Research Article
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Year 2025, Volume: 14 Issue: 2, 1151 - 1165, 30.06.2025
https://doi.org/10.17798/bitlisfen.1652418
https://izlik.org/JA37XP46BB

Abstract

References

  • K. Saplıoğlu and Y. S. Güçlü, “Combination of Wilcoxon test and scatter diagram for trend analysis of hydrological data,” J. Hydrol., vol. 612, no. July, p. 128132, Sep. 2022, doi: 10.1016/j.jhydrol.2022.128132.
  • M. Esit, R. Çelik, and E. Akbas, “Spatial and temporal variation of meteorological parameters in the lower Tigris–Euphrates basin, Türkiye: application of non-parametric methods and an innovative trend approach,” Water Sci. Technol., vol. 87, no. 8, pp. 1982–2004, Apr. 2023, doi: 10.2166/wst.2023.116.
  • M. A. Günen and U. H. Atasever, “Remote sensing and monitoring of water resources: A comparative study of different indices and thresholding methods,” Sci. Total Environ., vol. 926, p. 172117, May 2024, doi: 10.1016/j.scitotenv.2024.172117.
  • M. B. Yıldız, M. Kankal, S. Nacar, N. T. T. Linh, H. Van Hoa, and V. T. Nam, “Investigation of precipitation trends in Lower Mekong Delta River Basin of Vietnam by innovative trend analysis methods,” Theor. Appl. Climatol., vol. 155, no. 12, pp. 10033–10050, Dec. 2024, doi: 10.1007/s00704-024-05221-0.
  • M. A. Günen, K. F. Öztürk, and Ş. Aliyazıcıoğlu, “Optimizing Visibility of Historical Structures Using mWDE: Insights from the Kromni Valley, Gümüşhane, Türkiye,” Int. J. Eng. Geosci., vol. 10, no. 1, pp. 107–126, Feb. 2025, doi: 10.26833/ijeg.1529351.
  • H. B. Mann, “Nonparametric tests against trend,” Econometrica, vol. 13, no. 3, pp. 245–259, Jul. 1945, doi: 10.2307/1907187.
  • M. G. Kendall, Rank correlation methods. London: Griffin, 1975.
  • H. Tongal, “Spatiotemporal analysis of precipitation and extreme indices in the Antalya Basin, Turkey,” Theor. Appl. Climatol., vol. 138, no. 3–4, pp. 1735–1754, 2019, doi: 10.1007/s00704-019-02927-4.
  • S. Kumar, V. Merwade, J. Kam, and K. Thurner, “Streamflow trends in Indiana: Effects of long term persistence, precipitation and subsurface drains,” J. Hydrol., vol. 374, no. 1–2, pp. 171–183, Jul. 2009, doi: 10.1016/j.jhydrol.2009.06.012.
  • Y. Dinpashoh, R. Mirabbasi, D. Jhajharia, H. Z. Abianeh, and A. Mostafaeipour, “Effect of short-term and long-term persistence on identification of temporal trends,” J. Hydrol. Eng., vol. 19, no. 3, pp. 617–625, Mar. 2014, doi: 10.1061/(asce)he.1943-5584.0000819.
  • R. Sneyers, “On the statistical analysis of series of observations. WMO Tech,” Note, 143, 1, 1990.
  • A. Hess, H. Iyer, and W. Malm, “Linear trend analysis: a comparison of methods,” Atmos. Environ., vol. 35, no. 30, pp. 5211–5222, Oct. 2001, doi: 10.1016/S1352-2310(01)00342-9.
  • M. Mudelsee, “Trend analysis of climate time series: A review of methods,” Earth-Science Rev., vol. 190, pp. 310–322, Mar. 2019, doi: 10.1016/j.earscirev.2018.12.005.
  • S. Yue, P. Pilon, and G. Cavadias, “Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series,” J. Hydrol., vol. 259, no. 1–4, pp. 254–271, Mar. 2002, doi: 10.1016/S0022-1694(01)00594-7.
  • F. Wang et al., “Re-evaluation of the Power of the Mann-Kendall Test for Detecting Monotonic Trends in Hydrometeorological Time Series,” Front. Earth Sci., vol. 8, Feb. 2020, doi: 10.3389/feart.2020.00014.
  • M. Almazroui and Z. Şen, “Trend analyses methodologies in hydro-meteorological records,” Earth Syst. Environ., vol. 4, no. 4, pp. 713–738, Dec. 2020, doi: 10.1007/s41748-020-00190-6.
  • K. H. Hamed, “Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis,” J. Hydrol., vol. 349, no. 3–4, pp. 350–363, Feb. 2008, doi: 10.1016/j.jhydrol.2007.11.009.
  • C. Onyutha, “Identification of sub-trends from hydro-meteorological series,” Stoch. Environ. Res. Risk Assess., vol. 30, no. 1, pp. 189–205, Jan. 2016, doi: 10.1007/s00477-015-1070-0.
  • Z. Şen, “Crossing trend analysis methodology and application for Turkish rainfall records,” Theor. Appl. Climatol., vol. 131, no. 1–2, pp. 285–293, 2018, doi: 10.1007/s00704-016-1980-x.
  • Z. Şen, “Innovative trend significance test and applications,” Theor. Appl. Climatol., vol. 127, no. 3–4, pp. 939–947, Feb. 2017, doi: 10.1007/s00704-015-1681-x.
  • Z. Şen, “Partial trend identification by change-point successive average methodology (SAM),” J. Hydrol., vol. 571, pp. 288–299, Apr. 2019, doi: 10.1016/j.jhydrol.2019.02.007.
  • Z. Şen, “Moving Trend Analysis Methodology for Hydro-meteorology Time Series Dynamic Assessment,” Water Resour. Manag., May 2024, doi: 10.1007/s11269-024-03872-2.
  • Z. Şen, “Innovative trend analysis methodology,” J. Hydrol. Eng., vol. 17, no. 9, pp. 1042–1046, Sep. 2012, doi: 10.1061/(asce)he.1943-5584.0000556.
  • Z. Şen, E. Şişman, and I. Dabanli, “Innovative Polygon Trend Analysis (IPTA) and applications,” J. Hydrol., vol. 575, no. May, pp. 202–210, Aug. 2019, doi: 10.1016/j.jhydrol.2019.05.028.
  • G. Ceribasi, A. I. Ceyhunlu, and N. Ahmed, “Innovative trend pivot analysis method (ITPAM): a case study for precipitation data of Susurluk Basin in Turkey,” Acta Geophys., vol. 69, no. 4, pp. 1465–1480, Aug. 2021, doi: 10.1007/s11600-021-00605-6.
  • S. Alashan, “Combination of modified Mann-Kendall method and Şen innovative trend analysis,” Eng. Reports, vol. 2, no. 3, Mar. 2020, doi: 10.1002/eng2.12131.
  • Y. S. Güçlü, R. Acar, and K. Saplıoğlu, “Seasonally adjusted periodic time series for Mann-Kendall trend test,” Phys. Chem. Earth, Parts A/B/C, vol. 138, p. 103848, Jun. 2025, doi: 10.1016/j.pce.2024.103848.
  • R. M. Hirsch, J. R. Slack, and R. A. Smith, “Techniques of trend analysis for monthly water quality data,” Water Resour. Res., vol. 18, no. 1, pp. 107–121, Feb. 1982, doi: 10.1029/WR018i001p00107.
  • M. Şan, “Combined innovative trend analysis methods for seasonal trend testing,” J. Hydrol., vol. 649, p. 132418, Mar. 2025, doi: 10.1016/j.jhydrol.2024.132418.
  • ICPDR, “The International Commission for the Protection of the Danube River.” Accessed: Sep. 07, 2024. [Online]. Available: https://www.icpdr.org/
  • V.-M. Radu et al., “Overall assessment of surface water quality in the Lower Danube River,” Environ. Monit. Assess., vol. 192, no. 2, p. 135, Feb. 2020, doi: 10.1007/s10661-020-8086-8.
  • T. Mikami, The Climate of Japan, vol. 77. in Advances in Global Change Research, vol. 77. Singapore: Springer Nature Singapore, 2023. doi: 10.1007/978-981-99-5158-1.
  • H. E. Beck, N. E. Zimmermann, T. R. McVicar, N. Vergopolan, A. Berg, and E. F. Wood, “Present and future Köppen-Geiger climate classification maps at 1-km resolution,” Sci. Data, vol. 5, no. 1, p. 180214, Oct. 2018, doi: 10.1038/sdata.2018.214.
  • D. Machiwal, H. M. Meena, and D. V. Singh, “Overview of trend and homogeneity tests and their application to rainfall time series,” in Current Directions in Water Scarcity Research, 2022, ch. Chapter 34, pp. 599–620. doi: 10.1016/b978-0-323-91910-4.00034-0.
  • A. N. Pettitt, “A non-parametric approach to the change-point problem,” Appl. Stat., vol. 28, no. 2, pp. 126–135, 1979, doi: 10.2307/2346729.
  • D. R. Helsel, R. M. Hirsch, K. R. Ryberg, S. A. Archfield, and E. J. Gilroy, “Statistical methods in water resources,” Reston, VA, 2020. doi: https://doi.org/10.3133/tm4A3.
  • R. V. Hogg, E. A. Tanis, and D. L. Zimmerman, “Tests of Statistical Hypotheses,” in Probability and statistical inference, 9th editio., D. Lynch, Ed., Pearson, 2014, pp. 355–415.
  • R. Lindsey and L. Dahlman, “Climate Change: Global Temperature.” Accessed: Apr. 28, 2025. [Online]. Available: https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature
  • J. B. Wijngaard, A. M. G. Klein Tank, and G. P. Können, “Homogeneity of 20th century European daily temperature and precipitation series,” Int. J. Climatol., vol. 23, no. 6, pp. 679–692, May 2003, doi: 10.1002/joc.906.
  • S. Nacar, M. Kankal, and U. Okkan, “Evaluation of the suitability of NCEP/NCAR, ERA-Interim and, ERA5 reanalysis data sets for statistical downscaling in the Eastern Black Sea Basin, Turkey,” Meteorol. Atmos. Phys., vol. 134, no. 2, p. 39, Apr. 2022, doi: 10.1007/s00703-022-00878-6.

Seasonal Wilcoxon and Scatter Diagram Combination Trend Test

Year 2025, Volume: 14 Issue: 2, 1151 - 1165, 30.06.2025
https://doi.org/10.17798/bitlisfen.1652418
https://izlik.org/JA37XP46BB

Abstract

Climate change, one of the biggest problems of the last century, is of great concern. It is essential and common to examine the impacts of climate change as a holistic trend for different climatic and hydrological parameters with periodicity. However, considering the periodicity character of monthly, weekly, etc., is particularly important for analyzing and understanding seasonal trends. This is because seasonal trends help manage and regulate irrigation and agricultural activities and water resource systems. This study proposes the seasonal Wilcoxon and scatter diagram combination trend test (SWTT) method as an alternative to the seasonal Mann Kendall (SMK) method. This method is based on the combination Wilcoxon test and scatter diagram (CWTSD) trend test which assesses holistic trends. The data utilized for this study came from three sources: flow records from the Danube River, Romania, temperature records from Oxford, UK, and precipitation records from Kobe, Japan, to compare SWTT and SMK methods. The SWTT method shows very similar trends to the SMK, but the SWTT method takes a step forward because it is based on a graphical method providing a visual overview of seasonal trends. The SWTT can also be used as a regional trend test in the same way that the SMK method is used as a regional trend test by using station data instead of seasons The r-codes of the proposed method and the sample dataset are available at the related link.

Ethical Statement

The study is complied with research and publication ethics.

References

  • K. Saplıoğlu and Y. S. Güçlü, “Combination of Wilcoxon test and scatter diagram for trend analysis of hydrological data,” J. Hydrol., vol. 612, no. July, p. 128132, Sep. 2022, doi: 10.1016/j.jhydrol.2022.128132.
  • M. Esit, R. Çelik, and E. Akbas, “Spatial and temporal variation of meteorological parameters in the lower Tigris–Euphrates basin, Türkiye: application of non-parametric methods and an innovative trend approach,” Water Sci. Technol., vol. 87, no. 8, pp. 1982–2004, Apr. 2023, doi: 10.2166/wst.2023.116.
  • M. A. Günen and U. H. Atasever, “Remote sensing and monitoring of water resources: A comparative study of different indices and thresholding methods,” Sci. Total Environ., vol. 926, p. 172117, May 2024, doi: 10.1016/j.scitotenv.2024.172117.
  • M. B. Yıldız, M. Kankal, S. Nacar, N. T. T. Linh, H. Van Hoa, and V. T. Nam, “Investigation of precipitation trends in Lower Mekong Delta River Basin of Vietnam by innovative trend analysis methods,” Theor. Appl. Climatol., vol. 155, no. 12, pp. 10033–10050, Dec. 2024, doi: 10.1007/s00704-024-05221-0.
  • M. A. Günen, K. F. Öztürk, and Ş. Aliyazıcıoğlu, “Optimizing Visibility of Historical Structures Using mWDE: Insights from the Kromni Valley, Gümüşhane, Türkiye,” Int. J. Eng. Geosci., vol. 10, no. 1, pp. 107–126, Feb. 2025, doi: 10.26833/ijeg.1529351.
  • H. B. Mann, “Nonparametric tests against trend,” Econometrica, vol. 13, no. 3, pp. 245–259, Jul. 1945, doi: 10.2307/1907187.
  • M. G. Kendall, Rank correlation methods. London: Griffin, 1975.
  • H. Tongal, “Spatiotemporal analysis of precipitation and extreme indices in the Antalya Basin, Turkey,” Theor. Appl. Climatol., vol. 138, no. 3–4, pp. 1735–1754, 2019, doi: 10.1007/s00704-019-02927-4.
  • S. Kumar, V. Merwade, J. Kam, and K. Thurner, “Streamflow trends in Indiana: Effects of long term persistence, precipitation and subsurface drains,” J. Hydrol., vol. 374, no. 1–2, pp. 171–183, Jul. 2009, doi: 10.1016/j.jhydrol.2009.06.012.
  • Y. Dinpashoh, R. Mirabbasi, D. Jhajharia, H. Z. Abianeh, and A. Mostafaeipour, “Effect of short-term and long-term persistence on identification of temporal trends,” J. Hydrol. Eng., vol. 19, no. 3, pp. 617–625, Mar. 2014, doi: 10.1061/(asce)he.1943-5584.0000819.
  • R. Sneyers, “On the statistical analysis of series of observations. WMO Tech,” Note, 143, 1, 1990.
  • A. Hess, H. Iyer, and W. Malm, “Linear trend analysis: a comparison of methods,” Atmos. Environ., vol. 35, no. 30, pp. 5211–5222, Oct. 2001, doi: 10.1016/S1352-2310(01)00342-9.
  • M. Mudelsee, “Trend analysis of climate time series: A review of methods,” Earth-Science Rev., vol. 190, pp. 310–322, Mar. 2019, doi: 10.1016/j.earscirev.2018.12.005.
  • S. Yue, P. Pilon, and G. Cavadias, “Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series,” J. Hydrol., vol. 259, no. 1–4, pp. 254–271, Mar. 2002, doi: 10.1016/S0022-1694(01)00594-7.
  • F. Wang et al., “Re-evaluation of the Power of the Mann-Kendall Test for Detecting Monotonic Trends in Hydrometeorological Time Series,” Front. Earth Sci., vol. 8, Feb. 2020, doi: 10.3389/feart.2020.00014.
  • M. Almazroui and Z. Şen, “Trend analyses methodologies in hydro-meteorological records,” Earth Syst. Environ., vol. 4, no. 4, pp. 713–738, Dec. 2020, doi: 10.1007/s41748-020-00190-6.
  • K. H. Hamed, “Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis,” J. Hydrol., vol. 349, no. 3–4, pp. 350–363, Feb. 2008, doi: 10.1016/j.jhydrol.2007.11.009.
  • C. Onyutha, “Identification of sub-trends from hydro-meteorological series,” Stoch. Environ. Res. Risk Assess., vol. 30, no. 1, pp. 189–205, Jan. 2016, doi: 10.1007/s00477-015-1070-0.
  • Z. Şen, “Crossing trend analysis methodology and application for Turkish rainfall records,” Theor. Appl. Climatol., vol. 131, no. 1–2, pp. 285–293, 2018, doi: 10.1007/s00704-016-1980-x.
  • Z. Şen, “Innovative trend significance test and applications,” Theor. Appl. Climatol., vol. 127, no. 3–4, pp. 939–947, Feb. 2017, doi: 10.1007/s00704-015-1681-x.
  • Z. Şen, “Partial trend identification by change-point successive average methodology (SAM),” J. Hydrol., vol. 571, pp. 288–299, Apr. 2019, doi: 10.1016/j.jhydrol.2019.02.007.
  • Z. Şen, “Moving Trend Analysis Methodology for Hydro-meteorology Time Series Dynamic Assessment,” Water Resour. Manag., May 2024, doi: 10.1007/s11269-024-03872-2.
  • Z. Şen, “Innovative trend analysis methodology,” J. Hydrol. Eng., vol. 17, no. 9, pp. 1042–1046, Sep. 2012, doi: 10.1061/(asce)he.1943-5584.0000556.
  • Z. Şen, E. Şişman, and I. Dabanli, “Innovative Polygon Trend Analysis (IPTA) and applications,” J. Hydrol., vol. 575, no. May, pp. 202–210, Aug. 2019, doi: 10.1016/j.jhydrol.2019.05.028.
  • G. Ceribasi, A. I. Ceyhunlu, and N. Ahmed, “Innovative trend pivot analysis method (ITPAM): a case study for precipitation data of Susurluk Basin in Turkey,” Acta Geophys., vol. 69, no. 4, pp. 1465–1480, Aug. 2021, doi: 10.1007/s11600-021-00605-6.
  • S. Alashan, “Combination of modified Mann-Kendall method and Şen innovative trend analysis,” Eng. Reports, vol. 2, no. 3, Mar. 2020, doi: 10.1002/eng2.12131.
  • Y. S. Güçlü, R. Acar, and K. Saplıoğlu, “Seasonally adjusted periodic time series for Mann-Kendall trend test,” Phys. Chem. Earth, Parts A/B/C, vol. 138, p. 103848, Jun. 2025, doi: 10.1016/j.pce.2024.103848.
  • R. M. Hirsch, J. R. Slack, and R. A. Smith, “Techniques of trend analysis for monthly water quality data,” Water Resour. Res., vol. 18, no. 1, pp. 107–121, Feb. 1982, doi: 10.1029/WR018i001p00107.
  • M. Şan, “Combined innovative trend analysis methods for seasonal trend testing,” J. Hydrol., vol. 649, p. 132418, Mar. 2025, doi: 10.1016/j.jhydrol.2024.132418.
  • ICPDR, “The International Commission for the Protection of the Danube River.” Accessed: Sep. 07, 2024. [Online]. Available: https://www.icpdr.org/
  • V.-M. Radu et al., “Overall assessment of surface water quality in the Lower Danube River,” Environ. Monit. Assess., vol. 192, no. 2, p. 135, Feb. 2020, doi: 10.1007/s10661-020-8086-8.
  • T. Mikami, The Climate of Japan, vol. 77. in Advances in Global Change Research, vol. 77. Singapore: Springer Nature Singapore, 2023. doi: 10.1007/978-981-99-5158-1.
  • H. E. Beck, N. E. Zimmermann, T. R. McVicar, N. Vergopolan, A. Berg, and E. F. Wood, “Present and future Köppen-Geiger climate classification maps at 1-km resolution,” Sci. Data, vol. 5, no. 1, p. 180214, Oct. 2018, doi: 10.1038/sdata.2018.214.
  • D. Machiwal, H. M. Meena, and D. V. Singh, “Overview of trend and homogeneity tests and their application to rainfall time series,” in Current Directions in Water Scarcity Research, 2022, ch. Chapter 34, pp. 599–620. doi: 10.1016/b978-0-323-91910-4.00034-0.
  • A. N. Pettitt, “A non-parametric approach to the change-point problem,” Appl. Stat., vol. 28, no. 2, pp. 126–135, 1979, doi: 10.2307/2346729.
  • D. R. Helsel, R. M. Hirsch, K. R. Ryberg, S. A. Archfield, and E. J. Gilroy, “Statistical methods in water resources,” Reston, VA, 2020. doi: https://doi.org/10.3133/tm4A3.
  • R. V. Hogg, E. A. Tanis, and D. L. Zimmerman, “Tests of Statistical Hypotheses,” in Probability and statistical inference, 9th editio., D. Lynch, Ed., Pearson, 2014, pp. 355–415.
  • R. Lindsey and L. Dahlman, “Climate Change: Global Temperature.” Accessed: Apr. 28, 2025. [Online]. Available: https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature
  • J. B. Wijngaard, A. M. G. Klein Tank, and G. P. Können, “Homogeneity of 20th century European daily temperature and precipitation series,” Int. J. Climatol., vol. 23, no. 6, pp. 679–692, May 2003, doi: 10.1002/joc.906.
  • S. Nacar, M. Kankal, and U. Okkan, “Evaluation of the suitability of NCEP/NCAR, ERA-Interim and, ERA5 reanalysis data sets for statistical downscaling in the Eastern Black Sea Basin, Turkey,” Meteorol. Atmos. Phys., vol. 134, no. 2, p. 39, Apr. 2022, doi: 10.1007/s00703-022-00878-6.
There are 40 citations in total.

Details

Primary Language English
Subjects Water Resources Engineering
Journal Section Research Article
Authors

Murat Şan 0000-0001-7006-8340

Submission Date March 6, 2025
Acceptance Date May 9, 2025
Early Pub Date June 27, 2025
Publication Date June 30, 2025
DOI https://doi.org/10.17798/bitlisfen.1652418
IZ https://izlik.org/JA37XP46BB
Published in Issue Year 2025 Volume: 14 Issue: 2

Cite

IEEE [1]M. Şan, “Seasonal Wilcoxon and Scatter Diagram Combination Trend Test”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 14, no. 2, pp. 1151–1165, June 2025, doi: 10.17798/bitlisfen.1652418.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS