Assessment the Performance of IHACRES Model Using ARMAX and EXPUH Linear Methods (Case Study: Shoor River Basin in Ghaen)

Document Type : Research Article

Authors

• M. Fouladi Nasrabad ¹
• M. Amirabadizadeh ²
• M. Pourreza-Bilondi ³
• M. Yaghoobzadeh ⁴

¹ M.Sc of Water Resources, Department of Water Engineering and Science, University of Birjand, Birjand, Iran

² Assistant Professor, Department of Water Science and Engineering, Birjand University and Member of Drought and Climate Change Research Group

³ Associate Professor, Department of Water Engineering and Science, University of Birjand, Birjand, Iran and Member of Drought and Climate Change Research Group

⁴ Associate Professor, Department of Water Science and Engineering, Birjand University and Member of Drought and Climate Change Research Group

https://doi.org/10.22067/jsw.2022.74115.1122

Abstract

Introduction
The watershed acts as a hydrological unit regulating the quantity and quality of the water cycle, and human beings have incurred high costs due to ignorance of this complex cycle and lack of planning of projects in terms of the relationship between water management and community development.
Knowledge of features such as maximum flood discharge is essential for the design of hydraulic structures, such as dams, spillways, bridges, and culverts, in order to reduce potential damages and predict when peak discharges will be reached in the downstream areas when discussing flood warning. Rainfall-runoff modeling is one of the key tools in hydrology to achieve flood characteristics, such as peak rate and peak time. In current research, the performance of IHACRES model using "Hydromad" R package has been implemented to simulate flow in the Shoor river basin in Ghaen on a monthly scale. The model simulation was done to investigate the effect of selecting "ARMAX" and "EXPUH" methods in the linear part of the target function. Also, the modeling process and the optimized values of the model parameters were investigated.
Materials and Methods
The Shoor river basin with an area of 2412.92 square kilometers located in Ghaen between 59 degrees and 12 minutes to 59 degrees and 14 minutes east longitude and 33 degrees and 42 minutes to 33 degrees and 45 minutes north latitude. The study catchment with an average altitude of 1420 m above sea level and an average long-term annual rainfall of 173 mm has a dry climate. This river is the largest river in Ghaenat city which flows into Khaf Salt field. In this research, the IHACRES model was implemented using the Hydromad R package. To perform the flow simulation, precipitation, flow rate and temperature data on a monthly scale during the years 1998 to 2017 were used. The IHACRES model has two parts: the first part, which converts precipitation into effective precipitation at each time stage and the second part, which converts effective precipitation into modeled flow. These sections are called nonlinear and linear modules, respectively. To implement each of the sections of nonlinear modules and linear modules according to the data and conditions in the study area, methods with different parameters can be used. In this research, in the non-linear module section, the "CWI" method and in the linear module section, "ARMAX" and "EXPUH" methods have been used for proper routing in the "reverse" calibration section. In the validation section of the "ls" method, the performance criteria of KGE, NS and R² were used to evaluate the performance of the model in both calibration and validation process.
Result and Discussion
Comparison of obtained results in this study with previous studies showed that in terms of examining the performance of the model with the EXPUH linear method, the obtained results are consistent with the results of Sadeghi et al. (2015) and Lotfi Rad et al. (2015) and the model with the EXPUH linear method. The NS criteria has shown acceptable performance. According to the results of the model in the calibration section, in terms of evaluation criteria NS, KGE and , and in terms of simulation of peak flow values and the time to peak using EXPUH method in the linear part showed  better performance than ARMAX method. The value of these criteria in EXPUH method is equal to 0.86, 0.93, and 0.86 and in ARMAX method are equal to 0.7, 0.85 and 0.73, respectively. In the validation section, the evaluation criteria in EXPUH method were equal to 0.51, 0.63, and 0.54 and in ARMAX method were equal to 0.55, 0.73 and 0.65, respectively, indicating better performance of the model by ARMAX method. Comparison of the EXPUH method, and also the model with ARMAX method showed more accurate performance in terms of peak discharges, quantity and time of occurrence. The values of NS, KGE and evaluation criteria in this section were 0.51, 0.63, and 0.54 using EXPUH method and 0.55, 0.73 and 0.65 with ARMAX method, respectively.
Conclusion
According to the results, the IHACRES model using ARMAX method in the linear section resulted in more accurate performance than EXPUH method in simulation of peak flow values and time to peak.

Keywords

• Calibration
• IHACRES Model
• Rainfall-Runoff Simulation
• Shoor Ghaen River

20.1001.1.20084757.1401.36.1.2.4

Main Subjects

• Irrigation

جلد36 شماره1 سال1401

References

1. Abushandi E., and Merkel B. 2013. Modelling rainfall runoff relations using HEC-HMS and IHACRES for a single rain event in an arid region of Jordan. Water Resources Management 27(7): 2391-2409. https://doi.org/10.1007/s11269-013-0293-4.
2. Abushandi E.H., and Merkel B.J. 2011. Application of IHACRES rainfall-runoff model to the Wadi Dhuliel arid catchment, Jordan. Journal of Water and Climate Change 2(1): 56-71. https://doi.org/10.2166/wcc.2011.048.
3. , Ahmad A., Rezaei M., Rezvani Mahmoui A., and Mousavi H. 2016. River Flow Prediction Using Artificial Neural Network System (Case Study of Shurqain River), International Conference on New Research Achievements in Civil Engineering, Architecture and Urban Planning. (In Persian)
4. Ayele G.T., Teshale E.Z., Yu B., Rutherfurd I.D., and Jeong J. 2017. Streamflow and sediment yield prediction for watershed prioritization in the Upper Blue Nile River Basin, Ethiopia. Water. 9(10): 782. https://doi.org/10.3390/w9100782.
5. Ahmadi M., Moeini A., Ahmadi H., Motamedvaziri B., and Zehtabiyan G.R. 2019. 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, Parts A/B/C, 111: 65-77. https://doi.org/10.1016/j.pce.2019.05.002.
6. Baddoo T.D., Li Z., Guan Y., Boni K.R.C., and Nooni I.K. 2020. Data-Driven Modeling and the Influence of Objective Function Selection on Model Performance in Limited Data Regions. International Journal of Environmental Research and Public Health 17(11): 4132. https://doi.org/10.3390/ijerph17114132.
7. Box G.E. 1970. GM Jenkins Time Series Analysis: Forecasting and Control. San Francisco, Holdan-Day.
8. Chidaz A., Mohseni Sarvi M., Vafakhah M. 2009. Evaluation of HEC_HMS model for estimating flood hydrograph in Kasilian watershed, Watershed Management Research, No. 84. (In Persian with English abstract)
9. Dailari M., Darbandi S., Asadi E., and Samadian M. 2021. Simulation of effective parameters on river flow discharge trend using HACRES rainfall-runoff model in future periods (Case study: Zolachai River). Echo Hydrology 8(1): 177-193. (In Persian with English abstract)
10. Dooge J. 1973. Linear theory of hydrologic systems. Agricultural Research Service, US Department of Agriculture.
11. Hafezparast M., and Marabi S. 2021. Prediction of Discharge Using Artificial Neural Network and IHACRES Models Due to Climate Change. Journal of Renewable Energy and Environment 8(3): 75-85. https://doi.org/10.30501/jree.2021.257941.1162.
12. Jakeman A., Littlewood I., and Whitehead P. 1990. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. Journal of Hydrology 117(1-4): 275-300. https://doi.org/10.1016/0022-1694(90)90097-H.
13. Jakeman A., and Hornberger G. 1993. How much complexity is warranted in a rainfall‐runoff model? Water Resources Research 29(8): 2637-2649. https://doi.org/10.1029/93WR00877.
14. Karimi M., Malekinejad H., Abqari H., and Azizian M. 2012. Evaluation of Different Flood Hydrograph Simulation Methods Using HEC_HMS Software Package (Case Study: Chehel Gezi Watershed), Iranian Journal of Water Research 5(9): 28-39. (In Persian)
15. Karami F., and Bayati Khatibi M. 2019. Modeling soil erosion and sediment yield prioritization in Satar Khan Watershed using MUSLE and SWAT models: The Journal of Hydro Geomorphology 15(18): 115-137. (In Persian with English abstract)
16. Lotfirad M., Adib A., and Haghighi A. 2018. Estimation of daily runoff using of the semi-conceptual rainfall-runoff IHACRES model in the Navrood watershed (a watershed in the Gilan province. Iranian Journal of Ecohydrology 5(2): 449-460. (In Persian with English abstract). https://dx.doi.org/10.22059/ije.2017.234237.614.
17. Moriasi D.N., Arnold J.G., Van Liew M.W., Bingner R.L., Harmel R.D., and Veith T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50(3): 885-900. Doi: 10.13031/2013.23153.
18. Sadeghi S.H., Ghasemieh H., and Sadatinegad S.J. 2015. Performance Evaluation of the IHACRES Hydrological Model in Wet Areas (Case Study: Navrud Basin, Gillan) [Research]. Journal of Water and Soil Science 19(73): 73-83. https://doi.org/10.18869/acadpub.jstnar.19.73.73. (In Persian)
19. Sarraf A., and Ghasemi H. 2021. Calibration of the IHACRES hydrological model using multi-objective social spider optimization and search and rescue algorithms. Hydrogeomorphology. (In Persian). DOI: 22034/hyd.2021.45015.1581.
20. Yaghoubi M., and Massah Bavani A. 2014. Sensitivity analysis and comparison of capability of three conceptual models HEC-HMS, HBV and IHACRES in simulating continuous rainfall-runoff in semi-arid basins. Journal of the Earth and Space Physics 40(2): 153-172. (In Persian with English abstract). https://dx.doi.org/10.22059/jesphys.2014.50640.
21. Zeid Ali Nejad N., Naseri Sh., and Alijani F. 2020. (Simulation of the effect of climate change on Taraz-Haraksh river runoff, Khuzestan province, using NEX-GDDP data set and IHACRES rainfall-runoff model. Meteorology and Atmospheric Sciences 2(2): 162-178. (In Persian with English abstract)