Editorial Type:
Article
Article Type:
Research Article

A Long-Term Simulation of Land Surface Conditions at High Resolution over Continental China

Peng Ji aKey Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
bSchool of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing, China

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Xing Yuan aKey Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
bSchool of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing, China

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https://orcid.org/0000-0001-6983-7368
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Chunxiang Shi cNational Meteorological Information Center, China Meteorological Administration, Beijing, China

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Lipeng Jiang dCMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing, China

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Guoqing Wang eState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, China

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Kun Yang fMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China

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Online Publication:
16 Feb 2023
Print Publication:
01 Feb 2023
DOI:
https://doi.org/10.1175/JHM-D-22-0135.1
Page(s):
285–314
Restricted access

Abstract

With the improvement of meteorological forcings and surface parameters, high-resolution land surface modeling is expected to provide locally relevant information. Yet, its added value over the state-of-the-art global reanalysis products requires long-term evaluations over large areas, given uneven climate warming and significant land cover change. Here, the Conjunctive Surface–Subsurface Process version 2 (CSSPv2) model, with a reasonable representation of runoff generation, subgrid soil moisture variability and urban dynamics, is calibrated and used to perform a 6-km resolution simulation over China during 1979–2017. Evaluations against observations at thousands of stations and several satellite-based products show that the CSSPv2 has 67%, 29%, and 15% lower simulation errors for snow depth, evapotranspiration (ET), and surface and root-zone soil moisture, respectively, than nine global products. The median Kling–Gupta efficiency of the streamflow for 83 river basins is 0.66 after bulk calibrations, which is 0.38 higher than that of global datasets. The CSSPv2 also accurately simulates urban heat islands (UHIs) and the patterns and magnitudes of long-term snow depth, ET, and soil moisture trends. However, the global products do not detect UHIs and overestimate the trends (or show opposite trends) of snow depth and ET. Sensitivity experiments with coarse-resolution forcings and surface parameters reveal that advanced model physics and high-resolution surface parameters are vital for improved simulations of snow depth, ET, soil moisture, and UHIs, whereas high-resolution meteorological forcings are critical for modeling long-term trends. Our research emphasizes the substantial added value of long-term high-resolution land surface modeling to present global products at continental scales.

Significance Statement

Highly heterogeneous changes of terrestrial water and energy require kilometer-scale land surface information for the adaptation. High-resolution land surface modeling has been regarded as a promising approach to provide locally relevant information, but most applications are limited to a small region or a short period. By performing sets of 6-km resolution simulations over China during 1979–2017 with the Conjunctive Surface–Subsurface Process version 2 land model, here we show that high-resolution modeling has 15%–67% lower simulation errors of snow depth, streamflow, evapotranspiration, and soil moisture than nine global products, and the improvement is mainly attributed to the advances in model physical parameterizations and high-resolution surface parameters. Our results emphasize the great added value of kilometer-scale land surface modeling at continental scales.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xing Yuan, xyuan@nuist.edu.cn

Abstract

With the improvement of meteorological forcings and surface parameters, high-resolution land surface modeling is expected to provide locally relevant information. Yet, its added value over the state-of-the-art global reanalysis products requires long-term evaluations over large areas, given uneven climate warming and significant land cover change. Here, the Conjunctive Surface–Subsurface Process version 2 (CSSPv2) model, with a reasonable representation of runoff generation, subgrid soil moisture variability and urban dynamics, is calibrated and used to perform a 6-km resolution simulation over China during 1979–2017. Evaluations against observations at thousands of stations and several satellite-based products show that the CSSPv2 has 67%, 29%, and 15% lower simulation errors for snow depth, evapotranspiration (ET), and surface and root-zone soil moisture, respectively, than nine global products. The median Kling–Gupta efficiency of the streamflow for 83 river basins is 0.66 after bulk calibrations, which is 0.38 higher than that of global datasets. The CSSPv2 also accurately simulates urban heat islands (UHIs) and the patterns and magnitudes of long-term snow depth, ET, and soil moisture trends. However, the global products do not detect UHIs and overestimate the trends (or show opposite trends) of snow depth and ET. Sensitivity experiments with coarse-resolution forcings and surface parameters reveal that advanced model physics and high-resolution surface parameters are vital for improved simulations of snow depth, ET, soil moisture, and UHIs, whereas high-resolution meteorological forcings are critical for modeling long-term trends. Our research emphasizes the substantial added value of long-term high-resolution land surface modeling to present global products at continental scales.

Significance Statement

Highly heterogeneous changes of terrestrial water and energy require kilometer-scale land surface information for the adaptation. High-resolution land surface modeling has been regarded as a promising approach to provide locally relevant information, but most applications are limited to a small region or a short period. By performing sets of 6-km resolution simulations over China during 1979–2017 with the Conjunctive Surface–Subsurface Process version 2 land model, here we show that high-resolution modeling has 15%–67% lower simulation errors of snow depth, streamflow, evapotranspiration, and soil moisture than nine global products, and the improvement is mainly attributed to the advances in model physical parameterizations and high-resolution surface parameters. Our results emphasize the great added value of kilometer-scale land surface modeling at continental scales.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xing Yuan, xyuan@nuist.edu.cn

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  • Abatzoglou, J. T., S. Z. Dobrowski, S. A. Parks, and K. C. Hegewisch, 2018: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data, 5, 170191, https://doi.org/10.1038/sdata.2017.191.

    • Search Google Scholar
    • Export Citation

  • Alavi, N., S. Belair, V. Fortin, S. L. Zhang, S. Z. Husain, M. L. Carrera, and M. Abrahamowicz, 2016: Warm season evaluation of soil moisture prediction in the Soil, Vegetation, and Snow (SVS) scheme. J. Hydrometeor., 17, 2315–2332, https://doi.org/10.1175/JHM-D-15-0189.1.

    • Search Google Scholar
    • Export Citation

  • Anderson, S., and V. Radić, 2020: Identification of local water resource vulnerability to rapid deglaciation in Alberta. Nat. Climate Change, 10, 933–938, https://doi.org/10.1038/s41558-020-0863-4.

    • Search Google Scholar
    • Export Citation

  • Bastos, A., P. Ciais, P. Friedlingstein, S. Sitch, and S. Zaehle, 2020: Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv., 6, eaba2724, https://doi.org/10.1126/sciadv.aba2724.

    • Search Google Scholar
    • Export Citation

  • Bellucci, A., and Coauthors, 2015: Advancements in decadal climate predictability: The role of nonoceanic drivers. Rev. Geophys., 53, 165–202, https://doi.org/10.1002/2014RG000473.

    • Search Google Scholar
    • Export Citation

  • Bierkens, M. F. P., and Coauthors, 2015: Hyper-resolution global hydrological modelling: What is next? “Everywhere and locally relevant.” Hydrol. Processes, 29, 310–320, https://doi.org/10.1002/hyp.10391.

    • Search Google Scholar
    • Export Citation

  • Bonan, G. B., P. J. Lawrence, K. W. Oleson, S. Levis, M. Jung, M. Reichstein, D. M. Lawrence, and S. C. Swenson, 2011: Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. J. Geophys. Res., 116, G02014, https://doi.org/10.1029/2010JG001593.

    • Search Google Scholar
    • Export Citation

  • Chaney, N. W., P. Metcalfe, and E. F. Wood, 2016: HydroBlocks: A field-scale resolving land surface model for application over continental extents. Hydrol. Processes, 30, 3543–3559, https://doi.org/10.1002/hyp.10891.

    • Search Google Scholar
    • Export Citation

  • Chen, F., and Coauthors, 2011: The integrated WRF/urban modelling system: Development, evaluation, and applications to urban environmental problems. Int. J. Climatol., 31, 273–288, https://doi.org/10.1002/joc.2158.

    • Search Google Scholar
    • Export Citation

  • Cheng, Y., M. Huang, B. Zhu, G. Bisht, T. Zhou, Y. Liu, F. Song, and X. He, 2021: Validation of the Community Land Model version 5 over the contiguous United States (CONUS) using in situ and remote sensing data sets. J. Geophys. Res. Atmos., 126, e2020JD033539, https://doi.org/10.1029/2020JD033539.

    • Search Google Scholar
    • Export Citation

  • Choi, H. I., and X. Z. Liang, 2010: Improved terrestrial hydrologic representation in mesoscale land surface models. J. Hydrometeor., 11, 797–809, https://doi.org/10.1175/2010JHM1221.1.

    • Search Google Scholar
    • Export Citation

  • Choi, H. I., P. Kumar, and X. Z. Liang, 2007: Three-dimensional volume-averaged soil moisture transport model with a scalable parameterization of subgrid topographic variability. Water Resour. Res., 43, W04414, https://doi.org/10.1029/2006WR005134.

    • Search Google Scholar
    • Export Citation

  • Clark, M. P., and Coauthors, 2015: Improving the representation of hydrologic processes in Earth system models. Water Resour. Res., 51, 5929–5956, https://doi.org/10.1002/2015WR017096.

    • Search Google Scholar
    • Export Citation

  • Dai, Y. J., and Coauthors, 2003: The Common Land Model. Bull. Amer. Meteor. Soc., 84, 1013–1024, https://doi.org/10.1175/BAMS-84-8-1013.

    • Search Google Scholar
    • Export Citation

  • Dai, Y. J., R. E. Dickinson, and Y. P. Wang, 2004: A two-big-leaf model for canopy temperature, photosynthesis, and stomatal conductance. J. Climate, 17, 2281–2299, https://doi.org/10.1175/1520-0442(2004)017<2281:ATMFCT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dantec-Nédélec, S., and Coauthors, 2017: Testing the capability of ORCHIDEE land surface model to simulate Arctic ecosystems: Sensitivity analysis and site-level model calibration. J. Adv. Model. Earth Syst., 9, 1212–1230, https://doi.org/10.1002/2016MS000860.

    • Search Google Scholar
    • Export Citation

  • Deng, M., Meng, X., Lyv, Y., Zhao, L., Li, Z., Hu, Z., and Jing, H., 2020: Comparison of soil water and heat transfer modeling over the Tibetan Plateau using two Community Land Surface Model (CLM) versions. J. Adv. Model. Earth Syst., 12, e2020MS002189, https://doi.org/10.1029/2020MS002189.

    • Search Google Scholar
    • Export Citation

  • Dorigo, W., and Coauthors, 2017: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001.

    • Search Google Scholar
    • Export Citation

  • Duan, Q. Y., S. Sorooshian, and V. K. Gupta, 1994: Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol., 158, 265–284, https://doi.org/10.1016/0022-1694(94)90057-4.

    • Search Google Scholar
    • Export Citation

  • Fisher, R. A., and Koven, C. D., 2020: Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems. J. Adv. Model. Earth Syst., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453.

    • Search Google Scholar
    • Export Citation

  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Search Google Scholar
    • Export Citation

  • Gong, P., X. Li, and W. Zhang, 2019: 40-year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Sci. Bull., 64, 756–763, https://doi.org/10.1016/j.scib.2019.04.024.

    • Search Google Scholar
    • Export Citation

  • Gruber, A., T. Scanlon, R. van der Schalie, W. Wagner, and W. Dorigo, 2019: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology. Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019.

    • Search Google Scholar
    • Export Citation

  • Gudmundsson, L., J. Boulange, X. D. Hong, S. N. Gosling, and Z. Fang, 2021: Globally observed trends in mean and extreme river flow attributed to climate change. Science, 371, 1159–1162, https://doi.org/10.1126/science.aba3996.

    • Search Google Scholar
    • Export Citation

  • Guo, J., and X. L. Su, 2019: Parameter sensitivity analysis of SWAT model for streamflow simulation with multisource precipitation datasets. Hydrol. Res., 50, 861–877, https://doi.org/10.2166/nh.2019.083.

    • Search Google Scholar
    • Export Citation

  • Gupta, H. V., H. Kling, K. K. Yilmaz, and G. F. Martinez, 2009: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003.

    • Search Google Scholar
    • Export Citation

  • He, J., K. Yang, W. J. Tang, H. Lu, J. Qin, Y. Y. Chen, and X. Li, 2020: 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.

    • Search Google Scholar
    • Export Citation

  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation

  • Hulley, G., N. Malakar, and R. Freepartner, 2016: Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity Product (MxD21) Algorithm Theoretical Basis Document Collection-6. Jet Propulsion Laboratory California Institute of Technology Pasadena, 99 pp.

  • Ji, P., and X. Yuan, 2018: High-resolution land surface modeling of hydrological changes over the Sanjiangyuan region in the eastern Tibetan Plateau: 2. Impact of climate and land cover change. J. Adv. Model. Earth Syst., 10, 2829–2843, https://doi.org/10.1029/2018MS001413.

    • Search Google Scholar
    • Export Citation

  • Ji, P., X. Yuan, and X. Z. Liang, 2017: Do lateral flows matter for the hyperresolution land surface modeling? J. Geophys. Res. Atmos., 122, 12 077–12 092, https://doi.org/10.1002/2017JD027366.

    • Search Google Scholar
    • Export Citation

  • Ji, P., X. Yuan, and D. Li, 2020a: Atmospheric radiative processes accelerate ground surface warming over the southeastern Tibetan Plateau during 1998–2013. J. Climate, 33, 1881–1895, https://doi.org/10.1175/JCLI-D-19-0410.1.

    • Search Google Scholar
    • Export Citation

  • Ji, P., X. Yuan, Y. Jiao, C. Wang, S. Han, and C. Shi, 2020b: Anthropogenic contributions to the 2018 extreme flooding over upper Yellow River basin in China [in “Explaining Extreme Events of 2018 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 101 (1), S89–S94, https://doi.org/10.1175/BAMS-D-19-0105.1.

    • Search Google Scholar
    • Export Citation

  • Ji, P., X. Yuan, F. Ma, and M. Pan, 2020c: Accelerated hydrological cycle over the Sanjiangyuan region induces more streamflow extremes at different global warming levels. Hydrol. Earth Syst. Sci., 24, 5439–5451, https://doi.org/10.5194/hess-24-5439-2020.

    • Search Google Scholar
    • Export Citation

  • Ji, P., Yuan, X., Liang, X. Z., Jiao, Y., Zhou, Y., and Liu, Z., 2021: High‐resolution land surface modeling of the effect of long‐term urbanization on hydrothermal changes over Beijing metropolitan area. J. Geophys. Res. Atmos., 126, e2021JD034787, https://doi.org/10.1029/2021JD034787.

    • Search Google Scholar
    • Export Citation

  • Jia, B. H., J. G. Liu, Z. H. Xie, and C. X. Shi, 2018: Interannual variations and trends in remotely sensed and modeled soil moisture in China. J. Hydrometeor., 19, 831–847, https://doi.org/10.1175/JHM-D-18-0003.1.

    • Search Google Scholar
    • Export Citation

  • Jiang, L. M., P. Wang, L. X. Zhang, H. Yang, and J. T. Yang, 2014: Improvement of snow depth retrieval for FY3B-MWRI in China. Sci. China Earth Sci., 57, 1278–1292, https://doi.org/10.1007/s11430-013-4798-8.

    • Search Google Scholar
    • Export Citation

  • Jung, M., and Coauthors, 2019: The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8.

    • Search Google Scholar
    • Export Citation

  • Kollet, S. J., and R. M. Maxwell, 2008: Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resour. Res., 44, W02402, https://doi.org/10.1029/2007WR006004.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140, https://doi.org/10.1126/science.1100217.

    • Search Google Scholar
    • Export Citation

  • Lawrence, D., and Coauthors, 2019: The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst., 11, 4245–4287, https://doi.org/10.1029/2018MS001583.

    • Search Google Scholar
    • Export Citation

  • Lehner, B., K. Verdin, and A. Jarvis, 2008: New global hydrography derived from spaceborne elevation data. Eos, Trans. Amer. Geophys. Union, 89, 93–94, https://doi.org/10.1029/2008EO100001.

    • Search Google Scholar
    • Export Citation

  • Li, B. L., and Coauthors, 2019: Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges. Water Resour. Res., 55, 7564–7586, https://doi.org/10.1029/2018WR024618.

    • Search Google Scholar
    • Export Citation

  • Li, D., S. Malyshev, and E. Shevliakova, 2016: Exploring historical and future urban climate in the Earth system modeling framework: 1. Model development and evaluation. J. Adv. Model. Earth Syst., 8, 917–935, https://doi.org/10.1002/2015MS000578.

    • Search Google Scholar
    • Export Citation

  • Li, H., J. Sheffield, and E. F. Wood, 2010: Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J. Geophys. Res., 115, D10101, https://doi.org/10.1029/2009JD012882.

    • Search Google Scholar
    • Export Citation

  • Li, H.-Y., M. Huang, M. S. Wigmosta, Y. Ke, A. M. Coleman, L. R. Leung, A. Wang, and D. M. Ricciuto, 2011: Evaluating runoff simulations from the Community Land Model 4.0 using observations from flux towers and a mountainous watershed. J. Geophys. Res., 116, D24120, https://doi.org/10.1029/2011JD016276.

    • Search Google Scholar
    • Export Citation

  • Li, Q., T. Yang, and L. Li, 2022: Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation. Int. J. Climatol., 42, 4223–4239, https://doi.org/10.1002/joc.7459.

    • Search Google Scholar
    • Export Citation

  • Liang, M., and X. Yuan, 2021: Critical role of soil moisture memory in predicting the 2012 central United States flash drought. Front. Earth Sci., 9, 615969, https://doi.org/10.3389/feart.2021.615969.

    • Search Google Scholar
    • Export Citation

  • Liang, S., and Coauthors, 2021: The Global Land Surface Satellite (GLASS) products suite. Bull. Amer. Meteor. Soc., 102, E323–E337, https://doi.org/10.1175/BAMS-D-18-0341.1.

    • Search Google Scholar
    • Export Citation

  • Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99, 14 415–14 428, https://doi.org/10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation

  • Liang, X. Z., and Coauthors, 2012: Regional climate-weather research and forecasting model. Bull. Amer. Meteor. Soc., 93, 1363–1387, https://doi.org/10.1175/BAMS-D-11-00180.1.

    • Search Google Scholar
    • Export Citation

  • Liu, J., X. Yuan, J. Zeng, Y. Jiao, Y. Li, L. Zhong, and L. Yao, 2022: Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning. Hydrol. Earth Syst. Sci., 26, 265–278, https://doi.org/10.5194/hess-26-265-2022.

    • Search Google Scholar
    • Export Citation

  • Liu, L., Y. M. Ma, M. Menenti, X. Z. Zhang, and W. Q. Ma, 2019: Evaluation of WRF modeling in relation to different land surface schemes and initial and boundary conditions: A snow event simulation over the Tibetan Plateau. J. Geophys. Res. Atmos., 124, 209–226, https://doi.org/10.1029/2018JD029208.

    • Search Google Scholar
    • Export Citation

  • Liu, L., M. Menenti, Y. M. Ma, and W. Q. Ma, 2022: Improved parameterization of snow albedo in WRF + Noah: Methodology based on a severe snow event on the Tibetan Plateau. Adv. Atmos. Sci., 39, 1079–1102, https://doi.org/10.1007/s00376-022-1232-1.

    • Search Google Scholar
    • Export Citation

  • Ma, N., and Y. Zhang, 2022: Increasing Tibetan Plateau terrestrial evapotranspiration primarily driven by precipitation. Agric. For. Meteor., 317, 108887, https://doi.org/10.1016/j.agrformet.2022.108887.

    • Search Google Scholar
    • Export Citation

  • Ma, X., J. Jin, L. Zhu, and J. Liu, 2021: Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau. PeerJ, 9, e11040, https://doi.org/10.7717/peerj.11040.

    • Search Google Scholar
    • Export Citation

  • Ma, Y., and Coauthors, 2020: 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.

    • Search Google Scholar
    • Export Citation

  • Malik, M. J., R. van der Velde, Z. Vekerdy, and Z. B. Su, 2014: Improving modeled snow albedo estimates during the spring melt season. J. Geophys. Res. Atmos., 119, 7311–7331, https://doi.org/10.1002/2013JD021344.

    • Search Google Scholar
    • Export Citation

  • Martens, B., and Coauthors, 2017: GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017.

    • Search Google Scholar
    • Export Citation

  • Maxwell, R. M., and L. E. Condon, 2016: Connection between groundwater flow and transpiration partitioning. Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891.

    • Search Google Scholar
    • Export Citation

  • McNally, A., and Coauthors, 2017: A land data assimilation system for sub-Saharan Africa food and water security applications. Sci. Data, 4, 170012, https://doi.org/10.1038/sdata.2017.12.

    • Search Google Scholar
    • Export Citation

  • McNorton, J. R., and Coauthors, 2021: An urban scheme for the ECMWF integrated forecasting system: Single-column and global offline application. J. Adv. Model. Earth Syst., 13, e2020MS002375, https://doi.org/10.1029/2020MS002375.

    • Search Google Scholar
    • Export Citation

  • Meng, S., X. Xie, B. Zhu, and Y. Wang, 2020: The relative contribution of vegetation greening to the hydrological cycle in the Three-North region of China: A modelling analysis. J. Hydrol., 591, 125689, https://doi.org/10.1016/j.jhydrol.2020.125689.

    • Search Google Scholar
    • Export Citation

  • Miralles, D. G., T. R. H. Holmes, R. A. M. de Jeu, J. H. Gash, A. G. C. A. Meesters, and A. J. Dolman, 2011: 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.

    • Search Google Scholar
    • Export Citation

  • Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, 2007: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153.

    • Search Google Scholar
    • Export Citation

  • Muñoz-Sabater, J., and Coauthors, 2021: 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.

    • Search Google Scholar
    • Export Citation

  • Naz, B. S., S. Kollet, H. H. Franssen, C. Montzka, and W. Kurtz, 2020: A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015. Sci. Data, 7, 111, https://doi.org/10.1038/s41597-020-0450-6.

    • Search Google Scholar
    • Export Citation

  • Nijssen, B., G. M. O’Donnell, D. P. Lettenmaier, D. Lohmann, and E. F. Wood, 2001: Predicting the discharge of global river. J. Climate, 14, 3307–3323, https://doi.org/10.1175/1520-0442(2001)014<3307:PTDOGR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Niu, G.-Y., C. Paniconi, P. A. Troch, R. L. Scott, M. Durcik, X. Zeng, T. Huxman, and D. C. Goodrich, 2014: An integrated modelling framework of catchment-scale ecohydrological processes: 1. Model description and tests over an energy-limited watershed. Ecohydrology, 7, 427–439, https://doi.org/10.1002/eco.1362.

    • Search Google Scholar
    • Export Citation

  • Niu, Z., H. He, G. Zhu, X. Ren, L. Zhang, and K. Zhang, 2020: A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015. Sci. Data, 7, 369, https://doi.org/10.1038/s41597-020-00693-x.

    • Search Google Scholar
    • Export Citation

  • Oleson, K. W., G. B. Bonan, J. Feddema, M. Vertenstein, and C. S. B. Grimmond, 2008: An urban parameterization for a global climate model. Part I: Formulation and evaluation for two cities. J. Appl. Meteor. Climatol., 47, 1038–1060, https://doi.org/10.1175/2007JAMC1597.1.

    • Search Google Scholar
    • Export Citation

  • Oleson, K. W., and Coauthors, 2013: Technical description of version 4.5 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-503+STR, 420 pp., https://doi.org/10.5065/D6RR1W7M.

  • Orsolini, Y., and Coauthors, 2019: Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations. Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019.

    • Search Google Scholar
    • Export Citation

  • Pan, M., A. K. Sahoo, and E. F. Wood, 2014: Improving soil moisture retrievals from a physically-based radiative transfer model. Remote Sens. Environ., 140, 130–140, https://doi.org/10.1016/j.rse.2013.08.020.

    • Search Google Scholar
    • Export Citation

  • Peng, S., S. Piao, P. Ciais, J. Fang, and X. Wang, 2010: Change in winter snow depth and its impacts on vegetation in China. Global Change Biol., 16, 3004–3013, https://doi.org/10.1111/j.1365-2486.2010.02210.x.

    • Search Google Scholar
    • Export Citation

  • Qian, T., A. Dai, K. E. Trenberth, and K. W. Oleson, 2006: Simulation of global land surface conditions from 1948 to 2004: Part I: Forcing data and evaluations. J. Hydrometeor., 7, 953–975, https://doi.org/10.1175/JHM540.1.

    • Search Google Scholar
    • Export Citation

  • Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381.