Spatial calibration of the NOAH model parameters in the South American Land Data Assimilation System over the LBA/Amazon area using similarity-based measures
Goncalves, NASA/GSFC/ORAU, email@example.com
Bastidas, Utah University, firstname.lastname@example.org
Rosero, Utah University, email@example.com
Shuttleworth, University of Arizona, firstname.lastname@example.org
Toll, NASA/GSFC, email@example.com
Rosolem, University of Arizona, firstname.lastname@example.org
Land surface models (LSM) have been extensively used to better represent hydrological and land surface processes, providing lower boundary conditions for numerical climate and weather simulations. The determination of the spatially distributed LSM optimal parameters would greatly impact such numerical simulations. Parameter calibration techniques however rely on data availability which in general is provided by point measurements. We use the Shuffled Complex Evolution (SCE-UA) optimization algorithm and similarity concepts to estimate distributed parameter values for the NOAH LSM, incorporated into the Land Information System (LIS), a distributed framework developed at NASA/GSFC for Land Data Assimilation System (LDAS) applications. Flux towers from the Large-Scale Biosphere Atmosphere Experiment in Amazonia (LBA) are used as point ground measurements for the model calibration. This paper investigates the feasibility of combining the parameters estimated at the LBA sites, a similarity-based measure, the Hausdorff Norm (HN), and MODIS/AQUA ground surface temperature fields for calibration of parameters in the surrounding areas. This study is part of the group CD-36 of the LBA Synthesis Phase III and the South American LDAS (SALDAS) initiative at the GSFC/NASA Hydrological Sciences Branch.