Home / Research Article / Research Article
Back
Research Article Open access

Spatiotemporal Trends of Precipitation Analysis in Riverine Agriculture Area of Shabelle River Using Mann–Kendall Family and Innovative Trend Analysis

Abstract

The trend analysis of hydro-meteorological time series plays a vital role in water resource management and climate change, particularly in arid and semiarid regions. This study aims to investigate the long-term spatiotemporal variation in rainfall in four major agricultural cities situated near or on the banks of the Shabelle River. The analysis was conducted on an annual and seasonal basis using NASA Prediction of Worldwide Energy Resource Project (NASAPOWER) rainfall data from 1981 to 2020. Three widely used approaches—Mann–Kendall (MK), Modified Mann–Kendall (MMK), and Innovative Trend analysis (ITA) were employed to detect trends and variations in time series data. The findings indicate that all three methods revealed significant positive trends in annual rainfall for the four stations. Additionally, significant positive trends were observed in the Deyr and Hagaa seasons at significance levels of 0.1, 0.05, and 0.01. Although there were increasing trends in the Gu season across all stations, none of the methods showed a significant trend. The MK and MMK tests indicate an insignificant negative trend in the Jilaal season, which is the main dry season. Conversely, the ITA captured a significant negative trend in the Jilaal season for all four stations. This suggests that ITA is more effective at detecting significant hidden trends than MK and MMK. Based on these findings, the Deyr season, the second rainy season, exhibits an increasing trend at all stations, while the Gu season, the first main rainy season, shows an insignificant increasing trend. Interestingly, the Hagaa season, the second dry season, showed a highly significant increasing trend. The results presented in this study can serve as valuable information for policymakers and planners to make informed decisions related to water resource management, irrigation, and urban water usage.

Keywords

innovative trend analysis (ITA) | Mann–Kendall test | modified Mann–Kendall test | rainfall trend

References (39)

  1. Alashan, S. (2020). Testing and improving Type 1 error performance of Şen’s innovative trend analysis method. Theoretical and Applied Climatology, 142(3–4), 1015–1025. https://doi.org/10.1007/s00704-020-03363-5.
  2. Ayalew, D. (2012). Variability of rainfall and its current trend in Amhara Region, Ethiopia. African Journal of Agricultural Research, 7(10). DOI: 10.5897/AJAR11.698
  3. Basnyat, D. B. (2007). Water resources of Somalia (Technical Report No. W-11). FAO-SWALIM. https://www.faoswalim.org/resources/site
  4. Caloiero, T., Coscarelli, R., & Ferrari, E. (2018). Analysis of monthly rainfall trend in Calabria (Southern Italy) through the application of statistical and graphical techniques. [Journal title and volume information needed, 2(11)]. https://doi.org/10.3390/proceedings2110629
  5. Dixon, W. J. (1950). Analysis of extreme values. The Annals of Mathematical Statistics, 21(4), 488–506.
  6. Grubbs, F. E. (1950). Sample criteria for testing outlying observations. The Annals of Mathematical Statistics, 21(1), 27–58.
  7. Harka, A. E., Jilo, N. B., & Behulu, F. (2021). Spatial-temporal rainfall trend and variability assessment in the Upper Wabe Shebelle River Basin, Ethiopia: Application of innovative trend analysis method. Journal of Hydrology: Regional Studies, 37, Article 100915. https://doi.org/10.1016/j.ejrh.2021.100915.
  8. Ibrahim, Y. M. (2023). Estimation of crop water requirement for tomato plant in Afgoye, Somalia, using CROPWAT 8.0 model. Black Sea Journal of Agriculture, 6(6), 664–670. https://doi.org/10.47115/bsagriculture.1340670
  9. Ibrahim, Y. M., Karaca, C., & Büyüktaş, D. (2020). Water resource and irrigation in Somalia: A review. In Current researches in agriculture, forestry and aquaculture sciences (pp. 35–53). Duvar Publishing.
  10. Intergovernmental Panel on Climate Change. (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Cambridge University Press.
  11. Intergovernmental Panel on Climate Change. (2018). Global warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (V. Masson-Delmotte, P. Zhai, & H.-O. Pörtner, Eds.). Cambridge University Press.
  12. Kendall, M. G. (1957). Rank correlation methods. Charles Griffin.
  13. Kinyangi, J., Herrero, M., Omolo, A., Van De Steeg, J., & Thornton, P. (2009). Scoping study on vulnerability to climate change and climate variability in the Greater Horn of Africa: Mapping impacts and adaptive capacity. International Livestock Research Institute. https://cgspace.cgiar.org/
  14. Kisi, O. (2015). An innovative method for trend analysis of monthly pan evaporations. Journal of Hydrology, 527, 1123–1129. https://doi.org/10.1016/j.jhydrol.2015.06.009
  15. Kouman, K. D., Kabo-Bah, A. T., Kouadio, B. H., & Akpoti, K. (2022). Spatio-temporal trends of precipitation and temperature extremes across the north-east region of Côte d’Ivoire over the period 1981–2020. Climate, 10(5), Article 74. https://doi.org/10.3390/cli10050074
  16. Limbu, P. T., & Farah, M. (2023). Assessing spatio-temporal variations and trends of rainfall over Somalia from 1991 to 2020. Tanzania Journal of Engineering and Technology, 42(3), 189–200. https://doi.org/10.52339/tjet.v42i3.855.
  17. Machiwal, D., Gupta, A., Jha, M. K., & Kamble, T. (2019). Analysis of trend in temperature and rainfall time series of an Indian arid region: Comparative evaluation of salient techniques. Theoretical and Applied Climatology, 136(1–2), 301–320. https://doi.org/10.1007/s00704-018-2487-4
  18. Mallick, J., Talukdar, S., Alsubih, M., et al. (2021). Analysing the trend of rainfall in Asir Region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis and detrended fluctuation analysis. Theoretical and Applied Climatology, 143(1–2), 823–841.
  19. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259.
  20. Mekonen, A. A., & Berlie, A. B. (2020). Spatiotemporal variability and trends of rainfall and temperature in the northeastern highlands of Ethiopia. Modeling Earth Systems and Environment, 6(1), 285–300.
  21. Mohamed, J., Ali, D. A., Warsame, A. A., & Adam, M. B. (2022). Two phases of long-term shift in extreme precipitation in Somalia. Meteorology and Atmospheric Physics, 134(3).
  22. Mohan, P. R., Srinivas, C. V., Yesubabu, V., Baskaran, R., & Venkatraman, B. (2018). Simulation of a heavy rainfall event over Chennai in Southeast India using WRF: Sensitivity to microphysics parameterization. Atmospheric Research, 210, 83–99.
  23. Muchiri, P. W. (2007). Climate of Somalia (Technical Report No. W-01). FAO-SWALIM. https://www.faoswalim.org/resources/site
  24. Muchiri, P. W. (2007). Climate of Somalia (Technical Report No. W-01). FAO-SWALIM.
  25. Muse, N. M., Tayfur, G., & Safari, M. J. S. (2023). Meteorological drought assessment and trend analysis in Puntland Region of Somalia. Sustainability, 15(13), Article 10652.
  26. Musei, S. K., Nyaga, J. M., & Dubow, A. Z. (2021). SPEI-based spatial and temporal evaluation of drought in Somalia. Journal of Arid Environments, 184, Article 104296.
  27. National Aeronautics and Space Administration. (2022). Prediction of Worldwide Energy Resources (POWER) Data Access Viewer [Data set]. https://power.larc.nasa.gov/data-access-viewer/
  28. Opiyo, F. (2014). Trend analysis of rainfall and temperature variability in arid environment of Turkana. ResearchGate. https://www.researchgate.net/publication/264458956
  29. Osman, S. A., & Das, J. (2023). GIS-based flood risk assessment using multi-criteria decision analysis of Shebelle River Basin in southern Somalia. SN Applied Sciences, 5(5).
  30. Praveen, B., Talukdar, S., Shahfahad, M., et al. (2020). Analyzing trend and forecasting of rainfall changes in India using non-parametric and machine learning approaches. Scientific Reports, 10(1).
  31. Reshmi Mohan, P., Srinivas, C. V., Yesubabu, V., Baskaran, R., & Venkatraman, B. (2018). Simulation of a heavy rainfall event over Chennai in Southeast India using WRF: Sensitivity to microphysics parameterization. Atmospheric Research, 210, 83–99.
  32. Seenu, P. Z., & Jayakumar, K. V. (2021). Comparative study of innovative trend analysis technique with Mann-Kendall tests for extreme rainfall. Arabian Journal of Geosciences, 14(7).
  33. Şen, Z. (2012). Innovative trend analysis methodology. Journal of Hydrologic Engineering, 17(9), 1042–1046.
  34. Şen, Z. (2014). Trend identification simulation and application. Journal of Hydrologic Engineering, 19(3), 635–642.
  35. Sonali, P., & Nagesh Kumar, D. (2013). Review of trend detection methods and their application to detect temperature changes in India. Journal of Hydrology, 476, 212–227.
  36. Trenberth, K. E. (2011). Changes in precipitation with climate change. https://www.jstor.org/stable/24872346
  37. Umar, S., Lone, M. A., Goel, N. K., & Zakwan, M. (2022). Trend analysis of hydro-meteorological parameters in the Jhelum River Basin, northwestern Himalayas. Theoretical and Applied Climatology, 148(3–4), 1417–1428.
  38. Wang, Y., Xu, Y., Tabari, H., et al. (2020). Innovative trend analysis of annual and seasonal rainfall in the Yangtze. Atmospheric Research, 231.
  39. Yue, S., & Wang, C. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resources Management, 18.