Document Type : Research Article


Department of Water Engineering, Faculty of Agriculture and Natural Sciences, Razi University, P. O. Box: 09914412984, Kermanshah, Kermanshah, Iran.


Understanding of climate change and its impacts on river discharge has affected the quality and quantity of water and also supplying water requirements for drinking, agriculture and industry. Therefore, prediction of precipitation and temperature by climate models as well as simulation and optimization of their runoff with suitable models are very important. In this study, four climate models of the Fifth Coupled Model Inter comparison Project (CMIP5) and RCP8.5 scenario were used to forecast future precipitation and temperature for the next two periods including 2020-2052 and 2053-2085. Mean Observed Temperature-Precipitation (MOTP) method was used to reduce the uncertainty of climate models and the change factor method was used to downscale the climate data. Then, the Lumped-conceptual Identification of unit Hydrographs and Component flows from Rainfall, Evaporation and Stream flow data (IHACRES) model and multi-layer Artificial Neural Network (ANN) model were employed to estimate the effects of these parameters on the Khorramrood River runoff. The neural network model is written and implemented using Scikit-Learn library and the Python programming language. The comparison of performance of ANN models with different input variables like monthly precipitation, monthly precipitation of previous months, monthly discharge, monthly discharge of previous months, monthly temperature was made to find the best and most efficient network structure. The results showed that the precipitation in Khorramrood River basin based on the weighted combination model decreased by 8.18 % and 9.75 % in the first and the second periods, respectively, while the temperature increased by 1.85 and 4.22 °C, respectively. The discharge parameter in the calibration and validation period in the IHACRES model based on criteria to evaluate the parameters of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), The Coefficient of Determination (R), and the Nash-Sutcliffe Efficiency (NSE) performed better than the artificial neural network model. However, due to the small differences of these changes, the predictions were performed for both periods and using both models and the results indicated that future discharge in the IHACRES model decreased by 12.72 % during the first period and by 20.3 % in the second period, while the model of artificial neural network showed decrease rates of 2.12 % and 6.97 %, respectively.


Main Subjects

1.     Fu, G., Charles, S.P., Chiew, F.H., Teng, J., Zheng, H., Frost, A.J., Liu, W. and Kirshner, S., "Modelling runoff with statistically downscaled daily site, gridded and catchment rainfall series", Journal of Hydrology, Vol. 429, No. 7, (2013), 254-265. (
2.     Hao, X., Chen, Y., Xu, C. and Li, W., "Impacts of climate change and human activities on the surface runoff in the Tarim River Basin over the last fifty years", Water Resources Management, Vol. 22, (2008), 1159-1171. (
3.     Zhang, D., Chen, X., Yao, H. and Lin, B., "Improved calibration scheme of swat by separating wet and dry seasons", Ecological Modelling, Vol. 301, (2015), 54-61. (
4.     Risbey, J.S. and Entekhabi, D., "Observed Sacramento Basin stream flow response to precipitation and temperature changes and its relevance to climate impact studies", Journal of Hydrology, Vol. 184, No. 3-4, (1996), 209-223. (
5.     Ouyang, F., Zhu, Y., Fu, G., Lü, H., Yu, Zh. and Chen, X., "Impacts of climate change under CMIP5 RCP scenarios on streamflow in the Huangnizhuang catchment", Stochastic Environmental Research and Risk Assessment, Vol. 29, (2015), 1781-1795. (
6.     Hafezparast, M., Araghinejad, S., Fatemi, S.E. and Bressers, H., "A conceptual rainfall-runoff model using the auto calibrated NAM models in the Sarisoo River", Hydrology Current Research, Vol. 4, No. 1, (2013), 1-6. (
7.     Fu, G., Charles, S.P. and Chiew, F.H.S., "A two-parameter climate elasticity of streamflow index to assess climate change effects on annual streamflow", Water Resources Research, Vol. 43, No. 11, (2007), 1-12. (
8.     Liu, Z., Xu, Z., Charles, S.P., Fu, G. and Liu, L., "Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China", International Journal of Climatology, Vol. 31, No. 13, (2011), 6006-6060. (
9.     Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P.M., IPCC, Summary for policymakers, Climate change: The physical science basis, contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, (2013). (
10.   Ma, C., Pan, S., Wang, G., Liao, Y. and Xu, Y.P., "Changes in precipitation and temperature in Xingjian River Basin, China", Theoretical and Applied Climatology, Vol. 123, No. 3-4, (2016), 859-871. (
11.   Kharin, V.V., Zwiers, F.W., Zhang, X. and Wehner, M., "Changes in temperature and precipitation extremes in the CMIP5 ensemble", Journal of Climate Change, Vol. 116, (2013), 345-357. (
12.   Chadwick, R., Boutle, I. and Martin, G., "Spatial patterns of precipitation change in a CMIP5: Why the rich do not get richer in the tropics", Journal of Climate, Vol. 26, No. 11, (2013), 3503-3566. (
13.   Tan, M.L., Ficklin, D.L., Ibrahim, A.L. and Yusop, Z., "Impacts and uncertainties of climate change on stream flow of the Johor River Basin, Malaysia using a CMIP5 general circulation model ensemble", Journal of Water and Climate Change, Vol. 5, No. 4, (2014), 676-695. (
14.   Ahooghalandari, M., Khiadani, M. and Kothapalli, G., "Assessment of artificial neural networks and IHACRES models for simulating stream flow in Marillana catchment in the Pilbara, Western Australia", Australasian Journal of Water Resources, Vol. 19, No. 2, (2015), 116-126. (
15.   Sadeghi Loyeh, N. and Jamnani, M.R., "Comparison of different rainfall-runoff models performance: A case study of Liqvan catchment, Iran", European Water, Vol. 57, (2017), 315-322. (
16.   Ghanbarpour, M.R., Amiri, M., Zarei, M. and Darvari, Z., "Comparison of stream flow predicted in a forest watershed using different modelling procedures: ARMA, ANN, SWRRB, and IHACRES models", International Journal of River Basin Management, Vol. 10, No. 3, (2012), 281-292. (
17.   Karamouz, M., Fallahi, M., Nazif, S. and Farahani, M.R., "Long lead runoff simulation using data-driven models", International Journal of Civil Engineering, Vol. 10, No. 4, (2012), 328-336. (
18.   Sayahi, S., Shahbazi, A., Khademi, Kh., "Simulation of the climate change impact on monthly runoff of Dez watershed using IHACRES model", Journal of Water Science Engineering, Vol. 7, No. 15, (2017), 7-18. (In Farsi with English abstract). (
19.   Ahmadi, M., Moeini, A., Ahmadi, H., Motamedvaziri, B. and Zehtabiyan, Gh.R., "Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (Case study: Kan watershed, Iran)", Physics and Chemistry of the Earth, Vol. 111, (2019), 65-77. (
20.   Mitchell, T.D., "Pattern scaling: An examination of accuracy of the technique for describing future climates", Climatic Change, Vol. 60, (2003), 217-242. (
21.   Wilby, R.L. and Harris, I., "A framework for assessing uncertainties in climate change impacts: Low flow scenarios for the River Thames, UK", Water Resources Research, Vol. 42, No. 2, (2006), 1-10. (
22.   Massah Bavani, A. and Morid, A.R., "The impacts of climate change on water resources and agricultural production", Journal of Water Resources Research, Vol. 1, No.1, (2005), 40-47. (In Farsi with English abstract). (
23.   Moazami Goudarz, F., Sarraf, A. and Ahmadi, H., "Prediction of runoff within Maharlu basin for future 60 years using RCP scenarios", Arabian Journal of Geosciences, Vol. 13, No. 605, (2020). (
24.   Hansen, D.P., Ye, W., Jakeman, A.J., Cooke, R. and Sharma, P., "Analysis of the effect of rainfall and streamflow data quality and catchment dynamics on streamflow prediction using the rainfall-runoff model IHACRES", Environmental Software, Vol. 11, )1996(, 193-202. (
25.   Post, D.A. and Jakeman, A.J., "Predicting the daily stream flow of ungauged catchments in S.E. Australia by regionalizing the parameters of a lumped conceptual rainfall-runoff model", Ecological Modelling, Vol. 123, (1999), 91-104. (
26.   Schreider, S.Y., Jakeman, A.J. and Pittock, A.B., "Modelling rainfall-runoff from large catchment to basin scale: The Goulburn Valley, Victoria", Hydrological Processes, Vol. 10, (1996), 863-876. (;2-8).
27.   Haykin, S., Neural networks: A comprehensive foundation, 2nd Edition, Prentice Hall, Englewood Cliffs, New Jersey, USA, (1999), 1-696. (
28.   Bhattacharyya, B., Price, R.K. and Solomatine, D.P., "Machine learning approach to modelling sediment transport", Journal of  Hydraulic Engineering, Vol. 133, No. 4, (2007), 440-450. (
29.   Dawson, C.W. and Wilby, R.L., "Hydrological modelling using artificial neural networks", Progress in Physical Geography, Vol. 25, No. 1, (2001), 80-108. ( 10.1177/030913330102500104. 2001).
30.   Kamruzzaman, J. and Aziz, M.A., "Note on activation function in multilayer feed forward learning", Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), Honolulu, HI, USA,(2002). ((
31.   Asadollahfardi, G., Taklify, A. and Ghanbari, A., "Application of artificial neural network to predict TDS in Talkheh Rud River", Journal of Irrigation and Drainage Engineering, Vol. 138, No. 4, (2011), 363-370. (
32.   Bannem, J. and Shoemaker, C.A., "An analysis of high-flow sediment event data for evaluating model performance", Journal of Hydrological Processes, Vol. 19, (2005), 605-620. (
33.   Santhi, C., Arnold, J.G., Williams, J.R., Dugas, W.A., Srinivasan, R. and. Hauck, L.M., "Validation of the SWAT model on a large river basin with point and nonpoint sources", Journal of American Water Resources Assocciation, Vol. 37, No. 5, (2001), 1169-1188. (
34.   Sevat, E. and Dezetter, A., "Selection of calibration objective functions in the context of rainfall-runoff modeling in a Sudanese savannah area", Hydrological Science Journal, Vol. 36, No. 4, (1991), 307-330. (
35.   Boyle, D.P., Gupta, H.V. and Sorooshian, S., "Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods", Water Resources Research, Vol. 36, No. 12, (2000(, 3663-3674. (
36.   Legates, D.R. and McCabe, G.J., "Evaluating the use of “goodness-of-fit” measures in hydrologic and hydro climatic model validation", Water Resources Research, Vol. 35, No. 1, (1999(, 233-241. (
37.   Pourkheirollah, Z., Hafezparast, M. and Fatemi, S.E., "Changes in precipitation, temperature and discharge under RCP scenarios case study: Dehloran city", Proceedings of the The Second National Conference of Hydrology of Iran, Shahrekord University, (2017). (In Farsi with English abstract). (
38.   Tegegne, G., Kwan Park, D. and Kim, Y., "Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin", Journal of Hydrology: Regional Studies, Vol. 14, (2017), 49-66. (
39.   Zulkarnain, H., Shamsudin, S. and Harun, S., "Minimum input variance for modelling rainfall-runoff using ANN", Jurnal Teknologi, Vol. 69, No. 3, (2014), 113-118. (
40.   Zulkarnain, H., Shamsudin, S., Harun, S., Marlinda, A. and Hamidon, N., "Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: A case study in the northern area of Peninsular Malaysia", Environmental Earth Sciences, Vol. 74, No. 1, (2015), 463-477. (