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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/33128
Title: Impact of rain gauges distribution on the runoff simulation of a small mountain catchment in southern Ecuador
Authors: Sucozhañay Calle, Adrian Esteban
Celleri Alvear, Rolando Enrique
metadata.dc.ucuenca.correspondencia: Sucozhañay Calle, Adrian Esteban, addysc1@gmail.com
Keywords: Modeling uncertainty
Páramo ecosystem
Precipitation estimation
Rainfall monitoring
Rainfall-runoff modeling
metadata.dc.ucuenca.areaconocimientofrascatiamplio: 1. Ciencias Naturales y Exactas
metadata.dc.ucuenca.areaconocimientofrascatidetallado: 1.5.10 Recursos Hídricos
metadata.dc.ucuenca.areaconocimientofrascatiespecifico: 1.5 Ciencias de la Tierra y el Ambiente
metadata.dc.ucuenca.areaconocimientounescoamplio: 05 - Ciencias Físicas, Ciencias Naturales, Matemáticas y Estadísticas
metadata.dc.ucuenca.areaconocimientounescodetallado: 0521 - Ciencias Ambientales
metadata.dc.ucuenca.areaconocimientounescoespecifico: 052 - Medio Ambiente
Issue Date: 2018
metadata.dc.ucuenca.volumen: volúmen 10, número 9
metadata.dc.source: Water (Switzerland)
metadata.dc.identifier.doi: 10.3390/w10091169
metadata.dc.type: 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
Description: 
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
metadata.dc.ucuenca.urifuente: https://www.scimagojr.com/journalsearch.php?q=21100255400&tip=sid&clean=0
ISSN: 2073-4441
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