Assessment of flood discharge sensitivity to climate indexes in West Africa
<p>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, th...
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| Autores principales: | , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/f79c3acc7b8a48e993757d0bb060a6ed |
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| Sumario: | <p>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.</p> |
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