Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main objective of the present work is estimation and inter-comparison of infiltration models which are used to evaluate the infiltration rates of National Institute of Technology (NIT)-campus in district of Kurukshetra, Haryana (India) and for this study, field infiltration tests were carried out at ten different locations comprising of 109 observations by use of double ring infiltrometer. The potential of three infiltration models (Kostiakov, Modified Kostiakov and US-Soil Conservation Service (SCS)) were evaluated by least-square fitting to observed infiltration data. Three statistical comparison criteria including maximum absolute error (MAE), Bias and root mean square error (RMSE) were used to determine the best performing infiltration models. In addition, a novel infiltration model was developed from field tests data using nonlinear regression modeling which suggests improved performance out of other three models. In case of nonexistence of observed infiltration data, this novel model can be used to artificially generate infiltration data for NIT campus.

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