A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Often, the greatest source of uncertainty in predicting a satellite's trajectory in low Earth orbit originates from atmospheric neutral mass density variability caused by enhanced geomagnetic activity and solar EUV absorption. This work investigates the impacts of solar and geomagnetic index forecasting uncertainty on satellite drag and satellite maneuver decision-making. During an averaged point in the solar cycle, accurate index forecasts with reduced uncertainty are shown to provide significantly improved advance notice for dangerous conjunction events above 500 km. Below 500 km, forecast improvements are less impactful. This boundary of utility from forecast improvements shifts upward and downward during solar maximum and solar minimum, respectively. Improved index forecasts are shown to have little impact on making maneuver decisions 12-24 hours from a potential conjunction event, but are demonstrated to be very useful when trying to make maneuver decisions with more lead time. These improved forecasts of the space weather indices help in making actionable, durable conjunction predictions sooner than is currently possible.

Spreading and transport of the Maximum Salinity Core Layer (MSCL), defined as water with salinity higher than 36 psu, is investigated from hydrographic data sets collected along twelve cross-sections by the Sea education Association (SEA) between Tahiti and the equator from 2008 and 2015, the ARGO data objectively mapped from JAMSTEC and satellite remote sensing. Aquarius sea surface salinity data show that the MSCL occupied an area averaged over time of about 6.7 106 km² with an increase of the occupancy in 2015. The variation of the area occupied by the MSCL at the surface does not show any seasonal cycle. Hydrographic dataset are used to show the extent of the MSCL below the surface. The SEA dataset exhibits the spreading of the MSCL northward of Tahiti between isopycnals 24 σt and 25 σt and is observed as far north as 4°S. The MSCL is observed at the surface as far north as 12°S and the disappearance of the MSCL from the surface is due to input of fresher water from the equatorial region or from the west. Even tough ARGO data have a poorer vertical resolution, comparison with SEA dataset shows that the core of the MSCL is well determined. But ARGO data has a better time resolution and monthly data are available from 2001 to 2017. ARGO data allow us to visualize the whole volume of the MSCL and the westward extension of the MSCL. Geostrophic currents are calculated from ARGO data relative to the surface where surface currents are adjusted to velocities provided by the satellite derived OSCAR data set. The transport calculation shows that most of the water north of Tahiti is transported westward with the highest volume during an El Niño year.

Martian atmospheric dust is a major driver of weather, with feedbacks between atmospheric dust distribution, circulation changes from radiative heating and cooling driven by this dust, and winds that mobilize surface dust and distribute it in the atmosphere. Wind-driven mobilization of surface dust is a poorly understood process due to significant uncertainty about minimum wind stress, and whether saltation of sand particles is required. This study utilizes video of six Ingenuity helicopter flights to measure dust lifting during helicopter ascents, traverses, and descents. Dust mobilization persisted on take-off until the helicopter exceeded 3 m altitude, with dust advecting at 4-6 m/s. During landing, dust mobilization initiated at 2.3-3.6 m altitude. Extensive dust mobilization occurred during traverses at 5.1-5.7 m altitude. Dust mobilization threshold friction velocity of rotor-induced winds during landing are modelled at 0.4-0.6 m/s (factor of two uncertainty in this estimate), with higher winds required when the helicopter was over undisturbed terrain. Modeling dust mobilization from >5 m cruising altitude indicates mobilization by 0.3 m/s winds, suggesting non-saltation mechanisms like mobilization and destruction of dust aggregates. No dependence on background winds was seen for the initiation of dust lifting, but one case of takeoff in 7 m/s winds created a track of darkened terrain downwind of the helicopter, which may have been a saltation cluster. When the helicopter was cruising at 5-6 m altitude, recirculation was seen in the dust clouds.

In the northern Gulf of Mexico shelf, the Mississippi-Atchafalaya River System (MARS) impacts the carbonate system by delivering freshwater with a distinct seasonal pattern in both total alkalinity (Alk) and dissolved inorganic carbon (DIC), and promoting biologically-driven changes in DIC through nutrient inputs. However, how and to what degree these processes modulate the interannual variability in calcium carbonate solubility have been poorly documented. Here, we use an ocean-biogeochemical model to investigate the impact of MARS’s discharge and chemistry on interannual anomalies of aragonite saturation state (ΩAr). Based on model results, we show that the enhanced mixing of riverine waters with a low buffer capacity (low Alk-to-DIC ratio) during high-discharge winters promotes a significant ΩAr decline over the inner-shelf. We also show that increased nutrient runoff and vertical stratification during high-discharge summers promotes strong negative anomalies in bottom ΩAr, and less intense but significant positive anomalies in surface ΩAr. Therefore, increased MARS discharge promotes an increased frequency of suboptimal ΩAr levels for nearshore coastal calcifying species. Additional sensitivity experiments further show that reductions in the Alk-to-DIC ratio and nitrate concentration from the MARS significantly modify the simulated ΩAr spatial patterns, weakening the positive surface ΩAr anomalies during high-discharge summers or even producing negative surface ΩAr anomalies. Our findings suggest that riverine water carbonate chemistry is a main driver of interannual variability in ΩAr over river dominated ocean margins.

Perennial snowfields, such as those found in the Brooks Range in Alaska, are critical to alpine and arctic ecosystems, as they influence downslope hydrology, vegetation, geology, and serve as habitat for an array of wildlife, including caribou. Caribou are a crucial food and cultural resource for Alaska Native subsistence hunters. To understand extent changes and persistence of perennial snowfields, we developed a spatially distributed perennial snowfield melt model using the temperature melt index method, paired with multivariate binary logistic regression. Input data for calibration and evaluation of the model are a synthesis of climate reanalysis data and satellite imagery from both multi-spectral (optical) data and synthetic aperture radar (SAR). Temporal and spatial scale variations among input datasets, as well as variations in distribution and extents of the snowfields themselves, were accounted for using several methods. Snowfield metrics derived from remote sensing data were evaluated by comparison with field collected data. Probabilities of perennial snowfield melt at several thresholds were modeled using terrain-adjusted gridded temperature and net solar radiation data with a digital elevation model (DEM). Conditions of snowfield disappearance or persistence, from one melt season to the next, were derived from Sentinel-2 optical imagery. Pixel wise melt-onset and freeze-up dates were determined using a Sentinel-1 SAR backscatter intensity differencing approach. The model was calibrated in a focused study domain within the Brooks Range and evaluated in an alternate location around the Alaska Native village of Anaktuvuk Pass. Results of the perennial snowfield melt model indicate best performance at probability thresholds from 50% to 70%.  Local community members from the village of Anaktuvuk Pass were involved in field work decision making processes, as well as with data collection. The application of this model will be for quantifying one of many potential contributing factors to changes in arctic caribou herds that have been observed for some time by Alaska Native subsistence hunters. 

Wettability is one of the key controlling parameters for multiphase flow in porous media and significant for various geoscience applications such as geological storage of CO2, EOR, and energy storage. In this study, by the means of high-resolution imaging, a series of displacement experiments of brine invasion into oil-saturated microfluidic cells under various wettability conditions were conducted to investigate the wettability effects on displacement stability and fluid distributions. Displacements were studied using two oil fluid systems with M (the ratio of the viscosity of the defending fluid to that of the invading fluid) to be 0.85 and 100 respectively under three different flow rates (capillary number Ca ranging from 9.21 × 10−8 to 9.21 × 10−6). Then quantitative analysis of displacement patterns based on fluid distributions images under different flow conditions are performed. Remaining oil distributions are investigated and correlated with capillary forces and wettability conditions. By symmetrically varying the wettability of the flow cell to three different wettability conditions (water-wet, intermediate-wet and oil-wet), we found that as the viscous ratio (M)=0.85, at low capillary number 9.21 × 10−8 when capillary force is dominated, the displacement becomes more efficient as the flow cell becomes more hydrophobic. At small capillary number, residual oil saturation is decreased from 66.89% for water-wet to 59.22% for intermediate-wet and 50.45% for oil -wet. With the capillary number increased to 9.21 × 10−6 , the displacement pattern becomes more compact with 50.62% residual oil saturation. It is noted that with wettability altered from water-wet to oil-wet under the capillary number of 9.21 × 10−7, crossover occurs from stable flow into capillary-dominated flow displacement, resulting in lower displacement efficiency and lager residual oil saturation (Sor=59.77%). When the viscous ratio was increased from 0.85 to 100, the viscous fingering is found to be more obvious, resulting in lower the displacement efficiency as the viscous force dominates the displacement for all the flow conditions compared with fluid flows under M=0.85. Moreover, when the viscous force is increased by increasing flow rates, wettability effect on displacement efficiency and residual oil saturation is suppressed.

On 15 January 2022, the Hunga Tonga-Hunga Ha’apai submarine volcano erupted violently and triggered a giant atmospheric shock wave and tsunami. The exact mechanism of this extraordinary eruptive event, its size and magnitude are not well understood yet. In this work, we analyze data from the nearest ground-based receivers of Global Navigation Satellite System (GNSS) to explore the ionospheric total electron content (TEC) response to this event. We show that the ionospheric response consists of a giant TEC increase followed by a strong long-lasting depletion. We observe that the explosive event of 15 January 2022 began at 04:05:54UT and consisted of at least 5 explosions. Based on the ionospheric TEC data, we estimate the energy released during the main major explosion to be between 9 and 37 Megatons in TNT equivalent. This is the first detailed analysis of the eruption sequence scenario and the timeline from ionospheric TEC observations.

Main hydrological disasters that occur in the Republic of Moldova are floods. In the last 70 years (1947-2015) floods caused losses of 583 mil. US$. Thereby, a proportion of 55% of the damage was determined by flash floods and 40% - by fluvial floods. Increasing human impact on environment is considered to be the main factor for modifications of flood runoff regimes. Moldova features intense economic activity resulting in the fact that over 60% of the territory is used for agriculture and almost all rivers’ morphology is heavily modified. Therefore, the evaluation of anthropogenic impact on floods generation and propagation processes is of particular interest. Environmental change scenario modelling was performed to analyze the modification of flood wave hydrograph features with the help of the JAMS/J2000 hydrological model. 11 pilot basins have been utilized for this analysis, which represent different parts of the country. Assessment of land cover changes impact over the past 3 decades on flood runoff was performed by consecutive change of land cover layer for 1982 and 2013 for each flood event. The results showed that land cover changes caused a slight decrease of flood runoff in the northern part of the country (-4-10%) due to processes of basins naturalization in areas that were left fallow with undergoing natural succession. In the central and southern rivers basins an increase of flood features could be identified (+2-35%) because of intensified agricultural and urbanization processes. Assessment of reservoir impact on floods was performed on example of the Byc River, which is subject to change due to the Ghidighici reservoir. Thus, first, hydrological model was calibrated and validated for the Byc River natural runoff period and later applied for runoff simulation for the reservoir operation period. Differences of real and naturalized modeled runoff showed that Ghidighici reservoir causes a significant decrease of flood runoff which is from 3 up to 20 times lower than in the case of natural runoff modeling. Resulted hydrological models are well suited for land planning, hydraulic engineering and flood control. They can serve as support for authorities in decision making in flood protection and water resources management.

The Earth’s magnetotail contains a current sheet separating the anti-Sunward field of the southern lobe from the sunward-pointing northern lobe. Herein, we report tail current sheets that are supported by only electron currents. We examine one electron-only current sheet in detail, and briefly discuss ten others. Three current sheets are interpreted in terms of the time-evolution of reconnection onset. These current sheets show evidence of parallel electron heating, perpendicular ion heating, and current sheet expansion. These features are consistent with electron and ion behavior during traditional “electron-ion” reconnection. Ground-based and in-situ data show that electron-ion reconnection occurs shortly after each “pre-ion reconnection” electron-only reconnection event. This suggests that electron-only reconnection can act as a precursor to electron-ion reconnection. We note that five events occur shortly after a period of electron-ion reconnection, which suggests that electron-only reconnection is more than merely a precursor to ion reconnection.

The Kenyan ASALs (Arid and Semi-Arid Lands) through livestock production, contribute to over 12% of the 40% Agricultural GDP with further contribution through the tourism sector. They cover over 70% of the country and are home to both wildlife and pastoral communities. With dependence on rain-fed pastures, better management of the ASALs require near real time information on available resources. While information on vegetation conditions is important, other critical resources such as location of water, extent of unpalatable invasive species and other ancillary information is required for a comprehensive understanding of the condition of the ASALs. The Rangelands Decision Support tool was developed to address lack timely information for decision making in the ASALs which influences management of available pastures in dry and wet seasons, development of proper grazing plans, livestock movement, conflicts and implementation of conservation measures meant to rehabilitate degraded lands, management of scarce water resources and mitigation of the spread of invasive species. The tool automates data processing from acquisition to development of final products that consist of dekadal NDVI and monthly products (NDVI Z score, absolute anomalies and VCI (Vegetation Condition Index)). Users are able to select suitable products for specific assessment and produce maps at their monitoring units in PDF format. This research present a fully operational processing chain for the data incorporated in the tool and case studies demonstrating application of the different indicators for monitoring at different monitoring units.

Microbial transformation and decomposition of organic matter in sediments constitutes one of the largest fluxes of carbon in marine environments. Mineralization of sedimentary organic matter by microorganisms results in selective degradation such that bioavailable or accessible compounds are rapidly metabolized while more recalcitrant, complex compounds are preserved and buried in sediment (Mahmoudi et al., 2017). Recent studies have found that the ability to use different carbon sources appears to vary among microorganisms, suggesting that the availability of certain pools of carbon can be specific to the taxa that utilize the pool. This implies that organic matter mineralization in marine environments may depend on the metabolic potential of the microbial populations that are present and active. The goal of our study was to investigate the extent to which organic matter availability and transformation may be species-specific using sediment from Guaymas Basin (Gulf of California). We carried out time-series incubations using bacterial isolates and sterilized sediment in the IsoCaRB system (Beaupre et al., 2016) which allowed us to measure the production rates and natural isotopic signatures (δ13C and Δ14C) of microbially-respired CO2. Separate incubations using two different marine bacterial isolates (Vibrio sp. and Pseudoalteromonas sp.) and sterilized Guaymas Basin sediment under oxic conditions showed that the rate and total quantity of organic matter metabolized by these two species differs. Approximately twice as much CO2 was collected during the Vibrio sp. incubation compared to the Pseudoalteromonas sp. incubation. Moreover, the rate at which organic matter was metabolized by the Vibrio sp. was much higher than the Pseudoalteromonas sp. indicating the intrinsic availability of organic matter in sediments may depend on the species that is present and active. Isotopic analyses of microbially respired CO2 will be used to constrain the type and age of organic matter that is accessible to each species. Moreover, molecular analysis of subsamples collected from each incubation will link carbon utilization with the underlying gene expression. Our study sheds light on the degree to which the metabolic capacities of microorganisms affect carbon transformation in sedimentary environments.

Understanding how thermokarst lakes on arctic river deltas will respond to rapid warming is critical for projecting how carbon storage and fluxes will change in those vulnerable environments. Yet, this understanding is currently limited partly due to the complexity of disentangling significant interannual variability from the longer-term surface water signatures on the landscape, using the summertime window of optical spaceborne observations. Here, we rigorously separate perennial lakes from ephemeral wetlands on 12 arctic deltas and report distinct size distributions and climate trends for the two waterbodies. Namely, we find a lognormal distribution for lakes and a power-law distribution for wetlands, consistent with a simple proportionate growth model and inundated fractal topography, respectively. Furthermore, while no trend with temperature is found for wetlands, a statistically significant decreasing trend of mean lake size with warmer temperatures is found, attributed to colder deltas having deeper and thicker permafrost preserving larger lakes.

Far ultraviolet (FUV) imaging of the aurora from space provides great insight into dynamic coupling of the atmosphere, ionosphere and magnetosphere on global scales. To gain quantitative understanding of these coupling processes, the global distribution of auroral energy flux is required, but the inversion of FUV emission to derive precipitating auroral particles’ energy flux is not straightforward. Furthermore, the spatial coverage of FUV imaging from Low Earth Orbit (LEO) altitudes is often insufficient to achieve global mapping of this important parameter. This study seeks to fill these gaps left by the current geospace observing system using a combination of data assimilation and machine learning techniques. Specifically, this paper presents a new data-driven modeling approach to create instantaneous, global assimilative mappings of auroral electron total energy flux from Lyman-Birge-Hopfield (LBH) emission data from the Defense Meteorological System Program (DMSP) Special Sensor Ultraviolet Spectrographic Imager (SSUSI). We take a two-step approach; the creation of assimilative maps of LBH emission using optimal interpolation, followed by the conversion to energy flux using a neural network model trained with conjunction observations of in-situ auroral particles and LBH emission from the DMSP SSJ and SUSSI instruments. The paper demonstrates the feasibility of this approach with a model prototype built with DMSP data from February 17-23 2014. This study serves as a blueprint for a future comprehensive data-driven modeling of auroral energy flux that is complementary to traditional inversion techniques to take advantage of FUV imaging from LEO platforms for global assimilative mapping of auroral energy flux.

High-latitude winter warming was observed following strong tropical volcanism, which has long been believed to be due to the volcanic-induced positive NAO phase. However, recent works argue that this warming is caused by ENSO variability. Moreover, controversies exist about the sensitivity of ENSO to volcanism. Therefore, to better resolve the controversies related to post-volcanic ENSO and NAO sensitivity and associated high-latitude winter warming, we conducted ENSO-preconditioned volcanic experiments using GFDL-CM2.1 coupled model. Our simulations strongly suggest that the post-eruption Eurasian winter warming is caused by a post-eruption positive NAO phase and not by coexisting ENSO-preconditioned variability. Additionally, we find that the El Niño-preconditioned volcanic eruption enhances the El Niño phase; however, the neutral and La Niña-preconditioned volcanisms do not lead to ENSO–like response. These findings are helpful to better understand volcanic-induced circulation impacts and have important implications for the interpretation of model results and for post-volcanic prediction.

We investigate the flow-wise variation of the hydraulic conductivity inside a non-uniformly shaped fracture with permeable walls. Using lubrication theory for viscous flows, in conjunction with the Beavers–Joseph–Saffman boundary condition at the permeable walls, we obtain an analytical expression for the velocity profile, conductivity, and wall permeation velocity. These predictions highlight the effects of geometric variation (through the local slope of the aperture’s flow-wise variation), the permeability of the walls (through a dimensionless slip coefficient), and the effect of flow inertia (through a Reynolds number). The theory is validated against an OpenFOAM(R) solver for the Navier–Stokes equations subject to a tensorial slip boundary condition, showing good agreement. The mathematical results have implications on system-level (multiscale) modeling of hydraulically fractured reservoirs, in which the Darcy conductivity of each non-uniform passage must be accurately accounted for, throughout the fractured porous rock.

Lakes emit globally significant amounts of carbon dioxide (CO) to the atmosphere, but quantifying these rates for individual lakes is extremely challenging. The exchange of CO across the air-water interface is driven by physical, chemical, and biological processes in both the lake and the atmosphere that vary at multiple spatial and temporal scales. None of the methods we use to estimate CO flux fully capture this heterogeneous process. Here, we compared concurrent CO flux estimates from a single lake based on commonly used methods. These include floating chambers (FC), eddy covariance (EC), and two concentration gradient based methods labelled fixed (F-CO₂) and spatial (S-CO₂). At the end of summer, cumulative carbon fluxes were similar between EC, F-CO₂ and S-CO₂ methods (-4, -4 and -9.5 gC), while methods diverged in directionality of fluxes during the fall turnover period (-50, 43 and 38 gC). Collectively these results highlight the discrepancies among methods and the need to acknowledge the uncertainty when using any of them to approximate this heterogeneous process.

Despite advances in hydrological Deep Learning (DL) models using Single Task Learning (STL), the intricate relationships among multiple hydrological components and model inputs might not be comprehensively encapsulated. This study employed a Long Short-Term Memory (LSTM) neural network and the CAMELS dataset to develop a Multi-Task Learning (MTL) model, predicting streamflow and evapotranspiration across multiple basins. An optimal multi-task loss weight ratio was determined manually during the validation phase for all 591 selected basins with streamflow data-gaps under 5%. During test period, MTL showed median Nash-Sutcliffe Efficiency predictions for streamflow and evapotranspiration at 0.69 and 0.92, consistent with two STL models. The MTL’s strength appeared when predicting the non-target variable, surface soil moisture, using probes derived from LSTM cell states—representative of the internal DL model workings. This prediction showed a median correlation coefficient of 0.90, surpassing the 0.88 and 0.89 achieved by the streamflow and evapotranspiration STL models, respectively. This outcome suggests that MTL models could reveal additional rules aligned with hydrological processes through the inherent correlations among multiple hydrological variables, thereby enhancing their reliability. We termed this as “variable synergy,” where MTL can simultaneously predict varied targets with comparable STL performance, augmented by its robust internal representation. Harnessing this, MTL promises enhanced predictions for high-cost observational variables and a comprehensive hydrological model.

Atomic Hydrogen (H) is the most abundant constituent of the terrestrial exosphere. Its charge exchange interaction with ring current ions (H+ and O+) serves to dissipate magnetospheric energy during geomagnetic storms, resulting in the generation of energetic neutral atoms (ENAs). Determination of ring current ion distributions through modeling depends critically on the specification of the exospheric H density distribution. Furthermore, theoretical studies have demonstrated that ring current recovery rate after the storm onset directly correlates with the H density. Although measurements of H airglow emission at altitudes [3,6] Re exhibit storm-time variations, the H density distributions used in ring current modeling are typically assumed to be temporally static during storms. In this presentation, we will describe the temporal and spatial evolution of ring current ion densities in response to a realistically dynamic exospheric H density distribution using the Comprehensive Inner Magnetosphere-Ionosphere Model (CIMI). The exospheric densities used as input to the model are fully data-driven, derived as global, 3D, and time-dependent tomographic reconstructions of H emission data acquired from Lyman-alpha detectors onboard the NASA TWINS satellites during the geomagnetic storm that occurred on March 17, 2013. We will examine modeled ring current recovery rates using both dynamic and static reconstructions and evaluate the impact of realistic storm-time exospheric variability on the simulations.