Articles | Volume 17, issue 8
https://doi.org/10.5194/hess-17-3171-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-17-3171-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Aug 2013
Research article |
| 06 Aug 2013
Data compression to define information content of hydrological time series
S. V. Weijs
School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Station 2, 1015 Lausanne, Switzerland
N. van de Giesen
Water resources management, Delft University of Technology, Stevinweg 1, P.O. Box 5048, 2600 GA Delft, The Netherlands
M. B. Parlange
School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Station 2, 1015 Lausanne, Switzerland
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Cited
20 citations as recorded by crossref.
- Spatiotemporal variability of Indian rainfall using multiscale entropy R. Guntu et al. https://doi.org/10.1016/j.jhydrol.2020.124916
- Soil moisture: variable in space but redundant in time M. Mälicke et al. https://doi.org/10.5194/hess-24-2633-2020
- A brief history of information and disinformation in hydrological data and the impact on the evaluation of hydrological models K. Beven https://doi.org/10.1080/02626667.2024.2332616
- Advancing catchment hydrology to deal with predictions under change U. Ehret et al. https://doi.org/10.5194/hess-18-649-2014
- Stormwater Detention System Parameter Sensitivity and Uncertainty Analysis Using SWMM J. Knighton et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001382
- Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes D. Mihailović et al. https://doi.org/10.3390/e17052973
- Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al. https://doi.org/10.1016/j.jhydrol.2024.131774
- The role and value of distributed precipitation data in hydrological models R. Loritz et al. https://doi.org/10.5194/hess-25-147-2021
- The implication of information theory for estimating and detangling hydrological modelling uncertainty E. Findanis & A. Loukas https://doi.org/10.1016/j.jhydrol.2024.131941
- Robust informational entropy-based descriptors of flow in catchment hydrology I. Pechlivanidis et al. https://doi.org/10.1080/02626667.2014.983516
- Sensitivity of Entropy Method to Time Series Length in Hydrometric Network Design J. Keum & P. Coulibaly https://doi.org/10.1061/(ASCE)HE.1943-5584.0001508
- A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven https://doi.org/10.1002/wat2.1761
- Accounting for temporal variability for improved precipitation regionalization based on self-organizing map coupled with information theory R. Guntu et al. https://doi.org/10.1016/j.jhydrol.2020.125236
- Uncertainty quantification in watershed hydrology: Which method to use? A. Gupta & R. Govindaraju https://doi.org/10.1016/j.jhydrol.2022.128749
- Hydrologic State Influence on Riverine Flood Discharge for a Small Temperate Watershed (Fall Creek, United States): Negative Feedbacks on the Effects of Climate Change J. Knighton et al. https://doi.org/10.1175/JHM-D-16-0164.1
- Objective functions for information-theoretical monitoring network design: what is “optimal”? H. Foroozand & S. Weijs https://doi.org/10.5194/hess-25-831-2021
- On the dynamic nature of hydrological similarity R. Loritz et al. https://doi.org/10.5194/hess-22-3663-2018
- Debates: Does Information Theory Provide a New Paradigm for Earth Science? Sharper Predictions Using Occam's Digital Razor S. Weijs & B. Ruddell https://doi.org/10.1029/2019WR026471
- An Information Theory Approach to Identifying a Representative Subset of Hydro‐Climatic Simulations for Impact Modeling Studies I. Pechlivanidis et al. https://doi.org/10.1029/2017WR022035
- Streamflow Hydrograph Classification Using Functional Data Analysis C. Ternynck et al. https://doi.org/10.1175/JHM-D-14-0200.1
20 citations as recorded by crossref.
- Spatiotemporal variability of Indian rainfall using multiscale entropy R. Guntu et al. https://doi.org/10.1016/j.jhydrol.2020.124916
- Soil moisture: variable in space but redundant in time M. Mälicke et al. https://doi.org/10.5194/hess-24-2633-2020
- A brief history of information and disinformation in hydrological data and the impact on the evaluation of hydrological models K. Beven https://doi.org/10.1080/02626667.2024.2332616
- Advancing catchment hydrology to deal with predictions under change U. Ehret et al. https://doi.org/10.5194/hess-18-649-2014
- Stormwater Detention System Parameter Sensitivity and Uncertainty Analysis Using SWMM J. Knighton et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001382
- Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes D. Mihailović et al. https://doi.org/10.3390/e17052973
- Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al. https://doi.org/10.1016/j.jhydrol.2024.131774
- The role and value of distributed precipitation data in hydrological models R. Loritz et al. https://doi.org/10.5194/hess-25-147-2021
- The implication of information theory for estimating and detangling hydrological modelling uncertainty E. Findanis & A. Loukas https://doi.org/10.1016/j.jhydrol.2024.131941
- Robust informational entropy-based descriptors of flow in catchment hydrology I. Pechlivanidis et al. https://doi.org/10.1080/02626667.2014.983516
- Sensitivity of Entropy Method to Time Series Length in Hydrometric Network Design J. Keum & P. Coulibaly https://doi.org/10.1061/(ASCE)HE.1943-5584.0001508
- A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven https://doi.org/10.1002/wat2.1761
- Accounting for temporal variability for improved precipitation regionalization based on self-organizing map coupled with information theory R. Guntu et al. https://doi.org/10.1016/j.jhydrol.2020.125236
- Uncertainty quantification in watershed hydrology: Which method to use? A. Gupta & R. Govindaraju https://doi.org/10.1016/j.jhydrol.2022.128749
- Hydrologic State Influence on Riverine Flood Discharge for a Small Temperate Watershed (Fall Creek, United States): Negative Feedbacks on the Effects of Climate Change J. Knighton et al. https://doi.org/10.1175/JHM-D-16-0164.1
- Objective functions for information-theoretical monitoring network design: what is “optimal”? H. Foroozand & S. Weijs https://doi.org/10.5194/hess-25-831-2021
- On the dynamic nature of hydrological similarity R. Loritz et al. https://doi.org/10.5194/hess-22-3663-2018
- Debates: Does Information Theory Provide a New Paradigm for Earth Science? Sharper Predictions Using Occam's Digital Razor S. Weijs & B. Ruddell https://doi.org/10.1029/2019WR026471
- An Information Theory Approach to Identifying a Representative Subset of Hydro‐Climatic Simulations for Impact Modeling Studies I. Pechlivanidis et al. https://doi.org/10.1029/2017WR022035
- Streamflow Hydrograph Classification Using Functional Data Analysis C. Ternynck et al. https://doi.org/10.1175/JHM-D-14-0200.1
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