ﺑﺮرﺳﯽ ﻋﺪم ﻗﻄﻌﯿﺖ ﺷﺒﯿﻪ ﺳﺎزي ﺑﺎرش آﯾﻨﺪه (ﻣﻄﺎﻟﻌﻪ ﻣﻮردي :اﯾﺴﺘﮕﺎه ﻫﻤﺪﯾﺪي ﺑﺠﻨﻮرد و ﻣﺸﻬﺪ)

(Uncertainty analysis of rainfall projections (Case study: Bojnourd and Mashhad synoptic gauge station

ﺳﺎﺑﻘﻪ و ﻫﺪف: ﮐﻤﺒﻮد ﻣﻨﺎﺑﻊ آب ﺑﻪ دﻟﯿﻞ آﻟﻮدﮔﯽ ﻫﺎي زﯾﺴﺖ ﻣﺤﯿﻄﯽ و رﺷﺪ ﺟﻤﻌﯿﺖ ﺑﻪ ﻋﻨﻮان ﯾﮑﯽ از ﻣﺴﺎﺋﻞ ﭼﺎﻟﺶ ﺑﺮاﻧﮕﯿﺰ ﺟﻬﺎن اﻣﺮوز ﺗﺒﺪﯾﻞ ﺷﺪه اﺳﺖ .ﺑﻨﺎﺑﺮاﯾﻦ ارزﯾﺎﺑﯽ ﻣﻨﺎﺑﻊ آب آﯾﻨﺪه ﺑﺮاي ﻣﺪﯾﺮان و ﺳﯿﺎﺳﺘﮕﺬاران داراي اﻫﻤﯿﺖ اﺳﺖ .اﻣﺎ ﻋﻠﯽ رﻏﻢ ﭘﯿﺸﺮﻓﺖ ﻋﻠﻢ و در ﻧﺘﯿﺠﻪ دﻗﯿﻖ ﺗﺮ ﺷﺪن ﻣﺪل ﻫﺎي اﻗﻠﯿﻤﯽ در ارزﯾﺎﺑﯽ ﻫﺎ و ﭘﯿﺶ ﺑﯿﻨﯽ ﻫﺎي ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ ﻣﻨﺎﺑﻊ ﻣﺨﺘﻠﻔﯽ از ﻋﺪم ﻗﻄﻌﯿﺖ وﺟﻮد دارد ﮐﻪ ﻧﺎﺷﯽ از ﻓﻌﺎﻟﯿﺖ ﻫﺎي اﻧﺴﺎﻧﯽ و واﮐﻨﺶ ﻣﺘﻘﺎﺑﻞ ﺳﯿﺴﺘﻢ اﻗﻠﯿﻤﯽ در ﻣﻘﯿﺎس ﻫﺎي ﺑﺰرگ ﻣﮑﺎﻧﯽ و زﻣﺎﻧﯽ اﺳﺖ .ﺑﻨﺎﺑﺮاﯾﻦ، ﺑﻪ ﻣﻨﻈﻮر ﮐﺎرﺑﺮد ﻣﻮﻓﻘﯿﺖ آﻣﯿﺰ ﺷﺒﯿﻪ ﺳﺎزي ﭘﺎراﻣﺘﺮﻫﺎي ﻫﻮاﺷﻨﺎﺳﯽ در ﭘﮋوﻫﺶ ﻫﺎي ﮐﺎرﺑﺮدي ﻣﻨﺎﺑﻊ آب، ﺗﺤﻠﯿﻞ ﻋﺪم ﻗﻄﻌﯿﺖ ﺿﺮوري اﺳﺖ. در اﯾﻦ ﻣﻄﺎﻟﻌﻪ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻧﺘﺎﯾﺞ ﺷﺒﯿﻪ ﺳﺎزي ﻣﺎﻫﺎﻧﻪ ﺑﺎرش آﯾﻨﺪه ﺣﺎﺻﻞ از ﺗﺮﮐﯿﺐ ﭼﻬﺎر ﺳﻨﺎرﯾﻮ ﻧﺘﺸﺎر، ﭘﻨﺞ ﻣﺪل ﮔﺮدش ﻋﻤﻮﻣﯽ ﺟﻮ و دو ﻣﺪل ريزمقياس نما ( LARS-WS و SDSM) در دو اﯾﺴﺘﮕﺎه ﻫﻤﺪﯾﺪي ﻣﺸﻬﺪ و ﻫﻤﺪﯾﺪي ﺑﺠﻨﻮرد، ﻋﺪم ﻗﻄﻌﯿﺖ ﺷﺒﯿﻪ ﺳﺎزي ﺳﺮي زماني بارش در افق آتي اول ( 2040 - 2011) و افق آتي دوم ( 2070 - 2040 ) با دو روش باكس پلات و بوت استرپ بررسي شد. ﻣﻮاد و روش ﻫﺎ: اﯾﺴﺘﮕﺎه ﻫﺎي ﻫﻤﺪﯾﺪي ﺑﺠﻨﻮرد و ﻣﺸﻬﺪ ﮐﻪ داراي آﻣﺎر ﻣﻨﺎﺳﺐ و ﻗﺎﺑﻞ اﻃﻤﯿﻨﺎﻧﯽ ﺑﻮدﻧﺪ ﺑﺮاي اﻧﺠﺎم ﭘﮋوﻫﺶ اﻧﺘﺨﺎب ﺷﺪ .اﻃﻼﻋﺎت ﻣﻮرد ﻧﯿﺎز ﺷﺎﻣﻞ دﻣﺎي ﺣﺪاﮐﺜﺮ و ﺣﺪاﻗﻞ، ﺑﺎرش و ﺳﺎﻋﺎت آﻓﺘﺎﺑﯽ روزاﻧﻪ در دوره 1982تا2011 ﻣﯿﻼدي از ﻣﺮﮐﺰ آﻣﺎر ﺳﺎزﻣﺎن ﻫﻮاﺷﻨﺎﺳﯽ ﮐﺸﻮر ﺗﻬﯿﻪ شد.شبيه سازي سري زماني بارش خروجي مدل HadCM3 با سناريوي A1B, A2 , B2 , B1 مدل هاي NCPCM , CNCM3 با سناريوي A1B, مدل GFCM2 با سناريوي A1B و A2 و مدل CGCM3 با سناريوهاي A1B و A2 با دو مدل ريزمقياس گرداني آماري و در مجموع 10سناريوي مختلف براي بررسي عدم قطعيت شبيه سازي ها در دو اﻓﻖ آﯾﻨﺪه اول و دوم اﺳﺘﻔﺎده ﺷﺪ .در اﯾﻦ ﭘﮋوﻫﺶ دو روش ﺑﺎﮐﺲ- وﯾﺴﮑﺮ و روش ﻏﯿﺮﭘﺎراﻣﺘﺮي ﻓﺎﺻﻠﻪ اﻃﻤﯿﻨﺎن ﺑﻮت اﺳﺘﺮپ ﺟﻬﺖ ﺑﺮرﺳﯽ و ﮐﺎﻫﺶ ﻋﺪم ﻗﻄﻌﯿﺖ ﺷﺒﯿﻪ ﺷﺒﯿﻪ ﺳﺎزي ﻫﺎ ﺑﻪ ﮐﺎر ﺑﺮده ﺷﺪ .در ﮔﺎم اول ﺑﺎ اﺳﺘﻔﺎده از ﻧﻤﻮدار ﺑﺎﮐﺲ وﯾﺴﮑﺮ ﺷﺒﯿﻪ ﺳﺎزي ﻫﺎي ﭘﺮت ﺷﻨﺎﺳﺎﯾﯽ و حذف شدند.ﺎﺻﻠﻪ ﻣﯿﺎن دو ﭼﺎرك اول ( 25 درصد) و چارك سوم 0 75 درصد ) به عنوان دامنه عدم قطعيت لحاظ گرديد . در ﻣﺮﺣﻠﻪ ﺑﻌﺪ ﺑﺎ روش ﺑﻮت اﺳﺘﺮپ ﺑﺮاي ﺗﺨﻤﯿﻦ ﻗﺎﺑﻠﯿﺖ اﻃﻤﯿﻨﺎن ﺷﺒﯿﻪ ﺳﺎزي ﻫﺎ ﺑﺎ ﺗﻮﻟﯿﺪ داده ﻫﺎي ﺗﺼﺎدﻓﯽ، ﻓﺎﺻﻠﻪ اطمينان 95درﺻﺪي ﻣﺤﺎﺳﺒﻪ ﺷﺪ .در ﺑﺮآورد ﻋﺪم ﻗﻄﻌﯿﺖ و ﺗﻌﯿﯿﻦ ﺑﺎﻧﺪ ﻋﺪم ﻗﻄﻌﯿﺖ از ﻣﻘﺎدﯾﺮ پارامترهادر فضاي ﭘﺎراﻣﺘﺮﻫﺎ ﻧﻤﻮﻧﻪ ﺑﺮداري ﻣﯽ ﮔﺮدد .در اﯾﻦ ﺣﺎﻟﺖ ﻣﺪل ﺑﺮاي ﻫﺮ ﻣﺠﻤﻮﻋﻪ ﭘﺎراﻣﺘﺮﻫﺎ اﺟﺮا ﮔﺮدﯾﺪه و ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ اﯾﻦ ﺧﺮوﺟﯽ ﻫﺎ داﻣﻨﻪ ﻋﺪم قطعيت محاسبه مي گردد. ﯾﺎﻓﺘﻪ ﻫﺎ: در اﯾﺴﺘﮕﺎه ﻫﻤﺪﯾﺪي ﺑﺠﻨﻮرد، ﺑﻌﻀﯽ ﺷﺒﯿﻪ ﺳﺎزي ﻫﺎي ﻣﺎﻫﺎﻧﻪ با دو مدل3 CGCM و HadCM3 در افق اول آتي و مدل HadCM3 در افق دوم آﺗﯽ ﺑﺎ اﺳﺘﻔﺎده از ﻧﻤﻮدار ﺑﺎﮐﺲ- وﯾﺴﮑﺮ ﺑﻪ ﻋﻨﻮان داده ﭘﺮت ﺷﻨﺎﺳﺎﯾﯽ ﺷﺪ و ﺑﺮاي ﻣﺮﺣﻠﻪ ﺑﻌﺪي آﻧﺎﻟﯿﺰ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﻧﺸﺪ .در اﯾﺴﺘﮕﺎه ﻫﻤﺪﯾدي مشهد نيز اختلاف معني داري در شبيه سازي بعضي مدل هاي GCM و سناريوهاي انتشار مشاهده شد كه مربوط به مدل CGCM3 در دوماه ژانويه و مارس و مدل GFCM3 درماه هاي مربوط به ﻓﺼﻞ ﺗﺎﺑﺴﺘﺎن ﺑﻮد .ﺑﻌﺪ از ﺣﺬف ﺳﻨﺎرﯾﻮﻫﺎي ﭘﺮت ﻧﺘﺎﯾﺞ ﺑﯿﺎﻧﮕﺮ اﻧﺘﻈﺎر اﻓﺰاﯾﺶ ﺑﺎرش در ﻫﺮ دو اﯾﺴﺘﮕﺎه و در ﻫﺮ دو اﻓﻖ آﯾﻨﺪه اﺳﺖ .در ﻣﺮﺣﻠﻪ ﺑﻌﺪ ﺑﺎ روش ﺑﻮت اﺳﺘﺮپ ﻋﺪم ﻗﻄﻌﯿﺖ ﺧﺮوﺟﯽ ﺑﺮاي ﻣﺠﻤﻮﻋﻪ ﺷﺒﯿﻪ ﺳﺎزي ﻫﺎ ﻣﺤﺎﺳﺒﻪ ﺷﺪ .ﻧﺘﺎﯾﺞ در اﯾﺴﺘﮕﺎه ﻫﻤﺪﯾﺪي ﺑﺠﻨﻮرد ﺑﯿﺎﻧﮕﺮ ﺿﺨﺎﻣﺖ زﯾﺎد ﺑﺎﻧﺪ ﻋﺪم ﻗﻄﻌﯿﺖ در اﮐﺜﺮ ﻣﺎه ﻫﺎ ﺑﻪ ﺟﺰ در ﻣﺎه ﻫﺎي آﮔﻮﺳﺖ و اﮐﺘﺒﺮ اﺳﺖ .ﻫﻤﭽﻨﯿﻦ ﻣﻘﺎﯾﺴﻪ ﻣﻘﺎدﯾﺮ ﻣﯿﺎﻧﮕﯿﻦ ﺷﺒﯿﻪ ﺳﺎزي ﺑﺎرش ﻣﺎﻫﺎﻧﻪ آﯾﻨﺪه ﺑﺎ دوره ﭘﺎﯾﻪ ﺑﯿﺎﻧﮕﺮ اﻓﺰاﯾﺶ ﺑﺎرش در ﺷﺶ ﻣﺎﻫﻪ دوم ﻣﯿﻼدي در دو اﻓﻖ آﺗﯽ ﻧﺴﺒﺖ ﺑﻪ دوره ﭘﺎﯾﻪ اﺳﺖ .ﻧﺘﺎﯾﺞ در اﯾﺴﺘﮕﺎه ﻫﻤﺪﯾﺪي ﻣﺸﻬﺪ ﺑﯿﺎﻧﮕﺮ اﺧﺘﻼف ﺑﺴﯿﺎر ﮐﻢ ﻓﺎﺻﻠﻪ اﻃﻤﯿﻨﺎن وارﯾﺎﻧﺲ ﺷﺒﯿﻪ ﺳﺎزي ﻫﺎ در ﺷﺶ ﻣﺎﻫﻪ دوم ﻣﯿﻼدي دارد .در ﺻﻮرﺗﯽ ﮐﻪ در ﻫﺮ دو اﻓﻖ آﺗﯽ در ﻓﺼﻞ ﺑﻬﺎر ﺿﺨﺎﻣﺖ ﺑﺎﻧﺪ ﻣﺤﺪوده اﻃﻤﯿﻨﺎن ﺑﺴﯿﺎر زﯾﺎد ﺑﻮده، ﺑﻨﺎﺑﺮاﯾﻦ ﻋﺪم ﻗﻄﻌﯿﺖ ﭘﯿﺶ ﺑﯿﻨﯽ ﻣﺪل ﻫﺎ در دوره آﯾﻨﺪه ﺑﺮاي اﯾﻦ ﻓﺼﻞ زﯾﺎد اﺳﺖ . ﻧﺘﯿﺠﻪ ﮔﯿﺮي: در ﺑﯿﺶ ﺗﺮ ﻣﻄﺎﻟﻌﺎت ﻗﺒﻠﯽ در اﯾﺮان ﻃﯿﻒ ﮔﺴﺘﺮده اي از ﻋﺪم ﻗﻄﻌﯿﺖ ﻫﺎ در ﺑﺤﺚ ﭘﯿﺶ ﺑﯿﻨﯽ ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ را در درﻧﺘﯿﺠﻪ ﯾﺎﻓﺘﻪ، ﻧﻈﺮ ﻧﮕﺮﻓﺘﻨﺪ ﻫﺎي آن ﻫﺎ دﻗﯿﻖ ﺗﺮ از آن ﭼﻪ ﮐﻪ واﻗﻌﺎ ﻫﺴﺘﻨﺪ ﺑﻪ ﻧﻈﺮ ﻣﯽ رﺳﺪ .ﺑﻨﺎﺑﺮاﯾﻦ ﻧﺘﺎﯾﺞ آن ﻫﺎ ﮐﻢ ﺗﺮ ﻣﻮرد ﻗﺒﻮل ﭘﮋوﻫﺸﮕﺮان اﺳﺖ و ﺑﺮاي ﺳﯿﺎﺳﺘﮕﺬاران ﻣﻨﺎﺑﻊ آب ﮔﻤﺮاه ﮐﻨﻨﺪه اﺳﺖ .ﺑﻪ ﻧﻈﺮ ﭘﮋوﻫﺸﮕﺮان اﯾﻦ ﻣﻘﺎﻟﻪ روش اراﯾﻪ ﺷﺪه ﺗﺎ ﺣﺪودي ﻧﻘﺺ اﺳﺎﺳﯽ در ﺑﯿﺶ ﺗﺮ ﻣﻄﺎﻟﻌﺎت ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ در ﮐﺸﻮر را ﭘﻮﺷﺶ ﻣﯽ دﻫﺪ و ﻋﺪم در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﻋﺪم ﻗﻄﻌﯿﺖ در ﻣﻄﺎﻟﻌﺎت ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ ﻣﯽ ﺗﻮاﻧﺪ ﺑﻪ ﮐﻢ ﺑﻬﺎ دادن ﻃﯿﻒ وﺳﯿﻌﯽ از اﺛﺮات ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ منجر شود .

Background and Objectives: The scarcity of water resources caused by environmental pollution and population growth has become an issue of vital importance around the world. Assessing the water resources for the future is of great significance for water resources management and policy maker. Despite recent progress in developing reliable climate models, the different uncertainties inherent in climate change projections. Therefore, a successful application of a climate parameters simulation in applied water research strongly depends on uncertainty analysis of model output. Here we present a detailed and quantitative uncertainty assessment of rainfall for first future epoch (2011-2040) and second future epoch (2040-2070), based on the projections of wide range of rainfall projections resulting from the factorial combination of four emission scenarios, five GCMs and two downscaling methods (LARS-WG and SDSM) in Bojnourd and Mashhad synoptic stations. This enabled us to decompose the uncertainty in the ensemble of projections using Box-whisker plot and Bootstrapping method. Materials and Methods: Bojnourd and Mashhad synoptic stations based on the reliability of their data and long date series were chosen for this study. A 30- year base weather data (1982-2011) including daily precipitation, maximum and minimum temperature, solar radiations were obtained from Iranian meteorological organization. The uncertainty in precipitation change in response to the general circulation model (GCM) from HadCM3, NCPCM, CNCM3, GFCM2, CGCM3, SRES emission scenarios (A1B, A2, B1 and B2) and two downscaling method (SDSM and LARS-WG) was investigated in two future epochs. In this study, we evaluate the impact of uncertainty in climate change projections on the future precipitation by Box-whisker plots and Bootstrap technique. In the first step, the outliers were excluded by box-and-whisker plots. In the next step the precipitation projected which is reported by ten different scenarios, is then a vector of about 6000 bootstrap replications (500 per model), from which we take the 2.5th and 97.5th percentiles to calculate the range containing 95% of projected estimates. The fundamental idea of the model-based sampling theory approach to statistical inference is that the data arise as a sample from some conceptual probability distribution. Results: The GCM models show wide variation in their results, particularly for Bojnourd precipitation forecasting. According to Box-whisker graph in Bojnourd synoptic station (BSS), the projected precipitations by CGCM3 and HadCM3 in first and second epoch fall under the 2.5th and 97.5th percentiles. In Mashhad synoptic station (MSS) some scenarios projected precipitation significantly different from other scenarios which were belonging to CGCM3 in January and March and GFCM3 in summer months. On the basis of these results, it is clear that both stations will experience an increase in precipitation for epoch1 and epoch2, with the largest increase found for epoch2. In the next step confidence interval estimation by the bootstrap method is investigated for the uncertainty quantification of precipitation projections using the random sampling method. In BSS the confidence interval band is large in all month except in August and October. It is interesting that for MSS, the range in GCM predictions is relatively small for all seasons except in spring. This means that the uncertainty in climate predictions is considerably smaller for these months. Results also illustrate that the confidence interval band in Bojnourd station is wider than Mashhad station and suggest that precipitation projections is highly uncertain than in Mashhad Station. On the other hand in both stations climate predictions for the far future are more uncertain than climate predictions for the near future. Conclusion: All GCM and downscaling outputs are inherently uncertain because no model can ever fully describe physical systems. Most studies in the literature on the climate change projection do not capture the full range of plausible future climate variation, making their findings seem more precise than they actually are and as a result making them less credible among climate scientists and potentially misleading for policymakers. We feel that the methodological approach presented here addresses a fundamental shortcoming in the past research. We show that failing to account for climate uncertainty lead to a false sense of confidence about the likely future impacts of climate change, when in fact impacts are actually far less certain.