ریز مقیاس نمایی آماری و ارایه سناریوهای آتی رویدادهای حدی بارش درحوضه کشف رود

بر طبق گزارش‌های ipcc فراوانی و شدت رویدادهای حدی آب و هوایی تحت شرایط تغییر اقلیم افزایش یافته بطوریکه افزایش گازهای گلخانه‌ای و گرمایش زمین به شکل افزایش شدت، فراوانی و سهم رویدادهای فرین تجلی پیدا کرده است. در واقع گرمایش جهانی تغییر در متوسط متغیرهایی چون دما و بارش نیست بلکه در مجموع، افزایش رویدادهای حدی می‌باشد. تغییرات پیش بینی شده در رویدادهای حدی در نتیجه تغییر اقلیم و گرمایش جهانی در ارزیابی اثرات بالقوه تغییر اقلیم بر بخش های مختلف مانند آب، کشاورزی و مدیریت آب های سطحی شهری اهمیت زیادی دارد. در این راستا، در این مقاله میزان تغییرات رویدادهای حدی بارش حوضه کشف رود در آینده نزدیک (20402011) مورد بررسی قرار گرفته است. بدین منظور پس از ریزگردانی بارش در مقیاس روزانه و محاسبه نمایه های حدی بارش، توانمندی این مدل در شبیه‌سازی نمایه‌های صدک نودم، سهم بارش سنگین، بیشینه تعداد روزهای خشک متوالی و بیشینه بارش ماهانه طی دوره حاضر مورد بررسی قرار گرفت. نتایج نشان داد که امکان شبیه سازی الگوی تغییر ماهانه و مقدار بارش در مقیاس ماهانه در سطح قابل قبولی وجود دارد. اگرچه بین مقدار نمایه های حدی شبیه سازی شده و مشاهداتی تفاوت و خطا وجود داشت اما مدل، الگوی تغییرات ماهانه این نمایه ها را در اکثر ماه‌ها به خوبی شبیه سازی کرد. در ادامه بارش روزانه با استفاده از متغیرهای بزرگ مقیاس مدل hadcm3 تحت دو سناریوی a2 و  b2ریزگردانی شد و نمایه های حدی فوق برای دوره  20402011 محاسبه گردید. میزان تغییرات بارش و نمایه‌های حدی این دوره نسبت به دوره پایه 20001971 نشان داد مقدار بارش پیش بینی شده نسبت به دوره پایه، 3/3 درصد در سناریوی a2 و 6/3 درصد در سناریوی b2 کاهش می یابد و قابل ملاحظه ترین تفاوت در نمایه های حدی آتی، در فصل تابستان و تحت سناریوی a2 با افزایش بیشینه بارش، صدک نودم بارش و سهم بارش سنگین رخ خواهد داد.

Statistical Downscaling of Extremes of precipitation and construction of their future scenarios in the Kashfroud Basin

IntroductionThe Intergovernmental Panel on Climate Change (IPCC) stated that there is high confidence that recent climate changes have had discernible impacts on physical and biological systems. Impacts of climate change are felt most strongly through changes in extreme climate events, which are responsible for a major part of climaterelated economic losses (Jiang, et. al. 2012). The stateofthe art General Circulation Models (GCMs) can reproduce important processes in global and continental scale of atmosphere and predict future climate under different emission scenarios. Since spatial resolutions of GCMs are often coarse (hundreds of kilometer), there is a mismatch of scale between GCMs and the scale of interest for regional impacts.  Therefore, a range of downscaling methods have been developed to bridge the gap between the coarse resolution of the climate model outputs and the need for surface weather variables at finer spatial resolution (Wang et. al. 2011). Downscaling methods can be divided into two classes: dynamical downscaling (DD) and statistical (empirical) downscaling (SD). In this study, SD Model was evaluated by downscaling precipitation in the Kashafroud Basin. The statistical downscaling model (SDSM) used in our study here is a hybrid of a stochastic weather generator and regression methods (Wilby et al. 2001). This method includes a builtin transform functions in order to obtain secondary data series of the predictand and/or the predictor that have stronger correlations than the original data series (Wilby et al. 2004). Materials and MethodsStudy areaThe KashafRoud basin, located between   58° 2´ and 60° 8´ E and 35° 40´ and 40° 36´ N, totally has an  area of about 16500 km2. To the north east of the catchment is the HezarMasjed Mountain, to the south west is the Binaloud mountain and in the center of the catchment is the Mashhad plain. The climate of KashafRoud river basin ranges from severe semiarid to arid climate. The multiyear average precipitation and air temperature of the basin is about 220 mm and 12/2 °C respectively (Sayari et. al., 2011). DataThe data used for evaluation were largescale atmospheric data encompassing daily NCEP/NCAR reanalysis data during 19612001 and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model during 19612099. Areal average daily precipitation data of the KashafRoud basin (Mean of four weather stations daily precipitation data) during 19692001 was used for downscaling. Modeling of four extreme precipitation indices including the Maximum length of continuous dryspell, P90 percentile, Percentage of all precipitation from events greater than P90 percentile and the Maximum precipitation were investigated. MethodologyAs a first step, a quantitative statistical relationship between largescale atmospheric variables and localscale variables was established (Chen 2010) as:R=F (L)    in which R means the local predictand, L(l1, l2,..., ln) represents n largescale atmospheric predictors, and F is the built quantitative statistical relationship. SDSM uses largescale atmospheric variables to condition the rain occurrence as well as the rainfall amount in wet days. It can be expressed as follows (Wetterhall et al. 2009 Wilby et al.  2004): in which i is time (days), ωi is the conditional possibility of rain occurrence on day i,  is the normalized predictor, αj is the regression parameter and ωi−1 and αi−1 are the conditional probabilities of rain occurrence on day i−1 and lag1 day regression parameters, respectively. These two parameters are optional, depending on the study region and predictand. We used a uniformly distributed random number ri (0≤ri≤1) to determine the rain occurrence and supposed that rain would happen if ωi≤ri. On a wet day, rainfall can be expressed by a zscore as: in which Zi is the zscore on day i, βj is the calculated regression parameter, and βi−1 and Zi−1 are the regression parameter and the zscore on day i−1, respectively. As mentioned above, they are also optional ε is a random error term represented by the normal distribution N (0, ). Downscaling precipitationCalibration and validation of SDSMFirst, all of the 26 atmospheric variables in the region were taken as potential predictors, then most sensitive predictors for the region were analyzed month by month. The analysis results were integrated and finally, 3 predictors were selected for predictand (table 1). Table 1 Details of downscaling model in the study region for Daily precipitation (19691984)PredictorsVorticity at 500 hPa (p5_z)Divergence at 500 hPa (p5zh)850 hPa Ucomponent (P8_u)Model typeDailyFourth root modelConditional (amounts) and unconditional (occurrence) process ResultsThe results showed that the pattern of change and numerical value of precipitation can be reasonably simulated. Although some differences existed between values of observed and simulated indices but the pattern of change in most of months were good. In the next 30 years, total annual precipitation would decrease by about 3.3 % in A2 scenario and 3.6% in B2 scenario and summer might be the most distinct season among all the changes in extreme precipitation indices.