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Drought Monitoring and Assessment of climate parameters Variability in Koutiala and San Districts, Mali

American Journal of climate Change 2022年第3期11卷 230-249页

作者： Diarra Doukoro Georges Abbevi Abbey Traore Kalifa West African Science Service Center on climate Change and Adapted Land Use WASCAL Lome Togo DRP climate Change Disaster Risk Management University of Lomé Lome Togo Department of Geography University of Lomé Lome Togo Department of Agronomy Agricultural Enterprises Management University of Lomé Lome Togo Institute of Rural Economy in Sotuba Bamako Mali

In Mali, the annual temperature, rainfall, and evapotranspiration are high variables. Their distributions are unevenly spread from north to south. climate change strengthens to increase air temperature and evapotranspiration. It also increases the intense rainstorms and the risk of drought associated heat waves. Drought is considered a natural disaster among all hydrologic extremes. It causes severe damage to the environment, agriculture, and livelihoods relying on water resources. The present study evaluated the variation of drought indices from 1989 to 2019 in Koutiala and San districts, respectively. Therefore, the Standardized Precipitation Evapotranspiration Index (SPEI) was applied. Hence, the Mann-Kendall (MK) test was used and for 12-month time-scales. Trend analysis of monthly precipitation, temperature, and evapotranspiration has been done by using the MK test. Based on the analysis result, the climate of the Koutiala and San districts has been classified as moderate to severe drought category. However, this result clearly shows SPEI pattern changes in both districts. The monthly precipitation showed a significant decreasing trend in Koutiala and San districts. In comparison, the monthly temperature and evapotranspiration displayed an increasing trend in both districts.

关键词： Drought climate parameters SPEI Trend Mann-Kendall Koutiala San Mali

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climate Impact on the Productivity of Sugarcane Varieties in Ferke1 Industrial Plantations

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Agricultural Sciences 2023年第7期14卷 954-976页

作者： Sinali Dosso Fidèle Yoroba Benjamin Kouassi Kouakou Kouadio Adama Diawara Arsène Kobea Arona Diedhiou LAboratoire des Sciences de la Matière de l’Environnement et de l’Energie Solaire (LASMES) Université Félix Houphou&#235t Boigny Abidjan C&#244te d’Ivoire Laboratoire Mixte International Climat-Eau-Energy-Agriculture Université Félix Houphou&#235t Boigny Abidjan C&#244te d’Ivoire Geophysical Station of Lamto (GSL) N’Douci C&#244te d’Ivoire Université Grenoble Alpes IRD CNRS Grenoble INP IGE Grenoble France

This study assesses the climate impact on the productivity of five sugarcane varieties (R579, SP711406, M2593/92, M1400/86, and SP701006) in the industrial plantations of Ferké 1 sugar complex. It is a contribution to research efforts aimed at increasing the productivity of sugarcane varieties in the sugar fields. Also to support agricultural development and guarantee the income of planters. The sugarcane production data are from 2013 to 2017. Climatological data are measured and calculated continuously daily at the production site. In addition, the CMIP-5 (Coupled Model Intercomparison Project) climate database at 1º × 1º horizontal resolution was used for the predictability of crop yields of the 5 sugarcane varieties in the near future (2021-2050) and far future (2056-2075) to improve the quality of climate services to producers. The statistical methodological approach by multiple linear regression associated with the significativity test shows important and significant coefficients of determination (R2 > 0.90) between the yields of sugarcane varieties with certain climatic parameters such as minimum and maximum temperatures, insolation, global solar radiation, and potential evapotranspiration. The impact of rainfall has not been directly evaluated because the linear models do not explicitly show sensitivities to this parameter and the total water requirements for the plot are completely assured by irrigation. The future climate projections analyzed only from extreme thermal parameters (Tmax and Tmin) highlight their strong sensitivities with yields from an idealized model. In this model, we have assumed that the water supply needed by sugarcane is always met by irrigation on different plots. Moreover, linear models do not evolve fast enough in time and changes due to external environmental constraints are not too important at the plot scale. The projected thermic parameters can thus constitute a limiting factor for the

关键词： climate parameters Projections Yield Sugarcane Ferkessédougou

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Application of Machine Learning Methods on climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

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Engineering（科研） 2022年第2期14卷 85-93页

作者： Wend Yam Serge Boris Ouedraogo Sibiri Tiemounou Moumouni Djibo Ali Doumounia Serge Roland Sanou Moumouni Sawadogo Idrissa Guira François Zougmore Laboratoire Matériaux et Environnement Université Joseph KI-ZERBO Ouagadougou Burkina Faso Institut du Génie Informatique et Télécommunications Ecole Polytechnique de Ouagadougou Ouagadougou Burkina Faso Ecole Normale Supérieure Koudougou Burkina Faso Autorité de Régulation des Communications électroniques et des Postes Ouagadougou Burkina Faso Telecel Faso SA Ouagadougou Burkina Faso

Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.

关键词： Meteorological Visibility Commercial Microwave Link Machine Learning climate parameters Dust Attenuation

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