Simulation of Climatic Parameters using Statistical Microscale Models of SDSM and LARS in West Azerbaijan Province

Document Type : Research Article

Authors

1 Associate Professor, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran

2 M.S. graduate in Ecohydrology engineering, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran

10.22059/ije.2024.373803.1805

Abstract

Objective: The objective of this study is to evaluate the performance of the statistical downscaling models SDSM and LARS-WG in simulating minimum and maximum temperatures and precipitation at four stations (Urmia, Maku, Takab, and Mahabad) in West Azerbaijan province, with data from the periods 1987-2010 and 2020-2065.
Method: The study used the statistical downscaling models SDSM and LARS-WG to simulate temperature and precipitation variables at the selected stations. The observed data for the period 1987-2010 and the forecasted data for 2020-2065 were compared in both models. Additionally, to validate the SDSM model, the simulated parameters were compared with NCEP data and real observed data.
Results: The results showed that both models were more accurate in simulating temperature than precipitation. The SDSM model performed better in simulating daily temperature compared to LARS-WG, whereas the precipitation results from the LARS-WG model were slightly more accurate than those from the SDSM model. Additionally, the RMSE values for the SDSM and LARS-WG models for precipitation were 2.84 mm and 3.4 mm, respectively, while for maximum temperature, the RMSE values were 0.02°C and 0.29°C, respectively.
 
Conclusions: Based on the results, the SDSM model demonstrated higher accuracy in simulating both precipitation and temperature compared to the LARS-WG model. This model can be considered a reliable tool for predicting future changes in temperature and precipitation.

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Main Subjects

References

Alfieri, L. & Thielen, J. A. (2015). European precipitation index for extreme rain-storm and flash flood early warning. Meteorological Application, 22(1), 3–13.
Asakare, H. & Motalebizad, S. (2017). Comparing the performance of the SDSM models and those based on artificial neural networks in predicting the changes in minimum temperatures (station in case: urmia). The Journal of Spatial Planning and Geomatics, 21(4),140–60. (in Persian)
Baghanam, A. H., Eslahi, M., Sheikhbabaei, A. & Seifi, A. J. (2020). Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods. Theoretical and Applied Climatology, 141(3), 1135–50. (in Persian)
Bayatvarkeshi, M., Zhang, B., Fasihi, R., Adnan, R. M., Kisi, O. & Yuan, X. (2020). Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG. Water, 12(3), 666.
Dagnenet, M. & Disse, M. (2016). Analyzing the future climate change of Upper Blue Nile River Basin (UBNRB) using statistical down scaling techniques. Hydrology and Earth System Sciences Discussions, DOI: https://dx.doi.org/10.5194/hess-2016-543
Eingrüber, N. & Korres, W. (2022). Climate change simulation and trend analysis of extreme precipitation and floods in the mesoscale Rur catchment in western Germany until 2099 using Statistical Downscaling Model (SDSM) and the Soil & Water Assessment Tool (SWAT model). Science of The Total Environment, 838(1), 155775.
Habibzadeh, Y., Evazi, A. & Abdi, S. (2020). Evaluation of relevant areas of wheat cultivation with priority of reduction in water use in West Azerbaijan Province. Iranian Journal of Field Crop Science., 51(3), 205–20. (in Persian)
Hadi Ahmad, M., Abubakar, A., Ishak, Y. M., Shehu Danhassan, S., Jiahua, Z. & Alatalo, J. M. (2023). Modeling the influence of daily temperature and precipitation extreme indices on vegetation dynamics in Katsina State using statistical downscaling model (SDM). Ecological Indicators, 155, 110979.
Hassan, Z., Shamsudin, S. & Harun S. (2014). Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theoretical and Applied Climatology, 116(1), 243–57. https://doi.org/10.1007/s00704-013-0951-8
Hashmi, M. Z., Shamseldin, A. Y. & Melville, B. W. (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), 475–84.
Hasirchian, M., Zahabiyoun, B. & Khazaei, M. R. (2019). Assessment of SDSM model performance to investigate the effect of climate change on precipitation and temperature. Irrigation and Water Engineering, 9(2), 108–20. (in Persian)
Li, J., Li, Y., Zhang, T. & Feng, P. (2023). Research on the future climate change and runoff response in the mountainous area of Yongding watershed. Journal of Hydrology, 625, 130108.
Lotfi, M., Kamali, G. & Meshkatee, A. (2022). Varshavian V. Performance analysis of LARS-WG and SDSM downscaling models in simulating temperature and precipitation changes in the West of Iran. Modeling Earth Systems and Environment, 8, 1–11.
Phuong, D., Duong, N., Liem, T. & Nguen, D. (2020). Projections of Future Climate Change in the Vu Gia Thu Bon River Basin, Vietnam by Using Statistical DownScaling Model (SDSM), Water, 12(3), 755.
Roushangar, K. & Abdelzad, S. (2023). River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models. The Journal of Spatial Planning and Geomatics, 37(10), 3813–31. https://doi.org/10.1007/s11269-023-03527-8
Solomon, S. & Qin, D. (2007). Manning M, Chen Z, Marquis M, Averyt K, et al. IPCC fourth assessment report (AR4). Climate Change, 374.
Wilby, R. L., Dawson, C. W. & Barrow, E. M. (2002). sdsm — a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17(2), 145–57.
Wubneh, M. A., Worku, T. A., Fikadie, F. T., Aman, T. F. & Kifelew, M. S. (2022).  Climate change impact on lake tana water storage, upper blue nile basin, Ethiopia. Geocarto International, 37(25), 10278–300.
Zhai, X., Li, Y., Wang, H., Huang, G. & Li, Y. (2023). Assessment of the potential impacts of climate changes on Syr Darya watershed: A hybrid ensemble analysis method. Jurnal of Hydrolology, Regional Studies, 47, 101415.
 
Iranian journal of Ecohydrology
Volume 11, Issue 3
October 2024
Pages 374-394

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History

  • Receive Date: 23 July 2024
  • Revise Date: 18 August 2024
  • Accept Date: 27 August 2024
  • First Publish Date: 22 September 2024
  • Publish Date: 22 September 2024

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