مدل‌سازی ماهانه خشکسالی با استفاده از برونداد پس‌پردازش شده سامانه CFS.v2-RegCM4 در دوره 1982 تا 2010 (مطالعه موردی: ایران)

نوع مقاله : مقاله پژوهشی

نویسنده

پژوهشگاه هواشناسی و علوم جو، پژوهشکده اقلیم‌شناسی، مشهد، ایران.

چکیده

از آنجاکه کشور ایران در منطقه‌ای خشک و نیمه‌خشک واقع شده است، همواره در معرض کمبود بارش، دما و تبخیر بالا و در نتیجه خشکسالی‌های متعدد با اثرات مخرب بوده است. لذا پایش، مدل‌سازی و پیش‌بینی این پدیده، به‌ویژه در مقیاس ماهانه می‌تواند نقش به‌سزایی در مدیریت ریسک خشکسالی داشته باشد. هدف مطالعه حاضر، توسعه مدلی با قابلیت محاسبه شاخص‌های SPI و SPEI در پیش‌دید یک‌ماهه با بهره‌گیری از برونداد بارش و دمای مدل CFS.v2 که توسط مدل منطقه‌ای RegCM4 ریزمقیاس‌شده است، می‌باشد. ابتدا با استفاده از داده‌های دمای ریزمقیاس‌شده مدل CFS.v2 در دوره 2010-1982 و همچنین داده‌های ERA5، مقادیر تبخیر-تعرق پتانسیل ماهانه به روش هارگریوز-سامانی محاسبه شد؛ سپس داده‌های ریزمقیاس‌شده بارش و تبخیر-تعرق پتانسیل سامانه CFS.v2-RegCM توسط مدل‌های درخت تصمیم و ماشین بردار پشتیبان پس‌پردازش شدند. داده‌های ریزمقیاس‌شده به‌عنوان ورودی و داده‌های بازتحلیل ERA5 به‌عنوان داده‌های مرجع جهت آموزش (2002-1982) و آزمون (2010-2003) استفاده شدند. در پایان، از داده‌های ریزمقیاس‌شده و پس‌پردازش‌شده جهت محاسبه مقادیر SPI و SPEI یک‌ماهه مدل و از داده‌های ERA5 برای محاسبه مقادیر نظیر مرجع استفاده شد. مقایسه داده‌های پس‌پردازش‌شده بارش و تبخیر-تعرق پتانسیل با داده‌های نظیر بازتحلیل ERA5 نشان داد که مدل درخت تصمیم از کارایی بالاتری نسبت به مدل ماشین بردار پشتیبان برخوردار است. همچنین مقادیر SPI و SPEI محاسبه‌شده با داده‌های منتج از سامانه CFS.v2-RegCM4 که با مدل درخت تصمیم پس‌پردازش شده‌اند با مقادیر نظیر منتج از داده‌های بازتحیلی ERA5 همخوانی قابل‌قبولی داشتند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

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

نویسنده [English]

  • Yashar Falamarzi
Atmospheric Science and Meteorological Research Center, Climate Research Institute, Mashhad, Iran.
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Decision tree
  • Support vector machine
  • RegCM4
  • SPI
  • SPEI

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فیزیک زمین و فضا
دوره 49، شماره 2
تاریخ انتشار الکترونیکی: 8 شهریور 1402
شهریور 1402
صفحه 451-470

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