Environmental Erosion Research

fa سنجش قابلیت نقشه برداری رقومی خاک در ارزیابی خطر فرسایش با استفاده از معادله‌ی جهانی تلفات خاک اصلاح شده درحوضه آبخیز دامغان‌رود Erosion risk assessment using Revised Universal Soil Loss Equation and Digital Soil Mapping in Damghan Rood watershed. مدلسازی و تحلیل زمانی و مکانی رخداد انواع مختلف فرسایش محیطی مستخرج از پایان‌نامه / رساله / طرح پژوهشی <div style="text-align: justify;">فرسایش خاک، تحت تأثیر عواملی چون تغییر اقلیم،تخریب جنگل‌ها، کشاورزی نامناسب و مدیریت نادرست منابع، یکی از چالش‌های مهم زیست‌محیطی است. این پژوهش باهدف سنجش قابلیت نقشهبرداری رقومی خاک (DSM) در افزایش دقت ارزیابی خطر فرسایشخاک در حوضه آبخیز دامغان‌رود بااستفاده از مدل RUSLE  به منظور رفع محدودیت‌های موجود در مدل‌سازی خطر فرسایش در مقیاس‌های محلی انجام شد. تعداد 112 نقطه با استفاده از روش مکعب لاتین (cLHS) انتخاب و نمونهبرداری از خاک و پوششگیاهی صورت گرفت. نقشه‌های عوامل C، P، LS، K و R در نرم‌افزارهای R-STUDIO و QGIS تهیهشد. نتایج اولیه مدلسازی نشانداد که میانگین فرسایش سالانه در حوضه برابر با 58/36 تن در هکتار است. بررسی شرایط منطقه و دادههای واقعی نشاندهنده بیشبرآورد مدل بود. ازاینرو عامل C مجددا با استفاده از نقشه‌ی درصد پوشش و سنگوسنگریزه محاسبهشد و وارد مدل گردید. اینبار مقدار فرسایش 81/19 تن در هکتار برآرود گردید و دقت مدلسازی نزدیکبه 50 درصد افزایش یافت. طبقه‌بندی نقشه خطر فرسایش نشانداد که 86/19 درصد مساحت حوضه در کلاس فرسایش شدید (بیش از 30 تن در هکتار)، 53 درصد در کلاس فرسایش متوسط (10 تا 30 تن در هکتار) و 28 درصد در کلاس فرسایش کم (کمتر از 4 تن در هکتار) قرار دارد. بررسی بافت خاک نشاندادکه درصد بالای سیلت عامل اصلی فرسایش زیاد در برخی مناطق است، درحالیکه مناطق دارای خاک شنی یا پوششگیاهی مناسب، فرسایش کمتری را تجربه میکنند. این یافته‌ها نشان می‌دهد استفاده از DSM برای تهیه نقشههای ورودی مدل RUSLE و محاسبه عامل C با استفاده از دادههای درصد پوشش و سنگوسنگریزه، می‌تواند به‌طور دقیق‌تری دینامیک واقعی فرسایش را نشانداده و راهنمای مؤثری برای استراتژی‌های حفاظتی خاک ارائه دهد.</div> <div style="text-align: justify;"> </div> 1- Introduction Soil erosion is one of the major global environmental challenges that threatens soil quality, natural resources, ecosystems, agriculture, and water resources. This phenomenon has particularly severe negative impacts on environmental sustainability and food security in arid and semi-arid regions such as Iran. Numerous models have been developed to predict and manage soil erosion, and the RUSLE model has gained widespread use due to its effective performance in Iran's dry and semi-arid areas. Implementing this model at large scales requires accurate spatial data, which can be obtained by combining ground data with remote sensing and applying geostatistical methods such as Digital Soil Mapping (DSM). DSM, by integrating field data, remote sensing, and auxiliary variables, provides high accuracy in identifying spatial patterns of erosion and facilitates the production of maps with reduced uncertainty. The aim of this study is to improve the accuracy of soil erosion hazard assessment in the Damghan Rud watershed using the RUSLE model and integrating it with DSM and machine learning methods. This approach can help identify erosion-prone areas and enhance natural resource management. . 2. Methodology The present study was conducted in the Damghan Rud watershed area, located in the northwest of Damghan along the Damghan-Kiasar road, with an area of 1300 square kilometers. This region exhibits topographic diversity, including mountainous areas in the north and plains in the south, with an elevation range between 1390 and 1538 meters. The annual precipitation in the watershed varies between 57.75- and 249.22-mm. Soil sampling was performed using the Cubic Latin Hypercube Sampling (cLHS) method, and auxiliary variables such as Landsat 8 spectral bands, vegetation indices, and topographic features were utilized. In this study, 112 soil samples were collected from the depth of 0 to 30 cm, and physical and chemical analyses including clay, silt, sand, pH, EC, and organic matter content were conducted. To predict soil properties and the erodibility factor (K), Digital Soil Mapping (DSM) was employed using remote sensing data from Landsat 8, Digital Elevation Model (DEM), and Random Forest models. Model evaluation using statistical indicators such as R², RMSE, MAE, and ME showed that combining local data and remote sensing improved modeling accuracy and reduced errors. In estimating soil loss using the RUSLE model, the main formula A=RKLSCP was applied, where the rainfall factor (R) was calculated from the regional precipitation map and synoptic data, the soil erodibility factor (K) was determined based on the clay, silt, sand, organic matter content, and soil structure, the topographic factor (LS) was extracted using the DEM and empirical relations, and the vegetation cover factor (C) was assessed using the NDVI index and field data. 3- Results The results of the present study in the region indicate that the factors of rainfall (P), rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), and vegetation cover (C) play a key role in the extent of soil erosion. The average annual precipitation is 285 mm, and the average rainfall erosivity index is 226 MJ mm ha⁻¹ h⁻¹ y⁻¹, increasing from east to west and from south to north. The soil in the region has a high sand content (52.93%) and low organic carbon content (0.42%), which increases its erodibility (with an average K value of 0.038). The region's topography, with an average slope of 12.96% and an average elevation of 2018 meters, plays an important role in the intensity of erosion. Vegetation cover, with a C factor ranging from 0.54 to 1 and an average of 0.92, is effective in reducing erosion in areas with adequate vegetation cover. Overall, the erosion mapping revealed that the erosion risk class exceeding 30 tons per hectare covers the largest area (40.97%), while areas with less than 4 tons of erosion per hectare are primarily concentrated in the central and lower parts of the watershed. These findings highlight the importance of sustainable soil management and conservation measures in the region. 4- Discussion & Conclusions The present study was conducted to assess the potential of Digital Soil Mapping (DSM) in improving the accuracy of soil erosion hazard evaluation in the Damghan Rud watershed using the RUSLE model. The findings indicated that soil erosion is significantly influenced by various factors such as soil texture, topography, vegetation cover, and conservation practices. The RUSLE model estimated soil loss to be approximately 19.81 tons per hectare per year. However, when compared with the actual conditions of the region, it was observed that this model overestimates the erosion rates. The use of more accurate data, including vegetation cover percentage maps and gravel percentage, improved the model's accuracy. The study confirmed the significant impact of high sand content, slopes exceeding 10%, and reduced vegetation cover on increasing soil erosion. Additionally, conservation practices such as terracing and cultivation along contour lines reduced the P factor and were effective in controlling erosion. The results demonstrated that the RUSLE model has a high capability in estimating erosion, and combining field data with Digital Soil Mapping (DSM) enhances the modeling accuracy. This approach is recommended for similar regions.     فرسایش خاک, نقشه برداری رقومی, RUSLE, ارزیابی خطر فرسایش خاک. Climate change, Climate disasters, Erosion projection, Minab basin, RUSLE. 57 81 http://magazine.hormozgan.ac.ir/browse.php?a_code=A-10-710-3&slc_lang=fa&sid=1 Fatemeh Zahra Heydariyeh فاطمه زهرا حیدریه fheydariyeh@gmail.com 10031947532846009451 10031947532846009451 No Desertificatin Department, Faculty of Desert Studies, University of Semnan, Semnan رشته مدیریت و کنترل مناطق بیابانی، دانشکده کویر شناسی و گردشگری، دانشگاه سمنان Ali Asghar Zolfaghari علی اصغر ذوالفقاری azolfaghari@semnan.ac.ir 10031947532846009452 10031947532846009452 Yes Desertificatin Department, Faculty of Desert Studies, University of Semnan, Semnan گروه مدیریت و کنترل مناطق بیابانی، دانشکده کویرشناسی و گردشگری، دانشگاه سمنان Seyed Hasan Kaboli سید حسن کابلی hkaboi@semnan.ac.ir 10031947532846009453 10031947532846009453 No Desertificatin Department, Faculty of Desert Studies, University of Semnan, Semnan گروه مدیریت و کنترل مناطق بیابانی، دانشکده کویر شناسی و گردشگری، دانشگاه سمنان Shima Nikoo شیما نیکو shimanikoo@semnan.ac.ir 10031947532846009454 10031947532846009454 No Desertificatin Department, Faculty of Desert Studies, University of Semnan, Semnan گروه بیابانزدایی، دانشکده کویر شناسی و گردشگری، دانشگاه سمنان