اصلاح و بهبود کارائی منبع بارشی مبتنی بر رطوبت خاک SM2RAIN-ASCAT در سطح ایران در گام‌های زمانی روزانه و ماهانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشگاه بین المللی امام خمینی (ره)، قزوین، ایران

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

3 مدیر تحقیقات، موسسه تحقیقات هیدرولوژی، مرکز ملی مطالعات ایتالیا، پروجیا، ایتالیا

چکیده

تخمین مناسب بارش در مطالعات مختلفی همچون هواشناسی، هیدرولوژیکی، شبیه­سازی سیلاب و پایش خشکسالی از اهمیت بالایی برخوردار است. منبع بارشی ASCAT-SM2RAIN از جدیدترین تلاش­ها بمنظور تخمین بارش برمبنای تغییرات رطوبتی سطح خاک و حل معکوس بیلان آب-خاک می­باشد. پژوهش حاضر با هدف بررسی کارایی منبع بارش ASCAT-SM2RAIN در اقلیم­های مختلف ایران و در مقیاس­های روزانه و ماهانه به انجام رسیده است. لازم بذکر است که در تحقیق حاضر از مقادیر بارش منبع SM2RAIN-ASCAT براساس 54 ایستگاه سینوپتیک واقع در سطح کشور در بازه زمانی 2007 تا 2018 استفاده شده است. همچنین بهبود کارائی این منبع بارشی با حذف اریب از داده­ها از دیگر اهداف این پژوهش می­باشد که برای این منظور روش اصلاح اریبی نگاشت چندک مورد استفاده قرار گرفته است. نتایج نشان داد که منبع ASCAT-SM2RAIN در تخمین بارش ماهانه دارای عملکرد به مراتب بهتری نسبت به مقیاس روزانه در اکثر ایستگاه­های مورد مطالعه به غیر از ایستگاه­های واقع در نوار شمالی کشور،  است. در این مقیاس زمانی و در بیش از 67 درصد ایستگاه­های مورد بررسی مقدار شاخص CC بالاتر از 65/0 می­باشد. مقدار شاخص  RMSEدر مقیاس ماهانه در اقلیم­های مختلف نشان داد که منبع بارشی SM2RAIN-ASCAT در اقلیم­های خیلی­خشک تا خشک دارای خطای به مراتب کمتری نسبت به اقلیم­های مدیترانه­ای تا خیلی مرطوب می­باشد. حذف اریب از داده­ها با استفاده از روش نگاشت چندک نیز منجر به افزایش کارائی منبع SM2RAIN-ASCAT و کاهش هشدارهای غلط در بخش­های عمده­ای از ایران گردید. به عنوان مثال، مقادیر شاخص FAR در مقیاس روزانه و اقلیم­های مختلف با بهبودی معادل 8/17 تا 1/35 درصد و درگام زمانی ماهانه با بهبودی در حدود 6/30 تا 0/59 درصد روبرو بوده است. بنابراین منبع SM2RAIN-ASCAT به صورت خام از منابع ارزشمند در تخمین بارش ماهانه­ بویژه در اقلیم­های خیلی­خشک تا خشک می­باشد، که با تصحیح اریبی می­توان بر دقت منبع مذکور در اقلیم­های مختلف افزود.

کلیدواژه‌ها


عنوان مقاله [English]

Correcting and Improving the Performance of Soil Moisture-based Product (SM2RAIN-ASCAT) Over Iran at Daily and Monthly Time Scales

نویسندگان [English]

  • Sakine Koohi 1
  • Asghar Azizian 2
  • Luca Brocca 3
1 Water Engineering Dept./ Imam Khomeini International University, Qazvin, Iran
2 Assistant Professor in Water Engineering Department/ Imam Khomeini International University
3 Director of Research, Hydrology Group of the Research Institute for Geo-Hydrological Protection, Perugia, Italy
چکیده [English]

Estimating precipitation plays an important role in different studies, including meteorological, hydrological, flood simulation, and drought monitoring. SM2RAIN-ASCAT is the newest attempt to estimate precipitation using surface soil moisture and inverse solving the soil-water balance equation. This research addressed the performance of the SM2RAIN-ASCAT dataset over diverse climate regions of Iran at daily and monthly time scales in 54 synoptic stations located in Iran (2007-2018). In addition, improving the efficiency of this product using Quantile Mapping bias correction method is another objective of this study. Results showed that the performance of SM2RAIN-ASCAT at the monthly time scale was better than the daily time scale, and in most parts of Iran, the correlation coefficient between observed and estimated datasets was relatively high. At the monthly time step, 67% of the studied stations had a CC value higher than 0.65. Moreover, findings indicated that this product tended to overestimate in Iran's north and west parts. Based on the RMSE value at the monthly time scale, SM2RAIN-ASCAT performed well at extra-arid and arid regions compared to the Mediterranean and humid climate zones. Also, removing bias from the raw dataset increased the efficiency of SM2RAN-ASCAT in most parts of the studied areas. Based on contingency table metrics, the bias-corrected dataset's skills in detecting rainy days and false alarm ratio (FAR) improved significantly. For instance, the FAR metric averagely improved 26 and 45 % at daily and monthly time scales, respectively. Finally, results indicated that SM2RAIN-ASCAT is a valuable dataset for estimating monthly precipitation, especially in arid-desert to extra-arid climates. The accuracy of this dataset increases using bias correction in different climates.

کلیدواژه‌ها [English]

  • Rainfall
  • Soil-water balance
  • SM2RAIN algorithm
  • Bias correction
  • remote sensing
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