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dc.contributor.authorCalizaya, Elmer-
dc.contributor.authorMejía, Abel-
dc.contributor.authorBarboza Castillo, Elgar-
dc.contributor.authorCalizaya, Fredy-
dc.contributor.authorCorroto, Fernando-
dc.contributor.authorSalas López, Rolando-
dc.contributor.authorVásquez Pérez, Héctor Vladimir-
dc.contributor.authorTurpo Cayo, Efrain-
dc.coverage.spatialPerúes_PE
dc.date.accessioned2022-03-02T23:24:04Z-
dc.date.available2022-03-02T23:24:04Z-
dc.date.issued2021-12-10-
dc.identifier.citationCalizaya, E.; Mejía, A.; Barboza, E.; Calizaya, F.; Corroto, F.; Salas, R.; Vásquez, H.; Turpo, E. Modelling Snowmelt Runoff from Tropical Andean Glaciers under Climate Change Scenarios in the Santa River Sub-Basin (Peru). Water 2021, 13, 3535. doi: 10.3390/w13243535es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12955/1628-
dc.description.abstractEffects of climate change have led to a reduction in precipitation and an increase in temperature across several areas of the world. This has resulted in a sharp decline of glaciers and an increase in surface runoff in watersheds due to snowmelt. This situation requires a better understanding to improve the management of water resources in settled areas downstream of glaciers. In this study, the snowmelt runoff model (SRM) was applied in combination with snow-covered area information (SCA), precipitation, and temperature climatic data to model snowmelt runoff in the Santa River sub-basin (Peru). The procedure consisted of calibrating and validating the SRM model for 2005–2009 using the SRTM digital elevation model (DEM), observed temperature, precipitation and SAC data. Then, the SRM was applied to project future runoff in the sub-basin under the climate change scenarios RCP 4.5 and RCP 8.5. SRM patterns show consistent results; runoff decreases in the summer months and increases the rest of the year. The runoff projection under climate change scenarios shows a substantial increase from January to May, reporting the highest increases in March and April, and the lowest records from June to August. The SRM demonstrated consistent projections for the simulation of historical flows in tropical Andean glaciers.es_PE
dc.description.tableofcontentsAbstract. 1. Introduction. 2. Materials and methods. 3. Results. 4. Discussion. 5. Conclusions. References.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherMDPIes_PE
dc.relation.ispartofWater 2021, 13(24), 3535es_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_PE
dc.sourceInstituto Nacional de Innovación Agrariaes_PE
dc.source.uriRepositorio Institucional - INIAes_PE
dc.subjectCordillera Blanca (CB)es_PE
dc.subjectGlacierses_PE
dc.subjectClimate changees_PE
dc.subjectWateres_PE
dc.subjectGoogle earth engine (GEEes_PE
dc.subjectSnowmelt runoff model (SRM)es_PE
dc.titleModelling Snowmelt Runoff from Tropical Andean Glaciers under Climate Change Scenarios in the Santa River Sub-Basin (Peru)es_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#4.05.00es_PE
dc.identifier.journalWateres_PE
dc.relation.publisherversionhttps://doi.org/10.3390/w13243535es_PE
dc.publisher.countrySuizaes_PE
dc.identifier.doihttps://doi.org/10.3390/w13243535-
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