We investigate the applicability and performance of the plasma physics based WINDMI model to the analysis and identification of substorm onsets. There are several substorm onset criteria that have been developed into event lists, either from auroral observations or from auroral electrojet features. Five of these substorm onset lists are available at the SuperMAG website. We analyze these lists, aggregate them and use the WINDMI model to assess the identified events, emphasizing the loading unloading mechanism in substorm dynamics. The WINDMI model employs eight differential equations utilizing solar wind data measured at L1 by the ACE satellite as input to generate outputs such as the geotail current, the ring current and the field-aligned currents (FACs). In particular, the WINDMI model current output $I_1$ represents the westward auroral electrojet, which is related to the substorm SML index. We analyze a decade of solar wind and substorm onset data from 1998 to 2007, encompassing over 15,000 onsets. Our findings reveal a significant correlation, with WINDMI-derived enhancements in FAC coinciding with the identified substorm events approximately 40\% of the time. This suggests that a substantial proportion of substorms may be attributed to solar wind driving that results in the loading and unloading of energy in the magnetotail.

Understanding celerity, velocity, and their connections is crucial for unraveling subsurface hydrology and improving numerical models. This observational study investigated leakage in soil columns subjected to diverse treatments under natural rainfall, aiming to characterize celerity, velocity, and their relationships during leakage, and assess treatment effects on this relationship. Over three years with 13 rainfall events, velocities (0.00-0.26 m/h) and celerities (0.09-442.45 m/h) displayed significant disparities. A fixed kinematic ratio (k = 200) partitioned the celerity-velocity relationship into High-Ratio Area and Low-Ratio Area. Assuming liquid-phase changes affect mechanical wave celerity in a three-phase medium, these areas are hypothesized to correspond to dominant leakage-stage flow patterns: film flow and macropore flow. Both the High-Ratio Area and the Low-Ratio Area showed a strong adherence to a power function model, indicating a consistent non-linear celerity-velocity relationship across leakage scenarios. Treatments applied to soil columns influenced the distribution of celerity-velocity pairs and their interaction, demonstrating the potential to modify subsurface hydrological dynamics through targeted interventions.

Understanding streamflow generation at the catchment scale requires quantifying how different components of the system are linked, and how they respond to meteorological forcing. Here we use a data-driven nonlinear deconvolution and demixing approach, Ensemble Rainfall-Runoff Analysis (ERRA), to characterize and quantify dynamic linkages between precipitation, groundwater recharge, and streamflow in a mesoscale intensively farmed catchment. Streamflow in this catchment is flashy, but occurs at time lags that are too long to be plausibly attributed to overland flow runoff. Instead, the impulse responses of groundwater recharge to precipitation, and of streamflow to groundwater recharge, imply that this intermittent runoff is primarily driven by precipitation infiltrating to recharge groundwater, followed by linear-reservoir discharge of groundwater to streamflow. Streamflow increases nonlinearly with increasing precipitation intensity or groundwater recharge, and exhibits almost no runoff response to precipitation or recharge rates of less than 10 mm d−1. Groundwater recharge is both nonlinear, increasing more-than-proportionally with precipitation intensity, and nonstationary, increasing with antecedent wetness. Simulations with the infiltration model Hydrus-1D reproduce the observed water table time series reasonably well (NSE=0.70). However, the model’s impulse response is inconsistent with the observed impulse response estimated from measured precipitation and groundwater recharge, illustrating that goodness-of-fit statistics can be weak tests of model realism. Our analysis demonstrates how impulse responses estimated by ERRA can help quantify nonlinearity and nonstationarity in hydrologic processes, and can help clarify the mechanistic linkages between precipitation and streamflow at the catchment scale.

While most rivers and lakes follow predictable principles of hydrology and geology, a few defy the rules. Some rivers diverge rather than converge; some rivers flow two directions; some lakes have not one but two outlets; some watersheds have ambiguous boundaries. The scientific literature on these exceptions is sparse, scattered, and, in some cases, conflicting. We provide an authoritative overview of nine unusual natural drainages in North and South America, including river bifurcations and bifurcation lakes: Casiquiare River, Arroyo Partido, Wayambo River, Atchafalaya River, North Two Ocean Creek, Divide Creek, Committee’s Punch Bowl, Echimamish River, and Wollaston Lake. Most instances are found on flatlands and saddles. Some watershed boundaries are still unresolved or even dynamic, suggesting river formation in progress. We discuss the exploration, geophysical settings, hydrology, ecology, use, and management of these extraordinary drainages.

On October 8, 2023 (UTC), unique earthquakes occurred in the Izu-Ogasawara Arc, Japan, in which the P- and S-phases were barely visible and only the T-phase was evident, followed by tsunamis that reached islands in the Izu-Ogasawara Arc and a wide area of the Pacific coast of southwest Japan. In the location where the T-phase source was estimated, there is the Sofu Seamount which was previously unrecognized as an active submarine volcano. A bathymetric survey of the seamount conducted one month after the event revealed characteristics of the seamount with a caldera and a central cone. Compared to the bathymetry in 1987, the topography in the caldera had changed significantly such as a crater forming in the central cone. This seamount is likely to be an active volcano. The topographic changes on the caldera-sized scale that occurred at the caldera can be explained as a source of the October tsunami.

Near-term, iterative ecological forecasts can be used to help understand and proactively manage ecosystems. To date, more forecasts have been developed for aquatic ecosystems than other ecosystems worldwide, likely motivated by the pressing need to conserve these essential and threatened ecosystems and increasing availability of high-frequency data. Forecasters have implemented many different modelling approaches to forecast freshwater variables, which have demonstrated promise at individual sites. However, a comprehensive analysis of the performance of varying forecast models across multiple sites is needed to understand broader controls on forecast performance. Forecasting challenges (i.e., community-scale efforts to generate forecasts while also developing shared software, training materials, and best practices) present a useful platform for bridging this gap to evaluate how a range of modelling methods perform across axes of space, time, and ecological systems. Here, we analysed forecasts from the aquatics theme of the National Ecological Observatory Network (NEON) Forecasting Challenge hosted by the Ecological Forecasting Initiative. Over 100,000 probabilistic forecasts of water temperature and dissolved oxygen concentration for 1-30 days ahead across seven NEON-monitored lakes were submitted in 2023. We . For water temperature, we found that forecast skill degraded with increases in forecast horizons, process-based models and models that included air temperature as a covariate generally exhibited the highest forecast performance, and that the most skillful forecasts often Overall, the NEON Forecasting Challenge provides an exciting opportunity for a model inter-comparison to learn about the relative strengths of a diverse suite of models and advance our understanding of freshwater ecosystem predictability.

Since 2016, Antarctic sea ice area (SIA) has set three record summertime minimums occurring in 2017, 2022, and 2023. These recent extremes culminated in a record low SIA anomaly in austral winter 2023, when SIA was over 2 million km^2 below climatology, resulting from negative sea ice concentration (SIC) anomalies centered in the Ross, eastern Weddell, and East Antarctic Seas. We show that a fully-coupled Earth System Model run with historic and anthropogenic forcing and nudged to observed winds over 1950-2023 reproduces the observed austral winter 2023 SIC anomalies. In a sensitivity test that had the impact of 2023 ENSO conditions removed via regression, we show that the 2023 transition from La Niña to El Niño had a negligible impact. Next, using an ensemble initialized on January 1st 2023 nudged to past years' winds, we demonstrate that ~70% of the total Antarctic SIA anomaly was predictable six months in advance and driven by warm Southern Ocean sea surface temperatures that developed prior to 2023, with the remaining ~30%  attributable to 2023 atmospheric circulation. Finally, an ensemble forecast suggests that Antarctic SIA is likely to remain significantly below climatology in austral winter 2024 due to continuing warm Southern Ocean conditions.

Antarctic Bottom Water (AABW) is the densest water mass in the world and drives the lower limb of the global thermohaline circulation. AABW is formed in only four regions around Antarctica and Cape Darnley, East Antarctica, is the most recently discovered formation region. Here, we compile 40 years of oceanographic data for this region to provide the climatological oceanographic conditions, and review the water mass properties and their role in AABW formation. We split the region into three sectors (East, Central and West) and identified the main water masses, current regimes and their influence on the formation of Cape Darnley Bottom Water (CDBW). In the eastern sector, Prydz Bay, the formation of Ice Shelf Water preconditions the water (cold and fresh) that flows into the central sector to ~68.5◦E, enhancing sea ice production in Cape Darnley Polynya. This produces a high salinity variant of DSW (up to 35.15 g/kg) DSW that we coin Burton Basin DSW. In contrast, the western sector of the Cape Darnley Polynya produces a low salinity variant (up to 34.85 g/kg) we coin Nielsen Basin DSW. The resultant combined CDBW is the warmest (upper temperature bound of 0.05◦C) AABW formed around Antarctica with an upper bound salinity of ~34.845 g/kg. Our findings will contribute to planning future observing systems at Cape Darnley, determining the role CDBW plays in our global oceanic and climate systems, and modelling past and future climate scenarios.

The state of Colorado’s West Slope Basins are critical headwaters of the Colorado River and play a vital role in supporting Colorado’s local economy and natural environment. However, balancing the multi-sectoral water demands in the West Slope Basins is an increasing challenge for water managers. Internal variability - irreducible uncertainty stemming from interactions across non-linear processes within the hydroclimate system - complicates future vulnerability assessments. Climate change may exacerbate drought vulnerability in the West Slope Basins, with significant streamflow declines possible by mid-century. In this work, we introduce a novel multi-site Hidden Markov Model (HMM)-based synthetic streamflow generator to create an ensemble of streamflows for all six West Slope Basins that better characterizes the region’s hydroclimate and drought extremes. We capture the effects of climate change by perturbing the HMM to generate a climate-adjusted ensemble of streamflows that reflects plausible changes in climate. We then route both ensembles of streamflows through StateMod, the state of Colorado’s water allocation model, to evaluate spatially compounding drought impacts across the West Slope basins. Our results illustrate how drought events emerging from the system’s stationary internal variability in the absence of climate change can have significant impacts that exceed extreme conditions in the historical record. Further, we find that even relatively modest levels of plausible climate changes can cause a regime shift where extreme drought impacts become routine. These results can inform future Colorado River planning efforts, and our methodology can be expanded to other snow-dominated regions that face persistent droughts.

The adiabatic invariants (M, J, Φ) and the related invariants (M, K, L∗) have been established as effective coordinate systems for describing radiation belt dynamics at a theoretical level, and through numerical techniques, can be paired with in-situ observations to order phase-space density. To date, methods for numerical techniques to calculate adiabatic invariants have focused on empirical models such the Tsyganenko models TS05, T96, and T89. In this work, we develop methods based on numerical integration and variable step size iteration for the calculation of adiabatic invariants, applying the method to the Lyon-Fedder-Mobarry (LFM) global magnetohydrodynamics (MHD) simulation code, with optional coupling to the Rice Convection Model (RCM). By opening the door to adiabatic invariant modeling with MHD magnetic fields, the opportunity for exploratory modeling work of radiation belt dynamics is enabled. Calculations performed using LFM are cross-referenced with the same code applied to the T96 and TS05 Tsyganenko models evaluated on the LFM grid. Important aspects of the adiabatic invariant calculation are reviewed and discussed, including (a) sensitivity to magnetic field model used, (b) differences in the problem between quiet and disturbed geomagnetic states, and (c) the selection of key parameters, such as the magnetic local time step size for drift shell determination. The rigorous development and documentation of this algorithm additionally acts as preliminary step for future thorough reassessment of in-situ phase-space density results using alternative magnetic field models.

Underground storage in geologic formations will play a key role in the energy transition by providing low-cost storage of renewable fuels like hydrogen. The sealing qualities of caverns leached in salt and availability of domal salt bodies make them ideal for energy storage. However, unstable boundary shear zones of anomalous friable salt can enhance internal shearing and pose a structural hazard to storage operations. Considering the indistinct nature of internal salt heterogeneities when imaged with conventional techniques like reflection seismic surveys, we develop a method to map shear zones using seismicity patterns in the US Gulf Coast, the region with the world’s largest underground crude oil emergency supply. We developed and finetuned a machine learning algorithm using tectonic and local microearthquakes. The finetuned model was applied to detect microearthquakes in a 12-month nodal seismic dataset from the Sorrento salt dome. Clustered microearthquake locations reveal the three-dimensional geometry of two anomalous salt shear zones and their orientations were determined using probabilistic hypocenter imaging. The seismicity pattern, combined with borehole pressure measurements, and sonar surveys show the spatio-temporal evolution of cavern shapes within the salt dome. We describe how shear zone seismicity contributed to a cavern well failure and gas release incident that occurred during monitoring. Our findings show that caverns placed close to shear zones are more susceptible to structural damage. We propose a non-invasive technique for mapping hazards related to internal salt dome deformation that can be employed in high-noise industrial settings to characterize salt domes used for storage.

• Deep convection model shows that the divergence and vorticity are correlated in the polar vortices of Jupiter. • The correlation varies with the depth of the atmosphere and the resolution of the measurement. • The polar vortices at the top of atmosphere are likely sustained by the transfer of vorticity from deeper layers.

While rising global temperatures have altered global drought risk and are projected to continue to change large-scale hydroclimate, it has proved difficult to detect the influence of warming on drought-relevant variables at regional scales. In addition to the inherent difficulty in identifying signals in noisy data, detection and attribution studies generally rely on general circulation models, which may fail to accurately capture the characteristics of naturally forced and internal hydroclimate variability. Here, we use a long tree-ring based paleoclimate record of drought to estimate pre-industrial variability in the Palmer Drought Severity Index (PDSI), a commonly used metric of drought risk. Using a Bayesian framework, we estimate the temporal and spatial characteristics of hydroclimate variability prior to 1850. We assess whether observed twenty-first century PDSI is compatible with this pre-industrial variability or is better explained by a forced response that depends on global mean temperature. Our ressults suggest that global warming likely contributed to dry PDSI in Eastern Europe, the Mediterranean, and Arctic Russia and to wet PDSI in Northern Europe, East-central Asia, and Tibet.

Streamflow monitoring is very important for planning and management of water resources in watersheds, and their prediction accuracy is crucial for decision-making. The Niger River Basin is a transboundary resource, shared by nine West African Countries and Algeria and, a large portion of the population rely on the basin for rain-fed agriculture and hydropower. Over the years, the basin’s streamflow regime has been altered due to climate change, drought, desertification and establishment of Dams. This research describes a novel Deep Learning framework comprised of Bidirectional-Long Short-Term Memory (LSTM) requiring Antecedent Precipitation Index (API) and meteorological variables, preprocessed using Normal Quantile Transform (NQT) as input drivers and, compared with the Soil and Water Assessment Tool (SWAT+) for streamflow prediction. NQT-API-LSTM which considers catchment wetness and seasonality, was forced with reanalyzed climate (1979–2021) while, SWAT+ was driven with biophysical data and reanalyzed climate (2010–2020). The very high performance of both NQT-API-LSTM and SWAT+ models showed the models were reliable and can predict regulated flows with reasonable certainty. However, NQT-API-LSTM outperformed SWAT+ at Lokoja watershed and, realistically captured the influence of seasonal climate and regional groundwater dynamics from upstream catchments including the Sahara Desert on the Benue, Guinean, Sahelian and Sudan Flood. Overall, NQT-API-LSTM could be used successfully for watershed-scale streamflow prediction without the need for continuous ground support data, a benefit for sparsely gauged West African River Basins, while SWAT+ could be used as an alternative, particularly, to evaluate the watershed’s response to land use/land cover changes.

We investigate the causes of large $dB/dt$ events observed by SuperMAG, by comparing with the time-series of different types of geomagnetic activity, or “convection state”, for the duration of 2010. Spikes are found to occur predominantly in the pre-midnight and dawn sectors. We find that pre-midnight spikes are associated with substorm onsets. Dawn sector spikes are not directly associated with substorms, but with auroral activity occurring within the westward electrojet region. Azimuthally-spaced auroral features drift sunwards, producing Ps6 (10-20 min period) magnetic perturbations on the ground. The magnitude of $dB/dt$ is determined by the flow speed in the convection return flow region, which in turn is related to the strength of solar wind-magnetospheric coupling. Pre-midnight and dawn sector spikes can occur at the same time, as strong coupling favours both substorms and westward electrojet activity; however, the mechanisms that create them seem somewhat independent. The dawn auroral features share some characteristics with omega bands, but can also appear as north-south aligned auroral streamers. We suggest that these two phenomena share a single underlying cause.

Visible and microwave satellite measurements can provide the global whitecap fraction. The bubble clouds are three-dimensional structures, and a space-based lidar can provide complementary observations of the bubble depth. Here, we use lidar measurements of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite to quantify global bubble depth from depolarization. The relationship between CALIPSO bubble depth and wind speed from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 is similar to a recently derived relationship based on buoy measurements. The CALIPSO-based bubble depth data show global distributions and seasonal variations consistent with the high wind speed (> 7 m/s) but with some variance. We also found similarities between the CALIPSO bubble depth and the whitecap fraction from AMSR2 and WindSat. Our findings support using spaceborne lidar measurements to advance the understanding of the 3D bubble properties and ocean physics at high wind speeds.

While there is a consensus that water played at least some role in the formation of various Martian landforms, including valley networks (VNs), the specific mechanisms and climate conditions are still debated. Basin hypsometric curves, reflecting elevation distributions, offer insights into past processes and climates. Our study presents a global-scale comparison of basin hypsometry on Mars, Earth, the Moon, and artificial fractal surfaces. Results indicate Martian VN formation likely occurred under a climate more arid than hyper-arid Earth, or under more humid periods that were short-lived. Differences in hypsometric attributes between Mars and the Moon suggest VN formation on Mars involved precipitation-driven water flow. Additionally, impact cratering significantly influenced Martian surface conditions, potentially disrupting fluvial erosion processes. This comparative analysis sheds light on the complex interplay of climatic factors and geological processes in Martian landscape evolution.

A document by Pragallva Barpanda. Click on the document to view its contents.