Articles | Volume 24, issue 9
https://doi.org/10.5194/hess-24-4389-2020
© Author(s) 2020. 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-24-4389-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Sep 2020
Research article |
| 09 Sep 2020
| 09 Sep 2020
Adaptive clustering: reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements
Institute of Water Resources and River Basin Management, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany
Rik van Pruijssen
Institute of Water Resources and River Basin Management, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany
Marina Bortoli
Institute of Water Resources and River Basin Management, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany
Ralf Loritz
Institute of Water Resources and River Basin Management, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany
Elnaz Azmi
Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Erwin Zehe
Institute of Water Resources and River Basin Management, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany
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Cited
16 citations as recorded by crossref.
- Towards an Optimal Representation of Sub‐Grid Heterogeneity in Land Surface Models L. Torres‐Rojas et al. https://doi.org/10.1029/2022WR032233
- Scalability and Computational Performance of an Ecohydrological Model Using Machine Learning-Based Prediction N. Cortés-Torres et al. https://doi.org/10.3390/w18040466
- Preface: Linking landscape organisation and hydrological functioning: from hypotheses and observations to concepts, models and understanding C. Jackisch et al. https://doi.org/10.5194/hess-25-5277-2021
- When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al. https://doi.org/10.5194/hess-30-629-2026
- Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature M. Neri et al. https://doi.org/10.1016/j.jhydrol.2022.128555
- Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number A. Sikorska‐Senoner https://doi.org/10.1111/jfr3.12772
- Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California S. Adams et al. https://doi.org/10.5194/hess-27-3021-2023
- Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell https://doi.org/10.1016/j.jhydrol.2021.127287
- The role and value of distributed precipitation data in hydrological models R. Loritz et al. https://doi.org/10.5194/hess-25-147-2021
- Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1277-2025
- Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques P. Shi et al. https://doi.org/10.1016/j.envres.2024.118533
- Modeling streamflow variability at the regional scale: (2) Development of a bespoke distributed conceptual model F. Fenicia et al. https://doi.org/10.1016/j.jhydrol.2021.127286
- A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes R. Maxwell et al. https://doi.org/10.3390/w13243633
- To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. https://doi.org/10.5194/hess-28-2705-2024
- Fast urban flood modeling informing response decisions: Model development and future perspectives T. Duan et al. https://doi.org/10.1016/j.aei.2025.104152
- Reducing the computational cost of dynamic global vegetation models with locally weighted hierarchical clustering J. Priesner et al. https://doi.org/10.1140/epjs/s11734-026-02526-1
16 citations as recorded by crossref.
- Towards an Optimal Representation of Sub‐Grid Heterogeneity in Land Surface Models L. Torres‐Rojas et al. https://doi.org/10.1029/2022WR032233
- Scalability and Computational Performance of an Ecohydrological Model Using Machine Learning-Based Prediction N. Cortés-Torres et al. https://doi.org/10.3390/w18040466
- Preface: Linking landscape organisation and hydrological functioning: from hypotheses and observations to concepts, models and understanding C. Jackisch et al. https://doi.org/10.5194/hess-25-5277-2021
- When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al. https://doi.org/10.5194/hess-30-629-2026
- Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature M. Neri et al. https://doi.org/10.1016/j.jhydrol.2022.128555
- Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number A. Sikorska‐Senoner https://doi.org/10.1111/jfr3.12772
- Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California S. Adams et al. https://doi.org/10.5194/hess-27-3021-2023
- Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell https://doi.org/10.1016/j.jhydrol.2021.127287
- The role and value of distributed precipitation data in hydrological models R. Loritz et al. https://doi.org/10.5194/hess-25-147-2021
- Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1277-2025
- Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques P. Shi et al. https://doi.org/10.1016/j.envres.2024.118533
- Modeling streamflow variability at the regional scale: (2) Development of a bespoke distributed conceptual model F. Fenicia et al. https://doi.org/10.1016/j.jhydrol.2021.127286
- A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes R. Maxwell et al. https://doi.org/10.3390/w13243633
- To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. https://doi.org/10.5194/hess-28-2705-2024
- Fast urban flood modeling informing response decisions: Model development and future perspectives T. Duan et al. https://doi.org/10.1016/j.aei.2025.104152
- Reducing the computational cost of dynamic global vegetation models with locally weighted hierarchical clustering J. Priesner et al. https://doi.org/10.1140/epjs/s11734-026-02526-1
Saved (final revised paper)
Latest update: 11 Sep 2026
Short summary
In this paper we propose adaptive clustering as a new method for reducing the computational efforts of distributed modelling. It consists of identifying similar-acting model elements during the runtime, clustering them, running the model for just a few representatives per cluster, and mapping their results to the remaining model elements in the cluster. With the example of a hydrological model, we show that this saves considerable computation time, while largely maintaining the output quality.
In this paper we propose adaptive clustering as a new method for reducing the computational...
