The “Great Green Wall” of trees (GGW) is an emblematic Pan‐African initiative of re‐greening the Sahel through afforestation and assisted natural regeneration of trees in order to tackle desertification, soil degradation and to mitigate greenhouse gases. This study investigates (i.e., The Sahel Greenbelt) the potential impacts of the GGW and other assisted natural regeneration of trees on the frequency and intensity of extreme climate events over the Sahel and West Africa using the regional climate model (RegCM version 4.3). Our investigation shows that the Sahel greenbelt would increase significantly the number of rainy days (+9%) and the intensity of heavy rain events over the Sahel while extreme dry spells decrease (−4%). Important shifts appear in the modes of variability of all precipitation indices. These probability distribution shapes reveal tremendous intra‐seasonal variability as the new land use land cover (LULC) changes affect the regional climate. Changes in atmospheric circulation including increase of the moisture convergence and evapotranspiration appeared to be the main drivers of heavy rainfall changes. For temperature extremes, the maximum temperature shows significant decrease around the GGW area during summer and an increase in other seasons while the diurnal temperature range increases significantly without an evident change in temperature trends. Intra‐seasonal distributions of temperature extremes show less obvious changes compared to precipitation extremes. This investigation highlights the role of the planned and implemented re‐greening policies (i.e., afforestation by the GGW project and policies of assisted natural regeneration of trees) in affecting the frequency and the amplitude of some climatic extreme events (e.g., heavy rain events, maximum temperatures, etc.). These planned LULC policies need to be accounted for in the diagnostics and future projections of climate extremes over the region.
The sensitivity of mono-crystalline solar PV module towards dust accumulation, ambient temperature, relative humidity, and cloud cover is investigated from May to August 2015 for Niamey's environment. Two solar modules with the same characteristics have been used to carry out the impacts of the dust on the solar PV module. One of the modules is being cleaned every morning and the second one was used for monitoring the effect of dust accumulation onto the surface of the unclean module for May and June. The ambient temperature around the solar PV module was recorded at the same time with the output voltage and the output current to assess the impacts of ambient temperature on the PV conversion efficiency. In addition to these field test measurements, the solar radiation data measured in National Center of Solar Energy (CNES) of Niamey were also used. Also the relative humidity for the study area data obtained NASA power agro-climatology website was used. Results show that the dust accumulation has the greatest impact on the performance of the PV module followed by temperature, relative humidity and cloud cover. Exposing the module in 23 days has reduced the energy output by 15.29%. The power output and the conversion efficiency of the PV module have dropped by 2.6% and 0.49% respectively. The relative humidity also has reduced the energy output by 4.3 Wh/m 2 /day.
In this paper, we applied the Wavelet Transform Coherence (WTC) and phase analysis to analyze the relationship between the daily electricity demand (DED) and weather variables such as temperature, relative humidity, wind speed, and radiation. The DED data presents both seasonal fluctuations and increasing trend while the weather variables depict only seasonal variation. The results obtained from the WTC and phase analysis permit us to detect the period of time when the DED significantly correlates with the weather variables. We found a strong seasonal interdependence between the air temperature and DED for a periodicity of 256-512 days and 128-256 days. The relationship between the humidity and DED also shows a significant interdependence for a periodicity of 256-512 days with average coherence equal to 0.8. Regarding the radiation and wind speed, the correlation is low with average coherence less than 0.5. These results provide an insight into the properties of the impacts of weather variables on electricity demand on the basis of which power planners can rely to improve their forecasting and planning of electricity demand.
The accumulation of dust on the surface of solar panels reduces the amount of sunlight reaching the solar cells and results in a decrease in panel performance. To avoid this loss of production and thus, to improve the performance capacity, solar panels must be cleaned frequently. The West African region is well known for its high solar energy potential. However, this potential can be reduced by the high occurrence of dust storms during the year. This article aims to provide a contribution to the construction of a meteorological information service for solar panel cleaning operations at Diass solar plant in Senegal (Western Sahel). It is based on a full year in situ experiment comparing the power loss due to dust between solar panels cleaned at different frequencies and those not cleaned. The model to determine the cleaning frequencies is based on the deposition rate of airborne particles, the concentration of airborne particles, and the density of the dust that has a major impact on the power loss. Cleaning frequencies are presented at seasonal scale because in the study area, dust episodes differ according to the seasons. A cost–benefit analysis is also performed to demonstrate the advantage of using weather information service to support the dust cleaning operations at Diass plant. As results, it is found that cleaning every 3 weeks is required during the dry seasons, December–January–February and March–April–May. During the rainy season, cleaning every 5 weeks is recommended in June–July–August, while in September–October–November cleaning every 4 weeks is sufficient to maintain an optimal performance of the solar panel. The total costs of cleaning operations based on these results are reduced compared to the current costs of cleaning and the benefits are much higher than without cleaning action.
The present study examines the potential impact of climate change on daily electricity demand (DED) and climate variables in Niger at specific Global Warming Levels (GWL1.5, GWL2.0, GWL2.5, and GWL3.0). The principal component analysis (PCA) and the Multiple Linear Regression (MLR) model was utilized to build the electricity demand model. Furthermore, fourteen (14) regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX) were used for the study. The ability of the model ensemble-mean in reproducing the annual cycle of the climate variables was evaluated. The impact of climate change at specific GWLs on electricity demand and each climate variable is quantified. The MLR predicted the demand with a coefficient of determination R 2 equals to 0.808 and a root mean square error (RMSE) equals to 149.87. The residuals analysis indicated that the model complies with the regressions assumptions. The models projected an increase of electricity demand at all the GWLs. More than 75% of the models agree on the noticeable change in electricity demand. The results of the study showed how climate services could be used to quantify the impacts of climate change on electricity demand.
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