بررسی قدرت تفکیک مکانی مناسب داده‌های بارش روزانه پایگاه er-ainterim در شمال شرق ایران

در این پژوهش دقت 11 سری از داده‌های بازکاوی روزانه erainterim مربوط به مرکز اروپایی پیش‌بینی‌های میان‌مدت ecmwfبا تفکیک‌های مکانی 0.125 ، 0.25، 0.4،  0.5، 0.75، 1، 1.125، 1.5، 2، 2.5 و  3 درجه با داده‌های مشاهداتی 17 ایستگاه هواشناسی شمال شرق(خراسان شمالی، خراسان رضوی، خراسان جنوبی)، در یک دوره زمانی 26 ساله (2015-1990) موردبررسی قرار گرفت. برای این منظور از آماره‌های ضریب تبیین، شاخص توافق، اُریبی نسبی و nrmse استفاده شد. نتایج نشان دادند که بر اساس آماره nrmse دقت تمامی تفکیک‌های مکانی موردمطالعه زیر 10 قرار دارد که در محدوده عالی می باشد، اما سری داده‌های با تفکیک مکانی 0.4 درجه در سه نمایه آماری دیگر( ضریب تعیین،اریبی نسبی و ضریب توافق) بهترین عملکرد را دارا است و هماهنگی بهتری در مقایسه با سایر داده‌های era-interim دارد و می‌تواند برای تکمیل خلاءهای آماری و مناطق فاقد آمار مورداستفاده قرار گیرد. در سری داده‌های با تفکیک مکانی 0.4 درجه، بیشترین ضریب تبیین در بین ایستگاه‌های موردمطالعه در ایستگاه مشهد مشاهده شد که ممکن است ناشی از بالا بودن بارش آن، پایین بودن تغییرپذیری بارش آن در مقایسه با سایر ایستگاه‌ها و نیز سابقه آماری بالای این ایستگاه و لحاظ داده‌های آن در سامانه داده‌گواری جهانی داده‌ها است که به‌عنوان داده‌های پایه در آغازگری مدل پیش‌بینی عددی ecmwfاستفاده می‌شود. کمترین مقدار نرمال شده جذر میانگین مربع خطاها در ایستگاه‌های تقریباً پربارش شمالی منطقه شامل مشهد، بجنورد و قوچان و بیشترین خطای مقدار نرمال شده جذر میانگین مربع خطاها در ایستگاه‌های تقریباً خشک و کم بارش مشاهده شد. رفتار شاخص توافق کم و بیشتر شبیه nrmse است. قدر مطلق بایاس نسبی در تمامی ایستگاه‌های کمتر از 0.01 است که بیشترین خطای مربوطه در ایستگاه‌هایتقریباً کم بارش مرکز و جنوب منطقه مشاهده شد. با توجه به یافته‌های این پژوهش، داده‌های بازکاوی روزانه erainterim با تفکیک مکانی 0.4 درجه می‌توانند برای تکمیل خلاءهای آماری و مناطق فاقد آمار بارش در خراسان مورداستفاده قرار گیرد.

ERAInterim

Introduction  Climate model information usually is in large scales (> 100 km). It is often necessary to use downscaling methods to use this information. Downscaling can be defined as methods which interpret climate information in regional or local scale (10100 km) from the large grid (>100km) GCMs (Fung et al. 2011). Two main methods of downscaling are dynamical and statistical downscaling. Statistical downscaling methods are less computeintensive tasks which involve implementing local scale variables as a function of global climate model outputs (Chen et al. 2013). This paper predicts inflow into the Karaj dam reservoir using results of a Global climate model in different climate scenarios and downscaling methods (a total of 32 different runs). Material and methods Climate model and scenarios Intergovernmental Panel on Climate Change (IPCC) introduced four different greenhouse gas concentration (not emissions) trajectories (representative concentration pathways RCP) based on the amount of radiative forcing values at the end of the year 2100 namely RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 which all have been used in this study alongside CSIRO MK 3.6.0 Global Climate model (GCM). Statistical downscaling methods Based on choosing the predictors, observed data or historical GCMs simulation,  downscaling methods (DSM) can be divided into two major groups namely Bias Correction (BC) and Change Factor (CF) (Ho, Stephenson et al. 2012, Wang, Ranasinghe et al. 2016). In BC based methods, it is assumed that the change between GCM data and observed data remain constant in time and the CFbased method, it is presumed that change in observed climate variable is same as changes in climate model data, and precipitation occurrence probability will remain constant. For a better estimation of future climate conditions and understand the effect of selecting different downscaling methods two different BC methods and two different CF methods have been explained and used in the study. Karaj dam watershed is located in the central part of Northern Iran in Tehran province between 51.05 and 51.60 degrees North and 35.88 and 36.18 East. It is one of the main supplies of urban and agricultural water demand of Tehran. The area of the watershed is about 846.5 km2 and its average height is about 2826 MASL. Twelve (12) weather stations which had data with proper length were selected over the region. Spatial downscaling was used by averaging 4 grid points near the station. The inflow was predicted for a future period (20132045) and compared with both observed and modeled data in the base period (19852012). Future temperature and precipitation in different DSMRCPs have been plotted. Average monthly hydrograph and probability density functions of annual streamflow were compared. Runoffprecipitation simulation has been conducted using IHACRES software. Results The overall trend of temperature in different downscaling methods is rising and the uncertainty related to choosing DSM is more than choosing climate scenario. The range of changing temperature is wider in the CF method and choosing the overall DSM method (BC or CF) is more important than choosing submethod (MB or VB). Unlike temperature precipitation changes in not similar in different scenarios and DSMs. Although in some scenarios precipitation increases, in others decreases. Despite small differences, it seems that the overall trend of streamflow in the CSIRO model is decreasing. Streamflow in April decreased significantly. The range of streamflow in the future is wider than historical observation and uncertainty especially in extreme events is higher. RCP 8.5 has the greatest streamflow range in the future which shows less reliability in predictions. Discussions For better performance of infrastructures in the future, climate changes and their effect on streamflow should be predicted. This paper investigated the effect of choosing different statistical downscaling methods and climate scenarios (RCP) on the future streamflow in the Karaj dam basin, Tehran, Iran. There are changes in flow pattern and in most scenarios, two peaks in April and May are recorded. Least annual average flow is for RCP 6.0 and the greatest annual average flow is for RCP 2.8. The study showed that for better understanding and prediction of future condition, different downscaling methods should be considered as well as different climate scenarios. Choosing whether the BC or CF method has more effect than MB or VB selection. The paper used different scenarios and methods to predict future streamflow. Probabilistic approach showed the importance of considering uncertainty in streamflow prediction and the possible range of future changes which may be used in defining the reservoir operating rule.