An evaluation of single-site and multi-site statistical downscaling of SDSM–DC in terms of indices of climate extremes (Case study: Midwest of Iran)

Authors

1 Assistant professor of Climatology / University of Guilan

2 Tarbiat Modares University

3 University of Tehran

Abstract

Two single and multi-site statistical downscaling methods of Statistical Downscaling Model–Decision Centric (SDSM–DC) for daily temperature and precipitation are evaluated at nine stations located in the mountainous region of Iran’s Midwest. SDSM is best described as a single-site model, but it can be extended to multi-site applications via conditional resampling (CR-SDSM, Wilby et al. 2003; Harpham and Wilby 2005).
SDSM–DC (Wilby and Dawson 2013) is a hybrid of the stochastic weather generator and transfer function methods. Predictor selection is based on empirical relationships between GCM-scale predictors and single-site predictand variables. (Farajzadeh et al. 2015).
Applying SDSM to multi-site daily rainfall downscaling includes two steps: (1) the daily rainfall and temperature at a “marker” site (in this study, the area average amounts) is first downscaled by the single-site SDSM; (2) Daily rainfall amounts are then “resampled from the empirical distribution of area averages, conditional on the large-scale atmospheric forcing and the stochastic error term. The actual daily rainfall is determined by mapping the modeled normal cumulative distribution value onto the observed cumulative distribution of amounts at the marker site” (Wilby et al. 2003; Liu et al. 2013). Ultimately, the marker site rainfall is resampled to the constituent amount falling on the same day from each station in the multi-sites array (Harpham and Wilby 2005). Thus, if the marker series is based on an unweighted average of all sites, the conditional resampling will preserve both the areal average of the marker series and the spatial covariance of the multi-site rainfall (Wilby et al. 2003). Additionally, using area average, instead of individual sites as the marker series, reduces the risk of employing a nonhomogeneous/non-representative record and increases the signal to noise ratio of the predictand (Wilby et al. 2003; Liu et al. 2013). To downscale temperature, the same steps are applied but unconditionally using transfer function methods.
For statistical downscaling, two sets of data are generally required: (1) observational data for model calibration and validation, as predictands; and (2) synoptic-scale climate data from GCM and/or reanalysis, as predictors. In order for a better assessment of climate variability and change on local and regional scale, long-term time series of reliable climate data at fine-scale resolution are required (Vincent et al 2002; Mekis and Vincent 2011; Menne et al 2012). As mentioned before, for the Midwest of Iran, we selected nine synoptic stations with nearly complete data coverage for 1981–2010.
We used station data from two decades (1981–2000) for calibration and from one decade (2001–2010) for validation of daily values of minimum and maximum temperature, and total daily precipitation. To assess the accuracy and homogeneity of the observational data, we used different methods for quality control: the R packages RHtestsV3 (Wang and Feng 2010) and RHtests_dlyPrcp (Wang et al. 2010), based on penalized maximal t and F tests (Wang et al. 2007; Wang 2008b) that are embedded in a recursive testing algorithm (Wang 2008a); the R package Climatol (Guijarro 2012), which applies a type II linear regression model; and SDSM (Wilby and Dawson 2012, 2013) based on reanalysis predictor variables. Missing values are filled in by using the sequential k-nearest neighbor imputation method (Kim and Yi 2008) and homogeneity tests are applied both before and after infilling to assess infilling performance.
Predictor fields are extracted from the National Centers for Environmental Prediction (NCEP) Reanalysis (Kalnay et al. 1996) archives at resolutions of 2.5°×2.5°. As mentioned earlier, SDSM has its own methodology for predictor selection in which EOFs of NCEP reanalysis data over the domain (30° N, 42° E) and (40° N, 52° E) are screened separately for temperature and for precipitation. (Farajzadeh et al. 2015)
Results indicated that the methods are of widely varying complexity, with input requirements that range from single point predictors of temperature and precipitation to multivariate synoptic-scale fields. The period 1981-2000 is used for model calibration and 2001–2010 for validation, with performance assessed in terms of 27 Climate Extremes Indices (CLIMDEX). The sensitivity of the methods to large-scale anomalies and their ability to replicate the observed data distribution in the validation period are separately tested for each index by Pearson correlation and Kolmogorov–Smirnov (KS) tests, respectively. Combined tests are used to assess overall model performances. Single (multi)-site method of SDSM, passing 76%(81%), 16%(7%) and 14% (5%) of the Kolmogorov–Smirnov (KS), the Pearson correlation and the combined tests, performed well in terms of temperature and precipitation downscaling. Single-site method performed better than multi-site one at single sites; however, multisite method performance is better at regional downscaling. Correlation tests were passed less frequently than KS tests. Both methods downscaled temperature indices better than precipitation indices. Some indices, notably R20, R25, SDII, CWD, and TNx, were not successfully simulated by any of the methods. Model performance varied widely across the study region.

Keywords

Main Subjects


اوجی ر.، 1392، تحلیل عدم‌قطعیت روش‌های تک‌ایستگاهی و چندایستگاهی در ریزگردانی مقادیر حدی دما و بارش. رساله دکتری، دانشگاه تربیت مدرس.
جان‌آبکار ع.، حبیب‌نژاد، م.، سلیمانی، ک. و نقوی، ه.، 1393، حساسیت مدل ریز مقیاسنمایی SDSM به داده‌های بازتحلیل شده در مناطق خشک، دو فصلنامه علمی-پژوهشی خشک‌بوم، جلد چهارم، شماره دوم، 11-27.
جان‌آبکار ع.، حبیب‌نژاد، م.، سلیمانی، ک. و نقوی، ه.، 1392، بررسی میزان کارایی مدل SDSM در شبیه‌سازی شاخص‌های دمایی در مناطق خشک و نیمه خشک، فصلنامه علمی پژوهشی مهندسی آبیاری و آب، سال چهارم، شماره چهارده، 1-17.
دهقانی پور ا. ح.، حسن‌زاده، م. ج.، عطاری، ج. و عراقی‌نژاد، ش.، ۱۳۹۰، ارزیابی توانمندی مدل SDSM در ریز مقیاس نمایی بارش، دما و تبخیر (مطالعه موردی: ایستگاه سینوپتیک تبریز)، یازدهمین سمینار سراسری آبیاری و کاهش تبخیر، کرمان، دانشگاه شهید باهنر.
سلطان‌پور، ک. و حسامی‌کرمانی، م. ر.، ۱۳۸۸، ریز مقیاس نمایی بارندگی در تهران، هشتمین کنگره بین‌المللی مهندسی عمران، شیراز، دانشگاه شیراز.
علیزاده‌پهلوان ح. و زهرایی، ب.، 1393، مقایسه مدل‌های ریزمقیاس نمایی آماری در شبیه‌سازی بارش روزانه، مجموعه مقالات شانزدهمین کنفرانس ژئوفیزیک ایران، تهران.
قرمزچشمه ب.، رسولی، ع. ا.، رضائی‌بنفشه، م.، مساح‌بوانی، ع. ر. و خورشیددوست، ع. م.، 1393، بررسی اثر عوامل مورفو-اقلیمی بر دقت ریزمقیاس گردانی مدل SDSM، نشریه علمی-پژوهشی مهندسی و مدیریت آبخیز، جلد ششم، شماره دوم، 155-164.
کوهی م.، موسوی بایگی، م.، فرید حسینی، ع. ر.، ثنایی نژاد، ح. و جباری نوقابی، ه.، 1391، ریزمقیاس نمایی آماری و ارایه سناریوهای آتی رویدادهای حدی بارش درحوضه کشف رود، نشریه پژوهش‌های اقلیم شناسی، سال سوم، شماره دوازدهم، 35-53.
رضایی، م.، نهتانی، م.، جان‌آبکار، ع. و میرکازهی ریگی، م.، 1393، بررسی کارایی مدل ریزمقیاس‌نمایی آماری SDSM در پیشبینی پارامترهای دمایی در دو اقلیم خشک و فراخشک (مطالعه موردی:کرمان و بم). پژوهشنامه مدیریت حوزه آبخیز. سال پنجم. شماره 10. دانشگاه علوم کشاورزی و منابع طبیعی ساری، 117-131.
Benestad, R. E., 2010, Downscaling precipitation extremes, Theor. App. Climatol., 100, 1-21.
 
 
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, J. Clim., 25, 4366-4388, doi: 10.1175/JCLI-D-11-00408.1.
Chen, S. T., Yu, P. S. and Tang, Y. H., 2010, Statistical downscaling of daily precipitation using support vector machines and multivariate analysis, Journal of Hydrology, 385, 13-22.
Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R. K., Kwon, W. T., Laprise, R., Maga˜na Rueda, V., Means, L., Men´endez, C. G., R¨ais¨anen, J., Rinke, A., Sarr, A. and Whetton, P., 2007, Regional climate projections, In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Solomon, S., Quin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., Miller, H. L., (eds), Cambridge University Press, Cambridge.
Farajzadeh, M., Oji, R., Cannon, A. J., Ghavidel Y. and Massah Bavani, A., 2015, An evaluation of single-site statistical downscaling techniques in terms of indices of climate extremes for the Midwest of Iran. Theoretical and Applied Climatology 120, 377-390, doi 10.1007/s00704-014-1157-4,
Guijarro, J. A., 2012, climatol, Some tools for climatology: series homogenization, plus windrose and Walter&Lieth diagrams. R package version 2.1. http://CRAN.R-project.org/package=climatol.
Harpham, C., Wilby, R. L., 2005, Multi-site downscaling of heavy daily precipitation occurrence and amounts, J. Hydrol., 312, 235-255.
Hashemi, M. Z., Shamseldin, A. Y. and 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.
Haylock, M. R., Cawley, G. C., Harpham, C., Wilby, R. L. and Goodess, C. M., 2006, Downscaling heavy precipitation over the UK: a comparison of dynamical and statistical methods and their future scenarios, International Journal of Climatology, 26, 1397-1415, doi: 10.1002/joc.1318.
Kallache, M., Vrac, M., Naveau, P. and Michelangeli, P. A., 2011 Nonstationary probabilistic downscaling of extreme precipitation, J. Geophys. Res., 116, D05113, doi: 10.1029/2010JD014892.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, D., Mo, K. C., Ropelewski, C., Wang, J., Jenne, R. and Joseph, D., 1996, The NCEP/NCAR 40 year Reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-471.
Kim, K. Y. and Yi, G. S., 2008, CSBio lab., Information and Communications University Sequential KNN imputation method. R package version 1.0.1. http://csbio.icu.ac.kr.
Klein Tank, F., Zwiers, W. and Zhang, X., 2009, Guidelines on Analysis of extremes in a changing climate in support of informed decisions for adaptation, WMO-TD No. 1500, 56 pp.
Liu W., Guobin, F., Changming L. and Stephen, P. C., 2013, A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain, Theoretical Applied Climatology, 111, 585-600, doi: 10.1007/s00704-012-0692-0.
Liu, X. L., Coulibaly, P. and Evora, N., 2008, Comparison of data-driven methods for downscaling ensemble weather forecasts. Hydrology and Earth System Sciences, 12, 615-624.
Liu, Z., Xu, Z., Charles, S. P., Fu, G. and Liu, L., 2011, Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China. International Journal of Climatology, 31, 2006-2020.
Lu, X., 2006, Guidance on the development of climate scenarios within the framework of national communications from parties not included in annex I (NAI) to the United Nations Framework Convention on Climate Change (UNFCCC). National Communications Support Programme (NCSP), UNDP-UNEP-GEF.
Mekis, E. and Vincent, L. A, 2011, An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada, Atmosphere-Ocean, 49, 163-177, doi: 10.1080/07055900.2011.583910.
Menne, M. J., Durre, I., Vose, R.S., Gleason, B. and Houston, T. G., 2012, An overview of the Global Historical Climatology Network-Daily Database, J. Atmos. Ocean. Tech, 29, 897-910, doi: 10.1175/JTECH-D-11-00103.1.
Peterson, T. C. and Manton, M. J., 2008, Monitoring changes in climate extremes: a tale of international collaboration, Bull. Amer. Meteor. Soc., 89, 1266-1271, doi: http://dx.doi.org/10.1175/2008BAMS2501.1.
Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X. and Bronaugh, D., 2013, Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections, J. Geophys. Res. Atmos., doi: 10.1002/jgrd.50188.
Wang, X. L., 2008a, Accounting for autocorrelation in detecting mean-shifts in climate data series using the penalized maximal t or F test, J. App. Meteorol. Climatol, 47, 2423-2444.
Wang, X. L., 2008b, Penalized maximal F-test for detecting undocumented mean-shifts without trend-change, J. Atmos. Oceanic. Tech., 25, 368-384, doi:10.1175/2007/JTECHA982.1.
Wang, X.L., Chen, H., Wu, Y., Feng, Y. and Pu, Q., 2010, New techniques for the detection and adjustment of shifts in daily precipitation data series, J. App. Meteorol. Climatol, 49, 2416-2436.
Wang, X. L. and Feng, Y., 2010, RHtestsV3 user manual: Climate Research Division, Science and Technology Branch, Environment Canada, Toronto, ON, Canada. http://cccma.seos.uvic.ca/ETCCDMI/RHtest/RHtestsV3_UserManual.doc.
Wang, X. L., Wen, Q. H. and Wu, Y., 2007, Penalized maximal t test for detecting undocumented mean change in climate data series, J. App. Meteorol. Climatol, 46, 916-931, doi: 10.1175/JAM2504.1.
Wetterhall, F., B´ardossy, A., Chen, D., Halldin, S. and Xu, C., 2007a, Daily precipitation-downscaling techniques in three Chinese regions, Water Resources Research, 42, W11423, doi: 10.1029/2005WR004573.
Wetterhall, F., Halldin, S. and Xu, C. Y., 2007b, Seasonality properties of four statistical-downscaling methods in central Sweden, Theoretical and Applied Climatology, 87, 123-137.
Wilby, R. L. and Dawson, C. W., 2007, SDSM User Manual—A decision support tool for the assessment of regional climate change Impacts.
Wilby, R. L. and Dawson, C. W., 2012, The statistical down scaling model: insights from one decade of application, Int J Climatol, doi: 10.1002/joc.3544.
Wilby, R. L. and Dawson, C. W., 2013, Statistical down scaling model – decision centric (SDSM-DC) version 5.1 supplementary note, Loughborough University, Loughborough.
Wilby, R. L., Dawson, C. W. and Barrow, E. M., 2002, A decision support tool for the assessment of regional climate Impacts, Environmental Modeling & Software, http://dx.doi.org/10.1016/S1364-8152 (01)00060-3.
Wilby, R. L. and Fowler, H. J., 2010, Regional climate downscaling, in Modelling the Impact of Climate Change on Water Resources, Fung CF, Lopez A, New M (eds). Wiley-Blackwell, Chichester.
Wilby, R. L., Tomlinson, O. J. and Dawson, C. W., 2003, Multi-site simulation of precipitation by conditional resampling, Climate Research, 23, 183-194.
Wilby, R. L., 1997, Nonstationarity in daily precipitation series: implications for GCM downscaling using atmospheric circulation indices, International Journal of Climatology, 17, 439-454.
Wilby, R. L., 1998a, Modelling low-frequency rainfall events using weather pattern and frontal frequencies, Journal of Hydrology, 213, 381-392.
Wilby, R. L., 1998b, Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices, Climate Research, 10, 163-178.
Wilby, R. L., 2008a, Constructing climate change scenarios of urban heat island intensity and air quality, Environment and Planning B, Planning and Design, 35, 902-919.
Wilby, R. L., 2008b, Dealing with uncertainties of future climate: the special challenge of semi-arid regions, Proceedings of the Water Tribune, Expo Zaragoza, Spain.
Wilks, D. S., 2006, Statistical methods in the atmospheric sciences, 2nd ed. Elsevier Academic Press, California, USA.
Yang, T., Li, H., Wang, W., Xu, C. Y. and Yu, Z., 2012, Statistical downscaling of extreme daily precipitation, evaporation, and temperature and construction of future scenarios, Hydrological processes, 26, 3510-3523, doi: 10.1002/hyp.8427.
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson, T. C., Trewin, B. and Zwiers, F. W., 2011, Indices for monitoring changes in extremes based on daily temperature and precipitation data, WIREs Clim Change, 2, 851-870, doi: 10.1002/wcc.147.