Statistical Downscaling of Climatic Parameters using the XGBoost Model: A Study on Temperature and Relative Humidity in Arid Regions

Fouladi-Nasrabad, Mohamad; Pourreza-Bilandi, Mohsen; Amirabadizadeh, Mahdi; Javaheri, Mahna

doi:10.22077/jdcr.2025.9960.1166

Statistical Downscaling of Climatic Parameters using the XGBoost Model: A Study on Temperature and Relative Humidity in Arid Regions

نوع مقاله : مقاله پژوهشی

نویسندگان

Mohamad Fouladi-Nasrabad

Mohsen Pourreza-Bilandi

Mahdi Amirabadizadeh

Mahna Javaheri

Department of Water Engineering,Department, Faculty of Agriculture,University of Birjand, Birjand, Iran.

https://doi.org/10.22077/jdcr.2025.9960.1166

چکیده

Climate change and its effects on water resources and agriculture have made the accurate prediction of climatic parameters at the local scale even more necessary. General Circulation Models, due to their low spatial resolution, require downscaling for regional analyses. This study investigates the performance of the XGBoost machine learning model in the statistical downscaling of monthly mean temperature and relative humidity at the synoptic station of Qaen during the period from 1991 to 2015. In this research, the output of the GCM model, after correcting structural errors using the Chunk Mapping method,was used as input for the XGBoost model. The model's performance was evaluated using statistical criteria KGE, NSE, NRMSE, and R² in two phases: training and testing. The results indicated that the XGBoost model exhibited very good performance in downscaling mean temperature (with R² and NSE values close to one and low NRMSE in both phases) and acceptable performance for relative humidity. The model's stability in temperature simulation was evident, although there is a need for improvement in the model training process for relative humidity. The analysis of the distribution of simulated data showed that the model faces limitations in reproducing extreme temperature values (less than -10 degrees Celsius) and very high relative humidity (more than 80 percent), showing a greater tendency to simulate median temperature values. These findings are consistent with similar research in other parts of the world and confirm the high potential of XGBoost as an efficient tool in climate change studies, especially in arid regions.

کلیدواژه‌ها

Statistical Downscaling

XGBoost Model

Deep Learning Models

Climate Change

موضوعات

تغییراقلیم

1. Ali, S., Khan, S. D., Haq, M. U., Li, J., Virrantaus, K., & Chen, Y. (2023). Spatial downscaling of GRACE data based on XGBoost model for improved understanding of hydrological droughts in the Indus Basin Irrigation System (IBIS). Remote Sensing, 15(4), 873. https://doi.org/10.3390/rs15040873

2. Anandhi, A., Frei, A., Pierson, D. C., Schneiderman, E. M., Zion, M. S., Lounsbury, D., & Matonse, A. H. (2018). Examination of change factor methodologies for climate change impact assessment. Water Resources Research, 54(2), 1067-1086. https://doi.org/10.1002/2017WR021207

3. AkbariMotlagh., Ahmad A., Rezaei M., Rezvani Mahmoui A and Mousavi H. 2016, River Flow Prediction Using Artificial Neural Network System (Case Study of Shurqain River), International Conference on New Research Achievements in Civil Engineering, Architecture and Urban Planning. (in persian)

4. Chen, J., Brissette, F. P., Lucas-Picher, P., & Caya, D. (2020). Impacts of spatial resolution of global climate models on the statistical downscaling of precipitation. Climate Dynamics, 55(7-8), 1815-1837. https://doi.org/10.1007/s00382-020-05347-8

5. Chen, S., Wen, Z., Yang, P., Zhang, T., & Chen, J. (2022). Challenges and perspectives for high-resolution precipitation downscaling. Earth and Space Science, 9(11), e2022EA002453. https://doi.org/10.1029/2022EA002453

6. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM. https://doi.org/10.1145/2939672.2939785

7. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785

8. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.

9. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.

10. Daneshkhah, A., Ghorbani, M. A., Naganna, S. R., & Ghazvinian, P. H. (2020). Statistical downscaling of precipitation using machine learning techniques: A case study of Urmia Lake basin, Iran. Theoretical and Applied Climatology, 140(3-4), 1215-1231. https://doi.org/10.1007/s00704-020-03122-8

11. Dong, J., Zeng, W., Wu, L., Huang, J., Gaiser, T., & Srivastava, A. K. (2023). Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China. Engineering Applications of Artificial Intelligence, 117, 105579. https://doi.org/10.1016/j.engappai.2022.105579

12. El Htiti, M., Ouagabi, A., Lazaar, M., Bouziane, M., Hayani, A., & Guenoun, J. (2023). Machine-Learning-Based Downscaling of Hourly ERA5-Land Air Temperature over Mountainous Regions. Atmosphere, 14(4), 610. MDPI. https://www.mdpi.com/2073-4433/14/4/610

13. Fouladi Nasrabad, M., Amirabadizadeh, M. and Dastourani, M. (2024). Performance Evaluation of Two General Circulation Models for Downscaling Average Temperature in Birjand County. Integrated Watershed Management, 4(1), 30-45. doi: 10.22034/iwm.2024.2013786.1109

14. Ghahreman, B., Daneshvar, M. R. M., Zare, H., & Ebrahimi, M. (2022). Evaluating the performance of machine learning algorithms for seasonal precipitation downscaling in Iran. Water Resources Management, 36(1), 157-177. https://doi.org/10.1007/s11269-021-03004-5

15. Giri, R. K., Swain, S., Pingale, S. M., & Meshram, C. (2021). Statistical downscaling and projection of future temperature and precipitation using SVM, relevance vector machine and gaussian process regression over Narmada River basin, India. Stochastic Environmental Research and Risk Assessment, 35(6), 1189-1213. https://doi.org/10.1007/s00477-020-01949-x

16. Guo, J., Li, Y., Liu, H., Liu, Y., & Zhang, Y. (2022). Engineering Applications of Artificial Intelligence Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China.

17. Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance metrics: Implications for improving hydrological modelling. Journal of Hydrology, 377(1-2), 80-91.

18. Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., San Martín, D., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R., Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., ... & Cunillera, J. (2019). An intercomparison of statistical downscaling methods for projection of extreme precipitation in Europe. International Journal of Climatology, 39(9), 3751-3778. https://doi.org/10.1002/joc.5462

19. IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press.

20. Karimi, S., Ahmadi, F., & Massah Bavani, A. R. (2025, February 7). Assessing variations in meteorological parameters using global climate model (GCM) outputs and artificial neural networks. IWA Publishing. https://iwaponline.com/jwcc/article/16/2/531/107014/Assessing-variations-in-meteorological-parameters

21. Maraun, D., & Widmann, M. (2018). Statistical downscaling and bias correction for climate research. Cambridge University Press. https://doi.org/10.1017/9781107588783

22. Maraun, D., Huth, R., Gutiérrez, J. M., Martín, D. S., Dubrovsky, M., Fischer, A., Hertig, E., Soares, P. M. M., Bartholy, J., Pongracz, R., Widmann, M., Casado, M. J., Ramos, P., & Bedia, J. (2019). The VALUE perfect predictor experiment: evaluation of temporal variability. International Journal of Climatology, 39(9), 3786-3818. https://doi.org/10.1002/joc.5222

23. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282-290.

24. Niazkar, M., Menapace, A., Brentan, B., Piraei, R., Gonzalez, S., & Laudon, H. (2024). Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023). Environmental Modelling & Software, 174, 105971. https://doi.org/10.1016/j.envsoft.2024.105971

25. Nourani, V., Razzaghzadeh, Z., Baghanam, A. H., & Molajou, A. (2019). ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method. Theoretical and Applied Climatology, 137(3-4), 2111-2126. https://doi.org/10.1007/s00704-018-2722-5

26. Okkan, U., & Kirdemir, U. (2018). Statistical downscaling of monthly precipitation using linear regression, conditional metric-based models and spline interpolation. International Journal of Climatology, 38(5), 2421-2439. https://doi.org/10.1002/joc.5344

27. Parsa, M., Dehghani, M., Rezaei, M., & Klove, B. (2023). Downscaling precipitation using machine learning algorithms over Lake Urmia Basin, Iran. Water, 15(7), 1383. https://doi.org/10.3390/w15071383

28. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. https://doi.org/10.1038/s41586-019-0912-1

29. Sheikh Rabei, M. R., & et al. (2021). Comparison of the performance of SDSM and CCT models in climate change studies. Journal of Water and Climate Change, 12(1), 128-145.

30. Trigila, A., Iadanza, C., Bussettini, M., & Lastoria, B. (2015). Dissesto idrogeologico in Italia: pericolosità e indicatori di rischio (ISPRA - Rapporti 233/2015). ISPRA.

31. Wang, L., Chen, Y., & Yuan, Y. (2021). Support vector regression for precipitation downscaling in China's arid and semi-arid regions. Hydrological Processes, 34(11), 2517-2533.

32. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79-82.

33. Zare, H., Ghahreman, B., Daneshvar, M. R. M., & Ebrahimi, M. (2021). Performance assessment of support vector machine and artificial neural network models in statistical downscaling of daily precipitation (case study: Urmia Lake basin, Iran). Water Supply, 21(5), 2139-2155. https://doi.org/10.2166/ws.2021.008

34. Zhang, Y., Wu, Z., Liu, K., Lan, T., Chen, J., & Li, Z. (2023). Enhancing spatial resolution of GNSS-R soil moisture retrieval through XGBoost algorithm-based downscaling approach: A case study in the Southern United States. Remote Sensing, 15(18), 4576. https://doi.org/10.3390/rs15184576

35. Zhao, L., Feng, T., Li, X., Chen, S., & Zhang, J. (2022). Modelling Soil Temperature by Tree-Based Machine Learning Methods in Different Climatic Regions of China. Applied Sciences, 12(10), 5088. MDPI. https://www.mdpi.com/2076-3417/12/10/50