برآورد بار رسوب ماهانه‌ی ایستگاه‌های حوضه‌ی آجی‌چای با استفاده از مدل mpsiac و ریزمقیاس نمایی آبشاری

در این مقاله جهت برآورد میزان فرسایش و رسوب در زیرحوضه های حوضه ی آجی چای با توجه به نبود آمار کافی بار رسوب که یـکی از مسائل اساسی حوضه های کشور می باشد از مدل تجربی پسیاک اصلاح شده استفاده شده است. از آنجائی که میزان رسوب حاصل از این مدل متوسط سالانه می باشد، لذا در مرحله ی اول جهت محاسبه رسوب برای هر سال چگونگی تغییرات فاکتورهای نه گانه مدل فوق نسبت به زمان مورد بررسی قرار داده شده است. فاکتورهای که ماهانه (مثل بارش و رواناب) و یا سالانه (مثل پوشش گیاهی و کاربری اراضی) دستخوش تغییر هستند، نقش مستقیمی در محاسبه رسوب برای هر سال دارند. در مرحله ی دوم برای ریزمقیاس کردن رسوب سالانه ی حاصل از مدلسازی مرحله ی اول و با توجه به اینکه نمی توان رسوب سالانه را به نسبت مساوی برای تمام ماه های سال توزیع کرد، با استفاده از روش آبشاری میزان رسوب سالانه به ماهانه ریزمقیاس گردید. نتایج حاصل نشان می دهد بین بار رسوب برآورد شده با مدل پسیاک اصلاح شده و ریزمقیاس شده با مدل آبشاری، با نتایج مشاهداتی و ثبت شده همبستگی بالایی وجود دارد. از طرفی همانطوری که انتظار می ر فت از بین عوامل نه گانه مدل، دو عامل فرسایش رودخانه ای و فرسایش سطحی به ترتیب 13/6 و 13/4 بیشترین امتیاز را دارند همچنین کاربری اراضی و پوشش گیاهی با امتیازهای 13/4 و 11/5 نقش خود را در تولید و یا مهار رسوب به خوبی نشان می دهند. میزان رسوبدهی سالانه در کل حوضه 92/1 تن در هکتار می باشد که زیرحوضه 22 با توجه به شیب تند و پوشش گیاهی کاملا ضعیف با 3/88 تن در هکتار در سال بیشترین و زیرحوضه 1-14 با 1/0 تن در هکتار در سال کمترین مقدار تولید رسوب را در حوضه به خود اختصاص دادند.

Monthly Sediment Load Estimation of Aji Chay Basin Stations Using MPSIAC Model and Cascade Exponential SubScales

IntroductionIn this study, the MPSIAC model was used to consider the effects of the dominant factors in sediment production in order to estimate the rate of the erosion and sediment load in subbasins of the Aji Chay River.  Since the sediment rate of this model is the annual average, the variations of the nine fold factors of this model was examined in order to calculate the sediment for each year. Then, the annual and monthly sediment rates were quantified using a cascading method.MethodologyIn order to estimate the sediment production and the relationship between the degree of the sediment yield and the amount of production, equation (1) which was based on determining the scores of the factors considered in the PSIAC model and obtaining their total scores in each hydrological unit was used38.77e0.0353R =  Equation(1):        QS Qs=sediment yield (m3/km2/year)           R= sedimentation rate The PSIAC model specifies some variations for each factor, which is somewhat selective and requires an expert judgment. Johnson and Gombard (1982) have made the ninefold factors for this method as numerical equations.The estimated sediment rate using MPSIAC method is based on the annual average. Therefore, the variations of the factors of MPSIAC model were examined and compared to estimate the sediment for each year. Due to the fact that sediment is not the same throughout the year, it was not possible to equally consider annual sediment for all months of the year. Thus, for the purpose of the quantification of the monthly sediment, the cascading microscale was used through verifying the existing data and filling the deficiencies of the data. In the process of disintegration, the sediment, which was the annual sediment  in the initial intervals, was sequentially broken into smaller surfaces with specific coefficients and calibrated.        Equation(2): SNij = Sij      Equation(3): SijNky = Sk Results and discussionIn this paper, the annual sediment rate was estimated using remote sensing, GIS techniques, and the application of the experimental model of MPSIAC in hydrological units and its zoning in the area. Then, by inserting the DEM into the GIS environment and by modifying the ups and downs, the flow direction, the network of waterways, and the primary and secondary subbasins were produced. As a result, the production rate of the sediment and the scores of the each of the factors in the subbasins were calculated using the equations presented in the MPSIAC model. The results showed that there was a high correlation between the estimated sediment load with the MPSIAC model and the observed and recorded results.The results of the MPSIAC model for the estimated sediment rate were based on the annual average, so the existing data and ninefold factors of MPSIAC model, which were timeconsuming, were used for the monthly sedimentation. To measure the amount of the precipitation and runoff for different months of each statistical year and to study the amount and manner of changes in vegetation and land use in the studied area, the annual precipitation and annual erosion were calculated for each statistical year. Then, subscaling was done through the calculation of the subscale coefficients of annual to monthly sediment.ConclusionThe estimated sediment rate using MPSIAC model and observational and measured data of the sediment in the hydrometric stations of the Aji Chay basin has high accuracy and acceptable correlation. In addition, by comparing and verifying the available and measured data in the hydrometric stations of the AjiChay basin at low scales with extractive data of this method, it turns out that the sediment values can be estimated at low scales by specifying the subscale coefficients and calculating the sediment for each year.