Espacialização da precipitação pluvial por meio de krigagem e cokrigagem

Abstract

Resumo -O objetivo deste trabalho foi avaliar a cokrigagem ordinária, com dados de altitude e distância do mar, em comparação à krigagem ordinária, na espacialização da precipitação pluvial dos períodos anual, seco e úmido, no Estado do Espírito Santo. Os dados de altitude e distância do mar foram obtidos de pontos de amostragem em grades regulares e irregulares. Foram utilizados dados de 108 postos pluviométricos. A avaliação dos métodos e das variáveis foi realizada com base na validação cruzada, tendo-se considerado os erros dos valores preditos e o ajuste dos modelos de regressão linear para valores observados e preditos. A grade regular para amostragem das covariáveis apresentou melhor acurácia de predição em comparação à grade irregular. A cokrigagem produziu resultados mais acurados do que a krigagem, verificados por pequenas diferenças nos erros médios absolutos, capazes de produzir mapas estatisticamente diferentes. A interpolação por cokrigagem e o uso de grades regulares para amostragem são preferíveis, principalmente se as covariáveis são de fácil obtenção e de baixo custo.Termos para indexação: distribuição da precipitação, geoestatística, linha de costa, predição espacial. Rainfall spatialization by kriging and cokrigingAbstract -The objective of this work was to evaluate the ordinary co-kriging algorithm, using data from elevation and distance from the sea, compared to ordinary kriging, in the rainfall spatialization associated to wet, dry and annual periods, for the state of Espírito Santo, Brazil. Data of altitude and distance from the sea were obtained in sampling sites on regular and irregular grids. Data from 108 rain gauge stations were used. The evaluation of methods and variables based on cross-validation was performed, considering the errors of the predicted values and the fit of linear regression models for observed and predicted values. The regular grid sampling of the covariates showed the best prediction accuracy compared to the irregular grid. Cokriging produced more accurate results than kriging, checked by small differences in mean absolute errors which were able to produce statistically different maps. Cokriging interpolation and use of regular grids for sampling are preferable, mainly if the covariates are easy to obtain and inexpensive.

Space variability of phenological indicators of common bean crop

“…Standard error maps of kriging prediction were generated. These maps refer to the standard deviation of the prediction for any individual point (CUNHA et al, 2013), and they are obtained to have the source of information about the reliability of the interpolated values in the study area (FERRAZ et al, 2015). Cross-validation is a tool to evaluate alternative models of simple and crossed semivariograms, which will perform kriging and co-kriging, respectively.…”

Inter-relationships of Resistance to Penetration, Moisture and Soil Organic Matter with Irrigated Bean Yield in Mato Grosso do Sul, Brazil

“…Standard error maps of kriging prediction were generated. These maps refer to the prediction standard deviation for any individual point [11]; they are obtained to provide information about the confidence of the interpolated values in the area under study [12]. Cross-validation is a tool to evaluate alternative models of simple and crossed semivariograms, which will perform kriging and cokriging, respectively.…”

Spatial interpolators for improving the mapping of carbon stock of the arboreal vegetation in Brazilian biomes of Atlantic forest and Savanna

“…Kunkel et al (2011) applied co-kriging and multiple linear regression to predict the spatial distribution of soil C-stock across the watershed, and the authors observed that co-kriging successfully captured trends with respect to aspect and elevation. Cunha et al (2013) studied the rainfall spatial distribution in the Espirito Santo State, using kriging and co-kriging, and they found better performance using the multivariate technique.…”