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Thermokarst lake dynamics, which play an essential role in carbon release due to permafrost thaw, are affected by various geomorphological processes. In this study, we derive a three-dimensional (3D) Stefan equation to characterize talik geometry under a hypothetical thermokarst lake in the continuous permafrost region. Using the Euler equation in the calculus of variations, the lower bounds of the talik were determined as an extremum of the functional describing the phase boundary area with a fixed total talik volume. We demonstrate that the semi-ellipsoid geometry of the talik is optimal for minimizing the total permafrost thaw under the lake for a given annual heat supply. The model predicting ellipsoidal talik geometry was compared to talik thickness observations using transient electromagnetic (TEM) soundings in Peatball Lake on the Arctic Coastal Plain (ACP) of northern Alaska. The depth : width ratio of the elliptical sub-lake talik can characterize the energy flux anisotropy in the permafrost, although the lake bathymetry cross section may not be elliptic due to the presence of near-surface ice-rich permafrost. This theory suggests that talik development deepens lakes and results in more uniform horizontal lake expansion around the perimeter of the lakes, while wind-induced waves and currents are likely responsible for the elongation and orientation of shallow thermokarst lakes without taliks in certain regions such as the ACP of northern Alaska.

The belowground architecture of the critical zone (CZ) consists of soil and rock in various stages of weathering and wetness that acts as a medium for biological growth, mediates chemical reactions, and controls partitioning of hydrologic fluxes. Hydrogeophysical imaging provides unique insights into the geometries and properties of earth materials that are present in the CZ and beyond the reach of direct observation beside sparse wellbores. An improved understanding of CZ architecture can be achieved by leveraging the geophysical measurements of the subsurface. Creating categorical models of the CZ is valuable for driving hydrologic models and comparing belowground architectures between different sites to interpret weathering processes. The CZ architecture is revealed through a novel comparison of hillslopes by applying facies classification in the elastic-electric domain driven by surface-based hydrogeophysical measurements. Three pairs of hillslopes grouped according to common geologic substrates — granite, volcanic extrusive, and glacially altered — are classified by five different hydrofacies classes to reveal the relative wetness and weathering states. The hydrofacies classifications are robust to the choice of initial mean values used in the classification and noncontemporaneous timing of geophysical data acquisition. These results will lead to improved interdisciplinary models of CZ processes at various scales and to an increased ability to predict the hydrologic timing and partitioning. Beyond the hillslope scale, this enhanced capability to compare CZ architecture can also be exploited at the catchment scale with implications for improved understanding of the link between rock weathering, hydrochemical fluxes, and landscape morphology.

Thermokarst lake dynamics, which plays an essential role in carbon release due to permafrost thaw, is affected by various geomorphological processes. In this study, we derive a three-dimensional (3D) Stefan equation to characterize talik geometry under a hypothetical thermokarst lake in the continuous permafrost region. Using the Euler equation in the calculus of variations, the lower bounds of the talik were determined as an extremum of the functional describing the phase boundary area with a fixed total talik volume. We demonstrate that the semi-ellipsoid geometry of the talik is optimal for minimizing the total permafrost thaw under the lake for a given annual heat supply. The model predicting ellipsoidal talik geometry was verified by talik thickness observations using transient electromagnetic (TEM) soundings in Peatball Lake on the Arctic Coastal Plain (ACP) of Alaska. The lake width-depth ratio of the elliptic talik can characterize the energy flux anisotropy in the permafrost although the lake bathymetry cross section may not be elliptic due to the presence of near-surface ice-rich permafrost. This theory suggests that talik development stabilizes thermokarst lakes by ground subsidence due to permafrost thaw while wind-induced waves and currents are likely responsible for the elongation and orientation of thermokarst lakes in certain regions such as the ACP of northern Alaska.

The Permafrost Dynamics Observatory (PDO) combines L-band interferometric synthetic aperture radar (InSAR) and P-band polarimetric synthetic aperture radar (PolSAR) to simultaneously estimate the seasonal thaw depth and soil moisture profile of the active layer in permafrost regions. L-band InSAR can measure seasonal subsidence due to thawing of the active layer and P-band PolSAR backscatter is sensitive to subsurface soil moisture. A joint retrieval scheme is developed as both subsidence and soil moisture are essential to accurate active layer thickness (ALT) estimation. The PDO joint retrieval has been applied to airborne L- and P-band SAR data acquired over Arctic-boreal region during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign. In this paper, we describe the forward models and joint inversion used in the PDO retrievals and compare the results with in-situ ALT and soil moisture data estimated from ground-penetrating radar (GPR).

Agricultural water is of considerable interest to water managers and policymakers as irrigation—particularly flood irrigation—accounts for the largest portion of freshwater use. However, characterization of how and when flood applied water contributes to storage and adjacent surface water bodies via return flow remains limited, particularly relative to the volume of studies addressing surface processes. This study focuses on improving our understanding of return flow paths and explicitly quantifying subsurface return flow contributions using hydro-geophysical observations coupled with a vadose zone transport flow model. Our approach, which simulates return flow in agricultural fields in a small, heavily instrumented field site in northern Wyoming, leverages daily measured values of evapotranspiration obtained from a large aperture scintillometer and atmospheric conditions, and uses seismic data to characterize heterogeneous structure of the soil profiles at field scale. We use these high-resolution, site specific datasets to parameterize a process-based vadose zone flow model that quantifies hydrologic response to flood irrigation along the stream reach, allowing us to identify return flow fluxes out of the fields. Using our approach, we found that the majority of applied irrigation water (60%) left the fields through subsurface flow paths, while only 13% was consumed by vegetation. Subsurface runoff over less-permeable soil layers, governed by the subsurface structural characteristics, was identified as the dominant subsurface flow process contributing to fluxes out of the fields. Importantly, our findings about the importance of subsurface return flow paths nuance previous research which has emphasized surface flow paths. By improving our understanding of return flow processes through a high- resolution vadose zone model that leverages climatological, geophysical, and hydrological observations, this study advances our understanding of return flow processes. Understanding how subsurface characteristics influence return flow generation in semi-arid agriculturally productive regions will help managers and policy makers balance desires for improved irrigation efficiency against concerns about irrigation-dependent ecosystem services.

In permafrost regions, active layer thickness (ALT) observations measure the effects of climate change and predict hydrologic and elemental cycling. Often, ALT is measured through direct ground-based measurements. Recently, synthetic aperture radar (SAR) measurements from airborne platforms have emerged as a method for observing seasonal thaw subsidence, soil moisture, and ALT in permafrost regions. This study validates airborne SAR-derived ALT estimates in three regions of Alaska, USA using calibrated ground penetrating radar (GPR) geophysical data. The remotely sensed ALT estimates matched the field observations within uncertainty for 79% of locations. The average uncertainty for the GPR-derived ALT validation dataset was 0.14 m while the average uncertainty for the SAR-derived ALT in pixels coincident with GPR data was 0.19 m. In the region near Utqiaġvik, the remotely sensed ALT appeared slightly larger than field observations while in the Yukon-Kuskokwim Delta region, the remotely sensed ALT appeared slightly smaller than field observations. In the northern foothills of the Brooks Range, near Toolik Lake, there was minimal bias between the field data and remotely sensed estimates. These findings suggest that airborne SAR-derived ALT estimates compare well with in situ probing and GPR, making SAR an effective tool to monitor permafrost measurements.

Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms. In this study, which is the first in a two part series, we introduce a series of calibration techniques developed to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval to an airborne InSAR dataset acquired in 2017 by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and Western Canada. We demonstrate how InSAR measurement uncertainties are mitigated by these calibration methods and quantify remaining measurement uncertainties with a novel method of modeling interferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the impact of native SAR resolution on InSAR measurements, the limitation of using few interferograms per retrieval, and the implications of our findings for cross‐comparison of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by permafrost., Key Points We develop and present several calibration and postprocessing methods for seasonal subsidence estimation from interferometric synthetic aperture radar deformationNovel methods for phase referencing and uncertainty quantification due to nonergodicity within the multilook window are proposedResidual sources of uncertainty in active layer thickness estimation are discussed and quantified

Lake formation and drainage are pervasive phenomena in permafrost regions. Drained lake basins (DLBs) are often the most common landforms in lowland permafrost regions in the Arctic (50% to 75% of the landscape). However, detailed assessments of DLB distribution and abundance are limited. In this study, we present a novel and scalable remote sensing-based approach to identifying DLBs in lowland permafrost regions, using the North Slope of Alaska as a case study. We validated this first North Slope-wide DLB data product against several previously published sub-regional scale datasets and manually classified points. The study area covered >71,000 km2, including a >39,000 km2 area not previously covered in existing DLB datasets. Our approach used Landsat-8 multispectral imagery and ArcticDEM data to derive a pixel-by-pixel statistical assessment of likelihood of DLB occurrence in sub-regions with different permafrost and periglacial landscape conditions, as well as to quantify aerial coverage of DLBs on the North Slope of Alaska. The results were consistent with previously published regional DLB datasets (up to 87% agreement) and showed high agreement with manually classified random points (64.4–95.5% for DLB and 83.2–95.4% for non-DLB areas). Validation of the remote sensing-based statistical approach on the North Slope of Alaska indicated that it may be possible to extend this methodology to conduct a comprehensive assessment of DLBs in pan-Arctic lowland permafrost regions. Better resolution of the spatial distribution of DLBs in lowland permafrost regions is important for quantitative studies on landscape diversity, wildlife habitat, permafrost, hydrology, geotechnical conditions, and high-latitude carbon cycling.

Lakes in Arctic systems contribute to hydrologic storage, biogeochemical cycling, and permafrost thaw. Here, we have used surface nuclear magnetic resonance (NMR) measurements on lakes of Alaska’s North Slope to investigate the extent of permafrost thaw below lakes with different annual ice conditions. Our purpose is to understand if annual lake ice conditions are related to development of thawed permafrost below lakes. We investigated 10 lakes and two terrestrial permafrost control sites using surface NMR and direct measurement under spring conditions when lake ice is nearly at its thickest. We did not observe unfrozen water below our surveyed bedfast ice lakes, whereas unfrozen water (indicating permafrost thaw) was measured below floating ice lakes. We found that transitional ice lakes, ones that alternate between floating and bedfast ice conditions over multiyear timescales depending on winter ice growth and lake level conditions, have complex vertical unfrozen water content profiles attributed to sporadic periods of thaw. Based on that finding, we speculate that predicting the presence of talik based on remotely sensed lake ice conditions is unreliable. We applied a scheme to subtract the lake water signal from the NMR data and found the resulting inversions to be improved.

Understanding critical zone (CZ) structure below the first few meters of Earth’s surface is challenging and yet important to understand hydrologic and surface processes that influence life on Earth. Nuclear magnetic resonance (NMR) is an emerging geophysical tool that can quantify the volume of groundwater and pore-scale properties. Nuclear magnetic resonance has potential to aid in CZ studies, but it can be difficult to collect high-quality NMR data in weathered and fractured rock. We present data from seven surface NMR soundings and six borehole NMR profiles collected on a weathered and fractured granite in the Laramie Range, Wyoming. First, we show that it is possible to collect high-quality surface NMR data in a fractured rock. Second, we use the NMR data to delineate the weathering profile into three distinct zones—unsaturated saprolite, saturated saprolite, and fractured rock—and show that the surface NMR signal is dominated by saturated saprolite. Third, we show that lateral heterogeneity significantly reduces the surface NMR signal magnitude, which suggests that the boundary dividing saprolite and fractured rock is laterally heterogeneous. The NMR measurements, when combined with previously collected seismic refraction data, provide a unique opportunity to define the lateral heterogeneity of the boundary dividing saprolite and weathered bedrock in an eroding landscape underlain by crystalline rock.

This research demonstrates a new measurement and scaling approach to constrain the estimates of methane (CH4) fluxes emitted from permafrost thaw (thermokarst) lakes. Permafrost is estimated to store about 20% of the total terrestrial carbon (C) stock. Permafrost thawing releases C in part as CH4, however, there are large uncertainties in the global CH4 budget that limit the accuracy of climate change projections. Estimating how much C is released from permafrost is critical to overcome this knowledge gap. Lake CH4 fluxes are estimated by combining direct observations, geophysical mapping and satellite remote sensing along with a scaling strategy based on lake expansion rate. This research contributes to advance the understanding of CH4 fluxes from thermokarst lakes and improve atmospheric C models.

Seasonal subsidence measured by repeat-pass interferometric synthetic aperture radar (InSAR) can be used to infer the active layer thickness (ALT) in permafrost regions. The differential volume of soil water undergoing the phase change over the thaw season is one of the factors impacting the seasonal subsidence and is a function of both soil moisture profile and thaw depth. Without the information about soil moisture, this InSAR approach can have large biases in the ALT estimates when soil moisture profile is below saturation. Soil moisture and ALT can also be estimated from polarimetric synthetic aperture radar (PolSAR) backscatter observations but the sensing depth of the PolSAR approach is limited when deep ALT is present. In this paper, we integrated these two approaches and applied a joint retrieval method to estimate the soil moisture profiles and ALT from the L-band InSAR and P-band PolSAR data acquired over the Arctic-boreal region during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign.

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.

Thermokarst lakes are prevalent in Arctic coastal lowland regions and sublake permafrost degradation and talik development contributes to greenhouse gas emissions by tapping the large permafrost carbon pool. Whereas lateral thermokarst lake expansion is readily apparent through remote sensing and shoreline measurements, sublake thawed sediment conditions and talik growth are difficult to measure. Here we combine transient electromagnetic surveys with thermal modeling, backed up by measured permafrost properties and radiocarbon ages, to reveal closed-talik geometry associated with a thermokarst lake in continuous permafrost. To improve access to talik geometry data, we conducted surveys along three transient electromagnetic transects perpendicular to lakeshores with different decadal-scale expansion rates of 0.16, 0.38, and 0.58m/year. We modeled thermal development of the talik using boundary conditions based on field data from the lake, surrounding permafrost and a borehole, independent of the transient electromagnetics. A talik depth of 91m was determined from analysis of the transient electromagnetic surveys. Using a lake initiation age of 1400years before present and available subsurface properties the results from thermal modeling of the lake center arrived at a best estimate talk depth of 80m, which is on the same order of magnitude as the results from the transient electromagnetic survey. Our approach has provided a noninvasive estimate of talik geometry suitable for comparable settings throughout circum-Arctic coastal lowland regions.

Ground penetrating radar (GPR) is a powerful hydrogeophysical tool for estimating porosity and water content of geologic materials using radar wave velocities and appropriate petrophysical relations. In substrates with more than one layer of interest, surface-based multi-offset measurements require careful analysis to accurately retrieve physical properties for each layer. Frequently, Dix inversion is used to calculate interval velocities, however the assumptions and limitations of this approach are widely known. In particular for survey geometries and targets encountered with GPR, the assumptions inherent to Dix inversion are readily violated, and therefore inverse modeling is required to avoid velocity error. While the impact on velocity incurred by violating the assumptions of Dix inversion is well understood, the effects on water content estimates have not been widely reported and are therefore the subject of this work. In a subsurface representative of an unsaturated zone overlying an aquifer, error in excess of 50% in water content due to violating the assumptions of Dix inversion is demonstrated. Examples are shown using raytracing inversion to solve for subsurface water content structure that avoids the errors inherent to Dix inversion. These results are intended to underscore the importance of minimizing assumptions and using more correct physics when analyzing multi-offset GPR data, particularly due to the large potential errors that may be encountered if water content estimation is the main objective.

Freshwater lakes are an important source of atmospheric methane ([Formula: see text]); however, uncertainties associated with quantifying fluxes limit the accuracy of climate warming projections. Among emission pathways, ebullition (bubbling) is the principal and most challenging to account for given its spatial and temporal patchiness. When lakes freeze, many methane-rich bubbles escaping from lake-bottom sediments are temporarily trapped by downward-growing lake ice. Because bubble position is then seasonally fixed, we postulate that it should be possible to locate bubbles using a geophysical approach sensitive to perturbations in the ice-water interface and ice sheet structure generated by bubbles. We use ground-penetrating radar (GPR) to noninvasively quantify the amount of ebullition gas present in lake ice. To do this, an appropriate petrophysical transformation is required that relates radar wave velocity and volumetric gas content. We use laboratory experiments to show that electromagnetic models and volumetric mixing formulas were good representations of the gas volume-permittivity relationship. We found a standard deviation in dielectric permittivity between the models of 0.03, 0.03, and 0.02 for 20%, 50%, and 70% gas content, respectively. Second, by combining two GPR geometries (common and multioffset), we were able to locate bubbles and estimate gas volume with low uncertainty, with [Formula: see text] being the lowest uncertainty found and [Formula: see text] the largest. Finally, we found that GPR reflection patterns were associated with different previously identified ice-bubble classes. These geophysical results coupled with ancillary field measurements and ice-growth models also suggest how GPR can contribute to estimates of seasonal and annual ebullition fluxes over large spatiotemporal scales within and among lakes, thereby helping to reduce uncertainties in upscaled estimates of ecosystem methane emissions.

In this investigation, we compare the results of electrical resistivity measurements made by six commercially available instruments on the same line of electrodes to determine if there are differences in the measured data or inverted results. These comparisons are important to determine whether measurements made between different instruments are consistent. We also degraded contact resistance on one quarter of the electrodes to study how each instrument responds to different electrical connection with the ground. We find that each instrument produced statistically similar apparent resistivity results, and that any conservative assessment of the final inverted resistivity models would result in a similar interpretation for each. We also note that inversions, as expected, are affected by measurement error weights. Increased measurement errors were most closely associated with degraded contact resistance in this set of experiments. In a separate test we recorded the full measured waveform for a sing...

Arctic lakes located in permafrost regions are susceptible to catastrophic drainage. In this study, we reconstructed historical lake drainage events on the western Arctic Coastal Plain of Alaska between 1955 and 2017 using USGS topographic maps, historical aerial photography (1955), and Landsat Imagery (ca. 1975, ca. 2000, and annually since 2000). We identified 98 lakes larger than 10 ha that partially (>25% of area) or completely drained during the 62‐year period. Decadal‐scale lake drainage rates progressively declined from 2.0 lakes/yr (1955–1975), to 1.6 lakes/yr (1975–2000), and to 1.2 lakes/yr (2000–2017) in the ~30,000‐km2 study area. Detailed Landsat trend analysis between 2000 and 2017 identified two years, 2004 and 2006, with a cluster (five or more) of lake drainages probably associated with bank overtopping or headward erosion. To identify future potential lake drainages, we combined the historical lake drainage observations with a geospatial dataset describing lake elevation, hydrologic connectivity, and adjacent lake margin topographic gradients developed with a 5‐m‐resolution digital surface model. We identified ~1900 lakes likely to be prone to drainage in the future. Of the 20 lakes that drained in the most recent study period, 85% were identified in this future lake drainage potential dataset. Our assessment of historical lake drainage magnitude, mechanisms and pathways, and identification of potential future lake drainages provides insights into how arctic lowland landscapes may change and evolve in the coming decades to centuries.

Interactions and feedbacks between abundant surface waters and permafrost fundamentally shape lowland Arctic landscapes. Sublake permafrost is maintained when the maximum ice thickness (MIT) exceeds lake depth and mean annual bed temperatures (MABTs) remain below freezing. However, declining MIT since the 1970s is likely causing talik development below shallow lakes. Here we show high-temperature sensitivity to winter ice growth at the water-sediment interface of shallow lakes based on year-round lake sensor data. Empirical model experiments suggest that shallow (1m depth) lakes have warmed substantially over the last 30years (2.4°C), withMABT above freezing5 of the last 7years.This is incomparison to slower ratesofwarming in deeper (3 m) lakes (0.9°C), with already well-developed taliks. Our findings indicate that permafrost below shallow lakes has already begun crossing a critical thawing threshold approximately 70 years prior to predicted terrestrial permafrost thaw in northern Alaska.

Infiltration rate is the key parameter that describes how water moves from the surface into a groundwater aquifer during managed aquifer recharge (MAR). Characterization of infiltration rate heterogeneity in space and time is valuable information for MAR system operation. In this study, we utilized fiber optic distributed temperature sensing (FO-DTS) observations and the phase shift of the diurnal temperature signal between two vertically co-located fiber optic cables to characterize infiltration rate spatially and temporally in a MAR basin. The FO-DTS measurements revealed spatial heterogeneity of infiltration rate: approximately 78% of the recharge water infiltrated through 50% of the pond bottom on average. We also introduced a metric for quantifying how the infiltration rate in a recharge pond changes over time, which enables FO-DTS to be used as a method for monitoring MAR and informing maintenance decisions. By monitoring this metric, we found high-spatial variability in how rapidly infiltration rate changed during the test period. We attributed this variability to biological pore clogging and found a relationship between high initial infiltration rate and the most rapid pore clogging. We found a strong relationship (R2 = 0.8) between observed maximum infiltration rates and electrical resistivity measurements from electrical resistivity tomography data taken in the same basin when dry. This result shows that the combined acquisition of DTS and ERT data can improve the design and operation of a MAR pond significantly by providing the critical information needed about spatial variability in parameters controlling infiltration rates.

Active-layer thickness (ALT) is an important parameter for studying surface energy balance, ecosystems, and hydrologic processes in cold regions. We measured ALT along 10 routes with lengths ranging from 0.7 to 6.9 km located on the Alaska North Slope near Toolik Lake and the Happy Valley airstrip (between 68.475° and 69.150°N, and [Formula: see text] and [Formula: see text]). Using a ground-penetrating radar (GPR) system in a common-offset configuration, we measured the two-way traveltimes from the surface to the bottom of the active layer at the end of summer, when the thaw depth was greatest. We used 500 and 800 MHz antennas; the 500 MHz antenna provided suitable vertical resolution, while producing more unambiguous active-layer reflections in the presence of nonideal antenna coupling and active layer inhomogeneity. We derived ALT measurements and their uncertainties from GPR two-way traveltimes, with mechanical probing for velocity calibration. Using an empirical relationship between the wave velocity and soil volumetric water content (VWC), we found that the velocities were consistent with soil VWCs ranging from 0.46 to 0.63. In 31% of traces, the permafrost table horizon was identifiable, resulting in ALT measurements with uncertainties of generally less than 25%. The average ALT was 48.1 cm, with a standard deviation of 16.1 cm. We found distinct patterns of ALT spatial variability at different sites and different length scales. At some sites, the ALT at one point was effectively uncorrelated with ALT at other points separated by lag distances as small as tens of meters; for other sites, there was correlation at lag distances up to approximately 400 m. The ALT statistics were similar to nearby long-term in situ ALT measurements from the Circumpolar Active Layer Monitoring Network, through which yearly ALT measurements have been made since 1990.

Surface Nuclear Magnetic Resonance (NMR) is a non-invasive geophysical technique providing the ability to image and investigate aquifer properties. In order to produce reliable images and interpretations of subsurface properties accurate modelling of the underlying physics is required. In magnetic environments, where the background magnetic field varies spatially, challenges can arise that lead to difficulty accurately modelling the excitation process and interpreting the signal's time dependence. We demonstrate using field data collected in the Anangu Pitjantjatjara Yankunytjatjara (APY) Lands of South Australia that neglecting the influence of the magnetic environment can significantly alter the final images and interpretation of the subsurface structure and properties.

The assessment of hydrologic ecosystem services associated with flood irrigation in the western United States is particularly limited by a lack of data about return flows. Return flows, the portion of applied water that returns to adjacent surface and groundwater hydrologic systems during flood irrigation, may provide wildlife habitat, recharge aquifers, and supplement late season baseflows. To improve understanding about variability in the timing and volume of return flows to improve management and policy decisions associated with different irrigation management techniques, our study used an agricultural water balance that combined hydrologic, geophysical, and meteorological tools. We directly measure key components of the water balance and quantify error and uncertainty on a ∼100 ha portion of the Bear Creek watershed in the Upper Wind River Basin of Wyoming. Over the course of a single irrigation season with average precipitation, we found that of the 1,133,000 m3 of water applied to irrigated lands, 328,000 m3 (29 % of applied water) returned to Bear Creek and 487,000 m3 (43 % of applied water) was consumptively used by vegetation. Our data also suggest that 59,000 m3 (18 % of total return flows) occurred more than two weeks after irrigation had ceased. The percent of return flow for this study site is slightly lower than the median of 43 % for all reviewed studies and the percent of delayed return flow—return flow after irrigation has ceased—is slightly higher than the median finding of 14 % across available studies. We found that return flows from flood irrigation at this site are lower than generalized regional estimates and those found by other studies. Thus, our study indicates that the amount and timing of return flows are not necessarily coupled (i.e. lower overall volume did not correspond to lowered late season contribution at this site). In addition, we found that these return flows support hydrologic ecosystem services including, riparian vegetation (wildlife habitat), groundwater storage, and late season baseflows.

Snow water equivalence (SWE) is typically computed from snow weight by the SNOTEL system in the US. However, a snow pillow, the main snow weight sensor used by SNOTEL, requires a large, open, flat area (at least 9 square meters) and substantial maintenance costs. This article presents the snow water equivalence estimation (SWEE) algorithm that estimates the SWE evolution merely from continuous snow depth and temperature measurements using common sensors. The key component is a depth-averaged snow density model that is available in the literature, but is underutilized. Here, we demonstrate that the snow density model can estimate mass exchanges (SWE changes due to snowfall, erosion, deposition, and snowmelt) as well as the SWE. The SWEE algorithm can potentially increase the number of snow monitoring locations because snow depth and temperature sensors are considerably more accessible and economical than snow weighing sensor.