Comparison of MIKE NAM and AWBM models performance in simulation of daily runoff in Gokbad Catchment in Hamedan province

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Yasamin Sajadi Bami ¹ , Jahangir Porhemmat ² , Hossein Sedghi ³ , Navid Jalalkamali ⁴

1 - PhD Candidate in Water Resources Engineering, Department of Water Science and Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Facuty member of SCWMRI
3 - Professor, Department of Water science and Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - Assistan Professor, Department of Water Engineering,, Islamic Azad University, Kerman Branch, Kerman, Iran

Received: 2020-05-21 Accepted : 2020-11-11 Published : 2021-05-22

Keywords: Hamedan Gonbad basin, Rainfall-runoff models, Hydrological models, lumped model,

Abstract :

Apart from the understanding of the impact of land use and climate changes on the water cycle and hydrology regime, hydrological models are effective tools for designing and managing water resources. Currently, many hydrological models have been developed to simulate the basin, though choosing the right model is a challenge. To this end, a correct understanding of the model, its advantages, and limitations is necessary. In this regard, several studies have been conducted to evaluate the hydrological models performance in different regions and conditions. In the present study, the performance of two integrated hydrological and conceptual rainfall-runoff models of AWBM and MIKE NAM in the simulation of the average daily runoff in Gonbad Hamedan basin was investigated. Although both models are lumped models for rainfall-runoff process, the MIKE NAM model has a more complex structure compared to the AWBM. In addition to considering the initial conditions, MIKE NAM model is also capable of simulating snowmelt. The results of the runoff simulation during the calibration and validation periods were evaluated using two statistical indicators of the Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS). The NSE and PBIAS during the calibration and validation periods for the MIKE NAM model were 0.8, 6.3 and 0.71, -4.2; and 0.6, 14.33 and 0.55, -9.2 for AWBM model, respectively. The results showed that MIKE NAM model has a better performance in simulating daily runoff in Gonbad Moarref basin compared to the AWBM model.

References:

پرهمت، ج. و نظری‌پویا، ه. 1394. بررسی مدل‌های نفوذ در پوشش خاک اراضی مرتعی، مطالعه موردی: حوضه گنبددر استان همدان. نشریه علمی-پژوهشی مهندسی و مدیریت آبخیز، جلد 7 (4):458 -468.

رستمی خلج، م.، مقدم نیا، ع.، سلمانی، ح. و سپهوند، ع. 1395. بررسی مقایسه‌ای کارایی مدل‌های بارش رواناب AWBM، Sacramento، SimHyd، SMAR و Tank. فصلنامه اکوسیستم‌های طبیعی ایران، 7 (2): صفحات 39-47.

شاهویی، و. و پرهمت ج. 1398. ارزیابی و مقایسه دو مدل یکپارچه AWBM و نیمه توزیعی SWAT در شبیه‌سازی رواناب ماهانه رودخانه قره‌سو در استان کرمانشاه. مجله محیط زیست و مهندسی آب، 5، شماره 1، صفحات: 82–71.

گزارش حوضه معرف و زوجی گنبد همدان. 1394. (آرشیو اداره کل منابع طبیعی و آبخیزداری استان همدان، معاونت آبخیزداری).

 

Agrawal, N. and. Desmukh, T.S.2016. Rainfall Runoff Modeling using MIKE 11 Nam –A Review. IJISET - International Journal of Innovative Science. Engineering & Technology, 3(6):659-667.

Amir, M. S. I. I., Khan, M. M. K., Rasul, M.G., Sharma, R. H., & Akram, F. 2013. Automatic multi-objective calibration of a rainfall runoff model for the Fitzroy Basin, Queensland, Australia International Journal of Environmental Science and Development, 4(3), 311-315DOI: 10.7763/IJESD.2013.V4.361.

Boughton W. 2004. The Australian Water Balance Model. EnvironmentalModelling & Software 19(10): 943–956. DOI: 10.1016/j.envsoft.2003.10.007.

Boughton, W. 2006.A review of Australian model parameterization studies using large basin samples Experiments for Hydrological Model Parameterization: Results of the Model Parameter Experiment–MOPEX. IAHS Publ. 307, 2006.

Danish Hydraulic Institute. 2009. MIKE 11: A Modeling System for Rivers and Channels reference manual, 2009, 278–325.

Dai, ZH., Amatya, D., Sun, G., Tretctn, C., LI. CH.and LI, H. 2010. A comparision of MIKE SHE and DRAINMOD for modeling forested wetland in costal south Carolina, USA. XVIIth World Congress of the International Commission of Agricultural Engineering (CIGR) Hosted by the Canadian Society for Bioengineering (CSBE/SCGAB)Québec City, Canada June 13-17.

DHI 2004: MIKE 11 User & Reference Manual, Danish Hydraulic Institute, Denmark.

El-Nasr, A.A., Willems, P., &Timbe, L., Chrisyianes K. 2013. Comparative Analysis of the Runoff-Generation Using Lumped and Distributed Approaches, with Application to the Jeker Catchment in Belgium, 2013.

Galkate, R. V, Jaiswal, R. K., Thomas, T., Nayak T.R. 2014. Rainfall Runoff Modeling Using Conceptual NAM Model, International Conference on Sustainability and management strategy, Institute of Management and Technology, Nagpur, 2014.

Gayathri K Devi, Ganasri B Pa, Dwarakish G S.2015. A Review on Hydrological Models. Aquatic Procedia 4 (2015) 1001 – 1007.

Golmohammadi, G., Prasher, S., Madani, A. and Rudra, R. 2014. Evaluating Three Hydrological Distributed Watershed Models: MIKE-SHE, APEX, SWAT. Journal of Hydrology. 1: 20-39. doi:10.3390/hydrology1010020.

Gupta HV, Sorooshian S, Yapo PO. 1999. Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydrologic Engineering 4(2): 135–143.

Hafezparast, M., Araghinejad, S., Fatemi, S. E., Bressers, H. 2013. A conceptual rainfall-runoff model using the auto calibrated NAM Models in the Sarisoo River, Hydrology, 2013, 4(1), 1-6.

Haque, MD., Rahman, M., Hagare1, A.D. and Kibriha,G. 2014. Parameter uncertainty of the AWBM model when applied to an ungauged catchment, Hydrological processes hydrol. (2014),DOI: 10.1002/hyp.10283

Kumar, P., Lohani, A.K., and Nemai, A.K. 2019 Rainfall Runoff Modeling using Mike 11 Nam Model.Current World Environment,14 (1) 2019: 27-36ISSN: 0973-4929, https://www.cwejournal.org/pdf/vol14no1/Vol14_No1_p_27-36.pdf

Lan Anh N.T, Willems P., Boxall J.B &. Saul A.J. 2008. An evaluation of three lumped conceptual rainfall-runoff model at catchment scale. The 13th World Water Congress 2008

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., Veith T. L. 2007. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. American Society of Agricultural and Biological Engineers ISSN 0001−2351. 50(3): 885−900. Https://www.researchgate.net/publication/43261199_Model_Evaluation_Guidelines_for_Systematic_Quantification_of_Accuracy_in_Watershed_Simulations.

Nash JE, Sutcliffe J. 1970. River flow forecasting through conceptualmodels part I – a discussion of principles. Journal of Hydrology, 10(3)282–290.

Nielsen S.A., Hansen E. 1973. Numerical simulation of the rainfall runoff process on a daily basis. Nordic Hydrol, 1973;4: 171–190.

Odiyo, J. O., Phangisa, J. I., & Makungo, R.2012. Rainfall–runoff modelling for estimating Latonyanda River flow contributions to Luvuvhu River downstream of Albasini Dam. Physics and Chemistry of the Earth, Parts A/B/C, 2012, 50, 5-13.

Onyutha,C., 2016. Influence of Hydrological Model Selection on Simulation of Moderate and Extreme Flow Events: A Case Study of the Blue Nile Basin. Hindawi Publishing Corporation Advances in Meteorology Volume 2016, Article ID 7148326, 28 pages http://dx.doi.org/10.1155/2016/7148326

Oulgeris, C., Georgiou, P., Papadimos, D., & Papamichail, D. 2011. Evaluating three different model setups in the MIKE 11 NAM model. InAdvances in the Research of Aquatic Environment, 2011: 241-249.

Podger, G.M. 2003. Rainfall–runoff Library user guide [online].Cooperative Research Centre for Catchment Hydrology.Available from: www.toolkit.net.au/rrl [Accessed 15 April2015].

Refsgaard J. C. and Knudsen J. 1996. Operational validation andinter comparison of different types of hydrological models. WaterResources Research [online]. 32(7).: 2189-2202. Available:http://onlinelibrary.wiley.com/doi/10.1029/96WR00896/pdf:

Refsgaard. J. C. 1997. Parameterisation, calibration andvalidation of distributed hydrological models. Journal of Hydrology [online]. 198(1–4): 69-97. Available: http://www.sciencedirect.com/science/article/pii/S002216949603329

Sharma, K. D., Sorooshian, S. and Wheater, H. 2008. Hydrological Modelling in Arid and Semi-Arid Areas. New York. Cambridge University Press. 223 p. ISBN-13 978-0-511-37710-5

Wakigari SH. 2017. Evaluation of Conceptual Hydrological Models in Data Scarce Region of the Upper Blue Nile Basin: Case of the Upper Guder Catchment Hydrology 2017, 4, 59; doi: 10.3390/hydrology4040059 www.mdpi.com/journal/hydrology

Yu, b. and Z. Zhu. 2015.A comparative assessment of AWBM and SimHyd for forestedwatersheds. Hydrological Sciences Journal – Journal des Sciences Hydrologiques, 60 (7–8) 2015 http://dx.doi.org/10.1080/02626667.2014.961924