Articles | Volume 21, issue 10
https://doi.org/10.5194/hess-21-5273-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-5273-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Oct 2017
Research article |
| 19 Oct 2017
Performance of ensemble streamflow forecasts under varied hydrometeorological conditions
Harm-Jan F. Benninga
CORRESPONDING AUTHOR
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, the Netherlands
present address: Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the Netherlands
Martijn J. Booij
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, the Netherlands
Renata J. Romanowicz
Institute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland
Tom H. M. Rientjes
Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the Netherlands
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Cited
17 citations as recorded by crossref.
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- Hydropower System Operation and the Quality of Short-Term Hydrologic Ensemble Forecasts M. Osina Torres et al. 10.1061/JWRMD5.WRENG-5608
- Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees H. Roux et al. 10.5194/nhess-20-425-2020
- On the implementation of post-processing of runoff forecast ensembles J. Skøien et al. 10.1175/JHM-D-21-0008.1
- A Scalable Earth Observations‐Based Decision Support System for Hydropower Planning in Africa A. Koppa et al. 10.1111/1752-1688.12914
- Regional Adaptability of Global and Regional Hydrological Forecast System H. Wang et al. 10.3390/w15020347
- Exploring the role of the long short‐term memory model in improving multi‐step ahead reservoir inflow forecasting X. Luo et al. 10.1111/jfr3.12854
- Pre- and postprocessing flood forecasts using Bayesian model averaging T. Hegdahl et al. 10.2166/nh.2023.024
- Investigating the effects of local weather, streamflow lag, and global climate information on 1-month-ahead streamflow forecasting by using XGBoost and SHAP: two case studies involving the contiguous USA J. Liu et al. 10.1007/s11600-022-00928-y
- Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India U. Vegad & V. Mishra 10.5194/hess-26-6361-2022
- Simulation and forecasting of streamflows using machine learning models coupled with base flow separation H. Tongal & M. Booij 10.1016/j.jhydrol.2018.07.004
- NASA’s Global Precipitation Measurement Mission: Leveraging Stakeholder Engagement & Applications Activities to Inform Decision-making A. Portier et al. 10.1016/j.rsase.2022.100853
- Comparing Hydrological Postprocessors Including Ensemble Predictions Into Full Predictive Probability Distribution of Streamflow D. Biondi & E. Todini 10.1029/2017WR022432
- Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. 10.5194/hess-22-1831-2018
- Evaluation of ECMWF mid-range ensemble forecasts of precipitation for the Karun River basin M. Abedi et al. 10.1007/s00704-020-03160-0
- What can we learn from the projections of changes of flow patterns? Results from Polish case studies M. Piniewski et al. 10.1007/s11600-017-0061-6
- The critical role of uncertainty in projections of hydrological extremes H. Meresa & R. Romanowicz 10.5194/hess-21-4245-2017
15 citations as recorded by crossref.
- Research Trends of Hydrological Drought: A Systematic Review H. Hasan et al. 10.3390/w11112252
- Hydropower System Operation and the Quality of Short-Term Hydrologic Ensemble Forecasts M. Osina Torres et al. 10.1061/JWRMD5.WRENG-5608
- Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees H. Roux et al. 10.5194/nhess-20-425-2020
- On the implementation of post-processing of runoff forecast ensembles J. Skøien et al. 10.1175/JHM-D-21-0008.1
- A Scalable Earth Observations‐Based Decision Support System for Hydropower Planning in Africa A. Koppa et al. 10.1111/1752-1688.12914
- Regional Adaptability of Global and Regional Hydrological Forecast System H. Wang et al. 10.3390/w15020347
- Exploring the role of the long short‐term memory model in improving multi‐step ahead reservoir inflow forecasting X. Luo et al. 10.1111/jfr3.12854
- Pre- and postprocessing flood forecasts using Bayesian model averaging T. Hegdahl et al. 10.2166/nh.2023.024
- Investigating the effects of local weather, streamflow lag, and global climate information on 1-month-ahead streamflow forecasting by using XGBoost and SHAP: two case studies involving the contiguous USA J. Liu et al. 10.1007/s11600-022-00928-y
- Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India U. Vegad & V. Mishra 10.5194/hess-26-6361-2022
- Simulation and forecasting of streamflows using machine learning models coupled with base flow separation H. Tongal & M. Booij 10.1016/j.jhydrol.2018.07.004
- NASA’s Global Precipitation Measurement Mission: Leveraging Stakeholder Engagement & Applications Activities to Inform Decision-making A. Portier et al. 10.1016/j.rsase.2022.100853
- Comparing Hydrological Postprocessors Including Ensemble Predictions Into Full Predictive Probability Distribution of Streamflow D. Biondi & E. Todini 10.1029/2017WR022432
- Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. 10.5194/hess-22-1831-2018
- Evaluation of ECMWF mid-range ensemble forecasts of precipitation for the Karun River basin M. Abedi et al. 10.1007/s00704-020-03160-0
2 citations as recorded by crossref.
Discussed (final revised paper)
Latest update: 23 Nov 2024
Short summary
Accurate flood and low-streamflow forecasting are important. The paper presents a methodology to evaluate ensemble streamflow-forecasting systems for different lead times; low, medium and high streamflow; and related runoff-generating processes. We applied the methodology to a study forecasting system of the Biała Tarnowska River in Poland. The results provide valuable information about the forecasting system: in which conditions it can be used and how the system can be improved effectively.
Accurate flood and low-streamflow forecasting are important. The paper presents a methodology to...
