Non-parametric Hydrologic Data Assimilation

Abstract

Data assimilation, which relies on explicit knowledge of dynamical models, is a well-known approach that addresses models’ limitations due to various reasons, such as errors in input and forcing datasets. This approach, however, requires intensive computational efforts, especially for high dimensional systems such as distributed hydrological models. Alternatively, data-driven methods offer comparable solutions when the physics underlying the models are unknown. For the first time in a hydrological context, a non-parametric framework is implemented here to improve model estimates using available observations. This method uses Takens delay-coordinate method to reconstruct the dynamics of the system within a Kalman filtering framework, called the Kalman-Takens filter. A synthetic experiment is undertaken to fully investigate the capability of the proposed method by comparing its performance with that of a standard assimilation framework based on an adaptive unscented Kalman filter (AUKF). Furthermore, using terrestrial water storage (TWS) estimates obtained from the Gravity Recovery And Climate Experiment (GRACE) mission, both filters are applied to a real case scenario to update different water storages over Australia. In-situ groundwater and soil moisture measurements within Australia are used to further evaluate the results.

“Classically, data assimilation can be used to improve imperfect models by integrating available observations with the underlying physical model”. On the other hand, “data-driven methods offer comparable solutions when the physics underlying the models are unknown”.

—M. Khaki (This chapter is presented following Khaki et al. (2018f), “Non-parametric data assimilation scheme for land hydrological applications”)