Monthly Drought Modeling Using Post-Processed Output of CFS.v2-RegCM4 System during 1982-2010 (Case Study: Iran)

Document Type : Research Article

Author

Atmospheric Science and Meteorological Research Center, Climate Research Institute, Mashhad, Iran.

Abstract

Iran and its neighboring countries are located in an arid and semi-arid region, so they have been always affected by various climate disasters. Drought as one of the most important climatic hazards in the region, has attracted lots of researches. Drought could have negative impacts on different areas such as agriculture, water resources, industry, transportation and urbanization. Drought is basically a qualitative phenomenon that could have various definitions in different sectors. In a basic categorization, drought could be climatic, hydrological, agricultural and social droughts. Researches have always keen on defining appropriate indices for each kind of drought. Standardized Precipitation Index (SPI) and Standardized Precipitation and potential Evapotranspiration Index (SPEI) are good examples of climatic drought indices which have been widely used and popular between researchers.
While monitoring the changes of these indices are a good way of drought analysis, effective drought management also needs accurate forecast of drought. A widely applied approach to forecast drought is through using the outputs of Coupled atmosphere- land- ocean General Circulation Models (CGCMs). CGCMs simulate the atmosphere-land-ocean system in a simplified way. Although, these models have been developed a lot in recent years, the forecasts are not well enough to be used in small regions. Thus, downscaling and post-processing methods have been widely applied to increase the resolution and accuracy of CGCM’s outputs, respectively.
Downscaling methods could be in a way grouped into two categories: 1. Statistical, 2. Dynamical. Dynamical methods are based on physical theories and using parametrization and initial hypothesis. Statistical methods investigate the relationship between modeled variables and their corresponding observation in a period of time. This relationship is then applied to forecast that variable. Both mentioned methods have their own advantages and disadvantages. In recent years, combined dynamical and statistical approaches have been widely used. In this study, SPI and SPEI were forecasted using the outputs of CFS.v2. Firstly, the outputs of CFS.v2 in the reforecast period (1982-2010) were downscaled to 30km horizontal resolution using RegCM4. Due to limitation in computational power, the dynamical downscaling was limited to 1-month lead time. Then needed outputs for the rest of analysis were post-processed using Decision Tree (DT) and Support Vector Machine (SVM). The outputs of CFS.v2-RegCM4 system for precipitation and ERA5 were used as inputs and ideal outputs, respectively, of DT and SVM for calibration and validation. These post-processed outputs were then applied to calculate SPI. In order to calculate modeled SPEI, firstly, ET was computed using Hargreaves-Samani approach and the needed climatic modeled variables by CFS.v2-RegCM4 system. The reanalysis ERA5 data was utilized to calculate reanalysis ET. Then, in order to calibrate and validate DT and SVM models, modeled ET and reanalysis ET were applied as input and ideal output, respectively. Finally, the postprocessed precipitation and ET were employed to compute modeled SPEI. These modeled SPI and SPEI were spatially compared with SPI and SPEI computed using ERA5 data. The results showed that both DT and SVM improved the accuracy of precipitation and ET forecasted by CFS.v2-RegCM4 system. However, DT model performed much better than SVM. The ability of DT in post-processing precipitation was higher than it for ET. Comparing spatial pattern of modeled SPI and SPEI with reanalysis SPI and SPEI showed that DT model could replicate these values at an acceptable level of accuracy.

Keywords

Main Subjects

References

باباییان، ا.؛ خزانه‌داری، ل.؛ عباسی، ف.؛ مدیریان، ر.؛ کریمیان، م. و ملبوسی، ش. (1397). پیش‌بینی ماهانه خشکسالی در حوضه آبریز جنوب غرب کشور با استفاده از مدل CFS.v2.2. مجله تحقیقات منابع آب ایران، 14، 145-133.
دستورانی، م.؛ حبیبی‌پور، ا.؛ اختصاصی،م.؛ طالبی، ع. و محجوبی، ج. (1391). بررسی کارایی مدل درخت تصمیم در پیش‌بینی بارش (مطالعه موردی ایستگاه سینوپتیک یزد). مجله تحقیقات منابع آب ایران، 8، 27-14.
رحیمی بندرآبادی، س.؛ گودرزی، م. و رضیئی، ط. (1400). بررسی تغییرات اقلیمی حوضه دز با استفاده از ریزمقیاس‌نمایی دینامیکی. فصل‌نامه جغرافیای طبیعی، 13(53)، 19-37.
سعیدی پور م.؛ رادمنش ف.؛ اسلامیان س. و شریفی م. ر. (1398). تحلیل منطقه‌ای خشکسالی در حوضه آبریز کارون با استفاده از شاخص‌های SPI و SPEI. مجله علوم آب و خاک، ۲۳ (۲)، ۴۱۵-۳۹۷.
شیخ ربیعی، م.؛ پیروان، ح.؛ دانشکار آراسته، پ.؛ اکبری، م. و معتمدوزیری، ب. (1400). مقایسه ی کارایی مدل‌های SDSM و CCT در مطالعات تغییر اقلیم (مطالعه موردی حوزه آبخیز کرگانرود). هواشناسی و علوم جوّ، 4(2)، 128-146.
صمدی نقاب، س.؛ ساری صراف، ب.؛ ملبوسی، ش. و رستگارمقدم، م. (1397). بکارگیری داده‌های تحت شبکه  GPVدر پس پردازش خروجی مدل‌های دینامیکی جهت ارایه پیش‌بینی خشکسالی، دومین کنفرانس ملی آب و هواشناسی ایران.
طالبی، ع. و اکبری، ز. (1392).  بررسی کارایی مدل درختان تصمیم‌گیری در برآورد رسوبات معلق رودخانه‌ای (مطالعه موردی: حوضه سد ایلام. مجله علوم آب وخاک، 17، 121-109.
عساکره، ح. و غلامی، آ. (1400). شبیه سازی دمای بیشینۀ ایستگاه سینوپتیک قزوین با استفاده از ریزمقیاس نمایی آماری خروجی مدل CanESM2. فصلنامه علمی- پژوهشی اطلاعات جغرافیایی سپهر، 30(118)، 25-41.
Anthes, R.A., Hsie, E.-Y., & Kuo, Y.-H. (1987). Description of the Penn State/NCAR Mesoscale Model Version 4 (MM4). NCAR Boulder.
Babaeian, I., Karimian, M., Modiriyan, R., Falamarzi, Y., & Koohi, M. (2021). Future Precipitation and Temperature Projection over Eastern Provinces of Iran using Combined Dynamical–Statistical Downscaling Technique. Clim. Chang. Res., 2, 41–58. https://doi.org/10.30488/ccr.2020.252239.1026.
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees, Chapman & Hall/CRC Press, Boca Raton, FL.
Brunke, M.A., Broxton, P., Pelletier, J., Gochis, D., Hazenberg, P., Lawrence, D.M., Leung, L.R., Niu, G.Y., Troch, P.A., & Zeng, X. (2016). Implementing and evaluating variable soil thickness in the Community Land Model, version 4.5 (CLM4.5). J. Clim., 29, 3441–3461. https://doi.org/10.1175/JCLI-D-15-0307.1
Bürger, G. (1996). Expanded downscaling for generating local weather scenarios. Clim. Res., 7, 111–128. https://doi.org/10.3354/cr007111
Çimen, M. (2008). Estimation of daily suspended sediments using support vector machines. Hydrol. Sci. J., 53, 656–666. https://doi.org/10.1623/hysj.53.3.656
Crane, R.G., & Hewitson, B.C. (1998). Doubled CO2 precipitation changes for the Susquehanna basin: Down-scaling from the GENESIS general circulation model. Int. J. Climatol., 18, 65–76. https://doi.org/10.1002/(SICI)1097-0088(199801)18:165::AID-JOC2223.0.CO;2-9
Dai, A. (2001). Global precipitation and thundertstorm frequencies. Part I: Seasonal and interannual variations., J. Clim., 14, 1092–1111. https://doi.org/10.1175/1520-0442(2001)0141092:GPATFP2.0.CO;2
Emanuel, K.A. (1991). A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci., 48, 2313–2335. https://doi.org/10.1175/1520-0469(1991)0482313:asfrcc2.0.co;2.
Gnitou, G. T., Ma, T., Tan, G., Ayugi, B., Nooni, I. K., Alabdulkarim, A., & Tian, Y. (2019). Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa using large-scale spatial and temporal statistical metrics. Atmosphere, 10(12), 802.
Grenier, H., & Bretherton, C. S. (2001). A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. Monthly weather review, 129(3), 357-377.
Grell, G.A. (1993). Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Weather Rev., 121, 764–787. https://doi.org/10.1175/1520-0493(1993)1210764:PEOAUB2.0.CO;2.
Gunn, S.R. (1998). Support Vector Machines for Classification and Regression. Technical Report, University of Southampton, England.
Hand, D., Mannila, H., & Smyth, P. (2001). Principles of Data Mining”. The MIT Press. In A comprehensive, highlytechnical look at the math and science behind extracting useful information from large databases, 546.
Hargreaves, G.H., & Samani, Z.A. (1985). Reference Crop Evapotranspiration from Ambient Air Temperature. Pap. - Am. Soc. Agric. Eng.
Holtslag, A.A.M., De Bruijn, E.I.F., & Pan, H.L. (1990). A high-resolution air mass transformation model for short-range weather forecasting. Mon. Weather Rev., 118, 1561–1575. https://doi.org/10.1175/1520-0493(1990)1181561:AHRAMT2.0.CO;2.
IPCC. (2007). Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.
Izadi, N., Karakani, E.G., Saadatabadi, A.R., Shamsipour, A., Fattahi, E., & Habibi, M. (2021). Evaluation of era5 precipitation accuracy based on various time scales over iran during 2000–2018. Water (Switzerland) 13. https://doi.org/10.3390/w13182538.
Jouybari Moghaddam, Y., & Rostami, S. Q. (2018). Fusion of Markov Chain and SAX Method for Drought Probability Analysis (Case Study: Eastern District of Isfahan, Iran). Environmental Management Hazards, 5(3), 295-311. doi: 10.22059/jhsci.2018.267316.414.
Kain, J.S., & Fritsch, J.M. (1990). A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802. https://doi.org/10.1175/1520-0469(1990)0472784:AODEPM2.0.CO;2
Kain, J.S. (2004). The Kain--Fritsch convective parameterization: an update. J. Appl. Meteorol., 43, 170–181.
Karimi, M., Melesse, A.M., Khosravi, K., Mamuye, M., & Zhang, J. (2019). Analysis and prediction of meteorological drought using SPI index and ARIMA model in the Karkheh River Basin, Iran, in: Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation. Elsevier, 343–353. https://doi.org/10.1016/B978-0-12-815998-9.00026-9
Kiehl, J.T., Hack, J.J., Bonan, G.B., Boville, B.A., Briegleb, B.P., Williamson, D.L. & Rasch, P.J. (1996). Description of the NCAR Community Climate Model (CCM3). NCAR Tech. Note NCAR/TN-420+STR.
Kouhanestani, Z. K., Khorsandi, F., & Mokhtari, F. (2021). Zoning and determine the best indicator of drought in southwest Iran. In 2021 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.‏
Luo, L., & Zhang, Y. (2012). Did we see the 2011 summer heat wave coming? Geophys. Res. Lett. 39. https://doi.org/10.1029/2012GL051383
Malayeri, A.K., Saghafian, B., & Raziei, T. (2021). Performance evaluation of ERA5 precipitation estimates across Iran. Arab. J. Geosci., 14, 2676. https://doi.org/10.1007/s12517-021-09079-8
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology, 5(10), 179-184.
Mironov, D., & Raschendorfer, M. (2001). Evaluation of Empirical Parameters of the Technical Report No. 1 New LM Surface-Layer Parameterization Scheme: Results from Numerical Experiments Evaluation of Empirical Parameters of the New LM Including the Soil Moisture Analysis. DWD.
Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J. & Clough, S.A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. Atmos., 102, 16663–16682. https://doi.org/10.1029/97jd00237
Moghim, S., & Bras, R.L. (2017). Bias correction of climate modeled temperature and precipitation using artificial neural networks. J. Hydrometeorol., 18, 1867–1884. https://doi.org/10.1175/JHM-D-16-0247.1
Morid, S., Smakhtin, V., & Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and time series of drought indices. Int. J. Climatol., 27, 2103–2111. https://doi.org/10.1002/joc.1498.
NOAA. (2022). Operational CFSv2 7 Day Rotating Archive, https://cfs.ncep.noaa.gov/cfsv2/downloads.html.
Pal, J.S., Small, E.E., & Eltahir, E.A.B. (2000). Simulation of regional-scale water and energy budgets: Representation of subgrid cloud and precipitation processes within RegCM. J. Geophys. Res. Atmos., 105, 29579–29594. https://doi.org/10.1029/2000JD900415
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.T., Chuang, H.Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M.P., Van Den Dool, H., Zhang, Q., Wang, W., Chen, M., & Becker, E. (2014). The NCEP climate forecast system version 2. J. Clim., 27, 2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1
Shirmohammadi, B., Moradi, H., Moosavi, V., Semiromi, M.T., & Zeinali, A. (2013). Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: Southeastern part of east Azerbaijan province, Iran). Nat. Hazards 69, 389–402. https://doi.org/10.1007/s11069-013-0716-9
Smola, A. (1996). Regression estimation with support vector learning machines. Master’s thesis, Tech. Univ. M unchen.
Taghizadeh, E., Ahmadi-Givi, F., Brocca, L., & Sharifi, E. (2021). Evaluation of satellite/reanalysis precipitation products over Iran. Int. J. Remote Sens., 42, 3474–3497. https://doi.org/10.1080/01431161.2021.1875508
Tian, D., Wood, E., & Yuan, X. (2017). CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States. Hydrol. Earth Syst. Sci., 21, 1477–1490. https://doi.org/10.5194/hess-21-1477-2017
Tiedtke, M. (1989). A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Weather Rev., 117, 1779–1800. https://doi.org/10.1175/1520-0493(1989)1171779:ACMFSF2.0.CO;2
Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
Serrano V., Sergio M., Beguería, S., & López-Moreno, J., I. )2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23(7), 1696-1718.
Von Storch, H. (1999). On the use of “inflation” in statistical downscaling. J. Clim., 12, 3505–3506. https://doi.org/10.1175/1520-0442(1999)0123505:OTUOII2.0.CO;2.
Yin, Ch. (2011). Applications of self-organizing maps to statistical downscaling of major regional climate variables. Diss. University of Waikato.
Zarei, A. R., Shabani, A., & Moghimi, M. M. (2021). Accuracy assessment of the SPEI, RDI and SPI drought indices in regions of Iran with different climate conditions. Pure and Applied Geophysics, 178(4), 1387-1403.‏
Zhou, X., Huang, G., Li, Y., Lin, Q., Yan, D., & He, X. (2021). Dynamical downscaling of temperature variations over the Canadian prairie provinces under climate change. Remote Sensing, 13(21), 4350.
Journal of the Earth and Space Physics
Volume 49, Issue 2
online publication date: 30 Aug 2023
August 2023
Pages 451-470

Files

Share

How to cite

Statistics