Parameter sensitivity analysis for different complexity land surface models using multicriteria methods

[1] A multicriteria algorithm, the MultiObjective Generalized Sensitivity Analysis (MOGSA), was used to investigate the parameter sensitivity of five different land surface models with increasing levels of complexity in the physical representation of the vegetation (BUCKET, CHASM, BATS 1, Noah, and BATS 2) at five different sites representing crop land/pasture, grassland, rain forest, cropland, and semidesert areas. The methodology allows for the inclusion of parameter interaction and does not require assumptions of independence between parameters, while at the same time allowing for the ranking of several single-criterion and a global multicriteria sensitivity indices. The analysis required on the order of 50 thousand model runs. The results confirm that parameters with similar “physical meaning” across different model structures behave in different ways depending on the model and the locations. It is also shown that after a certain level an increase in model structure complexity does not necessarily lead to better parameter identifiability, i.e., higher sensitivity, and that a certain level of overparameterization is observed. For the case of the BATS 1 and BATS 2 models, with essentially the same model structure but a more sophisticated vegetation model, paradoxically, the effect on parameter sensitivity is mainly reflected in the sensitivity of the soil-related parameters.

[1]  K. Beven,et al.  Shenandoah Watershed Study: Calibration of a Topography‐Based, Variable Contributing Area Hydrological Model to a Small Forested Catchment , 1985 .

[2]  R. Koster,et al.  Modeling the land surface boundary in climate models as a composite of independent vegetation stands , 1992 .

[3]  G. Hornberger,et al.  Approach to the preliminary analysis of environmental systems , 1981 .

[4]  Xu Liang,et al.  Intercomparison of land-surface parameterization schemes: sensitivity of surface energy and water fluxes to model parameters , 2003 .

[5]  H Rabitz,et al.  Systems Analysis at the Molecular Scale , 1989, Science.

[6]  Ann Henderson-Sellers,et al.  Sensitivity of the biosphere-atmosphere transfer scheme (BATS) to the inclusion of variable soil characteristics , 1987 .

[7]  C. E. Desborough,et al.  Surface energy balance complexity in GCM land surface models , 1999 .

[8]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[9]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[10]  Lisa J. Graumlich,et al.  Interactive Canopies for a Climate Model , 1998 .

[11]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

[12]  M. Hodnett,et al.  Seasonal soil water storage changes beneath central Amazonian rainforest and pasture , 1995 .

[13]  J. Noilhan,et al.  Key results and implications from phase 1(c) of the Project for Intercomparison of Land-surface Parametrization Schemes , 1999 .

[14]  L. Bastidas,et al.  Parameter estimation for hydrometeorological models using multi-criteria methods , 1998 .

[15]  S. Manabe.  CLIMATE AND THE OCEAN CIRCULATION1 , 1969 .

[16]  Y. Xue,et al.  18-Year Land-Surface Hydrology Model Simulations for a Midlatitude Grassland Catchment in Valdai, Russia , 1997 .

[17]  Isabelle Braud,et al.  A simple soil-plant-atmosphere transfer model (SiSPAT) development and field verification , 1995 .

[18]  Soroosh Sorooshian,et al.  Evaluating model performance and parameter behavior for varying levels of land surface model complexity , 2006 .

[19]  H. Pan,et al.  A two-layer model of soil hydrology , 1984 .

[20]  B. Efron.  Computers and the Theory of Statistics: Thinking the Unthinkable , 1979 .

[21]  Ann Henderson-Sellers,et al.  Recent progress and results from the project for the intercomparison of landsurface parameterization schemes , 1998 .

[22]  Soroosh Sorooshian,et al.  Evaluation and Transferability of the Noah Land Surface Model in Semiarid Environments , 2005 .

[23]  M. Budyko,et al.  Heat balance of the Earth , 1964 .

[24]  Zong-Liang Yang,et al.  Simulations of a boreal grassland hydrology at Valdai, Russia: PILPS phase 2(d). , 2000 .

[25]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[26]  Ann Henderson-Sellers,et al.  Investigation of the sensitivity of the land-surface parameterization of the NCAR community climate model in regions of tundra vegetation. , 1987 .

[27]  Nong Shang,et al.  Parameter uncertainty and interaction in complex environmental models , 1994 .

[28]  W. J. Shuttleworth,et al.  Parameter estimation of a land surface scheme using multicriteria methods , 1999 .

[29]  I. E. Woodrow,et al.  A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .

[30]  D. Mocko,et al.  Simulation of high latitude hydrological processes in the Torne-Kalix basin : PILPS phase 2(e) - 2: Comparison of model results with observations , 2003 .

[31]  A. Saltelli,et al.  Sensitivity analysis: Could better methods be used? , 1999 .

[32]  A. Pitman.  The evolution of, and revolution in, land surface schemes designed for climate models , 2003 .

[33]  B. Efron.  Bootstrap Methods: Another Look at the Jackknife , 1979 .

[34]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[35]  R. Dickinson,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1993 .

[36]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere transfer scheme(BATS) version 1e as coupled to the NCAR community climate model , 1993 .

[37]  Soroosh Sorooshian,et al.  Sensitivity analysis of a land surface scheme using multicriteria methods , 1999 .

[38]  K. L. Driese,et al.  Aerodynamic roughness parameters for semi-arid natural shrub communities of Wyoming, USA , 1997 .

[39]  Andrew J. Pitman,et al.  Assessing the Sensitivity of a Land-Surface Scheme to the Parameter Values Using a Single Column Model , 1994 .

[40]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[41]  Ann Henderson-Sellers,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1995 .

[42]  P. Basham,et al.  Summary, Conclusions and Recommendations , 1989 .

[43]  Paul R. Houser,et al.  Surface flux measurement and modeling at a semi-arid Sonoran Desert site , 1996 .

[44]  J. Deardorff.  Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation , 1978 .

[45]  Soroosh Sorooshian,et al.  Impact of field‐calibrated vegetation parameters on GCM climate simulations , 2001 .

[46]  P. Rousseeuw.  Tutorial to robust statistics , 1991 .

[47]  R. Spear.  Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysis , 1980 .

[48]  Keith Beven,et al.  Bayesian estimation of uncertainty in land surface‐atmosphere flux predictions , 1997 .

[49]  Y. Xue,et al.  Use of midlatitude soil moisture and meteorological observations to validate soil moisture simulations with biosphere and bucket models , 1995 .

[50]  Kenneth E. Mitchell,et al.  Recent GCIP-sponsored advancements in coupled land-surface modeling and data assimilation in the NCEP Eta mesoscale model , 2000 .

[51]  Anton Beljaars,et al.  Cabauw Data for the Validation of Land Surface Parameterization Schemes , 1997 .

[52]  Roni Avissar,et al.  An Evaluation with the Fourier Amplitude Sensitivity Test (FAST) of Which Land-Surface Parameters Are of Greatest Importance in Atmospheric Modeling , 1994 .

[53]  Soroosh Sorooshian,et al.  Sensitivity analysis of the biosphere‐atmosphere transfer scheme , 1996 .

[54]  Luis A. Bastidas,et al.  Using a multiobjective approach to retrieve information on surface properties used in a SVAT model , 2004 .

[55]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.