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You are here : eLibrary : IAHR World Congress Proceedings : 34th Congress - Brisbane (2011) : THEME 7: Hydroinformatics : Filling missing data in streamflow series for supporting models
Filling missing data in streamflow series for supporting models
Author : Mijail Arias-Hidalgo, Gonzalo Villa-Cox, Ann van Griensven and Arthur E. Mynett
Several previous publications have addressed the problem of missing data in time series, which is a typical problem in many developing regions. This paper proposes an alternative when to develop a rainfall-runoff model to predict streamflows given rainfall series is time demanding or when such a data are simply not available. The Hodrick-Prescott filter has been employed in two modalities: heteroscedastic and homoscedastic by blocks (high and low discharges). The signal was split in trend and noise where the former is simulated and extended using Fourier Series and the latter is a function of the trend and computed using the Maximum Likelihood Estimation criterion for the unknown values. Afterwards, summing up the simulated trend and noise, a new total signal was obtained. The applicability of this technique was tested in some streamflow stations in Guayas River Basin (34000 Km2), Ecuador. Further models can be benefited from the products of these interpolations, either in setup stage (e.g. boundary conditions) or for further application such as recognition of overbanking periods.
File Size : 791,849 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 34th Congress - Brisbane (2011)
Article : THEME 7: Hydroinformatics
Date Published : 01/07/2011
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