Segmentation and Trend Analysis of Baseflow in Seyhan Basin

Abstract

This study highlights the importance of baseflow—one of the critical yet often overlooked components of the hydrological regime—in basin management by revealing the temporal dynamics of surface runoff and baseflow components in the Seyhan Basin through segmentation and trend analyses. By using the Recursive Digital Filter method baseflow was separated from daily mean discharge data, after which annual mean discharge and baseflow time series calculated from daily values were segmented to identify change points, and subjected to trend analysis. Case study was carried out on four streamflow gauging stations in the Seyhan Basin, which had continuous 30-year common records. The findings show that, at the annual scale, baseflow and total discharge in the Seyhan Basin move synchronously. Both components tend to increase during wet periods and decrease together during dry periods. This parallel and simultaneous pattern confirms that precipitation is the dominant factor in hydrological processes, and that precipitation deficits in the basin immediately affect both surface runoff and groundwater components— indicating that meteorological drought can trigger drought in both surface and groundwater resources. These results demonstrate that baseflow plays an indispensable role in preserving ecosystem integrity, meeting environmental flow requirements, and providing hydrological resilience against climate change. Furthermore, the study emphasizes the necessity of developing integrated watershed management strategies that focus on baseflow management, particularly under today’s conditions where climate variability is becoming increasingly significant. 

Keywords

• Trend analysis
• Mann-Kendall test
• Mann-Whitney test
• Segmentation
• Baseflow

SEYHAN HAVZASI TABAN AKIŞININ SEGMENTASYON VE EĞİLİM ANALİZİ

Abstract

Bu çalışma, Seyhan Havzasında yüzeysel akış ve taban akışı bileşenlerinin zamansal değişim dinamiklerini segmentasyon ve eğilim analizleri ile ortaya koyarak, hidrolojik rejimin kritik ancak sıklıkla göz ardı edilen bileşenlerinden biri olan taban akışını incelemektedir. Taban akışı, günlük ortalama akım verilerinden Tekrarlı Dijital Filtreleme yöntemi kullanılarak ayrılmış, ardından günlük değerlerden hesaplanan yıllık ortalama akım ve taban akışı zaman serileri segmentlere ayrılarak değişim noktaları belirlenmiş ve eğilim analizine tabi tutulmuştur. Uygulama, Seyhan Havzasındaki kesintisiz 30 yıllık ortak kaydı bulunan dört akım gözlem istasyonu üzerinde gerçekleştirilmiştir. Elde edilen bulgular, Seyhan Havzasında akarsu kesitindeki taban akışı ile toplam akımın yıllık zaman ölçeğinde eş zamanlı hareket ettiğini göstermektedir. Yağışlı dönemlerde her iki bileşen de artış eğilimi sergilemekte, kurak dönemlerde birlikte azalmaktadır. Paralel ve eş zamanlı bu gidiş, havzaya düşen yağışın yüzeysel akış ve taban akışı üzerinde etkili olduğunu göstermekte; havzada gözlenen yağış eksikliğinin de yine yüzeysel akış ve taban akışında azalmaya neden olduğunu teyit etmektedir. Çalışma, ayrıca iklim değişikliğinin giderek önem kazandığı günümüz koşullarında, taban akışı yönetimine odaklanan entegre havza yönetim stratejilerinin geliştirilmesinin gerekliliğine vurgu yapmaktadır.

Keywords

• Eğilim analizi
• Mann-Kendall testi
• Mann-Whitney testi
• Segmentasyon
• Taban akışı

References

1. Acar, R., Saplioglu, K. (2024) Using the Particle Swarm Optimization (PSO) Algorithm for Baseflow Separation and Determining the Trends for the Yesilirmak River (North Turkey). Russian Meteorology and Hydrology, 49(1), 40-51. doi.org/10.3103/S1068373924010060
2. Acar, R., Saplioglu, K. (2025) Using Meta-Heuristic Optimization Algorithms to Determine Baseflow and Comparing Their Temporal Performance. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 49, 1851-1869. doi.org/10.1007/s40996-024-01558-8
3. Aksoy, H. (1998) Kuruyan Akarsuların Günlük Akımlarının Modellenmesi, Doktora Tezi, İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, 247 s.
4. Aksoy, H. (2020) Surface water. In: Harmancioglu, N.B., Altinbilek, D. (eds.) Water Resources of Turkey, Springer, 127-158. doi.org/10.1007/978-3-030-11729-0_5
5. Aksoy, H., Gedikli, A., Unal, N.E., Kehagias, A. (2008) Fast segmentation algorithms for long hydrometeorological time series. Hydrological Processes, 22(23), 4600-4608. doi.org/10.1002/hyp.7064
6. Aksoy, H., Kurt, I., Eris, E. (2009) Filtered smoothed minima baseflow separation method. Journal of Hydrology, 372(1–4), 94–101. doi.org/10.1016/j.jhydrol.2009.03.037
7. Aksoy, H., Wittenberg, H. (2011) Nonlinear baseflow recession analysis in watersheds with intermittent streamflow. Hydrological Sciences Journal, 56(2), 226–237. doi.org/10.1080/02626667.2011.553614
8. Aksoy, H., Wittenberg, H. (2015) Baseflow recession analysis for flood‐prone Black Sea watersheds in Turkey. CLEAN–Soil, Air, Water, 43(6), 857-866. doi.org/10.1002/clen.201400199

9. Al-Faraj, F.A., Scholz, M. (2014) Incorporation of the flow duration curve method within digital filtering algorithms to estimate the base flow contribution to total runoff. Water Resources Management, 28, 5477–5489. doi.org/10.1007/s11269-014-0816-7
10. Arnold, J.G., Allen, P.M., Muttiah, R., Bernhardt, G. (1995) Automated base flow separation and recession analysis techniques. Groundwater, 33(6), 1010–1018. doi.org/10.1111/j.1745-6584.1995.tb00046.x
11. Bayazıt, M. (1981) Hidrolojide İstatistik Yöntemler, Teknik Üniversite Matbaası, İstanbul.
12. Bayazıt, M. (1996) İnşaat Mühendisliğinde Olasılık Yöntemleri. İTÜ İnşaat Fakültesi Matbaası, İstanbul.
13. Budyko, M.I. (1974) Climate and Life, Academic Press, New York. ISBN: 0-12-139450-6
14. Cavus, Y., Stahl, K., Aksoy, H. (2022) Revisiting Major Dry Periods by Rolling Time Series Analysis for Human-Water Relevance in Drought. Water Resources Management 36, 2725–2739. doi.org/10.1007/s11269-022-03171-8
15. Chapman, T.G. (1991) Comment on "Evaluation of Automated Techniques for Base Flow and Recession Analyses" by R. J. Nathan and T. A. McMahon, Water Resources Research, 27(7), 1783-1784. doi.org/10.1029/WR026i007p01465
16. Chen, H., Huang, S., Xu, Y.‐P., Teegavarapu, R.S.V., Guo, Y., Nie, H., Xie, H. (2024) Using baseflow ensembles for hydrologic hysteresis characterization in humid basins of Southeastern China. Water Resources Research, 60, e2023WR036195. doi.org/10.1029/2023WR036195
17. Chow, V.T., Maidment, D.R., Mays, L.W. (1988) Applied Hydrology, McGraw-Hill, ISBN: 0-07-010810-2
18. Çavuş, Y. (2025) Budyko eğrisinin uzun vadeli havza planlama çalışmalarında su bütçesi hesabı için kullanılması. Turkish Journal of Civil Engineering, 36(4), 67-88. doi.org/10.18400/tjce.1552201
19. Duncan, H.P. (2019) Baseflow separation – A practical approach. Journal of Hydrology, 575, 308–320. doi.org/10.1016/j.jhydrol.2019.05.040
20. Eckhardt, K. (2005) How to construct recursive digital filters for baseflow separation. Hydrological Processes, 19(2), 507-515. doi.org/10.1002/hyp.5675
21. Eris, E., Wittenberg, W. (2015) Estimation of baseflow and water transfer in karst catchments in Mediterranean Turkey by nonlinear recession analysis. Journal of Hydrology, 530, 500-507. doi.org/10.1016/j.jhydrol.2015.10.017
22. Fanta, B., Zaake, B.T., Kachroo, R.K. (2001) A study of variability of annual river flow of the southern African region. Hydrological Sciences Journal, 46(4), 513-524. doi.org/10.1080/02626660109492847
23. Gedikli, A., Aksoy, H., Unal, N.E. (2008) Segmentation algorithm for long time series analysis. Stochastic Environmental Research and Risk Assessment, 22(3), 291–302. doi.org/10.1007/s00477-007-0115-4
24. Gedikli, A., Aksoy, H., Unal, N.E. (2010) AUG-Segmenter: a user-friendly tool for segmentation of long time series. Journal of Hydroinformatics, 12(3), 318-328. doi.org/10.2166/hydro.2009.084
25. Gnann, S.J., Woods, R.A., Howden, N.J.K. (2019) Is there a baseflow Budyko curve? Water Resources Research, 55, 2838–2855. doi.org/10.1029/2018WR024464
26. Güçlü, Y.S., Acar, R., Saplıoğlu, K. (2025) Seasonally adjusted periodic time series for Mann-Kendall trend test. Physics and Chemistry of the Earth, Parts A/B/C, 138, 103848. doi.org/10.1016/j.pce.2024.103848
27. Hirsch, R.M., Helsel, D.R., Cohn, T.A., Gilroy, E.J. (1993) Statistical treatment of hydrologic data. In: Maidment D.R. (ed.) Handbook of Hydrology, New York, Mc-Graw Hill, 17.1-17.55.
28. Hubert, P. (2000) The segmentation procedure as a tool for discrete modeling of hydrometeorological regimes. Stochastic Environmental Research and Risk Assessment, 14, 297-304. doi.org/10.1007/PL00013450
29. Hughes, D.A., Hannart, P., Watkins, D. (2004) Continuous baseflow separation from time series of daily and monthly streamflow data. Water SA, 29(1), 43-48. doi.org/10.4314/wsa.v29i1.4945
30. Kendall, M. (1975) Rank Correlation Methods, 4th ed. Charles Griffin, London.
31. Leeming, K.A., Bloomfield, J.P., Coxon, G., Zheng, Y. (2025) Functional data analysis to investigate controls on and changes in the seasonality of UK baseflow. Hydrological Sciences Journal, 70(3), 522–534. doi.org/10.1080/02626667.2024.2434714
32. Lyne, V., Hollick, M. (1979) Stochastic time-variable rainfall-runoff modelling. Hydrology and Water Resources Symposium, Perth, Institution of Engineers Australia, 89–92.
33. Mann, H. (1945) Non-parametric test against trend. Econometrika, 13, 245-259.
34. Mohammed, H., Scholz, M. (2018) Flow–duration curve integration into digital filtering algorithms for simulating climate variability based on river baseflow. Hydrological Sciences Journal, 63(10), 1558–1573. doi.org/10.1080/02626667.2018.1519318
35. Nathan, R.J., McMahon, T.A. (1990) Evaluation of automated techniques for base flow and recession analyses. Water Resources Research, 26(7), 1465-1473. doi.org/10.1029/WR026i007p01465
36. Piggott, A.R., Moin, S., Southam, C. (2005) A revised approach to the UKIH method for the calculation of baseflow. Hydrological Sciences Journal, 50(5), 920. doi.org/10.1623/hysj.2005.50.5.911
37. Tanaka, K., Fujihara, Y., Watanabe, T., Kojiri, T., Ikebuchi, S. (2006) Projection of the impact of climate change on the surface energy and water balance in the Seyhan River Basin Turkey. Annual Journal of Hydraulic Engineering, JSCE, 50, 31-36.
38. UKIH (United Kingdom Institute of Hydrology) (1980) Low Flow Studies, research reports 1 and 3. Institute of Hydrology, Wallingford, Oxfordshire, UK
39. White, K.E., Sloto, R.A. (1990) Base-flow frequency characteristics of selected Pennsylvania streams. U.S. Geological Survey Water-Resources Investigations Report, 90–4160.
40. Wittenberg, H. (1994) Nonlinear analysis of flow recession curves. IAHS Publication, 221, 61-67.
41. Wittenberg, H. (1999) Baseflow recession and recharge as nonlinear storage processes. Hydrological Processes, 13(5), 715-726. doi.org/10.1002/(SICI)1099-1085(19990415)13:5<715::AID-HYP775>3.0.CO;2-N
42. Wittenberg, H., Sivapalan, M. (1999) Watershed groundwater balance estimation using streamflow recession analysis and baseflow separation. Journal of Hydrology, 219(1-2), 20-33. doi.org/10.1016/S0022-1694(99)00040-2
43. Wittenberg, H., Aksoy, H., Miegel, K. (2019) Fast response of groundwater to heavy rainfall. Journal of Hydrology, 571, 837-842. doi.org/10.1016/j.jhydrol.2019.02.037
44. Wong, H., Hu, B.Q., Ip, W.C., Xia, J. (2006) Change-point analysis of hydrological time series using grey relational method. Journal of Hydrology, 324, 323-338. doi.org/10.1016/j.jhydrol.2005.10.007
45. Xiong, L. Guo, S. (2004) Trend test and change-point detection for the annual discharge series of the Yangtze River at the Yichang hydrological station. Hydrological Sciences Journal, 49(1), 99-112. doi.org/10.1623/hysj.49.1.99.53998