ريز مقياس نمايي آماري و ارايه سناريوهاي آتي رويدادهاي حدي بارش درحوضه كشف رود

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

بر طبق گزارش‌هاي IPCC فراواني و شدت رويدادهاي حدي آب و هوايي تحت شرايط تغيير اقليم افزايش يافته بطوريكه افزايش گازهاي گلخانه‌اي و گرمايش زمين به شكل افزايش شدت، فراواني و سهم رويدادهاي فرين تجلي پيدا كرده است. در واقع گرمايش جهاني تغيير در متوسط متغيرهايي چون دما و بارش نيست بلكه در مجموع، افزايش رويدادهاي حدي مي‌باشد. تغييرات پيش بيني شده در رويدادهاي حدي در نتيجه تغيير اقليم و گرمايش جهاني در ارزيابي اثرات بالقوه تغيير اقليم بر بخش هاي مختلف مانند آب، كشاورزي و مديريت آب هاي سطحي شهري اهميت زيادي دارد. در اين راستا، در اين مقاله ميزان تغييرات رويدادهاي حدي بارش حوضه كشف رود در آينده نزديك (20402011) مورد بررسي قرار گرفته است. بدين منظور پس از ريزگرداني بارش در مقياس روزانه و محاسبه نمايه هاي حدي بارش، توانمندي اين مدل در شبيه‌سازي نمايه‌هاي صدك نودم، سهم بارش سنگين، بيشينه تعداد روزهاي خشك متوالي و بيشينه بارش ماهانه طي دوره حاضر مورد بررسي قرار گرفت. نتايج نشان داد كه امكان شبيه سازي الگوي تغيير ماهانه و مقدار بارش در مقياس ماهانه در سطح قابل قبولي وجود دارد. اگرچه بين مقدار نمايه هاي حدي شبيه سازي شده و مشاهداتي تفاوت و خطا وجود داشت اما مدل، الگوي تغييرات ماهانه اين نمايه ها را در اكثر ماه‌ها به خوبي شبيه سازي كرد. در ادامه بارش روزانه با استفاده از متغيرهاي بزرگ مقياس مدل HadCM3 تحت دو سناريوي A2 و  B2ريزگرداني شد و نمايه هاي حدي فوق براي دوره  20402011 محاسبه گرديد. ميزان تغييرات بارش و نمايه‌هاي حدي اين دوره نسبت به دوره پايه 20001971 نشان داد مقدار بارش پيش بيني شده نسبت به دوره پايه، 3/3 درصد در سناريوي A2 و 6/3 درصد در سناريوي B2 كاهش مي يابد و قابل ملاحظه ترين تفاوت در نمايه هاي حدي آتي، در فصل تابستان و تحت سناريوي A2 با افزايش بيشينه بارش، صدك نودم بارش و سهم بارش سنگين رخ خواهد داد.

Introduction The 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 Methods Study area The 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).   Data The 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.   Methodology As 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 precipitation Calibration and validation of SDSM First, 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) Predictors Vorticity at 500 hPa (p5_z) Divergence at 500 hPa (p5zh) 850 hPa Ucomponent (P8_u) Model type Daily Fourth root model Conditional (amounts) and unconditional (occurrence) process   Results The 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.