Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)

The Senegal River Basin, located in West Africa, has been affected by several droughts since the end of the 1960s. In its valley, which is densely populated and highly vulnerable to climate variability and water availability, agricultural activities provide the livelihood for thousands of people. In...

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Autores: Giraldo Osorio, Juan Diego, García Galiano, Sandra Gabriela
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2012
País:España
Institución:Universidad Politécnica de Cartagena(UPCT)
Repositorio:Repositorio Digital UPCT
OAI Identifier:oai:repositorio.upct.es:10317/13458
Acceso en línea:http://hdl.handle.net/10317/13458
https://www.sciencedirect.com/science/article/pii/S0022169412004052
Access Level:acceso abierto
Palabra clave:Dry spells
Non-stationary analysis
Senegal River Basin
Climate change
RCM
Monsoon season
Ingeniería Hidráulica
2502 Climatología
2508 Hidrología
1209 Estadística
2509.04 Hidrometeorología
id ES_2ef9e9bbc7f2e154b8fb73acfd7cdb60
oai_identifier_str oai:repositorio.upct.es:10317/13458
network_acronym_str ES
network_name_str España
repository_id_str
spelling Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)Giraldo Osorio, Juan DiegoGarcía Galiano, Sandra GabrielaDry spellsNon-stationary analysisSenegal River BasinClimate changeRCMMonsoon seasonIngeniería Hidráulica2502 Climatología2508 Hidrología1209 Estadística2509.04 HidrometeorologíaThe Senegal River Basin, located in West Africa, has been affected by several droughts since the end of the 1960s. In its valley, which is densely populated and highly vulnerable to climate variability and water availability, agricultural activities provide the livelihood for thousands of people. Increasing the knowledge about plausible trends of drought events will allow to improve the adaptation and mitigation measures in order to build ‘‘adaptive capacity’’ to climate change in West Africa. An innovative methodology for the non-stationary analysis of droughts events, which allows the prediction of regional trends associated to several return periods, is presented. The analyses were based on Regional Climate Models (RCMs) provided by the European ENSEMBLES project for West Africa, together with observed data. A non-stationary behaviour of the annual series of maximum length of dry spells (AMDSL) in the monsoon season is reflected in temporal changes in mean and variance. The non-stationary nature of hydrometeorological series, due to climate change and anthropogenic activities, is the main criticism to traditional frequency analysis. Therefore, in this paper, the modelling tool GAMLSS (Generalized Additive Models for Location, Scale and Shape), is applied to develop regional probability density functions (pdfs) fitted to AMDSL series for the monsoon season in the Senegal River Basin. The skills of RCMs in the representation of maximum length of dry spells observed for the period 1970–1990, are evaluated considering observed data. Based on the results obtained, a first selection of the RCMs with which to apply GAMLSS to the AMDSL series identified, for the time period 1970–2050, is made. The results of GAMLSS analysis exhibit divergent trends, with different value ranges for parameters of probability distributions being detected. Therefore, in the second stage of the paper, regional pdfs are constructed using bootstrapping distributions based on probabilistic models. In general, an increase in the mean and variance statistics of AMDSL at regional level are predicted, thereby increasing the lengths of dry spells associated with a low probability of occurrence (related to high return period) in the monsoon season.This work was performed within the framework of the EU FP6 Integrated Project AMMA. The ENSEMBLES data used in this work was funded by the EU FP6 Integrated Project ENSEMBLES (Contract No. 505539). The observed daily rainfall used, was collected by IRD (France). We appreciate the support of R&D Project CGL2008-02530/BTE of Spanish Ministry of Science and Innovation.ElsevierComisión EuropeaMinisterio de Ciencia e Innovación202420242012info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10317/13458https://www.sciencedirect.com/science/article/pii/S0022169412004052reponame:Repositorio Digital UPCTinstname:Universidad Politécnica de Cartagena(UPCT)Inglésinfo:eu-repo/grantAgreement/EC/FP6-SUSTDEV/004089info:eu-repo/grantAgreement/EC/FP6-SUSTDEV/505539info:eu-repo/grantAgreement/MICINN/Plan Nacional de I+D+i 2008-2011/CGL2008-02530%2FBTE004089505539CGL2008-02530/BTEinfo:eu-repo/semantics/openAccessoai:repositorio.upct.es:10317/134582026-05-15T06:39:02Z
dc.title.none.fl_str_mv Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)
title Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)
spellingShingle Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)
Giraldo Osorio, Juan Diego
Dry spells
Non-stationary analysis
Senegal River Basin
Climate change
RCM
Monsoon season
Ingeniería Hidráulica
2502 Climatología
2508 Hidrología
1209 Estadística
2509.04 Hidrometeorología
title_short Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)
title_full Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)
title_fullStr Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)
title_full_unstemmed Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)
title_sort Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)
dc.creator.none.fl_str_mv Giraldo Osorio, Juan Diego
García Galiano, Sandra Gabriela
author Giraldo Osorio, Juan Diego
author_facet Giraldo Osorio, Juan Diego
García Galiano, Sandra Gabriela
author_role author
author2 García Galiano, Sandra Gabriela
author2_role author
dc.contributor.none.fl_str_mv Comisión Europea
Ministerio de Ciencia e Innovación
dc.subject.none.fl_str_mv Dry spells
Non-stationary analysis
Senegal River Basin
Climate change
RCM
Monsoon season
Ingeniería Hidráulica
2502 Climatología
2508 Hidrología
1209 Estadística
2509.04 Hidrometeorología
topic Dry spells
Non-stationary analysis
Senegal River Basin
Climate change
RCM
Monsoon season
Ingeniería Hidráulica
2502 Climatología
2508 Hidrología
1209 Estadística
2509.04 Hidrometeorología
description The Senegal River Basin, located in West Africa, has been affected by several droughts since the end of the 1960s. In its valley, which is densely populated and highly vulnerable to climate variability and water availability, agricultural activities provide the livelihood for thousands of people. Increasing the knowledge about plausible trends of drought events will allow to improve the adaptation and mitigation measures in order to build ‘‘adaptive capacity’’ to climate change in West Africa. An innovative methodology for the non-stationary analysis of droughts events, which allows the prediction of regional trends associated to several return periods, is presented. The analyses were based on Regional Climate Models (RCMs) provided by the European ENSEMBLES project for West Africa, together with observed data. A non-stationary behaviour of the annual series of maximum length of dry spells (AMDSL) in the monsoon season is reflected in temporal changes in mean and variance. The non-stationary nature of hydrometeorological series, due to climate change and anthropogenic activities, is the main criticism to traditional frequency analysis. Therefore, in this paper, the modelling tool GAMLSS (Generalized Additive Models for Location, Scale and Shape), is applied to develop regional probability density functions (pdfs) fitted to AMDSL series for the monsoon season in the Senegal River Basin. The skills of RCMs in the representation of maximum length of dry spells observed for the period 1970–1990, are evaluated considering observed data. Based on the results obtained, a first selection of the RCMs with which to apply GAMLSS to the AMDSL series identified, for the time period 1970–2050, is made. The results of GAMLSS analysis exhibit divergent trends, with different value ranges for parameters of probability distributions being detected. Therefore, in the second stage of the paper, regional pdfs are constructed using bootstrapping distributions based on probabilistic models. In general, an increase in the mean and variance statistics of AMDSL at regional level are predicted, thereby increasing the lengths of dry spells associated with a low probability of occurrence (related to high return period) in the monsoon season.
publishDate 2012
dc.date.none.fl_str_mv 2012
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10317/13458
https://www.sciencedirect.com/science/article/pii/S0022169412004052
url http://hdl.handle.net/10317/13458
https://www.sciencedirect.com/science/article/pii/S0022169412004052
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/EC/FP6-SUSTDEV/004089
info:eu-repo/grantAgreement/EC/FP6-SUSTDEV/505539
info:eu-repo/grantAgreement/MICINN/Plan Nacional de I+D+i 2008-2011/CGL2008-02530%2FBTE
004089
505539
CGL2008-02530/BTE
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital UPCT
instname:Universidad Politécnica de Cartagena(UPCT)
instname_str Universidad Politécnica de Cartagena(UPCT)
reponame_str Repositorio Digital UPCT
collection Repositorio Digital UPCT
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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