Articles | Volume 16, issue 2
https://doi.org/10.5194/essd-16-775-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-16-775-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau
Ling Yuan
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
National Observation and Research Station for Qomolongma Special Atmospheric Processes and Environmental Changes, Dingri 858200, China
China-Pakistan Joint Research Center on Earth Sciences, Chinese Academy of Sciences, Islamabad 45320, Pakistan
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China
National Observation and Research Station for Qomolongma Special Atmospheric Processes and Environmental Changes, Dingri 858200, China
Kathmandu Center of Research and Education, Chinese Academy of Sciences, Beijing 100101, China
China-Pakistan Joint Research Center on Earth Sciences, Chinese Academy of Sciences, Islamabad 45320, Pakistan
Cunbo Han
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
National Observation and Research Station for Qomolongma Special Atmospheric Processes and Environmental Changes, Dingri 858200, China
China-Pakistan Joint Research Center on Earth Sciences, Chinese Academy of Sciences, Islamabad 45320, Pakistan
Binbin Wang
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
National Observation and Research Station for Qomolongma Special Atmospheric Processes and Environmental Changes, Dingri 858200, China
Kathmandu Center of Research and Education, Chinese Academy of Sciences, Beijing 100101, China
China-Pakistan Joint Research Center on Earth Sciences, Chinese Academy of Sciences, Islamabad 45320, Pakistan
Weiqiang Ma
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
National Observation and Research Station for Qomolongma Special Atmospheric Processes and Environmental Changes, Dingri 858200, China
China-Pakistan Joint Research Center on Earth Sciences, Chinese Academy of Sciences, Islamabad 45320, Pakistan
Related authors
Jiamin Wang, Kun Yang, Jiarui Liu, Xu Zhou, Xiaogang Ma, Wenjun Tang, Ling Yuan, and Zuhuan Ren
EGUsphere, https://doi.org/10.5194/egusphere-2025-1513, https://doi.org/10.5194/egusphere-2025-1513, 2025
Short summary
Short summary
Aerodynamic roughness length (z0) is a key parameter determining wind profiles in models, but most models neglect the urban effects. We proposed a low-cost method to estimate z0 at weather stations in built-up areas across China, and then developed a z0 dataset. Tests in the Weather Research and Forecasting model show that it significantly improves the simulation accuracy of wind speed at both 10-m and 100-m heights, supporting urban planning, air quality management, and wind energy projects.
Haipeng Yu, Guantian Wang, Zeyong Hu, Yaoming Ma, Maoshan Li, Weiqiang Ma, Lianglei Gu, Fanglin Sun, Hongchun Gao, Shujin Wang, and Fuquan Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-356, https://doi.org/10.5194/essd-2025-356, 2025
Preprint under review for ESSD
Short summary
Short summary
The Nagqu Observation Network, located in Central Tibetan Plateau (CTP), has functioned as the primary source of land-atmosphere interaction observations and published a near-surface meteorological observational dataset which spans a period of nine years (2014–2022) with hourly temporal resolution. This dataset will contribute to the understanding of the mechanism of land-atmosphere interactions on the TP and support comprehensive research of the energy-water cycle and climate change.
Binbin Wang, Yaoming Ma, Zeyong Hu, Weiqiang Ma, Xuelong Chen, Cunbo Han, Zhipeng Xie, Yuyang Wang, Maoshan Li, Bin Ma, Xingdong Shi, Weimo Li, and Zhengling Cai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-195, https://doi.org/10.5194/essd-2025-195, 2025
Preprint under review for ESSD
Short summary
Short summary
This study reveals distinct patterns in water, heat, and carbon exchange over the Tibetan Plateau. Heat transfer peaks in spring, while water vapor release is highest in summer. Most stations act as carbon sinks, but one in a forested valley is a carbon source, likely due to vegetation loss and human activity. The findings highlight the strong connections between water, heat, and carbon fluxes, offering valuable insights into climate change and weather forecasting.
Jiamin Wang, Kun Yang, Jiarui Liu, Xu Zhou, Xiaogang Ma, Wenjun Tang, Ling Yuan, and Zuhuan Ren
EGUsphere, https://doi.org/10.5194/egusphere-2025-1513, https://doi.org/10.5194/egusphere-2025-1513, 2025
Short summary
Short summary
Aerodynamic roughness length (z0) is a key parameter determining wind profiles in models, but most models neglect the urban effects. We proposed a low-cost method to estimate z0 at weather stations in built-up areas across China, and then developed a z0 dataset. Tests in the Weather Research and Forecasting model show that it significantly improves the simulation accuracy of wind speed at both 10-m and 100-m heights, supporting urban planning, air quality management, and wind energy projects.
Minqiang Zhou, Yilong Wang, Minzheng Duan, Xiangjun Tian, Jinzhi Ding, Jianrong Bi, Yaoming Ma, Weiqiang Ma, and Zhenhua Xi
EGUsphere, https://doi.org/10.5194/egusphere-2025-1293, https://doi.org/10.5194/egusphere-2025-1293, 2025
Short summary
Short summary
The Qinghai-Tibetan Plateau is a key system that impacts the global carbon balance. This study presents the greenhouse gas (GHG) mole fraction measurement campaign in May 2022 at Mt. Qomolangma station, including ground-based remote sensing and in situ measurements. The GHG measurements are carried out in this region for the first time and used for satellite validation.
Xu Zhou, Binbin Wang, Xiaogang Ma, Zhu La, and Kun Yang
The Cryosphere, 18, 4589–4605, https://doi.org/10.5194/tc-18-4589-2024, https://doi.org/10.5194/tc-18-4589-2024, 2024
Short summary
Short summary
The simulation of the ice phenology of Nam Co by WRF is investigated. Compared with the default model, improving the key lake schemes, such as water surface roughness length for heat fluxes and the shortwave radiation transfer for lake ice, can better simulate the lake ice phenology. The still existing errors in the spatial patterns of lake ice phenology imply that challenges still exist in modelling key lake and non-lake physics such as grid-scale water circulation and snow-related processes.
Cunbo Han, Yaoming Ma, Weiqiang Ma, Fanglin Sun, Yunshuai Zhang, Wei Hu, Hanying Xu, Chunhui Duan, and Zhenhua Xi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1963, https://doi.org/10.5194/egusphere-2024-1963, 2024
Preprint archived
Short summary
Short summary
Wind speed spectra analysis is very important for understanding boundary layer turbulence characteristics, atmospheric numerical model development, and wind energy assessment. However, wind speed spectra studies in mountainous areas are extremely scarce. In this study, using a 15-year time series of wind speed observed by a PBL tower and eddy-covariance tower at a site on the north slope of Mt. Everest, we investigated the characteristics of wind speed and wind speed spectrum.
Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li
Earth Syst. Sci. Data, 16, 3017–3043, https://doi.org/10.5194/essd-16-3017-2024, https://doi.org/10.5194/essd-16-3017-2024, 2024
Short summary
Short summary
Current models and satellites struggle to accurately represent the land–atmosphere (L–A) interactions over the Tibetan Plateau. We present the most extensive compilation of in situ observations to date, comprising 17 years of data on L–A interactions across 12 sites. This quality-assured benchmark dataset provides independent validation to improve models and remote sensing for the region, and it enables new investigations of fine-scale L–A processes and their mechanistic drivers.
Cunbo Han, Corinna Hoose, Martin Stengel, Quentin Coopman, and Andrew Barrett
Atmos. Chem. Phys., 23, 14077–14095, https://doi.org/10.5194/acp-23-14077-2023, https://doi.org/10.5194/acp-23-14077-2023, 2023
Short summary
Short summary
Cloud phase has been found to significantly impact cloud thermodynamics and Earth’s radiation budget, and various factors influence it. This study investigates the sensitivity of the cloud-phase distribution to the ice-nucleating particle concentration and thermodynamics. Multiple simulation experiments were performed using the ICON model at the convection-permitting resolution of 1.2 km. Simulation results were compared to two different retrieval products based on SEVIRI measurements.
Peizhen Li, Lei Zhong, Yaoming Ma, Yunfei Fu, Meilin Cheng, Xian Wang, Yuting Qi, and Zixin Wang
Atmos. Chem. Phys., 23, 9265–9285, https://doi.org/10.5194/acp-23-9265-2023, https://doi.org/10.5194/acp-23-9265-2023, 2023
Short summary
Short summary
In this paper, all-sky downwelling shortwave radiation (DSR) over the entire Tibetan Plateau (TP) at a spatial resolution of 1 km was estimated using an improved parameterization scheme. The influence of topography and different radiative attenuations were comprehensively taken into account. The derived DSR showed good agreement with in situ measurements. The accuracy was better than six other DSR products. The derived DSR also provided more reasonable and detailed spatial patterns.
Pei Zhang, Donghai Zheng, Rogier van der Velde, Jun Wen, Yaoming Ma, Yijian Zeng, Xin Wang, Zuoliang Wang, Jiali Chen, and Zhongbo Su
Earth Syst. Sci. Data, 14, 5513–5542, https://doi.org/10.5194/essd-14-5513-2022, https://doi.org/10.5194/essd-14-5513-2022, 2022
Short summary
Short summary
Soil moisture and soil temperature (SMST) are important state variables for quantifying the heat–water exchange between land and atmosphere. Yet, long-term, regional-scale in situ SMST measurements at multiple depths are scarce on the Tibetan Plateau (TP). The presented dataset would be valuable for the evaluation and improvement of long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change.
Maoshan Li, Wei Fu, Na Chang, Ming Gong, Pei Xu, Yaoming Ma, Zeyong Hu, Yaoxian Yang, and Fanglin Sun
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-257, https://doi.org/10.5194/acp-2022-257, 2022
Revised manuscript not accepted
Short summary
Short summary
Compared with the plain area, the land-atmosphere interaction on the Tibetan Plateau (TP) is intense and complex, which affects the structure of the boundary layer. The observed height of the convective boundary layer on the TP under the influence of the southern branch of the westerly wind was higher than that during the Asian monsoon season. The height of the boundary layer was positively correlated with the sensible heat flux and negatively correlated with latent heat flux.
Yunshuai Zhang, Qian Huang, Yaoming Ma, Jiali Luo, Chan Wang, Zhaoguo Li, and Yan Chou
Atmos. Chem. Phys., 21, 15949–15968, https://doi.org/10.5194/acp-21-15949-2021, https://doi.org/10.5194/acp-21-15949-2021, 2021
Short summary
Short summary
The source region of the Yellow River has an important role in issues related to water resources, ecological environment, and climate changes in China. We utilized large eddy simulation to understand whether the surface heterogeneity promotes or inhibits the boundary-layer turbulence, the great contribution of the thermal circulations induced by surface heterogeneity to the water and heat exchange between land/lake and air. Moreover, the turbulence in key locations is characterized.
Lian Liu, Yaoming Ma, Massimo Menenti, Rongmingzhu Su, Nan Yao, and Weiqiang Ma
Hydrol. Earth Syst. Sci., 25, 4967–4981, https://doi.org/10.5194/hess-25-4967-2021, https://doi.org/10.5194/hess-25-4967-2021, 2021
Short summary
Short summary
Albedo is a key factor in land surface energy balance, which is difficult to successfully reproduce by models. Here, we select eight snow events on the Tibetan Plateau to evaluate the universal improvements of our improved albedo scheme. The RMSE relative reductions for temperature, albedo, sensible heat flux and snow depth reach 27%, 32%, 13% and 21%, respectively, with remarkable increases in the correlation coefficients. This presents a strong potential of our scheme for modeling snow events.
Zhipeng Xie, Yaoming Ma, Weiqiang Ma, Zeyong Hu, and Genhou Sun
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-260, https://doi.org/10.5194/tc-2021-260, 2021
Preprint withdrawn
Short summary
Short summary
Wind-driven snow transport greatly influences spatial-temporal distribution of snow in mountainous areas. Knowledge of the spatiotemporal variability of blowing snow is in its infancy because of inaccuracies in satellite-based blowing snow algorithms and the absence of quantitative assessments. Here, we present the spatiotemporal variability and magnitude of blowing snow events, and explore the potential links with ambient meteorological conditions using near surface blowing snow observations.
Cunbo Han, Yaoming Ma, Binbin Wang, Lei Zhong, Weiqiang Ma, Xuelong Chen, and Zhongbo Su
Earth Syst. Sci. Data, 13, 3513–3524, https://doi.org/10.5194/essd-13-3513-2021, https://doi.org/10.5194/essd-13-3513-2021, 2021
Short summary
Short summary
Actual terrestrial evapotranspiration (ETa) is a key parameter controlling the land–atmosphere interaction processes and water cycle. However, the spatial distribution and temporal changes in ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Results have been validated at six eddy-covariance monitoring sites and show high accuracy.
Zhipeng Xie, Weiqiang Ma, Yaoming Ma, Zeyong Hu, Genhou Sun, Yizhe Han, Wei Hu, Rongmingzhu Su, and Yixi Fan
Hydrol. Earth Syst. Sci., 25, 3783–3804, https://doi.org/10.5194/hess-25-3783-2021, https://doi.org/10.5194/hess-25-3783-2021, 2021
Short summary
Short summary
Ground information on the occurrence of blowing snow has been sorely lacking because direct observations of blowing snow are sparse in time and space. In this paper, we investigated the potential capability of the decision tree model to detect blowing snow events in the European Alps. Trained with routine meteorological observations, the decision tree model can be used as an efficient tool to detect blowing snow occurrences across different regions requiring limited meteorological variables.
Yanbin Lei, Tandong Yao, Kun Yang, Lazhu, Yaoming Ma, and Broxton W. Bird
Hydrol. Earth Syst. Sci., 25, 3163–3177, https://doi.org/10.5194/hess-25-3163-2021, https://doi.org/10.5194/hess-25-3163-2021, 2021
Short summary
Short summary
Lake evaporation from Paiku Co on the TP is low in spring and summer and high in autumn and early winter. There is a ~ 5-month lag between net radiation and evaporation due to large lake heat storage. High evaporation and low inflow cause significant lake-level decrease in autumn and early winter, while low evaporation and high inflow cause considerable lake-level increase in summer. This study implies that evaporation can affect the different amplitudes of lake-level variations on the TP.
Maoshan Li, Xiaoran Liu, Lei Shu, Shucheng Yin, Lingzhi Wang, Wei Fu, Yaoming Ma, Yaoxian Yang, and Fanglin Sun
Hydrol. Earth Syst. Sci., 25, 2915–2930, https://doi.org/10.5194/hess-25-2915-2021, https://doi.org/10.5194/hess-25-2915-2021, 2021
Short summary
Short summary
In this study, using MODIS satellite data and site atmospheric turbulence observation data in the Nagqu area of the northern Tibetan Plateau, with the Massman-retrieved model and a single height observation to determine aerodynamic surface roughness, temporal and spatial variation characteristics of the surface roughness were analyzed. The result is feasible, and it can be applied to improve the model parameters of the land surface model and the accuracy of model simulation in future work.
Ziyu Huang, Lei Zhong, Yaoming Ma, and Yunfei Fu
Geosci. Model Dev., 14, 2827–2841, https://doi.org/10.5194/gmd-14-2827-2021, https://doi.org/10.5194/gmd-14-2827-2021, 2021
Short summary
Short summary
Spectral nudging is an effective dynamical downscaling method used to improve precipitation simulations of regional climate models (RCMs). However, the biases of the driving fields over the Tibetan Plateau (TP) would possibly introduce extra biases when spectral nudging is applied. The results show that the precipitation simulations were significantly improved when limiting the application of spectral nudging toward the potential temperature and water vapor mixing ratio over the TP.
María P. González-Dugo, Xuelong Chen, Ana Andreu, Elisabet Carpintero, Pedro J. Gómez-Giraldez, Arnaud Carrara, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 755–768, https://doi.org/10.5194/hess-25-755-2021, https://doi.org/10.5194/hess-25-755-2021, 2021
Short summary
Short summary
Drought is a devastating natural hazard and difficult to define, detect and quantify. Global meteorological data and remote-sensing products present new opportunities to characterize drought in an objective way. In this paper, we applied the surface energy balance model SEBS to estimate monthly evapotranspiration (ET) from 2001 to 2018 over the dehesa area of the Iberian Peninsula. ET anomalies were used to identify the main drought events and analyze their impacts on dehesa vegetation.
Genhou Sun, Zeyong Hu, Yaoming Ma, Zhipeng Xie, Jiemin Wang, and Song Yang
Hydrol. Earth Syst. Sci., 24, 5937–5951, https://doi.org/10.5194/hess-24-5937-2020, https://doi.org/10.5194/hess-24-5937-2020, 2020
Short summary
Short summary
We investigate the influence of soil conditions on the planetary boundary layer (PBL) thermodynamics and convective cloud formations over a typical underlying surface, based on a series of simulations on a sunny day in the Tibetan Plateau, using the Weather Research and Forecasting (WRF) model. The real-case simulation and sensitivity simulations indicate that the soil moisture could have a strong impact on PBL thermodynamics, which may be favorable for the convective cloud formations.
Yaoming Ma, Zeyong Hu, Zhipeng Xie, Weiqiang Ma, Binbin Wang, Xuelong Chen, Maoshan Li, Lei Zhong, Fanglin Sun, Lianglei Gu, Cunbo Han, Lang Zhang, Xin Liu, Zhangwei Ding, Genhou Sun, Shujin Wang, Yongjie Wang, and Zhongyan Wang
Earth Syst. Sci. Data, 12, 2937–2957, https://doi.org/10.5194/essd-12-2937-2020, https://doi.org/10.5194/essd-12-2937-2020, 2020
Short summary
Short summary
In comparison with other terrestrial regions of the world, meteorological observations are scarce over the Tibetan Plateau.
This has limited our understanding of the mechanisms underlying complex interactions between the different earth spheres with heterogeneous land surface conditions.
The release of this continuous and long-term dataset with high temporal resolution is expected to facilitate broad multidisciplinary communities in understanding key processes on the
Third Pole of the world.
Felix Nieberding, Christian Wille, Gerardo Fratini, Magnus O. Asmussen, Yuyang Wang, Yaoming Ma, and Torsten Sachs
Earth Syst. Sci. Data, 12, 2705–2724, https://doi.org/10.5194/essd-12-2705-2020, https://doi.org/10.5194/essd-12-2705-2020, 2020
Short summary
Short summary
We present the first long-term eddy covariance CO2 and H2O flux measurements from the large but underrepresented alpine steppe ecosystem on the central Tibetan Plateau. We applied careful corrections and rigorous quality filtering and analyzed the turbulent flow regime to provide meaningful fluxes. This comprehensive data set allows potential users to put the gas flux dynamics into context with ecosystem properties and potential flux drivers and allows for comparisons with other data sets.
Cited articles
Alaoui, A. and Goetz, B.: Dye tracer and infiltration experiments to investigate macropore flow, Geoderma, 144, 279–286, https://doi.org/10.1016/j.geoderma.2007.11.020, 2008.
Baik, J., Liaqat, U. W., and Choi, M.: Assessment of satellite- and reanalysis-based evapotranspiration products with two blending approaches over the complex landscapes and climates of Australia, Agr. Forest Meteorol., 263, 388–398, https://doi.org/10.1016/j.agrformet.2018.09.007, 2018.
Bibi, S., Wang, L., Li, X., Zhou, J., Chen, D., and Yao, T.: Climatic and associated cryospheric, biospheric, and hydrological changes on the Tibetan Plateau: a review, Int. J. Climatol., 38, 1–17, https://doi.org/10.1002/joc.5411, 2018.
Biermann, T., Babel, W., Ma, W., Chen, X., Thiem, E., Ma, Y., and Foken, T.: Turbulent flux observations and modeling over a shallow lake and a wet grassland in the Nam Co basin, Tibetan Plateau, Theor. Appl. Climatol., 116, 301–316, https://doi.org/10.1007/s00704-013-0953-6, 2014.
Blyth, E. and Harding, R. J.: Methods to separate observed global evapotranspiration into the interception, transpiration, and soil surface evaporation components, Hydrol. Process., 25, 4063–4068, https://doi.org/10.1002/hyp.8409, 2011.
Camillo, P. J. and Gurney, R. J.: A resistance parameter for bare soil evaporation models, Soil Sci., 141, 95–105, https://doi.org/10.1097/00010694-198602000-00001, 1986.
Chang, Y., Qin, D., Ding, Y., Zhao, Q., and Zhang, S.: A modified MOD16 algorithm to estimate evapotranspiration over the alpine meadow on the Tibetan Plateau, China, J. Hydrol., 561, 16–30, https://doi.org/10.1016/j.jhydrol.2018.03.054, 2018.
Che, T., Li, X., Liu, S., Li, H., Xu, Z., Tan, J., Zhang, Y., Ren, Z., Xiao, L., Deng, J., Jin, R., Ma, M., Wang, J., and Yang, X.: Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China, Earth Syst. Sci. Data, 11, 1483–1499, https://doi.org/10.5194/essd-11-1483-2019, 2019.
Chen, D., Xu, B., Yao, T., Guo, Z., Cui, P., Chen, F., Zhang, R., Zhang, X., Zhang, Y., Fan, J., Hou, Z., and Zhang, T.: Assessment of past, present, and future environmental changes on the Tibetan Plateau, Kexue Tongbao/Chinese Sci. Bull., 60, 3025–3035, https://doi.org/10.1360/N972014-01370, 2015.
Chen, X., Su, Z., Ma, Y., Yang, K., Wen, J., and Zhang, Y.: An Improvement of Roughness Height Parameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau, J. Appl. Meteorol. Clim., 52, 607–622, https://doi.org/10.1175/JAMC-D-12-056.1, 2013.
Chen, X., Su, Z., Ma, Y., Liu, S., Yu, Q., and Xu, Z.: Development of a 10-year (2001–2010) 0.1° data set of land-surface energy balance for mainland China, Atmos. Chem. Phys., 14, 13097–13117, https://doi.org/10.5194/acp-14-13097-2014, 2014.
Chen, X., Massman, W. J., and Su, Z.: A Column Canopy-Air Turbulent Diffusion Method for Different Canopy Structures, J. Geophys. Res.-Atmos., 124, 488–506, https://doi.org/10.1029/2018JD028883, 2019.
Chen, X., Su, Z., Ma, Y., Trigo, I., and Gentine, P.: Remote Sensing of Global Daily Evapotranspiration based on a Surface Energy Balance Method and Reanalysis Data, J. Geophys. Res.-Atmos., 126, e2020JD032873, https://doi.org/10.1029/2020JD032873, 2021.
Chen, Y., Yang, K., Tang, W., Qin, J., and Zhao, L.: Parameterizing soil organic carbon's impacts on soil porosity and thermal parameters for Eastern Tibet grasslands, Sci. China Earth Sci., 55, 1001–1011, https://doi.org/10.1007/s11430-012-4433-0, 2012.
Chen, Y., Xia, J., Liang, S., Feng, J., Fisher, J. B., Li, X., Li, X., Liu, S., Ma, Z., Miyata, A., Mu, Q., Sun, L., Tang, J., Wang, K., Wen, J., Xue, Y., Yu, G., Zha, T., Zhang, L., Zhang, Q., Zhao, T., Zhao, L., and Yuan, W.: Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China, Remote Sens. Environ., 140, 279–293, https://doi.org/10.1016/j.rse.2013.08.045, 2014.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils, Water Resour. Res., 20, 682–690, https://doi.org/10.1029/WR020i006p00682, 1984.
Cui, J., Tian, L., Wei, Z., Huntingford, C., Wang, P., Cai, Z., and Wang, L.: Quantifying the Controls on Evapotranspiration Partitioning in the Highest Alpine Meadow Ecosystem, Water Resour. Res., 56, https://doi.org/10.1029/2019WR024815, 2020.
Dan, J., Gao, Y., and Zhang, M.: testing and Attributing Evapotranspiration Deviations Using Dynamical Downscaling and Convection-Permitting Modeling over the Tibetan Plateau, Water, 13, 2096, https://doi.org/10.3390/w13152096, 2017.
de Kok, R. J., Kraaijenbrink, P. D. A., Tuinenburg, O. A., Bonekamp, P. N. J., and Immerzeel, W. W.: Towards understanding the pattern of glacier mass balances in High Mountain Asia using regional climatic modelling, The Cryosphere, 14, 3215–3234, https://doi.org/10.5194/tc-14-3215-2020, 2020.
Denef, K., Galdo, I. D., Venturi, A., and Cotrufo, M. F.: Assessment of Soil C and N Stocks and Fractions across 11European Soils under Varying Land Uses, Open J. Soil Sci., 03, 297–313, https://doi.org/10.4236/ojss.2013.37035, 2013.
Dore, S., Montes-Helu, M., Hart, S. C., Hungate, B. A., Koch, G. W., Moon, J. B., Finkral, A., and Kolb, T. E.: Recovery of ponderosa pine ecosystem carbon and water fluxes from thinning and stand-replacing fire, Glob. Change Biol., 18, 3171–3185, https://doi.org/10.1111/j.1365-2486.2012.02775.x, 2012.
Ding, J., Chen, L., Ji, C., Hugelius, G., Li, Y., Liu, L., Qin, S., Zhang, B., Yang, G., Li, F., Fang, K., Chen, Y., Peng, Y., Zhao, X., He, H., Smith, P., Fang, J., and Yang, Y.: Decadal soil carbon accumulation across Tibetan permafrost regions, Nat. Geosci., 10, 420–424, https://doi.org/10.1038/ngeo2945, 2017.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Farouki, O. T.: The thermal properties of soils in cold regions, Cold Reg. Sci. Technol., 5, 67–75, https://doi.org/10.1016/0165-232X(81)90041-0, 1981.
Fischer, M. L., Billesbach, D. P., Berry, J. A., Riley, W. J., and Torn, M. S.: Spatiotemporal variations in growing season exchanges of CO2, H2O, and sensible heat in agricultural fields of the Southern Great Plains, Earth Interact., 11, 1–12, https://doi.org/10.1175/EI231.1, 2007.
Gan, R., Zhang, Y., Shi, H., Yang, Y., Eamus, D., Cheng, L., Chiew, F., and Yu, Q.: Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems, Ecohydrology, 11, e1974, https://doi.org/10.1002/eco.1974, 2018.
Good, S. P., Noone, D., and Bowen, G.: Hydrologic connectivity constrains partitioning of global terrestrial water fluxes, Science, 349, 175–177, https://doi.org/10.1126/science.aaa5931, 2015.
Guo, X., Tian, L., Wang, L., Yu, W., and Qu, D.: River recharge sources and the partitioning of catchment evapotranspiration fluxes as revealed by stable isotope signals in a typical high-elevation arid catchment, J. Hydrol., 549, 616–630, https://doi.org/10.1016/j.jhydrol.2017.04.037, 2017.
Han, C., Ma, Y., Chen, X., and Su, Z.: Trends of land surface heat fluxes on the Tibetan Plateau from 2001 to 2012, Int. J. Climatol., 37, 4757–4767, https://doi.org/10.1002/joc.5119, 2017.
Han, C., Ma, Y., Wang, B., Zhong, L., Ma, W., Chen, X., and Su, Z.: Long-term variations in actual evapotranspiration over the Tibetan Plateau, Earth Syst. Sci. Data, 13, 3513–3524, https://doi.org/10.5194/essd-13-3513-2021, 2021.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first high-resolution meteorological forcing dataset for land process studies over China, Sci. Data., 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
Högström, U.: Review of some basic characteristics of the atmospheric surface layer, Bound. Lay. Meteorol., 78, 215–246, https://doi.org/10.1007/BF00120937, 1996.
Immerzeel, W. W., Van Beek, L. P. H., and Bierkens, M. F. P.: Climate change will affect the Asian water towers, Science, 328, 1382–1385, https://doi.org/10.1126/science.1183188, 2010.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B., Elmore, A., Emmer, A., Feng, M., Fernández, A., Haritashya, U., Kargel, J., Koppes, M., Kraaijenbrink, P., Kulkarni, A., Mayewski, P., Nepal, S., Pacheco, P., Painter, T., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.: Importance and vulnerability of the world' 's water towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020.
Irmak, S. and Mutiibwa, D.: On the dynamics of canopy resistance: Generalized linear estimation and relationships with primary micrometeorological variables, Water Resour. Res., 46, W08526, https://doi.org/10.1029/2009WR008484, 2010.
Jarvis, P. G.: The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the Field, Philos. T. R. Soc. Lond. B., 273, 593–610, https://doi.org/10.1098/rstb.1976.0035, 1976.
Jiang, Y., Yang, K., Qi, Y., Zhou, X., He, J., Lu, H., Li, X., Chen, Y., Li, X., Zhou, B., Mamtimin, A., Shao, C., Ma, X., Tian, J., and Zhou, J.: TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1/30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations, Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, 2023.
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., De Jeu, R., Dolman, A., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball, J., Law, B., Montagnani, L., Mu, Q., Mueller, B., Oleson, K., Papale, D., Richardson, A., Roupsard, O., Running, S., Tomelleri, E., Viovy, N., Weber, U., Williams. C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline in the global land evapotranspiration trend due to limited moisture supply, Nature, 467, 951–954, https://doi.org/10.1038/nature09396, 2010.
Kang, S., Xu, Y., You, Q., Flügel, W. A., Pepin, N., and Yao, T.: Review of climate and cryospheric change in the Tibetan Plateau, Environ. Res. Lett., 5, 015101, https://doi.org/10.1088/1748-9326/5/1/015101, 2010.
Khan, M. S., Liaqat, U. W., Baik, J., and Choi, M.: Stand-alone uncertainty characterization of GLEAM, GLDAS, and MOD16 evapotranspiration products using an extended triple collocation approach, Agr. Forest Meteorol., 252, 256–268, https://doi.org/10.1016/j.agrformet.2018.01.022, 2018.
Kuang, X. and Jiao, J. J.: Review on climate change on the Tibetan plateau during the last half century, J. Geophys. Res.-Atmos., 121, 3979–4007, https://doi.org/10.1002/2015JD024728, 2016.
Kutsch, W. L., Aubinet, M., Buchmann, N., Smith, P., Osborne, B., Eugster, W., Wattenbach, M., Schrumpf, M., Schulze, E., Tomelleri, E., Ceschia, E., Bernhofer, C., Béziat, P., Carrara, A., Di Tommasi, P., Grunwald, T., Jones, M., Magliulo, V., Moureaux, C., Olioso, A., Sanz, M., Saunders, M., S?gaard, H., and Ziegler, W.: The net biome production of full crop rotations in Europe, Agr. Ecosyst. Environ., 139, 336–345, https://doi.org/10.1016/j.agee.2010.07.016, 2010.
Kool, D., Agam, N., Lazarovitch, N., Heitman, J. L., Sauer, T. J., and Ben-Gal, A.: A review of approaches for evapotranspiration partitioning, Agr. Forest Meteorol., 184, 56–70, https://doi.org/10.1016/j.agrformet.2013.09.003, 2014.
Koster, R. D. and Suarez, M. J.: The Influence of Land Surface Moisture Retention on Precipitation Statistics, J. Climate, 9, 2551–2567, https://doi.org/10.1175/1520-0442(1996)009<2551:TIOLSM>2.0.CO;2, 1996.
Lawrence, D. M., Thornton, P. E., Oleson, K. W., and Bonan, G. B.: The Partitioning of Evapotranspiration into Transpiration, Soil Evaporation, and Canopy Evaporation in a GCM: Impacts on Land–Atmosphere Interaction, J. Hydrometeorol., 8, 862–880, https://doi.org/10.1175/JHM596.1, 2007.
Lehmann, P., Merlin, O., Gentine, P., and Or, D.: Soil texture effects on surface resistance to bare soil evaporation, Geophys. Res. Lett., 45, 10398–10405, https://doi.org/10.1029/2018GL078803, 2018.
Lemone, M. A., Chen, F., Alfieri, J. G., Cuenca, R. H., Hagimoto, Y., Blanken, P., Niyogi, D., Kang, S., Davis, K., and Grossman, R. L.: NCAR/CU surface, soil, and vegetation observations during the International H2O Project 2002 field campaign, B. Am. Meteorol. Soc., 88, 65–81, https://doi.org/10.1175/BAMS-88-1-65, 2007.
Letts, M. G., Comer, N. T., Roulet, N. T., Skarupa, M. R., and Verseghy, D. L.: Parametrization of peatland hydraulic properties for the Canadian land surface scheme, Atmos. Ocean., 38, 141–160, https://doi.org/10.1080/07055900.2000.9649643, 2000.
Leuning, R., Zhang, Y. Q., Rajaud, A., Cleugh, H., and Tu, K.: A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman–Monteith equation, Water Resour. Res., 44, W10419, https://doi.org/10.1029/2007WR006562, 2010.
Li, S., Hao, X., Du, T., Tong, L., Zhang, J., and Kang, S.: A coupled surface resistance model to estimate crop evapotranspiration in the arid region of northwest China, Hydrol. Process., 28, 2312–2323, https://doi.org/10.2136/vzj2018.04.0072, 2013.
Li, S., Zhang, L., Kang, S., Tong, L., Du, T., Hao, X., and Zhao, P.: Comparison of several surface resistance models for estimating crop evapotranspiration over the entire growing season in arid regions, Agr. Forest Meteorol., 208, 1–15, https://doi.org/10.1016/j.agrformet.2015.04.002, 2015.
Li, S., Wang, G., Sun, S., Chen, H., Bai, P., Zhou, S., Huang, Y., Wang, J., and Deng, P.: Assessment of Multisource Evapotranspiration Products over China Using Eddy Covariance Observations, Remote Sens., 10, 1692, https://doi.org/10.3390/rs10111692, 2018.
Li, S., Wang, G., Sun, S., Fiifi Tawia Hagan, D., Chen, T., Dolman, H., and Liu, Y.: Long-term changes in evapotranspiration over China and attribution to climatic drivers during 1980–2010, J. Hydrol., 59, https://doi.org/10.1016/j.jhydrol.2021.126037, 2021.
Li, X., Liang, S., Yuan, W., Yu, G., Cheng, X., Chen, Y., Zhao, T., Feng, J., Ma, Z., Ma, M., Liu, S., Chen, J., Shao, C., Li, S., Zhang, X., Zhang, Z., Sun, G., Chen, S., Ohta, T., Varlagin, A., Miyata, A., Takagi, K., Saiqusa, N., and Kato, T.: Estimation of evapotranspiration over the terrestrial ecosystems in China, Ecohydrology, 7, 139–149, https://doi.org/10.1002/eco.1341, 2014a.
Li, X., Wang, L., Chen, D., Yang, K., and Wang, A.: Seasonal evapotranspiration changes (1983–2006) of four large basins on the Tibetan Plateau, J. Geophys. Res.-Atmos., 119, 13079–13095, https://doi.org/10.1002/2014JD022380, 2014b.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan, W., Zhang, X., Zhao, X., and Zhou, J.: The global land surface satellite (GLASS) product suite, B. Am. Meteorol. Soc., 102, E323–E337, https://doi.org/10.1175/BAMS-D-18-0341.1, 2021.
Liu, J., Chai, L., Dong, J., Zheng, D., Wigneron, J. P., Liu, S., Zhou, J., Xu, T., Yang, S., Song, Y., Qu, Y., and Lu, Z.: Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method, Remote Sens. Environ., 255, 112225, https://doi.org/10.1016/j.rse.2020.112225, 2021.
Liu, S., Lu, L., Mao, D., and Jia, L.: Evaluating parameterizations of aerodynamic resistance to heat transfer using field measurements, Hydrol. Earth Syst. Sci., 11, 769–783, https://doi.org/10.5194/hess-11-769-2007, 2007.
Liu, S. M., Xu, Z. W., Wang, W. Z., Jia, Z. Z., Zhu, M. J., Bai, J., and Wang, J. M.: A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem, Hydrol. Earth Syst. Sci., 15, 1291–1306, https://doi.org/10.5194/hess-15-1291-2011, 2011.
Liu, S. M., Li, X., Xu, Z. W., Che, T., Xiao, Q., Ma, M. G., Liu, Q. H., Jin, R., Guo, J. W., Wang, L. X., Wang, W. Z., Qi, Y., Li, H. Y., Xu, T. R., Ran, Y. H., Hu, X. L., Shi, S. J., Zhu, Z. L., Tan, J. L., Zhang, Y., and Ren, Z. G.: The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China, Vadose Zone J., 17, 180072, https://doi.org/10.2136/vzj2018.04.0072, 2018.
Ma, N. and Zhang, Y.: Increasing Tibetan Plateau terrestrial evapotranspiration primarily driven by precipitation, Agr. Forest Meteorol., 317, 108887, https://doi.org/10.1016/j.agrformet.2022.108887, 2022.
Ma, N., Zhang, Y., Guo, Y., Gao, H., Zhang, H., and Wang, Y.: Environmental and biophysical controls on the evapotranspiration over the highest alpine steppe, J. Hydrol., 529, 980–992, https://doi.org/10.1016/j.jhydrol.2015.09.013, 2015a.
Ma, N., Zhang, Y., Xu, C.-Y., and Szilagyi, J.: Modeling actual evapotranspiration with routine meteorological variables in the data-scarce region of the Tibetan Plateau: Comparisons and implications, J. Geophys. Res.-Biogeo., 120, 1638–1657, https://doi.org/10.1002/2015JG003006, 2015b.
Ma, N., Szilagyi, J., Zhang, Y., and Liu, W.: Complementary-Relationship-Based Modeling of Terrestrial Evapotranspiration Across China During 1982–2012: Validations and Spatiotemporal Analyses, J. Geophys. Res.-Atmos., 124, 4326–4351, https://doi.org/10.1029/2018JD029850, 2019.
Ma, Y., Hu, Z., Xie, Z., Ma, W., Wang, B., Chen, X., Li, M., Zhong, L., Sun, F., Gu, L., Han, C., Zhang, L., Liu, X., Ding, Z., Sun, G., Wang, S., Wang, Y., and Wang, Z.: A long-term (2005–2016) dataset of hourly integrated land–atmosphere interaction observations on the Tibetan Plateau, Earth Syst. Sci. Data, 12, 2937–2957, https://doi.org/10.5194/essd-12-2937-2020, 2020.
Ma, Y., Chen, X., and Yuan, L.: Long term variations of monthly terrestrial evapotranspiration over the Tibetan Plateau (1982–2018)[DS/OL], V2, Science Data Bank [data set], CSTR:31253.11.sciencedb.00020, https://doi.org/10.11922/sciencedb.00020 2021.
Merlin, O., Stefan, V. G., Amazirh, A., Chanzy, A., Ceschia, E., Er-Raki, S., and Khabba, S.: Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta-analysis approach, Water Resour. Res., 52, 3663–3684, https://doi.org/10.1002/2015WR018233, 2016.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Miralles, D. G., Jiménez, C., Jung, M., Michel, D., Ershadi, A., McCabe, M. F., Hirschi, M., Martens, B., Dolman, A. J., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets, Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, 2016.
Monteith, J. L.: Evaporation and environment, Symp. Soc. Exp. Biol., 19, 205–234, 1965.
Mu, Q., Heinsch, F. A., Zhao, M., and Running, S. W.: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sens. Environ., 111, 519–536, https://doi.org/10.1016/j.rse.2007.04.015, 2007.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Novick, K. A., Stoy, P. C., Katul, G. G., Ellsworth, D. S., Siqueira, M. B. S., Juang, J., and Oren, R.: Carbon dioxide and water vapor exchange in a warm temperate grassland, Oecologia, 138, 259–274, https://doi.org/10.1007/s00442-003-1388-z, 2004.
Ortega-Farias, S., Poblete-Echeverría, C., and Brisson, N.: Parameterization of a two-layer model for estimating vineyard evapotranspiration using meteorological measurements, Agr. Forest Meteorol., 150, 276–286, https://doi.org/10.1016/j.agrformet.2009.11.012, 2010.
Paulson, C. A.: The Mathematical Representation of Wind Speed and Temperature Profiles in the Unstable Atmospheric Surface Layer, J. Appl. Meteorol., 9, 857–861, https://doi.org/10.1175/1520-0450(1970)009<0857:tmrows>2.0.co;2, 1970.
Peng, J., Loew, A., Chen, X., Ma, Y., and Su, Z.: Comparison of satellite-based evapotranspiration estimates over the Tibetan Plateau, Hydrol. Earth Syst. Sci., 20, 3167–3182, https://doi.org/10.5194/hess-20-3167-2016, 2016.
Phillips, T. J., Klein, S. A., Ma, H. Y., Tang, Q., Xie, S., Williams, I. N., Joseph, A., David, R., and Margaret, S.: Using ARM observations to evaluate climate model simulations of land-atmosphere coupling on the U.S. Southern Great Plains, J. Geophys. Res.-Atmos., 122, 11524–11548, https://doi.org/10.1002/2017JD027141, 2017.
Ramoelo, A., Majozi, N., Mathieu, R., Jovanovic, N., Nickless, A., and Dzikiti, S.: Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa, Remote Sens.-Basel, 6, 7406–7423, https://doi.org/10.3390/rs6087406, 2014.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrovem B., Radakovich, J., Bosilovich, M., Entin, J., Walker, J., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Sakaguchi, K. and Zeng, X.: Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3.5), J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2008JD010834, 2009.
Schlesinger, W. H. and Jasechko, S.: Transpiration in the global water cycle, Agr. Forest Meteorol., 189–190, 115–117, https://doi.org/10.1016/j.agrformet.2014.01.011, 2014.
Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B., Dazlich, D. A., and Bounoua, L.: A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation, J. Climate, 9, 676–705, https://doi.org/10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2, 1996.
Shi, Q. and Liang, S.: Surface-sensible and latent heat fluxes over the Tibetan Plateau from ground measurements, reanalysis, and satellite data, Atmos. Chem. Phys., 14, 5659–5677, https://doi.org/10.5194/acp-14-5659-2014, 2014.
Sobrino, J. A., Jiménez-Muñoz, J. C., and Paolini, L.: Land surface temperature retrieval from LANDSAT TM 5, Remote Sens. Environ., 90, 434–440, https://doi.org/10.1016/j.rse.2004.02.003, 2004.
Song, L., Zhuang, Q., Yin, Y., Zhu, X., and Wu, S.: Spatio-temporal dynamics of evapotranspiration on the Tibetan Plateau from 2000 to 2010, Environ. Res. Lett., 12, 014011, https://doi.org/10.1088/1748-9326/aa527d, 2017.
Su, Z.: The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrol. Earth Syst. Sci., 6, 85–100, https://doi.org/10.5194/hess-6-85-2002, 2002.
Sun, S. F.: Moisture and heat transport in a soil layer forced by atmospheric conditions, Master thesis, Dept. of Civil Engineering, University of Connecticut, 72, 1982.
Tang, J. Y. and Riley, W. J.: A new top boundary condition for modeling surface diffusive exchange of a generic volatile tracer: theoretical analysis and application to soil evaporation, Hydrol. Earth Syst. Sci., 17, 873–893, https://doi.org/10.5194/hess-17-873-2013, 2013.
Thom, A. S.: Momentum, mass and heat exchange of vegetation, Q. J. Roy. Meteor. Soc., 98, 124–134, https://doi.org/10.1002/qj.49709841510, 1972.
Wang, B., Ma, Y., Su, Z., Wang, Y., and Ma, W.: Quantifying the evaporation amounts of 75 high-elevation large dimictic lakes on the Tibetan Plateau, Sci. Adv., 6, eaay8558, https://doi.org/10.1126/sciadv.aay8558, 2020.
Wang, G., Lin, S., Hu, Z., Lu, Y., Sun, X., and Huang, K.: Improving Actual Evapotranspiration Estimation Integrating Energy Consumption for Ice Phase Change Across the Tibetan Plateau, J. Geophys. Res.-Atmos., 125, e2019JD031799, https://doi.org/10.1029/2019JD031799, 2020.
Wang, W., Li, J., Yu, Z., Ding, Y., Xing, W., and Lu, W.: Satellite retrieval of actual evapotranspiration in the Tibetan Plateau: components partitioning, multi-decadal trends and dominated factors identifying, J. Hydrol., 559, 471–485, https://doi.org/10.1016/j.jhydrol.2018.02.065, 2018.
Wang, Y., Lv, W., Xue, K., Wang, S., Zhang, L., Hu, R., Zeng, H., Xu, X., Li, Y., Jiang, L., Hao, Y., Du, J., Sun, J., Dorji, T., Piao, S., Wang, C., Luo, C., Zhang, Z., Chang, X., Zhang, M., Hu, Y., Wu, T., Wang, J., Li, B., Liu, P., Zhou, Y., Wang, A., Dong, S., Zhang, X., Gao, Q., Zhou, H., Shen, M., Wilkes, A., Miehe, G., Zhao, X., and Niu, H.: Grassland changes and adaptive management on the Qinghai–Tibetan Plateau, Nat. Rev. Earth. Environ., 3, 668–683, https://doi.org/10.1038/s43017-022-00330-8, 2022.
Wei, Z., Yoshimura, K., Wang, L., Miralles, D. G., Jasechko, S., and Lee, X.: Revisiting the contribution of transpiration to global terrestrial evapotranspiration, Geophys. Res. Lett., 44, 2792–2801, https://doi.org/10.1002/2016GL072235, 2017.
Wieder, W. R., Boehnert, J., Bonan, G. B., and Langseth, M.: Regridded Harmonized World Soil Database v1.2. Data set, Oak Ridge National Laboratory Distributed Active Archive Center [data set], Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1247, 2014.
Wilcox, B. P., Breshears, D. D., and Seyfried, M. S.: Water balance on rangelands, in: Encyclopedia of Water Science, edited by: Stewart, B. A. and Howell, T. A., Marcel Dekker Inc, New York, 791–794, http://www.cprl.ars.usda.gov/wmru/pdfs/DekkerEvettTDR.pdf (last access: 1 February 2024), 2003.
Wu, C., Hu, B. X., Huang, G., and Zhang, H.: Effects of climate and terrestrial storage on the temporal variability of actual evapotranspiration, J. Hydrol., 549, 388–403, https://doi.org/10.1016/j.jhydrol.2017.04.012, 2017.
Xu, X., Dong, L., Zhao, Y., and Wang, Y.: Effect of the Asian Water Tower over the Qinghai-Tibet Plateau and the characteristics of atmospheric water circulation, Kexue Tongbao/Chinese Science Bulletin, 64, 2830–2841, https://doi.org/10.1360/TB-2019-0203, 2019.
Yang, K., Koike, T., Ishikawa, H., Kim, J., Li, X., Liu, H., Liu S., Ma Y., and Wang, J.: Turbulent flux transfer over bare-soil surfaces: Characteristics and parameterization, J. Appl. Meteorol. Clim., 47, 276–290, https://doi.org/10.1175/2007JAMC1547.1, 2008.
Yang, K., He, J., Tang, W., Qin, J., and Cheng, C. C. K.: On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau, Agr. Forest Meteorol., 150, 38–46, https://doi.org/10.1016/j.agrformet.2009.08.004, 2010.
Yang, K., Wu, H., Qin, J., Lin, C., Tang, W., and Chen, Y.: Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review, Global Planet. Change, 112, 79–91, https://doi.org/10.1016/j.gloplacha.2013.12.001, 2014.
Yang, Y., Liu, Y., Li, M., Hu, Z., and Ding, Z.: Assessment of reanalysis flux products based on eddy covariance observations over the Tibetan Plateau, Theor. Appl. Climatol., 138, 275–292, https://doi.org/10.1007/s00704-019-02811-1, 2019.
Yao, T., Thompson, L., Yang, W., Yu, W., Gao, Y., Guo, X., Yang, X., Duan, K., Zhao, H., Xu, B., Pu, J., Lu, A., Xiang, Y., Kattel, D., and Joswiak, D.: Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings, Nat. Clim. Change, 2, 663–667, https://doi.org/10.1038/nclimate1580, 2012.
Yao, Y., Liang, S., Cheng, J., Liu, S., Fisher, J. B., Zhang, X., Jia, K., Zhao, X., Qin, Q., Zhao, B., Han, S., Zhou, G., Li, Y., and Zhao, S.: MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm, Agr. Forest Meteorol., 171–172, 187–202, https://doi.org/10.1016/j.agrformet.2012.11.016, 2013.
Yin, Y., Wu, S., Zhao, D., Zheng, D., and Pan, T.: Modeled effects of climate change on actual evapotranspiration in different eco-geographical regions in the Tibetan Plateau, J. Geogr. Sci., 23, 195–207, https://doi.org/10.1002/eco.1341, 2013.
You, Q., Xue, X., Peng, F., Dong, S., and Gao, Y.: Surface water and heat exchange comparison between alpine meadow and bare land in a permafrost region of the Tibetan Plateau, Agric. For. Meteorol., 232, 48–65, https://doi.org/10.1016/j.agrformet.2016.08.004, 2017.
Yu, G. R., Wen, X. F., Sun, X. M., Tanner, B. D., Lee, X., and Chen, J. Y.: Overview of ChinaFLUX and evaluation of its eddy covariance measurement, Agr. Forest Meteorol., 137, 125–137, https://doi.org/10.1016/j.agrformet.2006.02.011, 2006.
Yuan, L.: A Monthly 0.05∘ Terrestrial Evapotranspiration Dataset (1982–2018) for the Tibetan Plateau, National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Terre.tpdc.271913, 2021.
Yuan, L., Ma, Y., Chen, X., Wang, Y., Li, Z.: An enhanced MOD16 evapotranspiration model for the Tibetan Plateau during the unfrozen season, J. Geophys. Res.-Atmos., 126, e2020JD032787, https://doi.org/10.1029/2020JD032787, 2021.
Zhang, G., Yao, T., Xie, H., Yang, K., Zhu, L., Shum, C. K., Bolch, T., Yi, S., Allen, S., Jiang, L., Chen, W., and Ke, C.: Response of Tibetan Plateau lakes to climate change: Trends, patterns, and mechanisms, Earth-Sci. Rev., 28, 103269, https://doi.org/10.1016/j.earscirev.2020.103269, 2020.
Zhang, K., Kimball, J. S., Nemani, R. R., and Running, S. W.: A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006, Water Resour. Res., 46, W09522, https://doi.org/10.1029/2009WR008800, 2010.
Zhang, L. M., Luo, Y. W., Liu, M., Chen, Z., Su, W., He, H., Zhu, Z., Sun, X., Wang, Y., Zhou, G., Zhao, X., Han, S., Ouyang, Zhu., Zhang, X., Zhang, Y., Liu, Q., Hao, Y., Yan, J., Zhang, D., Li, Y., Wang, A., Wu, J., Li, F., Zhao, F., Shi, P., Zhang, Y., He, Y., Lin, L., Song, Q., Wang, H.,, Liu, Y., and Yu, G.: Carbon and water fluxes observed by the Chinese Flux Observation and Research Network (2003–2005), Sci. Data., 4, https://doi.org/10.11922/csdata.2018.0028.zh, 2019 (in Chinese).
Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q., and Yang, Y.: Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017, Remote Sens. Environ., 222, 165–182, https://doi.org/10.1016/j.rse.2018.12.031, 2019.
Zhang, Y., Peña-Arancibia, J. L., McVicar, T. R., Chiew, F. H. S., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y., Miralles, D., and Pan, M.: Multi-decadal trends in global terrestrial evapotranspiration and its components, Sci. Rep., 6, 19124, https://doi.org/10.1038/srep19124, 2016.
Zhao, H., Zeng, Y., Lv, S., and Su, Z.: Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau, Earth Syst. Sci. Data, 10, 1031–1061, https://doi.org/10.5194/essd-10-1031-2018, 2018.
Zheng, C., Jia, L., and Hu, G.: Global Land Surface Evapotranspiration Monitoring by ETMonitor Model Driven by Multi-source Satellite Earth Observations, J. Hydrol., 613, 128444, https://doi.org/10.1016/j.jhydrol.2022.128444, 2022.
Zhong, L., Ma, Y., Hu, Z., Fu, Y., Hu, Y., Wang, X., Cheng, M., and Ge, N.: Estimation of hourly land surface heat fluxes over the Tibetan Plateau by the combined use of geostationary and polar-orbiting satellites, Atmos. Chem. Phys., 19, 5529–5541, https://doi.org/10.5194/acp-19-5529-2019, 2019.
Zohaib, M., Kim, H., and Choi, M.: Evaluating the patterns of spatiotemporal trends of root zone soil moisture in major climate regions in East Asia, J. Geophys. Res.-Atmos., 122, 7705–7722, https://doi.org/10.1002/2016JD026379, 2017.
Short summary
Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration...
Special issue
Altmetrics
Final-revised paper
Preprint