Core Ideas: Time‐lapse imaging reveals subsurface flow paths driven by snowmelt. Vertical flow occurs in the same location, but in a different amount, under varying water input. Regolith geometry, observed by seismic refraction, drives partitioning to deep flow. Understanding water partitioning and storage on mountain hillslopes is important for vadose zone hydrologic modeling, water balance calculations, and predicting landslides. Revealing the dynamics of unsaturated water movement through time remains challenging due to the scarcity of spatially continuous observations at dense time sampling intervals. We investigate heterogeneous vertical flow using time‐lapse electrical resistivity tomography over a span of 2 yr and use seismic refraction to determine the bedrock location on a hillslope with snow‐dominated hydrology. We expect that heterogeneities in vertical unsaturated flow are driven by highly conductive zones in soil structure and higher localized water content due to surface topographic depressions and a long‐term sustained snowpack relative to other locations, resulting in increased water availability. The results show that vertical flow heterogeneity and partitioning to deep flow occurred under contrasting precipitation inputs. We determine that topographic and vegetation cover heterogeneities on the hillslope and snow accumulation exert control on the location of partitioning to deep flow, likely due to macropores, roots, boulders, and localized water availability. We conclude that the regolith–bedrock boundary has a slope reversal that allows water to accumulate to drive heterogeneous flow.

Topography and land cover characteristics can have significant effects on infiltration, runoff, and erosion processes on watersheds. The ability to model the timing and routing of surface water and erosion is affected by the resolution of the digital elevation model (DEM). High resolution ground-based Light Detecting and Ranging (LiDAR) technology can be used to collect detailed topographic and land cover characteristic data. In this study, a method was developed to remove vegetation from ground-based LiDAR data to create high resolution DEMs. Research was conducted on intensively studied rainfall–runoff plots on the USDA-ARS Walnut Gulch Experimental Watershed in Southeast Arizona. LiDAR data were used to generate 1 cm resolution digital surface models (DSM) for 5 plots. DSMs created directly from LiDAR data contain non-surface objects such as vegetation cover. A vegetation removal method was developed which used a slope threshold and a focal mean filter method to remove vegetation and create bare earth DEMs. The method was validated on a synthetic plot, where rocks and vegetation were added incrementally. Results of the validation showed a vertical error of ±7.5 mm in the final DEM.

A portion of water not consumed by crops during flood irrigation can flow back across the surface or through the subsurface to adjacent surface water bodies and streams as return flow. Few studies have directly addressed subsurface processes governing return flow and the importance of structural complexity on hydrologic process representation. It is challenging to measure and model these subsurface flow paths using traditional hydrologic observations. In this study, we assess the impact of subsurface structural complexity on vadose zone flow representation in a two‐dimensional transport model by varying structural complexity derived from background geophysical data. We assessed four model structures each with three soil types of homogeneous hydrologic properties, two of which were evaluated with and without an anisotropy factor. Wetting front arrival times, derived from time‐lapse electrical resistivity measurements during flood irrigation field experiments, were used to evaluate the different representations of soil profile structures. These data indicated both vertical and lateral preferential flow in the subsurface during flood irrigation. Inclusion of anisotropy in the saturated hydraulic conductivity field improved the ability to model subsurface hydrologic behavior when flow processes shifted from uniform to heterogeneous flow, as occurs with lateral subsurface return flow under flood irrigation driven by a large pressure gradient. This reduced the need for detailed spatial discretization to represent these observed subsurface flow processes. The resulting simple three‐layer model structure was better able to model both the vertical and lateral flow processes than a more complex geospatial structure, suggesting that overinterpretation of smoothed inverted profiles could lead to misrepresentation of the subsurface structure.

Habitat suitability analysis is complex and requires integration of multiple attributes at a range of spatial scales. We use fine- and broad-scale variables within a spatial modelling framework to determine the suitability of greater sage-grouse nesting habitat using fuzzy logic. Fuzzy theory allows for a landscape component to have full, partial, or no membership in a particular management target. We used seven biophysical and anthropogenic variables correlated with nest site locations to build the fuzzy model. Using field and remotely sensed data and expert opinion, we developed fuzzy functions to define numerical membership values describing relationships between landscape characteristics and nesting habitat suitability. We used a fuzzy gamma overlay function to define five suitability classes in the resulting habitat map. Using a fuzzy logic model, we were able to spatially distinguish the suitability of nesting habitat using fine- and broad-scale attributes and characterise the uncertainty of the habitat model.