<?xml version="1.0" encoding="UTF-8"?>
<records>
<record>
<language>eng</language>
<publisher>Science and Education Publishing</publisher>
<journalTitle>American Journal of Water Resources</journalTitle>
<eissn>2333-4819</eissn>
<publicationDate>2025-09-17</publicationDate>
<volume>13</volume>
<issue>3</issue>
<startPage>104</startPage>
<endPage>120</endPage>
<doi>10.12691/ajwr-13-3-5</doi>
<publisherRecordId>AJWR20251335</publisherRecordId>
<documentType>article</documentType>
<title language="eng">Analysis of the Spatio-temporal Evolution of Extreme Precipitation Indices Over the Period 1980 - 2020 in the Cavally Watershed (West Africa)</title>
<authors>
<author>
<name>Lou Moin Sandrine Tivoli</name>
<email>sandrine.tivoli20@inphb.ci</email>
<affiliationId>1</affiliationId>
</author>
<author>
<name>Koffi Eug¨¨ne Kouakou</name>
<affiliationId>1</affiliationId>
</author>
<author>
<name>Kouadio Assemien Fran?ois Yao</name>
<affiliationId>2</affiliationId>
</author>
<author>
<name>Amani Michel Kouassi</name>
<affiliationId>2</affiliationId>
</author>

</authors>
<affiliationsList>
<affiliationName affiliationId="1">Joint Research and Innovation Unit for Engineering Sciences and Techniques, Felix Houphouet-Boigny National Polytechnic Institute of Yamoussoukro, Yamoussoukro, C?te dĄ¯Ivoire</affiliationName>

<affiliationName affiliationId="2">Department of Geological and Mining Sciences, University of Man, Man, C?te dĄ¯Ivoire</affiliationName>

</affiliationsList>
<abstract language="eng">Heavy rainfall is recognized as the main factor in the flood risk occurrence. Analysis of daily meteorological data provides insight into the current situation and helps manage future extreme events with a view to preventing flood risks in a given region. This study aimed to determine the spatio-temporal evolution of 10 rainfall indices over the period 1983-2020 in order to highlight the risk of flooding in the Cavally watershed. The daily data used were processed and then integrated into the RClimdex model to calculate the 10 rainfall indices, which are: PRCPTOT, CWD, RX1day, RX5day, RX10, RX20, RX25, R95p, R99p, and SDII. The Man-Kendall trend test and Sen's slope estimator were used in the analysis of meteorological data. Although not all trends are significant, the results show a general downward trend in total annual precipitation (PRCPTOT), the number of days with heavy and very heavy precipitation (R10 and R20), and the number of very wet days (R95p). On the other hand, maximum 1-day precipitation amount (RX1day), maximum 5-day precipitation amount (RX5day), and the number of extremely wet days (R99p) showed an upward trend. These positive trends are causing increasingly frequent flooding in the central and southern parts of the study area. It is therefore necessary to implement adaptation measures to protect this heavily agricultural area.</abstract>
<fullTextUrl format="pdf">https://pubs.sciepub.com/ajwr/13/3/5/ajwr-13-3-5.pdf</fullTextUrl>
<keywords language="eng"><keyword>Rainfall indices</keyword>
<keyword>climate change</keyword>
<keyword>flood</keyword>
<keyword>Cavally watershed</keyword>
</keywords>
</record>
</records>
