Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 25 June 2019 / Approved: 26 June 2019 / Online: 26 June 2019 (13:50:18 CEST)

How to cite: H. Koubodana, D.; Tall, M.; Amoussou, E.; Mumtaz, M.; Adounkpe, J.; Atchonouglo, K. Trend Analysis of Hydroclimatic Historical Data and Future Scenarios of Climate Extreme Indices over Mono River Basin in West Africa. Preprints 2019, 2019060267 (doi: 10.20944/preprints201906.0267.v1). H. Koubodana, D.; Tall, M.; Amoussou, E.; Mumtaz, M.; Adounkpe, J.; Atchonouglo, K. Trend Analysis of Hydroclimatic Historical Data and Future Scenarios of Climate Extreme Indices over Mono River Basin in West Africa. Preprints 2019, 2019060267 (doi: 10.20944/preprints201906.0267.v1).

Abstract

This paper performs non-parametric Mann Kendall (MK) trend analysis of historical hydroclimatic data (1961-2016), an ensemble climate model validation and a computation of 16 Expert Team on Climate Change Detection and Indices (ETCCDI) temperature and rainfall extremes indices. The climate indices are evaluated using MK test and annual trend analysis for two Representative Concentration Pathways (RCP4.5 & RCP8.5) future scenarios from 2020 to 2045 over Mono River Basin (MRB) in Togo. The annual and seasonal trend analyses are assessed on historical potential evapotranspiration, mean temperature, rainfall and discharge data. Results show positive and negative trends of hydroclimatic data over MRB from1961 to 2016. Mean temperatures increase significantly in most of the stations while a negative non-significant trend is noticed for rainfall. Meanwhile, the discharge presents a significant seasonal and annual trend for three gauge stations (Corrokope, Nangbéto and Athiémé). 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 climatic indices under RCP4.5 and RCP8.5 scenarios shows a significant annual trend of some extreme climatic 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.

Subject Areas

Trend analysis, Extremes indices, Climate change, ETCCDI

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