دانلود مقاله ترجمه شده کاربرد مدل WEPP در شبیه سازی تولید رسوب و رواناب از حوزه آبخیز مرتفع – مجله الزویر

فهرست مطالب:

 چکیده
مقدمه
توصیف منطقه مورد مطالعه
کشاورزی دامنه ای
نگاهی بر مدل حوزه ای WEPP
روش
داده های هواشناسی
داده های هیدرولوژیک
اندازه گیری میدانی خواص خاک
مدل ارتفاع رقومی برای تعیین خصوصیات ابخیز
داده های ماهواره ای
کاربرد مدل wepp
ورودی های مدل
ارزیابی عملکرد مدل
معیارهای ارزیابی مدل
نتایج و بحث
شبیه سازی رواناب
آنالیزهای حساسیت
شبیه سازی تولید رسوب
ارزیابی سناریوهای مدیریت ساختمان و پوشش بنیان
مدیریت کارامد حوزه
ارزیابی عملیات مدیریت زراعی
تیمار شخم
مدیریت ساختمانی
اثر مجموعه بهترین عملیات مدیریتی و ساختارهای حفاظتی
نتیجه گیری

بخشی از ترجمه:

مدل WEPP تولید رسوب و رواناب را به طور رضایت بخشی در شرایط شیب و بارش زیاد حوزه با ضریب ناش سات کلیف بیش از ۰٫۸۷ درصد و انحراف معیار ۵٫۲۳ شبیه سازی کرد. مقایسه بین مقادیر شبیه سازی و اندازه گیری شده تولید رسوب و رواناب نشان داد که مدل مقادیر بالا را کم تر از مقدار واقعی براورد می کند. مطالعات بیشتر در خصوص اجزای زیزسطحی پارامتر های مدل می تواند برای بهبود قدرت براورد مدل مفید باشد.
نتایج شبیه سازی نشان می دهد که سویا و بادام زمینی پتانسل تعویض با برنج دیم برای کاهش تولید رسوب به ترتتیب تا ۲۹٫۶۰ و ۲۷٫۷۰ دارند.
نتایج شبیه سازی نشان می دهد که جایگزینی عملیات شخم سنتی با سیستم بدون شخم و کولتیواتور زارعی می تواند منجر به کاهش تولید رسوب تا ۲۱٫۸۸ و ۱۳٫۱۴ % شود.
نتایج شبیه سازی نشان می دهد که نصب ۲۶ سد سنگی متخلخل و موانع رسوب گیر در حوزه امروی می تواند تولید رسوب را تا ۵۴٫۶درصد کاهش دهد.
نتایج به وضوح نشان داد که مدیریت شخم وزراعی و کنترل سازه ای به تنهایی قادر به کاهش تولید رسوب ورواناب نمی باشند. شبیه سازی مجموعه روش های کنترلی بیولوژیکی، شخم و سازه ای دارای پتانسیل کاهش تولید رسوب تا ۷۸٫۴۰% می باشد. تناوب کشت ذرت با سویا در ارتفاعات برای کاهش فرسایش خاک و رفع نیازهایی غذایی لازم است.
مدل واسنجی و اعتبارسنجی شده WEPP می تواند به طور موفقیت آمیزی برای توسعه راهبرد های بیولوژیک و ساختمانی در شرایط با شیب و بارش زیاد شرق هیمالیا مورد استفاده قرار گیرد.

بخشی از مقاله انگلیسی:

۱٫ Introduction Food security and environment protection are the most challenging task the world is facing today. Land and water resources are important components of environment. Degradation of the quality of these resources will affect the quality of the environment. Major portion of the food consumed by human being comes from the land. Due to demographic pressure land use is intensified and erosion prone area is brought under subsistence agriculture leading to land degradation. Soil erosion is an irreversible phenomenon causing land degradation and deterioration of surface water quality. It is caused due to inappropriate land use and poor management. Soil degradation is responsible for making 0.3–۰٫۸% of the world’s arable land unsuitable for agricultural production every year and an additional 200 million ha of cropped area would be required over the next 30 years to feed the increasing population (den Biggelaar et al., 2004; Lafond et al., 2006). Therefore, this precious finite resource must be safeguarded against all kinds of degradation and deterioration for sustainability of agricultural production and environment protection. In India, out of the total geographical area of 329 million ha, approximately 145 million ha of the total land resource is subjected to various degrees of wind and water erosion, which cause a loss of 5.3 million Mg of soil every year. In eastern Himalayan region of India, soil erosion by water is a major factor causing land degradation. About one third area of the region suffers from various forms of land degradation problems (Sehgal and Abrol, 1994). Practice of traditional slash-and-burn agriculture on steep slopes, intense rainfall, undulating and sloppy land surface compounds the problem many folds. About 386 900 ha area in the region is affected by shifting cultivation and as high as 76.6 Mg ha1 yr1 of soil is lost due to this system of farming (Satapathy, 1996). Eighty percent area is under threat of moderate to severe erosion. To combat the threat of land degradation, we need to understand physical process of erosion in relation of topography, land use and management. Watershed being a natural drainage unit should form the basis for planning various land uses and conservation measures to optimize the use of soil and water resources to increase and sustain agricultural production. In order to develop a comprehensive plan for soil and water conservation, it is essential to estimate runoff and soil loss resulting from different crop and structure-based management practices. Among the available tools for soil erosion assessment, simulation models are quite important because appropriate models can be used to evaluate a variety of management scenarios without costly and time consuming field tests (Pieri et al., 2007). In recent past a trend of testing and using physical process based runoff and soil erosion prediction models both at field as well as at watershed scales has gained momentum. These physical process based models, often with an explicit attempt to describe runoff and erosion processes, are better equipped to evaluate the impacts of management interventions and help make management decisions aimed at preserving land productivity and environment quality (Yu and Rosewell, 2001). Before application, such models need to be properly calibrated and validated using measured hydrologic data of the watershed. Lack of reliable field-measured data for calibration and validation of models is the major limitation for their application. Runoff and soil loss data can be obtained through field measurements and watershed attributes could be derived using remotely sensed data and digital elevation model of the watershed (Amore et al., 2004; Baigorria and Romero, 2007). In hilly watersheds spatial and temporal variability in terms of soil, land use/land cover, topography, rainfall and biotic interventions are large. The runoff, sediment, fertilizers, pesticides and other pollutants, resulting from the agricultural activities cause deterioration of quality of surface and ground water. Methods for identification of sources of pollutants and their quantification are essential for their control. Distributed parameter watershed models are applicable for this type of assessment. Review of literatures revealed that CREAMS (Knisel, 1980), ANSWERS (Beasley et al., 1982), SWRRB (Arnold et al., 1990), AGNPS (Young et al., 1989), EPIC (Sharpley and Williams, 1990) and SWAT (Arnold et al., 1998) are probably the most important models and widely used worldwide for hydrologic and pollutants transport modeling. The main erosion components of these models are based on concepts originally formulated in the Universal Soil Loss Equation (USLE) model (Wischmeier and Smith, 1978), which relate the total amount of soil loss to six factors: rainfall erosivity, soil texture, slope length and grade, soil cover and conservation practice. Runoff components of these models use the SCS–CN method to predict runoff (Bingner, 1990; Schröder, 2000). Among these models only SWAT, AGNPS and ANSWERS are the distributed parameters physically based watershed scale models mostly used for low slope conditions (Arnold et al., 1998; Bingner et al., 1992; Schröder, 2000). Any model used for computing potential soil loss from an area must deal with a large number of parameters concerning vegetation, management, soil, topography and climate (Amore et al., 2004). Performance of a model depends upon number of parameters it is considering for runoff and soil loss computation based on scientific theory. Performance of such model may not differ much on different scales. Topography and nature of rainfall are quite dominant parameters in generation of runoff and soil erosion particularly in hilly watersheds. Water Erosion Prediction Project (WEPP) is one such process based model developed for estimating soil loss, and selecting catchment’s management practices for conservation planning for field and small catchments (Flanagan and Nearing, 1995; Lane et al., 1997). The model was used successfully world wide (Yu and Rosewell, 2001; Huang et al., 1996; Amore et al., 2004; Pieri et al., 2007; Baigorria and Romero, 2007; Shen et al., 2009; Shen et al., 2010) including India (Pandey et al., 2008) for estimating runoff and soil loss from different land use and crop management practices. Performance of the WEPP model was compared with the ANSWERS (Bhuyan et al., 2002), EPIC (Bhuyan et al., 2002), and SWAT (Shen et al., 2010). They found that the performance of the WEPP model was at par with the ANSWERS and better than the EPIC and SWAT in simulating different management scenario for reduction of sediment yield.

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