Neural network approach for modeling future natural river flows: assessing climate change impacts on the Tagus River

Simple item page
cg.contributor.affiliationUniversidade de Vigoen_US
cg.contributor.affiliationUniversidade de Lisboaen_US
cg.contributor.affiliationUniversidade da Coruñaen_US
cg.contributor.affiliationUniversidade Federal do Rio de Janeiroen_US
cg.contributor.donorEducación e Universidadeen_US
cg.contributor.donorEuropean Union NextGeneration EU/PRTRen_US
cg.contributor.donorPortuguese Fundação para a Ciência e a Tecnologiaen_US
cg.identifier.doihttps://doi.org/10.1016/j.ejrh.2025.102191en_US
cg.identifier.iwmilibraryH053638en_US
cg.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S2214581825000151/pdfft?md5=0eb2cd3a4778aedddde5a0b7d9e1256f&pid=1-s2.0-S2214581825000151-main.pdfen_US
cg.issn2214-5818en_US
cg.journalJournal of Hydrology: Regional Studiesen_US
dc.contributor.authorFernandez-Novoa, D.en_US
dc.contributor.authorSoares, P. M.en_US
dc.contributor.authorGarcia-Feal, O.en_US
dc.contributor.authorCostoya, X.en_US
dc.contributor.authorTrigo, R. M.en_US
dc.contributor.authorGomez-Gesteira, M.en_US
dc.date.accessioned2025-03-26T06:04:53Zen_US
dc.date.available2025-03-26T06:04:53Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/173866en_US
dc.titleNeural network approach for modeling future natural river flows: assessing climate change impacts on the Tagus Riveren_US
dcterms.abstractStudy region: Tagus River basin (Iberian Peninsula). Study focus: An innovative methodology is developed to analyze the impact of climate change on the hydrological cycle. Initially, natural river flow is reconstructed to address the challenge posed by river regulation, which complicates accurate hydrological modeling and can obscure the true impact of climate change. The Iber+ hydrodynamic model is applied to account for downstream reservoir contributions, which allows reversing their influence. Then, neural networks of varying configurations, with specific requirements such as data bucketing, are trained to replicate river flow utilizing recorded precipitation and temperature datasets, subjected to validation procedures. A multi-model ensemble is constructed to address uncertainties inherent in modeling future hydrological climate scenarios. This ensemble, supplied with climate model data, derives historical and projected river flows, allowing analysis of their temporal evolution. New hydrological insights for the region: The findings affirm the efficacy of the proposed methodology and reveal, for the considered high-risk SSP5–8.5 scenario, the intensification of the Tagus hydrological cycle. Within the inherent uncertainty of climate models, average ensemble outputs indicate a reduction of about −20 % in available water at the end of the century, especially critical during summer, with an almost 600 % rise in dry months. Average ensemble results also indicate an increase in flooding events, with extreme floods that currently have five-year frequency, projected to double by the century’s end.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.bibliographicCitationFernandez-Novoa, D.; Soares, P. M.; Garcia-Feal, O.; Costoya, X.; Trigo, R. M.; Gomez-Gesteira, M. 2025. Neural network approach for modeling future natural river flows: assessing climate change impacts on the Tagus River. Journal of Hydrology: Regional Studies, 58:102191. [doi:https://doi.org/10.1016/j.ejrh.2025.102191]en_US
dcterms.issued2025-01-17en_US
dcterms.licenseCC-BY-NC-4.0en_US
dcterms.typeJournal Articleen_US

Files

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Water management outputs