ارزیابی مقایسه‌ای مدل‌های SDSM، IDW و LARS-WG برای شبیه‌سازی و ریز مقیاس کردن دما و بارش

نوع مقاله : مقالات پژوهشی


1 دانشگاه صنعتی اصفهان

2 دانشگاه تبریز


یکی از مشکلات خروجی مدل‌های GCM بزرگ مقیاس بودن آنها است که استفاده از ابزارهای ریز مقیاس را برای تبدیل داده‌های بزرگ مقیاس جهانی به داده‌های اقلیمی برای منطقه‌ مورد، نظر ضروری کرده است. بدین منظور، مدل‌ها و روش‌های مختلفی توسعه یافته‌اند که قطعیت و صحت نتایج هر کدام از آنها در منطقه مورد نظر می‌بایست بررسی گردد تا بتوان به نتایج واقعی‌تری در آینده دست یافت. در مطالعه حاضر، عملکرد مدل‌های SDSM، IDW و LARS-WG برای ریزمقیاس کردن داده‌های دما و بارش ایستگاه سینوپتیک پارس آباد، مقایسه و ارزیابی شدند. کالیبراسیون و صحت‌سنجی دو مدل SDSM و LARS-WG در مورد دما نشان داد که دو مدل دارای توانایی بیشتری در شبیه سازی دما نسبت به بارش می‌باشند و در تمام مدل‌ها، برای بیشتر ماه‌های گرم، افزایش دما مشاهده گردید. بطورکلی، نتایج نشان دادند که هر سه مدل عملکرد مشابه و خوبی برای شبیه‌سازی و ریز مقیاس کردن داده‌های دما دارند. در مورد بارش نتایج سه مدل تفاوت قابل توجه‌ای نسبت به یکدیگر نشان دادند و شدت کاهش و افزایش بارش نسبت به دوره پایه در مدل IDW نسبت به دو مدل دیگر بیشتر و در مدل LARS-WG نسبت به دو مدل دیگر کمتر است. اما در مورد تبخیر و تعرق محاسبه شده، نتایج دو مدل SDSM و IDW حاکی از افزایش تبخیر و تعرق در تمامی ماه‌ها حتی به میزان ناچیز و حداکثر در اوخر بهار و تابستان می‌باشد. درحالی‌که، تبخیر و تعرق محاسبه شده در مدل LARS-WG برآورد بسیار پایین‌تری را نسبت به دوره پایه نشان داده است که حاکی از توانایی پایین مدل در محاسبه این متغیر می‌باشد.


عنوان مقاله [English]

Comparative Assessment of SDSM, IDW and LARS-WG Models for Simulation and Downscaling of Temperature and Precipitation

نویسندگان [English]

  • Z. Dehghan 1
  • F. Fathian 2
  • S. Eslamian 1
1 Isfahan University of Technology
2 University of Tabriz
چکیده [English]

Introduction: According to the fifth International Panel on Climate Change (IPCC) report, increasing concentrations of CO2 and other greenhouse gases resulting from anthropogenic activities have led to fundamental changes on global climate over the course of the last century. The future global climate will be characterized by uncertainty and change, and this will affect water resources and agricultural activities worldwide. To estimate future climate change resulting from the continuous increase of greenhouse gas concentration in the atmosphere, general circulation models (GCMs) are used. Resolution of the output of the GCM models is one of the problems of these models. Using downscaling tools to convert global large-scale data to climate data for the study area is essential. These techniques are used to convert the coarse spatial resolution of the GCMs output into a fine resolution, which may involve the generation of station data of a specific area using GCMs climatic output variables. The objectives of this study are, therefore, to investigate and evaluate the statistical downscaling approaches.
Materials and Methods: Different models and methods have been developed which the uncertainty and validation of results in each of them in the study area should be investigated to achieve the more real results in the future. In the present study, the performance of SDSM, IDW and LARS-WG models for downscaling of the temperature and precipitation data of Pars Abad synoptic station were compared and investigated. IDW technique is based on the functions of the inverse distances in which the weights are characterized by the inverse of the distance and normalized, so their aggregate equivalents one. SDSM is categorized as a hybrid model, which utilized a linear regression method and a stochastic weather generator. The GCM’s outputs (named as predictors) are used to a linearly condition local-scale weather generator parameters at single stations. LARS-WG is a stochastic weather generator and it is widely used for the climate change assessment. This model uses the observed daily weather data, to compute a set of parameters for probability distributions of weather variables, which are used to generate synthetic weather time series of arbitrary length by randomly selecting values from the appropriate distributions. In this study, data from the Pars Abad meteorological station, which was used as the data for the baseline period, was also used to predict climate variables. The record of data is 30 years (1971-2000), and the mean temperature and precipitation are 13.7 and 283 mm per year, respectively. The driest month is August, which receives less than 5 mm of rain. Most of the rainfall occurs in April, averaging at 47 mm. July is the warmest month of the year, with an average temperature of 28.9 oC, and January is the coldest, with an average temperature of -2.3 °C. Precipitation differs by 42.8 mm between the driest and wettest months of the year and the average temperature varies by 31.2 °C.
Results and Discussion: The calibration and validation results of the SDSM and LARS-WG models in the case of temperature showed that two models have better abilities for temperature simulation in comparison with precipitation data and, in all models, the increasing temperature was observed for most of the warm months. In the case of precipitation, the results of three models have considerable different towards each other and changes intensity of decreasing and increasing precipitation compared to the baseline in IDW model is higher and in LARS-WG model is lower than two other models. But, in case of calculated evapotranspiration, the results of SDSM and IDW models indicate the increasing evapotranspiration in the all months even modest and its maximum value is in last spring and summer. While, calculated evapotranspiration by using LARS-WG model has showed the lower estimation than the baseline period which implies the low ability of model to calculate this model. In general, scenario A2 resulted in more increases in temperature than B2 in each time period. Whereas, in the case of rainfall, the results for each time period were different. For ETo, in comparison to the baseline, both A2 and B2 scenarios showed an increase during both time periods.
Conclusion: In general, the results showed that all three models have similar and good performance for simulating and downscaling of temperature and precipitation data. Therefore, these three models can be adopted to study climate change impacts on natural phenomenon.

کلیدواژه‌ها [English]

  • Pars Abad
  • Climate change
  • Climate data
  • GCMs models
1- Abbasi, F., Babaeian, A., Habibi Now Khandan, M., Mokhtari., L. G., Malboso, Sh. and Askari, Sh. A. 2010. Evaluation the impact of climate change on precipitation and temperature in the next decade with the MAGICC-SCENGEN. Model, Journal of Physical Geography, 72: 91-109.
2- Ashofteh, P.S. and Massah Bavani, A.R. 2010. Effects uncertainties of climate change on precipitation and temperature basin Aydoghmosh in periods 2040-2069, Journal of Soil and Water Science, 9: 2.
3- Hadinia, H., Pirmoradian, N., and Ashrafzadeh, A. 2013. Evaluation of GCM models for predictions reference evapotranspiration under different scenarios of climate change (Case Study Rasht). The First National Conferenceo on Climate.
4- Huang, J., Zhang, J., Zhang, Z., Yu Xu, C., Wang, B. and Yao, J. 2011. Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method, Stoch Environ Res Risk Assess, 25: 781–792.
5- Irwin, S. E., Rubaiya, S., Leanna M., King and Simonovic, S. P. 2012. Assessment of climatic vulnerability in the Upper Thames River basin: Downscaling with LARS-WG. Department of Civil and Environmental Engineering The University of Western Ontario London, Ontario, Canada.
6- Jalali, H., and KHanjar, S. 2007 Study fluctuations of temperature using time series and probability distribution models (Case study: Kermanshah), Journal of Geographic Space, 27: 115-132.
7- Jones, R. N. 2000. Managing uncertainty in climate change projections-issues for impact assessment, Journal of Climatic Change, 45: 403–419.
8- Kohi, M. A. and Sanaei Nejad, H. 2013. Study of climate change scenarios on the variable reference evapotranspiration based on the results of the two downscaling methods in Urmia region, Journal of Irrigation and Drainage, 4(7): 574-559.
9- Ehteramian, K., Ohamadi, G. N., Bannyan, M. and Ali Zadeh, A. 2012. Impacts of climate change scenarios on wheat yield determined by evapotranspiration calculation agriculture, 99(3): 279–286.
10- Lotze-Campen, H. and Schellnhuber H. J. 2009. Climate impacts and adaptation options in agriculture: what we know and what we don't know. Potsdam Institute for Climate Impact Research (PIK).
11- Massah Bavani, A. R, and Morid, S. and Mohammadzadeh, M. 2011 Comparison of downscaling and AOGCM models in the study of the effect of climate change on regional scale. Journal of Earth and Space Physics, 36(4): 99-110.
12- Massah Bavani, A. R, and Morid, S. 2005. Effects of climate change on water resources and agricultural production of the zayandeh rood Isfahan basin. Journal of Water Resources Research, 1(1).
13- Meteorology site. www.irimo.ir
14- Nakicenovic, N., Alcamo, J., Davis, G., De Vries, B., Fenhann, J., Gaffin, S., Gregory, K., Grubler, A., Jung, T.Y. and Kram, T. 2000. Special report on emissions scenarios: a special report of Working Group III of the Intergovernmental Panel on Climate Change. Pacific Northwest National Laboratory, Richland, WA(US), Environmental Molecular Sciences Laboratory(US).
15- Nguyen. 2007. Dynamical downscaling of GCM outputs Statiscal downscaling of community of climate system model monthly tempreture and precipitation project.
16- Rajabi, A., Sedghi, H., Eslamian, S. and Musavi, H. 2010. Comparison of LARS-WG and SDSM downscaling models in Kermanshah (Iran), Journal of Ecology, Environment and Conservation, 16(4): 465-474.
17- Rajabi, A. and Shabanlou, S. 2013 The analysis of uncertainty of climate change by means of SDSM model (Case Study: Kermanshah), Sciences Journal, 23(10): 1392-1398,
18- Sailor, D., Hu, T., Li, X. and Rosen, J. 2000. A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change. Renewable energy Journal, 19: 359-378.
19- Salehnia, N. Alizadeh, S. and Sayari, N. 2015, compared two downscaling models LARS-WG and ASD in prediction of temperature and precipitation under climate change and in different climate conditions. Journal of Irrigation and Drainage, 8(2): 233-242.
20- Samadi, S., Ehteramian, K. and Sarraf, B. S. 2011. SDSM ability in simulate predictors for climate detecting over Khorasan province, Social and Behavioral Sciences journal, 19.
21- Semenov, M. A. and Barrow, E. M. 2002. A Stochastic Weather Generator for Use in Climate Impact Studies. User Manual.
22- Sydkably, H., Akhundali, A.M., Massah Bavani, A.R. and Radmanesh, F. 2012. Presentation downscaling model of climate data based on non-parametric nearest neighbor (K-NN), Journal of Soil and Water (Agricultural Science and Technology), 26(4): 779-808.
23- Wilby, R. L. and Harris, I. 2006, A framework for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames, UK. Water Resources Research.
24- Wilby, R.L. and C.W. Dawson. 2007. SDSM-A Decision SuportTool for the Assessment of Regional Climate Change Impacts. User Manual.
25- Wilby, R.L., Dawson, C.W. and Barrow, E.M. 2002. SDSM- A Decision SuportTool for the Assessment of Regional Climate Change Impacts, Journal of Environmental Modeling and Software, 17: 147-159.
26- Wilby, R.L., Charles, S.P., Zorita, E., Timbal, B., Whetton, P. and Mearns, L.O. 2004. Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change, available from the DDC of IPCC TGCIA 27.
27- Zia Hashmi, M., 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, Stoch. Environ. Res. Risk Assess, 25: 475–484.