برآورد مقدار رسوبگذاری جادۀ جنگلی با مدل فرسایش سطحی واشینگتن و ارزیابی میزان اثر ورود متغیر دیوارۀ خاکریزی بر آن

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته دکتری جنگلداری، دانشکدۀ منابع طبیعی، دانشگاه گیلان، صومعه‌سرا، ایران

2 استاد گروه جنگلداری، دانشکدۀ منابع طبیعی، دانشگاه گیلان، صومعه‌سرا، ایران

3 استادیار گروه مرتع و آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه گیلان، صومعه‌سرا، ایران

4 استاد گروه مهندسی جنگل، دانشکدۀ فنی بورسا، ترکیه

10.22034/ijf.2023.376647.1908

چکیده

جاده‌های جنگلی یکی از منابع تولید رسوب در حوضه‌های آبخیز کوهستانی محسوب می‌شوند که پیش‌بینی مقدار رسوبات حاصل از آنها اهمیت زیادی در فرایندهای مدیریتی دارد. یکی از کارامدترین مدل‌های تخمین مقدار رسوبات حاصل از جاده‌های جنگلی، مدل فرسایش سطحی واشینگتن است که در جنگل‌های ایالات متحده تولید و برای استفاده به کل جهان عرضه شد؛ اما محدودیت اصلی آن استفاده نکردن از داده‌های مربوط به دامنۀ خاکریزی است. از این‌رو با توجه به ویژگی‌های خاص جنگل‌های شمال ایران از جمله شیب زیاد دامنه‌ها و نوع گونه‌های درختی و درختچه‌ای، این عامل به‌عنوان فاکتور احتمالی در تغییر نتایج مدل بررسی شد. این پژوهش در سری سه طرح جنگلداری چلوند واقع در حوزه یک جنگل‌های شمال ایران انجام گرفت. طول کل جاده‌های بررسی‌شده 5/5 کیلومتر بود که به 48 قطعه نمونه با ویژگی‌های همگن تقسیم و اطلاعات لازم برای اجرای مدل در آنها برداشت شد. نتایج مدل، مقدار تخمینی رسوبات حاصل از سطح جاده و دامنۀ خاکبرداری را 786/94 تن در کیلومتر بر سال برآورد کرد. سپس اطلاعات مربوط به دامنۀ خاکریزی نیز وارد و مدل دوباره اجرا شد. مقدار رسوبات حاصل از دامنۀ خاکریزی 4/3 تن بر کیلومتر در سال پیش‌بینی شد. مقایسۀ نتایج مدل در حضور و نبود اطلاعات دامنۀ خاکریزی با استفاده از آزمون تی نشان داد که تفاوت مشاهده‌شده بین نتایج حاصل معنی‌دار نیست. از این‌رو می‌توان نتیجه گرفت که اضافه کردن ویژگی‌های مربوط به دامنۀ خاکریزی نمی‌تواند تأثیر معنی‌داری در تغییر نتایج نهایی حاصل از مدل در منطقۀ پژوهش داشته باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Estimating the amount of forest road sedimentation employing Washington Surface Erosion Model and evaluating the effect of fillslope variables on it

نویسندگان [English]

  • A Ghomi Motazeh 1
  • R Naghdi 2
  • E Taghvaye Salimi 3
  • A.E Akay 4
1 Ph.D. of Forestry, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran
2 Prof., Dept. of Forestry, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran
3 Assistant Prof., Dept. of Range and Watershed management, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran
4 Prof., Dept. of Forest Engineering and Technologies, Bursa Technical University, Bursa, Turkey
چکیده [English]

Forest roads are recognized as one of the primary sources of sediment production in mountainous watersheds. Predicting the amount of sediment they produce is crucial for effective management. The WARSEM model, developed in the forests of the United States and used globally, is one of the most efficient models for estimating sediment from forest roads. However, a significant limitation of this model is the lack of fillslope data. Given the unique characteristics of the northern forests of Iran, such as steep slopes, this study investigated the potential impact of this factor on the model's results. The study was conducted in the third district of the Chelavand forestry project, located in the natural resources area of Astara city in Northwestern Iran. The total length of the roads studied was 5.5 km, divided into 48 samples with homogeneous characteristics. Essential information was collected for the implementation of the model. The model estimated the amount of road and cutslope sediment to be 94.786 tons per kilometer per year. After incorporating information about the fillslope area, the model was run again. The predicted amount of sediment from the fillslope area was 3.4 tons/km per year. A t-test analysis revealed that the observed difference between the results was not significant. Therefore, it can be concluded that adding features related to the fillslope area does not significantly affect the final results of the model in the study area.

کلیدواژه‌ها [English]

  • Forest road
  • Erosion
  • Predictive models
  • Sedimentation
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