Evaluation of SDSM, LARS-WG, and ANN Methods in Downscaling of Temperature and Precipitation for Two Different Climates

Helmi, Mahtab; Zeraati Neyshabouri, Sepideh; Amirabadizadeh, Mahdi; Yaghoobzadeh, Mostafa

doi:10.22077/jdcr.2023.6996.1049

Document Type : Original Article

Authors

¹ PhD Student in Water Resources, Department of Sciences and Water Engineering, University of Birjand, Birjand, Iran.

² PhD Student in Water Resources, Department of Sciences and Water Engineering, University of Birjand, Birjand, Iran

³ Associate Professor, Department of Water Engineering,Department and Research Group of Drought and Climate Change, Faculty of Agriculture,University of Birjand, Birjand, Iran.

https://doi.org/10.22077/jdcr.2023.6996.1049

Abstract

General Circulation Models are one of the important and practical models in regional scale climate change studies. Since the simulation of climate parameters in these models is done on a large spatial and temporal scale, their output is scaled down using different methods. In this study, the results of three downscaling models SDSM, LARS-WG and ANN has been compared in the climatic parameters of daily rainfall and minimum and maximum temperature in two stations of Taybad (hot and dry) and Qouchan (cold and moderate) in Khorasan Razavi province. For this purpose, the 20-year period of rainfall and minimum and maximum temperature data of the stations between 1986-2005 was used. To compare the accuracy of the models, the indicators of coefficient R2, RMSE and MAE were used. The results showed that LARS-WG and SDSM models have the least amount of error in simulating minimum and maximum daily temperature. The average MAE of the SDSM model error for the minimum temperature parameter in the two stations of Taybad and Qouchan is 0.61 and 0.71. The error value of the mentioned parameter in the LARS-WG model is 0.65 and 0.43 in the two stations of Taybad and Qouchan. On the other hand, in the ANN model, the absolute mean of the minimum temperature error is 1.78 in Taybad station and 1.73 in Qouchan station. Finally, SDSM and LARS-WG models have higher accuracy in simulating daily temperature climatic parameters and especially the minimum temperature parameter in Taybad and Qouchan stations compared to ANN model.

Keywords

Main Subjects

Climate change

References

Alizadeh, H. & Zahraei, B. (2014). Comparison of Statistical Downscaling Models in Simulation of the Daily Rainfall. The Sixteenth Conference of Iran Geophysics, 13 May 2014. Tehran, Iran. P.128-132. [In Persian]

Amirabadizadeh, M., Nazeri Tahroudi, M. & Zeynali, M.J. (2018). Evaluation of the Accuracy of Artificial Intelligence and Regression Models for the Simulation of Daily Temperature, Journal of Meteorology and Atmospheric Sciences, 1(1), 65-76. [In Persian]

Artlert, K., Chaleeraktrakoon, Ch. & Nguyen, V. (2013). Modeling and Analysis of Rainfall Processes in the Context of Climate Change for Mekong, Chi and Mun River Basins (Thailand), Hydro- environment Research, 7 (1), 2-17.

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, 21 (4), 140-160. [In Persian]

Ashraf, B., Mousavi Baygi, M., Kamali, G.A. & Davari. K. (2011). Prediction of Seasonal Variations of Climatological Parameters over Next 20 Years by Using Statistical Downscaling Method of HADCM3 Data (Case Study: Khorasan Razavi Province), Journal of Water and Soil, 25 (4), 940-952. [In Persian]

Bucchignani, E., Mercogliano, P., Panitz, H. & Montesarchio, M. (2018). Climate Change Projections for the Middle East–North Africa Domain with COSMO-CLM at Different Spatial Resolutions, Advances in Climate Change Research, 9 (1), 66-80.

Delghandi, M., Joorablou, S. & Ganji Nowroozi, Z. (2023). The impact of climate change on severity, duration and magnitude of drought using SPI and RDI in the Semnan region, Journal of Drought and Climate change Research, 1 (1), 1-18.

Duan, K. & Mei, Y. (2014). A comparison study of three statistical downscaling methods and their model-averaging ensemble for precipitation downscaling in China, Theoretical and Applied Climatology, 116, 707-719.

Erfanian, M., Ansari, H. & Alizadeh, A. (2012). Forecasting Precipitation and Temperature Using Artificial Neural Networks (Case Study: Mashhad Station), Journal of Arid Regions Geographic Studies, 3 (11), 53-73. [In Persian]

Ghasemifar, E., Alijani, B. & Salighe, M. (2016). Investigating the temperature changes of the southern shores of the Caspian Sea using three modelsو LARSWG, SDSM and Artificial Neural Network, Journal of Natural Geography, 9 (4), 23-41. [In Persian]

Goudarzi, M., Salahi, B. & Hosseini, S. A. (2016). Performance Assessment of LARS-WG and SDSM Downscaling Models in Simulation of Climate Changes in Urmia Lake Basin, Iranian Journal of Watershed Management Science and Engineering, 9 (31), 11-22. [In Persian]

Gulacha Metekiya, M. & Mulungu Deogratias, M.M. (2017) Generation of Climate Change Scenarios for Precipitation and Temperature at Local Scales Using SDSM in Wami-Ruvu River Basin Tanzania, Physics and Chemistry of The Earth. 100, 62-72.

Hashemi, M.Z., Shamsedin, 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, 475-484.

Hassan, H., Aramaki, T., Hanaki, K., Matsu, T. & Wilby, R.L. (1998). Lake Stratification and Temperature Profiles Simulated Using Downscaled GCM Output, Water Science and Technology, 38 (11), 217-226.

Hosseini, A., 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 (1), 1-16.

Helmi, M. & Shahidi, A. (2023). The using of SPI and SPEI indices in evaluating the effect of drought on quality of surface water resources (Case study: Kashafroud river), Journal of Drought and Climate change Research, 1 (1), 83-96.

Jafari Godeneh, M., Salajegh, A. & Haghighi, P. (2019). Forecast Comparative of Rainfall and Temperature in Kerman County Using LARS-WG6 Models, EcoHydrology, 7 (2), 529-538. [In Persian]

Jafarzadeh, A., Khashei-Siuki, A. & Shahidi, A. (2016). assessment of statistical downscaling methods LARS-WG & SDSM in forecast of climate parameter variation, Journal of Water and Soil conservation, 23 (4), 309-322. [In Persian]

Kilsby, C., Jones, P., Burton, A. & Ford, A. (2007). A daily weather generator for use in climate change studies, Environmental Modelling & Software, 22, 1705-1719.

Kundu, S., Khare, D. & Mondal, A. (2017). Individual and combined impacts of future climate and land use changes on the water balance, Ecological Engineering, 105, 42-57.

Maan, A. K., Jayadevi, D. A. & James, A. P. (2016). A survey of memristive threshold logic circuits, IEEE Transactions on Neural Networks and Learning Systems, 28 (8), 1734-1746

Naderi, S., Goodarzi, M. & Ghadmi Dehno, M. (2017). The Effect of Climate Change on Climatic Parameters in Seymareh Basin, Iranian Journal of Watershed Management Science and Engineering, 11 (39), 69-76. [In Persian]

Nazif, S., Karamouz, M., Falahi, M. & Rahimi Farahani, M. (2009). Long lead rainfall prediction using statistical downscaling and artificial neural network modeling, International Journal of Science & Technology, 16 (2), 166-172.

Ramezani Moghadam, J., Yaghoubzadeh, M. & Jafarzadeh, A. (2018). Examination of Feature Selection Methods for Downscaling of Daily Precipitation in Two Different Climates, Journal of Water and Soil, 32 (4), 831-848. [In Persian]

Refsgaard, J.C., Atnbjerg-Nielsen, K., Drews, M., Halsnӕs, K., Jeppesen, E., Madsen, H., Markandya, A., Olesen, J.E., Porter, J.R. & Christensen, J.H. (2013). The Role of Uncertainty in Climate Change Adaptation Strategies – A Danish Water Management Example, Mitigation and Adaptation Strategies for Global Change, 18 (3), 337-359.

Rezaei, M., Nohtani, M., Abkar, A., Rezaei, M. & Mirkazehi Rigi, M. (2014). Performance Evaluation of Statistical Downscaling Model (SDSM) in Forecasting Temperature Indexes in Two Arid and Hyper Arid Regions (Case Study: Kerman and Bam), Journal of Watershed Management Research, 5 (10), 117-131. [In Persian]

Salajegheh, A., Rafiei Sardoii, E., Moghadamnia, A., Malekian, Ar., Araghinejad, S., Khalighi Sigarodii, Sh. & Saleh Pourjam, A. (2017). Performance Assessment of LARS-WG and SDSM Downscaling Models in Simulation of Precipitation and Temperature, Iranian Journal of Soil and Water Research, 48 (2), 253-262. [In Persian]

Samadi, S., Ehteramian, K. & Sarraf, B.S. (2011). SDSM Ability in Simulate Predictors for Climate Detecting over Khorasan Province, Procedia-Social and Behavioral Sciences, 3, 741-749.

Sobhani, B., Eslahi, M. & Babaeian, I. (2017). Comparison Of Statistical Downscaling in Climate Change Models to Simulate Climate Elements in Northwest Iran, Physical Geography Research Quarterly, 49 (2), 301-325. [In Persian]

Tao, X.E., Chen, H., Xu, C.Y., Hou, Y.K. & Jie, M.X. (2015). Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China, Water Science and Engineering, 4, 273-281.

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 Modeling and Software, 17 (2), 145-157.

Wilks, D. (1992). Adapting stochastic weather generation algorithms for climate change studies, Climate Change, 22, 67-84.

Wilks, D.S. & Wilby, R.L. (1999). The Weather Generation game: a review of Stochastic Weather Models, Progress in Physical Geography, 23 (3), 329-357.

Yoosefdoost, I., Khashei-Siuki, A., Mohammadrezapour, O. & Tabari, H. (2021). Evaluating Performance of Four Statistical Downscaling Models (SDSM) of Precipitation and Temperature Data under the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) Scenario, Journal of Climate Research, 12 (45), 43-66. [In Persian]

Zarfeshani, A. & Jahangir, M.H., (2021). The Isfahan values of Temperature and Precipitation Forecast Based on Two Fine scale models LARS-WG and SDSM and Artificial Neural Network Method, Iranian journal of Irrigation and Drainage, 15 (1), 23-49. [In Persian]

Zulkarnian, H., Shamsudin, S. & Harun, S. (2014). Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature, Theoretical and Applied Climatology, 116 (1-2), 243-257.

Evaluation of SDSM, LARS-WG, and ANN Methods in Downscaling of Temperature and Precipitation for Two Different Climates

References

References

Volume 1, Issue 4 - Serial Number 4February 2024Pages 105-118

Volume 1, Issue 4 - Serial Number 4
February 2024
Pages 105-118