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Assessing the main drivers of low flow series in Turkey

Author

Listed:
  • Muhammet Yılmaz

    (Erzurum Technical University)

  • Fatih Tosunoğlu

    (Erzurum Technical University)

Abstract

Over the past decades, low flow has been highly impacted by climate change across Turkey, and it is important to investigate low flow trends and drivers of this change to guide water resources management. The standard normal homogeneity test (SNHT), the Pettitt test, and the Buishand range test were used for homogeneity analysis. A comprehensive assessment of trends and variability in low flows from 88 flow stations located in 26 river basins across Turkey and their attributions in a changing climate were presented. The Mann–Kendall (MK) and modified Mann–Kendall test (mMK) were utilized to detect the significance of trends, while Sen’s slope method was used to identify the magnitude of trends. According to the trend test results, the low flow records of 34 stations indicated statistically decreasing trends, whereas those of four stations indicated a statistically increasing trend. We also analyzed how climate variables affect low flow variations. Correlations between climate variables (temperature and precipitation) and large-scale climate models with low flow were determined by Spearman's Rho test. North Atlantic Oscillation (NAO), Western Mediterranean Oscillation (WeMO), Southern Oscillation Index (SOI), and Pacific Decadal Oscillation (PDO) were investigated for their relation with the low flow variability. The results showed that low flow data were generally positively correlated with precipitation, and this result was particularly pronounced on the annual scale. Unlike precipitation, low flow series have a negative correlation with temperature data, and correlations are clearer during dry periods. In most areas, NAO and SO were negatively correlated with low flow patterns in Turkey, while PDO and WeMO were positively correlated with low flow series. The results indicate that low flows in most regions are more sensitive to precipitation than temperature and large-scale climate models. In addition, this research reported that the use of seasonal indices made some seasonal correlations more pronounced than their annual counterparts. These results suggest that seasonal climate indices can be potential candidates for low flow prediction.

Suggested Citation

  • Muhammet Yılmaz & Fatih Tosunoğlu, 2023. "Assessing the main drivers of low flow series in Turkey," 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. 115(3), pages 1927-1953, February.
    • Handle: RePEc:spr:nathaz:v:115:y:2023:i:3:d:10.1007_s11069-022-05621-3
    DOI: 10.1007/s11069-022-05621-3
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    References listed on IDEAS

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    1. Jonghun Kam & Justin Sheffield, 2016. "Changes in the low flow regime over the eastern United States (1962–2011): variability, trends, and attributions," Climatic Change, Springer, vol. 135(3), pages 639-653, April.
    2. Subhasis Mitra & Puneet Srivastava, 2017. "Spatiotemporal variability of meteorological droughts in southeastern USA," 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. 86(3), pages 1007-1038, April.
    3. Ricardo Trigo & José L. Zêzere & Maria Rodrigues & Isabel Trigo, 2005. "The Influence of the North Atlantic Oscillation on Rainfall Triggering of Landslides near Lisbon," 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. 36(3), pages 331-354, November.
    4. I. Masih & S. Uhlenbrook & S. Maskey & V. Smakhtin, 2011. "Streamflow trends and climate linkages in the Zagros Mountains, Iran," Climatic Change, Springer, vol. 104(2), pages 317-338, January.
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