Relleno de series diarias de precipitación, temperatura mínima, máxima de la región norte del Urabá Antioqueño

Abstract

ResumenLos estudios agroclimáticos requieren la utilización de series cronológicas de las variables meteorológicas, que generalmente presentan datos faltantes limitando su uso, por lo que los métodos de relleno son utilizados. La finalidad de este trabajo fue determinar la confiablidad de los métodos de relleno: U.S National Weather Servicie (WS), deductivo racional (RD), la regresión múltiple (RM) y regresión lineal (RL) y de ellos utilizar el mejor para rellenar los datos de las series de precipitación, temperatura máxima y temperatura mínima en el periodo 2006-2009 de las estaciones ubicadas en la zona Norte del eje bananero del Urabá Antioqueño. La estación base fue la estación Pista Indira, y las estaciones Uniban, Turbo, Aeropuerto y Prado Mar se utilizaron como vecinas. La estimación del error se hizo con la: raíz cuadrada del cuadrado medio del error (RCCME), coeficiente de determinación (R 2 ), error medio absoluto (MAE), error relativo (RE) e Índice de concordancia de Willmott (d). Los métodos RM y RL presentaron RCCME y MAE similares a los del WS, lo que con llevaría a tener errores similares pero debido a que la R 2 de los primeros fueron menores de 0.8 y el índice (d) para WS fue mayor o similar a lo demás método, se utilizó este último para el relleno de las series.Palabras claves: deductivo, distancia inversa, regresión. AbstractAgroclimatic studies require the use of time series of meteorological variables, which generally present missing data limiting its use, so filling methods are used. The purpose of this study was to determine the reliability of filling methods: US National Weather Servicie (WS), rational deductive (RD), multiple regression (MR) and linear regression (LR) and from them use the best to fill the data series of precipitation, maximum and minimum temperature during the period 2006-2009 of the stations located in the northern area on b anana axis from Urabá Antoquia. The base station was Pïsta Indira, and Uniban, Turbo, Aeropuerto and Prado Mar stations were used as neighbors. Standard error was made with: square root of the mean squared error (RCCME), coefficient of determination (R 2 ), mean absolute error (MAE), relative error (RE) and Willmott's concordance index (d). RM and RL methods presented similar RCCME and MAE to those of WS, which would lead to have similar errors but due to R 2 of the first were 0.8 and the index (d) for WS was higher or similar to the other method, the latter was used to fill in the series.Keywords: deductive, inverse distance, regression. IntroducciónEl clima es un recurso natural que afecta la producción agraria, por lo tanto es importante tener conocimiento de los recursos disponibles del ambiente. Las interacciones que se producen en el área del cultivo, debajo de la superficie del suelo, la interfase suelo-aire y la capa límite de la atmósfera, proporciona una guía esencial para las decisiones estratégicas en planificación a largo plazo de los sistemas agrícolas (WMO, 1981).En las estaciones climatológicas es común que los registros estén inco...

Remote sensing-based estimation of precipitation data (2000-2015) in Ecuador's Loja province

“…The coefficient of determination represents the percentage of variation in the response variable, which a linear model explains. In other words, there is a good correlation between the data (Toro et al, 2017), which obtained similar results when filling in missing data using the linear correlation method for the Ambi River basin.…”

“…It is an average that considers the inverse of the squared distance as a weighting factor, thus distributing the contribution of each auxiliary station by weight, based on Eq. 2 (Chow et al, 1994), cited by (Toro et al, 2017).…”

Analysis of a new spatial interpolation weighting method to estimate missing data applied to rainfall records

“…Since there is not a well-established criterion of what to consider as an acceptable percentage of missing data in a time series dataset (Dong and Peng, 2013), an assumption was made to consider datasets with daily rainfall with at most 25% of missing data. This approach allowed to have a more reliable dataset, even though rainfall time series with higher percentages of missing data have been considered in other studies (Malek et al, 2010;Campozano et al, 2015;Toro-Trujillo et al, 2015). Table II shows a summary of the main features of the selected weather stations, which cover 82% of Tabasco's municipalities (14 of 17).…”

“…Geostatistical estimation methods based on theoretical foundations (particularly kriging) are based on showing the proportion in which the variance between points in space changes and is expressed in a semi-variogram. However, this procedure is limited because it relies on a certain amount of data to produce a reliable and adequate variogram (Toro-Trujillo et al, 2015). Regardless of having a high number of points to interpolate, some authors argue that kriging does not show improvements with respect to the IDW method (Wagner et al, 2012).…”