Evaluation of SIMHYD Rainfall-Runoff Model Efficiency in Climate Change Conditions

Razaghian, Hadi; Shahedi, Kaka; Mohseni, Behrouz

doi:10.29252/jwmr.9.17.216

Volume 9, Issue 17 (9-2018) jwmr 2018, 9(17): 216-225 | Back to browse issues page

‎ 10.29252/jwmr.9.17.216

‎ 20.1001.1.22516174.1397.9.17.24.6

Mendeley

Zotero

RefWorks

Razaghian H, Shahedi K, Mohseni B. (2018). Evaluation of SIMHYD Rainfall-Runoff Model Efficiency in Climate Change Conditions. jwmr. 9(17), 216-225. doi:10.29252/jwmr.9.17.216
URL: http://jwmr.sanru.ac.ir/article-1-816-en.html

Evaluation of SIMHYD Rainfall-Runoff Model Efficiency in Climate Change Conditions

Hadi Razaghian

, Kaka Shahedi

, Behrouz Mohseni

Abstract: (3542 Views)

Babolroud watershed and Mazandaran province of such as are that in recent years, different extreme events have been happened. On this basis, emphasize necessity to investigate further on impact of climate change on watershed runoff. This work is done by climate change and rainfall-runoff models that able to simulate and calculate of climate changes impact on hydrologic components Including precipitation, temperature, evapotranspiration and runoff. In this study, using this method, the data model HadCm3 general circulation of the atmosphere with the use of LARS WG model according to A2 (pessimist), B1(optimist) two scenarios for the time periods 2046-2065 and 2080-2099 be downscaled. Then predicted variables were introduced to SIMHYD rainfall-runoff model. The simulated daily runoff in the period 1982-2011, were selected the best period of calibration and verification with regard to the duration and optimizing statistical parameters and model sensitivity analysis process, in order to minimize the simulation error. The results showed a reasonable match of the runoff changes pattern between the observed and simulated. So that relatively high values of coefficients of determination (R2=0.73) and Nash-Sutcliffe (0.53) during calibration, validation, indicated the model efficiency to simulating common and minimal flow. The results, showed some changes in the average annual rate SIMHYD, +23 to +58 percent that the highest increase rate in October and November and the largest decline in July and August in the future years are. The situation of low rainfall months will be shift to more drought and rainy months toward the flooding.

Keywords: Sensitivity analysis, Simulation, HadCm3 Model, Nash-Sutcliffe

Full-Text [PDF 1820 kb] (1371 Downloads)

Type of Study: Research | Subject: هواشناسی
Received: 2017/06/19 | Revised: 2018/09/25 | Accepted: 2017/12/4 | Published: 2018/09/26

References

1. Akhavan, S., A. Rajabi and S. Shabanloo. 2013. Investigation of Climate Change and Effect on Runoff in Future Periods on the Shahrchay Watershed. 2th National Conference Climate Change and Effect on Agriculture and Environment, Center of Agriculture and Natural Resources Research of Azarbaijan-Gharbi Province, 9 pp (In Persian).

2. Arnell, N.W., M.B. Charlton and J.A. Lowe. 2014. The Effect of Climate Policy on the Impacts of Climate Change in the UK. Journal of Hydrology, 510: 424-435. [DOI:10.1016/j.jhydrol.2013.12.046]

3. Ashofteh, P.S. and A.R. Massah. 2012. Investigation of AOGCM Model Uncertainty and Emission Scenarios of Greenhouse Gases Impact on the Basin Runoff under Climate Change: Case study in Gharanghu Basin, East Azerbaijan. Iran-Water Resources Research, 8: 36-47 (In Persian).

4. Bakhtiari, B., Sh. Purmusavi and N. Sayari. 2015. Impact of Climate Change on Intensity-Duration-Frequency Curves of Precipitation: Case study in Babolsar station. Iranian Journal of Irrigation and Drainage, 8: 694-704 (In Persian).

5. Behmanesh, J., A. Jabari, M. Montaseri and H. Rezaei. 2014. Comparing AWBM and SYMHYD Models in Rainfall-Runoff Modeling: Case study in Nazlouchay Catchment in West Azarbijan. Geography and Environmental Planning Journal, 52: 39-42 (In Persian).

6. Chen, J., F.P. Brissette, D. Chaumont and M. Braun. 2013. Performance and Uncertainty Evaluation of Empirical Downscaling Methods in Quantifying the Climate Change Impacts on Hydrology over two North American River Basins. Journal of Hydrology, 479: 200-214. [DOI:10.1016/j.jhydrol.2012.11.062]

7. Gosain, A.K., S. Rao and D. Basuray. 2006. Climate Change Impact Assessment on Hydrology of Indian River Basins. Current Science, 90: 346-353.

8. Guardiola, M., P.A. Troch, D.D. Breshears, T.E. Huxman, M.B. Switanek, M. Durcik and N.S. Cobb. 2011. Decreased Stream flow in Semi-arid Basins Following Drought-Induced Tree Die-off: A Counter-Intuitive and Indirect Climate Impact on Hydrology. Journal of Hydrology, 406: 225-233. [DOI:10.1016/j.jhydrol.2011.06.017]

9. Hoseinzadeh-chahkandak, M. and S.M. Tabatabaei. 2015. Performance Comparison of SYMHYD and Sacramento Models in Run-off Simulation of Karaj Basin. 14th National Conference on Hydraulic, Department of Civil Engineering University of Sistan and Baluchestan, 13 pp (In Persian).

10. IPCC. 2010. Summary for Policy Makers Climate Change: The Physical Science Basis. Contribution of Working Group I to the fifth Assessment Report. Cambridge University Press, 881 pp.

11. Jabari, A., J. Behmanesh and A. Jabari. 2012. Modeling of Daily Water Balance in Basins by Method SIMHYD: Case Study in Nazlouchay Basin, 3th National Conference of Water Comprehensive Management, 11 pp (In Persian).

12. Kamal, A.R. and A.R. Massah Bavani. 2010. Climate Change and Variability Impact in Basin's Runoff with Interference of Tow Hydrology Models Uncertainty. Journal of Water and Soil, 24: 920-931 (In Persian).

13. Khosh ravesh, M., M. Raeini and E. Nikzad Tehrani. 2017. Application of Continuous Rainfall-Runoff HMS-SMA Model in Estimating Flood and Drought Frequencies of the Neka Basin under Climate Change A2 Emissions Scenario using HadCM3 Model. Journal ofWatershed Management Research, 14: 128-151 (In Persian). [DOI:10.29252/jwmr.7.14.140]

14. Kopytkovskiy, M., M. Geza and E. McCray. 2015. Climate-Change Impacts on Water Resources and Hydropower Potential in the Upper Colorado River Basin. Journal of Hydrology: Regional Studies 3: 473-493. [DOI:10.1016/j.ejrh.2015.02.014]

15. Kumar, A., R. Singh, P. Jena, C. Chatterjee and A. Mishra. 2015. Identification of the Best Multi-Model Combination for Simulating River Discharge. Journal of Hydrology, 525: 313-325. [DOI:10.1016/j.jhydrol.2015.03.060]

16. Leibundgut, C. and Ch. Külls. 2007. Rainfall Runoff Relationships of the Semiarid Kuiseb Basin Institut für Hydrologic der Albert-Ludwigs-Universität Freiburgi. Br Matti Gerspacher.

17. Li, F., Y. Zhang, X. Zongxue, T. Jin, C. Liud, W. Liua and F. Mpelasokab. 2013. The Impact of Climate Change on Runoff in the Southeastern Tibetan Plateau. Journal of Hydrology, 505: 188-201. [DOI:10.1016/j.jhydrol.2013.09.052]

18. Littlewood, L.G., R.T. Clarke, W. Collischonn and B.F.W. Croke. 2007. Predicting Daily Stream flow using Rainfall Forecasts, a Simple Loss Module and Unit Hydrographs: Two Brazilian catchments. Environmental Modeling and Software, 22: 1229-1239. [DOI:10.1016/j.envsoft.2006.07.004]

19. Motovilov, Y.G., L. Gottschalk, K. Engeland and A. Rohde. 1999. Validation of a Distributed Hydrological Model against Spatial Observations. Agricultural and Forest Meteorology, 98-99: 257-277. [DOI:10.1016/S0168-1923(99)00102-1]

20. Nash, J.E. and I.V. Sutcliffe. 1970. River Flow Forecasting Through Conceptual Models Part 1- a Discussion of Principles. Journal of Hydrology: 10: 282-290. [DOI:10.1016/0022-1694(70)90255-6]

21. Podger, G. 2005. RRL: Rainfall Runoff Library, USER GUIDE; CRC for Catchment Hydrology, Australia.

22. Pope, V.D., M.L. Gallani, P.R. Rowntree and R.A. Stratton. 92000. The impact of new physical parameterizations in the Hadley Centre climate model HadAM3. Climate Dynamics, 16: 123-146. [DOI:10.1007/s003820050009]

23. Sanikhani, H., M. Gohardoost and M. Sadeghi. 2016. The Impacts of Climate Change on Runoff of Ghareh-Chay Basin inMarkazi Province. Journal ofWatershed Management Research, 13: 12-22 (In Persian). Iran (In Persian). [DOI:10.18869/acadpub.jwmr.7.13.22]

24. Sarkar, J. and J.R. Chicholikar. 2016a. Future Climate Change Scenario at Hot Semi-arid Climate of Ahmedabad (23.04°N, 72.38°E), India Based on Statistical Downscaling by LARS-WG Model. Asian Journal of Water, Environment and Pollution, 13: 35-42. [DOI:10.3233/AJW-160005]

25. Semenov, M.A. and P. Stratonovitch. 2010. The use of Multi-model Ensembles from Global Climate Models for Impact Assessments of Climate Change. Climate Research, 41: 1-14. [DOI:10.3354/cr00836]

26. Silwal, G., R. Kayastha and P. Mool. 2016. Application of Temperature Index Model for Estimating Daily Discharge of Sangda River Basin, Mustang, Nepal. Journal of Climate Change, 2: 15-26. [DOI:10.3233/JCC-160002]

27. Surfleet, C.G. and D. Tullos. 2013. Variability in Effect of Climate Change on Rain-on-Snow Peak Flow Events in a Temperate Climate. Journal of Hydrology, 479: 24-34. [DOI:10.1016/j.jhydrol.2012.11.021]

28. Yaghoubi, M. and A.R. Massah Bavani. 2014. Temperature and Rainfall Simulation of Future Period on Azam-Harat River by Using Lars-WG Model. Climate Change Conference and a Way toward Sustainable Future, NGO of Earth Supporters Population, 10 pp (In Persian).

29. Zarghami, M., A. Abdi, I. Babaeian, Y. Hassanzadeh and R. Kanani. 2011. Impacts of Climate Change on Runoffs in East Azarbaijan, Iran. Journal of Global and Planetary Change, 1698: 1-10.

30. Zhang, X., D. Watersb and E. Robin. 2013. Evaluation of SYMHYD, Sacramento and GR4J Rainfall Runoff Models in Two Contrasting Great Barrier Reef Catchments, 20th International Congress on Modeling and Simulation. Adelaide, Australia, 1-6 December 2013, www.mssanz.org.au/modsim 2013.