Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Kankal, MuratPublication Index: search.filters.publicationIndex.Scopus

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  • Article
    In-Depth Analysis of Drought Trend in Semiarid Saïs Plateau and Middle Atlas Region in Morocco
    (Springer int Publ Ag, 2025) Qadem, Zohair; Tayfur, Gokmen; Kankal, Murat
    This study explores the spatiotemporal properties of droughts and their evolution in the semiarid Sa & iuml;s Plateau and the Middle Atlas regions in Morocco. The methodology includes the analysis of 36 years of precipitation data recorded at 16 meteorological stations, the use of the standardized precipitation index (SPI) at different temporal scales of short term (1 and 3 months), medium term (6 months), and long term (12 and 24 months) to capture historical droughts, and the application of the Mann-Kendall test to assess the drought trends. The wet and dry periods in the Sa & iuml;s Plateau and Middle Atlas regions are almost evenly distributed, with 65% in the "near-normal" condition. The "extremely dry" period, with a value of 1%, was lower than the "extremely humid" period (1.65%). There is no topographically and climatically significant difference between the Sa & iuml;s Plateau and the Middle Atlas in terms of droughts, which are found to decrease over time in both regions. The results of Mann-Kendall test reveal that the short-term trends are generally positive, except at the Boulemane station (- 0.06 for SPI-1, - 0.13 for SPI-3). The long-term precipitation increases in the Middle Atlas region, particularly at the Ait Khabach (0.33 for SPI-24) and the Imouzzer (0.36 for SPI-24) stations. On the Sa & iuml;s Plateau, the trends are positive at the stations of Fez City (0.16 for SPI-24) and Dar Elarsa (0.27 for SPI-24). Significant trends are more pronounced at longer time scales.
  • Article
    Citation - WoS: 29
    Citation - Scopus: 34
    Prediction of Suspended Sediment Concentration From Water Quality Variables
    (Springer Verlag, 2014) Bayram, Adem; Kankal, Murat; Tayfur, Gökmen; Önsoy, Hızır
    This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.