Probabilistic Forecasts of Streamflow Scenarios Using ESP Approach (Case study: Halil River)

Document Type : Research Paper


Assistant Professor, Department of Ecology, Environmental Research Center, Institute of Science and High Technology and Environmental Science, Graduate University of Advanced Technology


Stream flow forecast is a fundamental tool that could be utilized for appropriate water resource management. The enhancement of accuracy as well as the increase of time horizon for stream flow forecasts is essential especially for agricultural sector which is the most vulnerable sector during water shortages. In this regard, the application of stochastic approaches like Ensemble Streamflow Prediction (ESP) procedure for long-term forecast with respect to streamflow uncertainty can be proposed. The ESP procedure produces streamflow forecasts in the form of multiple hydrographs, each a possible realization of seasonal streamflow (Day, 1985). One example of ensemble streamflow forecasts is the National Weather Service’s ESP procedure. Faber and Stedinger (2001) successfully combined reservoir operation models with updated information from ESP of the National Weather Service (Faber and Stedinger, 2001). Using ESP forecasts, Eum et al. (2011) also developed a procedure to calculate optimal water release curtailments during droughts using a future value function derived with a sampling stochastic dynamic programming model.
The main objective of this paper is to present a probabilistic approach and forecast the inflows to Jiroft dam reservoir. In this regards, using ESP approach as well as Artificial Neural Networks (ANNs) the 1- to 12- month ahead probabilistic scenarios of Halil river were forecasted.


Main Subjects

1- Alcázar, J., Palau, A. and Vega-Garcı, C., 2008. A neural net model for environmental flow estimation at the Ebro River Basin, Spain. Journal of hydrology, 349(1-2), pp.44-55.
2- Aqil, M., Kita, I., Yano, A. and Nishiyama, S., 2006. Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool. Journal of Environmental Management, 10, pp.1-9.
3- ASCE Task Committee on Application of Artificial Neural Networks in Hydroloy, 2000. Artificial neural networks in hydrology, I: preliminary concepts. Journal of Hydrologic Engineering,  5(2), pp. 115-123.
4- Besaw, L.E., Rizzo, D.M., Bierman, P.R. and Hackett, W.R., 2010. Advances in ungauged streamflow prediction using artificial neural networks. Journal of Hydrology, 386(1-4), pp.27–37.
5- Chow, V.T., Maidment, D.R. and Larry W., 1981. Applied Hydrology.
6- Dawson, C.W., Abrahart, R.J., Shamseldin, A.Y. and Wibly, R.L., 2006. Flood estimation at  ungauged sites using artificial neural networks. Journal of Hydrology, 319, pp.391-409.
7- Day, G.N., 1985. Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management, 111(2), pp.157-170.
8-  Demirel, M.C., Venancio, A. and Kahya, E., 2009. Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Advances in Engineering Software, 40(7), pp.467-473.
9- Eum, H., Kim, Y.O. and Palmer, R., 2011. Optimal drought management using sampling stochastic dynamic programming with a hedging rule. Journal of Water Resources Planning and Management. 137(1), pp.113-122.
10- Faber, B.A. and Stedinger, J.R., 2001. Reservoir optimization using sampling SDP with ensemble steamflow prediction (ESP) forecasts. Journal of Hydrology, 249, pp.113-133.
11- = 11722
12- Karamooz, M. and Araghinezhad, SH., 2005. Advanced hydrology, Amirkabir University of Technology Press, Tehran, 465p, (In Persian).
13- Kim, Y.O., Eum, H., Lee E.G. and Ko, I.H., 2007. Optimizing operational policies of a korean multireservoir system using sampling stochastic dynamic programming with ensemble streamflow prediction. Journal of Water Resources Planning and Management, 133(1), pp.4-14.
14- Kisi, O., 2005. Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish Journal of Engeering and Environmental Science, 29, pp.9-20.
15-  Nayaka, P.C., Sudheerb, K.P., Ranganc, D.M. and Ramasastri, K.S., 2004. A neuro- fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291, pp.52–66.
16- Saghafian, B., Anvari, S. and Morid, S., 2013. Effect of SOI and spatial climatic data on ANN, ANFIS and K-NN models in the stream flow forecasts. Expert Systems Journal, 30(4), pp.367–380.
17- Sedki, A., Ouazar, D. and El Mazoudi, E., 2009. Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting. Expert Systems with Applications, 36(3), pp.4523-4527.
18- Shiri, J. and Kisi, O., 2010. Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. Journal of Hydrology, 394(3-4), pp.486-493.
19- Wang, W., Van Gelderp, H.A.J.M., Vrijling, J.K. and Ma, J., 2006. Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology, 324, pp.383–399.
20- Zounemat Kermani, M. and Teshnehlab, M., 2007. Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Applied Soft Computing, Available online at
Volume 41, Issue 3
November 2018
Pages 75-87
  • Receive Date: 27 October 2016
  • Revise Date: 28 July 2016
  • Accept Date: 12 February 2017
  • First Publish Date: 23 October 2018