Journal of Advanced Informatics in Water, Soil, and Structure

Journal of Advanced Informatics in Water, Soil, and Structure

Assessment of LARS-WG and Change Factor Downscaling Models in Simulating Climate Variables (Case study: Golmakan station)

Document Type : Research Article

Authors
Mahtab Helmi
sepideh zeraati neyshabouri
Mostafa Yaghoubzadeh
Department of Water Engineering, University of Birjand, Birjand, Iran.
10.22048/wss.2024.440537.1008
Abstract
Today, climate change caused by the increase in greenhouse gases is considered one of the important global issues and has led to anomalies in the global climate system. Downscaling methods play a fundamental role in improving the accuracy of General Circulation model outputs (GCMs). Among these, statistical downscaling methods have more efficiency due to their easy and inexpensive calculations compared to dynamic downscaling methods and are used more. In this study, the results of two statistical downscaling models, LARS-WG and Change Factor (CF) or Delta, in simulating temperature and precipitation parameters under three emissions scenarios (RCP2.6, RCP4.5, and RCP8.5) shortly period (2021-2040) were considered based on observational data from the Golmakan synoptic station in the base period (1975-2005). To evaluate the accuracy of the mentioned methods in estimating the variables, statistical evaluation criteria such as Nash-Sutcliffe efficiency, Root Mean Square Error, and correlation coefficient were used. Ultimately, based on the research findings, the LARS-WG model showed less error in simulating minimum and maximum daily temperature and precipitation in the study area and performed better compared to the Change Factor method in climate prediction.
Keywords
Downscaling
GCM
Golmakan
Precipitation
Temperature

Subjects

  1. Bakhtiari, S., Mirahmadi, M., Golestani Kermani, S., & Bakhtiari, B. (2021). Identifying the trend of air and soil temperature changes using parametric and non-parametric tests at a station in the southeast of Iran. Journal of Meteorological Organization (Nivar), 45(112-113), 16-27.
  2. Barrow, E., & Yu, G. (2005). Climate scenarios for Alberta. A report prepared for the Prairie Adaptation Research Collaborative (PARC) in cooperation with Alberta Environment. Alberta Environment, Regina, Saskatchewan. (pp: 73).
  3. de Assis Paiva, D., & Sáfadi, T. (2021). Study of tests for trend in time series. Brazilian Journal of Biometrics, 39(2), 311-333.‏
  4. Eblaghian, A., Akhondali, A.M., Radmanesh, F., & Zarei, H. (2019). Trend Analysis of Temperature, Precipitation, and Relative Humidity Changes in Iran. Journal of Irrigation Sciences and Engineering, 42(3), 197-212.
  5. Eftekhari, M., Eslaminezhad, S. A., Ghaemi, M., Ghorani, M., & Akbari, M. (2023). Analyzing and predicting drought in arid and semi-arid regions by using atmospheric general circulation model and RCP scenarios. Journal of Hydrosciences & Environment, 7(13).
  6. Fenta Mekonnen, D., & Disse, M. (2018). Analyzing the future climate change of the Upper Blue Nile River basin using statistical downscaling techniques. Hydrology and Earth System Sciences, 22(4), 2391-2408.‏
  7. Gumus, B., Oruc, S., Yucel, I., & Yilmaz, M. T. (2023). Impacts of climate change on extreme climate indices in Türkiye driven by high-resolution downscaled CMIP6 climate models. Sustainability, 15(9), 7202.
  8. Hashemi, 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), 475-484.
  9. Helmi, M., Zeraati Neyshabouri, S., Amirabadizadeh, M., & Yaghoubzadeh, M. (2024). Evaluation of SDSM, LARS-WG, and ANN Methods in Down Scaling of Temperature and Precipitation for Two Different Climates. Journal of Drought and Climate Change Research, 1(4), 105-118.
  10. Jafary Godeaneh, M., Salajeghe, A., & Haghighi, P. )2020(. Forecast Comparative of Rainfall and Temperature in Kerman County Using LARS-WG6 Models. Iranian Journal of Ecohydology, 7(2), 529-538.
  11. Jahangir, M.H, Soltani, K., Nohegar, A., & Sadatinejad, S.J. )2019 (. Modeling and routing of surface evaporation from the Amir Kabir reservoir using the Mann-Kendall and neural network technology. Watershed Engineering and Management, 10(4), 635-644.
  12. Khazaei, S., Barati, R., Ghandehar, A., & Sadeghifard, M.R. )2019). Rainfall Trend Analysis using Innovative-Şen Method and Comparison with Traditional Methods (Case Study: Khorasan Razavi Province). Journal of Water and Sustainable Development, 6(1), 41-50.
  13. Li, B., Su, H., Chen, F., Wu, J., & Qi, J. )2013). The changing characteristics of drought in China from 1982 to 2005. Natural hazards, 68(2), 723-743.
  14. Mahmoudi, P., Khosravi, M., Masoodian, S.A., & Alijani, B. )2016 (. Studying the Trend of Changes in the Frequency of Days with Frost-pervasive and Semi-pervasive Conditions. Journal of Geography and Planning, 19(54), 303-327.
  15. Minville, M., Brissette, F., & Leconte, R. (2008). Uncertainty of the impact of climate change on the hydrology of a Nordic watershed. Journal of Hydrology, 358, 70-83.
  16. Nasiri, B., & Yarmoradi, Z. (2017). Predicting the Climatic Parameters Changes of Lorestan Province in the next 50 years using the HADCM3 model. Scientific- Research Quarterly of Geographical Data, 26(101), 143-154.
  17. Salehnia, N., Alizadeh, A., & Sayari, N. )2013(. Comparison of downscaling method LARS-WG & ASD in forecast rain and temperature in different climates. Journal of Irrigation and Drainage, 2, 233-245.
  18. Setoudeh, F. (2014). Statistical Analysis and Modelling of Annual Mean Temperature in Gorgan Station (1956-2005). Journal of Meteorological Organization (Nivar), 38(86-87), 73-83.
  19. Sharghi, E., Nourani, V., & Aghaei Lahroudi, F. )2017, September(. Evaluation of climate change effects on temperature and precipitation parameters of Tabriz station in 2020-2049 using LARS-WG model and change factor method. 16th Iranian Hydraulic Conference.
  20. Viner, D. (2012). Spatial Downscaling. Retrieved from http://www.cccsn.ec.gc.ca/?page=downscaling.
  21. Wilby, R. L., Dawson, C.W., & Barrow, E.M. (2002). SDSM- A Decision Support Tool for The Assessment of Regional Climate Change Impacts. Journal of Environmental Modelling and Software, 17, 147-159.
  22. Zahiri, Z., Ghazavi, R., Omidvar, E., & Davudirad, A. )2020(. Comparison of LARS-WG and SDSM Downscaling Models for Prediction Temperature and Precipitation Changes under RCP Scenarios. Arid Regions Geographic Studies, 11(40), 39-52.
  23. Zarfeshani, A., & Jahangir, M.H. (2021). The Isfahan values of Temperature and Precipitation Forecast are Based on Two Fine-scale models, LARS_WG and SDSM, and an Artificial Neural Network Method. Iranian Journal of Irrigation and Drainage, 15(1), 38-49.
Journal of Advanced Informatics in Water, Soil, and Structure
Volume 1, Issue 1 - Serial Number 1
January 2025
Pages 86-97

  • Receive Date 29 January 2024
  • Revise Date 15 June 2024
  • Accept Date 03 July 2024
  • First Publish Date 01 January 2025
  • Publish Date 01 January 2025