Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean

In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been app...

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Detalles Bibliográficos
Autores: Marcos, Raül, Llasat, Mª Carmen, Quintana-Seguí, Pere, Turco, Marco
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/107843
Acceso en línea:https://hdl.handle.net/2117/107843
https://dx.doi.org/10.1016/j.scitotenv.2017.08.010
Access Level:acceso abierto
Palabra clave:Seasonal climate forecasting
Climatology
Bias correction
Seasonal forecast
Water management
Climate services
ECMWF System 4
Reservoir
Clima--Observacions
Climatologia
Àrees temàtiques de la UPC::Energies
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spelling Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern MediterraneanMarcos, RaülLlasat, Mª CarmenQuintana-Seguí, PereTurco, MarcoSeasonal climate forecastingClimatologyBias correctionSeasonal forecastWater managementClimate servicesECMWF System 4ReservoirClima--ObservacionsClimatologiaÀrees temàtiques de la UPC::EnergiesIn this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981–2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter.We thank the Catalan Water Agency for the hydrological data provided. We acknowledge the AEMET and ECMWF for the ECMWF System 4 ensemble re-forecast data. We also acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-_eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu). Raül Marcos thanks the Ministerio de Educación Cultura y Deporte for the FPU (grant reference AP2010-0999) and the Agustí Pedro i Pons University Foundation funding for international research projects. Marco Turco was supported by the Spanish Juan de la Cierva Programme(IJCI-2015-26953).Peer ReviewedElsevier20182018-01-0120172017-09-21journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/107843https://dx.doi.org/10.1016/j.scitotenv.2017.08.010reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 IJCI-2015-26953 IJCI-2015-26953open accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 4.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/4.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1078432026-05-27T15:37:01Z
dc.title.none.fl_str_mv Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean
title Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean
spellingShingle Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean
Marcos, Raül
Seasonal climate forecasting
Climatology
Bias correction
Seasonal forecast
Water management
Climate services
ECMWF System 4
Reservoir
Clima--Observacions
Climatologia
Àrees temàtiques de la UPC::Energies
title_short Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean
title_full Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean
title_fullStr Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean
title_full_unstemmed Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean
title_sort Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean
dc.creator.none.fl_str_mv Marcos, Raül
Llasat, Mª Carmen
Quintana-Seguí, Pere
Turco, Marco
author Marcos, Raül
author_facet Marcos, Raül
Llasat, Mª Carmen
Quintana-Seguí, Pere
Turco, Marco
author_role author
author2 Llasat, Mª Carmen
Quintana-Seguí, Pere
Turco, Marco
author2_role author
author
author
dc.subject.none.fl_str_mv Seasonal climate forecasting
Climatology
Bias correction
Seasonal forecast
Water management
Climate services
ECMWF System 4
Reservoir
Clima--Observacions
Climatologia
Àrees temàtiques de la UPC::Energies
topic Seasonal climate forecasting
Climatology
Bias correction
Seasonal forecast
Water management
Climate services
ECMWF System 4
Reservoir
Clima--Observacions
Climatologia
Àrees temàtiques de la UPC::Energies
description In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981–2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-09-21
2018
2018-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/107843
https://dx.doi.org/10.1016/j.scitotenv.2017.08.010
url https://hdl.handle.net/2117/107843
https://dx.doi.org/10.1016/j.scitotenv.2017.08.010
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 IJCI-2015-26953 IJCI-2015-26953
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 4.0 Spain
http://creativecommons.org/licenses/by-nc-nd/4.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 4.0 Spain
http://creativecommons.org/licenses/by-nc-nd/4.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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