Simultaneous estimation of both hydrological and ecological parameters in an ecohydrological model by assimilating microwave signal

DOI Web Site Web Site 被引用文献12件 参考文献63件 オープンアクセス
  • Yohei Sawada
    Department of Civil Engineering, School of Engineering University of Tokyo Tokyo Japan
  • Toshio Koike
    Department of Civil Engineering, School of Engineering University of Tokyo Tokyo Japan

書誌事項

公開日
2014-07-27
資源種別
journal article
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1002/2014jd021536
公開者
American Geophysical Union (AGU)

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説明

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

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