scholarly journals Best-fit probability distribution models for monthly rainfall of Northeastern Brazil

2021 ◽  
Author(s):  
Patricia de Souza Medeiros Pina Ximenes ◽  
Antonio Samuel Alves da Silva ◽  
Fahim Ashkar ◽  
Tatijana Stosic
Keyword(s):  
Northeastern Brazil ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Distribution Models ◽  
Drought Period ◽  
Natural Systems ◽  
Generalized Pareto ◽  
Log Normal ◽  
Monthly Precipitation Data ◽  
Parameter Distributions

Abstract The analysis of precipitation data is extremely important for strategic planning and decision-making in various natural systems, as well as in planning and preparing for a drought period. The drought is responsible for several impacts on the economy of Northeast Brazil (NEB), mainly in the agricultural and livestock sectors. This study analyzed the fit of 2-parameter distributions gamma (GAM), log-normal (LNORM), Weibull (WEI), generalized Pareto (GP), Gumbel (GUM) and normal (NORM) to monthly precipitation data from 293 rainfall stations across NEB, in the period 1988–2017. The maximum likelihood (ML) method was used to estimate the parameters to fit the models and the selection of the model was based on a modification of the Shapiro-Wilk statistic. The results showed the chosen 2-parameter distributions to be flexible enough to describe the studied monthly precipitation data. The GAM and WEI models showed the overall best fits, but the LNORM and GP models gave the best fits in certain months of the year and regions that differed from the others in terms of their average precipitation.

scholarly journals Assessment of different methods for estimation of missing data in precipitation studies

2016 ◽  
Vol 48 (4) ◽  
pp. 1032-1044 ◽  
Author(s):  
Mohammad-Taghi Sattari ◽  
Ali Rezazadeh-Joudi ◽  
Andrew Kusiak
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Regression Method ◽  
Linear Regression Method ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Multiple Regression Method ◽  
Multiple Imputation Method ◽  
Estimation Of Missing Data ◽  
Monthly Precipitation Data

The outcome of data analysis depends on the quality and completeness of data. This paper considers various techniques for filling in missing precipitation data. To assess suitability of the different methods for filling in missing data, monthly precipitation data collected at six different stations was considered. The complete sets (with no missing values) are used to predict monthly precipitation. The arithmetic averaging method, the multiple linear regression method, and the non-linear iterative partial least squares algorithm perform best. The multiple regression method provided a successful estimation of the missing precipitation data, which is supported by the results published in the literature. The multiple imputation method produced the most accurate results for precipitation data from five dependent stations. The decision-tree algorithm is explicit, and therefore it is used when insights into the decision making are needed. Comprehensive error analysis is presented.

scholarly journals Analysis of total monthly precipitation of Susurluk Basin in Turkey using innovative polygon trend analysis method

2020 ◽  
Author(s):  
Gokmen Ceribasi ◽  
Ahmet Iyad Ceyhunlu
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Meteorological Parameters ◽  
Global Climate ◽  
Data Series ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Analysis Method ◽  
Distinctive Features ◽  
Monthly Precipitation Data

Abstract The effects of climate change caused by global warming can be seen in changes of climate variables such as precipitation, humidity, and temperatures. These effects of global climate change can be interpreted as a result of the examination of meteorological parameters. One of the most effective methods to investigate these effects is trend analysis. The Innovative Polygon Trend Analysis (IPTA) method is a trend analysis method that has emerged in recent years. The distinctive features of this method compared with other trend methods are that it depends on time series and can compare data series among themselves. Therefore, in this study, the IPTA method was applied to total monthly precipitation data of Susurluk Basin, one of Turkey's important basins. Data from ten precipitation observation stations in Susurluk Basin were used. Data were provided by the General Directorate of State Meteorology Affairs. The length of this data series was 12 years (2006–2017). As a result of the study, since there is no regular polygon in IPTA graphics of each station, it is seen that precipitation data varies by years. While this change is seen increasingly at some stations, it is seen decreasingly at other stations.

scholarly journals Time series analysis on precipitation with missing data using stochastic SARIMA

MAUSAM ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 617-624
Author(s):  
SHARMA M. K. ◽  
OMER MOHAMMED ◽  
KIANI SARA
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Analysis ◽  
Stochastic Model ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Missing Observations ◽  
Full Data ◽  
Sarima Model ◽  
Monthly Precipitation Data

This paper presents an application of the Box-Jenkins methodology for modeling the precipitation in Iran. Linear stochastic model known as multiplicative seasonal ARIMA was used to model the monthly precipitation data for 44 years. Missing data occurred in between for 34 months for some reason. To fill the gap a SARIMA model was fitted based on the first 180 available observations and the missing observations were substituted by the forecasts for the next 34 months. Then a SARIMA model was fitted for the full data. The result showed that the fitted model represent the full data well.

scholarly journals Comparison of the Methods to Estimate Missing Values in Monthly Precipitation Data

2017 ◽  
Vol 7 (6) ◽  
pp. 2168
Author(s):  
Wahidah Sanusi ◽  
Wan Zawiah Wan Zin ◽  
Usman Mulbar ◽  
Muhammad Danial ◽  
Syafruddin Side
Keyword(s):  
Missing Values ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Monthly Precipitation Data
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