Southeastern Greenland (SEG) provides a complex habitat area consisting of dozens of deep fjords that connect land-based ice with the open ocean. Within these fjords, glacial ice mixes with sea ice and intricate topography can create niche local conditions. This area is important for a number of species, including polar bears, and better understanding of both land ice and floating glacial ice as biological habitat motivates the need to study the physical environment itself. Studying SEG, however, posed a variety of challenges, including difficult access for in-situ work or instrument deployment, cloudy conditions that can obscure optical satellite instruments, and steep, complex terrain that can complicate identification of surface conditions. Here, we discuss our work to leverage several remote sensing products to determine the geospatial patterns of landfast sea ice and solid glacial ice during 2015-2019 in five SEG fjords with high polar bear use. We further connect these data with measurements of solid glacial ice discharge and glacial and terrestrial freshwater flux across SEG. The landfast sea ice season in our focus fjords is quite short, extending on average only ~2-4 months, and including substantial variability. Because of the fjord connections to marine-terminating glaciers, however, solid glacial ice creates a potential alternative ice platform on the fjord surfaces that can complement the short sea ice season. Challenges remain in automating this type of surface classification, and we discuss how our manual digitization work can be leveraged to support other ongoing efforts to create machine learning surface identification algorithms.

Previous approaches to modify the surface albedo of Arctic ice have been proposed to slow the rate of ice melt or to increase ice thickness. (Seitz 2011, Desch 2018, Field 2018) Sea ice, glacial ice, and ice-covered thermokarst lakes are all known to melt aided by surface albedo feedback loops. In this process, melting of snow and/or the formation of melt ponds on the surface of the ice causes a lowering of surface albedo, which causes more absorption of sunlight, further warming the ice and increasing the rate of melt. In this study on a freshwater pond, we demonstrate the real-time dynamics of melting and a reduction of ice melt rate by 26% via surface albedo modification.

Earth observation (EO) and geographic information technology (GIT) offer a wide range of data and tools to aid in data management, research, decision-making, and tracking, as well as the ability to test and assess options by providing high-resolution and spatially explicit information in both temporal and spatial domains. However, capacity and gender gap at individual and institutional levels in the HKH region has limited the adoption and operational use of EO data and GIT in various applications as was evident by SERVIR phase I and phase II analysis. In this regard, as part of our capacity-building processes in the HKH region, we held a series of “Empowering women in GIT” events in 2021 to promote and support young women to become aware of and pursue careers in EO and GIT. Virtual trainings were delivered to the young women of five countries i.e. Nepal, Pakistan, Afghanistan, Bhutan and Bangladesh of the HKH region building the capacity of ~235 individuals from ~166 institutes accommodating many professionals from the academic institutes as well. The trainings were given on various aspects of EO and GIT from basic to advanced level including the use of open-source EO data and tools for terrestrial applications along with a focus on the SERVIR services, and applications on drought monitoring, forest cover analysis, HiWAT streamflow prediction, stream water delineation etc. Analysis from the training assessment showed an enhancement (>50%) in the knowledge and skills from low to higher levels. The effectiveness of the training judged by relevancy and quality surveys demonstrated that the trainings were of significant high quality, with >85 percent of the participants responding positively. Our efforts to close the gender gap in EO and GIT had a positive outcome, with most participants expressing confidence in using the information in their respective professional domains and research areas. Academic institutions have been the key in spreading knowledge on EO and GIT, thus, building the capacity of academic professionals would help in the foundation of institutional and individual capacity to transfer knowledge. Focused capacity development can contribute to bridge the gender and capacity gap, and underpin the achievement of best practices and innovations that might guide towards better adoption of EOand GIT in the HKH region.

Since its launch, in September 2018, ICESat-2’s Advanced Topographic Laser Altimeter System (ATLAS) has collected high-resolution measurements of Arctic sea ice by sampling the surface every 70 cm along-track. We utilize the high-resolution capabilities of ATLAS with a novel algorithm called the University of Maryland-Ridge Detection Algorithm (UMD-RDA) to investigate sea ice topography across a range of scales. Applying the UMD-RDA to the ATL03 Global Geolocated Photon product we measure surface roughness and derive the frequency, height, width, and angle of individual pressure ridge sails. Aggregating data from multiple orbit crossings per day we investigate ridge characteristics at length-scales varying from 1 km (individual floes) to the pan-Arctic scale (central Arctic Ocean). Here, we present an evaluation of pressure ridge characteristics during the winter seasons of 2018/19, 2019/20, and 2020/21, comparing results from distinct regions with varying ice conditions. Near-coincident, independent observations of pressure ridges with Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) lidar data, OIB near-coincident Continuous Airborne Mapping By Optical Translator (CAMBOT) high-resolution (~15 cm) optical imagery, and WorldView high-resolution (~30 cm) panchromatic satellite imagery are used to evaluate the accuracy of our ICESat-2 ridge detection scheme. There are many potential use-cases for a high-resolution sea ice topography data product within the community, ranging from navigational hazard mitigation to ecological studies of marine mammal habitats. We discuss plans for releasing these data products and discuss the improvements such data would make within high-resolution sea ice models.

Recent observations of faults in the shallow regions of subduction zones have revealed slow slip events that nucleate up-dip of the locked zone. Clay-rich sediments are prevalent at shallow depths and a large body of experimental work has shown that these sediments have a tendency towards velocity-strengthening frictional behavior, although velocity-weakening behavior is observed as well. Models of deeper slow slip, down-dip of the locked zone, generally require velocity-weakening behavior for events to nucleate. Here I show that slow slip events can nucleate and propagate on shallow, velocity-strengthening thrust faults, in a numerical model of a thrust fault dipping in a homogeneous, elastic half-space. This behavior is due to the broken symmetry of the thrust fault geometry, and is similar to behavior previously reported on bi-material, and poro-elastic faults. The interaction of the fault with the free surface (i.e. the sea floor) creates a coupling between normal stress on the fault and fault slip. This coupling allows velocity-strengthening slow slip events to nucleate, and becomes stronger at shallower depths. Here I conduct a parameter analysis, and show how this behavior is limited to certain values of the frictional and elastic parameters on the fault.

Despite advancements in computational science, nonlinear geophysical processes still present important modeling challenges. Physical sensors (such as satellites, AUVs, or buoys) can collect data at specific points or regions, but these are often scarce or inaccurate. Here, we present a method to build improved spatio-temporal models that combine dynamics, inferred from high-fidelity numerical models (reanalysis data), and data from sensors. We are motivated by a data set of ocean temperature where sensor measurements are only available at the surface of the ocean. We first employ reanalysis data in the form of a 3D temperature field, and apply standard principal component analysis (PCA) at every ocean surface coordinate. For each coordinate, the vertical structure of the field can be represented with just two PCA modes and their corresponding time coefficients, significantly reducing the dimensionality of the data. Next, a conditionally Gaussian model, implemented through a temporal convolutional neural network, is built to predict the time coefficients of the PCA modes (i.e. vertical structure), as well as their variance, as a function of the surface temperature. These probabilistic predictions are made with the satellite data as input, and they are used with the PCA modes to stochastically reconstruct the full temperature field. The estimated temperature field is then combined with data from buoys through a multi-fidelity Gaussian process regression scheme, where the buoys have the highest fidelity and the satellite-based predictions have lower fidelity. The techniques described provide a framework for building less expensive and more accurate models of conditionally Gaussian estimates for full 3D fields, and they can be applied to geophysical systems where data from both sensors and numerical simulations are available. We implement these techniques to estimate the full 3D temperature field of the Massachusetts and Cape Cod Bay where temperature can serve as a useful indicator for ocean acidification. Finally, we discuss how the developed ideas can be leveraged to make more informed decisions about optimal in-situ sampling and path planning.

Agriculture is a significant source of carbon dioxide (CO2) and methane (CH4) and is the dominant source of anthropogenic nitrous oxide (N2O) emissions. Changes in agricultural land management practices that reduce overall greenhouse gas (GHG) emissions have been suggested to help mitigate climate change, but a better understanding of the timing, magnitude, and drivers of GHG fluxes is needed. Alfalfa agroecosystems may be significant sources of N2O given their ability to increase N inputs through symbiotic N2 fixation and frequent irrigation events that create conditions for hot moments of N2O production. However, few studies have explored long-term N2O emissions and their associated drivers in alfalfa ecosystems. We collected over 108,000 CO2, CH4 and N2O soil flux measurements over four years using cavity ring-down spectroscopy from a conventional flood-irrigated alfalfa field in California, USA. This ecosystem was a consistent source of N2O (annual mean: 624.4 ± 27.8 mg N2O m-2 yr-1, range: 263.6. ± 5.6 to 901.9 ± 74.5 mg N2O m-2 yr-1) and a small net sink of CH4 (annual mean: -53.5 ± 2.5 mg CH4 m-2 yr-1, range: -78.2 ± 8.8 to -31.6 ± 2.5 mg CH4 m-2 yr-1). Soil CO2 fluxes averaged 4925.9 ± 13.5 g CO2 m-2 yr-1 and were greater than other alfalfa ecosystem estimates, likely driven by elevated temperatures and plant productivity throughout the growing season. Hot moments of N2O emissions represented only 0.2% to 1.1% of annual measurements but were 31.6% to 56.8% of the annual flux. We found that both the magnitude and the contribution of N2O hot moments to annual emissions decreased over time. Normalized difference vegetation index (NDVI), soil temperature, moisture, and O2 were all significantly correlated with soil CO2, N2O, and CH4 fluxes, although associations varied across both soil depth and timescales. Our results suggest that flood-irrigated alfalfa is a significant source of agricultural N2O emissions, and that plant productivity and soil moisture effects on O2 availability may modulate the net GHG budget of alfalfa agroecosystems.

There are a limited number of studies covering the temporal evolution and spatial distribution of under-ice meltwater and false bottoms for Arctic sea ice. At the same time, they both have a significant effect on the desalinization of sea ice and the ice bottom melt rates. Additionally, these observations are an important part of the meltwater budget. The MOSAiC drifting expedition was aimed to collect field data of coupled processes between ice, ocean, and atmosphere. During the melt season ice cores were collected every week from the unponded first- (FYI) and second-year level ice (SYI) of the investigated ice floe. In addition, ice mass balance buoys were installed in the vicinity of two coring sites, but in ponded areas. This allowed for the comparison of snow, ice, melt pond, under-ice meltwater layer, and false bottom thickness evolution, as well as ice and water physical parameters. Despite the 130 m distance between unponded and ponded FYI sites, the thickness of both under-ice meltwater layer and false bottoms was almost identical. For the SYI, the thicker unponded area had a draft below the meltwater layer and experienced only an ice bottom temperature rise to -1.2°C, while for thinner ponded SYI under-ice meltwater layer was observed. The depth of the seawater and under-ice meltwater layer interface was similar for FYI and SYI. The temperature of under-ice meltwater was close to 0°C, above its freezing point with pronounced diurnal cycles. The under-ice meltwater layer formed three weeks earlier below SYI than below FYI. Due to presence of under-ice meltwater, the FYI bulk salinity decreased from both top and bottom to bulk values below typical for multiyear ice due to only top surface flushing. The thickness of under-ice meltwater layer was stable, around 47 cm for FYI and 26 cm for SYI, in contrast to gradually increasing water equivalent of melted snow and ice. This imbalance indicates a significant horizontal transfer of meltwater.

Faults and fractures may emplace fresh material onto Europa’s surface, originating from shallow reservoirs within the ice shell or directly from the subsurface ocean. Ménec Fossae is a region of particular interest, as it displays, within a relatively small area, the interaction of several geological features such as bands, double ridges, chaotic terrains, and fossae. These features might affect the emplacement of buried material and subsequent exposure of fresh volatiles, prime targets for the upcoming JUICE and Europa Clipper missions in order to assess Europa’s astrobiological potential. Previous studies already revealed that a deep central trough is present at Ménec Fossae, flanked by several subparallel minor troughs and by few asymmetrical scarps with lobate planforms. The presence of such features has motivated this study, given its potential to provide clear indications on the tectonic regime involved. Through detailed geomorphological-structural mapping on Galileo Solid State Imager data and terrain analysis on Digital Terrain Models, we could develop a novel hypothesis on the formation mechanisms that might have been involved in the study area. We propose that Ménec Fossae has been shaped by transtensional (strike-slip with a major extensional component) tectonic activity, as indicated by the orientation and relationship of the tectonic features present. The shear heating related to such a tectonic setting possibly led to the formation of a shallow water reservoir, that in turn could have generated the observed chaotic terrains, double ridges, and fossae. These results strengthen the case for widely distributed shallow water reservoirs within Europa’s ice shell.

Oceanic transform faults accommodate plate motions through both seismic and aseismic slips. However, deformation partition and slip mode interaction at these faults remain elusive mainly limited by rare observations. We use one-year ocean bottom seismometer data collected in 2008 to detect and locate earthquakes at the westernmost Gofar transform fault. The ultra-fast slipping rate of Gofar results in ~30,000 earthquakes during the observational period, providing an excellent opportunity to investigate interrelations between the slip mode, seismicity, and fault architecture at an unprecedented resolution. Earthquake distribution indicates that the ∼100 km long Gofar transform fault is distinctly segmentated into five zones, including one zone contouring a M6 earthquake that was captured by the experiment. Further, a barrier zone east of the M6 earthquake hosted abundant foreshocks preceding the M6 event and halted its active seismicity afterwards. The barrier zone has two layers of earthquakes at depth, and they responded to the M6 earthquake differently. Additionally, a zone connecting to the East Pacific Rise had quasi-periodic earthquake swarms. The seismicity segmentation suggests that the Gofar fault has multiple slip modes occurring in adjacent fault patches. Spatiotemporal characteristics of the earthquakes suggest that complex fault architecture and fluid-rock interaction play primary roles in modulating the slip modes at Gofar, possibly involving multiple concurrent physical processes.

Ammonium contamination is one of the most nocive forms of groundwater contamination caused due to the extensive use of nitrogen fertilizers. Contaminant transport is governed by the adsorption mechanism, which varies as the contaminant migrates through different types of soils. It is important to understand the adsorption mechanism of ammonium ions and the factors influencing them to predict and mitigate their contamination. This work attempts to thoroughly investigate the effects of clay content present in the soil on the adsorption and eventually on the retardation of ammonium ions movement in the porous media. Ammonium ions transport was examined for three soil types with different clay proportions, by column and batch experiments. The experimental results were verified by simulating ammonium ion migration by numerical modeling using HYDRUS 2D software. It was observed the ammonium ions adsorption increases with the increase in the clay content of the soil. Therefore, greater content of clay in the soil enhances the retardation of ammonium migration in the soil. Further, it can be construed that the soil clay fraction ensures a reduction in deeper penetration of ammonium ions as a contaminant into the subsurface.

We developed a numerical thermodynamics laboratory called “Thermolab” to study the effects of the thermodynamic behavior of non-ideal solution models on reactive transport processes in open systems. The equations of state of internally consistent thermodynamic datasets are implemented in MATLAB functions and form the basis for calculating Gibbs energy. A linear algebraic approach is used in Thermolab to compute Gibbs energy of mixing for multi-component phases to study the impact of the non-ideality of solution models on transport processes. The Gibbs energies are benchmarked with experimental data, phase diagrams and other thermodynamic software. Constrained Gibbs minimization is exemplified with MATLAB codes and iterative refinement of composition of mixtures may be used to increase precision and accuracy. All needed transport variables such as densities, phase compositions, and chemical potentials are obtained from Gibbs energy of the stable phases after the minimization in Thermolab. We demonstrate the use of precomputed local equilibrium data obtained with Thermolab in reactive transport models. In reactive fluid flow the shape and the velocity of the reaction front vary depending on the non-linearity of the partitioning of a component in fluid and solid. We argue that non-ideality of solution models has to be taken into account and further explored in reactive transport models. Thermolab Gibbs energies can be used in Cahn-Hilliard models for non-linear diffusion and phase growth. This presents a transient process towards equilibrium and avoids computational problems arising during precomputing of equilibrium data.

A long-standing problem in paleoseismic studies is to distinguish between aseismic and seismic slips along faults. We address this problem by characterizing the magnetic fabrics of rock samples around mesoscale normal faults of aseismic origin, and comparing them to those around co-seismic normal faults. Detailed sampling profiles along traverses, <0.2 m up to ~20 m from both sides of the faults, indicate symmetric and unchangeable magnetic fabrics of deformation origin that are coaxial with the regional strain field. These results are essentially different from magnetic fabrics detected around co-seismic normal faults, which show fault-related fabrics with asymmetric and changeable orientations. The analysis demonstrates two end-member cases of magnetic fabrics in association with aseismic and seismic slips along faults. We suggest that magnetic fabric analysis provides a powerful and efficient tool to characterize inelastic deformation around faults, enabling to distinguish between seismic and aseismic slip histories.

Six conceptually different models of steady groundwater flow and conservative transport are applied to the heterogeneous MADE aquifer. Their predictive capability is assessed by comparing the modelled and observed longitudinal mass distributions at different times of the plume in the MADE-1 experiment, as well as at a later time. The models differ in their conceptualization of the heterogeneous aquifer structure, computational complexity, and use of permeability data obtained from various observation methods (DPIL, Grain Size Analysis, Pumping Tests and Flowmeter). Models depend solely on aquifer structural and flow data, without calibration by transport observations. Comparison of model results by various measures, i.e. peak location, bulk mass and leading tail, reveals that the predictions of the solute plume agree reasonably well with observations if the models are underlined by a few parameters of close values: mean velocity, a parameter reflecting log-conductivity variability and a horizontal length scale related to conductivity spatial correlation. From practitioners perspective the robustness of the models is an important and useful property. The model comparison provides insight into relevant features of transport in heterogeneous aquifers. After further validation by additional field experiments or by numerical simulations, the results can be used to provide guidelines for users in selecting conceptual aquifer models, characterization strategies, quantitative models and implementation for particular goals.

Kepler-411 is a K2V-type star with an average rotation period of 10.52 days, radius of 0.79 Rsun and mass of 0.83 Msun. This active star has at least four planets, three of them eclipse the star, the three larger planets are mini Neptunes with radii of 2.2, 3.47 and 3.46 Earth radii, and periods of 3.0, 7.8 and 58.0 days, respectively. This star was observed by the Kepler satellite for about 600 days showing a total number of 195 transit for planet Kepler-411 b, 76 transits Kepler-411c and 10 transits for planet Kepler-411d. When a planet transits its host star, it may occult a spot causing a detectable signal in the light curve. In this work we apply the model described in Silva (2003), to characterize the starspots, which resulted in the detection of a total of 45 spots in Kepler-411b, 143 spots in Kepler-411c transits and 10 spots in Kepler-411d transits. Analysis of the spots detected on the different transit latitudes of these planets yields a differential shear of 0.050 rd/d or a relative differential rotation of 8.3%, assuming a solar like rotation profile. Also, a total of 66 flares and superflares were detected on the light curve. Here, we discuss the relationship between the size and temperature of the starspots with statistical data on flares and superflares.

Two traverses have been conducted for validation of the NASA Ice, Cloud, and land Satellite 2 on the flat interiors of the Antarctic and Greenland ice sheets. GNSS data collected on three separate 88S Traverses intersect 20% of the ICESat-2 reference ground tracks and have height errors of 5.6 and 8.0 cm. GNSS data from the monthly Summit Traverse in Greenland have 0.9 cm of bias and 5.7 cm precision. Data from these traverses were used to assess heights from ICESat-2, CryoSat-2, and Airborne Topographic Mapper (ATM). ICESat-2 heights have better than ±2.9 cm bias and better than ±6.7 cm precision. ATM heights have better than 9.3 cm bias and better than ±9.6 cm precision. CryoSat-2 heights have more than 20 cm of bias and more than ±40 cm precision. These best-case results are from the ice-sheet interiors but provide a characterization of the quality of satellite and airborne altimetry.

We use a newly developed global Hall MHD code to investigate how reconnection drives magnetotail asymmetries in small magnetospheres. Here, we consider a scaled-down, Earth-like magnetosphere where we have artificially inflated the ion inertial length ($\delta_i$) to one Earth radius (the real Earth’s $\delta_i\approx 1/15-1/20 R_E \approx 300-400\unit{km}$ in the magnetotail). This results in a magnetotail width on the order of $30 \delta_i$, slightly smaller than Mercury’s tail and much smaller than Earth’s. At this small size, we find that the Hall effect has significant impact on the global flow pattern, changing from a symmetric, Dungey-like convection under resistive MHD to an asymmetric pattern similar to that found in previous Hall MHD simulations of Ganymede’s subsonic magnetosphere as well as other simulations of Mercury’s using multi-fluid or embedded kinetic physics. We demonstrate that the Hall effect is sufficient to induce a dawnward asymmetry in observed dipolarization front locations and find quasi-periodic global scale dipolarizations under steady, southward solar wind conditions. On average, we find a thinner current sheet dawnward; however, the measured thickness oscillates with the dipolarization cycle. During the flux-pileup stage, the dawnward current sheet can be thicker than the duskward sheet. This could be an explanation for recent observations that suggest Mercury’s current sheet is actually thicker on the duskside: a sampling bias due to a longer-lasting “thick’ state in the sheet.

Private groundwater wells have the potential to be an unmonitored source of contaminants that can harm human health for millions of people throughout the United States. Developing models that predict potential exposure to contaminants, such as nitrate, could guide sampling efforts and allow the residents to take action to reduce their risk. Machine learning models have been successful in predicting nitrate contamination using geospatial information such as proximity to nitrate sources or soil type, but previous models have not considered meteorological factors that change temporally. In this study, we test random forest (regression and classification) and linear regression models to predict nitrate contamination of wells using rainfall and temperature records over the previous 180-days. We trained and tested models for (1) all of North Carolina, (2) each geographic region in North Carolina, (3) a three-county region with high density animal agriculture, and (4) a three-county region with a low density of animal agriculture. All regression models had poor predictive performance (R2 = 0.04) for all areas tested. The random forest classification model for the coastal plain region showed fair agreement (Cohen’s kappa = 0.23) when trying to predict whether contamination occurred. All other classification models had slight or poor predictive performance. Our results show that temporal changes in rainfall and temperature alone are not enough to predict nitrate contamination in most areas of North Carolina but show potential in the coastal plain region.