Vertical spatial sensitivity and effective depth of exploration (d e ) of low-induction-number (LIN) instruments over a layered soil were evaluated using a complete numerical solution to Maxwell's equations. Previous studies using approximate mathematical solutions predicted a vertical spatial sensitivity for instruments operating under LIN conditions that, for a given transmitter-receiver coil separation (s), coil orientation, and transmitter frequency, should depend solely on depth below the land surface. When not operating under LIN conditions, vertical spatial sensitivity and d e also depend on apparent soil electrical conductivity (s a ) and therefore the induction number (b). In this new evaluation, we determined the range of s a and b values for which the LIN conditions hold and how d e changes when they do not. Two-layer soil models were simulated with both horizontal (HCP) and vertical (VCP) coplanar coil orientations. Soil layers were given electrical conductivity values ranging from 0.1 to 200 mS m 21. As expected, d e decreased as s a increased. Only the least electrically conductive soil produced the d e expected when operating under LIN conditions. For the VCP orientation, this was 1.6s, decreasing to 0.8s in the most electrically conductive soil. For the HCP orientation, d e decreased from 0.76s to 0.51s. Differences between this and previous studies are attributed to inadequate representation of skin-depth effect and scattering at interfaces between layers. When using LIN instruments to identify depth to water tables, interfaces between soil layers, and variations in salt or moisture content, it is important to consider the dependence of d e on s a .

Accurate description of the soil water retention curve (SWRC) at low water contents is important for simulating water dynamics and biochemical vadose zone processes in arid environments. Soil water retention data corresponding to matric potentials of less than −10 MPa, where adsorptive forces dominate over capillary forces, have also been used to estimate soil specific surface area (SA). In the present study, the dry end of the SWRC was measured with a chilled‐mirror dew point psychrometer for 41 Danish soils covering a wide range of clay (CL) and organic carbon (OC) contents. The 41 soils were classified into four groups on the basis of the Dexter number (n = CL/OC), and the Tuller‐Or (TO) general scaling model describing water film thickness at a given matric potential (<−10 MPa) was evaluated. The SA estimated from the dry end of the SWRC (SA_SWRC) was in good agreement with the SA measured with ethylene glycol monoethyl ether (SA_EGME) only for organic soils with n > 10. A strong correlation between the ratio of the two surface area estimates and the Dexter number was observed and applied as an additional scaling function in the TO model to rescale the soil water retention curve at low water contents. However, the TO model still overestimated water film thickness at potentials approaching ovendry condition (about −800 MPa). The semi–log linear Campbell‐Shiozawa‐Rossi‐Nimmo (CSRN) model showed better fits for all investigated soils from −10 to −800 MPa and yielded high correlations with CL and SA. It is therefore recommended to apply the empirical CSRN model for predicting the dry part of the water retention curve (−10 to −800 MPa) from measured soil texture or surface area. Further research should aim to modify the more physically based TO model to obtain better descriptions of the SWRC in the very dry range (−300 to −800 MPa).

[1] Water levels in aquifers typically vary in response to time-varying rates of recharge, suggesting the possibility of inferring time-varying recharge rates on the basis of longterm water level records. Presumably, in the southwestern United States (Arizona, Nevada, New Mexico, southern California, and southern Utah), rates of mountain front recharge to alluvial aquifers depend on variations in precipitation rates due to known climate cycles such as the El Niño-Southern Oscillation index and the Pacific Decadal Oscillation. This investigation examined the inverse application of a one-dimensional analytical model for periodic flow described by Lloyd R. Townley in 1995 to estimate periodic recharge variations on the basis of variations in long-term water level records using southwest aquifers as the case study. Time-varying water level records at various locations along the flow line were obtained by simulation of forward models of synthetic basins with applied sinusoidal recharge of either a single period or composite of multiple periods of length similar to known climate cycles. Periodic water level components, reconstructed using singular spectrum analysis (SSA), were used to calibrate the analytical model to estimate each recharge component. The results demonstrated that periodic recharge estimates were most accurate in basins with nearly uniform transmissivity and the accuracy of the recharge estimates depends on monitoring well location. A case study of the San Pedro Basin, Arizona, is presented as an example of calibrating the analytical model to real data.

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We have developed a screening method for simplifying groundwater models by delineating areas within the domain that can be represented using steady‐state groundwater recharge. The screening method is based on an analytical solution for the damping of sinusoidal infiltration variations in homogeneous soils in the vadose zone. The damping depth is defined as the depth at which the flux variation damps to 5% of the variation at the land surface. Groundwater recharge may be considered steady where the damping depth is above the depth of the water table. The analytical solution approximates the vadose zone diffusivity as constant, and we evaluated when this approximation is reasonable. We evaluated the analytical solution through comparison of the damping depth computed by the analytic solution with the damping depth simulated by a numerical model that allows variable diffusivity. This comparison showed that the screening method conservatively identifies areas of steady recharge and is more accurate when water content and diffusivity are nearly constant. Nomograms of the damping factor (the ratio of the flux amplitude at any depth to the amplitude at the land surface) and the damping depth were constructed for clay and sand for periodic variations between 1 and 365 d and flux means and amplitudes from nearly 0 to 1 × 10−3 m d−1. We applied the screening tool to Central Valley, California, to identify areas of steady recharge. A MATLAB script was developed to compute the damping factor for any soil and any sinusoidal flux variation.

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

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[1] Simulations of infiltration during three ephemeral streamflow events in a coarsegrained alluvial channel overlying a less permeable basin-fill layer were conducted to determine the relative contribution of transient infiltration at the onset of streamflow to cumulative infiltration for the event. Water content, temperature, and piezometric measurements from 2.5-m vertical profiles within the alluvial sediments were used to constrain a variably saturated water flow and heat transport model. Simulated and measured transient infiltration rates at the onset of streamflow were about two to three orders of magnitude greater than steady state infiltration rates. The duration of simulated transient infiltration ranged from 1.8 to 20 hours, compared with steady state flow periods of 231 to 307 hours. Cumulative infiltration during the transient period represented 10 to 26% of the total cumulative infiltration, with an average contribution of approximately 18%. Cumulative infiltration error for the simulated streamflow events ranged from 9 to 25%. Cumulative infiltration error for typical streamflow events of about 8 hours in duration in is about 90%. This analysis indicates that when estimating total cumulative infiltration in coarse-grained ephemeral stream channels, consideration of the transient infiltration at the onset of streamflow will improve predictions of the total volume of infiltration that may become groundwater recharge.