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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/22053
Title: Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes
Other Titles: Journal of Hydrology
Authors: Buytaert, Wouter
Célleri, Rolando
Willems, Patrick
Bievre, Bert De
Wyseure, Guido
Keywords: Rainfall Variability
Mountain Environments
Issue Date: 15-Oct-2006
metadata.dc.ucuenca.paginacion: Páginas 413-421
volumen 329; número 3-4
metadata.dc.identifier.doi: doi:10.1016/j.jhydrol.2006.02.031
metadata.dc.type: Article
Particularly in mountain environments, rainfall can be extremely variable in space and time. For many hydrological applications such as modelling, extrapolation of point rainfall measurements is necessary. Decisions about the techniques used for extrapolation, as well as the adequacy of the conclusions drawn from the final results, depend heavily on the magnitude and the nature of the uncertainty involved. In this paper, we examine rainfall data from 14 rain gauges in the western mountain range of the Ecuadorian Andes. The rain gauges are located in the western part of the rio Paute basin. This area, between 3500 and 4100 m asl, consists of mountainous grasslands, locally called páramo, and acts as major water source for the inter-Andean valley. Spatial and temporal rainfall patterns were studied. A clear intraday pattern can be distinguished. Seasonal variation, on the other hand, is low, with a difference of about 100 mm between the dryest and the wettest month on an average of about 100 mm month−1, and only 20% dry days throughout the year. Rain gauges at a mutual distance of less than 4000 m are strongly correlated, with a Pearson correlation coefficient higher than 0.8. However, even within this perimeter, spatial variability in average rainfall is very high. Significant correlations were found between average daily rainfall and geographical location, as well as the topographical parameters slope, aspect, topography. Spatial interpolation with thiessen gives good results. Kriging gives better results than thiessen, and the accuracy of both methods improves when external trends are incorporated.
URI: http://dspace.ucuenca.edu.ec/handle/123456789/22053
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