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Título : | Impact of rain gauges distribution on the runoff simulation of a small mountain catchment in southern Ecuador |
Autor: | Sucozhañay Calle, Adrian Esteban Celleri Alvear, Rolando Enrique |
Correspondencia: | Sucozhañay Calle, Adrian Esteban, addysc1@gmail.com |
Palabras clave : | Modeling uncertainty Páramo ecosystem Precipitation estimation Rainfall monitoring Rainfall-runoff modeling |
Área de conocimiento FRASCATI amplio: | 1. Ciencias Naturales y Exactas |
Área de conocimiento FRASCATI detallado: | 1.5.10 Recursos Hídricos |
Área de conocimiento FRASCATI específico: | 1.5 Ciencias de la Tierra y el Ambiente |
Área de conocimiento UNESCO amplio: | 05 - Ciencias Físicas, Ciencias Naturales, Matemáticas y Estadísticas |
ÁArea de conocimiento UNESCO detallado: | 0521 - Ciencias Ambientales |
Área de conocimiento UNESCO específico: | 052 - Medio Ambiente |
Fecha de publicación : | 2018 |
Volumen: | volúmen 10, número 9 |
Fuente: | Water (Switzerland) |
metadata.dc.identifier.doi: | 10.3390/w10091169 |
Tipo: | ARTÍCULO |
Abstract: | In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km 2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges. View Full-Text |
Resumen : | In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km 2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges. View Full-Text |
URI : | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85052783498&origin=inward |
URI Fuente: | https://www.scimagojr.com/journalsearch.php?q=21100255400&tip=sid&clean=0 |
ISSN : | 2073-4441 |
Aparece en las colecciones: | Artículos
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