Advances in Atmospheric Sciences

, Volume 36, Issue 1, pp 79–92 | Cite as

Subdaily to Seasonal Change of Surface Energy and Water Flux of the Haihe River Basin in China: Noah and Noah-MP Assessment

  • Fuqiang Yang
  • Li DanEmail author
  • Jing Peng
  • Xiujing Yang
  • Yueyue Li
  • Dongdong Gao
Original Paper


The land surface processes of the Noah-MP and Noah models are evaluated over four typical landscapes in the Haihe River Basin (HRB) using in-situ observations. The simulated soil temperature and moisture in the two land surface models (LSMs) is consistent with the observation, especially in the rainy season. The models reproduce the mean values and seasonality of the energy fluxes of the croplands, despite the obvious underestimated total evaporation. Noah shows the lower deep soil temperature. The net radiation is well simulated for the diurnal time scale. The daytime latent heat fluxes are always underestimated, while the sensible heat fluxes are overestimated to some degree. Compared with Noah, Noah-MP has improved daily average soil heat flux with diurnal variations. Generally, Noah-MP performs fairly well for different landscapes of the HRB. The simulated cold bias in soil temperature is possibly linked with the parameterized partition of the energy into surface fluxes. Thus, further improvement of these LSMs remains a major challenge.

Key words

land surface model Haihe River Basin soil temperature soil moisture surface energy flux seasonal cycle 


本文利用自动站观测资料, 选取海河流域的四种典型下垫面对Noah-MP和Noah陆面过程模式的模拟性能进行评估. 结果表明, 两种离线模式能较好模拟土壤温度和湿度, 特别是在雨季. 虽然模式模拟的蒸发量偏低, 但基本能模拟出作物下垫面的各能量分量的平均分布和季节变率. Noah陆面过程模式对深层土壤温度的模拟偏低. 两种陆面过程模式都能较好模拟净辐射的日变化. 白天的潜热辐射模拟偏低, 而感热辐射则有不同程度的模拟偏高. 与传统的Noah陆面过程模式相比, 虽然Noah-MP模式模拟的土壤热通量日变率更大, 但土壤热通量均值也更接近观测值. 总体来说, Noah-MP模式在海河流域各下垫面的模拟性能都有不同程度的提高. 离线模式模拟的土壤温度偏低可能与陆面能量各通量间的参数化有关, 因此, 提高模式在这些方面的参数化能力仍然是当前改善模式性能所面临的主要挑战.


陆面过程模式 海河流域 土壤温度 土壤湿度 地表能量平衡 季节变化 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This study was supported by a project of the National Key Research and Development Program of China (Grant No. 2016YFA0602501), a project of the National Natural Science Foundation of China (Grant Nos. 41630532 and 41575093). The dataset was provided by the Cold and Arid Regions Science Data Center at Lanzhou ( We gratefully acknowledge Prof. Shaomin LIU of Beijing Normal University for providing the Haihe multiscale surface flux and meteorological elements observational experiments.


  1. Blyth, E., J. Gash, A. Lloyd, M. Pryor, G. P. Weedon, and J. Shuttleworth, 2010: Evaluating the JULES land surface model energy fluxes using FLUXNET data. Journal of Hydrometeorology, 11(2), 509–519, Scholar
  2. Cai, X. T., Z.-L. Yang, Y. L. Xia, M. Y. Huang, H. L. Wei, L. R. Leung, and M. B. Ek, 2014a: Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS test bed. J. Geophys. Res., 119(24), 13 751–13 770, Scholar
  3. Cai, X. T., Z.-L. Yang, C. H. David, G.-Y. Niu, and M. Rodell, 2014b: Hydrological evaluation of the Noah-MP land surface model for the Mississippi River Basin. J. Geophys. Res., 119(1), 23–38, Scholar
  4. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn state-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon. Wea. Rev., 129(4), 569–585,<0569:CAALSH>2.0.CO;2.Google Scholar
  5. Chen, F., and Coauthors, 2014: Modeling seasonal snowpack evolution in the complex terrain and forested Colorado Headwaters region: a model intercomparison study. J. Geophys. Res., 119(24), 13 795–13 819, Scholar
  6. Chen, H. S., R. E. Dickinson, Y. J. Dai, and L. M. Zhou, 2011: Sensitivity of simulated terrestrial carbon assimilation and canopy transpiration to different stomatal conductance and carbon assimilation schemes. Climate Dyn., 36(5), 1037–1054, Scholar
  7. Chen, Y. Y., K. Yang, D. G. Zhou, J. Qin, and X. F. Guo, 2010: Improving the Noah land surface model in arid regions with an appropriate parameterization of the thermal roughness length. Journal of Hydrometeorology, 11(4), 995–1006, Scholar
  8. Chen, Y. Y., K. Yang, W. J. Tang, J. Qin, and L. Zhao, 2012: Parameterizing soil organic carbon’s impacts on soil porosity and thermal parameters for Eastern Tibet grasslands. Science China Earth Sciences, 55(6), 1001–1011, Scholar
  9. Dan, L., and J. J. Ji, 2007: The surface energy, water, carbon flux and their intercorrelated seasonality in a global climate-vegetation coupled model. Tellus B: Chemical and Physical Meteorology, 59(3), 425–438, Scholar
  10. Dan, L., F. Q. Cao, and R. Gao, 2015: The improvement of a regional climate model by coupling a land surface model with eco-physiological processes: a case study in 1998. Climatic Change, 129(3–4), 457–470, Scholar
  11. De Gonc¸alves, L. G. G., and Coauthors, 2013: Overview of the large-scale biosphere-atmosphere experiment in Amazonia data model intercomparison project (LBA-DMIP). Agricultural and Forest Meteorology, 182–183, 111–127, Scholar
  12. Dickinson, R. E., 1995: Land-atmosphere interaction. Rev. Geophys., 33(S2), 917–922, Scholar
  13. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale eta model. J. Geophys. Res., 108(D22), 8851, Scholar
  14. Gao, Y. H., K. Li, F. Chen, Y. S. Jiang, and C. G. Lu, 2015: Assessing and improving Noah-MP land model simulations for the central Tibetan Plateau. J. Geophys. Res., 120(18), 9258–9278, Scholar
  15. Gayler, S., and Coauthors, 2014: Incorporating dynamic root growth enhances the performance of Noah-MP at two contrasting winter wheat field sites. Water Resour. Res., 50(2), 1337–1356, Scholar
  16. Gulden, L. E., E. Roseroe, Z. L. Yang, T. Wagener, and G. Y. Niu, 2008: Model performance, model robustness, and model fitness scores: a new method for identifying good land-surface models. Geophys. Res. Lett., 35(11), L11404, Scholar
  17. Jia, Z. Z., S. M. Liu, Z. W. Xu, Y. J. Chen, and M. J. Zhu, 2012: Validation of remotely sensed evapotranspiration over the Hai River Basin, China. J. Geophys. Res., 117(D13), D13113, Scholar
  18. Jin, J., X. Gao, Z.-L. Yang, R. C. Bales, S. Sorooshian, R. E. Dickinson, S. F. Sun, and G. X. Wu, 1999: Comparative analyses of physically based snowmelt models for climate simulations. J. Climate, 12(8), 2643–2657,<2643:CAOPBS>2.0.CO;2.CrossRefGoogle Scholar
  19. Liu, S. M., Z. W. Xu, Z. L. Zhu, Z. Z. Jia, and M. J. Zhu, 2013: Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China. J. Hydrol., 487, 24–38, Scholar
  20. Niu, G.-Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116(D12), D12109, Scholar
  21. Pan, Y., C. Zhang, H. L. Gong, P. J.-F. Yeh, Y. J. Shen, Y. Guo, Z. Y. Huang, and X. J. Li, 2017: Detection of human-induced evapotranspiration using GRACE satellite observations in the Haihe River basin of China. Geophys. Res. Lett., 44(1), 190–199, Scholar
  22. Peng, J., and L. Dan, 2015: Impacts of CO2 concentration and climate change on the terrestrial carbon flux using six global climate-carbon coupled models. Ecological Modelling, 304, 69–83, Scholar
  23. Peters-Lidard, C. D., E. Blackburn, X. Liang, and E. F. Wood, 1998: The effect of soil thermal conductivity parameterization on surface energy fluxes and temperatures. J. Atmos. Sci., 55(7), 1209–1224,<1209:TEOSTC>2.0.CO;2.CrossRefGoogle Scholar
  24. Pilotto, I. L., D. A. Rodr´iguez, J. Tomasella, G. Sampaio, and S. C. Chou, 2015: Comparisons of the Noah-MP land surface model simulations with measurements of forest and crop sites in Amazonia. Meteor. Atmos. Phys., 127(6), 711–723, Scholar
  25. Pitman, A. J., 2003: The evolution of, and revolution in, land surface schemes designed for climate models. International Journal of Climatology, 23(5), 479–510, Scholar
  26. Rosero, E., Z.-L. Yang, T. Wagener, L. E. Gulden, S. Yatheendradas, and G.-Y. Niu, 2010: Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season. J. Geophys. Res., 115(D3), D03106, Scholar
  27. Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Technical Note, NCAR/TN-475+STR, 125 pp.Google Scholar
  28. Twine, T. E., and Coauthors, 2000: Correcting eddy-covariance flux underestimates over a grassland. Agricultural and Forest Meteorology, 103(3), 279–300, Scholar
  29. Yang, K., and J. M. Wang, 2008: A temperature predictioncorrection method for estimating surface soil heat flux from soil temperature and moisture data. Science in China Series D: Earth Sciences, 51(5), 721–729, Scholar
  30. Yang, K., T. Koike, B. S. Ye, and L. Bastidas, 2005: Inverse analysis of the role of soil vertical heterogeneity in controlling surface soil state and energy partition. J. Geophys. Res., 110(D8), D08101, Scholar
  31. Yang, Z.-L., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins. J. Geophys. Res., 116(D12), D12110, Scholar
  32. Zhang, G., F. Chen, and Y. J. Gan, 2016: Assessing uncertainties in the Noah-MP ensemble simulations of a cropland site during the Tibet Joint International Cooperation program field campaign. J. Geophys. Res., 121(16), 9576–9596, Scholar
  33. Zheng, D. H., R. Van Der Velde, Z. B. Su, J. Wen, M. J. Booij, A. Y. Hoekstra, and X. Wang, 2015: Under-canopy turbulence and root water uptake of a Tibetan meadow ecosystem modeled by Noah-MP. Water Resour. Res., 51(7), 5735–5755, Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Fuqiang Yang
    • 1
    • 2
  • Li Dan
    • 1
    Email author
  • Jing Peng
    • 1
  • Xiujing Yang
    • 1
    • 2
  • Yueyue Li
    • 2
    • 3
  • Dongdong Gao
    • 4
  1. 1.Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  4. 4.School of Atmospheric SciencesChengdu University of Information TechnologyChengduChina

Personalised recommendations