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PROJECTION OF INTENSITY DURATION FREQUENCY CURVES IN A CONTEXT OF CLIMATE CHANGE IN THE CITY OF LOME (WEST AFRICA).

Authors: Gbafa Kodjvi Senanou , Afio Ayarema; Samah Ouro-Djobo E.; Tiem Sonnou and Kokou Kouami.;

PROJECTION OF INTENSITY DURATION FREQUENCY CURVES IN A CONTEXT OF CLIMATE CHANGE IN THE CITY OF LOME (WEST AFRICA).

Abstract

Urban hydrology models are tools for dimensioning and diagnosing drainage systems for runoff from impervious surfaces. These models undergo an irreversible upheaval in the face of climate changes that impact rainfall intensities. This study aims to analyze the effect of climate change on rainfall intensities in the city of Lom? through the elaboration of Intensity Time Frequency (IDF) curves for future climate for three (03) normals from 2011 to 2100. The analysis of rainfall intensities in a changing climate was carried out using the IDF_CC version 1.2 tool on 50 years pluviographers data of the Lom? airport synoptic station. The efficiency of retention basins in a changing climate was determined by comparing the threshold intensity with the theoretical intensities. The results show a large difference between the reference curves and the existing curves obtained from the same historical data. The results also indicate a significant difference in rainfall intensities between current climate and the three future periods considered, regardless of the scenario used and the return period. These results, however, demonstrate a downward trend in intensities for RCP 2.6 and an upward trend in rainfall intensities using the RCP 8.5 scenario which is in fact related to very high greenhouse gas emissions. The hydrological response of retention basins in a changing climate gives very low threshold intensities compared to projections. Drainage of urban stormwater in a changing climate is not effective.

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Keywords

Climate change Intensity duration frequency curves urban hydrology.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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