Imputation of Missing Daily Rainfall Data Case Study of Quindio River Basin
DOI:
https://doi.org/10.18041/1909-2458/ingeniare.18.539Keywords:
Rainfall, Spatial interpolation, Weighting methodsAbstract
This paper shows the results obtained when five methods for imputing missing daily rainfall were applied to records of eight hydrological stations located in the Quindío river basin, on the west-center part of Colombia. With the purpose of preserving the presence of no rainfall data, were considered calculate the empirical probabilities of first-order Markov chains. The five methods were implemented with a recursive algorithm which initializes missing data with the average daily rainfall. After this, the algorithm runs iteratively, replacing the previous run missing data imputations, it runs until the maximum difference between two successive imputations is smaller than a threshold value. Data imputed by the Statistical Measure Weighting Method conserves the measures of central tendency from each station daily rainfall record when it includes missing data.
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References
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F.-W. Chen and C.-W. Liu, “Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan”, Paddy and Water Environment, vol.10, n°. 3, pp. 209- 222, 2012.
J.-W. Kim and Y.A. Pachepsky, “Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation”, Journal of Hydrology, n°.
, pp. 305-314, 2010.
B. Ahrens. “Distance in spatial interpolation of daily rain gauge data”, Hydrol. Earth Syst. Sci, n°. 10, pp. 197-208, 2006.
R.S.V. Teegavarapu and V. Chandramouli, “Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records”, Journal of Hydrology,
n°. 312, pp. 191-206, 2005.
Y. Xia, P. Fabian, A. Stohl and M. Winterhalter, “Forest climatology: estimation of missing values for Bavaria, Germany”, Agricultural and Forest Meteorology, n°. 96, pp. 131-144, 1999.
Corporación Autónoma Regional del Quindío. Proyecto de reglamentación de las aguas del río Quindío y sus tributarios. Subdirección de Ejecución de Políticas Ambientales, Corporación Autónoma Regional del Quindío, 2011.
Corporación Autónoma Regional del Quindío. Boletín Meteorológico CRQ 2005. Subdirección de Ejecución de Políticas Ambientales, Corporación Autónoma Regional del Quindío, 2006.
L. Campozano, E. Sanchez, A. Aviles and E. Samaniego, “Evaluation of infilling methods for time series of daily precipitation and temperature: The case of the Ecuadorian Andes”, Maskana,
vol. 5, n°. 1, pp. 99-115, 2014.
M.A. Malek, S.M. Shamsuddin and S. Harun, “Restoration of hydrological data in the presence of missing data via Kohonen Self Organizing Maps”, en New Trends in Technologies, B. Ramov.
Ed., 2010. New York, USA: Editorial Intech, pp. 223-242.
B. Hingray, C. Picouet and A. Musy, Hydrology: A Science for Engineers. New York, USA: CRC Press. Data required for hydrological analysis and modeling, Precipitation, 2015.
M.M. Hasan and B.F.W. Croke (2013). Filling gaps in daily rainfall data: a statistical approach. Conference presented at 20th International Congress on Modelling and Simulation, Adelaide,
Australia.
B.E. Vieux, Distributed Hydrologic Modeling Using GIS. Norwell, USA: Kluwer Academic Publishers, 2004. Surface Generation: Producing spatial data from point measurements, Surface
Generators, 2004.
Python Software Foundation. Python Language Reference, version 2.7. Disponible en: http://www.python.org
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Published
2015-01-01
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Artículos
How to Cite
1.
García Reinoso PL. Imputation of Missing Daily Rainfall Data Case Study of Quindio River Basin. ingeniare [Internet]. 2015 Jan. 1 [cited 2025 Apr. 7];(18):73-86. Available from: https://revistas.unilibre.edu.co/index.php/ingeniare/article/view/539
