پديد آورندگان :
استواري ، ياسر نويسنده دانشجوي دكتري گروه علوم خاك، دانشكده كشاورزي، دانشگاه شهركرد , , قرباني دشتكي، شجاع نويسنده دانشيار گروه علوم خاك، دانشكده كشاورزي، دانشگاه شهركرد , , بهرامي ، حسينعلي نويسنده دانشيار گروه علوم خاك، دانشكده كشاورزي، دانشگاه تربيت مدرس , , نادري، مهدي نويسنده دانشكده كشاورزي-دانشگاه شهركرد , , عباسي ، مژگان نويسنده استاديار گروه جنگلداري، دانشكده منابع طبيعي و علوم زمين، دانشگاه شهركرد ,
كليدواژه :
فرسايندگي , كريجينگ , شاخص فورنيه , فرسايش پذيري
چكيده فارسي :
فرسايش پذيري خاك يكي از عوامل مهم در تخمين درست مقدار فرسايش خاك و ارايه راهكارهاي مبارزه با اين پديده است. اهداف اين مطالعه، اندازه گيري واقعي مقدار فرسايش-پذيري خاك ؛ مقايسه مقدار واقعي فرسايشپذيري با مقدار برآوردي توسط رابطه ي ويشماير و اسميت (1978)؛ و تهيه نقشه تغييرات مكاني فرسايش پذيري و پارامترهاي موثر بر آن در منطقه بالادست سد سيوند واقع در استان فارس بود. براي اين منظور،40 كرت استاندارد درشيب‌هاي 9% ايجاد و در طي يك سال مقدار هدر رفت خاك ها حاصل از 4 رخداد بارندگي در اين كرت ها اندازه گيري شد. ميانگين سالانه شاخص فرسايندگي از رابطه ي اصلاح‌شده فورنيه محاسبه شد. نقشه ها با استفاده از روش زمين آماري كريجينگ و در محيط GIS ترسيم گرديد. نتايج نشان داد كه ميانگين مقدار هدر رفت خاك در كرت ها t ha?1 ya-1 8/5 و ميانگين شاخص فرسايندگي باران MJ mm ha-1 h-1ya-1 7/255 بود. ميانگين فرسايش پذيري اندازه گيري شده در كرت هاي آزمايشي و برآوردي با استفاده از رابطه ي ويشماير و اسميت به ترتيب t h MJ?1 014/0 و t h MJ?1030/0 شد كه بر اساس، رابطه ي ويشماير و اسميت در برآورد فرسايش پذيري به‌طور ميانگين مقدار اين فاكتور را 2 برابر بيشتر از مقدار اندازه-گيري شده تخمين ميزند. براي هر دو فرسايش پذيري اندازه گيري شده و برآورد شده مدل سمي واريوگرام نمايي با دامنه تاثير به ترتيب 3399 و 5439 متر بهترين برازش را داشت. درحالي‌كه براي خصوصيات مادهآلي، كربنات كلسيم معادل، پايداري خاك دانه ها و نفوذپذيري كه بيشترين همبستگي را با فرسايش پذيري واقعي داشتند، مدل كروي واريوگرام بهترين توصيف را داشت. نقشه هاي پهنه بندي نشان داد كه مقدار فرسايشپذيري از قسمت هاي مركزي و مسطح منطقه كه تحت كشت گياهان پوششي قرار دارد به سمت نواحي مرتفع حاشيه اي كه فاقد پوشش گياهي مناسب است افزايش مي يابد.
چكيده لاتين :
Introduction: Soil erosion is one the most serious environmental problems that causes great soil losses and threatens sustainable agriculture. Erosion causes severe land degradation and soil productivity loss, and generally consider as hazard of health society. For these reasons, preventing soil erosion is very important for managing and conserving the natural resources. Soil erodibility is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. In the RUSLE model, the soil erodibility is considered as K factor. This factor has been directly measured as the mean rate of soil loss from standard plots divided by erosivity factor. The main aims of this study were to (1) measuring the erodibility factor in the field with standard plots, (2) comparing the measured erodibility with estimated K factor using the Wischmeier and Smith (1978) equation, (3) investigating the effects of soil easily parameters on erodibility factor and (4) assessing the spatial variation of soil erodibility factor in the Simakan watershed with an area of 350 km2.
Methodology: The Semikan watershed, which mainly has calcareous soil with more than 40% lime (total carbonates), is located in the central of Fars province, between 30°06ʹ-30°18ʹN and 53°05ʹ-53°18ʹE (WGS? 1984, zone 39°N) with an area of about 350 km2. For this study, 40 standard plots, which are 22.1×1.83 m with a uniform ploughed slope of 9% in the upslope/downslope direction, were installed in the slopes of 8-10% and the deposit of each plot was collected after rainfall. The erosivity factor was calculated using using the modified form of Fournier index developed by Arnoldus (1977). From each plot three samples were sampled and some physicochemical properties including soil texture, organic matter, water aggregate stability, pH, EC were analyzed. Soil permeability in each plot was directly measured using double ring method. The Variowin and GIS software were used for geoestatistical analyze (kriging) and mapping the soil erodibility factor and other parameters, respectively.
Results and discussion: According to triangle texture diagram, most soil samples were present in Loam, Clay loam and Sandy clay loam. Descriptive statistics of easily measurable soil properties are given in Table 1. Clay content was found to range between 17.9 and 41.9% with an average value of 26.7%. Similarly, sand content varied from 24.1 to 56.0% with an average of 41.1%. The K has positive significant correlation with silt content (r= 0.47, p < 0.01) and very fine sand content (r=0.43, p < 0.01). The K factor had negative significant correlation with content of sand (r= -0.32, p < 0.05), OM (r= -0.60, p < 0.001), CaCO3 (r= -0.052, p < 0.01), water-aggregate stability (r= -0.58, p < 0.01) and permeability (r=-0.77, p < 0.001). The contents of very fine sand and silt positively correlate with K due to their high susceptibility to soil detachment and transport by rainfall and runoff The K factor had the most significant correlation with permeability. Yu et al. (2006) and Vaezi el al. (2008) also reported the negative correlation between the K factor and permeability. There was a significant difference (p < 0.001) between measurement in the plots and estimated values derived from Wischmeier and Smith (1978) equation values of soil erodibility factor in the study area. A comparison between measured and estimated K values proved that the measured soil erodibility factor values were from 1.08 to 3.57 with average 2.18 times smaller than the estimated values. The results indicated that the correlation between the measured and estimated K factor was significant with R2= 0.269 between the two. The semi-variograms of the estimated and measured soil erodibility factors were fitted using exponential models. While for other parameters the spherical model was the best qualification. Amounts of error of the models fitted to the semi-variograms were calculated using the proportion nugget (C0) to the sill (C0+C= 1). The amounts of error of the fitted models to the experimental semi-variograms of the estimated and measured soil erodibility factors were 2.8×10-5 and 9.8×10-6, respectively. The maps showed that the trend of K factor for both measured and estimated in the south part of the study area is nearly the same. However, from center to north of the study area measured and estimated K factor have different distribution.
Conclusion: based on the results, Organic matter (r=-0.60) and permeability (r= -0.77) had high significant correlation with K factor. Although the content of lime has not been considered in Wischmeier-Smith and RUSLE model, we found significantly decrease of K because of its strong effects on aggregate stability and soil permeability. The kriging method and exponential model were acceptable tools for investigation the spatial distribution of erodibility factor.
