Contribution of rainfall variability to salts’ dynamics in the region of Mahdia (Tunisian Sahel)
The wide use of irrigation areas in the region of Mahdia is considered to be one of the main factors of soil salinity increase. The present study aims to evaluate the impact of extreme rainfall events on soil salinity. To fill the missing values in our rainfall dataset, we applied the artificial neu...
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Veröffentlicht in: | Arabian journal of geosciences 2019-02, Vol.12 (4), p.1-11, Article 123 |
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creator | Farhat, Lokmen El Amri, Asma Khlifi, Slaheddine Fourati, Marwa Majdoub, Rajouene |
description | The wide use of irrigation areas in the region of Mahdia is considered to be one of the main factors of soil salinity increase. The present study aims to evaluate the impact of extreme rainfall events on soil salinity. To fill the missing values in our rainfall dataset, we applied the artificial neural network algorithm. In addition, we used the RClimDex software to gain further insights on the number of rainy days’ variability over the period between 1960 and 2017. Moreover, to study the presence/absence of a trend in the rainfall time series, we used the modified Mann-Kendall (MM-K) test. Furthermore, to establish the current saline profiles, a drilling operation was carried out at four agricultural plots. These profiles were compared to previous ones observed in 2011. The statistical analysis of the precipitations’ time series reveals that the highest probability of extreme rainfall events is associated with the MS El Djem. The indices generated by the RClimDex software shows a slight increasing trend of days with rainfall ranging between 1 and 25 mm and a slight decreasing trend of days with rainfall ranging between 26 and 50 mm. However, the MM-K test shows an absence of any significant trend for all studied parameters. In addition, results show that the exceptional precipitations, which occurred in autumn 2016 have, indeed, leached the salts that were being retained at the soil surface layers. For instance, in the Ouled Chamekh Center plot, salinity has decreased by around 32%, between 2011 and 2017. |
doi_str_mv | 10.1007/s12517-019-4291-6 |
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The present study aims to evaluate the impact of extreme rainfall events on soil salinity. To fill the missing values in our rainfall dataset, we applied the artificial neural network algorithm. In addition, we used the RClimDex software to gain further insights on the number of rainy days’ variability over the period between 1960 and 2017. Moreover, to study the presence/absence of a trend in the rainfall time series, we used the modified Mann-Kendall (MM-K) test. Furthermore, to establish the current saline profiles, a drilling operation was carried out at four agricultural plots. These profiles were compared to previous ones observed in 2011. The statistical analysis of the precipitations’ time series reveals that the highest probability of extreme rainfall events is associated with the MS El Djem. The indices generated by the RClimDex software shows a slight increasing trend of days with rainfall ranging between 1 and 25 mm and a slight decreasing trend of days with rainfall ranging between 26 and 50 mm. However, the MM-K test shows an absence of any significant trend for all studied parameters. In addition, results show that the exceptional precipitations, which occurred in autumn 2016 have, indeed, leached the salts that were being retained at the soil surface layers. 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The present study aims to evaluate the impact of extreme rainfall events on soil salinity. To fill the missing values in our rainfall dataset, we applied the artificial neural network algorithm. In addition, we used the RClimDex software to gain further insights on the number of rainy days’ variability over the period between 1960 and 2017. Moreover, to study the presence/absence of a trend in the rainfall time series, we used the modified Mann-Kendall (MM-K) test. Furthermore, to establish the current saline profiles, a drilling operation was carried out at four agricultural plots. These profiles were compared to previous ones observed in 2011. The statistical analysis of the precipitations’ time series reveals that the highest probability of extreme rainfall events is associated with the MS El Djem. The indices generated by the RClimDex software shows a slight increasing trend of days with rainfall ranging between 1 and 25 mm and a slight decreasing trend of days with rainfall ranging between 26 and 50 mm. However, the MM-K test shows an absence of any significant trend for all studied parameters. In addition, results show that the exceptional precipitations, which occurred in autumn 2016 have, indeed, leached the salts that were being retained at the soil surface layers. For instance, in the Ouled Chamekh Center plot, salinity has decreased by around 32%, between 2011 and 2017.</description><subject>Agricultural management</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Computer programs</subject><subject>Drilling</subject><subject>Dynamics</subject><subject>Earth and Environmental Science</subject><subject>Earth science</subject><subject>Earth Sciences</subject><subject>Leaching</subject><subject>Neural networks</subject><subject>Original Paper</subject><subject>Precipitation</subject><subject>Probability theory</subject><subject>Profiles</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Salts</subject><subject>Software</subject><subject>Soil</subject><subject>Soil layers</subject><subject>Soil salinity</subject><subject>Soils</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Surface layers</subject><subject>Time series</subject><subject>Variability</subject><issn>1866-7511</issn><issn>1866-7538</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kL1OwzAUhS0EEqXwAGyWWGAI-DqO7Yyo4k8qYqDMlpPYravUKbaD1I3X4PV4ElK1gonpnuF850ofQudAroEQcROBFiAyAmXGaAkZP0AjkJxnosjl4W8GOEYnMS4J4ZIIOULVpPMpuKpPrvO4szho561uW_yhg9OVa13a4NThqNsUvz-_cLPxeuXqiJ3HaWFwMPM9-qwXjdP4ctZ7F532-FUvTHt1io6GwWjO9neM3u7vZpPHbPry8DS5nWY15TJlvCmIBEtJwxpreAGlbXhumGWlJqVkGjhUzOSiKGRpgYGtWMOZKKtaGCrrfIwudrvr0L33Jia17Prgh5eKghAlzQsqhxbsWnXoYgzGqnVwKx02CojaqlQ7lWpQqbYqFR8YumPi0PVzE_6W_4d-ACy3d0o</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Farhat, Lokmen</creator><creator>El Amri, Asma</creator><creator>Khlifi, Slaheddine</creator><creator>Fourati, Marwa</creator><creator>Majdoub, Rajouene</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0002-5675-4309</orcidid></search><sort><creationdate>20190201</creationdate><title>Contribution of rainfall variability to salts’ dynamics in the region of Mahdia (Tunisian Sahel)</title><author>Farhat, Lokmen ; El Amri, Asma ; Khlifi, Slaheddine ; Fourati, Marwa ; Majdoub, Rajouene</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c268t-6d5081f20d4dfe6519fd63e4f49a0984a161b4e375589f141fb4d6479bc7e28c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Agricultural management</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Computer programs</topic><topic>Drilling</topic><topic>Dynamics</topic><topic>Earth and Environmental Science</topic><topic>Earth science</topic><topic>Earth Sciences</topic><topic>Leaching</topic><topic>Neural networks</topic><topic>Original Paper</topic><topic>Precipitation</topic><topic>Probability theory</topic><topic>Profiles</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Salts</topic><topic>Software</topic><topic>Soil</topic><topic>Soil layers</topic><topic>Soil salinity</topic><topic>Soils</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Surface layers</topic><topic>Time series</topic><topic>Variability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Farhat, Lokmen</creatorcontrib><creatorcontrib>El Amri, Asma</creatorcontrib><creatorcontrib>Khlifi, Slaheddine</creatorcontrib><creatorcontrib>Fourati, Marwa</creatorcontrib><creatorcontrib>Majdoub, Rajouene</creatorcontrib><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Arabian journal of geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Farhat, Lokmen</au><au>El Amri, Asma</au><au>Khlifi, Slaheddine</au><au>Fourati, Marwa</au><au>Majdoub, Rajouene</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Contribution of rainfall variability to salts’ dynamics in the region of Mahdia (Tunisian Sahel)</atitle><jtitle>Arabian journal of geosciences</jtitle><stitle>Arab J Geosci</stitle><date>2019-02-01</date><risdate>2019</risdate><volume>12</volume><issue>4</issue><spage>1</spage><epage>11</epage><pages>1-11</pages><artnum>123</artnum><issn>1866-7511</issn><eissn>1866-7538</eissn><abstract>The wide use of irrigation areas in the region of Mahdia is considered to be one of the main factors of soil salinity increase. The present study aims to evaluate the impact of extreme rainfall events on soil salinity. To fill the missing values in our rainfall dataset, we applied the artificial neural network algorithm. In addition, we used the RClimDex software to gain further insights on the number of rainy days’ variability over the period between 1960 and 2017. Moreover, to study the presence/absence of a trend in the rainfall time series, we used the modified Mann-Kendall (MM-K) test. Furthermore, to establish the current saline profiles, a drilling operation was carried out at four agricultural plots. These profiles were compared to previous ones observed in 2011. The statistical analysis of the precipitations’ time series reveals that the highest probability of extreme rainfall events is associated with the MS El Djem. The indices generated by the RClimDex software shows a slight increasing trend of days with rainfall ranging between 1 and 25 mm and a slight decreasing trend of days with rainfall ranging between 26 and 50 mm. However, the MM-K test shows an absence of any significant trend for all studied parameters. In addition, results show that the exceptional precipitations, which occurred in autumn 2016 have, indeed, leached the salts that were being retained at the soil surface layers. For instance, in the Ouled Chamekh Center plot, salinity has decreased by around 32%, between 2011 and 2017.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12517-019-4291-6</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-5675-4309</orcidid></addata></record> |
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subjects | Agricultural management Algorithms Artificial neural networks Computer programs Drilling Dynamics Earth and Environmental Science Earth science Earth Sciences Leaching Neural networks Original Paper Precipitation Probability theory Profiles Rain Rainfall Salinity Salinity effects Salts Software Soil Soil layers Soil salinity Soils Statistical analysis Statistical methods Surface layers Time series Variability |
title | Contribution of rainfall variability to salts’ dynamics in the region of Mahdia (Tunisian Sahel) |
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