Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena

Ground-penetrating radar (GPR) reflection tomography algorithms allow non-invasive monitoring of water content changes resulting from flow in the vadose zone. The approach requires multi-offset GPR data that are traditionally slow to collect. We automate GPR data collection to reduce the survey time significantly, thereby making this approach to hydrologic monitoring feasible. The method was evaluated using numerical simulations and laboratory experiments that suggest reflection tomography can provide water content estimates to within 5 % vol vol−1–10 % vol vol−1 for the synthetic studies, whereas the empirical estimates were typically within 5 %–15 % of measurements from in situ probes. Both studies show larger observed errors in water content near the periphery of the wetting front, beyond which additional reflectors were not present to provide data coverage. Overall, coupling automated GPR data collection with reflection tomography provides a new method for informing models of subsurface hydrologic processes and a new method for determining transient 2-D soil moisture distributions

Meteoric 36-Cl in the Contiguous United States

The use of 36C1 as an environmental tracer of hydrologic processes has become increasingly popular over the last twenty years. The use of this isotope in hydrogeology has been limited, however, by uncertainty in the input function for 36C1 entering groundwater. To address this problem and to evaluate current 36C1/C1 distribution models, 36C1/C1 was measured for 183 groundwater samples collected from sites located throughout the United States. On the continental scale, values of 36C11C1 range from less than 100 x 10-15 in coastal regions to over 1,200 x 1015 in Wyoming. This distribution is dominated by the spatial variability of stable chloride fallout. The depositional flux of 36C1 is approximately constant over the United States with a mean of 21.3+2.7 atoms 111-2S-1 which is equivalent to a global flux of 13.6+1.7 atoms r62s4. A simple model developed in this study, which assumes constant 36C1 deposition, consistently yields high estimates of 36C11C1 in the southwestern part of the country. This discrepancy may indicate that the relative amount of dry to total fallout is an important factor in controlling 36C1/C1 in arid areas and requires further investigation. Variability of 36C11C1 at the local scale is primarily controlled by subsurface sources of chloride. Auxiliary data, such as bromide concentrations, are often useful for identifying and interpreting these localized processes. In this study, three schemes for correcting this effect are explored: i) binary mixing models, ii) scaling by chloride concentrations and iii) scaling by chloride-bromide ratios.hydrology collectio

Resolving precipitation induced water content profiles by inversion of dispersive GPR data: A numerical study

Surface-based ground-penetrating radar (GPR) measurements have significant potential for monitoring dynamic hydrologic processes at multiple scales in time and space. At early times during infiltration into a soil, the zone above the wetting front may act as a low-velocity waveguide that traps GPR waves, thereby causing dispersion and making interpretation of the data using standard methods difficult. In this work, we show that the dispersion is dependent upon the distribution of water within the waveguide, which is controlled by soil hydrologic properties. Simulations of infiltration were performed by varying the n-parameter of the Mualem–van Genuchten equation using HYDRUS-1D; the associated GPR data were simulated to evaluate the influence of dispersion. We observed a notable decrease in wave dispersion as the sharpness of the wetting front profile decreased. Given the sensitivity of the dispersion effect to the wetting front profile, we also evaluated whether the water content distribution can be determined through inversion of the dispersive GPR data. We found that a global grid search combined with the simplex algorithm was able to estimate the average water content when the wetted zone is divided into 2 layers. This approach was incapable, however, of representing the gradational nature of the water content distribution behind the wetting front. In contrast, the shuffled complex evolution algorithm was able to constrain a piece-wise linear function to closely match the shallow gradational water content profile. In both the layered and piece-wise linear case, the sensitivity of the dispersive data dropped sharply below the wetting front, which in this case was around 20 cm, i.e., twice the average wavelength, for a 900 MHz GPR survey. This study demonstrates that dispersive GPR data has significant potential for capturing the early-time dynamics of infiltration that cannot be obtained with standard GPR analysis approaches

Resolving Infiltration-Induced Water Content Profiles by Inversion of Dispersive Ground-Penetrating Radar Data

Ground-penetrating radar (GPR) data were collected before, during, and after a 24-min-long forced infiltration event in a large sand tank. High spatial and temporal resolution were achieved by automation of the radar system, thereby allowing these data to be collected during the course of the experiment while continuously changing the distance between the antennas through offsets ranging between 0.17 and 2.17 m. These multi-offset data showed evidence of a phenomenon known as waveguide dispersion during early infiltration times (5–10 min), indicating that a shallow layer of high water content was present. The GPR data exhibiting this dispersive behavior were used to fit water content profiles for the wetting front, i.e., the waveguide, with time using either a blocky-layer model or a piecewise linear function. Results from the separate inversions showed good agreement with in situ soil moisture measurements and a calibrated unsaturated flow model. The piecewise linear model, however, was able to honor the gradational nature of the hydrologically induced waveguide and was in better agreement with the observed soil moisture data. Furthermore, the piecewise linear model returned a water content profile that showed a consistent progression of the wetting front with time, whereas a less consistent progression of the wetting front was observed for the blocky-layer model