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Investigation of spatiotemporal variability of some precipitation indices in Seyhan Basin, Turkey: monotonic and sub-trend analysis

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  • Cihangir Koycegiz

    (Konya Technical University)

  • Meral Buyukyildiz

    (Konya Technical University)

Abstract

Irregular precipitation regimes have important effects on the increase in the incidence and severity of meteorological disasters, the use of water resources, the decrease in the variety and amount of agricultural products, and on biodiversity. Therefore, investigating the temporal and spatial variations of precipitation is vital important in the future planning and management both water resources and of agricultural activities. In this study, it is aimed to investigate annual and seasonal time scales the spatial variability and temporal trends of concentration, seasonality and aggressiveness of precipitation in Seyhan Basin (Turkey), which has different topography and climate characteristics. For this purpose, nonparametric indices such as the Precipitation Concentration Index (PCI), Seasonality Index (SI) and Modified Fournier Index (MFI) were used. To calculate these indices, monthly precipitation data of 7 stations for the period 1970–2019 were used. While monotonic trends in the PCI, SI and MFI series were analyzed using the classical Mann–Kendall test, sub-trends were examined using Onyutha's test, which is an innovative method. The presence of monotonic and sub-trends was evaluated at the 5% significance level. Analyses performed on both annual and seasonal scales showed that generally higher index values were obtained at stations in the south of the basin and lower indices values were obtained in other parts of the basin. The results of MK and Onyutha trend tests applied to annual total precipitation and PCI, SI and MFI values are similar. In general, insignificant positive trends were determined in the annual total precipitation and index values at the stations in the south of the basin, while insignificant negative trends were determined in the other regions.

Suggested Citation

  • Cihangir Koycegiz & Meral Buyukyildiz, 2023. "Investigation of spatiotemporal variability of some precipitation indices in Seyhan Basin, Turkey: monotonic and sub-trend analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 2211-2244, March.
    • Handle: RePEc:spr:nathaz:v:116:y:2023:i:2:d:10.1007_s11069-022-05761-6
    DOI: 10.1007/s11069-022-05761-6
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    References listed on IDEAS

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    1. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
    2. Ruqayah Mohammed & Miklas Scholz, 2019. "Climate Variability Impact on the Spatiotemporal Characteristics of Drought and Aridityin Arid and Semi-Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5015-5033, December.
    3. Xu, Meng & Shang, Pengjian, 2018. "Analysis of financial time series using multiscale entropy based on skewness and kurtosis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1543-1550.
    4. Muhammad S. Ashraf & Ijaz Ahmad & Noor M. Khan & Fan Zhang & Ahmed Bilal & Jiali Guo, 2021. "Streamflow Variations in Monthly, Seasonal, Annual and Extreme Values Using Mann-Kendall, Spearmen’s Rho and Innovative Trend Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 243-261, January.
    5. Mehmet Dikici, 2022. "Drought Analysis for the Seyhan Basin with Vegetation Indices and Comparison with Meteorological Different Indices," Sustainability, MDPI, vol. 14(8), pages 1-17, April.
    6. Farre, Imma & Faci, Jose Maria, 2006. "Comparative response of maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) to deficit irrigation in a Mediterranean environment," Agricultural Water Management, Elsevier, vol. 83(1-2), pages 135-143, May.
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