بررسی تاثیر اصلاح اریبی بر بهبود کیفی داده‌های بارش ریز مقیاس سازی شده NEX-GDDP

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 استاد گروه مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

3 استادیار مؤسسه تحقیقات حفاظت از خاک و آبخیزداری، سازمان تحقیقات کشاورزی، آموزش و ترویج، تهران، ایران.

چکیده

استفاده از داده‌های دما و بارش ریزمقیاس‌سازی شده مرکز تبادلات داده‌های زمینی ناسا (NEX-GDDP) به عنوان یکی از محصولات مستخرج از مدل‌های اقلیمی جهانی به سرعت در حال گسترش است. بررسی کیفی داده‌های بارش این محصول در ایران می‌تواند پژوهشگران را در استفاده آگاهانه از آن در مطالعات هیدرولوژی و منابع آب یاری نماید. در این پژوهش ابتدا میزان کارایی داده‌های بارش ماهانه مدل‌ ACCESS1-0 از محصول NEX-GDDP با داده‎های مدل GCM متناظر با آن و نیز داده‌های مشاهداتی ایستگاه‌های واقع در هشت ناحیه همگن بارشی ایران مورد مقایسه قرارگرفت و سپس میزان بهبود کیفی مقادیر بعد از اصلاح اریبی به روش نگاشت چندک (QM) با استفاده از پنج تابع SSPLINE، QUANT، PTF، RQUANT و DIST مقایسه شد. بررسی کارایی داده‌های بارش ماهانه مدل منتخب از محصول NEX-GDDP در مقایسه با داده‌های GCM خام آن نشان داد که آماره‌‌های R ،PBIAS ،NSE  و KGE به ترتیب در %75، %100، %100 و %88 ایستگاه‌های مورد مطالعه به مقدار قابل توجهی بهبود یافته است و همبستگی داده‌های اصلاح اریبی شده با داده‌‎های مشاهداتی در %50 ایستگاه‌ها، PBIAS در تمامی ایستگاه‌ها، NSE و KGE نیز به ترتیب در %75 و %62/5 ایستگاه‌ها بهبود یافته است. این بررسی همچنین نشان داد که از میان توابع مورد استفاده، تابع RQUANT بهترین کارایی را در اصلاح اریبی داده‌ها داراست.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigating the Effect of Bias Correction on Quality Improvement of NEX-GDDP Downscaled Precipitation Data

نویسندگان [English]

  • Vahid Ghalami 1
  • Bahram Saghafian 2
  • Tayeb Raziei 3
1 Ph.D. Candidate, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 Assistant Professor, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREO), Tehran, Iran.
چکیده [English]

The use of temperature and precipitation data from the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) is increasingly expanding as one of the products derived from the global climate models. Investigating the quality of this product’s precipitation data in Iran can help the researchers to consciously use them in hydrological and water resources practices. In this study, first, the degree of improvement of the monthly precipitation data of ACCESS1-0 model from the NEX-GDDP product was investigated against the corresponding GCM model as well as the observational data measured at stations located in eight homogeneous precipitation regions of Iran. Then the NEX-GDDP data was bias-corrected using the Quantile Mapping (QM) method through using the SSPLINE, QUANT, PTF, RQUANT and DIST functions. Comparison of monthly precipitation data of the selected models of the NEX-GDDP product with its raw GCM data showed that R, PBIAS, NSE, and KGE statistics have significantly improved respectively in 75%, 100%, 100%, and 88% of the studied stations. The correlation between the bias-corrected data and the observational data was also improved in 50% of the stations, the NSE and KGE were improved respectively in 75% and 62.5% of the stations, and PBIAS was improved in all stations. This study also showed that among the used bias correction functions, the RQUANT had the best performance.

کلیدواژه‌ها [English]

  • Bias Correction
  • NEX-GDDP Dataset
  • Precipitation
  • QM
  • GCM
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