In situ measurements of soil saturated hydraulic conductivity: Assessment of reliability through rainfall-runoff experiments

Abstract

The saturated hydraulic conductivity, K s , is a soil property that has a key role in the partitioning of rainfall into surface runoff and infiltration. The commonly used instruments and methods for in situ measurements of K s have frequently provided conflicting results. Comparison of K s estimates obtained by three classical devices-namely, the double ring infiltrometer (DRI), the Guelph version of the constant-head well permeameter (GUELPH-CHP) and the CSIRO version of the tension permeameter (CSIRO-TP) is presented. A distinguishing feature in this study is the use of steady deep flow rates, obtained from controlled rainfall-runoff experiments, as benchmark values of K s at local and field-plot scales, thereby enabling an assessment of these methods in reliably reproducing repeatable values and in their capability of determining plot-scale variation of K s . We find that the DRI grossly overestimates K s , the GUELPH-CHP gives conflicting estimates of K s with substantial overestimation in laboratory experiments and underestimation at the plot scale, whereas the CSIRO-TP yields average K s values with significant errors of 24% in the plot scale experiment and 66% in laboratory experiments. Although the DRI would likely yield a better estimate of the nature of variability than the GUELPH-CHP and CSIRO-TP, a separate calibration may be warranted to correct for the overestimation of K s values. The reasons for such discrepancies within and between the measurement methods are not yet fully understood and serve as motivation for future work to better characterize the uncertainty associated with individual measurements of K s using these methods and the characterization of field scale variability from multiple local measurements.

Measurement Method Has a Larger Impact Than Spatial Scale For Plot‐Scale Field‐Saturated Hydraulic Conductivity (Kfs) After Wildfire and Prescribed Fire in Forests

“…The synthesis here indicates that positive head applied as the surface boundary condition has an effect when greater than 0.5 cm, with a power-law relation between K fs and positive (i.e. For example, Morbidelli et al (2017) compared TI, FHP, and SSRS K fs estimates and found that TI and SSRS methods produced similar mean K fs , but TI K fs was more variable, whereas FHP produced K fs estimates that were larger on average by nearly a factor of 5 compared to SSRS. The minimum ponded pressure head used in the datasets reported here was 0.5 cm, so that is the lower limit of ponded head that this analysis can identify but if there is a significant threshold pressure head it may be smaller.…”

“…Work in unburned conditions also shows sensitivity to ponded head. For example, Morbidelli et al (2017) compared TI, FHP, and SSRS K fs estimates and found that TI and SSRS methods produced similar mean K fs , but TI K fs was more variable, whereas FHP produced K fs estimates that were larger on average by nearly a factor of 5 compared to SSRS. K fs studies in burned conditions suggested larger positive heads applied at the surface boundary condition by FHP and CHP may induce infiltration into water repellent porous media by exceeding the water entry value (Nyman et al, 2010;Ebel et al, 2012) or activating preferential flow paths (Nyman et al, 2014;Langhans et al, 2016) that may not be accessed by TI and SSRS.…”

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

“…Estimation of K s at the point scale requires a solution to an inverse problem that involves measurements of other quantities (e.g., soil moisture content and soil-water suction) and use of an infiltration model (e.g., Corradini et al, 1997;Green & Ampt, 1911;Parlange et al, 1982;Philip, 1957;Richards, 1931). Apart from problems noted with such point estimates from measuring instruments (Boers et al, 1992;Cook & Broeren, 1994;Jabro & Evans, 2006;Morbidelli et al, 2017;Verbist et al, 2013), a very large number of these measurements are needed to assess the nature of spatial variability at the field scale because of the high degree of spatial variability found in the infiltration properties of natural soils (Russo & Bresler, 1981). Field studies (Loague & Gander, 1990;Morbidelli et al, 2017;Nielsen et al, 1973;Sharma et al, 1987) have shown that even seemingly uniform areas manifest large variations in K s .…”

“…Apart from problems noted with such point estimates from measuring instruments (Boers et al, 1992;Cook & Broeren, 1994;Jabro & Evans, 2006;Morbidelli et al, 2017;Verbist et al, 2013), a very large number of these measurements are needed to assess the nature of spatial variability at the field scale because of the high degree of spatial variability found in the infiltration properties of natural soils (Russo & Bresler, 1981). Field studies (Loague & Gander, 1990;Morbidelli et al, 2017;Nielsen et al, 1973;Sharma et al, 1987) have shown that even seemingly uniform areas manifest large variations in K s . Several studies, such as Dagan and Bresler (1983), have stated that K s exhibits the maximum spatial variability among infiltration parameters and can be represented using a lognormal random field.…”

A test of water pouring height and run intermittence effects on single‐ring infiltration rates

“…Saturated hydraulic conductivity, K s , of the surface soil layer is the most important soil parameter that influences rainfall partition into infiltration and surface runoff (Assouline & Mualem, , ), and it can be determined with infiltrometer runs. However, the K s data collected with these techniques could be thought to be unusable for hydrological studies since several investigations have shown that the K s results were much higher than those obtained by other methods, or they were incompatible with any rainfall excess even if surface runoff was measured (Bagarello, Di Stefano, Iovino, & Sgroi, ; Cerdà, , ; Morbidelli et al, ; van de Giesen, Stomph, & De Ridder, ; Williams & Bonell, ). This inconsistency can depend on different factors such as blocking of some large pores by massive air inclusion during natural events or crust formation and associated particle sorting decreasing overall permeability during the storm (Van de Giesen et al, ) but not, or not at the same rate, during the infiltration run.…”