Non-parametric data assimilation scheme for land hydrological applications

Khaki, M., Hamilton, F., Forootan, E. ORCID: https://orcid.org/0000-0003-3055-041X, Hoteit, I., Awange, J. and Kuhn, M. 2018. Non-parametric data assimilation scheme for land hydrological applications. Water Resources Research 54 (7) , pp. 4946-4964. 10.1029/2018WR022854

Preview

PDF - Accepted Post-Print Version Download (2MB) | Preview

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 data sets. 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 nonparametric 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 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. The Kalman‐Takens filter successfully improves the estimated water storages at levels comparable to the AUKF results, with an average root‐mean‐square error reduction of 37.30% for groundwater and 12.11% for soil moisture estimates. Additionally, the Kalman‐Takens filter, while reducing estimation complexities, requires a fraction of the computational time, that is, ∼8 times faster compared to the AUKF approach.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Earth and Environmental Sciences
Publisher:	American Geophysical Union (AGU)
ISSN:	0043-1397
Date of First Compliant Deposit:	20 June 2018
Date of Acceptance:	13 June 2018
Last Modified:	13 Nov 2023 10:42
URI:	https://orca.cardiff.ac.uk/id/eprint/112666

Citation Data

Cited 10 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item