全球河川モデルへのデータ同化手法の適用-アマゾン川流域を対象にした仮想実験-  [in Japanese] APPLICATION OF DATA ASSIMILATION FOR A GLOBAL RIVER MODEL: A VIRTUAL EXPERIMENT AT THE AMAZON BASIN  [in Japanese]

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Abstract

本研究ではSWOT衛星の擬似観測を全球河川モデルCaMa-FloodにLETKFでデータ同化する仮想実験を,アマゾン川流域全体を対象に行なった.水面標高の疑似観測を同化することで,陸面流出量に誤差を含んだシミュレーションに関しても地表水動態の再現精度を向上できた.比較的小さな大陸河川を対象とした既往研究とは異なり大河川の流域全体が対象のデータ同化では,上流の補正効果が下流へ伝搬するため,上流からの流入量が大きな地点では擬似観測が無い時でも河川流量の再現精度が大幅に向上した.局所的な陸面流出量に対し,上流からの河川流量が大きい地点では局所的な観測で補正できず,上流からの補正効果の伝搬で補正が達成された.このように,データ同化は大流域河川においても地表水動態の時空間変動推定の高度化に有効であると示された.

 We carried out a virtual experiment of data assimilation, which assimilates virtual SWOT satellite observation data into global river model CaMa-Flood with LETKF, at the Amazon basin. Assimilating virtual SWOT observed water surface height improved reproducibility of land surface water dynamics such as river discharge, even for simulations using bias corrupted runoff as external forcing. Unlike previous researches which set relatively small scale rivers as target, we set the whole basin of large scale river as target, allowing correction effect to flow down rivers in this research. This caused reproducibility improvement of river discharge, especially at the downstream where catchment area is large. For locations where correction is difficult because of large river discharge comparing with local runoff, discharge was improved by correction effect propagated from river upstream. This research showed that data assimilation method is valid to improve estimation of spatio-temporal land surface water dynamics variability even for large scale rivers.

Journal

  • Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering)

    Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 73(4), I_175-I_180, 2017

    Japan Society of Civil Engineers

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