Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall

Goovaerts, Pierre

doi:10.1016/s0022-1694(00)00144-x

Cited by 1,327 publications

(1,031 citation statements)

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“…The multiple regression results were improved by analyzing the variogram functions of the residual errors. The variogram function describes the average dissimilarity between the residual errors in relation to their spatial distance (Goovaerts, 1997). Sample variograms could be fitted to spherical models ( Figure 2) that finally were used to obtain maps of the residual errors by ordinary kriging.…”

Section: Case Studymentioning

confidence: 99%

“…Several interpolation methods are used for climatic maps. Examples of methods are traditional hand-interpolated isopleths (Burrough & McDonnell, 1998), and computational techniques involving spatial Thiessen polygon interpolators, inverse distance weighting, cubic splining, trend surface analysis, thin plate splines (Hutchinson, 1991;Lennon & Turner, 1995;Saveliev et al, 1998;Felicísimo-Pérez et al, 2001), Kriging (Dingman et al, 1988;Bigg, 1991;Philips et al, 1992) or co- Kriging (Goovaerts, 2000) are frequently used. Multiple regression analysis is another method widely used to compute the distribution of climatic variables from discrete data (Collins & Bolstad, 1996;Goodale et al, 1998;Ninyerola, 2000;Ninyerola et al, 2000;Lennon & Turner, 1995;Valeriano & Picini, 2000;Silva et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Modelling air temperature for the state of São Paulo, Brazil

Rodríguez‐Lado¹,

Sparovek²,

Vidal‐Torrado³

et al. 2007

Sci. agric. (Piracicaba, Braz.)

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Spatial modelling of air temperature (maximum, mean and minimum) of the State of São Paulo (Brazil) was calculated by multiple regression analysis and ordinary kriging. Climatic data (mean values of five or more years) were obtained from 256 meteorological stations distributed uniformly over the State. The correlation between the climatic dependent variables, with latitude and altitude as independent variables was significant and could explain most of the spatial variability. The coefficients of determination (P < 0.05) varied in the range of 0.924 and 0.953, showing that multiple regression analysis is an accurate method for the modelling of air temperature for the State of São Paulo. Finally, these regression equations were used together with the kriged maps of the residual errors to build 15 digital maps of air temperature using a 0. O período mínimo das séries climáticas utilizadas foi de cinco anos. Os resultados das análises de regressão apresentaram uma boa correlação entre as variáveis dependentes analisadas (temperaturas médias, máximas e mínimas) com a latitude, e a altitude como variáveis independentes. Os coeficientes de determinação (P < 0,05) variam entre 0,924 e 0,953 indicando que a regressão múltipla é um método preciso de estimativa da temperatura do ar no Estado de São Paulo. As equações de regressão obtidas foram utilizadas, em conjunto com mapas dos resíduos interpolados por krigagem, para a elaboração de 15 mapas de temperatura do ar sobre um modelo de elevação digital de 0,5 km 2 de resolução espacial com a ajuda de geoprocessamento.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling air temperature for the state of São Paulo, Brazil

Rodríguez‐Lado¹,

Sparovek²,

Vidal‐Torrado³

et al. 2007

Sci. agric. (Piracicaba, Braz.)

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“…A estimativa da variabilidade espacial de chuvas é importante para a determinação do potencial agrícola de uma região e identificação de áreas de risco, onde elevados índices de precipitação pluvial podem levar às inundações (Goovaerts, 2000) e a movimentos de massa ao longo de encostas (Porto Junior et al, 2012).…”

Section: Introductionunclassified

“…O método de cokrigagem, para fins de espacialização da precipitação pluvial, envolve geralmente variáveis topográficas (Goovaerts, 2000). Estas variáveis têm baixo custo como fonte de informação secundária, se obtidas de modelos digitais de elevação (Goovaerts, 2000).…”

Section: Introductionunclassified

“…Estas variáveis têm baixo custo como fonte de informação secundária, se obtidas de modelos digitais de elevação (Goovaerts, 2000). Dessas variáveis, a altitude tem sido mais utilizada como covariável, e estudos têm mostrado melhorias na acurácia da predição de chuvas com sua utilização na cokrigagem (Diodato & Ceccarelli, 2005;Moral, 2010;Viola et al, 2010).…”

Section: Introductionunclassified

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Espacialização da precipitação pluvial por meio de krigagem e cokrigagem

Cunha

Lani

Santos³

et al. 2013

Pesq. agropec. bras.

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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.

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Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km²); Cyprus, 1980–2010

et al. 2014

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High-resolution gridded daily data sets are essential for natural resource management and the analyses of climate changes and their effects. This study aims to evaluate the performance of 15 simple or complex interpolation techniques in reproducing daily precipitation at a resolution of 1 km 2 over topographically complex areas. Methods are tested considering two different sets of observation densities and different rainfall amounts. We used rainfall data that were recorded at 74 and 145 observational stations, respectively, spread over the 5760 km 2 of the Republic of Cyprus, in the Eastern Mediterranean. Regression analyses utilizing geographical copredictors and neighboring interpolation techniques were evaluated both in isolation and combined. Linear multiple regression (LMR) and geographically weighted regression methods (GWR) were tested. These included a step-wise selection of covariables, as well as inverse distance weighting (IDW), kriging, and 3D-thin plate splines (TPS). The relative rank of the different techniques changes with different station density and rainfall amounts. Our results indicate that TPS performs well for low station density and large-scale events and also when coupled with regression models. It performs poorly for high station density. The opposite is observed when using IDW. Simple IDW performs best for local events, while a combination of step-wise GWR and IDW proves to be the best method for large-scale events and high station density. This study indicates that the use of step-wise regression with a variable set of geographic parameters can improve the interpolation of large-scale events because it facilitates the representation of local climate dynamics.

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Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall

Cited by 1,327 publications

References 20 publications

Modelling air temperature for the state of São Paulo, Brazil

Modelling air temperature for the state of São Paulo, Brazil

Espacialização da precipitação pluvial por meio de krigagem e cokrigagem

Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km²); Cyprus, 1980–2010

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Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall

Cited by 1,327 publications

References 20 publications

Modelling air temperature for the state of São Paulo, Brazil

Modelling air temperature for the state of São Paulo, Brazil

Espacialização da precipitação pluvial por meio de krigagem e cokrigagem

Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980–2010

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Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km²); Cyprus, 1980–2010