Document Type

Presentation

Publication Date

8-1-2014

Abstract

The presence of hydro-meteorological series gaps is a common problem in hydrological and water engineering applications. This is also the case for the Ecuadorian hydrological data-series, showing many gaps of short term duration. This study focuses on the Paute River Basin, in the Southern Ecuadorian Andes. It is one of the most monitored basins in Ecuador, with 25 rainfall observed sites during the period 1963 - 1990. These series suffer of about 20% of missing data. Two techniques were evaluated comparing their efficiency in the filling of missing gaps. The first one is a traditional one based on multiple linear regressions based on logarithmic transformations converting the data to normalized standard variables. The second one is a newly proposed technique based on quantile perturbation factors, following four steps: i. Identification of the station with the highest monthly correlation; ii. selection and ranking of the stations for which the correlation is significant; iii. gap filling based on the stations with the highest significant correlation by linear regressions; and iv. the application of a quantile based correction factor to each filled value (depending on its empirical exceedance probability). For their evaluation, three uninterrupted daily rainfall series were selected. Data values were removed from these series in a random way, simulating about 20% of missing data. The two filling techniques were applied and results inter-compared by means of different statistical criteria. Results indicate that the proposed technique is an efficient method for filling missing gaps. It shows a higher performance for extreme rainfall intensities (high/low intensities). The traditional regression based gap filling method has the disadvantage that it averages out the intensities; hence causes biases in the upper and lower tail of the frequency distribution of the individual values. One disadvantage of the proposed method is the double counting of high/low extremes events.

Comments

Session R49, Data Processing: Reconstruction, Gap Filling, and Error Corrections

 
 

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