ◽  
Atmospheric Water

scholarly journals Atmospheric Water Vapor Budget and its Long-term Trend over the Tibetan Plateau

2020 ◽  
Author(s):  
Hongru Yan ◽  
Jianping Huang ◽  
Yongli He ◽  
Yuzhi Liu ◽  
Tianhe Wang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Tibetan Plateau ◽  
Atmospheric Water Vapor ◽  
The Tibetan Plateau ◽  
Atmospheric Water ◽  
Term Trend ◽  
Long Term Trend ◽  
Water Vapor Budget

The Effect of Atmospheric Water Vapor on Neutron Count in the Cosmic-Ray Soil Moisture Observing System

2013 ◽  
Vol 14 (5) ◽  
pp. 1659-1671 ◽  
Author(s):  
R. Rosolem ◽  
W. J. Shuttleworth ◽  
M. Zreda ◽  
T. E. Franz ◽  
X. Zeng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Vapor ◽  
Neutron Transport ◽  
Cosmic Ray ◽  
Atmospheric Water Vapor ◽  
Fast Neutrons ◽  
Atmospheric Conditions ◽  
Atmospheric Water ◽  
Neutron Measurement ◽  
Neutron Intensity

Abstract The cosmic-ray method for measuring soil moisture, used in the Cosmic-Ray Soil Moisture Observing System (COSMOS), relies on the exceptional ability of hydrogen to moderate fast neutrons. Sources of hydrogen near the ground, other than soil moisture, affect the neutron measurement and therefore must be quantified. This study investigates the effect of atmospheric water vapor on the cosmic-ray probe signal and evaluates the fast neutron response in realistic atmospheric conditions using the neutron transport code Monte Carlo N-Particle eXtended (MCNPX). The vertical height of influence of the sensor in the atmosphere varies between 412 and 265 m in dry and wet atmospheres, respectively. Model results show that atmospheric water vapor near the surface affects the neutron intensity signal by up to 12%, corresponding to soil moisture differences on the order of 0.10 m3 m−3. A simple correction is defined to identify the true signal associated with integrated soil moisture that rescales the measured neutron intensity to that which would have been observed in the atmospheric conditions prevailing on the day of sensor calibration. Use of this approach is investigated with in situ observations at two sites characterized by strong seasonality in water vapor where standard meteorological measurements are readily available.

scholarly journals Atmospheric Water Vapor Budget and Its Long‐Term Trend Over the Tibetan Plateau

2020 ◽  
Vol 125 (23) ◽  
Author(s):  
Hongru Yan ◽  
Jianping Huang ◽  
Yongli He ◽  
Yuzhi Liu ◽  
Tianhe Wang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Tibetan Plateau ◽  
Atmospheric Water Vapor ◽  
The Tibetan Plateau ◽  
Atmospheric Water ◽  
Term Trend ◽  
Long Term Trend ◽  
Water Vapor Budget

scholarly journals Observation of the atmospheric water vapor stable isotopes in three dimensions over the Tibetan Plateau

2019 ◽  
Vol 64 (27) ◽  
pp. 2822-2829
Author(s):  
Zeqing He ◽  
Tandong Yao ◽  
Baiqing Xu ◽  
Guangjian Wu ◽  
Xiaowei Niu ◽  
...  
Keyword(s):  
Stable Isotopes ◽  
Water Vapor ◽  
Tibetan Plateau ◽  
Atmospheric Water Vapor ◽  
Three Dimensions ◽  
The Tibetan Plateau ◽  
Atmospheric Water

scholarly journals Nongrowing Season CO2 Emissions Determine the Distinct Carbon Budgets of Two Alpine Wetlands on the Northeastern Qinghai—Tibet Plateau

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1695
Author(s):  
Chenggang Song ◽  
Fanglin Luo ◽  
Lele Zhang ◽  
Lubei Yi ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Co2 Emissions ◽  
Growing Season ◽  
Atmospheric Water Vapor ◽  
Tibet Plateau ◽  
Co2 Fluxes ◽  
Carbon Budgets ◽  
Atmospheric Water ◽  
Boosted Regression Tree ◽  
Qinghai Tibet Plateau

Alpine wetlands sequester large amounts of soil carbon, so it is vital to gain a full understanding of their land-atmospheric CO2 exchanges and how they contribute to regional carbon neutrality; such an understanding is currently lacking for the Qinghai—Tibet Plateau (QTP), which is undergoing unprecedented climate warming. We analyzed two-year (2018–2019) continuous CO2 flux data, measured by eddy covariance techniques, to quantify the carbon budgets of two alpine wetlands (Luanhaizi peatland (LHZ) and Xiaobohu swamp (XBH)) on the northeastern QTP. At an 8-day scale, boosted regression tree model-based analysis showed that variations in growing season CO2 fluxes were predominantly determined by atmospheric water vapor, having a relative contribution of more than 65%. Variations in nongrowing season CO2 fluxes were mainly controlled by site (categorical variable) and topsoil temperature (Ts), with cumulative relative contributions of 81.8%. At a monthly scale, structural equation models revealed that net ecosystem CO2 exchange (NEE) at both sites was regulated more by gross primary productivity (GPP), than by ecosystem respiration (RES), which were both in turn directly controlled by atmospheric water vapor. The general linear model showed that variations in nongrowing season CO2 fluxes were significantly (p < 0.001) driven by the main effect of site and Ts. Annually, LHZ acted as a net carbon source, and NEE, GPP, and RES were 41.5 ± 17.8, 631.5 ± 19.4, and 673.0 ± 37.2 g C/(m2 year), respectively. XBH behaved as a net carbon sink, and NEE, GPP, and RES were –40.9 ± 7.5, 595.1 ± 15.4, and 554.2 ± 7.9 g C/(m2 year), respectively. These distinctly different carbon budgets were primarily caused by the nongrowing season RES being approximately twice as large at LHZ (p < 0.001), rather than by other equivalent growing season CO2 fluxes (p > 0.10). Overall, variations in growing season CO2 fluxes were mainly controlled by atmospheric water vapor, while those of the nongrowing season were jointly determined by site attributes and soil temperatures. Our results highlight the different carbon functions of alpine peatland and alpine swampland, and show that nongrowing season CO2 emissions should be taken into full consideration when upscaling regional carbon budgets. Current and predicted marked winter warming will directly stimulate increased CO2 emissions from alpine wetlands, which will positively feedback to climate change.

scholarly journals Profiling of atmospheric water vapor with MIR and LASE

2002 ◽  
Vol 40 (6) ◽  
pp. 1211-1219 ◽  
Author(s):  
J.R. Wang ◽  
P. Racette ◽  
M.E. Triesky ◽  
E.V. Browell ◽  
S. Ismail ◽  
...  
Keyword(s):  
Water Vapor ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water

scholarly journals New Gridded Product for the Total Columnar Atmospheric Water Vapor over Ocean Surface Constructed from Microwave Radiometer Satellite Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2402
Author(s):  
Weifu Sun ◽  
Jin Wang ◽  
Yuheng Li ◽  
Junmin Meng ◽  
Yujia Zhao ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Atmospheric Water Vapor ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Fusion Product ◽  
Mean Deviation ◽  
Atmospheric Water ◽  
Absolute Deviation ◽  
Better Than

Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radiometer (MR). The accuracy of this water vapor fusion product was validated using radiosonde water vapor observations. The comparative results show that the overall mean deviation (Bias) is smaller than 0.6 mm; the root mean square error (RMSE) and standard deviation (SD) are better than 3 mm, and the mean absolute deviation (MAD) and correlation coefficient (R) are better than 2 mm and 0.98, respectively.

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