Evaluating model performance and parameter behavior for varying levels of land surface model complexity

[1] This paper investigates model performance and parameter behavior for a range of land surface model (LSM) complexity across a variety of vegetated surfaces. Although LSMs are used routinely in regional and global climate (and weather) prediction, there has been limited rigorous testing of these models across a range of biomes. A systems-based approach is used to compare five commonly used LSMs (BUCKET, CHASM, BATS1e, BATS2, and Noah) across five different vegetated sites (pasture, short grass, cropland, tropical rain forest, and semiarid desert). Results indicate that there is no “perfect” model and that additional complexity (defined as additional physical representation in a model) does not necessarily equate to improved performance. In general, the medium complexity BATS1e model has the most consistent performance, with overall lower errors across the sites. Results also indicate that prescribed parameter meanings are not consistent across the various LSM formulations. A comparison of BATS1e and BATS2 parameters reveals significant differences in behavior across the study sites. These findings have key implications for general application of a single model across a range of global biomes and for model intercomparison studies where parameters are preassigned to participating models.

[1]  Soroosh Sorooshian,et al.  Parameter sensitivity analysis for different complexity land surface models using multicriteria methods , 2006 .

[2]  Luis A. Bastidas,et al.  Constraining a physically based Soil‐Vegetation‐Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach , 2005 .

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

[4]  Zong-Liang Yang,et al.  The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2(c) Red–Arkansas River basin experiment:: 1. Experiment description and summary intercomparisons , 1998 .

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

[6]  B. Croke,et al.  Predictions in catchment hydrology: an Australian perspective , 2001 .

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

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

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

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

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

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

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

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

[15]  Soroosh Sorooshian,et al.  Toward improved streamflow forecasts: value of semidistributed modeling , 2001 .

[16]  Mrinal K. Sen,et al.  Optimal parameter and uncertainty estimation of a land surface model: A case study using data from Cabauw, Netherlands , 2003 .

[17]  G. Hornberger,et al.  Empirical equations for some soil hydraulic properties , 1978 .

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

[19]  Hoshin V. Gupta,et al.  Exploring the relationship between complexity and performance in a land surface model using the multicriteria method , 2002 .

[20]  S. Sorooshian,et al.  A multistep automatic calibration scheme for river forecasting models , 2000 .

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

[22]  Matthew F. McCabe,et al.  Calibration of a land surface model using multiple data sets , 2005 .

[23]  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 .

[24]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods , 2000 .

[25]  G. Kuczera.  Efficient subspace probabilistic parameter optimization for catchment models , 1997 .

[26]  Soroosh Sorooshian,et al.  Multi-objective global optimization for hydrologic models , 1998 .

[27]  Soroosh Sorooshian,et al.  Constraining Land Surface and Atmospheric Parameters of a Locally Coupled Model Using Observational Data , 2005 .

[28]  W. J. Shuttleworth,et al.  Evaporation from Amazonian rainforest , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[29]  A. Jakeman,et al.  How much complexity is warranted in a rainfall‐runoff model? , 1993 .

[30]  George Kuczera,et al.  Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms , 1999 .

[31]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[32]  Luis A. Bastidas,et al.  Multicriteria parameter estimation for models of stream chemical composition , 2002 .

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

[34]  T. Gan,et al.  Automatic Calibration of Conceptual Rainfall-Runoff Models: Optimization Algorithms, Catchment Conditions, and Model Structure , 1996 .

[35]  H. Wheater,et al.  A Large‐Scale Grid‐Based Hydrological Model of the Severn and Thames Catchments , 1996 .

[36]  Thomas Meixner,et al.  Stream chemistry modeling of two watersheds in the Front Range, Colorado , 2000 .

[37]  Luis A. Bastidas,et al.  Calibrating a land surface model of varying complexity using multicriteria methods and the Cabauw dataset , 2002 .

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

[39]  Gérard Dedieu,et al.  Calibrating a Coupled SVAT–Vegetation Growth Model with Remotely Sensed Reflectance and Surface Temperature—A Case Study for the HAPEX-Sahel Grassland Sites , 2000 .

[40]  Stein Beldring,et al.  Multi-criteria validation of a precipitation–runoff model , 2002 .

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

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

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

[44]  Zong-Liang Yang,et al.  Impacts of data length on optimal parameter and uncertainty estimation of a land surface model , 2004 .