Estimating Field-scale Variability in Soil Saturated Hydraulic Conductivity from Rainfall-Runoff Experiments

Saturated hydraulic conductivity (Ks) is among the important soil properties that influence the partitioning of rainfall into surface and subsurface waters. Point estimates of Ks are difficult to determine and exhibit large spatial variability in fields. Often, data from field-scale rainfall-runoff experiments are used to assess the properties of the Ks random field that are required in the use of field-scale infiltration models. Standard methods of calibration are confounded by non-uniqueness and identifiability problems associated with experimental data. In this study, a new method that employed a field-averaged infiltration model and Monte Carlo simulations was used to obtain the possible range of distributions of Ks that would describe experimental observations over a field for a rainfall event. An information-theoretic approach was subsequently adopted to consolidate the ranges of Ks distributions over multiple rainfall events to yield the best range of Ks distributions. The method was applied to data from several rainfall-runoff events observed under natural conditions over an experimental field characterized by a silty loam soil and a small surface slope. Results suggest the existence of numerous parameter combinations that could satisfy the experimental observations over a single rainfall event, and high variability of these combinations among different events, thereby providing insights regarding the identifiable space of Ks distributions from individual rainfall experiments. Validation results showed that the method provides a realistic estimate of our ability to quantify the spatial variability of Ks in natural fields from rainfall-runoff experiments.