Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1189-2021
© Author(s) 2021. 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-25-1189-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Mar 2021
Research article |
| 08 Mar 2021
| 08 Mar 2021
Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden
Swedish Meteorological and Hydrological Institute, 601 76
Norrköping, Sweden
Louise Crochemore
Swedish Meteorological and Hydrological Institute, 601 76
Norrköping, Sweden
INRAE, UR Riverly, 69100 Villeurbanne, France
Ilias G. Pechlivanidis
Swedish Meteorological and Hydrological Institute, 601 76
Norrköping, Sweden
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26 citations as recorded by crossref.
- Probabilistic Forecast of Ecological Drought in Rivers Based on Numerical Weather Forecast from S2S Dataset C. Cai et al. 10.3390/w16040579
- A Model for Assessing the Importance of Runoff Forecasts in Periodic Climate on Hydropower Production S. Hao et al. 10.3390/w15081559
- A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
- Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets C. Brendel et al. 10.1016/j.envsoft.2024.106094
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- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
- Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. 10.1080/02626667.2022.2104646
- A framework of integrating heterogeneous data sources for monthly streamflow prediction using a state-of-the-art deep learning model W. Xu et al. 10.1016/j.jhydrol.2022.128599
- Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System S. Harrigan et al. 10.5194/hess-27-1-2023
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting J. Musuuza et al. 10.1029/2022WR033655
- Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework H. Llauca et al. 10.1016/j.ejrh.2023.101381
- Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling D. Araya et al. 10.5194/hess-27-4385-2023
- A co-generation success story: Improving drinking water management through hydro-climate services C. Cantone et al. 10.1016/j.cliser.2023.100399
- Spatiotemporal deep learning rainfall-runoff forecasting combined with remote sensing precipitation products in large scale basins S. Zhu et al. 10.1016/j.jhydrol.2022.128727
- Predictability of daily streamflow for the great rivers of South America based on a simple metric I. Petry et al. 10.1080/02626667.2022.2139620
- Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models H. Liu et al. 10.1002/hyp.14410
- Hydrological drought forecasts using precipitation data depend on catchment properties and human activities S. Sutanto et al. 10.1038/s43247-024-01295-w
- Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. 10.1016/j.gsf.2022.101349
- Characterization of Bias during Meteorological Drought Calculation in Time Series Out-of-Sample Validation K. Mammas & D. Lekkas 10.3390/w13182531
- Stochastic optimization of a mixed moving average process for controlling non-Markovian streamflow environments H. Yoshioka et al. 10.1016/j.apm.2022.11.009
- The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting S. Hauswirth et al. 10.5194/hess-27-501-2023
- Robustness of hydrometeorological extremes in surrogated seasonal forecasts K. Klehmet et al. 10.1002/joc.8407
- Quantifying multi-year hydrological memory with Catchment Forgetting Curves A. de Lavenne et al. 10.5194/hess-26-2715-2022
- Hydrological regimes explain the seasonal predictability of streamflow extremes Y. Du et al. 10.1088/1748-9326/acf678
- A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling B. Yifru et al. 10.2166/nh.2024.016
Latest update: 20 Nov 2024
Short summary
The Swedish hydrological warning service is extending its use of seasonal forecasts, which requires an analysis of the available methods. We evaluate the simple ESP method and find out how and why forecasts vary in time and space. We find that forecasts are useful up to 3 months into the future, especially during winter and in northern Sweden. They tend to be good in slow-reacting catchments and bad in flashy and highly regulated ones. We finally link them with areas of similar behaviour.
The Swedish hydrological warning service is extending its use of seasonal forecasts, which...
