Complexity entropy-analysis of monthly rainfall time series in northeastern Brazil

2021 ◽  
Vol 143 ◽  
pp. 110623
Author(s):  
Antonio Samuel Alves Silva ◽  
Rômulo Simões Cezar Menezes ◽  
Osvaldo A. Rosso ◽  
Borko Stosic ◽  
Tatijana Stosic
Keyword(s):  
Time Series ◽  
Monthly Rainfall ◽  
Northeastern Brazil ◽  
Rainfall Time Series ◽  
Entropy Analysis

Multiscale entropy analysis of monthly rainfall time series in Paraíba, Brazil

2021 ◽  
Vol 151 ◽  
pp. 111296
Author(s):  
Sílvio Fernando Alves Xavier ◽  
Érika Fialho Morais Xavier ◽  
Jader Silva Jale ◽  
Tatijana Stosic ◽  
Carlos Antonio Costa dos Santos
Keyword(s):  
Time Series ◽  
Monthly Rainfall ◽  
Multiscale Entropy ◽  
Rainfall Time Series ◽  
Entropy Analysis

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

scholarly journals Estimation of deterministic component of monthly rainfall time series : A case study for Pantnagar

MAUSAM ◽  
2021 ◽  
Vol 69 (3) ◽  
pp. 449-458
Author(s):  
MANISHA MADHAV NAVALE ◽  
P. S. KASHYAP ◽  
SACHIN KUMAR SINGH ◽  
DANIEL PRAKASH KUSHWAHA ◽  
DEEPAK KUMAR ◽  
...  
Keyword(s):  
Time Series ◽  
Monthly Rainfall ◽  
Rainfall Time Series ◽  
Deterministic Component

Neural networks adaptive wavelets for predictions of the northeastern Brazil monthly rainfall anomalies time series

1996 ◽  
Author(s):  
Weigang Li ◽  
Leonardo D. Sa ◽  
G. S. Prasad ◽  
A. G. Nowosad ◽  
Mauricio Bolzan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Monthly Rainfall ◽  
Northeastern Brazil ◽  
Adaptive Wavelets ◽  
Rainfall Anomalies

scholarly journals Prediction of monsoon rainfall for a mesoscale Indian catchment based on stochastical downscaling and objective circulation patterns

2005 ◽  
Vol 2 (5) ◽  
pp. 1961-1993
Author(s):  
E. Zehe ◽  
A. K. Singh ◽  
A. Bárdossy
Keyword(s):  
Time Series ◽  
Classification Scheme ◽  
Decadal Variability ◽  
Monthly Rainfall ◽  
Temperate Climate ◽  
Time Variability ◽  
Rainfall Time Series ◽  
Time Step ◽  
North West ◽  
Circulation Patterns

Abstract. In this study a stochastical approach for generating rainfall time series based on objective circulation patterns (CP is applied to the mesoscale Anas catchment in North West India. This CP based approach was developed and successfully applied in the humid and temperate climate of Central Europe. The objective of the study was to find out whether this approach is transferable to a catchment in North West India with a totally different semi arid climate. For the Anas catchment it was possible to identify a CP classification scheme consisting of 12 CPs defined in a window between 5° N 40° E and 35° N 95° E, which explained the space-time variability of observed rainfall at 10 stations in the Anas catchment. Based on the classification scheme, NCAR pressure data from 500 hPa level were classified into a CP time series for the period of 1964–1994, which was in turn used as meteorological forcing for multivariate stochastical rainfall simulations with a daily time step. On the monthly time scale the model performed well. Except for stations Udaigarh and Bhabra the average annual cycle of monthly rainfall and rainy days in a month was matched well. The frequency distributions of monthly rainfall at different stations were also captured well. Correlation coefficients between simulated and observed monthly rainfall were larger than 0.85 at each station. Within a long term simulation of 30 years the model yielded promising predictions for monthly as well as for seasonal rainfall totals, but showed also clear deficiencies in capturing the very extremes and inter-decadal variability of monsoon strength. In this respect, the introduction of additional predictors such as SST anomalies and wind direction classes promised the most substantial model improvements.

FLUCTUATION ANALYSIS OF MONTHLY RAINFALL TIME SERIES

2010 ◽  
Vol 09 (02) ◽  
pp. 219-228 ◽  
Author(s):  
JORGE O. PIERINI ◽  
LUCIANO TELESCA
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Scaling Behavior ◽  
Monthly Rainfall ◽  
Fluctuation Analysis ◽  
Rainfall Time Series ◽  
Scaling Exponents ◽  
Times Series ◽  
Detrended Fluctuation

The monthly rainfall time series, spanning more than a century, recorded in several sites in the middle Argentina were analyzed. The power spetral density (PSD) method reveals the presence of annual and semi-annual cyclic fluctuations. The detrended fluctuation analysis (DFA) performed on the residual times series (after removing the periodicities) shows a scaling behavior, characterized by DFA scaling exponents ranging between 0.54 and 0.58. These findings could contribute to a better understanding of rainfall dynamics.

scholarly journals Analysis of the Characteristics of Monthly Rainfall Pattern in Katsina

2019 ◽  
pp. 1-13
Author(s):  
Emmanuel Vezua Tikyaa ◽  
Francis Oladele Anjorin ◽  
Emmanuel Joseph
Keyword(s):  
Time Series ◽  
Nonlinear Analysis ◽  
Chaotic Behavior ◽  
Monthly Rainfall ◽  
Skewed Distribution ◽  
Rainfall Pattern ◽  
Largest Lyapunov Exponent ◽  
Rainfall Time Series ◽  
Correlation Exponent ◽  
Increasing Trend

Aims: This paper seeks to analyse the characteristics of monthly rainfall pattern in Katsina City in a view to unveiling the trends and describing its dynamics so that adequate recommendations can be made for its modelling. Study Design: The analysis involves a complete statistical, trend, spectral and nonlinear analysis of the monthly rainfall time series recorded in Katsina. Place and Duration of Study: Location: Katsina City, Katsina State, Nigeria from 1990 to 2015; a period of 26 years. Methodology: Secondary data of daily rainfall recorded in Katsina city from 1990 to 2015 was collected from the Nigerian Meteorological Agency (NiMet), and monthly averages were taken to obtain the monthly rainfall data. The data was then subjected to statistical, trend, spectral and nonlinear analysis techniques to reveal the behavioural patterns in the rainfall and also to reveal its underlying dynamics for its future modelling and prediction. Results: The outcome of this analysis indicates that the monthly rainfall in Katsina exhibits an increasing trend with high variance and right-skewed distribution requiring a maximum of 6 independent variables to model its dynamics. The correlation exponent plot reached a saturation value of 5.892 confirming that the monthly rainfall in Katsina over the last 26 years exhibits low dimensional chaotic behavior while the largest Lyapunov exponent for the monthly rainfall time series in Katsina was also computed and found to be positive, having a value of 0.006055/month confirming the presence of deterministic chaos dynamics and is predictable for the next 165 months. Conclusion: Since from the findings of this work it is confirmed that the rainfall in Katsina exhibits chaotic behavior with an increasing trend, it is recommended that more drainages and dams be built to provide steady supply of water for agricultural and domestic purposes as well as curtail the menace of flooding and drought which may occur as a result of global warming and climate change.

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