Joint model state-parameter retrieval through the evolutionary data assimilation approach

Abstract

Data assimilation (DA) methods have been widely used to improve model state estimation by merging model outputs with observations. Traditionally, studies have focused on updating model state variables but recent studies have augmented model parameters alongside model state variables to improve the estimation procedure. The updated model ensemble members represent a compromised estimation between prediction and observation. The compromise, which is usually in objective space subject to agreement between observation and model predictions, is important. However, few studies have actually employed DA procedures to investigate the updated members in decision space, through examination of the temporal changes of model states and parameters. Usually, the model states and parameters evolve/change: (i) subject to changes in observation, (ii) to account for the varied uncertainties in different land surface conditions, and (iii) due to their intricate connection with hydrologic conditions which evolve across assimilation time periods. Moreover, the update procedure in most DA methods is controlled predominantly by matchings between observation and model predictions with limited impact from decision space through model state variables and parameters. As a result, DA procedures are needed to tightly link the compromise in objective space to decision space, with the capability to examine the temporal changes of model states and parameters.