Abstract

Abstract. Floods are natural disasters that widely affect people and goods. Its frequency and magnitude are projected to substantially increase due to the ongoing environmental change. At regional and national levels, some efforts have been made in predicting floods at a short-term range. However, the usefulness of flood prediction increases as the time lead increases. The objective of this work is therefore to investigate flood sensitivity to climate indexes in West Africa as a basis for seasonal flood forecasting. The methodology consists of optimizing the relationship between Annual Maximal Discharge (AMD), a proxy for flood discharge and various climate indexes using correlation coefficient, linear regression and statistical modeling based on 56 river gauging stations across West Africa. The climate indexes considered are the Sea Surface Temperature (SST) of the Tropical Northern Atlantic (TNA), SST of the Tropical Southern Atlantic (TSA), the Sea Level Pressure (SLP) of the Southern Oscillation Indexes (SOI) and the detrended El-Nino Southern Oscillation indexes. It was found that SOI/SLP indexes are the most strongly related to the AMD for the investigated stations with generally high, positive, and statistically significant correlation. The TSA/SST indexes indicated both positive and negative statistically significant correlations with river discharge in the region. The percentage change in AMD per unit change in SOI/SLP for most of the statistically significant stations is within 10 % and 50 % indicating a strong relationship between these two variables. This relationship could serve as a basis for seasonal flood forecasting in the study area.

Highlights

  • Sea Surface Temperature (SST) plays a remarkable role in spatial and temporal rainfall variability world widely

  • This study evaluated the relationship between the annual maximal discharge (AMD) and four climate indexes using statistical methods

  • The results indicated a strong relationship between Annual Maximal Discharge (AMD) and the climate indexes

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Summary

Introduction

Sea Surface Temperature (SST) plays a remarkable role in spatial and temporal rainfall variability world widely. The teleconnection between different phases of ENSO or ElNina and rainfall variability over Africa have been found in many studies (Nicholson and Selato, 2000; Nicholson and Kim, 1997). A clear relationship between rainfall and SST has been demonstrated for Eastern Africa (Mutai and Ward, 2000), Sahelian Africa (Dyer et al, 2017; Giannini et al, 2004) and Western Guinean Africa (Balas et al, 2007).

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