HR: 15:30h
AN: H53F-07    [Abstracts]
TI: Do improved hydrological representations in the Community Noah Land Surface Model yield a more robust model?
AU: * Rosero, E
EM: erosero@mail.utexas.edu
AF: Jackson School of Geosciences, The University of Texas at Austin, 1 University Station C1140 , Austin, TX 78712, United States
AU: Gulden, L E
EM: gulden@mail.utexas.edu
AF: Jackson School of Geosciences, The University of Texas at Austin, 1 University Station C1140 , Austin, TX 78712, United States
AU: Yang, Z
EM: liang@mail.utexas.edu
AF: Jackson School of Geosciences, The University of Texas at Austin, 1 University Station C1140 , Austin, TX 78712, United States
AU: Niu, G
EM: niu@geo.utexas.edu
AF: Jackson School of Geosciences, The University of Texas at Austin, 1 University Station C1140 , Austin, TX 78712, United States
AU: Chen, F
EM: feichen@ucar.edu
AF: National Center for Atmospheric Research, Research Applications Laboratory, Boulder, CO 80304, United States
AU: Mitchell, K
EM: Kenneth.Mitchell@noaa.gov
AF: National Centers for Environmental Prediction, NOAA Science Center , Camp Springs, MD 20746, United States
AU: Gochis, D J
EM: gochis@ucar.edu
AF: National Center for Atmospheric Research, Research Applications Laboratory, Boulder, CO 80304, United States
AB: Understanding the strength of land-memory mechanisms such as the storage of water near the surface as soil moisture and the nature and seasonal progression of growing vegetation remains a challenge. New parameterization schemes aim to capture such processes with realism within land-surface models. We investigate how the augmentation of the latest version of the unified Noah LSM, jointly maintained at NCEP and NCAR, with additional land memory processes (i.e. groundwater and dynamic vegetation) impacts the performance and robustness of the model. It is hypothesized that increased physical realism in conceptual models enhances their robustness, making them less sensitive to the choice of parameter values. At different locations, we systematically perform offline, multiobjective parameter estimation on four different versions of Noah: 1) Standard Noah LSM. 2) Noah-GW, equipped with a simple groundwater scheme, which describes the groundwater dynamics in an unconfined aquifer, efficiently representing the groundwater impacts on soil moisture. 3) Noah-DV, equipped with a short-term dynamic vegetation scheme, which describes the vegetation carbon budgets controlled by photosynthesis and respiration processes and allocating assimilated carbon to roots, stems, wood, and leaves. 4) Noah-DVGW, equipped both with groundwater and dynamic phenology module. The models are constrained with observations of surface energy budget components (sensible, latent, and soil heat fluxes) and soil temperature, moisture, and matric potential. Data was collected from May-June 2002 during the International H2O Project (IHOP-02) at locations representing the major types of land cover in the Oklahoma Panhandle: grassland, pasture, and croplands. The comparison of obtained optimal model structures (e.g., parameter sets, model covariances) allows us to draw conclusions regarding the robustness of the new parameterization to the choice of parameter values. We successively test if new, more realistic model structures significantly change the location of the optimal parameter set with respect to the standard formulation. Such an experimental setup enables us to determine whether addition of new parameters alters the structure of the model such that parameter values for existing parameterizations change. Additionally we can single out the contribution of individual parameterization enhancements to the overall improvement in performance.
DE: 1830 Groundwater/surface water interaction
DE: 1840 Hydrometeorology
DE: 1843 Land/atmosphere interactions (1218, 1631, 3322)
DE: 1846 Model calibration (3333)
DE: 1851 Plant ecology (0476)
SC: Hydrology [H]
MN: 2007 Joint Assembly