ارزیابی عملکرد داده‌های بازتحلیل ‌شده Era-Interim در تخمین بارش روزانه و ماهانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی آب/ دانشگاه بین المللی امام خمینی قزوین

2 استادیار گروه مهندسی آب دانشگاه بین المللی امام خمینی قزوین

چکیده

تخمین صحیح بارش  در شبیه­سازی سیلاب، پایش خشکسالی و مدیریت منابع آب امری ضروری و مهم بشمار می­آید. در حال حاضر بخش­های عمده­ای از جهان، فاقد ایستگاه­های اندازه­گیری بارش زمینی هستند و حتی در صورت وجود از نظر زمانی و مکانی دارای پوشش مناسبی نیستند و همین مساله مطالعات منابع آب را با چالشی اساسی روبرو می­نماید. یکی از مهم­ترین منابع بارشی موجود، پایگاه­های بارشی مدل مبنا می­باشد که با تلفیق فن­آوری­های ماهواره­ای، مدلهای سطح زمین (LSMs) و مدل­های عمومی گردش جو (GCMs) داده­های شبکه­بندی شده با توان تفکیک مکانی و زمانی بالا را برای تمامی نقاط دنیا ارائه می­نماید. این گزینه می‌تواند کمبود اطلاعات ایستگاه‌های زمینی را به ویژه در مناطقی که از این حیث با کمبود مواجه هستند تا حدود زیادی برطرف سازد. در پژوهش حاضر به ارزیابی عملکرد یکی از مهم­ترین پایگاه­های بارشی  مدل مبنا به نام پایگاه ECMWF در گام­های زمانی روزانه و ماهانه در سطح حوضه آبریز سفیدرود (در بازه زمانی 2000 تا 2008) پرداخته شده است. همچنین برای ارزیابی هرچه بهتر پایگاه مذکور از داده­های بارش مبتنی بر سنجش از دور TRMM نیز استفاده گردید. نتایج حاصل از ارزیابی عملکرد پایگاه بارش ECMWF در سطح این حوضه در دو مقیاس زمانی روزانه و ماهانه حاکی از آن است که این منبع دارای همبستگی بالایی با ایستگاه­های زمینی به ویژه در بخش­های جنوبی، مرکزی و غربی حوضه است. به عنوان مثال در هر دو گام زمانی روزانه و ماهانه، همبستگی بین متوسط داده­های بارشی این منبع با داده­های بارش زمینی به ترتیب در حدود 83/0 و 94/0 برآورد گردید در حالیکه در صورت استفاده از پایگاه TRMM مقادیر مذکور به ترتیب معادل 32/0 و 57/0 بدست آمد. برخلاف پایگاه­های بارشی بازتحلیل شده، یکی از نقاط ضعف پایگاه­های بارشی همچون TRMM، تخمین ضخامت ابر و میزان آب قابل بارش توسط آن، تنها بر اساس تکنیک­های مبتنی بر سنجش از دور می­باشد. همچنین از نظر آماره­های طبقه­بندی، پایگاه بارش ECMWF در هر دو گام زمانی روزانه و ماهانه با دارا بودن مقادیر کم شاخص FAR (گزارش­های اشتباه)، مقادیر بالای شاخص Accuracy (صحت پیش­بینی­های درست) و نیز مقدار بالا در تشخیص روزهای بارانی (POD) دارای عملکرد بسیار مناسبی می­باشد. از آنجائی که حوضه آبریز سفیدرود با توجه به وسعت زیاد دارای تنوع اقلیمی، توپوگرافیکی و پوشش گیاهی متفاوتی است، نتایج بدست آمده در آن می­تواند راهنمای مناسبی برای استفاده در حوضه­های مشابه مدنظر قرار گیرد. لذا در حوضه­های فاقد آمار که امکان دسترسی به داده­های زمینی برای ارزیابی عملکرد پایگاه­های بارش مختلف میسر نمی­باشد، استفاده از این منبع بارشی ارزشمند می­تواند سودمند باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Assessing the Accuracy of European Center for Medium Range Weather Forecasts (ECMWF) Reanalysis Datasets for Estimation of Daily and Monthly Precipitation

نویسندگان [English]

  • Asghar Azizian 1
  • hadi ramezani etedali 2
1 Assistant Professor in Water Engineering Department/ Imam Khomeini International University
2 Assistant Professor in Water Engineering Dept/ IKIU University, Qazvin, Iran
چکیده [English]

An accurate estimation of precipitation is important and necessary for flood simulation, drought monitoring and water resources management. Currently, most parts of the world are suffering from the lack of the rain gauge observations and the spatial coverage of ground observations aren’t enough and continues. One of the most important precipitation datasets is the model-based precipitation datasets, by which the satellite techniques, the general circulation models (GCMs) and the land surface models (LSMs) are integrated to provide high temporal and high resolution datasets for all parts of the world. This datasets can compensate the lack of adequate ground observation gauges or can be considered as an alternative for ground observations, especially in ungauged regions. In this research the accuracy of the most important reanalysis datasets, called ECMWF, for estimation of daily and monthly precipitation over the SefidRood watershed for the time period of 2000-2008 was investigated. In addition, for better assessment of the proposed precipitation datasets, TRMM dataset was used. Findings on daily and monthly time scales, show that the correlation coefficient (CC) between observed and ECMWF dataset is so remarkable, especially in south, central and west parts of the study area. For instance, the CC values of the average precipitation of ECMWF data versus gauge datasets in both daily and monthly time steps were estimated to be about 0.83, 0.94, respectively, while the CC values for TRMM dataset versus gauge datasets were estimated to be 0.32 and 0.57, respectively. In contrast to reanalysed datasets, one of the most important weakness of the precipitation datasets such as TRMM is that they estimate the rainfall only based on the cloud thickness and its available water. Moreover, according to the categorical verification statistics in both time spans, ECMWF due to having low value of false alarm ratio (FAR) and high values for accuracy and probability of detection (POD) yields acceptable results over the SefidRood watershed. SefidRood watershed is a large scale region and contains different climate and topographical conditions and hence the results of this research can be used as an appropriate guidance for other similar areas. Based on the findings in this study it’s highly recommended for using this rainfall dataset as one of the best alternatives for ground observations, especially in data sparse regions that accessing to ground datasets is so hard or almost impossible.  

کلیدواژه‌ها [English]

  • Reanalysis datasets
  • ECMWF
  • Rainfall estimation
  • remote sensing
  • SefidRood Catchment

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