تهيه داده هاي تلفيقي بارش ماهانه براي حوضه هاي آبريز جنوب غرب كشور با بكارگيري داده هاي شبكه اي APHRODITE و مشاهداتي

Providing Monthly Composite APHRODITE-Observed Precipitation Data over Iran’s Southwest Watersheds

حوضه هاي آبريز جنوب غرب كشور شامل كرخه، كارون بزرگ، جراحي و زهره سهم چشمگيري در تامين منابع آبي مورد نياز بخش هاي كشاورزي، شرب، صنعت و نيروگاههاي برقابي منطقه و برخي استان هاي واقع در شرق زاگرس دارند. وجود داده هاي پايه ماهانه بارش با تراكم مناسب از نيازهاي اساسي براي مديريت منابع آبي منطقه از قبيل تصميم گيري در مورد زمان مناسب آبگيري و تخليه سدها، واسنجي و پس پردازش برونداد مدل هاي پيش بيني ماهانه بارش و پيش آگاهي خشكسالي مي باشد. بررسي تعدادايستگاههاي سنجش بارش تحت پوشش وزارت نيرو و سازمان هواشناسي كشور نشان مي دهد كه حدود 34 درصد از منطقه فاقد تراكم مناسب از ايستگاههاي اندازه گيري بارش مي باشد. براي پركردن خلاء هاي آماري از داده هاي بازكاوي افروديت كه توسط سازمان هواشناسي ژاپن و با همكاري اتحاديه هواشناسي آسيا در تامين داده هاي بارش كشورهاي آسيايي تهيه شده است، استفاده گرديد. داده هاي شبكه اي افروديت در دو تفكيك مكاني 0/5×0/5 درجه و 0/25×0/25 درجه در دسترس هستند. بدين منظور در ابتدا صحت داده هاي بارش افروديت با داده هاي ديدباني بررسي گرديد. پس از حصول اطمينان از كارايي داده هاي افروديت و اعمال تصحيحات مربوطه، داده ها مذكور جايگزين داده هاي ديدباني مخدوش يا شبكه هاي فاقد داده شدند. به دليل عدم به روزرساني داده هاي APHRODITE بر روي غرب آسيا، دوره مطالعاتي محدود به دوره 2007-1987 ميلادي گرديد. بررسي ها نشان داد، داده هاي تلفيقي ديدباني-افروديت بدست آمده به خوبي قادر به نمايش الگوهاي بارش حوضه هاي آبريز جنوب غرب كشور به ويژه در ماههاي سرد و پر بارش مي باشد.

Introduction: Southeast watersheds of Iran including Great Karoon, Karkheh, Jarrahi and Zohreh have the most significant contribution in the water supply of the agriculture, industry, drinking water and hydroelectric power plants over Iran. 25 percent of the country’s electricity is produced from hydroelectric power plants located in this region. The existence of a monthly relatively high resolution gridded precipitation dataset is of the most important needs of water resources management for such as deciding on the suitable time of dewatering and discharge of dams, calibration of dynamical monthly forecasting models and drought early warning. Even considering all observation stations governed by Meteorological Administration and Ministry of Power, the density of stations is not so enough to use them for calibration of hydro-climate model outputs. To overcome this deficiency, one way to fill the gap is using bias corrected global gridded precipitation dataset such as APHRODITE, CMORPH, PRESIANN and other newly generated data. Material and Methods: Watershed of Karkheh, great Karoon, Jarrahi and Zohreh are the area of study which covers southwest provinces of Khuzestan, Kermanshah, Ilam, Chaharmohal-Bakhtiari, Kohkiluyeh and Buyerahmad, Isfahan, Hamadan, Fars and Lorestan, which is shown in figure 2. There are 135 observation station in the area of study which governs by Iran Meteorological Organization and Ministry of Power. Area of study covers by 75 grids of 0.5×0.5 degree latitude and longitude. For each grid there is an APHRODITE precipitation data. In the 34% of grids, there is no observation station. The main goal of this study is to attribute a reliable monthly precipitation data to all grids without any observation station. Period of APHRODITE data set is 1987-2007, which is same to observation period. Firstly regional bias of APHRODITE data set has been computed by comparing observed precipitation with APHRODITE one. Then bias corrected APHRODITE precipitation (Composite APHRODITE Observation dataset) has been placed in non-observation grids. Efficiency of composite precipitation data has been determined by statistical parameters of bias, correlation and Nash-Sutcliff indices. Results and Discussion: In this research the results have been evaluated at monthly and seasonal time scales. In the case of seasonal time scale, we found that the minimum APHRODITE’s bias of 1.2 mm has been occurring in summer, while the maximum bias has been occurring in winter by 40.9mm. It means that the bias is high in the rainy season. Seasonal correlations were statistically acceptable in 0.05 significant levels, showing same seasonal fluctuations in APHRODITE and rain gage data. To provide seasonal composite APHRODITE-Observed precipitation gridded data set, mean seasonal bias of APHRODITE has been removed, while preserving seasonal fluctuation. The highest spatial correlation of 0.8 was detected in autumn, while it was about 0.7 for spring and winter. The minimum seasonal correlation was in summer by 0.5. There were also a good agreement between area averaged observation and APHRODITE data, when considering statistical indices of bias, Nash-Sutcliff and relative percentage errors. Results show the cumulative distribution function of APRODITE data is behind of the observed cumulative distribution function data, meaning that APHRODITE reaches its maximum earlier than observation data. This implies that APHRODITE cannot capture well the extreme monthly precipitation. Monthly correlations are approximately greater than 0.9, but the only exception is September with a correlation coefficient of 0.52. All correlations are significant in 0.05 levels. The highest spatial correlation was occurred in Novembers. Monthly Nash-Sutcliff was 0.96 in monthly time series. The categorical percentage score was 94.1%. These results strongly confirm that APHRODITE precipitation data is a good option for replacement in grid cells without observations. The number of observation stations per cell is varied from 1 to 7. We found that the maximum monthly correlations occur in grid cells of 0.5×0.5 degree latitude and longitude which having at least 3 observation stations. The three-station bias has been applied to APHRODITE data, then bias-removed data has been replaced with grid cells without observations. Spatial patterns of new composite APHRODITE-observation data set has good agreement with observation in the areas having intense observation stations. They also can capture well the spatial precipitation distribution of rainy areas located in the center of basin and low rainfall areas located in the southwest of the region. The results of this research can be used in calibration of dynamical seasonal forecasting outputs, drought early warning and rain-runoff simulation.