Throughout the annals of human history, various religious traditions have featured savior figures, divine beings whose purpose is to guide, inspire, and offer salvation to their followers. In Manichaeism, Mithra, sometimes referred to as "Mihr," was considered one of the divine beings who played a significant role in the salvation of souls. Manichaeans believed in a dualistic cosmology with the struggle between light and darkness, and Mithra was associated with the forces of light. On the other hand, in the vast tapestry of Buddhist beliefs and prophecies, one figure stands out as a beacon of hope for the future-Maitreya, the Buddha of loving-kindness and compassion. Revered across various Buddhist traditions, Maitreya is believed to be the fifth and future Buddha who will come to guide humanity toward enlightenment when the teachings of Gautama Buddha have faded into obscurity. The connections and adaptations between figures like Maitreya and Mithra demonstrate the complex nature of religious and cultural exchanges throughout history. This study proposes that Maitreya and Mithra are the same figure, based on historical and linguistic similarities.

Although adequately detailed kerosene chemical-combustion Arrhenius reaction-rate suites were not readily available for combustion modeling until ca. the 1990’s (e.g., Marinov [1998]), it was already known from mass-spectrometer measurements during the early Apollo era that fuel-rich liquid oxygen + kerosene (RP-1) gas generators yield large quantities (e.g., several percent of total fuel flows) of complex hydrocarbons such as benzene, butadiene, toluene, anthracene, fluoranthene, etc. (Thompson [1966]), which are formed concomitantly with soot (Pugmire [2001]). By the 1960’s, virtually every fuel-oxidizer combination for liquid-fueled rocket engines had been tested, and the impact of gas phase combustion-efficiency governing the rocket-nozzle efficiency factor had been empirically well-determined (Clark [1972]). Up until relatively recently, spacelaunch and orbital-transfer engines were increasingly designed for high efficiency, to maximize orbital parameters while minimizing fuels and structural masses: Preburners and high-energy atomization have been used to pre-gasify fuels to increase (gas-phase) combustion efficiency, decreasing the yield of complex/aromatic hydrocarbons (which limit rocket-nozzle efficiency and overall engine efficiency) in hydrocarbon-fueled engine exhausts, thereby maximizing system launch and orbital-maneuver capability (Clark; Sutton; Sutton/Yang). The combustion community has been aware that the choice of Arrhenius reaction-rate suite is critical to computer engine-model outputs. Specific combustion suites are required to estimate the yield of high-molecular-weight/reactive/toxic hydrocarbons in the rocket engine combustion chamber, nonetheless such GIGO errors can be seen in recent documents. Low-efficiency launch vehicles also need larger fuels loads to achieve the same launched mass, further increasing the yield of complex hydrocarbons and radicals deposited by low-efficiency rocket engines along launch trajectories and into the stratospheric ozone layer, the mesosphere, and above. With increasing launch rates from low-efficiency systems, these persistent (Ross/Sheaffer [2014]; Sheaffer [2016]), reactive chemical species must have a growing impact on critical, poorly-understood upper-atmosphere chemistry systems.

The Regional Climate Modeling system (RegCM) has undergone a significant evolution over the years, leading for example to the widely used versions RegCM4 and RegCM4-NH. In response to the demand for higher resolution, a new version of the system has been developed, RegCM5, incorporating the non-hydrostatic dynamical core of the MOLOCH weather prediction model. In this paper we assess the RegCM5’s performance for 5 CORDEX-CORE domains, including a pan-European domain at convection-permitting resolution. We find temperature biases generally in the range of -2 to 2 degrees Celsius, higher in the northernmost regions of North America and Asia during winter, linked to cloud water overestimation. Central Asia and the Tibetan Plateau show cold biases, possibly due to sparse station coverage. The model exhibits a prevailing cold bias in maximum temperature and warm bias in minimum temperature, associated with a systematic overestimation of lower-level cloud fraction, especially in winter. Taylor diagrams indicate a high spatial temperature pattern correlation with ERA5 and CRU data, except in South America and the Caribbean region. The precipitation evaluation shows an overestimation in South America, East Asia, and Africa. RegCM5 improves the daily precipitation distribution compared to RegCM4, particularly at high intensities. The analysis of wind fields confirms the model’s ability to simulate monsoon circulations. The assessment of tropical cyclone tracks highlights a strong sensitivity to the tracking algorithms, thus necessitating a careful model interpretation. Over the European region, the convection permitting simulations especially improve the diurnal cycle of precipitation and the hourly precipitation intensities.

We propose a framework to assess monthly GCM precipitation and temperature simulations with the aim of achieving robust annual and seasonal climatic projections. The approach is based on a Past Performance Index (PPI) inspired by the Kling-Gupta Efficiency (KGE) and accounts for climatological averages, interannual variability, seasonal cycle, monthly probabilistic distribution and spatial patterns of climatological means. The PPI formulation is flexible enough to include additional evaluation metrics and weight them differently, enabling the diagnostics and classification of GCMs in a simple diagram that shows the joint performance for precipitation and temperature. We demonstrate the utility of this approach to evaluate 27 CMIP6 models and constrain the spread of projections in five regions with very different climates across continental Chile. We also examine the degree of correspondence between the ensemble of models classified as ‘satisfactory’ based on the PPI and the capability of GCMs to reproduce teleconnection responses to El Niño Southern Oscillation and the Southern Annular Mode. The results show that the approach is useful to discriminate models that do not reproduce the seasonal precipitation cycle and to narrow the spread of projected annual and seasonal changes. The best models, according to the PPI, do not necessarily overlap with those that replicate historically observed teleconnections, suggesting that the latter criterion complements our GCM assessment framework. Finally, we show that model features that can be improved through bias correction can be excluded from the model evaluation process to avoid culling models that reproduce historically observed teleconnections.

Though precise, most LiDARs are vulnerable to position and pointing errors as deviations from the expected principal axis lead to projection errors on target. While fidelity of location/pointing solutions can be high, determination of uncertainty remains relatively limited. As a result, NASA’s 2021 Surface Topography and Vegetation Incubation Study Report lists vertical (horizontal, geolocation) accuracy as an associated parameter for all (most) identified Science and Application Knowledge Gaps, and identifies maturation of Uncertainty Quantification (UQ) methodologies on the STV Roadmap for this decade. The presented generalized Polynomial Chaos Expansion (gPCE) based method has wide ranging applicability to improve positioning, geolocation uncertainty estimates for all STV disciplines, and is extended from the bare earth to the bathymetric lidar use case, adding complexity introduced by entry angle, wave structure, and sub-surface roughness. This research addresses knowledge gaps in bathy-LiDAR measurement uncertainty through a more complete description of total aggregated uncertainties, from system level to geolocation, by applying a gPCE-UQ approach. Currently, the standard approach is the calculation of the Total Propagated Uncertainty, which is often plagued by simplifying approximations (e.g. strictly Gaussian uncertainty sources) and ignored covariances. gPCE intrinsically accounts for covariance between variables to determine uncertainty in a measurement, without manually constructing a covariance matrix, through a surrogate model of system response. Additionally, gPCE allows arbitrarily high order uncertainty estimates (limited only by the one-time computational cost of computing gPCE coefficients), accurate representation of non-Gaussian sources of error (e.g. wave height energy distributions), and direct integration of measurement requirements into the design of LiDAR systems, by trivializing the computation of global sensitivity analysis.

Arctic warming under increased CO2 peaks in winter, but is influenced by summer forcing via seasonal ocean heat storage. Yet changes in atmospheric heat transport into the Arctic have mainly been investigated in the annual mean or winter, with limited focus on other seasons. We investigate the full seasonal cycle of poleward heat transport modelled with increased CO2 or with individually applied Arctic sea-ice loss and global sea-surface warming. We find that a winter reduction in dry heat transport is driven by Arctic sea-ice loss and warming, while a summer increase in moist heat transport is driven by sub-Arctic warming and moistening. Intermodel spread in Arctic warming controls spread in seasonal poleward heat transport. These seasonal changes and their intermodel spread are well-captured by down-gradient diffusive heat transport. While changes in moist and dry heat transport compensate in the annual-mean, their opposite seasonality may support non-compensating effects on Arctic warming.

Holistic approaches are needed to investigate the capacity of current water resource operations and infrastructure to sustain water supply and critical ecosystem health under projected drought conditions. Drought vulnerability is complex, dynamic, and challenging to assess, requiring simultaneous consideration of changing water demand, use and management, hydrologic system response, and water quality. We are bringing together a community of scientists from the U.S. Geological Survey, National Center for Atmospheric Research, Department of Energy, and Cornell University to create an integrated human-hydro-terrestrial modeling framework, linking pre-existing models, that can explore and synthesize system response and vulnerability to drought in the Delaware River Basin (DRB). The DRB provides drinking water to over 15 million people in New York, New Jersey, Pennsylvania, and Delaware. Critical water management decisions within the system are coordinated through the Delaware River Basin Commission and must meet requirements set by prior litigation. New York City has rights to divert water from the upper basin for water supply but must manage reservoir releases to meet downstream flow and temperature targets. The Office of the Delaware River Master administers provisions of the Flexible Flow Management Program designed to manage reservoir releases to meet water supply demands, habitat, and specified downstream minimum flows to repel upstream movement of saltwater in the estuary that threatens Philadelphia public water supply and other infrastructure. The DRB weathered a major drought in the 1960s, but water resource managers do not know if current operations and water demands can be sustained during a future drought of comparable magnitude. The integrated human-hydro-terrestrial modeling framework will be used to identify water supply and ecosystem vulnerabilities to drought and will characterize system function and evolution during and after periods of drought stress. Models will be forced with consistent input data sets representing scenarios of past, present, and future conditions. The approaches used to unify and harmonize diverse data sets and open-source models will provide a roadmap for the broader community to replicate and extend to other water resource issues and regions.

Using Unoccupied Aerial Systems (UASs) to collect image data for field phenotyping enables the coverage of large spatial scales, but the extraction of phenotypes at the level of an individual plant remains a challenge. Phenotypes are generally extracted at the plot level after manual delineation of the plots in an orthomosaic image. However, this approach is impractical when individual plant phenotypes are of interest, such as in mutant screenings, heterozygous population trials, and other cases where individual plants in a plot or a landscape may be very diverse. For mutant studies in particular, UASs have had limited utility since one typically wants to identify individual plants with outlier phenotypes. Current mutant population screenings require scientists to walk through the field on a regular basis to manually identify and record plants of interest. If UAS software could be designed to identify these plants, much larger studies could be conducted with lower labor costs. Here, we use deep Convolutional Neural Network (CNN) models to detect individual maize plants in raw UAS images followed by the extraction of individual phenotypes. The predictions from the CNN models are used to identify plants in the orthomosaic image to derive information on plot location. For the extraction of phenotypes, plant pixels are segmented from the background and traits including color-based indices and canopy coverage are calculated. Individual plant height is derived from the corresponding Digital Surface Model (DSM). This project will contribute to a more efficient approach for the phenotyping of individual plants using UAS images.

Conventional engineering measures, such as surge barriers and mobile floodgates, are being adopted in many coastal cities worldwide, threatened by the increasing flooding hazard due to rising sea levels. Famous examples include London, the Netherlands, New Orleans, St. Petersburg and Venice. However, the question of how flood regulation affects the morphodynamic evolution of shallow tidal embayments still lingers. Storm-surge barriers may importantly modify the propagation of tides, surges and wind waves, changing sediment transport and, thus, the morphological evolution of regulated tidal environments, in particular in sediment-starved systems. Combining field data and numerical modelling, we investigate the effect of the Mo.S.E. storm-surge barriers, designed to protect Venice from flooding, on the morphodynamic evolution of the Venice lagoon. Artificial reduction of water levels within the lagoon affects the interaction between tide propagation and wind waves, increasing sediment resuspension on tidal flats. Resuspended sediment hardly accumulates on salt marshes, contributing to their vertical accretion and offsetting the negative effect of relative sea-level rise, owing to the reduction of marsh flooding determined by reduced water levels. Although barrier closures temporarily reduce the sediment export toward the open sea, this does not point to preserve the characteristic lagoonal morphology, hindering salt-marsh accumulation and promoting tidal-flat deepening and channel infilling. We conclude that the operations of flood barriers can promote a significant loss of geomorphological diversity, which will critically impact the ecosystem services provided by the shallow tidal environments they are meant to protect, thus increasing the costs related to their conservation and restoration.

Perfluoroalkyl acids (PFAAs), a group of synthetic compounds associated with adverse human health impacts, are commonly found in effluent discharged from wastewater treatment facilities. When that effluent is used for irrigation, the fate of PFAAs depends strongly on vadose zone solute retention properties and loading history. The relative importance of PFAA retention factors under natural conditions remains uncertain, and the historical record of effluent PFAA concentrations is limited. Using soil cores collected from the Penn State Living Filter (irrigated with treated wastewater effluent for nearly 60 years), we evaluated PFAA transport under near-natural conditions, and estimated historical PFAA concentrations in the irrigated effluent. Total perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) masses stored in soils in 2014 were more than 450 times greater than the masses applied during the 2020 effluent irrigation. Equilibrium piston-flow transport models reproduced the observed PFOS and PFOA profiles, allowing us to estimate historical effluent PFOS and PFOA concentrations: 70-170 ng L-1 and 1000-1300 ng L-1, respectively. Estimated concentrations were comparable to concentrations measured in other wastewater effluents in the 1990s and 2000s, indicating that when interpreted with transport modeling, wastewater-irrigated soils function as integrated records of historical PFAA loading. Simulated PFOS breakthrough to groundwater occurred 50 years after the start of wastewater irrigation, while simulated PFOA breakthrough occurred after only 10 years of irrigation. Thus, while wastewater irrigation of soils facilitates retention and reduces effluent PFAA loading to surface waters, the resulting increased PFAA storage in soils potentially creates long-term sources of PFAAs to groundwater.

Magmas readily react with their surroundings, which may be other magmas or solid rocks. Such reactions are important in the chemical and physical evolution of magmatic systems and the crust, for example, in inducing volcanic eruptions and in the formation of ore deposits. In this contribution, we conceptually distinguish assimilation from other modes of magmatic interaction and discuss and review a range of geochemical (+/- thermodynamical) models used to model assimilation. We define assimilation in its simplest form as an end-member mode of magmatic interaction in which an initial state (t0) that includes a system of melt and solid wallrock evolves to a later state (tn) where the two entities have been homogenized. In complex natural systems, assimilation can refer more broadly to a process where a mass of magma wholly or partially homogenizes with materials derived from wallrock that initially behaves as a solid. The first geochemical models of assimilation used binary mixing equations and then evolved to incorporate mass balance between a constant-composition assimilant and magma undergoing simultaneous fractional crystallization. More recent tools incorporate energy and mass conservation in order to simulate changing magma composition as wallrock undergoes partial melting. For example, the Magma Chamber Simulator utilizes thermodynamic constraints to document the phase equilibria and major element, trace element, and isotopic evolution of an assimilating and crystallizing magma body. Such thermodynamic considerations are prerequisite for understanding the importance and thermochemical consequences of assimilation in nature, and confirm that bulk assimilation of large amounts of solid wallrock is limited by the enthalpy available from the crystallizing resident magma. Nevertheless, the geochemical signatures of magmatic systems-although dominated for some elements (particularly major elements) by crystallization processes-may be influenced by simultaneous assimilation of partial melts of compositionally distinct wallrock.

Seismologists working with fiber-optic sensing, commonly referred to as Distributed Acoustic Sensing (DAS), have yet to find an established way of automatically detecting signals of interest within its recordings. We propose a new research perspective within the field by examining the output of a DAS array as an image and processing the image to find signals of interest. In this manuscript, we show an example of such a method, where we automatically detect seismic events of interest within two different DAS datasets, finding, respectively 99 % and 96 % of the local earthquakes previously identified within the data by manual analysis. The method is based on simple image processing and computer vision techniques, which clean the image, and, ideally, leave nothing but the signal of interest. These simple image processing steps yield promising results, indicating that computer vision and image processing might have an immediate impact in geophysical applications of fiber-optic sensing.

Machine Learning and Remote sensing method to determine the relationship between Climate and Groundwater Recharge. Adya Aiswarya Dash1, Abhijit Mukherjee1,2,3. 1Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, West Bengal 721302, India 2School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India 3Applied Policy Advisory for Hydrogeoscience (APAH) Group, Indian Institute of Technology Kharagpur, West Bengal 721302, India Abstract Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

In the Arctic, the spatial distribution of boreal forest cover and soil profile transition characterizing the taiga-tundra ecological transition zone (TTE) is experiencing an alarming transformation. The SIBBORK-TTE model provides a unique opportunity to predict the spatiotemporal distribution patterns of vegetation heterogeneity, forest structure change, arctic-boreal forest interactions, and ecosystem transitions with high resolution scaling across broad domains. Within the TTE, evolving climatological and biogeochemical dynamics facilitate moisture signaling and nutrient cycle disruption, i.e. permafrost thaw and nutrient decomposition, thereby catalyzing land cover change and ecosystem instability. To demonstrate these trends, in situ ground measurements for active layer depth were collected to cross-validate below-ground-enhanced modeled simulations from 1980-2017. Shifting trends in permafrost variability (i.e. active layer depth) and seasonality were derived from model results and compared statistically to the in situ data. The SIBBORK-TTE model was then run to project future below-ground conditions utilizing CMIP6 scenarios. Upon visualization and curve-integrated analysis of the simulated freeze-thaw dynamics, the calculated performance metric associated with annual active layer depth rate of change yielded 76.19%. Future climatic conditions indicate an increase in active layer depth and shifting seasonality across the TTE. With this novel approach, spatiotemporal variation of active layer depth provides an opportunity for identifying climate and topographic drivers and forecasting permafrost variability and earth system feedback mechanisms.

Ice cliff distribution plays a major role in determining the melt of debris-covered glaciers but its controls are largely unknown. We assembled a dataset of 37537 ice cliffs and determined their characteristics across 86 debris-covered glaciers within High Mountain Asia (HMA). We complemented this dataset with the analysis of 202 cliff formation events from multi-temporal UAV observations for a subset of glaciers. We find that 38.9% of the cliffs are stream-influenced, 19.5% pond-influenced and 19.7% are crevasses. Surface velocity is the main predictor of cliff distribution at both local and glacier scale, indicating its dependence on the dynamic state and hence evolution stage of debris-covered glacier tongues. Supraglacial ponds contribute to maintaining cliffs in areas of thicker debris, but this is only possible if water accumulates at the surface. Overall, total cliff density decreases exponentially with debris thickness as soon as debris gets thicker than 10 cm.

Quantifying tectonic stress magnitudes is crucial in understanding crustal deformation processes, fault geomechanics, and variable plate interface slip behaviors in subduction zones. The Hikurangi Subduction Margin (HSM), New Zealand is characterized by along-strike variation in interface slip behavior, which may be linked to tectonic stress variations within the overriding plate. This study constrains in-situ stress magnitudes of the shallow (<3km) overriding plate of the HSM to better understand its tectonics and how they relate to larger scale subduction dynamics. Results reveal σ3: Sv ratios of 0.6-1 at depths above 650-700 m TVD and 0.92-1 below this depth interval along the HSM and SHmax: Sv ratios of 0.95-1.81 in the central HSM, and 0.95-3.12 in the southern HSM. These stress ratios suggest a prevalent thrust to strike-slip (σ1=SHmax) faulting regime across the central and southern HSM. In the central HSM, the presence of NNE-NE striking reverse faults co-existing with a modern σ1 aligned ENE-WSW (SHmax) suggests that overtime the stress state here evolved from a contractional to a strike-slip state, where the compressional direction changes from perpendicular (NW-SE) to subparallel (ENE-WSW) to the Hikurangi margin. This temporal change in stress state may be explained by forearc rotation, likely combined with development of upper plate overpressures. In the southern HSM, the modern WNW-ESE/ NW-SE σ1 (SHmax) and pre-existing NNE-NE striking reverse faults indicate that stress state remains contractional and subparallel (NW-SE) to the Hikurangi margin overtime. This may reflect the interseismic locked nature of the plate interface.

Over the last decade, the evolution of survey acquisition design composed of numerous receivers and shot points has exponentially increased the seismic data volume. As a result, the demand for efficient algorithms for detecting seismic first arrivals has been growing. Machine learning methods, especially convolutional neural networks, have shown great potential in previous studies. However, most existing methods lack generalization ability or be sensitive to noise. This paper proposes a new picking strategy based on the single-to multi-domain selftrained network to achieve automated picking for the first arrivals. Our self-trained network learns from a single domain to multiple domains while rejecting the wrong predicted picking and assigning the pseudo-first-arrivals-times to the unlabeled dataset. The model inherits the advantage from both single-and multi-domain picking strategies; it has high generalizability and noise robustness. In addition, the model is designed for a limited amount of manually annotated data to reduce human labor and total seismic processing time. In experiments on an open hard rock seismic multi-survey dataset, our picking method outperforms the benchmark on most evaluation metrics. Remarkably, our approach achieves approximately 10 % accuracy improvement against Lalor site data with a sampling rate that differs from training data. The experimental results demonstrate our method’s high generalizability and robustness for unseen datasets.

Seismic tomography of Earth’s mantle images abundant slab remnants, often located in close proximity to active subduction systems. The impact of such remnants on the dynamics of subduction remains under explored. Here, we use simulations of multi-material free subduction in a 3-D spherical shell geometry to examine the interaction between visco-plastic slabs and remnants that are positioned above, within and below the mantle transition zone. Depending on their size, negatively buoyant remnants can set up mantle flow of similar strength and length scales as that due to active subduction. As such, we find that remnants located within a few hundred km from a slab tip can locally enhance sinking by up to a factor 2. Remnant location influences trench motion: the trench advances towards a remnant positioned in the mantle wedge region, whereas remnants in the sub-slab region enhance trench retreat. These motions aid in rotating the subducting slab and remnant towards each other, reducing the distance between them, and further enhancing the positive interaction of their mantle flow fields. In this process, the trench develops along-strike variations in shape that are dependent on the remnant’s location. Slab-remnant interactions may explain the poor correlation between subducting plate velocities and subducting plate age found in recent plate tectonic reconstructions. Our results imply that slab-remnant interactions affect the evolution of subducting slabs and trench geometry. Remnant-induced downwelling may also anchor and sustain subduction systems, facilitate subduction initiation, and contribute to plate reorganisation events.