Trend analysis and ARIMA modeling for forecasting precipitation pattern in Wadi Shueib catchment area in Jordan

  • Odai Al BalasmehEmail author
  • Richa Babbar
  • Tapas Karmaker
Original Paper


In this present work, the daily precipitation data obtained from five gauging stations were analyzed to find the trend and prediction of precipitation for water deficit area of Wadi Shueib catchment in Jordan. Mann–Kendall (MK) test with Sen’s slope estimator and innovative trend analysis (ITA) were carried out for monthly, average, and seasonal data, derived from daily precipitation for a minimum of 44 years of record. The ITA method detected trend at all stations, in different trend levels (low, medium, and high), while MK test detected no trend except at two stations during the same period. The Box–Jenkins forecasting method with autoregressive integrated moving average (ARIMA) model was used to predict the changes in precipitation for projected years. Best-fit ARIMA models were found based on diagnostic check procedure in Box–Jenkins method. The best-fit ARIMA models, validated with 10 years of data (2007–2016), were used for predicting precipitation up to 2026, and ITA was used to find the trend of precipitation in the future. The future trend shows that the high level (heavy rain) is decreasing at all stations and low level (normal rain) is increasing, except in the month of December, which shows an increasing trend. This observed pattern warrants effective water management strategies for already water-stressed area of Wadi Shueib catchment in Jordan.


Mann–Kendall test Innovative trend analysis Box–Jenkins method ARIMA model t Test hypothesis 


  1. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Tank AMGK, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Kumar KR, Revadekar J, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez-Aguirre JL (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res-Atmos 38(5):2423–2434Google Scholar
  2. Al-Masaeid K. (2010). Community-Based Rangeland Rehabilitation Project in Jordan. Report of the International Workshop on Water Harvesting Techniques and Practices and Their Roles in Enhancing Rural Livelihoods, held in Beirut, Lebanon on 26th Sep–2nd Oct 2010Google Scholar
  3. Amirataee B, Montaseri M, Sanikhani H (2016) The analysis of trend variations of reference evapotranspiration via eliminating the significance effect of all autocorrelation coefficients. Theor Appl Climatol 126(1–2):131–139CrossRefGoogle Scholar
  4. Atilgan A, Tanriverdi C, Yucel A, OZ H, Degirmenci H (2017) Analysis of long-term temperature data using Mann–Kendall trend test and linear regression methods: the case of the southeastern Anatolia region. Scientific Papers Series A Agronomy LX:455–462Google Scholar
  5. Ay M, Kisi O (2015) Investigation of trend analysis of monthly total precipitation by an innovative method. Theor Appl Climatol 120(3–4):617–629CrossRefGoogle Scholar
  6. Bellu A, Sanches Fernandes LF, Cortes RMV, Pacheco FAL (2016) A framework model for the dimensioning and allocation of a detention basin system: the case of a flood-prone mountainous watershed. J Hydrol 533:567–580CrossRefGoogle Scholar
  7. Borges PA, Franke J, Silva FDS, Weiss H, Bernhofer C (2014) Differences between two climatological periods (2001–2010 vs. 1971–2000) and trend analysis of temperature and precipitation in Central Brazil. Theor Appl Climatol 116:191–202CrossRefGoogle Scholar
  8. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control, Revised edn. Holden-Day, San FranciscoGoogle Scholar
  9. Chattopadhyay G, Chakraborthy P, Chattopadhyay S (2012) Mann–Kendall trend analysis of tropospheric ozone and its modeling using ARIMA. Theor Appl Climatol 110(3):321–328CrossRefGoogle Scholar
  10. Chen L, Lai X (2011) Comparison between ARIMA and ANN models used in short-term wind speed forecasting. Asia-Pacific Power and Energy Engineering Conference, APPEEC 1–4.
  11. Chowdhury DR, Sen D (2017) Artificial neural network based trend analysis and forecasting model for course selection. Int J Comput Sci Eng 5(2):20–26Google Scholar
  12. Climate Change (2007). “The physical science basis”, Contribution of Working Group I to the Fourth Assessment Report of the IPCC (ISBN 978 0521 88009-1 Hardback; 978 0521 70596-7 Paperback)Google Scholar
  13. Eni D, Adeyeye F (2015) Seasonal ARIMA modeling and forecasting of rainfall in Warri Town, Nigeria. Journal of Geoscience and Environment Protection 3:91–98CrossRefGoogle Scholar
  14. Feng G, Cobb S, Abdo Z, Fisher DK, Ouyang Y, Adeli A, Jenkins JN (2016) Trend analysis and forecast of precipitation, reference evapotranspiration, and rainfall deficit in the Blackland Prairie of Eastern Mississippi. J Appl Meteorol Climatol 5:1425–1439CrossRefGoogle Scholar
  15. Gajbhiye S, Meshram C, Mirabbasi R, Sharma SK (2016) Trend analysis of rainfall time series for Sindh river basin in India. Theor Appl Climatol 125(3–4):593–608CrossRefGoogle Scholar
  16. Gaynor PE, Kirkpatrick RC (1994) Introduction to time-series modeling and forecasting in business and economics. McGraw-Hill, New YorkGoogle Scholar
  17. Gong DY, Wang SW (2006) Severe summer rainfall in China associated with enhanced global warming. Clim Res 16:51–59CrossRefGoogle Scholar
  18. Hamed KH, Rao AR (1998) A modified Mann–Kendall trend test for autocorrelated data. J Hydrol 204(1):182–196CrossRefGoogle Scholar
  19. Helsel D R Hirsch R M (2002) Statistical methods in water resources. Techniques of water resources investigations, chapter A3, Book 4, U.S. Geological Survey, pp 522. [Available online at] Accessed 4th Jan 2019
  20. Hongmei L, Tianjun Z, Chao L (2010) Decreasing trend in global land monsoon precipitation over the past 50 years simulated by a coupled climate model. Adv Atmos Sci 27(2):285–292CrossRefGoogle Scholar
  21. Hu C, Xu Y, Han L, Yang L, Xu G (2016) Long-term trends in daily precipitation over the Yangtze River Delta region during 1960–2012, eastern China. Theor Appl Climatol 125(1–2):131–147CrossRefGoogle Scholar
  22. Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, LondonGoogle Scholar
  23. Kisi O (2015) An innovative method for trend analysis of monthly pan evaporations. J Hydrol 527:1123–1129CrossRefGoogle Scholar
  24. Kisi O, Ay M (2014) Comparison of Mann–Kendall and innovative trend method for water quality parameters of the Kizilirmak River, Turkey. J Hydrol 513:362–375CrossRefGoogle Scholar
  25. Kool J (2016) Sustainable development in the Jordan Valley “Final Report of the Regional NGO Master Plan”. ISBN 978-3-319-30036-8Google Scholar
  26. Kumar V, Jain SK, Singh Y (2010) Analysis of long-term rainfall trends in India. Hydrol Sci J 55(4):484–496CrossRefGoogle Scholar
  27. Lan F, Changhe L, Biao Y, Zhao C (2012) Long-term trends of precipitation in the North China plain. J Geogr Sci 22(6):989–1001CrossRefGoogle Scholar
  28. Longobardi A, Villani P (2010) Trend analysis of annual and seasonal rainfall time series in the Mediterranean area. Int J Climatol 30:1538–1546Google Scholar
  29. Lu Y, AbouRizk SM (2009) Automated Box–Jenkins forecasting modelling. Autom Constr 18(5):547–558CrossRefGoogle Scholar
  30. Mann HB (1945) Nonparametric tests against trend. Econometrica 13(3):245–259CrossRefGoogle Scholar
  31. Maragatham RS (2012) Trend analysis of rainfall data—a comparative study of existing methods. International Journal of Physics and Mathematical Sciences 2(1):13–18Google Scholar
  32. Meshram SG, Singh VP, Meshram C (2017a) Long-term trend and variability of precipitation in Chhattisgarh state, India. Theor Appl Climatol 129(3–4):729–744CrossRefGoogle Scholar
  33. Meshram SG, Singh SK, Meshram C, Deo RC, Ambade B (2017b) Statistical evaluation of rainfall time series in concurrence with agriculture and water resources of Ken River basin, Central India (1901–2010). Theor Appl Climatol 134(3–4):1231–1243Google Scholar
  34. Mohammadi K, Eslami HR, Dayyani Dardashti S (2005) Comparison of regression, ARIMA and ANN models for reservoir inflow forecasting using snowmelt equivalent (a case study of Karaj). J Agric Sci Technol 7:17–30Google Scholar
  35. Mohan S, Vedula S (1995) Multiplicative seasonal Arima model for long-term forecasting of inflows. Water Resour Manag 9:115–126CrossRefGoogle Scholar
  36. Narasimha Murthy KV, Saravana R, Vijaya Kumar K (2018) Modeling and forecasting rainfall patterns of southwest monsoons in north-east India as a SARIMA process. Meteorog Atmos Phys 130(1):99–106CrossRefGoogle Scholar
  37. Palizdan N, Falamarzi Y, Huang YF, Lee TS, Ghazali AH (2014) Regional precipitation trend analysis at the Langat River basin, Selangor, Malaysia. Theor Appl Climatol 117:589–606CrossRefGoogle Scholar
  38. Peña D, Tiao GC, Tsay RS (2001) A course in time series analysis, 1st edn. Wiley, New YorkGoogle Scholar
  39. Plessis WPD (1999) Linear regression relationships between NDVI, vegetation and rainfall in Etosha National Park, Namibia. J Arid Environ 42:235–260CrossRefGoogle Scholar
  40. Ranhao S, Baiping Z, Jing T (2008) A multivariate regression model for predicting precipitation in the Daqing Mountains. Mt Res Dev 28(3–4):318–325CrossRefGoogle Scholar
  41. Riepl D (2013) Knowledge-based decision support for integrated water resources management with an application for Wadi Shueib, Jordan. KIT Scientific Publishing, KarlsruheGoogle Scholar
  42. Rizeei HM, Pradhan B, Saharkhiz MA (2018) Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region. Arab J Geosci 11:53CrossRefGoogle Scholar
  43. Sanikhani H, Kisi O, Mirabbasi R, Meshram SG (2018) Trend analysis of rainfall pattern over the Central India during 1901–2010. Arab J Geosci 11:437CrossRefGoogle Scholar
  44. Santos RMB, Sanches Fernandes LF, Moura JP, Pereira MG, Pacheco FAL (2014) The impact of climate change, human interference, scale and modeling uncertainties on the estimation of aquifer properties and river flow components. J Hydrol 519:1297–1314CrossRefGoogle Scholar
  45. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389CrossRefGoogle Scholar
  46. Şen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17(9):1042–1046CrossRefGoogle Scholar
  47. Şen Z (2013) Trend identification simulation and application. J Hydrol Eng 19:635–642. CrossRefGoogle Scholar
  48. Sun M, Kim G (2016) Quantitative monthly precipitation forecasting using cyclostationary empirical orthogonal function and canonical correlation analysis. J Hydrol Eng 21(1):04015045 1–13CrossRefGoogle Scholar
  49. Sun M, Li X, Kim G (2018) Precipitation analysis and forecasting using singular spectrum analysis with artificial neural networks. Clust Comput:1–8.
  50. Tarawneh QY, Chowdhury S (2018) Trends of climate change in Saudi Arabia: implications on water resources. Climate 6(8):1–19. CrossRefGoogle Scholar
  51. Terêncio DPS, Sanches Fernandes LF, Cortes RMV, Pacheco FAL (2017) Improved framework model to allocate optimal rainwater harvesting sites in small watersheds for agro-forestry uses. J Hydrol 550:318–330CrossRefGoogle Scholar
  52. Terêncio DPS, Sanches Fernandes LF, Cortes RMV, Moura JP, Pacheco FAL (2018) Rainwater harvesting in catchments for agroforestry uses: a study focused on the balance between sustainability values and storage capacity. Sci Total Environ 613-614:1079–1092CrossRefGoogle Scholar
  53. Wang W-C, Chau K-W, Cheng C-T, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306CrossRefGoogle Scholar
  54. Wang HR, Wang C, Lin X, Kang J (2014) An improved ARIMA model for precipitation simulations. Nonlinear Process Geophys 21:1159–1168CrossRefGoogle Scholar
  55. Wang WC, Chau KW, Xu DM, Chen XY (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manag 29(8):2655–2675CrossRefGoogle Scholar
  56. Werz H (2006) The use of remote sensing imagery for groundwater risk intensity mapping in the Wadi Shueib, Jordan. PhD thesis, Department of Applied Geology, University of Karlsruhe, GermanyGoogle Scholar
  57. Zhang Q, Wang B-D, He B, Peng Y, Ren M-L (2011) Singular spectrum analysis and ARIMA hybrid model for annual runoff forecasting. Water Resour Manag 25(11):2683–2703CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringThapar Institute of Engineering and TechnologyPatialaIndia

Personalised recommendations