Analysis of spatio-temporal changes of precipitation to estimate R factor in rusle at Kartalkaya Dam

Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Geological Engineering, Turkey

Approval Date: 2018




In recent years, soil erosion models have been developed all over the world. The most common model, RUSLE requires a lot of detailed information and extensive laboratory studies. One of the RUSLE parameter, rainfall factor, is identified as the erosivity factor of precipitation. This parameter depends on duration, intensity and frequency of rainfall events. The difficulty in calculating rainfall factor is the lack of minute-based precipitation data in many parts of Turkey. The aim of this study is to calculate R factor based on available precipitation data and determine the sensitivity of the R parameter using different methods with GIS tools in Kartalkaya Dam catchment. Firstly, the relationship between precipitation and physiogeographic parameters of study area is examined and stations are classified based on their location and the main factors cause precipitation. Then, RUSLE rainfall factor is calculated by using minute based data. To estimate rainfall factor based on monthly and annual rainfall data, Modified Fournier Index (MFI) is calculated. The relationship between MFI and R values show that there is a strength correlation between these two parameters with a coefficient determination (R2) value of 0.78. It vi has been estimated that compare to mean annual precipitation and rainfall factor relationship (R2=0.64), MFI calculation significantly improve R-factor estimation. Rainfall erosivity maps are constructed with calculated R and MFI values and also based on their relation. Due to low number of stations and complexity of environmental features, physio-geographic parameters of study area are also utilized as secondary information in an effort to improve interpolation of rainfall factor. The results show that using elevation as the secondary information significantly improves the estimations over IDW interpolations.