Journal of Meteorological Research

, Volume 31, Issue 6, pp 1167–1182 | Cite as

Analysis of parameter sensitivity on surface heat exchange in the Noah land surface model at a temperate desert steppe site in China

Regular Articles

Abstract

The dominant parameters in the Noah land surface model (LSM) are identified, and the effects of parameter optimization on the surface heat exchange are investigated at a temperate desert steppe site during growing season in Inner Mongolia, China. The relative impacts of parameters on surface heat flux are examined by the distributed evaluation of local sensitivity analysis (DELSA), and the Noah LSM is calibrated by the global shuffled complex evolution (SCE) against the corresponding observations during May–September of 2008 and 2009. The differences in flux simulations are assessed between the Noah LSM calibrated by the SCE with 27 parameters and 12 dominant parameters. The systematic error, unsystematic error, root mean squared error, and mean squared error decompositions are used to evaluate the model performance. Compared to the control experiment, parameter optimization by the SCE using net radiation, sensible heat flux, latent heat flux, and ground heat flux as the objective criterion, respectively, can obviously reduce the errors of the Noah LSM. The calibrated Noah LSM is further validated against flux observations of growing season in 2010, and it is found that the calibrated Noah LSM can be applied in the longer term at this site. The Noah LSM with 12 dominant parameters calibrated performs similar to that with 27 parameters calibrated.

Keywords

parameter optimization sensitivity analysis temperate desert steppe 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abramowitz, G., R. Leuning, M. Clark, et al., 2008: Evaluating the performance of land surface models. J. Climate, 21, 5468–5481, doi: 10.1175/2008JCLI2378.1.CrossRefGoogle Scholar
  2. Alfieri, J. G., D. Niyogi, P. D. Blanken, et al., 2008: Estimation of the minimum canopy resistance for croplands and grasslands using data from the 2002 International H2O Project. Mon. Wea. Rev., 136, 4452–4469, doi: 10.1175/2008MWR2524.1.CrossRefGoogle Scholar
  3. Cai, X. T., Z. L. Yang, Y. L. Xia, et al., 2014: Assessment of simulated water balance from Noah, Noah‐MP, CLM, and VIC over CONUS using the NLDAS test bed. J. Geophys. Res., 119, 13751–13770, doi: 10.1002/2014JD022113.Google Scholar
  4. Chen, F., and Y. Zhang, 2009: On the coupling strength between the land surface and the atmosphere: From viewpoint of sur-face exchange coefficients. Geophys. Res. Lett., 36, L10404, doi: 10.1029/2009GL037980.CrossRefGoogle Scholar
  5. Chen, F., K. Mitchell, J. Schaake, et al., 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251–7268, doi: 10.1029/95JD02165.CrossRefGoogle Scholar
  6. Chen, F., Z. Janjic, and K. Mitchell, 1997: Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Bound.-Layer Meteor., 85, 391–421, doi: 10.1023/A:1000531001463.CrossRefGoogle Scholar
  7. Chen, Y. Y., K. Yang, D. G. Zhou, et al., 2010: Improving the Noah land surface model in arid regions with an appropriate parameterization of the thermal roughness length. J. Hydrometeor., 11, 995–1006, doi: 10.1175/2010JHM1185.1.CrossRefGoogle Scholar
  8. Duan, Q. Y., V. K. Gupta, and S. Sorooshian, 1993: Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl., 76, 501–521, doi: 10.1007/BF00939380.CrossRefGoogle Scholar
  9. Ek, M. B., K. E. Mitchell, Y. Lin, et al., 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational Mesoscale Eta Model. J. Geophys. Res., 108, 8851, doi: 10.1029/2002JD003296.CrossRefGoogle Scholar
  10. Guan, X. D., J. P. Huang, and R. X. Guo, 2017: Changes in aridity in response to the global warming hiatus. J. Meteor. Res., 31, 117–125, doi: 10.1007/s13351-017-6038-1.CrossRefGoogle Scholar
  11. Gupta, H. V., H. Kling, K. K. Yilmaz, et al., 2009: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol., 377, 80–91, doi: 10.1016/j.jhydrol.2009.08.003.CrossRefGoogle Scholar
  12. Herman, J. D., P. M. Reed, and T. Wagener, 2013: Time-varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior. Water Resour. Res., 49, 1400–1414, doi: 10.1002/wrcr.20124.CrossRefGoogle Scholar
  13. Hornberger, M. G., and C. R. Spear, 1981: Approach to the preliminary analysis of environmental systems. J. Environ. Manage., 12, 7–18.Google Scholar
  14. Hu, Y. Q., Y. J. Qi, and X. L. Yang, 1990: Preliminary analyses about characteristics of microclimate and heat energy budget in HEXI Gobi (Huayin). Plateau Meteor., 9, 113–119. (in Chinese)Google Scholar
  15. Li, J., Q. Y. Duan, W. Gong, et al., 2013: Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis. Hydrol. Earth Syst. Sci., 17, 3279–3293, doi: 10.5194/hess-17-3279-2013.CrossRefGoogle Scholar
  16. Li, J. D., Y. P. Wang, Q. Y. Duan, et al., 2016: Quantification and attribution of errors in the simulated annual gross primary production and latent heat fluxes by two global land surface models. J. Adv. Model. Earth Syst., 8, 1270–1288, doi: 10.1002/2015MS000583.CrossRefGoogle Scholar
  17. Ma, Y. M., O. Tsukamoto, J. M. Wang, et al., 2002: Analysis of aerodynamic and thermodynamic parameters on the grassy marshland surface of Tibetan Plateau. Prog. Nat. Sci., 12, 36–40.Google Scholar
  18. Marcé, R., C. E. Ruiz, and J. Armengol, 2008: Using spatially distributed parameters and multi-response objective functions to solve parameterization of complex applications of semi-distributed hydrological models. Water Resour. Res., 44, W02436, doi: 10.1029/2006WR005785.CrossRefGoogle Scholar
  19. Morris, M. D., 1991: Factorial sampling plans for preliminary computational experiments. Technometrics, 33, 161–174, doi: 10.2307/1269043.CrossRefGoogle Scholar
  20. Oliphant, A. J., C. S. B. Grimmond, H. N. Zutter, et al., 2004: Heat storage and energy balance fluxes for a temperate deciduous forest. Agric. For. Meteor., 126, 185–201, doi: 10.1016/j.agrformet.2004.07.003.CrossRefGoogle Scholar
  21. Rakovec, O., M. C. Hill, M. P. Clark, et al., 2014: Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models. Water Resour. Res., 50, 409–426, doi: 10.1002/2013WR014063.CrossRefGoogle Scholar
  22. Rode, M., U. Suhr, and G. Wriedt, 2007: Multi-objective calibration of a river water quality model-information content of calibration data. Ecological Modelling, 204, 129–142, doi: 10.1016/j.ecolmodel.2006.12.037.CrossRefGoogle Scholar
  23. Rosero, E., Z. L. Yang, T. Wagener, et al., 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, D03106, doi: 10.1029/2009JD012035.CrossRefGoogle Scholar
  24. Rosero, E., L. E. Gulden, Z. L. Yang, et al., 2011: Ensemble evaluation of hydrologically enhanced Noah-LSM: Partitioning of the water balance in high-resolution simulations over the Little Washita River experimental watershed. J. Hydrometeor., 12, 45–64, doi: 10.1175/2010JHM1228.1.CrossRefGoogle Scholar
  25. Rosolem, R., H. V. Gupta, W. J. Shuttleworth, et al., 2012: Towards a comprehensive approach to parameter estimation in land surface parameterization schemes. Hydrol. Processes, 27, 2075–2097, doi: 10.1002/hyp.9362.CrossRefGoogle Scholar
  26. Saltelli, A., 2002: Making best use of model evaluations to compute sensitivity indices. Comput. Phys. Commun., 145, 280–297, doi: 10.1016/S0010-4655(02)00280-1.CrossRefGoogle Scholar
  27. Sen, O. L., L. A. Bastidas, W. J. Shuttleworth, et al., 2001: Impact of field-calibrated vegetation parameters on GCM climate simulations. Quart. J. Roy. Meteor. Soc., 127, 1199–1223, doi: 10.1002/qj.49712757404.CrossRefGoogle Scholar
  28. Shangguan, W., Y. J. Dai, B. Y. Liu, et al., 2013: A China data set of soil properties for land surface modeling. J. Adv. Model. Earth Syst., 5, 212–224, doi: 10.1002/jame.20026.CrossRefGoogle Scholar
  29. Sobol, I. M., 1993: Sensitivity analysis for nonlinear mathematical models. Math. Mod. Comput. Exp., 1, 407–414.Google Scholar
  30. Tang, Y., P. Reed, T. Wagener, et al., 2007: Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation. Hydrol. Earth Syst. Sci., 3, 793–817, doi: 10.5194/hess-11-793-2007.CrossRefGoogle Scholar
  31. Trier, S. B., M. A. LeMone, F. Chen, et al., 2011: Effects of surface heat and moisture exchange on ARW-WRF warm-season precipitation forecasts over the central United States. Wea. Forecasting, 26, 3–25, doi: 10.1175/2010WAF2222426.1.CrossRefGoogle Scholar
  32. Twine, T. E., W. P. Kustas, J. M. Norman, et al., 2000: Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteor., 103, 279–300, doi: 10.1016/S0168-1923(00)00123-4.CrossRefGoogle Scholar
  33. van Werkhoven, K., T. Wagener, P. Reed, et al., 2008: Characterization of watershed model behavior across a hydroclimatic gradient. Water Resour. Res., 44, W01429, doi: 10.1029/2007WR006271.Google Scholar
  34. Verhoef, A., B. J. J. M. van den Hurk, A. F. G. Jacobs, et al., 1996: Thermal soil properties for vineyard (EFEDA-I) and savanna (HAPEX-Sahel) sites. Agric. For. Meteor., 78, 1–18, doi: 10.1016/0168-1923(95)02254-6.CrossRefGoogle Scholar
  35. Wang, G. S., J. Xia, and J. Chen, 2009: Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: A case study of the Chaobai River basin in northern China. Water Resour. Res., 45, W00A11, doi: 10.1029/2007WR006768.Google Scholar
  36. Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapour transfer. Quart. J. Roy. Meteor. Soc., 106, 85–100, doi: 10.1002/qj.49710644707.CrossRefGoogle Scholar
  37. Wilczak, J. M., S. P. Oncley, and S. A. Stage, 2001: Sonic anemometer tilt correction algorithms. Bound.-Layer Meteor., 99, 127–150, doi: 10.1023/A:1018966204465.CrossRefGoogle Scholar
  38. Willmott, C. J., 1981: On the validation of models. Phys. Geogr., 2, 184–194.Google Scholar
  39. Wilson, K., A. Goldstein, E. Falge, et al., 2002: Energy balance closure at FLUXNET sites. Agric. For. Meteor., 113, 223–243, doi: 10.1016/S0168-1923(02)00109-0.CrossRefGoogle Scholar
  40. Yang, F. L., and G. S. Zhou, 2011: Characteristics and modeling of evapotranspiration over a temperate desert steppe in Inner Mongolia, China. J. Hydrol., 396, 139–147, doi: 10.1016/j.jhydrol.2010.11.001.CrossRefGoogle Scholar
  41. Yang, K., T. Koike, H. Ishikawa, et al., 2008: Turbulent flux transfer over bare-soil surfaces: Characteristics and parameterization. J. Appl. Meteor. Climatol., 47, 276–290, doi: 10.1175/2007JAMC1547.1.CrossRefGoogle Scholar
  42. Yin, J. F., X. W. Zhan, Y. F. Zheng, et al., 2016: Improving Noah land surface model performance using near real time surface albedo and green vegetation fraction. Agric. For. Meteor., 218–219, 171–183, doi: 10.1016/j.agrformet.2015.12.001.Google Scholar
  43. Zeng, X. B., Z. Wang, and A. H. Wang, 2012: Surface skin temperature and the interplay between sensible and ground heat fluxes over arid regions. J. Hydrometeor., 13, 1359–1370, doi: 10.1175/JHM-D-11-0117.1.CrossRefGoogle Scholar
  44. Zhang, G., G. S. Zhou, F. Chen, et al., 2014a: A trial to improve surface heat exchange simulation through sensitivity experiments over a desert steppe site. J. Hydrometeor., 15, 664–684, doi: 10.1175/JHM-D-13-0113.1.CrossRefGoogle Scholar
  45. Zhang, G., G. S. Zhou, F. Chen, et al., 2014b: Analysis of the variability of canopy resistance over a desert steppe site in Inner Mongolia, China. Adv. Atmos. Sci., 31, 681–692, doi: 10.1007/s00376-013-3071-6.CrossRefGoogle Scholar
  46. Zhang, Q., X. Y. Cao, G. A. Wei, et al., 2002: Observation and study of land surface parameters over Gobi in typical arid region. Adv. Atmos. Sci., 19, 121–135, doi: 10.1007/s00376-002-0039-3.CrossRefGoogle Scholar
  47. Zilitinkevich, S. S., 1995: Non-local turbulent transport: Pollution dispersion aspects of coherent structure of convective flows. Air Pollution III, Vol. I, Air Pollution Theory and Simulation, Power H., N. Moussiopoulos, and C. A. Brebbia, Eds., Computational Mechanics Publications, Boston, Mass, 53–60.Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  2. 2.National Center for Atmospheric ResearchBoulderUSA

Personalised recommendations