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Improving Flood Forecasting Skill with the Ensemble Kalman Filter

Improving Flood Forecasting Skill with the Ensemble Kalman Filter

dc.creatorVergara, Humberto
dc.creatorHong, Yang
dc.creatorGourley, Jonathan
dc.date2016-03-05
dc.date.accessioned2020-08-21T20:40:12Z
dc.date.available2020-08-21T20:40:12Z
dc.identifierhttps://revistas.unbosque.edu.co/index.php/RevTec/article/view/1294
dc.identifier10.18270/rt.v13i1.1294
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/10699
dc.descriptionThe purpose of this particular work was to explore the benefits and drawbacks of sequential state updating for flood forecasting and identify factors or mechanisms affecting the updating process and thus controlling its performance. The Ensemble Kalman filter was employed to assimilate hourly streamflow observations into a simple but widely used conceptual rainfall-runoff model for flood prediction purposes. Ensembles were constructed by perturbing model forcing and parameters. Parametric perturbations were obtained from multiple model calibrations with an optimization algorithm. Errors in streamflow observations were characterized through an innovative yet simple empirical model. A sensitivity analysis was performed to evaluate the improvement of the first guess forecast. Additionally, the forecast skill was assessed as a function of lead-time. It was found that the improvement is mainly reflected in runoff volume, while the peak timecan be deteriorated as a trade-off of the assimilation process. Overall, ensemble-based models with sequential data assimilation outperformed the best-calibrated deterministic models for lead times of at least 1.5 days.en-US
dc.descriptionThe purpose of this particular work was to explore the benefits and drawbacks of sequential state updating for flood forecasting and identify factors or mechanisms affecting the updating process and thus controlling its performance. The Ensemble Kalman filter was employed to assimilate hourly streamflow observations into a simple but widely used conceptual rainfall-runoff model for flood prediction purposes. Ensembles were constructed by perturbing model forcing and parameters. Parametric perturbations were obtained from multiple model calibrations with an optimization algorithm. Errors in streamflow observations were characterized through an innovative yet simple empirical model. A sensitivity analysis was performed to evaluate the improvement of the first guess forecast. Additionally, the forecast skill was assessed as a function of lead-time. It was found that the improvement is mainly reflected in runoff volume, while the peak timecan be deteriorated as a trade-off of the assimilation process. Overall, ensemble-based models with sequential data assimilation outperformed the best-calibrated deterministic models for lead times of at least 1.5 days.es-AR
dc.descriptionThe purpose of this particular work was to explore the benefits and drawbacks of sequential state updating for flood forecasting and identify factors or mechanisms affecting the updating process and thus controlling its performance. The Ensemble Kalman filter was employed to assimilate hourly streamflow observations into a simple but widely used conceptual rainfall-runoff model for flood prediction purposes. Ensembles were constructed by perturbing model forcing and parameters. Parametric perturbations were obtained from multiple model calibrations with an optimization algorithm. Errors in streamflow observations were characterized through an innovative yet simple empirical model. A sensitivity analysis was performed to evaluate the improvement of the first guess forecast. Additionally, the forecast skill was assessed as a function of lead-time. It was found that the improvement is mainly reflected in runoff volume, while the peak timecan be deteriorated as a trade-off of the assimilation process. Overall, ensemble-based models with sequential data assimilation outperformed the best-calibrated deterministic models for lead times of at least 1.5 days.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherUniversidad El Bosquees-ES
dc.relationhttps://revistas.unbosque.edu.co/index.php/RevTec/article/view/1294/901
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dc.sourceJournal of Technology; Vol 13 No 1 (2014): Tecnología para hábitos y estilos de vida sostenibles; 9-27en-US
dc.sourceRevista de Tecnología; ##issue.vol## 13 ##issue.no## 1 (2014): Tecnología para hábitos y estilos de vida sostenibles; 9-27es-AR
dc.sourceRevista de Tecnología; Vol. 13 Núm. 1 (2014): Tecnología para hábitos y estilos de vida sostenibles; 9-27es-ES
dc.source1692-1399
dc.titleImproving Flood Forecasting Skill with the Ensemble Kalman Filteres-AR
dc.titleImproving Flood Forecasting Skill with the Ensemble Kalman Filteres-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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