Articles | Volume 23, issue 2
https://doi.org/10.5194/hess-23-1015-2019
© Author(s) 2019. 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-23-1015-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Feb 2019
Research article |
| 19 Feb 2019
| 19 Feb 2019
Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory
Stephanie Thiesen
CORRESPONDING AUTHOR
Institute of Water Resources and River Basin Management, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany
Paul Darscheid
Institute of Water Resources and River Basin Management, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany
Uwe Ehret
Institute of Water Resources and River Basin Management, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany
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- Estimation of catchment response time using a new automated event-based approach E. Nagy et al. 10.1016/j.jhydrol.2022.128355
- On the Automation of Flood Event Separation From Continuous Time Series H. Oppel & B. Mewes 10.3389/frwa.2020.00018
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- Assessment of water inrush risk based comprehensive cloud model: a case study in a lead zinc mine, China Q. Li et al. 10.1007/s13146-022-00827-9
- Optimizing parameter learning and calibration in an integrated hydrological model: Impact of observation length and information P. Jiang et al. 10.1016/j.jhydrol.2024.131889
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- Histogram via entropy reduction (HER): an information-theoretic alternative for geostatistics S. Thiesen et al. 10.5194/hess-24-4523-2020
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Latest update: 20 Nov 2024
Short summary
We present a data-driven approach created to explore the full information of data sets, avoiding parametric assumptions. The evaluations are based on Information Theory concepts, introducing an objective measure of information and uncertainty. The approach was applied to automatically identify rainfall-runoff events in discharge time series, however it is generic enough to be adapted to other practical applications.
We present a data-driven approach created to explore the full information of data sets, avoiding...
