A Novel Deep Learning Approach for Data Assimilation of Complex
Hydrological Systems
Abstract
In hydrological research, data assimilation (DA) is widely used to fuse
the information contained in process-based models and observational data
to reduce simulation uncertainty. However, many popular DA methods are
limited by low computational efficiency or their reliance on the
Gaussian assumption. To address these limitations, we propose a novel DA
method called DA(DL), which leverages the capabilities of deep learning
(DL) to model non-linear relationships and recognize complex patterns.
DA(DL) first generates a large volume of training data from the prior
ensemble, and then trains a DL model to update the system knowledge
(e.g., model parameters in this study) from multiple predictors. For
highly non-linear models, an iterative form of DA(DL) can be
implemented. Additionally, strategies of data augmentation and local
updating are proposed to enhance DA(DL) for problems involving small
ensemble size and the equifinality issue, respectively. In two
hydrological DA cases involving Gaussian and non-Gaussian distributions,
DA(DL) shows promising performance compared to two ensemble smoother
(ES) methods, i.e., ES(K) and ES(DL), which respectively apply the
Kalman- and DL-based updates. Potential improvements to DA(DL) can be
made by designing better DL model architectures, imposing physical
constraints to the training of the DL model, and further updating other
important variables like model states, forcings and error terms.