نقشه برداری زمانی مکانی کربن آلی خاک با استفاده از رویکرد مدلسازی معکوس : مطالعه موردی حوضه آبخیز زاینده‌رود استان اصفهان

آب و خاک

مقاله 5، دوره 39، شماره 3 - شماره پیاپی 101، مرداد و شهریور 1404، صفحه 312-291   

نوع مقاله: مقالات پژوهشی

شناسه دیجیتال (DOI): 10.22067/jsw.2025.94129.1488

نویسندگان

شیما معصومی¹؛ حمیدرضا متین فر^{* 1}؛ سید روح اله موسوی²

¹گروه علوم و مهندسی خاک، دانشگده کشاورزی، دانشگاه لرستان، ایران

²گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تهران، کرج، ایران

چکیده

تغییرات زمانی و مکانی کربن آلی خاک تحت تأثیر عوامل متعددی از جمله کاربری اراضی، شرایط اقلیمی، توپوگرافی و فعالیت‌های انسانی قرار دارد. با توجه به جایگاه کربن آلی در بهبود کیفیت خاک، پژوهش حاضر با هدف بررسی تغییرات زمانی و مکانی کربن آلی خاک (SOC) با استفاده از رویکرد مدلسازی معکوس بر مبنای مدل مکانی تهیه شده در سال 2024 و تعمیم آن به سال‌های ماقبل 2015، 2010 و 2000 با استفاده از متغیرهای محیطی در حوضه آبخیز زاینده رود انجام گردید. همچنین سه مدل یادگیری ماشین جنگل تصادفی (RF)، رگرسیون بردار پشتیبان (SVR) و درخت تقویت شده با گرادیان افراطی (XGBoost) برای بررسی ارتباط بین فاکتورهای محیطی و SOC به همراه دو رویکرد کمی سازی عدم قطعیت پیش‌بینی شامل بوتسراپت و k-فولد استفاده گردید. نتایج اعتبارسنجی کارایی مدل‌های یادگیری ماشین حاکی از دقت بالاتر مدل RF نسبت به دو مدل دیگر بود. اگرچه بطور مشابه در هر سه مدل روند دقت پیش‌بینی SOC از سال 2024 به 2000 کاهش یافت. همچنین رویکرد عدم قطعیت بوتسراپت بر اساس آماره‌های انحراف معیار و میانگین عدم قطعیت از اطمینان بالاتری در پیش‌بینی SOC برخوردار بود. اهمیت نسبی متغیرهای محیطی نیز نشان دهنده فاکتورهای اقلیمی و توپوگرافی از درجه اهمیت بالاتری در پیش‌بینی SOC بودند. همچنین، تغییرات مکانی SOC با روند الگوی پراکنش متغیرهای اقلیمی و توپوگرافی همخوانی داشت، به طوری که مناطق با بارش و ارتفاع بیشتر به‌همراه دمای پایین‌تر، محتوای SOC بیشتری را نشان دادند. نقشه‌های پیش-بینی مکانی و زمانی نیز بیانگر افزایش سطح اراضی با محتوای SOC بسیار پایین (کمتر از 5/0 درصد) در طی دوره زمانی بلند مورد بررسی از 2000 تا 2024 بود. بطورکلی رویکرد مورد استفاده در این پژوهش می‌تواند به‌عنوان یک چارچوب مناسب برای دست‌یابی به روند تغییرات ویژگی‌های خاک در مناطقی باشد، که بطور عمده فاقد پایگاه داده‌های اطلاعات مکانی خاک در گذشته می‌باشند.

کلیدواژه‌ها

نقشه‌برداری رقومی متغیرهای محیطی؛ الگوریتم‌های یادگیری ماشین؛ تغییرپذیری زمانی و مکانی

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