[A-CG45] 陸域〜沿岸域における⽔・⼟砂動態

[ACG45-10] Prospects of Assimilating Remote Sensing Data into a Hydrodynamic Model in Amazon Basin

*Menaka Revel¹、Dai Yamazaki¹、Shinjiro Kanae² (1.Global Hydrological Forecast Center, Institute of Industrial Science, The University of Tokyo、2.Department of Civil and Environmental Engineering, Tokyo Institute of Technology )

キーワード：data assimilation, river discharge, satellite altimetry

Assessing the terrestrial water cycle is important to human livelihood. Although large-scale hydrodynamic models were used to estimate surface water dynamics, they consist of non-negligible uncertainties. Data assimilation can be used to estimate comprehensive water dynamics by combining satellite altimetry data into a hydrodynamic model. However, the hydrodynamic models are not mature enough to assimilate the satellite altimetry data directly. To overcome the limitations of the models in estimating river discharge, we assimilated the normalized values into the CaMa-Flood hydrodynamic model and compared them with direct value assimilation. The hydrological data assimilation was performed using a physically-based empirical localization method which builds upon the local ensemble transformation Kalman filter. The river discharge was improved in most of the river reaches in normalized data assimilation compared to direct assimilation. More than 60% of the river gauges have improved their accuracy of the river discharge estimations in the Amazon basin, by assimilating normalized satellite altimetry data with respect to model simulations. Moreover, the river discharges were estimated well with a median Nash-Sutcliffe Efficiency value of 0.47 by the normalized value assimilation. The low flows and the sudden secondary peaks were well captured by the normalized data assimilations. Hence, the normalized assimilation of satellite altimetry data into a continental-scale hydrodynamic model can withstand the limitations of current modelling capabilities. With the upcoming Surface Water and Ocean Topography satellite mission, the ability of data assimilation to estimate accurate river discharge would be enhanced with the methods developed here.