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An Appraisal on the Interpolation Methods Used for Predicting Spatial Variability of Field Hydraulic Conductivity

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Abstract

Knowledge of spatial variability of soil hydraulic conductivity is a key parameter for hydrological modeling. Spatial variability of soil hydraulic conductivity, Ks is attributed to soil heterogeneities associated with texture/structure, different initial conditions, meteorological changes, and clogging. The spatial variability is quantified by employing interpolation methods to point measurements performed in the field. There are not many studies reported in the literature that deals with the critical evaluation of the relative performance of different interpolation methods for predicting spatial variability of hydraulic conductivity. The primary objective of this study is to perform a critical evaluation of five interpolation methods, namely Kriging, Inverse Distance Weighted, Natural Neighbor, Spline and Trend for the spatial prediction of hydraulic conductivity. The accuracy of different methods was assessed by comparing the predicted values with the measured hydraulic conductivity of selected locations. It was noted that the Kriging method with exponential model gave a better spatial prediction as compared to other methods. The spatial variability of Ks was found to be in the same pattern as that of the percentage variation of the sand fraction for both the sites investigated in this study. It was further noted that the prediction of Ks was found to be more precise for those stations with a higher percentage of sand. A sudden transition of soil type from sand to silt was found to influence the accuracy of spatial prediction.

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  1. Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

    Biplab Ghosh & Sreeja Pekkat

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  1. Biplab Ghosh
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  2. Sreeja Pekkat
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Correspondence to Sreeja Pekkat.

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Ghosh, B., Pekkat, S. An Appraisal on the Interpolation Methods Used for Predicting Spatial Variability of Field Hydraulic Conductivity. Water Resour Manage 33, 2175–2190 (2019). https://doi.org/10.1007/s11269-019-02248-1

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  • DOI: https://doi.org/10.1007/s11269-019-02248-1

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