Regional streamflow prediction in northwest Spain: A comparative analysis of regionalisation schemes

UDC.coleccionInvestigaciónes_ES
UDC.departamentoEnxeñaría Civiles_ES
UDC.grupoInvEnxeñaría da Auga e do Medio Ambiente (GEAMA)es_ES
UDC.institutoCentroCITEEC - Centro de Innovación Tecnolóxica en Edificación e Enxeñaría Civiles_ES
UDC.journalTitleJournal of Hydrology: Regional Studieses_ES
UDC.startPage101427es_ES
UDC.volume47es_ES
dc.contributor.authorCea, Luis
dc.contributor.authorFarfán-Durán, Juan F.
dc.contributor.otherEnxeñaría da Auga e do Medio Ambiente (GEAMA)es_ES
dc.date.accessioned2024-02-15T14:51:36Z
dc.date.available2024-02-15T14:51:36Z
dc.date.issued2023
dc.descriptionFinanciado para publicación en acceso aberto: Universidade da Coruña/CISUGes_ES
dc.description.abstract[Abstract:] Study Region: The present study was conducted in 24 watersheds located in the region of Galicia, in the northwest of Spain, covering an extension of approximately 13,000 km. Study focus: This study is focused on the application and evaluation of different schemes for streamflow Prediction in Ungauged Basins (PUB). The MHIA model (Spanish acronym for Modelo HIdrológico Agregado), is first used to reproduce the observed time series of discharge in several gauged basins. Then, six different regionalisation schemes are applied to transfer the hydrological model parameters to ungauged catchments. For that purpose, we explore and compare two physical similarity, two spatial proximity and two regression-based regionalisation schemes. Output averaging (also known as ensemble modelling) as well as parameter averaging implementations of the physical similarity and spatial proximity methods are analysed. New hydrological insights: The most efficient methods are those based on output averaging, with acceptable success rates (SR) in 88% of the cases. On the other hand, the parameter averaging-based methods have the lowest SR. The methods based on spatial proximity output averaging provide the best performance when the receptor basin has a sufficient number of nearby donor basins. On the other hand, the methods based on physical similarity output averaging show a better performance in areas where there is a low density of donor catchments. The regression-based methods showed the lowest performance in all cases. The existence of correlations between the performance of the regionalisation schemes and the area of the receptor catchments was observed, with higher performances in large basins than in small basins.es_ES
dc.description.sponsorshipThis study has received financial support from the Galician government (Xunta de Galicia) as part of its pre-doctoral fellowship program (Axudas de apoio á etapa predoutoral 2019) Register No ED481A-2019/014. Funding for open access charge: Xunta de Galicia (project No ED431C 2022/010) and Universidade da Coruña/CISUGes_ES
dc.description.sponsorshipXunta de Galicia; ED481A-2019/014es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2022/010es_ES
dc.identifier.citationFarfán, J. F., & Cea, L. (2023). Regional streamflow prediction in northwest Spain: A comparative analysis of regionalisation schemes. Journal of Hydrology: Regional Studies, 47, 101427. https://doi.org/10.1016/j.ejrh.2023.101427es_ES
dc.identifier.doi10.1016/j.ejrh.2023.101427
dc.identifier.urihttp://hdl.handle.net/2183/35620
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.urihttps://doi.org/10.1016/j.ejrh.2023.101427es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectUngauged basines_ES
dc.subjectRegionalisationes_ES
dc.subjectSpatial proximityes_ES
dc.subjectPhysical similarityes_ES
dc.subjectArtificial neural networkses_ES
dc.subjectHydrological modeles_ES
dc.titleRegional streamflow prediction in northwest Spain: A comparative analysis of regionalisation schemeses_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationd914d106-6715-40cf-b743-1e240f37dc94
relation.isAuthorOfPublication86fc26ef-d2cd-4c4d-8219-d9fe0f37914f
relation.isAuthorOfPublication.latestForDiscoveryd914d106-6715-40cf-b743-1e240f37dc94

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
FarfanJuanF_2023_JoHRS_47_101427.pdf
Size:
9.31 MB
Format:
Adobe Portable Document Format
Description: