[1] The evolution of permafrost in cold regions is inextricably connected to hydrogeologic processes, climate, and ecosystems. Permafrost thawing has been linked to changes in wetland and lake areas, alteration of the groundwater contribution to streamflow, carbon release, and increased fire frequency. But detailed knowledge about the dynamic state of permafrost in relation to surface and groundwater systems remains an enigma. Here, we present the results of a pioneering $1,800 line-kilometer airborne electromagnetic survey that shows sediments deposited over the past $4 million years and the configuration of permafrost to depths of $100 meters in the Yukon Flats area near Fort Yukon, Alaska. The Yukon Flats is near the boundary between continuous permafrost to the north and discontinuous permafrost to the south, making it an important location for examining permafrost dynamics. Our results not only provide a detailed snapshot of the present-day configuration of permafrost, but they also expose previously unseen details about potential surface -groundwater connections and the thermal legacy of surface water features that has been recorded in the permafrost over the past $1,000 years. This work will be a critical baseline for future permafrost studies aimed at exploring the connections between hydrogeologic, climatic, and ecological processes, and has significant implications for the stewardship of Arctic environments.

The passage of the Sustainable Groundwater Management Act in California has highlighted a need for cost-effective ways to acquire the data used in building conceptual models of the aquifer systems in the Central Valley of California. One approach would be the regional implementation of the airborne electromagnetic (AEM) method. We acquired 104 line-kilometers of data in the Tulare Irrigation District, in the Central Valley, to determine the depth of investigation (DOI) of the AEM method, given the abundance of electrically conductive clays, and to assess the usefulness of the method for mapping the hydrostratigraphy. The data were high quality providing, through inversion of the data, models displaying the variation in electrical resistivity to a depth of approximately 500 m. In order to transform the resistivity models to interpreted sections displaying lithology, we established the relationship between resistivity and lithology using collocated lithology logs (from drillers' logs) and AEM data. We modeled the AEM response and employed a bootstrapping approach to solve for the range of values in the resistivity model corresponding to sand and gravel, mixed coarse and fine, and clay in the unsaturated and saturated regions. The comparison between the resulting interpretation and an existing cross section demonstrates that AEM can be an effective method for mapping the large-scale hydrostratigraphy of aquifer systems in the Central Valley. The methods employed and developed in this study have widespread application in the use of the AEM method for groundwater management in similar geologic settings.

[1] Hydraulic conductivity (K) is one of the most important parameters of interest in groundwater applications because it quantifies the ease with which water can flow through an aquifer material. Hydraulic conductivity is typically measured by conducting aquifer tests or wellbore flow (WBF) logging. Of interest in our research is the use of proton nuclear magnetic resonance (NMR) logging to obtain information about water-filled porosity and pore space geometry, the combination of which can be used to estimate K. In this study, we acquired a suite of advanced geophysical logs, aquifer tests, WBF logs, and sidewall cores at the field site in Lexington, Nebraska, which is underlain by the High Plains aquifer. We first used two empirical equations developed for petroleum applications to predict K from NMR logging data: the Schlumberger Doll Research equation (K SDR ) and the Timur-Coates equation (K T-C ), with the standard empirical constants determined for consolidated materials. We upscaled our NMR-derived K estimates to the scale of the WBF-logging K(K WBF-logging ) estimates for comparison. All the upscaled K T-C estimates were within an order of magnitude of K WBF-logging and all of the upscaled K SDR estimates were within 2 orders of magnitude of K WBF-logging . We optimized the fit between the upscaled NMR-derived K and K WBF-logging estimates to determine a set of site-specific empirical constants for the unconsolidated materials at our field site. We conclude that reliable estimates of K can be obtained from NMR logging data, thus providing an alternate method for obtaining estimates of K at high levels of vertical resolution.

SI to Inch/Pound Multiply By To obtain Length meter (m) 3.281 foot (ft) kilometer (km) 0.6214 mile (mi) Area square meter (m 2) 0.0002471 acre hectare (ha) 2.471 acre Volume cubic meter (m 3) 0.00081 acre-foot Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:°F =(1.8×°C)+32 Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:°C =(°F-32)/1.8 Datum information used in this report Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 (NGVD29). Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83). Elevation, as used in this report, refers to distance above the vertical datum.

[1] The need for sustainable management of fresh water resources is one of the great challenges of the 21st century. Since most of the planet's liquid fresh water exists as groundwater, it is essential to develop non-invasive geophysical techniques to characterize groundwater aquifers. A field experiment was conducted in the High Plains Aquifer, central United States, to explore the mechanisms governing the non-invasive Surface NMR (SNMR) technology. We acquired both SNMR data and logging NMR data at a field site, along with lithology information from drill cuttings. This allowed us to directly compare the NMR relaxation parameter measured during logging, T 2 , to the relaxation parameter T 2 * measured using the SNMR method. The latter can be affected by inhomogeneity in the magnetic field, thus obscuring the link between the NMR relaxation parameter and the hydraulic conductivity of the geologic material. When the logging T 2 data were transformed to pseudo-T 2 * data, by accounting for inhomogeneity in the magnetic field and instrument dead time, we found good agreement with T 2 * obtained from the SNMR measurement. These results, combined with the additional information about lithology at the site, allowed us to delineate the physical mechanisms governing the SNMR measurement. Such understanding is a critical step in developing SNMR as a reliable geophysical method for the assessment of groundwater resources. Citation: Knight, R.,