Resumen

CAMPOS-ARANDA, Daniel Francisco. Simultaneous Estimation of Hydrologic Annual Data Missing in Multiple Sites. Ing. invest. y tecnol. [online]. 2015, vol.16, n.2, pp.295-306. ISSN 2594-0732.

The deduction of annual missing data in hydrological records is necessary to integrate series with a common period, which are required in simulation studies of hydraulic systems and several regional flood estimation methods. Besides, the statistical estimates become more reliable and accurate when full and extensive series are utilized. Multiple linear regression (MLR) allows estimating annual missing data based on close records that have dependence or correlation with the incomplete sequence. The Beale-Little algorithm is based in MLR where each record considered as a dependent variable and the rest as regressors; uses all available information, not only the common data period and leads to a simultaneous estimation of annual missing values in the records processed. Three numerical applications of the Beale-Little algorithm are described to estimate annual missing data of runoff volume and maximum flow in the system Tempoal River and Upper Grijalva River of the Hydro-logical Regions No. 26 (Panuco) and No. 30 (Grijalva-Usumacinta), which has five hydrometric stations, four of which are complete. The conclusions pointed out the advantages of the procedure described and illustrated numerically and recommend its systematic application given its ease of implementation.

Palabras llave : Beale-Little algorithm; multiple linear regression; linear correlation coefficient; Tempoal River; Upper Grijalva River.

        · resumen en Español     · texto en Español     · Español ( pdf )