Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 2
Articles | Volume 29, issue 2
https://doi.org/10.5194/hess-29-335-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-335-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 2
Research article
 | 
20 Jan 2025
Research article |  | 20 Jan 2025

State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models

Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu

Supplement

https://doi.org/10.5194/hess-29-335-2025-supplement

Data sets

The China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0) near-real-time product dataset National Meteorological Information Centre http://data.cma.cn/data/detail/dataCode/NAFP_CLDAS2.0_NRT.html

Model code and software

The Parallel Data Assimilation Framework (PDAF) (v2.1) L. Nerger https://doi.org/10.5281/zenodo.7861829

OpenDA-Association/OpenDA: OpenDA 3.1.1 W. Kramer et al. https://doi.org/10.5281/zenodo.8018104

Uncertainty Quantification Python Laboratory (UQ-PyL) (v1.0 Windows Binary) C. Wang et al. http://www.uq-pyl.com/

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Short summary
Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping to better prepare for and respond to floods.
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