Measurement and inference of profile soil-water dynamics at different hillslope positions in a semiarid agricultural watershed

Dynamics of profile soil water vary with terrain, soil, and plant characteristics. The objectives addressed here are to quantify dynamic soil water content over a range of slope positions, infer soil profile water fluxes, and identify locations most likely influenced by multidimensional flow. The instrumented 56 ha watershed lies mostly within a dryland (rainfed) wheat field in semiarid eastern Colorado. Dielectric capacitance sensors were used to infer hourly soil water content for approximately 8 years (minus missing data) at 18 hillslope positions and four or more depths. Based on previous research and a new algorithm, sensor measurements (resonant frequency) were rescaled to estimate soil permittivity, then corrected for temperature effects on bulk electrical conductivity before inferring soil water content. Using a mass‐conservation method, we analyzed multitemporal changes in soil water content at each sensor to infer the dynamics of water flux at different depths and landscape positions. At summit positions vertical processes appear to control profile soil water dynamics. At downslope positions infrequent overland flow and unsaturated subsurface lateral flow appear to influence soil water dynamics. Crop water use accounts for much of the variability in soil water between transects that are either cropped or fallow in alternating years, while soil hydraulic properties and near‐surface hydrology affect soil water variability across landscape positions within each management zone. The observed spatiotemporal patterns exhibit the joint effects of short‐term hydrology and long‐term soil development. Quantitative methods of analyzing soil water patterns in space and time improve our understanding of dominant soil hydrological processes and provide alternative measures of model performance. Key Points Temperature‐corrected capacitance sensor readings infer soil water content Temporal changes in soil water content infer space‐time dynamics of water flux Pattern analysis may improve process understanding and model evaluation