Climate dynamics and trends have significant environmental and socioeconomic impacts; however, in the Benin Republic, they are generally studied with diverse statistical methods ignoring the nonstationarity, nonlinearity, and self-similarity characteristics contained in precipitation time series. This can lead to erroneous conclusions and an unclear understanding of climatic dynamics. Based on daily precipitation data observed in the six synoptic stations of Benin Republic, in the period from 1951 to 2010, we have proposed (i) determining the local trends of precipitations, (ii) investigating precipitation nonlinear dynamics, and (iii) assessing climatic shift in the study period by Ensemble Empirical Mode Decomposition (EEMD) and Multifrac...
We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzin...
Rainfall, one of the most important climate variables, is commonly studied due to its great heteroge...
Central African citizens are highly vulnerable to extreme hydroclimatic events due to excess precipi...
This study analyzed the long-term memory (LTM) in precipitation over Bénin synoptic stations from 19...
Fractal analysis is important for characterizing and modeling rainfall’s space-time variations in hy...
Since climate trends are getting considerable attention in recent years, we aimed in this study to c...
Multifractal detrended fluctuation analysis (MFDFA) method can examine higher-dimensional fractal an...
Climate change has severe impacts on natural resources, food production and consequently on food sec...
In this presentation the scaling properties of rainfall time-series generated by a climate model are...
National audienceMultifractal techniques are applied to the study of rainfall daily time series over...
Observed rainfall data (1961–2016) were used to analyze variability, trends and changes of extreme p...
This study analyzed the trends of extreme daily rainfall indices over the Ouémé basin using the obse...
As a preliminary attempt to apply multifractal techniques to climate model simulations, Royer et al ...
Rainfall in the tropical Africa is prone to large temporal and spatial variability. The well-documen...
To better assess the occurrence of climate variability and change and related effects on crop produc...
We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzin...
Rainfall, one of the most important climate variables, is commonly studied due to its great heteroge...
Central African citizens are highly vulnerable to extreme hydroclimatic events due to excess precipi...
This study analyzed the long-term memory (LTM) in precipitation over Bénin synoptic stations from 19...
Fractal analysis is important for characterizing and modeling rainfall’s space-time variations in hy...
Since climate trends are getting considerable attention in recent years, we aimed in this study to c...
Multifractal detrended fluctuation analysis (MFDFA) method can examine higher-dimensional fractal an...
Climate change has severe impacts on natural resources, food production and consequently on food sec...
In this presentation the scaling properties of rainfall time-series generated by a climate model are...
National audienceMultifractal techniques are applied to the study of rainfall daily time series over...
Observed rainfall data (1961–2016) were used to analyze variability, trends and changes of extreme p...
This study analyzed the trends of extreme daily rainfall indices over the Ouémé basin using the obse...
As a preliminary attempt to apply multifractal techniques to climate model simulations, Royer et al ...
Rainfall in the tropical Africa is prone to large temporal and spatial variability. The well-documen...
To better assess the occurrence of climate variability and change and related effects on crop produc...
We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzin...
Rainfall, one of the most important climate variables, is commonly studied due to its great heteroge...
Central African citizens are highly vulnerable to extreme hydroclimatic events due to excess precipi...