Temperature Forecasting as a Means of Mitigating Climate Change and Its Effects: A Case Study of Mali

Utibe Akpan Billy, Sunday O Udo, Igwe O Ewona, Mfon D Umoh, Agbor Mfongang

Abstract


Temperature forecasts and trend analyzes were carried out for several locations in Mali as an important tool for warning of potentially threatening weather events such as severe heat waves, storms, droughts and floods, which could pose a great risk to humans and their environment. Five locations (Segou, Sikasso, Kayes, Gao and Taoudenni) across Mali (170 00’N – 40 00’W) were chosen for this research work. Satellite data of annual temperature obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) database for 35 years (1985-2019) was used for this work. The Mann-Kendall trend test was carried out for various locations to observe and study the trend. Four Models including Auto Regressive and Integrated Moving Average (ARIMA), Exponential smoothening (ETS), TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components) and the linear model were employed to forecast average temperature for 10 years for all the locations. The model that produces the best forecast at the 95% confidence level is expected to have the lowest Root Mean Square Error (RMSE) value. The results showed that no significant trends were recorded at the considered locations. The linear model produced the best forecast for Segou, Kayes and Taoudenni, while the TBATS model produced the best forecast for Gao and the ARIMA model produced the best forecast for Sikasso.

Citation: Billy, U., Udo, S., Ewona, I., Umoh, M., & Mfongang, A. (2023). Temperature Forecasting as a Means of Mitigating Climate Change and Its Effects: A Case Study of Mali. Trends in Renewable Energy, 9(2), 167-179. doi:http://dx.doi.org/10.17737/tre.2023.9.2.00158


Keywords


Trend Analysis; Forecast; Temperature; Mali; Mann-Kendall; Models

Full Text:

FULL TEXT (PDF)

References


Fabbri, K. (2015). Indoor thermal comfort perception. A Questionnaire Approach Focusing on Children; Springer: New York City, NY, USA.

Dube, T., Moyo, P., Ncube, M., & Nyathi, D. (2016). The impact of climate change on agro-ecological based livelihoods in Africa: A review. Dube T, Moyo P, Mpofu M, Nyathi D (2016), The impact of climate change on agro-ecological based livelihoods in Africa: A review, Journal of Sustainable Development, 9(1), 256-267.

Aune, J. B., Coulibaly, A., & Giller, K. E. (2017). Precision farming for increased land and labour productivity in semi-arid West Africa. A review. Agronomy for sustainable development, 37, 1-10.

Yang, K., Yu, Z., Luo, Y., Zhou, X., & Shang, C. (2019). Spatial‐temporal variation of lake surface water temperature and its driving factors in Yunnan‐Guizhou Plateau. Water Resources Research, 55(6), 4688-4703.

Aragón, F. M., Oteiza, F., & Rud, J. P. (2021). Climate change and agriculture: Subsistence farmers’ response to extreme heat. American Economic Journal: Economic Policy, 13(1), 1-35.

Dissanayake, D. M. S. L. B., Morimoto, T., Murayama, Y., Ranagalage, M., & Handayani, H. H. (2018). Impact of urban surface characteristics and socio-economic variables on the spatial variation of land surface temperature in Lagos City, Nigeria. Sustainability, 11(1), 25.

Ibitoye M. O., Aderibigbe O. G., Adegboyega S. &Adebola A. (2017). Spatio temporal analysis of land surface temperature variations in the rapidly developing Akure and its environs, southwestern Nigeria using Land sat data. Ethiopian Journal of Environmental Studies and Management 10(3):389, DOI:10.4314/ejesm.v10i3.9.

Ogunjobi, K. O., Adamu, Y., Akinsanola, A. A., & Orimoloye, I. R. (2018). Spatio-temporal analysis of land use dynamics and its potential indications on land surface temperature in Sokoto Metropolis, Nigeria. Royal Society open science, 5(12), 180661.

Jaseena, K. U., & Kovoor, B. C. (2022). Deterministic weather forecasting models based on intelligent predictors: A survey. Journal of King Saud University-Computer and Information Sciences, 34(6), 3393-3412.

Paglia, E., & Parker, C. (2021). The intergovernmental panel on climate change: guardian of climate science. Guardians of Public Value: How Public Organisations Become and Remain Institutions, 295-321.

Daramola, M. T., Eresanya, E. O., & Erhabor, S. C. (2017). Analysis of rainfall and temperature over climatic zones in Nigeria. J Geogr Environ Earth Sci Int, 11, 1-14.

Yusuf, N., Okoh, D., Musa, I., Adedoja, S., & Said, R. (2017). A study of the surface air temperature variations in Nigeria. The Open Atmospheric Science Journal, 11(1).

Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American meteorological Society, 93(4), 485-498.

Stoner, A. M., Hayhoe, K., Yang, X., & Wuebbles, D. J. (2013). An asynchronous regional regression model for statistical downscaling of daily climate variables. International Journal of Climatology, 33(11), 2473-2494.

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.




DOI: http://dx.doi.org/10.17737/tre.2023.9.2.00158

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Utibe Akpan Billy, Sunday O Udo, Igwe O Ewona, Mfon D Umoh, Agbor Mfongang

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 License.
Copyright @2014-2024 Trends in Renewable Energy (ISSN: 2376-2136, online ISSN: 2376-2144)