A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation. (October 2019)

• Record Type: Journal Article
• Authors: Zhang, Qiuru; Shi, Liangsheng; Holzman, Mauro; Ye, Ming; Wang, Yakun; Carmona, Facundo; Zha, Yuanyuan
• Is Part Of: Advances in water resources. Volume 132(2019)
• Publication Date: 2019-10
• Subjects: Soil moisture -- Data assimilation -- Model structural error -- Data-driving -- Machine learning; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2019.103407
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: We propose a dynamic data-driven approach based on Gaussian process regression to estimate model structural error in soil moisture data assimilation. Gaussian process error model can represent the underlying model structural error. The proposed hybrid method (EnKF-GP) outperforms the standard EnKF. Abstract: Attributing to the flexibility in considering various types of observation error and model error, data assimilation has been increasingly applied to dynamically improve soil moisture modeling in many hydrological practices. However, accurate characterization of model error, especially the part caused by defective model structure, presents a significant challenge to the successful implementation of data assimilation. Model structural error has received limited attention relative to parameter and input errors, mainly due to our poor understanding of structural inadequacy and the difficulties in parameterizing structural error. In this paper, we present a dynamic data-driven approach to estimate the model structural error in soil moisture data assimilation without the need for identifying error generation mechanism or specifying particular form for the error model. The error model is based on the Gaussian process regression and then integrated into the ensemble Kalman filter (EnKF) to form a hybrid method for dealing with multi-source model errors. Two variants of the hybrid method in terms of two different error correction manners are proposed. The effectiveness of the proposed method is tested through a suit of synthetic cases and a real-world case. Results demonstrate the potential of the proposed hybrid method for estimating model structural error and providing improved model predictions. Compared to the traditional EnKF without explicitly considering the model structural error, parameter compensation issue is obviously reduced and soil moisture retrieval is substantially improved.