This paper investigates an error analysis of the K-factor by EPIC model and the Standard Table. Experiments accomplished to find various contents of soil and K-factor are calculated using the EPIC model and Standard Table is used for standard K values to consider them as true values. Kriging Interpolation is used to find unknown K-factors values in the Gurushikhar study area for both K-factor values. The prediction map for the K-factor is generated for the EPIC model, the Standard Table, and the Error of K-factor using the Kriging interpolation method. In the next section cross-validation comparison of the resulting maps is done. Results of high elevation regions show high negative errors and in plain regions, it shows positive errors. The regression function is generated using the collected and interpolated data. It also shows the high difference in multiplicative factor for “EPIC model K-factor” map and “K-Factor Error map” then “Standard Table Kfactor map” and “Error map”. Histograms for all sample points for Clay, Silt, Soil, and OMC are also created to understand the relationship with the most erroneous frequencies. The result shows that error is dependent on texture (Clay, Silt, and Soil) not on OMC in this study area.

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This study assesses temporal variation in rainfall erosivity of Gurushikhar, Rajasthan, (India) on a monthly precipitation basis in the form of the USLE/RUSLE R-factor. The objective of the paper is to theoretically calculate rainfall erosivity when the unavailability of high temporal resolution pluviographic rainfall data such as Indian condition. In the study, the rainfall erosivity has been calculated using the Modified Fourier Index. The results show that the annual rainfall erosivity factor (R) value highest in the year 2017 and lowest in 1974. Conferring to an examination through NASA, earth’s global superficial temperatures in 2017 ranked as second warmest since 1880. Therefore, the rainfall amount was more in 2017 compared to past years, and also rainfall erosivity value suddenly increased in 2017, achieved the highest value. They concluded that the heavy precipitation events in the year are lead to an increase in rainfall erosivity value and risk of soil erosion.