In the present study, soil erosion assessment of Dikrong river basin of Arunachal Pradesh (India) was carried out. The river basin was divided into 200×200 m grid cells. The Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing software) provided spatial input data and the USLE was used to predict the spatial distribution of the average annual soil loss on grid basis. The average rainfall erositivity factor (R) for Dikrong river basin was found to be 1,894.6 MJ mm ha −1 h −1 year −1 . The soil erodibility factor (K) with a magnitude of 0.055 t ha h ha −1 MJ −1 mm −1 is the highest, with 0.039 t ha h ha −1 MJ −1 mm −1 is the least for the watershed. The highest and lowest value of slope length factor (LS) is 53.5 and 5.39 respectively for the watershed. The highest and lowest values of crop management factor (C) were found out to be 0.004 and 1.0 respectively for the watershed. The highest and lowest value of conservation factor (P) were found to be 1 and 0.28 respectively for the watershed. The average annual soil loss of the Dikrong river basin is 51 t ha −1 year −1 . About 25.61% of the watershed area is found out to be under slight erosion class. Areas covered by moderate, high, very high, severe and very severe erosion potential zones are 26.51%, 17.87%, 13.74%, 2.39% and 13.88% respectively. Therefore, these areas need immediate attention from soil conservation point of view.
Rainfall is one of the fundamental components of the hydrological cycle as its accurate estimation is necessary for planning, designing and operation of water resources development programmes. In the present study, monthly stochastic model was developed using rainfall data for Doimukh (Itanagar), Arunachal Pradesh, India. The rainfall series was assumed to be composed of deterministic and stochastic components. The trend component was found to be non-significant. Fourier series analysis was used to identify the periodic component. Three harmonics were found significant. The stochastic component was modeled by fitting into auto regressive model of order 6. The Mean and standard deviation of the generated series were found to be close to the historical values. The value of absolute error, relative error, correlation coefficient and Nash-Sutcliffe coefficient indicated a high degree of model fitness to the observed data series.
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