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

Carvalho, José Maria Franco de; Monteiro, José Eduardo Boffinho Almeida; Nakai, Alan Massaru; Assad, Eduardo Delgado

doi:10.1590/0102-7786324006

Cited by 25 publications

(20 citation statements)

References 26 publications

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“…This is also the time when highest productivity is experienced in the northern Indian Ocean. The impact of missing data on statistical inference is potentially significant and are therefore prone to biased estimates 69 ; but filling the data-gaps is equally challenging. This paper presents a method to fill gaps in remote sensed data by using sophisticated statistical tools of moderate complexity.…”

Section: Discussionmentioning

confidence: 99%

Gap-Filling of Ocean Color Over the Tropical Indian Ocean Using Monte-Carlo Method

Modi¹,

Roxy²,

Ghosh³

2021

Preprint

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Continuous remote-sensed daily fields of ocean color now span over two decades; however, it still remains a challenge to examine the ocean ecosystem processes, e.g., phenology, at temporal frequencies of less than a month. This is due to the presence of significantly large gaps in satellite data caused by clouds, sun-glint, and hardware failure; thus, making gap-filling a prerequisite. Commonly used techniques of gap-filling are limited to single value imputation, thus ignoring the error estimates. Though convenient for datasets with fewer missing pixels, these techniques introduce potential biases in datasets having a higher percentage of gaps, such as in the tropical Indian Ocean during the summer monsoon, the satellite coverage is reduced up to 40% due to the seasonally varying cloud cover. In this study, we fill the missing values in the tropical Indian Ocean with a set of plausible values (here, 10,000) using the classical Monte-Carlo method and prepare 10,000 gap-filled datasets of ocean color. Contrary to the previously used gap-filled datasets, the ecological indicators derived using our gap-filled datasets also quantifies uncertainty indicating the likelihood of estimates. Quantification of uncertainty is critical to address the importance of underlying datasets and hence, motivating future observations.

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Section: Discussionmentioning

confidence: 99%

Gap-Filling of Ocean Color Over the Tropical Indian Ocean Using Monte-Carlo Method

Modi¹,

Roxy²,

Ghosh³

2021

Preprint

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“…Missing data were estimated using the multiple imputation method, which simulates missing data multiple times. The multiple imputation method has been successfully applied to infill hydro-meteorological data in different studies (de Carvalho et al 2017;Sattari et al 2017;Ekeu-wei et al 2018). In the current study, the statistical XLSTAT software was used to generate multiple imputations, whereby five imputations were applied as suggested by Schafer & Olsen (1998).…”

Section: Data Quality Control and Statistical Analysismentioning

confidence: 99%

Trend analysis of selected hydro-meteorological variables for the Rietspruit sub-basin, South Africa

Banda

Dzwairo

Singh

2021

Journal of Water and Climate Change

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Identifying hydro-meteorological trends is critical for assessing climate change and variability both at a basin and regional level. This study examined the long- and short-term trends from stream discharge, temperature, and rainfall data around the Rietspruit sub-basin in South Africa. The data were subjected to homogeneity testing before performing the trend tests. Inhomogeneity was widely detected in discharge data, hence no further analyses were performed on such data. Temperature and rainfall trends and their magnitudes at yearly, seasonal, and monthly time steps were identified after applying the non-parametric Mann-Kendall and Sen's slope estimator. The possible starting point of a trend was determined by performing the sequential Mann-Kendall test. This study revealed a combination of upward and downward trends in both temperature and rainfall data for the time steps under observation. For rainfall on an annual basis, there were no statistically significant monotonic trends detected, although non-significant downward trends were dominant. However, significant decreasing rainfall trends were observed in dry and low rainfall months, which were April, August, September, and November. In contrast, significant upward temperature trends were detected at the Vereeniging climate station at an annual scale and in October, November, spring, and winter. The findings are critical for climate risk management and reduction decisions for both near and long-term timescales.

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“…É possível ainda que se realizem projetos de redirecionamento de recursos hídricos (abastecimento de água), levando em consideração a pouca homogeneidade na distribuição de chuvas em escala global ou mesmo em escala regional, como é o caso do sertão nordestino brasileiro (a transposição do rio São Francisco é um exemplo de tentativa de redirecionamento). Dessa forma, é necessário que o registro convertido em séries históricas apresente uma margem de erro aceitável e uma continuidade, originando uma base de dados consistente de modo a permitir o adequado tratamento estatístico das informações e o emprego dos resultados nos estudos de viabilidade de obras (Carvalho et al, 2017;Farias, Santos & Silva, 2018).…”

Section: Introductionunclassified

Uso de metodologias de preenchimento de falhas para estimativas de dados de precipitação

Dias

Soares

2021

RSD

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No presente trabalho, as metodologias para preenchimento de falhas em séries temporais de precipitação foram utilizadas, não como ferramenta auxiliar no preenchimento de lacunas provocadas por erros, mas sim como um meio de prever integralmente os dados de pluviometria para o município de Catanduva, baseando-se em dados dos municípios de São Carlos (SC), Franca (FR) e Votuporanga (VT), localizados no estado de São Paulo. Objetivando assim, analisar e avaliar a viabilidade do uso dos métodos da ponderação regional, da regressão linear simples e da regressão linear múltipla, verificando o desempenho e a correlação entre os dados estimados e os dados reais (registrados pelo posto pluviométrico de Catanduva). Foram utilizadas informações de séries históricas com 30 anos de observações mensais fornecidas pelo Instituto Nacional de Meteorologia (INMET) para a aplicação dos métodos citados. Comparando os dados estimados aos dados medidos pode-se afirmar que o método da regressão linear múltipla foi o que melhor simulou a realidade dos eventos observados e que as estimativas têm melhor desempenho para os meses menos chuvosos e quando são utilizados dados pluviométricos de regiões próximas.

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

Cited by 25 publications

References 26 publications

Gap-Filling of Ocean Color Over the Tropical Indian Ocean Using Monte-Carlo Method

Gap-Filling of Ocean Color Over the Tropical Indian Ocean Using Monte-Carlo Method

Trend analysis of selected hydro-meteorological variables for the Rietspruit sub-basin, South Africa

Uso de metodologias de preenchimento de falhas para estimativas de dados de precipitação

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