Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2661-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-2661-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2023
Research article |
| 19 Jul 2023
| 19 Jul 2023
Data worth analysis within a model-free data assimilation framework for soil moisture flow
Yakun Wang
Key Laboratory of Agricultural Soil and Water Engineering of in Arid and Semiarid Areas, Ministry of Education, Northwest A & F University, Yangling, Shaanxi 712100, China
Xiaolong Hu
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
Lijun Wang
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
Jinmin Li
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
Lin Lin
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
Kai Huang
Guangxi Key Laboratory of Water Engineering Materials and Structures, Guangxi Institute of Water Resources Research, Nanning 530023, China
Liangsheng Shi
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
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Short summary
To avoid overloaded monitoring cost from redundant measurements, this study proposed a non-parametric data worth analysis framework to assess the worth of future soil moisture data regarding the model-free unsaturated flow models before data gathering. Results indicated that (1) the method can quantify the data worth of alternative monitoring schemes to obtain the optimal one, and (2) high-quality and representative small data could be a better choice than unfiltered big data.
To avoid overloaded monitoring cost from redundant measurements, this study proposed a...
