Nivar

Nivar

Evaluation of LARS-WG and SDSM model in investigating the effects of climate change on climatic variables in the south of Kerman province

Document Type : Original Article

Authors
Kamal Omidvar 1
Hasan Bahaddin Beigi 2
Forough Mohammadi Ravari 3
1 professor of climatology and faculty member of the Department of Geography, Yazd University, Yazd, Iran.
2 graduate of Master's degree in Climatology, Yazd University, Yazd, Iran.
3 Ph.D student of Climatology, Yazd University, Yazd, Iran.
10.30467/nivar.2025.487884.1312
Abstract
Abstract
This study aims to evaluate the performance of the LARS-WG and SDSM models in simulating the trends of extreme precipitation and temperature indices and their impact on climate change in the southern region of Kerman province from 1991 to 2021. In this research, extreme precipitation and temperature data were downscaled using the SDSM and LARS-WG models, considering the A1 and B2 scenarios, as well as four RCP scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) to assess future climate change in the study area. The results of the study showed that the maximum temperature at the Kahnuj and Jiroft stations has shifted from summer (June and July) to autumn (September and October). Additionally, the predictive models indicated a reduction in future temperature compared to the observational period, which may be due to increased cloud cover in the southern regions of the country and the use of models with lower Equilibrium Climate Sensitivity (ECS). Moreover, the forecasting models revealed that the LARS-WG model outperformed the SDSM model in simulating precipitation trends and minimum temperature, as indicated by its lower statistical error parameters. However, the SDSM model produced more accurate results in analyzing maximum temperature trends than the LARS-WG model. The overall projected temperature changes in both models and under both A1 and B2 scenarios indicate a general increase in temperature at all studied stations compared to the baseline period. The general trend of precipitation changes in the SDSM model showed an increase of 12.8 mm under scenario A1 and 12.9 mm under scenario B2. However, the LARS-WG model projected a decrease in precipitation at all studied stations compared to the baseline period. Among the four RCP scenarios, all except RCP2.6 projected a decrease in precipitation. Overall, the results suggest that the LARS-WG model provides better projections for precipitation, while the SDSM model performs better for temperature. Additionally, the findings indicate a decline in precipitation and an increase in extreme temperature in the southern region of the province, particularly a decreasing trend in annual precipitation. Consequently, the recurrence of drought events in this region can be attributed to decreasing precipitation, rising temperatures, and, to some extent, climate change.
Keywords
"Climate change"
"south of Kerman province"
"extreme precipitation and temperature profiles"
"SDSM model"
Subjects
1.    Al Faraj, F., Scholz, M., & Tigkas, D. (2014). Sensitivity of surface runoff to drought and climate change: Application for shared river basins. Water, 6(10), 3033–3048.
2.    Babaeian, I., Najafi Nik, Z., Zabol Abbasi, F., Habibi Nokhandan, M., Adab, H., & Malbousi, S. (2009). Evaluation of climate change in Iran for the period 2010–2039 using downscaled data from the ECHO G general circulation model. Geography and Development, 7(16), 135–152. (in Persian)
3.    Canadian Climate Change Scenarios Network (CCCSN). (2012). Environment Canada climate scenarios. Retrieved from http://www.cccsn.ec.gc.ca/?page=pred-hadcm3
4.    Ebrahimi, H. (2005). Investigation of the impact of climate change on agricultural water demand in Mashhad Plain (Doctoral dissertation, Islamic Azad University, Science and Research Branch). (in Persian)
5.    Gavrilov, M. B., Tosić, I., Marković, S. B., Unkašević, M., & Petrović, P. (2016). Analysis of annual and seasonal temperature trends using the Mann Kendall test in Vojvodina, Serbia. Időjárás, 120(2), 183–198.
6.    Gupta, S., Gupta, A., Himanshu, S. K., & Singh, R. (2020). Analysis of extreme rainfall events over the upper catchment of Sabarmati River Basin in western India using extreme precipitation indices. Theoretical and Applied Climatology, 142(3–4), 1091–1105.
7.    Halabian, A. H., & Keykhosravi Kiani, M. S. (2020). Evaluation of extreme precipitation indices in Iran. Quarterly Journal of Spatial Planning (Geography), 10(4), 24–45. (in Persian)
8.    Ilderami, G., Nouri, H., & Karami, M. (2016). Evaluation of drought and climate change in the coming period using atmospheric general circulation models: Case study of the Gorgan River–Qarasu watershed, Iran. Quarterly Journal of Geographical Studies of Arid Regions, 7(26), 67–82. (in Persian)
9.    IPCC. (2007). Climate change 2007: The physical science basis. Cambridge University Press.
10.    Jones, P. D. (1999). Surface air temperature and its variations over the last 150 years. Reviews of Geophysics, 37(2), 173–199.
11.    Jones, P. D., Horton, E. B., Folland, C. K., Hulme, M., Parker, D. E., & Basnett, T. A. (1999). The use of indices to identify changes in climatic extremes. Climatic Change, 42(1), 131–149.
12.    Karl, T. R., Knight, R. W., & Baker, B. (2000). The record breaking global temperatures of 1997 and 1998: Evidence for an increase in the rate of global warming? Geophysical Research Letters, 27(5), 719–722.
13.    Mohammadi, Y. (2020). Trend analysis and prediction of precipitation and temperature at selected stations in Fars Province using general circulation models (Master’s thesis, University of Yazd). (in Persian)
14.    Molina Navarro, E., Trolle, D., Martínez Pérez, S., Sastre Merlín, A., & Jeppesen, E. (2014). Hydrological and water quality impact assessment of a Mediterranean reservoir under climate change and land use management scenarios. Journal of Hydrology, 509, 354–366.
15.    Nury, A. H., & Alam, M. J. B. (2013). Performance study of global circulation model HADCM3 using SDSM for temperature and rainfall in north eastern Bangladesh. Journal of Scientific Research, 6(1), 87–96.
16.    Omidvar, K., & Khosravi, Y. (2010). Investigation of changes in selected climatic elements along the northern coasts of the Persian Gulf using the Mann Kendall test. Geography and Environmental Planning, 21(2), 33–46. (in Persian)
17.    Omidvar, K., & Salari, H. (2013). Trend analysis of temperature and precipitation changes in western and northwestern Iran using parametric and nonparametric methods. Journal of Geography and Environmental Planning, 11(37), 271–288. (in Persian)
18.    Pińskwar, I., Choryński, A., Graczyk, D., & Kundzewicz, Z. W. (2019). Observed changes in extreme precipitation in Poland: 1991–2015 versus 1961–1990. Theoretical and Applied Climatology, 135(1–2), 773–787.
19.    Qanqermeh, A., & Roshan, G. (2012). Prospects of the effects of global warming on temperature changes in northwestern Iran. Geography and Development, 10(26), 183–198. (in Persian)
20.    Rezaei, M., Nahtani, M., Abkar, A., Rezaei, M., & Rigi, M. (2014). Evaluation of the efficiency of the statistical downscaling model SDSM in predicting temperature parameters in arid and hyper arid climates. Watershed Management Research, 5(10), 117–131. (in Persian)
21.    Safari, M. (2021). Trend analysis of extreme precipitation indices in Fars Province (Master’s thesis, University of Yazd). (in Persian)
22.    Sadeghat Zadeh, Z., Tashkaryan, V., Mobasheri, M. H., Homayouni, M. S., Forough Bakhsh, A., Mansouri, A., & Sepehrimanesh, M. (2013). Fars Province studies (4th ed.). Iran Schoolbook Publishing Company. (in Persian)
23.    Tank, A. K., Wijngaard, J. B., Können, G. P., Böhm, R., Demarée, G., Gocheva, A., … Heino, R. (2002). Daily dataset of 20th century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology, 22(12), 1441–1453.
24.    Wang, X., Yang, T., Shao, Q., Acharya, K., Wang, W., & Yu, Z. (2012). Statistical downscaling of extremes of precipitation and temperature and construction of their future scenarios in an elevated and cold zone. Stochastic Environmental Research and Risk Assessment, 26(3), 405–418. 

  • Receive Date 09 November 2024
  • Revise Date 26 March 2025
  • Accept Date 07 April 2025
  • Publish Date 21 March 2026