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

• Published in: Water resources management 2019-04, Vol.33 (6), p.2175-2190
• Main Authors: Ghosh, Biplab, Pekkat, Sreeja
• Format: Article
• Language: English
• Subjects: Accuracy; Atmospheric Sciences; Civil Engineering; Earth and Environmental Science; Earth Sciences; Environment; Evaluation; Geotechnical Engineering & Applied Earth Sciences; Hydraulic conductivity; Hydraulics; Hydrogeology; Hydrology; Hydrology/Water Resources; Initial conditions; Interpolation; Kriging interpolation; Mathematical models; Measurement methods; Modelling; Predictions; Sand; Silt; Soil; Soil conductivity; Soil types; Soils; Spatial variations; Statistical methods; Variability
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-019-02248-1

Knowledge of spatial variability of soil hydraulic conductivity is a key parameter for hydrological modeling. Spatial variability of soil hydraulic conductivity, K s 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 K s 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 K s 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.