Investigation of Uncertainties in a Rainfall-Runoff Conceptual Model for Simulation of Basin using Bayesian Method

Document Type : Research Article


1 Ph.D Candidate, School of Environment, College of Engineering, University of Tehran

2 Associate Professor, School of Environment, College of Engineering, University of Tehran


Taleghan River is among the most important rivers in Iran due to its flow to Taleghan Dam, supplying drinking water to this region in addition to being one of the sources for northwestern part of the city of Tehran. Long-term river discharge data are needed to design hydroelectric power stations and manage water resources. With the existing monitoring stations being scattered and not providing sufficient hydrological data for the basin, we employ rainfall-runoff models which are popular tools for expanding hydrological data over time and space. In this paper, the feasibility of applying a conceptual rainfall-runoff model called HYMOD to a part of Taleghan River Basin is investigated. The generalized probability estimation method was used for model calibration and uncertainty analysis. The results show that the observed discharges are satisfactorily consistent with the observations, indicating that the hydrological model is working well and applying HYMOD to estimate long time series of river discharge in the study area is turning reasonable results.


[1]. Deletic A, Dotto CB, McCarthy DT, Kleidorfer M, Freni G, Mannina G, Uhl M, Henrichs M, Fletcher TD, Rauch W, Bertrand-Krajewski JL. Assessing uncertainties in urban drainage models. Physics and Chemistry of the Earth, Parts A/B/C. 2012;42:3-10.
[2]. Quan Z, Teng J, Sun W, Cheng T, Zhang J. Evaluation of the HYMOD model for rainfall–runoff simulation using the GLUE method. Proceedings of the International Association of Hydrological Sciences. 2015;368:180-185.
[3]. Zhang JL, Li YP, Huang GH, Baetz BW, Liu J. Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach. Water research. 2017;116:159-181.
[4]. Cu Thi P, Ball JE, Dao NH. Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam. Water. 2018;10(11):1641.
[5]. Herman JD, Reed PM, Wagener T. Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior. Water Resources Research. 2013;49(3):1400-1414.
[6]. Alipour MH, Kibler KM. A framework for streamflow prediction in the world’s most severely data-limited regions: Test of applicability and performance in a poorly-gauged region of China. Journal of hydrology. 2018;557:41-54.
[7]. Sobol IM. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and computers in simulation. 2001;55(1-3):271-280.
[8]. Shen M, Chen J, Zhuan M, Chen H, Xu CY, Xiong L. Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology. Journal of Hydrology. 2018;556:10-24.
[9]. Thompson DB. The rational method, regional regression equations, and site-specific flood-frequency relations. Civil Engineering department Texas Tech University; 2006:1-7.
[10].            Cooper VA, Nguyen VT, Nicell JA. Calibration of conceptual rainfall–runoff models using global optimisation methods with hydrologic process-based parameter constraints. Journal of Hydrology. 2007;334(3-4):455-466.
[11].            Chen H, Xu CY, Guo S. Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of hydrology. 2012;434:36-45.
[12].            Cooper VA. On automatic calibration of conceptual rainfall runoff models using optimisation techniques. Canada: National Library of Canada; 2002.
[13].            Andréassian V, Le Moine N, Perrin C, Ramos MH, Oudin L, Mathevet T, Lerat J, Berthet L. All that glitters is not gold: the case of calibrating hydrological models. Hydrological Processes. 2012;26(14):2206
[14].            Kirchner JW. Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resources Research. 2006;42(3).
[15].            Beven K. Prophecy, reality and uncertainty in distributed hydrological modelling. Advances in water resources. 1993;16(1):41-51.
[16].            Beven K. A manifesto for the equifinality thesis. Journal of hydrology. 2006;320(1-2):18-36.
[17].            Efstratiadis A, Dialynas YG, Kozanis S, Koutsoyiannis D. A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence. Environmental Modelling & Software. 2014;62:139-152.
[18].            van Emmerik T, Steele-Dunne SC, Judge J, van de Giesen N. Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress. IEEE Transactions on Geoscience and Remote Sensing. 2015;53(7):3855-3869.
[19].            Chiew FH, Kirono DG, Kent DM, Frost AJ, Charles SP, Timbal B, Nguyen KC, Fu G. Comparison of runoff modelled using rainfall from different downscaling methods for historical and future climates. Journal of Hydrology. 2010;387(1-2):10-23.
[20].            Arsenault R, Poulin A, Côté P, Brissette F. Comparison of stochastic optimization algorithms in hydrological model calibration. Journal of Hydrologic Engineering. 2014;19(7):1374-1384.
[21].            Song JH, Her Y, Suh K, Kang MS, Kim H. Regionalization of a Rainfall-Runoff Model: Limitations and Potentials. Water. 2019;11(11):2257.
[22].            Vrugt JA, Diks CG, Gupta HV, Bouten W, Verstraten JM. Improved treatment of
uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation. Water resources research. 2005;41(1).
[23].            Abdolvandi AF, Eslamian SS, Heidarpour M, Babazadeh H. Simultaneous simulation of both surface and groundwater resources using system dynamics approach (Case Study: Taleghan Dam). Advances in Environmental Biology. 2013:562-571.
[24].            Vafakhah M, Nouri A, Alavipanah SK. Snowmelt-runoff estimation using radiation SRM model in Taleghan watershed. Environmental earth sciences. 2015;73(3):993-1003.
[25].            Antonetti M, Zappa M. How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps. Hydrology & Earth System Sciences. 2018;22(8):4425-4447.
[26].            Boyle DP, Gupta HV, Sorooshian S. Multicriteria calibration of hydrologic models. Calibration of Watershed Models, edited by: Duan, Q., Gupta, H., Sorooshian, S., Rousseau, A., Turcotte, R., AGU. 2003:185-196.
[27].            Vrugt JA, Ter Braak CJ, Clark MP, Hyman JM, Robinson BA. Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resources Research. 2008;44(12).
Volume 7, Issue 1
April 2020
Pages 223-236
  • Receive Date: 07 October 2019
  • Revise Date: 11 February 2020
  • Accept Date: 11 February 2020
  • First Publish Date: 20 March 2020
  • Publish Date: 20 March 2020