Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul

Bioscience Journal

Elias Rodrigues da Cunha¹  Francisco Eduardo Torres¹  Victor Matheus Bacani¹  Larissa Pereira Ribeiro¹  Givanildo Gois¹  Caio Cézar Guedes Corrêa¹  José Francisco de Oliveira-Junior²  Paulo Eduardo Teodoro³ 

[1] ;Department of Environmental Sciences, Forest Institute, Federal University of Rio de Janeiro (UFRRJ), 23890-000, Seropédica, Rio de Janeiro, Brazil.;M.Sc. Student of Plant Production in Universidade Estadual de Mato Grosso do Sul, University Unit of Aquidauana (UEMS/UUA), 79200-000, Aquidauana, Brazil.;

关键词: correlation;    multiple linear regression;    path analysis.;   

DOI  :  10.14393/BJ-v32n1a2016-29387

来源: DOAJ

【 摘 要 】

Adjustment of multiple linear regression equations has allowed estimating the value of a certain climatological variable according to geographical coordinates with acceptable degree of accuracy. The aim of this study was to verify if the average monthly rainfall could be estimated according to the altitude, latitude and longitude in Mato Grosso do Sul State (MS). Rainfall data of 32 stations of MS were collected from 1954 to 2013. It were formed 384 time series (12 months × 32 sites), with different numbers of years of observations in each series. On each of the 384 monthly rainfall time series it was calculated the average (a), at least 30 years of observation, forming 12 matrices 32 x 4 (32 sites x 4 variables: altitude, latitude, longitude and monthly rainfall). It was estimated for each matrix the Pearson's linear correlation coefficient among the variables, performing the multicollinearity diagnosis for each matrix. Correlations were unfolded by path analysis in direct and indirect effects and in each month it was used the multiple linear regression model. The altitude and latitude have greater effect on the spatial distribution of rainfall in MS. The multiple linear regression equations generated in this study will subsidize researches of crop zoning, indication for sowing times, irrigation, determination of yield potential, climate risks zoning and credit and agricultural insurance.

【 授权许可】

Unknown