Document Type : Research Paper


Department of Dams and Water Resources Engineering, College of Engineering, University of Mosul, Mosul, Iraq


Flow disaggregation models, which are one of the stochastic generation techniques, play a crucial role in the planning, design, and operation of water resource management systems and related projects. One distinguishing feature of these models is their ability to address the issue of missing observed data and compensate for it. They also enable the rescaling of data from a higher temporal level to a lower temporal scale. Data at lower temporal scales are typically required to address hydraulic and operational design problems in water resource projects. There are two main approaches to disaggregation flow data: the parametric approach and the non-parametric approach.One of the advantages of the disaggregation model is its ability to distribute flow data values from a key station to several sub-stations, both temporally and/or spatially, while preserving the basic statistical properties of the time series obtained from the model (mean, standard deviation, minimum, maximum, and correlation coefficient) for the observed data.In the current study, a non-parametric approach was used for the purpose of disaggregation approach. It is assumed that there is aggregated discharge data at a key station, and this data will be disaggregated into a corresponding series of discharges temporally and spatially at sub-stations that are statistically similar, using the SAMS 2010 platform program (Stochastic Analysis, Modeling, and Simulation). Annual and monthly discharge data for five stations measuring discharges on the Tigris River System in Iraq were used, including the Mosul Dam station on the Tigris River, the Asmawah station on the Khazir River, the Askiklik station on the Upper Zab, the Dibs Dam station on the Lower Zab, and the Baiji station on the Tigris River, covering a time span of twenty-three years. The statistical results of the disaggregation approach were compared with their observed counterparts and showed good agreement in most years and months and for all stations. Based on this, the method is recommended disaggregation of the data when decisions required water management strategies in these regions.


Main Subjects

  1. B. Rajagopalan, Salas, J. D.Salas, and U. Lall, "Stochastic methods for modeling precipitation and streamflow", Advances in data-based approaches for hydrologic modeling and forecasting , pp. 17–52, 2010.
  2. J. R. Prairie, B. Rajagopalan, T. J. Fulp, E. A. Zagona, "Statistical nonparametric model for natural salt estimation", Journal of Environmental Engineering, Vol.131, Issue:1, pp.130–138, 2005. doi: 10.1061/(asce)0733-9372(2005)131(1)130-138
  3. U. Lall, and A. Sharma, " A nearest neighbor bootstrap for resampling hydrologic time series", Water Resources Research, Vol.32, Issue: 3, pp.679–693, 1996. doi: 10.1029/95WR02966.
  4. Y. I. Moon, and U. Lall, (1994) "A kernel quantite function estimator for flood frequency analysis", Water Resources Research, Vol.30, Issue :11, pp.3095-3103, 1994. doi: 10.1029/94WR01217.
  5. N. Şarlak, and Ş. Tiğrek, " Noktasal Taşkın Frekans Fanalizi: Göksu Nehri ve Kayraktepe Barajı Vaka Analizi", Journal of the Faculty of Engineering and Architecture of Gazi University, Vol.31, Issue: 4, pp.1095-1103, 2016. doi: 10.17341/gummfd.79436.
  6. K. Grantz, B. Rajagopalan, M. Clark, and E. Zagona " A technique for incorporating large‐scale climate information in basin‐scale ensemble streamflow forecasts" Water Resources Research ,Vol. 41,Issue:10, 2005. doi: 10.1029/WR003467.
  7. V.V. Srinivas, and K. Srinivasan, "Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows" Journal of Hydrology, Vol.302,Issue: 1, pp.307–330,2005. doi: 10.1016/j.jhydrol.2006.01.023.
  8. U.Lall, "Recent advances in nonparametric function estimation: Hydrologic applications", Reviews of Geophysics, Vol.33, Issue:2, pp.1093–1102,1995. doi: 10.1029/95RG00343.
  9. D.N.Kumar, U. Lall, and M.R. Petersen,"Multisite disaggregation of monthly to daily streamflow", Water Resources Research,Vol.36 Issue:7, pp.1823–1833.2000. doi: 10.1029/2000WR900049.
  10. F.A.Filho, and U. Lall," Seasonal to interannual ensemble streamflow forecasts for Ceara, Brazil: Applications of a mutlivariate, semiparametric algorithm", Water Resource Research, Vol.39, Issue: 11, pp.1-11,2003. doi: 10.1029 /WR001373.
  11. T. Lee, J.D. Salas, J. Keedy, D. Frevert, and T. Fulp, "Stochastic Modeling and Simulation of the Colorado River Flows", World Environmental and Water Resources Congress, 10 p.2007. doi: 10.1061/40927(243)423.
  12. K.Nowak, J. Prairie, and B. Rajagopalan, "Development of stochastic flow sequences based on observed data", Washington, DC: Allen Institute.2008.
  13. C.Bracken, B. Rajagopalan, and J. Prairie, "A Multisite Seasonal Ensemble Streamflow Forecasting Technique", Journal of Water Resource Research, Vol.46, pp. 1-12,2009. doi: 10.1029/2009WR007965.
  14. S.K. Regonda, B. Rajagopalan, M. Clark, and E. Zagona, "A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin", Water Resources Research, Vol.42,Issue:9, 2006. doi: 10.1029/2005WR004653.
  15. J. Prairie, B. Rajagopalan, U. Lall, and T. Fulp, "A stochastic nonparametric technique for space‐time disaggregation of streamflows",Water Resources Research, Vol.43, Issue:3,2007. doi: 10.1029/2005WR004721.
  16. T. Lee, J. D.Salas, J. Prairie, "An enhanced nonparametric streamflow disaggregation model with genetic algorithm", Water Resource Research, Vol.46, 14 p, 2010. doi: 10.1029/2009WR007761.
  17. S.H.AL-Zakar, N. Şarlak, O.M. Mahmood, "Disaggregation of Annual to Monthly Streamflow: A Case Study of Kızılırmak Basin (Turkey)", Advances in Meteorology, Vol.2017, 16 p, 2017. doi: 10.9790/4861-0901023443.
  18. T. Lee, and T. Ouarda, "Randomized block nonparametric temporal disaggregation of hydrological variables RB-NPD (version1.0) – model development" Geoscientific Model Development Discussion, 2023.,2023.
  19.  G. Castellanos-Osorio , A. López-Ballesteros , J. Pérez- Sánchez , and J. Senent," Disaggregated monthly SWAT+ model versus daily SWAT+ model for estimating environmental flows in Peninsular Spain", Journal of Hydrology, Vol.623, August 2023. 10.1016 /j.jhydrol.2023.129837.
  20. M. Velpuri, G. Titas,and U. N.V. , " A Multi criteria Decision Making based nonparametric method of fragments to disaggregate daily precipitation", Journal of Hydrology, Vol. 617, Part A, 2023. doi: 10.24996/ijs.2023.64.6.22.
  21. H. Bolouki, and M. Fazeli, "Evaluation of Multivariate Rainfall Disaggregation Performance Using MuDRain Model (Case Study: North East of Hormozgan Province", A mirkabir Journal of Civil Engineering, Vol.54 (12), pp.4657–4676, 2023. doi: 10.1029/96WR00488.
  22. H. Khalid Hameed, K. Ahmed Abdullah ,and R.Hoobi Irzooki, "Seepage Simulation of the Proposed Makhool Dam in North Iraq", Iraqi Journal of Science, Vol. 64,Issue:6, pp.2934-2945,2023.
  23. D. Koutsoyiannis, and A. Manetas, "Simple disaggregation by accurate adjusting procedures", Water Resource Research, Vol.32, pp. 2105– 2117, 1996.
  24.  O.G.B.Sveinsson , T.S. Lee, J. D. Salas, W. L. Lane, D. K. Frevert, and T.B.M.J. Ouarda," Stochastic Analysis, Modeling, and Simulation (SAMS) Version 2010 ".USER's MANUAL, Colorado State University, March 2011. doi: 10.1016/j.jhydrol.2011.08.027.
  25. R. Younus Ahmad Hassan, A. Mohammad Younes, "Prediction of Daily Flow to the Great Zab River Using Artificial Neural
    Network Models", Al
    -Rafidain Engineering Journal (AREJ) , Vol.28, Issue:.2, pp. 163-172,2023.
  26. S. Hazim Dawood, "Meteorological Estimations for selected stations in the North of Iraq", Al-Rafidain Engineering Journal (AREJ), Vol.17, Issue:1, 2009.
  27. A. Acharya, T.C. Piechota, H. Stephen, "Modeled streamflow response under cloud seedine in the North Platte River watershed", Journal of Hydrologic Engineering, Vol. 409, Issue:1-2, pp.305-314, 2011.