Alternative methodology to gap filling for generation of monthly rainfall series with GIS approach

Bielenki, Claudio; Santos, Franciane Mendonça dos; Povinelli, Silvia Cláudia Semensato; Mauad, Frederico Fábio

doi:10.1590/2318-0331.231820170171

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…In this module, statistical parameters were also implemented for model efficiency evaluation and data comparison. We chose to enter NHSS equations for calculations of Adjustment coefficient (AC) (Equation 13), Efficiency (EF) (Equation 14), 15), Root Mean Square Error (RMSE) (Equation 16), Maximum Error (ME) (Equation 17), and Mean Difference (MD) (Equation 18), which can be used in various studies as validations of models of soil nutrient dynamics, comparative analysis of different methods of obtaining variables, comparison of different models of estimation of infiltration rates or methods of solid sediment discharge, among others (LENGNICK;FOX, 1994;SENTELHAS et al, 1997;ALVES SOBRINHO et al, 2003;BRITO et al, 2009;SANTOS et al, 2012;BIELENKI JUNIOR et al, 2018;MARQUES et al, 2019…”

Section: Statistical Module Of Nhssmentioning

confidence: 99%

A computational tool for hydrosedimentological and statistical calculations

Santos

Mauad

Miranda

et al. 2021

Eng. Sanit. Ambient.

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This paper presents a new computational tool called NH SEDIMENT AND STATISTIC which performs hydrosedimentological and statistical calculations using Visual Basic. This computational tool was developed for studies related to calculations of sediment transport in rivers. The tool includes hydrosedimentological methods for calculating suspension loads, bed loads, and total solid discharge. In addition, it provides the user with the possibility of performing statistical tests such as the Kolmogorov-Smirnov normality test, F test and χ2 test of variance, Student's t-test, non-parametric Wilcoxon test, and statistical parameter calculations. The NH SEDIMENT AND STATISTIC automatically calculates and provides the main results for each of the methods, allowing the user to draw their own conclusions. This proposed computational tool supports hydrosedimentological studies, and is reliable and easy to use, contributing to the reduction of sediment-related problems in the areas of hydraulic engineering, geology, and soil and water conservation. Furthermore, this tool may be used in transdisciplinary scientific areas for complete planning and management of water resources.

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Section: Statistical Module Of Nhssmentioning

confidence: 99%

A computational tool for hydrosedimentological and statistical calculations

Santos

Mauad

Miranda

et al. 2021

Eng. Sanit. Ambient.

Self Cite

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“…Others, such as the kNN weighted averaging method, consist in generating reference series in a location formed by the weighted average of the data observed in neighboring stations (Beguería et al, 2019). Yet other approaches consider multiple linear regression models (Mora et al, 2014;Tardivo and Berti, 2014;Serrano-Notivoli et al, 2017), the Inverse Distance Weighting algorithm (Bielenki Junior et al, 2018;Lu and Wong, 2008;Armanuos et al, 2020) and, more recently, strategies such as tree based methods and machine learning algorithms (Körner et al, 2018;Bellido-Jiménez et al, 2021), or the combination of several gap-filling methods (Armanuos et al, 2020;Longman et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Does the gap-filling method influence long-term (1950–2019) temperature and precipitation trend analyses?

Padial-Iglesias¹,

Pons²,

Serra³

et al. 2022

GeoFocus

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Incomplete climatic series require gap-filling approaches so they can be used in homogeneous longterm spatiotemporal trend analyses. Monthly mean Temperature (MT) and Precipitation (PR) databases from the meteorological stations of the Iberian Peninsula have a high percentage of data gaps: 80.21 % and 73.25 % for the period 1950-1979 (P1), and 61.82 % and 58.03 % for the period 1980-2019 (P2). The different gap-filling methods of the Emmental software were tested to determine their performance and whether the gap-filling method influences these trend analyses. The nonparametric Theil-Sen approach and the Mann-Kendall test were used to assess the trend magnitude and its significance. The results showed (i) similar patterns between the evaluated methods, but with (ii) spatial differences, especially during P1. (iii) The comparison between standardized gap-filled and unfilled series did not show significant differences for MT and PR, although a reduction in the trend variability occurred in the first case (filled). (iv) Summer mean temperatures showed the largest warming trend (0.27 °C/decade), while autumn showed the smallest (0.21 °C/decade) (median data for P1 and P2). Overall, an increase of 1.45 °C occurred in the entire period (annual median). (v) PR did not show any clear trend in any month in the entire period. This research has shown how climate trends can be affected by a reduction in data variability due to the application of gap filling methods. Although accounting for variability is of crucial importance for climate analysis, ignoring discontinuities in derived climatic surfaces causes greater spatiotemporal inconsistencies in derived climate products.

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“…A ocorrência de falhas nos dados de precipitação disponíveis (ou ausência deles) é comum e pode ocorrer por conta dos erros provocados pela ausência do operador do aparelho pluviométrico, pelo registro incorreto, por falhas mecânicas, por perda dos registros realizados ou mesmo pela falta de verba para continuidade da operação. Para superar esses empecilhos existem as chamadas metodologias para preenchimento de falhas, que permitem prever a ocorrência de chuvas em determinada região baseando-se em dados obtidos em regiões próximas (Ventura, Santana, Martins, Figueiredo, 2016;Bier, Ferraz, 2017;Bielenki Junior et al, 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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Alternative methodology to gap filling for generation of monthly rainfall series with GIS approach

Cited by 9 publications

References 17 publications

A computational tool for hydrosedimentological and statistical calculations

A computational tool for hydrosedimentological and statistical calculations

Does the gap-filling method influence long-term (1950–2019) temperature and precipitation trend analyses?

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

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