Assessment of Single site versus Multi-site Downscaling Methods on Estimation of Rainfall Extreme Values

Document Type : Research Article


1 M.Sc. Student, Department of Civil Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran

2 Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran


Extreme weather conditions have an important role on strategic planning of water resource and developing adaptation plans and natural disaster management. Therefore, it is necessary to present a detailed perspective of upcoming extreme patterns of rainfall events. In the context of climate change, pattern extraction of extreme events can only be achieved by using of daily downscaling methods. In the current paper, two single site downscaling methods SDSM, DMDM and a multisite approach based on Singular Value Decomposition (SVD) technique are used and their results are evaluated. The case study is located on Tehran province with over 10 precipitation and synoptic stations in the period 1985 to 2005. The used climate change scenarios were generated for (2021-2050) period. In addition, the daily NCEP/NCAR dataset and results of climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) were achieved from the Canadian Centre for Climate Modeling and Analysis.
For each downscaling models, based on their own concepts, suitable predictors have been selected via backward stepwise regression as a preprocessing step (Hessami et al. 2008). The implemented multisite approach is based on combination of two multiple regression models to simulate precipitation amount and occurrence and also using SVD to capture stochastic behavior of precipitation to preserve accurately the space–time statistical properties of daily precipitation (Khalili and Nguyen, 2017). Beside the SDSM as a regression based downscaling method (Wilby et al. 2002), DMDM as a regression based tool box including Multiple Linear Regression (MLR), Ridge Regression (RR), Multivariate Adaptive Regression Splines (MARS) and Model Tree (MT) have been used as well (Tavakol et al. 2013b).
To achieve the goal of the current research, temporal downscaling method to simulate extreme precipitation values is needed. In this regard, numerical model based on scaling invariant concept is used to do temporal downscaling (Nguyen et al. 2007). The sub daily extreme rainfall, are estimated from daily downscaled rainfalls by analyzing the non-central moments of observed rainfalls, single time regime (from 6 h to 24 h) and using scaling factor. Finally, as the major output of this study, Intensity Duration Frequency (IDF) curve is calculated affected by climate change in the period 2020 to 2050. The results based on statistical assessment both in calibration and validation periods of daily precipitation show the effectiveness of SDSM and DMDM models, respectively, in terms of long-term monthly average, and multisite model in preserving the trend of computed information in comparison with observed values. Based on uncertainty assessments results, DMDM provided the most precsion results versus the other methods over the study area. In addition, the models performance rank in estimating unseen station belong to the DMDM and multisite and SDSM methods, respectively. In the second step, quantities of IDF curves for return periods of (2-100) years and durations of (6, 12, 24 hour) at Mehrabad station are estimated using the results of coupled three different spatial downscaling and GEV distribution. Results show higher accuracy of DMDM and SDSM models respectively in comparison with multisite model. Based on the linear structure of SDSM and Multisite downscaling models versus the complex structure of DMDM, it seems that limitations of linear methods cause DMDM to be superior to the other ones. In addition, evaluation of the results of extreme values by three different climate change scenarios based on the DMDM downscaling model indicates an increase in rainfall intensity using scenario RCP8.5 and a decrease under scenarios RCP4.5 and RCP2.6.


Main Subjects

اوجی، ر.، 1392، تحلیل‌عدم‌ قطعیت روش‌های‌ تک‌ایستگاهی وچندایستگاهی در ریزگردانی مقادیر حدی دما وبارش، رساله دکتری، دانشگاه تربیت مدرس.
اوجی، ر.، 1396، مقایسه ریزگردانی تک ایستگاهی و چندایستگاهی فرین‌های دما و بارش (مطالعه موردی: سواحل جنوبی دریای خزر)، م. فیزیک زمین و فضا، 44 (2)، 397-410.
پهلوان، ح.، 1392، توسعه روش‌های مقیاس کاهی آماری با هدف ارزیابی اثرات نغییر اقلیم بر رویدادهای حدی بارش، پایان نامه کارشناسی‌ارشد، دانشکده فنی دانشگاه تهران.
Alaya, M. B., Ouarda, T. and Chebana, F., 2018, Non-Gaussian spatiotemporal simulation of multisite daily precipitation: downscaling framework. Climate dynamics, 50, 1-15.
Bates, B. C., Charles, S. P. and Hughes, J. P., 1998, Stochastic downscaling of numericalclimate model simulations. Environmental Modelling & Software, 13, 325-331.
Binesh, N., Niksokhan, M. H. and Sarang, A., 2018, A Study of Rainfall and Urban Runoff Flow Regime under Future Climate Condition (Case Study: West Flood-Diversion Catchment in Tehran). Amirkabir Journal of Civil Engineering, 5(11), 815–826.
Bürger, G., Murdock, T. Q., Werner, A. T., Sobie, S. R. and Cannon, A. J., 2012, Downscaling extremes—An intercomparison of multiple statistical methods for present climate. Journal of Climate, 25(12), 4366-4388.
Chandler, R. E. and Wheater, H. S., 2002, Analysis of rainfall variability using generalized linear models: a case study from the west of Ireland, Water Resources Research, 38(10), 10-1.
Fowler, H. J., Blenkinsop, S. and Tebaldi, C., 2007, Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(8), 1547-157.
Herath, H., Sarukkalige, P. R. and Nguyen, V., 2015, Downscaling approach to develop future sub-daily IDF relations for Canberra Airport Region, Australia. Proceedings of the International Association of Hydrological Sciences, 369, 147-155.
Hessami, M., Gachon, P., Ouarda, T. B. and Sthilaire, A., 2008, Automated regression-based statistical downscaling tool. Environmental Modelling & Software, 23, 813-834.
Huth, R., 1999, Statistical downscaling in central Europe: evaluation of methods and potential predictors. Climate Research, 13, 91-101.
Jeong, D. I., Sthilaire, A., Ouarda, T. B. and Gachon, P., 2012, Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator. Climatic Change, 114, 567-591.
Khalili, M. and Van Nguyen, V. T., 2017, An efficient statistical approach to multi-site downscaling of daily precipitation series in the context of climate change. Clim. Dyn. 49, 2261–2278.
khalili, M., Van Nguyen, V. T. and Gachon, P., 2013, A statistical approach to multi‐site multivariate downscaling of daily extreme temperature series. International Journal of Climatology, 33, 15-32.
Khan, M. S., Coulibaly, P. and Dibike, Y., 2006, Uncertainty analysis of statistical downscaling methods. Journal ofHydrology, 319, 357-382.
Koutsoyiannis, D. and Xanthopoulos, T., 1990, A dynamic model for short-scale rainfall disaggregation. Hydrological Sciences Journal, 35, 303-322.
Li, X. and Babovic, V., 2018, Multi-site multivariate downscaling of global climate model outputs: an integrated framework combining quantile mapping, stochastic weather generator and Empirical Copula approaches. Climate Dynamics, 52, 5775-5799.
Menabde, M., Seed, A. and Pegram, G., 1999, A simple scaling model for extreme rainfall. Water Resources Research, 35, 335-339.
Nguyen, V. T. V., Nguyen, T. D. and Cung, A., 2007, A statistical approach to downscaling of sub-daily extreme rainfall processes for climate-related impact studies in urban areas. Water science and technology: water supply, 7(18) , 192-198.
Pahlavan, H. A., Zahraie, B., Nasseri, M. and Varnousfaderani, A. M. 2018, Improvement of multiple linear regression method for statistical downscaling of monthly precipitation. International journal of environmental science and technology, 15(9), 1897-1912.
Rodriguez Iturbe, I., Cox, D. R. and Isham, V., 1987, Some models for rainfall based on stochastic point processes. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 410, 269-288.
Seguí, P. Q., Ribes, A., Martin, E., Habets, F. and Boe, J., 2010, Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins. Journal of hydrology, 383, 111-124.
Singh, M. P., 2018, Efficient Multi-site Statistical Downscaling Model for Climate Change. Motilal Nehru National Institute Of Technology Allahabad.
Tavakol-Davani, H., Nasseri, M., Tavakol-Davani, H. and Esmaeili, N., 2013a, Uncertainty Evaluation of Climate Change Scenarios Based on Ensemble Prediction, 6th International Perspective on Water Resources and the Environment, Izmir, Turkey.
Tavakol-Davani, H., Nasseri, M. and Zahraie, B., 2013b, Improved statistical downscaling of daily precipitation using SDSM platform and data‐mining methods. International Journal of Climatology, 33, 2561-2578.
Tavakolifar, H., Shahghasemi, E. and Nazif, S., 2017, Evaluation of climate change impacts on extreme rainfall events characteristics using a synoptic weather typing-based daily precipitation downscaling model. Journal of Water and Climate Change, 8, 388-411.
Vandal, T., Bhatia, U. and Ganguly, A. R., 2017, Statistical Downscaling in Climate with State-of-the-Art Scalable Machine Learning, In Large-Scale Machine Learning in the Earth Sciences, Chapman and Hall/CRC, 55-72.
Wilby, R. L. and Dawson, C. W., 2013, The statistical downscaling model: insights from one decade of application. International Journal of Climatology, 33, 1707-1719.
Wilby, R. L., Dawson, C. W. and Barrow, E. M., 2002, SDSM—a decision support tool for the assessment of regional climate changeimpacts. Environmental Modelling & Software, 17, 145-157.
Wilby, R. L., Wigley, T., Conway, D., Jones, P., Hewitson, B., Main, J. and Wilks, D., 1998, Statistical downscaling of general circulation model output: A comparison of methods. Water resources research, 34, 2995-3008.
Wilks, D., 1998, Multisite generalization of a daily stochastic precipitation generation model. Journal of Hydrology, 210, 178-191.
Xu, C. Y., 1999, From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches. Progress in physical Geography, 23, 229-249.