تعیین بهترین رابطۀ دبی‌ـ رسوب معلق بر اساس تقسیم‏ بندی‏ های زمانی و ضرایب تصحیح مختلف (مطالعۀ موردی: رودخانۀ بشار)

نوع مقاله : پژوهشی

نویسندگان

1 ‏آموختۀ کارشناسی ارشد سازه‏ های آبی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان

2 استادیار گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان

10.22059/ije.2023.356861.1719

چکیده

استفاده از معادلات پیچیدۀ هیدرولیکی برای تخمین رسوب نیاز به استفاده از داده‏های زیادی دارد که در بسیاری از رودخانه‌ها در دسترس نیستند. بنابراین، برای برآورد رسوب از معادلات ساده‏تری همچون منحنی سنجه استفاده می‏شود. در برخی موارد منحنی‏های سنجه دقت مناسبی ندارند. برای رفع این مشکل، از ضرایب اصلاحی و تقسیم‏بندی داده‏ها بر مبنای عوامل مختلف استفاده می‏شود. تحقیق حاضر، با هدف امکان‏سنجی استفاده از روش‏های مختلف اصلاحی و تقسیم‏بندی داده‏ها برای افزایش دقت در استفاده از این منحنی‏ها در ایستگاه‏های پاتاوه، قلات و شاه‌مختار رودخانۀ بشار استان کهگیلویه و بویراحمد انجام شد. مد‏ل‏های مختلف منحنی سنجه با استفاده از 7 شاخص خطاسنجی ارزیابی شدند. نتایج نشان داد هرگونه تقسیم‏بندی زمانی داده‏ها، دقت تخمین رسوب را افزایش می‏دهد و تقسیم‏بندی ماهانه و فصلی داده‏ها، به ‏طور میانگین، به‏ترتیب 61 و 36 دقت تخمین‏ها را افزایش داد. اگرچه استفاده از مدل حد واسط داده‏ها 70 درصد امتیاز بیشتری از مدل سالیانه داشت‌، استفاده از روش اصلاحی فائو موجب کاهش 22 درصدی دقت برآوردها شد. به طور کلی، با وجود تأثیر مثبت مدل‌های اقلیمی، هیدرولوژیکی و پوشش گیاهی بر افزایش دقت معادلات سنجه، به‌ترتیب به میزان 35، 28 و 20 درصد، هیچ‏یک مدل برتر نبودند. نتایج تحقیق تأیید کرد که تولید و انتقال رسوب معلق در رودخانه تحت تأثیر عوامل مختلفی از جمله عوامل اقلیمی و پوشش گیاهی قرار دارد. توجه به این تغییرات در استفاده از معادلات دبی‌ـ رسوب موجب افزایش دقت در برآورد رسوب رودخانه می‏شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Determining the best discharge-suspended sediment relationship based on different time classifications and correction coefficients (Case study: Bashar River)

نویسندگان [English]

  • Javad Daneshi 1
  • Amir Naserin 2
  • Saied Jalily 2
1 Graduated Student in Water Structures, Agricultural Sciences and Natural Resources University of Khuzestan, Khuzestan, Iran
2 Assistant Professor, Department of Water Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Khuzestan, Iran
چکیده [English]

Using complex hydraulic equations to estimate river sedimentation requires a lot of data that is not available in many rivers. Therefore, simpler equations such as the Sediment rating curve (SRC) are used to estimate river sediment load. In some cases, rating curves are not accurate enough. To solve this problem, applying correction coefficients and temporal classification of discharge and sediment data based on the different factors. The present study was conducted with the aim of evaluating the effect of different correction methods and data classification to increase the accuracy of SRC in the Bashar river, southwest of Iran. The models were examined by 7 statistical criteria. The results showed that any temporal classification of the data increases the accuracy of sediment estimation and applying monthly and seasonal models, on average, increased the accuracy of estimates by 36 and 61 percent. Although using the middle of the classes model had 70% more points than the annual model. However, using the FAO correction method caused a 22% decrease in the accuracy of the estimates. In general, despite the positive effect of climatic, hydrological and vegetation models on increasing the accuracy of SRC, by 35, 28 and 20%, respectively, none of the models were superior. The results confirmed that the production and transfer of suspended sediment in the river are influenced by various factors such as climatic factors and especially the presence of vegetation. Paying attention to these changes in the use of discharge-sediment equations increases the accuracy of SRCs.

کلیدواژه‌ها [English]

  • Climate Condition
  • Flood
  • Rating curve
  • Vegetation

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اکوهیدرولوژی
دوره 10، شماره 1
فروردین 1402
صفحه 113-125

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سابقه مقاله

  • تاریخ دریافت: 11 دی 1401
  • تاریخ بازنگری: 11 بهمن 1401
  • تاریخ پذیرش: 12 اسفند 1401

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