Evaluation and comparison of performance of SDSM and CLIMGEN models in simulation of climatic variables in Qazvin plain

Authors

1 Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Faculty of Soil Science Engineering, Shahid Bahonar University of Kerman, kerman, iran.

Abstract

Climate change is found to be the most important global issue in the 21st century, so to monitor its trend is of great importance. Atmospheric General Circulation Models because of their large scale computational grid are not able to predict climatic parameters on a point scale, so small scale methods should be adapted. Among downscaling methods, statistical methods are used as they are easy to run. Two famous models, ClimGen and SDSM, were studied for daily total precipitation and temperature data in Qazvin station. For this purpose, three steps of models calibration, verification and simulation, in Qazvin station were performed and model performances in terms of similarities in produced data with those using parameters such as root mean square error (RMSE), coefficient of determination (R2) and Nash coefficient (NSE) were assessed. The results in climatic range showed that Climgen outperform in rainfall data generation while SDSM outperforms in simulating average temperatures. However, both models have high potential to simulate temperature and precipitation.

Keywords

References

Abassi, F., S. Malbusi, I. Babaeian, M. Asmari, R.  Borhani, 2010. Climate change prediction of south Khorasan Province During 2010-2039 by using statistical Downscaling of ECHO-G Data. Journal of Water and Soil. 24(2); 218-233.
Abkar, A., M. Habibnajad., K. Soleimani., H. Naghavi, 2013. Investigation efficiency SDSM model to simulate temperature indexes in arid and semiarid regions. Journal of Irrigation and Water Engineering. 4(14); 1-17.
Babaeian, I., Z. Najafi Nik., F. Zabol-Abbasi., M. Habibi- Nokhandan, H. Adab., & S. Malbusi, 2009. Climate change assessment over Iran using Statistical downscaling of ECHO-G outputs during 2010-2039. Iranian Journal of Geography and Development, 7; 135-152.
Bazrafshan J., Khalili A., Horfar A.F., Torabi PeletKalleh S., Hejam S. Comparison of the Performance of ClimGen and LARS-WG Models in Simulating the Weather Factors for Diverse Climates of Iran. Iran-Water Resources Research. 5(1); 44-57.
Chen, H., C. Xu., S. Guo, 2012. Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of hydrology. 434; 36-45.
Cheng, C., S. Li., G. Li., H. Auld, 2008. Statistical downscaling of hourly and daily climate scenarios for various meteorological variables in South-central Canada. Theoretical and Applied Climatology, 91(1-4); 129-147.
Etemadi, H., S. Samadi, M. Sharifikia, 2014. Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland. Climate dynamics, 42(11-12); 2899-2920.
Farzaneh, M., S. Samadi., A. Akbarpour, S. Eslamian, 2011. The introduction of small-scale predictors selected for statistical-regression in the sub-basin of Northern Karun Beheshtabad. First Conference Applied Research on Water Resources of Iran, Kermanshah, Kermanshah University.
Goodarzi, M., S. Jahanbakhsh., M. Rezaee., A. Ghafouri, M.H. Mahdian, 2011. Assessment of climate change statistical downscaling methods in a single site in Kermanshah, Iran. American-Eurasian Journal of Agricultural and Environmental Science, 6(5); 564-572.
Goyal, MK., CSP. Ojha, 2012. Downscaling of precipitation on a lake basin: evaluation of rule and decision tree induction algorithms. Hydrol Res 43(3); 215–230.
Harpham, C., R. L. Wilby, 2005. Multi-site downscaling of heavy daily precipitation occurrence and amounts. Journal of Hydrology, 312(1); 235-255.
Hashmi, M., A.­Y. Shamsedin, B.W. Melville, 2011. Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stochastic Environmental Research and Risk Assessment, 25(4); 475-484.
Hejarpour A., Yousefi M., Kamkar B. 2014. Evaluation of LARS-WG,WeatherMan and CLIMGEN models for simulating climatic parameters in three different climate (Gorgan, Mashhad, Gonbad), Geography and development. 12(35); 201-216.  
Holden, N. M., A. J. Brereton., R. Fealy, J. Sweeney,  2003. Possible change in Irish climate and its impact on barley and potato yields.Agricultural and Forest  Meteorology, 116(3); 181-196.
Intergovernmental Panel on Climate Change (IPCC), 2007. Working Group III Report, Mitigation of Climate Change, Chapter6, Residential and commercial buildings. M. Levine (USA) and D. U¨rge-Vorsatz (Hungary), coordinating lead authors. Geneva, Switzerland: Intergovernmental Panel on Climate Change.
Kabiri, R., V. R. Bai, A. Chan, 2015. Assessment of hydrologic impacts of climate change on the runoff trend in Klang Watershed, Malaysia. Environmental Earth Sciences. 73(1); 27-37.
Kou, X., Ge, J., Wang, Y., Zhang, C (2007). Validation of the weather generator CLIMGEN with daily precipitation data from the Loess Plateau, China. Journal of Hydrology. 347.
Mahdizadeh, S., M. Meftah halghi, A. Mosaedi, S. Seyyed Ghasemi, 2011. Study of precipitation variation due to climate change (Case study: Golestan dam basin). Journal of Water and Soil Conservation. 18(3); 1-17.
McKague K., R. Rudra, J. Ogilvie, I. Ahmed, B. Gharabaghi, 2005. Evaluation of Weather Generator ClimGen for Southern Ontario. Canadian Water Resources Journal. 30(4); 315–330.
Rezaee Zaman, M., S. Morid, M. Delavar, 2013. Evaluate the effects of climate change on Hydro climatologic variables in Siminerood basin. Journal of Water and Soil. 27(6); 1247-1259.
Samadi, S., W. CAME, H. Moradkhani, 2013. Uncertainty analysis of statistical downscaling models using Hadley center coupled model. Theory Appl Climatol .114; 673-690.
Taei Semiromi, S., H.M. Moradi., M., Khodagholi, 2014, Simulation and Prediction of Climate Variables by Multiple Linear Models SDSM and Global Circulation Models in Bar Neyshabour watershed, Iran, in preparation, 16 pages.
Tatsumi, K., T. Oizumi, Y. Yamashiki, 2013. Introduction of daily minimum and maximum temperature change signals in the Shikoku region using the statistical downscaling method by GCMs. Hydrological Research Letters, 7(3); 48-53.
Wilby, R. L., C. W. Dawson, E.M. Barrow, 2002. SDSM—a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17(2); 145-157.
Wilby, R. L., W.C. Dawson, 2007. SDSM 4.2- A decision support tool for the assessment of regional climate change impacts, SDSM manual version 4.2, Environment Agency of England and Wales: 94pp.
Yano, T., M, Aydin, T. Haraguchi, 2007. Impact of climate change on irrigation demand and crop growth in a Mediterranean environment of Turkey. Sensors. 7(10); 2297-2315.
Zhang, X.C., 2003. Evaluation of CLIMGEN precipitation parameters and their implication on WEPP runoff and erosion prediction. Transactions of  American Society of Agricultural Engineers.46.
Zhang, X. C., M. A. Nearing, 2005. Impact of Climate change on Soil erosion, runoff, and wheat Productivity in Central Oklahoma. Catena. 61; 185-195.
Zhang, X.C., W.Z. Liu., J. Chen, 2011. Trend and uncertainty analysis of simulated climate change impacts with multiple GCMs and emission scenarios. Agricultural and Forest Meteorology. 151; 1297-1304.
Zulkarnain, H., S. Shamsudin, S. Harun, 2014. Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theoretical and applied limatology 116; 243-257. 

Files

Share

How to cite

Statistics