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Simultaneous estimation of both hydrological and ecological parameters in an ecohydrological model by assimilating microwave signal
Author(s) -
Sawada Yohei,
Koike Toshio
Publication year - 2014
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2014jd021536
Subject(s) - leaf area index , environmental science , remote sensing , data assimilation , satellite , microwave , radiometer , vegetation (pathology) , water content , microwave radiometer , brightness temperature , biomass (ecology) , atmospheric radiative transfer codes , radiative transfer , meteorology , ecology , geography , computer science , geology , physics , medicine , telecommunications , geotechnical engineering , pathology , quantum mechanics , biology , astronomy
Abstract To improve the skill of reproducing land‐atmosphere interactions in weather, seasonal, and climate prediction systems, it is necessary to simulate correctly and simultaneously the surface soil moisture (SSM) and terrestrial biomass in land surface models. Despite the performance of hydrological and ecosystem models depends highly on parameter calibration, a method for parameter estimation in ungauged areas has yet to be established. We develop an autocalibration system that can simultaneously estimate both hydrological and ecological parameters by assimilating a microwave signal that is sensitive to both SSM and terrestrial biomass. This system comprises a hydrological model that has a physically based, sophisticated soil hydrology scheme, a dynamic vegetation model that can estimate vegetation growth and senescence, and a radiative transfer model that can convert land surface condition into brightness temperatures in the microwave region. By assimilating microwave signals from the Advanced Microwave Scanning Radiometer for Earth Observing System, the system simultaneously optimizes the parameters of these models. We test this approach at three in situ observation sites under different hydroclimatic conditions. Estimated SSM exhibits good agreement with ground‐based in situ observed SSM, and estimated leaf area index (LAI) is also improved by the optimization, compared with satellite‐observed LAI. The root‐mean‐square error of SSM and LAI at all sites, estimated by the model with optimized parameters, is much less than that estimated by the model with default parameters. Using microwave satellite brightness temperature data sets, this system offers the potential to calibrate parameters of both hydrological and ecosystem models globally.