Parameter sensitivity analysis and optimization of Noah land surface model with field measurements from Huaihe River Basin, China

详细信息    查看全文

• 作者：Ting Hou ; Yonghua Zhu ; Haishen Lü…
• 关键词：Noah LSM ; Huaihe River ; Sensitivity analysis ; RSA ; Sobol’s method ; PEST
• 刊名：Stochastic Environmental Research and Risk Assessment (SERRA)
• 出版年：2015
• 出版时间：July 2015
• 年：2015
• 卷：29
• 期：5
• 页码：1383-1401
• 全文大小：5,010 KB
• 参考文献：Baginska B, Milne-Home W, Cornish P (2003) Modelling nutrient transport in Currency Creek, NSW with AnnAGNPS and PEST. Environ Model Softw 18:801-08
  Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569-85View Article
  Chen F, Mitchell K, Schaake J, Xue Y, Pan H-L, Koren V, Duan QY, Ek M, Betts A (1996) Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J Geophys Res 101:7251-268View Article
  Chen F, Janji? Z, Mitchell K (1997) Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Bound Layer Meteorol 85:391-21View Article
  Chen Y, Yang K, Zhou D, Qin J, Guo X (2010) Improving the Noah land surface model in arid regions with an appropriate parameterization of the thermal roughness length. J Hydrometeorol 11:995-006View Article
  Demaria EM, Nijssen B, Wagener T (2007) Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model. J Geophys Res 112:D11113View Article
  Doherty J (2004) PEST-model independent parameter estimation user manual. Watermark Numerical Computing, Brisbane
  Doherty J, Johnston JM (2003) Methodologies for calibration and predictive analysis of a watershed model. J Am Water Resour Assoc 39:251-65View Article
  Ek M, Mahrt L (1991) OSU 1-D PBL model user’s guide. Department of Atmospheric Sciences, Oregon State University, Corvallis
  Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res 108:8851View Article
  Fieberg J, Jenkins KJ (2005) Assessing uncertainty in ecological systems using global sensitivity analyses: a case example of simulated wolf reintroduction effects on elk. Ecol Model 187:259-80View Article
  Freer J, Beven K, Ambroise B (1996) Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour Res 32:2161-173View Article
  Hall J, Tarantola S, Bates P, Horritt M (2005) Distributed sensitivity analysis of flood inundation model calibration. J Hydraul Eng 131:117-26View Article
  Hamby DM (1994) A review of techniques for parameter sensitivity analysis of environmental models. Environ Monit Assess 32:135-54View Article
  Helton JC, Davis FJ (2003) Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf 81:23-9View Article
  Hogue TS, Bastidas L, Gupta H, Sorooshian S, Mitchell K, Emmerich W (2005) Evaluation and transferability of the Noah land surface model in semiarid environments. J Hydrometeorol 6:68-4View Article
  Hornberger GM, Spear RC (1981) Approach to the preliminary analysis of environmental systems. J Environ Manag 12:7-8
  Koren V, Schaake J, Mitchell K, Duan QY, Chen F, Baker JM (1999) A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J Geophys Res 104:19569-9585View Article
  Lence BJ, Takyi AK (1992) Data requirements for seasonal discharge programs: an application of a regionalized sensitivity analysis. Water Resour Res 28:1781-789View Article
  Levenberg K (1944) A method for the solution of certain problems in least squares. Q Appl Math 2:164-68
  Li L, Xu C-Y (2014) The comparison of sensitivity analysis of hydrological uncertainty estimates by GLUE and Bayesian method under the impact of precipitation errors. Stoch Environ Res Risk Assess 28:491-04View Article
  Liang X, Guo J (2003) Intercomparison of land-surface parameterization schemes: sensitivity of surface energy and water fluxes to model parameters. J Hydrol 279:182-09View Article
  Liu YB, Batelaan O, De Smedt F, Poórová J, Velcická L (2005) Automated calibration applied to a GIS-based flood simulation model using PEST. In: van Alphen J, van Beek E, Taal M (eds) Floods, from defence to management. Taylor-Francis Group, London, pp 317-26
  Mahrt L, Ek M (1984) The influence of atmospheric stability on potential evaporation. J Clim Appl Meteorol 23:222-34View Article
  Mahrt L, Pan H (1984) A two-layer model of soil hydrology. Bound Layer Meteorol 29:1-0View Article
  Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431-41View Article
  McCuen RH (1973) The role of sensitivity analysis in hydrologic modeling. J Hydrol 18:37-3View Article
  McKay MD, Beckman RJ, Conover WJ (1979) Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239-45
  Muleta MK, Nicklow JW (2005) Sensitivity and uncertainty analysis coupled with automatic calibration for a distribut
• 作者单位：Ting Hou (1)
  Yonghua Zhu (1)
  Haishen Lü (1)
  Edward Sudicky (2)
  Zhongbo Yu (1)
  Fen Ouyang (1)
  
  1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China
  2. Department of Earth and Environmental Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Mathematical Applications in Environmental Science
  Mathematical Applications in Geosciences
  Probability Theory and Stochastic Processes
  Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
  Numerical and Computational Methods in Engineering
  Waste Water Technology, Water Pollution Control, Water Management and Aquatic Pollution
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1436-3259

文摘

This study aims to identify the parameters that are most important in controlling the Noah land surface model (LSM), the analysis of parameter interactions, and the evaluation of the performance of parameter optimization using the parameter estimation software PEST. We found it necessary to analyze parameter sensitivity in order to properly simulate hydrological variables such as latent heat flux in the Huaihe River Basin, China. The parameters under study in the Noah LSM link thermodynamic and hydrological parts into a complete model. To our knowledge, this parameter interaction in the Noah LSM has never been studied before. There are, however, several studies concerning the influence of vegetation types and climate conditions on parameter sensitivity of the Noah LSM. Three sensitivity analysis methods, the including local sensitivity analysis method SENSAN, regional sensitivity analysis, and Sobol’s method, were tested. Five experimental sites in the Huaihe River Basin were chosen to perform the simulations. The results show that the Noah LSM parameter sensitivities were impacted by the choice of the analysis method. The local method SENSAN often produced significant differences in results compared to the two global methods. The parameter interactions investigated made a significant contribution towards elucidating how one process influences another in the Noah LSM. The results show that parameters were not transferable solely based on vegetation types but also rely on climate conditions. According to the sensitivity analysis results, four sensitive parameters were chosen to be optimized using the PEST method. PEST is a widely used method for estimating parameters in models. Root-mean-square error was used to evaluate the effect of the optimization. Generally in all sites, the optimized parameters values perform better than the original parameter values.