Trend Analysis of Hydro-climatic Historical Data and Future Scenarios of Climate Extreme Indices over Mono River Basin in West Africa

H. Djan’na Koubodana^{1, 2,} , Julien Adounkpe¹, Moustapha Tall^{1, 3}, Ernest Amoussou⁴, Kossi Atchonouglo² and Muhammad Mumtaz⁵

¹West Africa Science Service Centre on Climate change and Adapted Land Use, WASCAL-Climate Change and Water Resources, University of Abomey Calavi, 03 BP 526 Cotonou, Benin

²Faculty of Sciences, University of Lomé, Po. Box 1515 Lomé, Togo

³Laboratoire de Physique de l'Atmosphère et de l'Océan, Ecole Supérieure Polytechnique, Université Cheikh Anta Diop, BP 5085, Dakar, Senegal

⁴Laboratoire Pierre PAGNEY, Climat, Eau, Ecosystème et Développement (LACEEDE), 03 BP1122, Cotonou, Bénin

⁵Department of Management Sciences, the University of Haripur, Pakistan

Cite this paper:
H. Djan’na Koubodana, Julien Adounkpe, Moustapha Tall, Ernest Amoussou, Kossi Atchonouglo and Muhammad Mumtaz. Trend Analysis of Hydro-climatic Historical Data and Future Scenarios of Climate Extreme Indices over Mono River Basin in West Africa. American Journal of Rural Development. 2020; 8(1):37-52. doi: 10.12691/ajrd-8-1-5

Abstract

Climate change impacts considerably on water balance components and needs to be evaluated through trend analysis or climate models scenarios extremes. The objective of this paper is to perform non-parametric Mann Kendall (MK) trend analysis on historical hydro-climatic data (1961-2016), to validate an ensemble climate model and to compute temperature and rainfall extremes indices. The climate indices are evaluated using MK test and annual trend analysis for two future scenarios (2020- 2045) over Mono River Basin (MRB) in Togo. Results show positive and negative trends of hydro-climatic data over MRB from 1961 to 2016. The average temperature increases significantly in most of the stations while a negative non-significant trend of rainfall is noticed. Meanwhile, the discharge presents a significant seasonal and annual trend Corrokope, Nangbéto and Athiémé gauge stations. Validation of the ensemble climate models reveals that the model under-estimates observations at Sokode, Atkakpamé and Tabligbo stations, however linear regression and spatial correlation coefficients are higher than 0.6. Moreover, the percentage of bias between climate model and observations are less than 15% at most of the stations. Finally, the computation of extreme climate indices under RCP4.5 and RCP8.5 scenarios shows a significant annual trend of some extreme climate indices of rainfall and temperature at selected stations between 2020 and 2045 in the MRB. Therefore, relevant governmental politics are needed to elaborate strategies and measures to cope with projected climate changes impacts in the country.

Keywords:
trend analysis extremes indices climate change ETCCDI

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