برآورد بار رسوب ماهانه ي ايستگاه هاي حوضه ي آجي چاي با استفاده از مدل MPSIAC و ريزمقياس نمايي آبشاري

Monthly Sediment Load Estimation of Aji Chay Basin Stations Using MPSIAC Model and Cascade Exponential Sub-Scales

در اين مقاله جهت برآورد ميزان فرسايش و رسوب در زيرحوضه هاي حوضه ي آجي چاي با توجه به نبود آمار كافي بار رسوب -كه يـكي از مسايل اساسي حوضه هاي كشور مي باشد- از مدل تجربي پسياك اصلاح شده استفاده شده است. از آنجايي كه ميزان رسوب حاصل از اين مدل متوسط سالانه مي باشد، لذا در مرحله ي اول جهت محاسبه رسوب براي هر سال چگونگي تغييرات فاكتورهاي نه گانه مدل فوق نسبت به زمان مورد بررسي قرار داده شده است. فاكتورهاي كه ماهانه (مثل بارش و رواناب) و يا سالانه (مثل پوشش گياهي و كاربري اراضي) دستخوش تغيير هستند، نقش مستقيمي در محاسبه رسوب براي هر سال دارند. در مرحله ي دوم براي ريزمقياس كردن رسوب سالانه ي حاصل از مدلسازي مرحله ي اول و با توجه به اينكه نمي توان رسوب سالانه را به نسبت مساوي براي تمام ماه هاي سال توزيع كرد، با استفاده از روش آبشاري ميزان رسوب سالانه به ماهانه ريزمقياس گرديد. نتايج حاصل نشان مي دهد بين بار رسوب برآورد شده با مدل پسياك اصلاح شده و ريزمقياس شده با مدل آبشاري، با نتايج مشاهداتي و ثبت شده همبستگي بالايي وجود دارد. از طرفي همانطوري كه انتظار مي ر فت از بين عوامل نه گانه مدل، دو عامل فرسايش رودخانه اي و فرسايش سطحي به ترتيب 6/13 و 4/13 بيشترين امتياز را دارند همچنين كاربري اراضي و پوشش گياهي با امتيازهاي 4/13 و 5/11 نقش خود را در توليد و يا مهار رسوب به خوبي نشان مي دهند. ميزان رسوبدهي سالانه در كل حوضه 92/1 تن در هكتار مي باشد كه زيرحوضه 22 با توجه به شيب تند و پوشش گياهي كاملا ضعيف با 88/3 تن در هكتار در سال بيشترين و زيرحوضه 1-14 با 0/1 تن در هكتار در سال كمترين مقدار توليد رسوب را در حوضه به خود اختصاص دادند.

Introduction In 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 sub-basins 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. Methodology In 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 used 38.77e0.0353R = 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 nine-fold 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 micro-scale 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 discussion In 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 sub-basins were produced. As a result, the production rate of the sediment and the scores of the each of the factors in the sub-basins 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 nine-fold factors of MPSIAC model, which were time-consuming, 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, sub-scaling was done through the calculation of the sub-scale coefficients of annual to monthly sediment. Conclusion The 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 sub-scale coefficients and calculating the sediment for each year.