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برآورد نقشههای فرسایندگی و بارش در مناطقی با ایستگاه بارانسنجی محدود (مطالعه موردی: استان سمنان) | ||
تحقیقات آب و خاک ایران | ||
دوره 53، شماره 9، آذر 1401، صفحه 2027-2044 اصل مقاله (2.14 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2022.343710.669279 | ||
نویسندگان | ||
الهام امینی1؛ علی اصغر ذوالفقاری* 2؛ سید حسن کابلی2؛ محمد رحیمی1 | ||
1گروه بیابانزدایی، دانشکده کویرشناسی، دانشگاه سمنان، سمنان، ایران | ||
2گروه مدیریت مناطق خشک، دانشکده کویرشناسی، دانشگاه سمنان، سمنان، ایران | ||
چکیده | ||
برای برآورد فرسایش آبی معادلات تجربی زیادی ارائه شده که معادله جهانی فرسایش خاک اصلاح شده (RUSLE) یکی از پرکاربردترین این معادلات بود. یکی از فاکتورها این معادله، فرسایندگی باران (R) میباشد. برای محاسبه مستقیم R نیاز به دادههای دقیقهای بارش است که در تعداد محدودی از ایستگاههای سینوپتیک وجود دارد و دسترسی به این دادهها با مشکلاتی همراه است. در این پژوهش با استفاده از دادههای در دسترس مانند متوسط بارش سالانه، مقدار فرسایندگی باران برآورد شد. استان سمنان با وسعتی برابر با 96816 کیلومتر مربع، دارای تعداد محدودی ایستگاه سینوپتیک و بارانسنجی است، که برآورد فرسایندگی باران را در این استان، دشوار میکند. در این مطالعه به منظور برطرف ساختن کمبود ایستگاههای بارانسنجی و سینوپتیک، از متغیرهای کمکی شامل ارتفاع (DEM)، پوشش گیاهی نرمال شده (NDVI)، دمای سطح زمین (LST) و دادههای شبکهای جهانی بارش ""Open Land Map Precipitation(LMP) که بیشترین ارتباط را با بارش داشتند، استفاده شد. برای این منظور ابتدا با استفاده از دادههای کمکی و مدل غیرخطی جنگل تصادفی (RF) نقشه بارش استان تهیه شد. در ایستگاههای سینوپتیک مقدار فرسایندگی بر اساس شاخص EI30 و متوسط بارندگی سالانه ایستگاهها تعیین و ارتباط بین بارش و فرسایندگی باران با استفاده از رگرسیون غیرخطی تعیین شد. نتایج نشان داد مقدار مجذور میانگین مربعات خطا RMSE)) و ضریب همبستگی (r) مدل RF در برآورد بارش ایستگاههای مورد توجه برابر با 9/16 میلیمتر و (p<0.01) 98/0 بود، که نشان از دقت بالای این مدل در برآورد بارش استان میباشد. نقشه بارش استان نشان داد که میزان بارش سالانه مناطق مختلف استان بین 420-70 میلیمتر متغیر میباشد. نقشههای طبقهبندی بارش نشان داد که نیمی از استان دارای بارش کمتر از 100 میلیمتری میباشد. 30 درصد استان بارشی بین 100 تا 150 میلیمتری دارند و تنها حدود 17 درصد از سطح استان بارشی بیش از 150 میلیمتر را دارا است. بررسی رگرسیونهای خطی و غیرخطی نشان داد که تابع توانی به خوبی قادر به برآورد فرسایندگی باران با استفاده از دادههای متوسط بارش سالانه بود. بهطوری که ضریب همبستگی معادلهی که فرسایندگی را به عنوان تابعی از بارش برآورد میکند، برابر با **96/0 بدست آمد. بیشترین و کمترین مقادیر فرسایندگی باران به ترتیب برابر با 380 و 39 MJha-1mm h-1year-1 در مناطق شمالی و جنوبی استان بدست آمد. نتایج این مطالعه نشان داد که استفاده از شیوههای نوین دادهکاوی جهت تهیه نقشه بارش و مدلسازی و پردازش در محیط برنامهنویسی در مناطق-ی با تعداد معدود ایستگاههای بارانسنجی، تهیه نقشههای دقیق بارش و فرسایندگی باران را ممکن میسازد. | ||
کلیدواژهها | ||
متغیرهای کمکی؛ دادههای شبکهای بارش؛ مدل جنگل تصادفی | ||
عنوان مقاله [English] | ||
Estimation of Rainfall Erosivity Map in Areas with Limited Number of Rainfall Station (Case study: Semnan Province) | ||
نویسندگان [English] | ||
Elham Amini1؛ Ali Zolfaghari2؛ Hasan Kaboli2؛ Mohammad Rahimi1 | ||
1Department of desertification, Faculty of Desert Science, Semnan University, Semnan, Iran. | ||
2Department of Arid lands management, Faculty of Desert Science; Semnan University. Iran. | ||
چکیده [English] | ||
Water erosion is one of the most important challenges of agriculture and watershed management in the world and it has been considered by many researchers. To estimate water erosion, many experimental models have been proposed, of which the Revised Universal Soil Loss Equation (RUSLE) is one of the most widely used models for estimation of soil erosion. Rainfall erosivity (R) is one of the factors in this model. Direct calculation of R required meteorological gauge stations which are available at a limited number of synoptic stations. In this study, the attempt was to estimate rainfall erosivity using available data such as annual rainfall. Semnan province, with an area of 96816 km2, has a limited number of synoptic and rain gauge stations, makes it difficult to estimate rain erosivity in this province. In this study the auxiliary variables including digital elevation model (DEM), normalized vegetation index (NDVI), land surface temperature (LST) and global precipitation network data "Open Land Map Precipitation" (LMP) were used for spatial prediction of annual rainfall. The rainfall map of the study area was prepared using auxiliary data and using random forest (RF) model. Also in synoptic stations, the amount of erosivity was determined based on the EI30 index and average annual rainfall. Finally, the relation between rainfall and erosivity and annual rainfall was determined using nonlinear regression. Root mean square error (RMSE) and correlation coefficient (r) of RF model for prediction of annual rainfall were 16.9 mm and 0.98, respectively. The results of the rainfall map in the study area showed that the rainfall varied between 70-420 mm year-1. Rainfall classification maps showed that near the half of the study area has annual rainfall less than 100 mm, 30% of the province has annual rainfall of between 100 and 150 mm and only about 17% of the province has annual rainfall more than 150 mm year-1. The maximum and minimum values of erosivity were 380 and 39 MJha-1mm h-1year-1 in the northern and southern part of the study area, respectively. Our results indicated using new method of data mining, it is possible to spatial prediction of rainfall and erosivity, especially in areas with small number of synoptic stations. | ||
کلیدواژهها [English] | ||
Auxiliary variables, Network precipitation data, Random Forest model | ||
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