Contribution of Satellite Observations in the Optical and Microphysical Characterization of Aerosols in Burkina Faso, West Africa

In this work, we proceed to an optical and microphysical analysis of the observations reversed by the MODIS, SeaWiFS, MISR and OMI sensors with the aim of proposing the best-adapted airborne sensor for better monitoring of aerosols in Burkina Faso. To this end, a comparison of AOD between satellite observations and in situ measurements at the Ouagadougou site reveals an underestimation of AERONET AOD except for OMI which overestimates them. Also, an inter-comparison done based on the linear regression line representation shows the correlation between the aerosol models incorporated in the airborne sensor inversion algorithms and the aerosol population probed. This can be seen through the correlation coefficients R which are 0.84, 0.64, 0.55 and 0.054 for MODIS, SeaWiFS, MISR and OMI respectively. Furthermore, an optical analysis of aerosols in Burkina Faso by the MODIS sensor from 2001 to 2016 indicates a large spatial and temporal variability of particles strongly dominated by desert dust. This is corroborated by the annual and seasonal cycles of the AOD at 550 nm and the Angström coefficient measured in the spectral range between 412 nm and 470 nm. A zoom on a few sites chosen according to the three climatic zones confirms the majority presence of mineral aerosols in Burkina Faso, whose maxima are observed in spring and summer.

Fluctuating Asymmetry of Elopes lacerta (Valenciennes, 1847) Otoliths in the Western African Waters

The importance of this study lies in the in-depth exploration of the ecological diversity of otoliths in Elopes lacerta, based on the analysis of data from 260 individuals collected from various sites, including the Porto Novo lagoon, the Cotonou lagoon, Lake Nokou, and the Atlantic coast in southern Benin. The results highlight significant variations in otolith morphology, establishing relevant links with the biological parameters of the fish at each site. Exploration of the asymmetry between the right and left sides reveals notable distinctions between these two aspects. Analysis of otolith shape thus emerges as a valuable tool for discriminating between stocks and providing a better understanding of ecological variations. The local diversity observed highlights the crucial importance of implementing adaptive management strategies to ensure effective conservation of the species and its habitat.

Spatio-Temporal Analysis of Climate Change Impact on Future Wind Power Potential in Burundi (East Africa)

This paper assessed climate change impact on future wind power potential across highlands and western lowlands of Burundi. Hourly observed and MERRA-2 data were considered for the historical period 1980-2016, and a Multi-model ensemble for future projections data of eight selected Regional Climate Models under RCP 4.5 and RCP 8.5 over the periods 2019-2040 and 2071-2100 was used. Variability and trend analysis were adopted using standardized index and Mann-Kendall’s test, respectively while wind power density (WPD) in quartiles was adopted for changes distribution. As results, diurnal wind speeds (WS) were higher from 9:00 AM to 2:00 PM, while monthly wind speeds reached the maximum during summer time. An increasing trend in WPD was detected all over the studied area. Over the period 2019-2040, the lowest WPD change is projected at Northern Highlands (NHL) under RCP 4.5 with 28.04 W·m−2 while the highest WPD change of 47.35 W·m−2 is projected under RCP 8.5 at Southern Imbo plain (SIP). As for the period 2071-2100, the highest change is expected at SIP under RCP 8.5 with 152.39 W·m−2 while the minimum change of 83.96 W·m−2 is projected under RCP 4.5 at NHL. The findings showed that areas nearby the Lake Tanganyika are expected to have high positive WDP changes.

Future Extremes Temperature: Trends and Changes Assessment over the Mono River Basin, Togo (West Africa)

This study assessed the extreme temperatures trends and changes over Mono River Basin under the highest greenhouse gas emission scenario RCP8.5. Simulations of five (5) regional climate models (RCMs) provided by Africa-CORDEX program were selected from the eighth (8) considered. Future trends in temperature percentiles, including extremes, are used to assess changes in the distribution of daily temperature over Mono Basin in Togo. Changes of temperature and Extreme low (high) temperatures from the baseline period 1961-2010 were computed for future (2051-2100). This analysis reveals that in the north of the basin, for the positive trends, the maximum is 0.82°C·year-1 given by model MPI-ESM2 at Tchamba while the strongest negative change is 0.26°C·year-1 given by model MIROC at Sotouboua. In the south of the basin, the strongest negative trend is of 0.03°C·year-1 given by model (A) CNRM-CMA5. The maximum ones of the trends for models-mean are all positive except at Anié. Higher percentiles of minimum and maximum temperature will increase at a greater rate than the lower percentiles during dry and rainy seasons (with differences more pronounced for maximum values) over the north. Concerning future changes, almost all the RCMs predicted an increase of maximum and minimum temperatures over most parts of the Mono Basin, particularly in the north. Finally, results predicted an increase of TX90P (TX10P) and TN90P (TN10P) from 10% to 45% (13% to 40%) and 0% to 35% (12% Mean value), respectively over Mono Basin.

Variability of Future Rainfall over the Mono River Basin of West-Africa

This study assessed the rainfall trends and changes over Mono river basin under the highest greenhouse gas emission scenario RCP8.5. Simulations of eight regional climate models (RCMs) provided by Africa-CORDEX program were considered. To analyze the performance of a set of regional climate models, the MBE (mean bias error), the RMSE (root mean square error), the volume bias (VB), correlation coefficient (R2) and the t-Test statistics were calculated. The precipitation concentration index (PCI), Mann-Kendall trend test, Theil-Sen’s slope estimator (β), and relative percentage change methods were also adopted for data analysis. Changes from the baseline period 1981-2010 were computed for far future (2061-2090 and 2071-2100). As results, the analysis herein highlighted the multi-models’ mean ability to simulate the Mono river basin rainfall adequately. Two distinct patterns emerged from the calculated PCI indicating that stations in southern basin will have moderate, irregular, and strongly irregular rainfall concentrations, whereas stations in northern basin will have irregular and strongly irregular rainfall concentrations. Significant declining in the rainfall was detected in most stations for the future period. The evolution of the monthly average rainfall amounts will be broadly characterized by a decrease and increase between 32.4 and 12% with late rainy seasons. It is understood that future changes in rainfall distribution and trends will affect the availability of water for crops, which should affect the productivity of rain fed agriculture.

Testing Some f(R,T) Gravity Models from Energy Conditions

We consider f(R,T) theory of gravity, where R is the curvature scalar and T is the trace of the energy momentum tensor. Attention is attached to the special case, f(R,T)=R+2f(T) and two expressions are assumed for the function f(T),(a1Tn+b1)/(a2Tn+b2) and a3Inq(b3Tm), where a1,a2 ,b1,b2,n,a3 ,b3,q and m are input parameters. We observe that by adjusting suitably these input parameters, energy conditions can be satisfied. Moreover, an analysis of the perturbations and stabilities of de Sitter solutions and power-law solutions is performed with the use of the two models. The results show that for some values of the input parameters, for which energy conditions are satisfied, de Sitter solutions and power-law solutions may be stables.

A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of attention.Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds.This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmenta-tion of crops and weeds in color images.Agronomic images of two different databases were used for the segmentation algorithms.Using the thresholding technique,everything except plants was removed from the images.Afterward,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive algorithm.The comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels algorithms.The proposed algorithm pro-vided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%.Based on the confusion matrix,the true-positive and true-negative values were 0.9952 and 0.8985 representing the true classification rate of crops and weeds,respectively.The results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.