Estimation of Field‐Scale Variability in Soil Saturated Hydraulic Conductivity From Rainfall‐Runoff experiments

Saturated hydraulic conductivity ( K s ) is among the important soil properties that influence the partitioning of rainfall into surface and subsurface waters. Point estimates of K s are difficult to determine and exhibit large spatial variability in fields. Often, data from field‐scale rainfall‐runoff experiments are utilized to assess the properties of the K s random field that are required in the use of field‐scale infiltration models. Standard methods of calibration are confounded by nonuniqueness 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 K s that would describe experimental observations over a field for a rainfall event. A Shannon information‐theoretic approach was subsequently adopted to consolidate the ranges of K s distributions over multiple rainfall events to yield the best range of K s 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 K s distributions from individual rainfall experiments. Validation results showed that the method provides a realistic estimate of our ability to quantify the spatial variability of K s in natural fields from rainfall‐runoff experiments.