Identifiability of soil and ecosystem states and parameters of integrated subsurface-land surface-atmosphere models by multivariate data assimilation

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We will implement a strategy to assimilate land surface temperature by simultaneously considering multiple observations during a day to update sensitive land surface parameters and states. We will develop an operator to assimilate leaf area index and update the dynamical vegetation states and sensitive ecosystem parameters. Synthetic experiments estimate the value of these data together with observed soil moisture. In particular, we will detect under realistic scenarios including multiple sources of uncertainty and several unknown parameters, whether we can improve parameter estimation, and to what extent the assimilation of certain data types improves the state of other compartments. Synthetic tests are carried out first for the land component of TerrSysMP-PDAF (CLM-ParFlow-PDAF) using a joint test case with P5, P6 and P7. We will contribute to the experiments made with the complete TerrSysMP-PDAF coordinated by C1. Finally, tests will be made for the Rur catchment.