| Popis: |
The assessment of the terrestrial water cycle is critical to human life. Even though large-scale hydrodynamic models were utilized to simulate surface water dynamics, they have non-negligible uncertainty. Combining satellite altimetry data into a hydrodynamic model using data assimilation is useful to determine comprehensive water dynamics. However, the hydrodynamic models are not yet competent enough to incorporate satellite altimetry data directly. We conducted anomaly and normalized value assimilation schemes into the CaMa-Flood hydrodynamic model to address the constraints of the models in estimating river discharge by direct assimilation of satellite altimetry. We used a physically-based empirical localization method that builds upon the local ensemble transformation Kalman filter. We used satellite altimetry data from ENVISAT, Jason 1, and Jason 2 for 2002-2014. In most river reaches normalized data assimilation enhanced river discharge over direct and anomaly assimilation. In comparison to model simulations, more river gauges have improved their river discharge estimation accuracy by assimilating normalized satellite altimetry data. Furthermore, the normalized value assimilation accurately calculated river discharges. 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 modeling capabilities. On the verge of the Surface Water and Ocean Topography satellite mission, the estimation of river discharge at the global scale can be realized with the methods developed here. |
