USGS Science⌕ Search
Home/Archive/USGS

USGS · 70196863

Unsaturated zone CO2, CH4, and δ13C-CO2 at an arid region low-level radioactive waste disposal site

Christopher H. Conaway·Michelle Ann Walvoord·Randall B. Thomas·Christopher Green·R.J. Baker·James J. Thordsen·David A. Stonestrom·Brian J. Andraski
View original source ↗Export metadata ↓

Abstract

Elevated tritium, radiocarbon, Hg, and volatile organic compounds associated with low-level radioactive waste (LLRW) at the USGS Amargosa Desert Research Site (ADRS) have stimulated research on factors and processes that affect contaminant gas distribution and transport. Consequently, we examined the sources, mixing, and biogeochemistry of CO 2 and CH 4 , two additional important species in the unsaturated zone at ADRS. In spring 2015 and 2016, shallow unsaturated zone gas samples were collected from the 1.5-m depth both inside and outside the LLRW disposal area. Samples also were collected from two 110-m-deep multilevel gas-sampling boreholes and a distant background site. These samples were analyzed for CO 2 mole fraction ( x CO 2 ) and C isotopic composition (δ 13 C-CO 2 ) and CH 4 mole fraction ( x CH 4 ). Graphical analysis of the results indicates mixing of CO 2 characteristic of the root zone (δ 13 C −18 to −19‰), deep soil gas of the capillary fringe (−13 to −15‰), and CO 2 produced by microbial respiration of organic matter disposed in the LLRW trenches (−22 to −25‰). Distribution of CH 4 overall reflects atmospheric sources and production in anaerobic microzones in the LLRW area and methanotrophy in the undisturbed shallow subsurface outside the LLRW area. Although x CH 4 reflecting lateral transport from the LLRW area is decreasing with time in the deep profiles, deep unsaturated zone x CO 2 has changed little in recent decades. The results imply that CH 4 and δ 13 C-CO 2 may serve as good tracers of anthropogenic effects in the unsaturated zone even when CO 2 primarily reflects natural processes.

Explore related subjects

Vadose Zone JournalArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Christopher H. Conaway, Michelle Ann Walvoord, Randall B. Thomas, Christopher Green, R.J. Baker, James J. Thordsen, David A. Stonestrom, Brian J. Andraski. 2018-06-14. Unsaturated zone CO2, CH4, and δ13C-CO2 at an arid region low-level radioactive waste disposal site. https://doi.org/10.2136/vzj2017.11.0200

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Network-wide assessment of soil water content calibration and sensitivity to biomass proxies using cosmic-ray neutron sensing in the Roaring Fork Basin, Colorado

Soil water content (SWC) is a key state variable of the climate system but is often uncertain in water balance monitoring, especially in alpine environments. SWC measurements can be challenging in alpine environments due to the topography and rocky soils. In 2022, the US Geological Survey's Next Generation Water Observing System Program began research to evaluate water balance monitoring technologies, including cosmic-ray neutron sensors (CRNS). This work evaluated the uncertainty resulting from network-wide calibration of CRNS for SWC monitoring in an alpine watershed and investigated the stability of the calibration parameters across space and time, focusing on potential influence of biomass dynamics. Fifteen stations with moderated and unmoderated (bare) CRNS were deployed and made operational within the Roaring Fork Basin in west-central Colorado. The root mean squared error of the network-wide calibration using the moderated CRNS was 0.042 or 0.047 cm 3 cm −3 , depending on the calibration equation used. Relative SWC dynamics from CRNS were correlated with the in situ probes with a correlation coefficient of 0.91 or 0.87 (depending on calibration equation). We did not find significant relationships between the calibration parameters and stationary site-specific variables. However, the calibration parameters derived from in situ probe SWC dynamics varied over time and were correlated with biomass proxies of cumulative growing degree-day, cumulative growing season index, and bare neutron counts. Future use of the CRNS network can leverage the reliable relative SWC data from network-wide calibration for watershed modeling and continue to research sensitivity of bare neutron measurements to biomass dynamics.

Colorado↗
USGS2025

Identifying preferential flow from soil moisture time series: Review of methodologies

Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially-distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods, to highlight important information for readers who want to identify PF from soil moisture data, and to inform those who aim to develop new methods or improve existing ones.

Vadose Zone Journal↗
USGS2023

Field evaluation of semi-automated moisture estimation from geophysics using machine learning

Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025–0.046 cm 3 /cm 3 ). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

Connecticut↗