Impact of observation thresholds in the assessment of field-scale soil saturated hydraulic conductivity

Abstract

The soil saturated hydraulic conductivity (Ks) plays a key role in the partitioning of rainfall into runoff and infiltration. Field-scale variability of Ks is often represented as a lognormal random field, and its parameters are frequently estimated by calibrating areal-average infiltration models with infiltration observations obtained under natural/artificial rainfall conditions. Such observations are obtained in terms of censored moments as infiltration is controlled by only a fraction of the Ks random field during a rainfall event. In this study, the impact of such observations on the maximum likelihood estimates (MLE) of the Ks distribution parameters is evaluated. Based on the data from several rainfall-runoff events observed under natural conditions over an experimental field, the results demonstrate the role of temporal variation of rainfall in resolving the Ks field for a rainfall event. Validation results show that the performance of the MLEs is impacted by both the fraction of the Ks field resolved by a rainfall event and its capacity to capture the variability of Ks. Finally, recommendations are provided as to what constitutes a good calibration event. The impact of incorporating reliability of the calibration events in consolidating information was tested using an information-theoretic measure.