Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones

Carvalho, José Maria Franco de; Nakai, Alan Massaru; Monteiro, J. M. G.

doi:10.1590/0102-778631220150025

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…A série temporal total de precipitação Cabaceiras de 1928 a 2017 compreende 32868 registros diários, dos quais apenas 2,04% apresentaram ausência de informações, o que de forma alguma comprometeria as análises desta pesquisa. Mesmo assim, as falhas foram preenchidas de acordo com a metodologia proposta e descrita por Carvalho et al (2016).…”

Section: Resultsunclassified

Tendências em índices extremos de precipitação em Cabaceiras (PB) para períodos distintos

Costa

Gomes

Silva

et al. 2020

Rev. Bras. Geog. Fis.

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Este trabalho teve como objetivo aplicar e estudar 11 índices de extremos de precipitação formulados pelo ETCCDI (Expert Team on Climate Change Detection and Indices, www.clivar.org/organization/etccdi), para a cidade de Cabaceiras-PB, utilizando dados diários de precipitação contínuos de 90 anos. Os índices foram calculados para o comprimento total da série, 1928 a 2017, assim como para três segmentos de 30 anos (1928-1957, 1958-1987 e 1988-2017). Os resultados evidenciaram que para muitos índices, tendências opostas e estatisticamente significativas podem ser observadas a depender do subperíodo estudado, assim como haver diferença entre estas tendências e as obtidas ao analisar-se o período total dos dados. Exemplos disso aconteceram para os índices R1mm, R10mm, R20mm, CWD e PRCPTOT. Trends in extreme precipitation indexes in Cabaceiras (PB) for different periods A B S T R A C TThis work aimed to apply and analyze 11 precipitation extremes indexes formulated by ETCCDI (Expert Team on Climate Change Detection and Indices, www.clivar.org/organization/etccdi), for the city of Cabaceiras, located in the Borborema mesoregion and microregion of Paraíba Oriental Cariri. A municipality in the semiarid region, it has the title of municipality where it rains less in Brazil, with an annual average of just over 300mm. Daily 90-year continuous precipitation data were used for the extreme indices, with the time series analyzed for four distinct periods, the total length of the series, 1928 to 2017, as well as three 30-year segments (1928-1957, 1958- 1987 and 1988-2017). The results showed that for many indices, opposite and significant trends can be observed depending on the sub period studied, as well as differences between these trends and those obtained by analyzing the total data period. The R1, R10 and R20mm indices show significant negative trends in the 1928-1957 sub period, but positive in the following two sub periods, reflecting a significant positive trend in the total period from 1928 to 2017. Other interesting examples are CDD indices for consecutive dry days, and PRCPTOT, for total annual rainfall with rainfall greater than 1mm. The CDD showed significant positive trend only in the 1928-1957 sub period, but non-significant negative trends in the subsequent sub periods, reflecting non-significant negative trends in the total length of the series. The PRCPTOT index shows behavior opposite to the CDD index, with a significant negative trend in the 1928-1957 sub period, positive in 1958-1987 and negative again in 1988-2017, but for the total length of the series the trend is positive and significant. These results show that the analysis of extreme trends is noticeably sensitive to the sample of the analyzed period, and may not reflect the reality of the time series the longer the total length of the time series, and need to be used with caution.Keywords: climate variability, dry and wet periods, semiarid.

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Section: Resultsunclassified

Tendências em índices extremos de precipitação em Cabaceiras (PB) para períodos distintos

Costa

Gomes

Silva

et al. 2020

Rev. Bras. Geog. Fis.

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“…This statistics quantifies the relative variation of the mean square error from the method of multiple imputation (MSEmod) regarding Kriging and Co-Kriging (MSEkrig and MSEcockri). The positive values of SS indicate that the model improved the forecasts (Carvalho et al, 2016;Carvalho et al, 2011;Libonati et al, 2008).…”

Section: Methodsmentioning

confidence: 90%

Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults

Carvalho

Monteiro

Nakai

et al. 2017

Rev. bras. meteorol.

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Modeling by multiple enchained imputation is an area of growing importance. However, its models and methods are frequently developed for specific applications. In this study the model for multiple imputation was used to estimate daily rainfall data. Daily precipitation records from several meteorological stations were used, obtained from system AGRITEMPO for two homogenous climatic zones. Modelo de Imputação Múltipla para Estimar Dados de Precipitação Diária e Preenchimento de FalhasResumo A modelagem por imputação múltipla com cadeias encadeadas é uma área de importância crescente cujos modelos e métodos têm sido muitas vezes desenvolvidos para lidar com aplicações específicas. No presente estudo, o modelo de imputação múltipla foi utilizado para estimar os dados de precipitação diária. Foram utilizados registros de precipitação de várias estações meteorológicas, obtidos a partir do sistema Agritempo para duas zonas climaticamente homogéneas. Os valores de precipitação obtidos para duas datas fixas (02 de Maio de 2005 e 20 de Janeiro de 2005) usando o modelo de imputação múltipla foram comparados com as técnicas geoestatisticas de krigagem ordinária e cokrigagem ordinária com a altitude como variável auxiliar. O modelo de imputação múltipla foi mais de 16% melhor para a primeira Zona e mais de 23% melhor para a segunda zona, comparadas com as estimativas de precipitação obtidas pelas técnicas

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“…The closer the SS value is to 1.0, the more reliable the estimation. An SS value of 1.0 indicates a perfect estimation of the missing data [57][58][59]. The skill score index (SS) is calculated as follows:…”

Section: Similarity Index (S-index)mentioning

confidence: 99%

Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation

Armanuos

Al‐Ansari

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

2020

Atmosphere

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The results of metrological, hydrological, and environmental data analyses are mainly dependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity and trend tests were performed to check the data. The results of the different methods were compared using the mean absolute error (MAE), root-mean-square error (RMSE), coefficient of efficiency (CE), similarity index (S-index), skill score (SS), and Pearson correlation coefficient (r Pearson ). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides the most accurate estimations with r Pearson of 0.945, mean absolute error of 22.90 mm, RMSE of 33.695 mm, similarity index of 0.999, CE index of 0.998, and skill score of 0.998. When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and r Pearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR), expectation maximization (EM), and multiple imputations (MI) methods gave the lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world.

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Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones

Cited by 14 publications

References 22 publications

Tendências em índices extremos de precipitação em Cabaceiras (PB) para períodos distintos

Tendências em índices extremos de precipitação em Cabaceiras (PB) para períodos distintos

Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults

Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation

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