An accurate estimation of the shale permeability is essential to understand heterogeneous organic-rich shale reservoir rocks and predict the complexity of pore fluid transport in the rocks. However, predicting the matrix permeability by traditional models is still challenging because they require information often measured from core measurements. First, Kozeny’s equation (Kozeny, 1927) uses porosity and specific surface area of solid grains. However, it is difficult to characterize the specific surface area values or grain sizes from the logs. Second, Herron’s method (Herron, 1987) has been used for predicting permeability based on the mineral contents provided by well log data in conventional sandstone reservoirs. However, the predictive accuracy is low due to the different pore network structures of the shales. In this study, we estimate shale matrix permeability by a combined exploratory data analysis (EDA) and nonlinear regression estimation from the wireline logs. First, we conduct a bivariate correlation analysis for permeability and rock properties in core measurements. According to the correlation and Shapley value sensitivity test, we find that permeability change has a significant effect on the variation in porosity. Also, we investigate a nonlinear behavior between porosity and permeability. Second, we derive a nonlinear polylogarithmic estimation function of porosity to permeability, comparing it to the multivariate linear regression of porosity and clay volume fraction. As a result, a cubic logarithmic function of porosity significantly improves the fitting performance of the permeability values, better than the traditional methods. Moreover, we generate the permeability logs from the calibrated porosity logs, and they imply better shale permeability prediction as well. Since we can invert the porosity distribution from seismic data, this approach can provide a more accurate permeability estimation and reliable fluid flow modeling for shale and mudrock.

Meter-scale AUV mapping of 85-km of the summit and rift zones of Axial Seamount shows systematic variation in morphology of the lava flows with depth and distance from the caldera. ROV sampling reveals flow age and chemistry variations. Each rift zone has a steady downward slope of ~2° outside the caldera. In the caldera and first few km down the rift zones, flows are predominantly channelized sheet flows with collapses along the channels. Mid-rift, drained inflated hummocky flows consisting of complex mounds with tumuli and lava lakes, and narrow ridges of hummocky mounds become common. On the axis of the south rift, the first cones with craters occur 3.2 km from the caldera at 1600 m depth, and broad inflated hummocky flows emplaced through complex lava tubes first appear 6 km down-rift at 1715 m. On the north rift, similar cones and complex flows appear at 10.5 km down-rift at 1725 m depth. The historical flows exemplify this variation: on the upper south rift as channelized sheet flows in 1998 and 2011 erupted from fissures that extended 6.5 km down-rift; on the middle north rift, inflated hummocky flows up to 126 m thick erupted in 2015 from fissures 17.5 km from the caldera; and on the distal south rift, a narrow ridge of coalesced hummocks 160 m tall formed during the 2011 eruption. Older and older lavas remain exposed at greater distances from the more active summit and upper rift zones. Deep on the rift zones, stellate and steep cones with smooth talus slopes occur that did not feed expansive flows, despite being constructed of hotter, less viscous, near-primary magmas. These cones are first observed on both rift zones at 1800 m depth and 18 km from the caldera. Deeper still, emanating from both distal rift axes beginning ~30 km from the caldera, lie voluminous inflated sheet and inflated hummocky flows 30 to 135 m thick with combined area of over 150 km2. The plagioclase-phyric voluminous flows on the south rift erupted ~1250 years ago, and the aphyric ones on the north rift ~13,000 years ago. Eruption rate is the most likely cause of the flow morphology changes since estimated magma viscosity does not correlate with flow morphology. Lateral transport through long dikes would slow magma delivery unless dike widths are large. The near-primary magmas may have risen through narrow conduits from the mantle to the distal rifts.

This research targets disentangling shallow causes of anthropogenically-induced subsidence in a reclaimed and urbanized coastal plain. The study area is around the city of Almere, in the South Flevoland polder, the Netherlands, which is among the countries’ fastest subsiding areas. The procedure consists of integrating synthetic Interferometric Synthetic Aperture Radar (InSAR) data with high-resolution phreatic groundwater and lithoclass models, and a database containing construction details. The two main parts of the workflow are isolation of the InSAR points of structures without a pile foundation and a data assimilation procedure by Ensemble Smoothing with Multiple Data Assimilation. The shrinkage of surficial clay beds by phreatic groundwater level lowering is identified to be the main cause of shallow subsidence in the area, with an average contribution of 6 mm/year. The history-matched physics-based model predicts that one meter drop in phreatic groundwater level now translates into 10 millimeter of subsidence in the next five years. Also, this study showed that a groundwater deficiency due to severe dry periods should be considered as an accelerator of subsidence in both the short- and long-term planning. To ensure a robust network to estimate future subsidence, we advise on a consistent monitoring strategy of the phreatic groundwater level.

Substantial progress on machine learning (ML) models and graphical processing units (GPUs) has stimulated emerging research in applications of ML to earth science. As snow is a vital component of the global hydroclimate system, precise snowpack prediction is of considerable value for science and society. In this work, we have trained three different ML models (LSTM, CNN and Attention) to predict daily snow water equivalent (SWE) with both dynamic and static features in the Western Contiguous United States from Snow Telemetry (SNOTEL) observations. Dynamic features include precipitation, minimum and maximum temperature, minimum and maximum relative humidity, specific humidity, solar radiation and wind velocity. Static features are latitude, longitude, elevation, diurnal anisotropic heating (DAH) index and topographic radiative aspect (TRASP) index. This choice of features allows us to produce high-resolution maps of regional SWE for a given set of input meteorological conditions. The importance and the sensitivity of input variables will be tested by several explainable AI methods including feature permutation and integrated gradient. The ML-based dataset is further up-sampled and compared with the 4km gridded SWE dataset from the National Snow & Ice Data Center (NSIDC), which is from a physical-based model. Future SWE estimates are also produced under climate conditions projected by CMIP class models, along with associated uncertainty estimates based on our sensitivity analysis. The ML models are demonstrated to be a fast and accurate method of producing high-resolution SWE estimates with minimal computing power.

The seasonal deposition and sublimation of CO2 constitute a major element in the Martian volatile cycle. Here, we propose to use the shadow variations of the ice blocks at the foot of the steep scarps of the North Polar Layered Deposits (NPLDs) to infer the vertical evolution of the seasonal deposits at high polar latitudes. We conduct an experiment at a steep scarp centered at (85.0°N, 151.5°E). We show that the average thickness of the seasonal deposits due to snowfalls in Mars Year 31 is 0.97±0.13 m at Ls = 350.7° in late winter, which then gradually decreases in springtime. The large snow depth measured makes us wonder if snowfalls are more frequent and violent than previously thought. Meanwhile, we show that the average frost thickness due to direct condensation in Mars Year 31 reaches 0.64±0.18 m at Ls = 350.7° in late winter and quasi-linearly decreases towards the summer solstice. Combined, the total thickness of the seasonal cover in Mars Year 31 reaches 1.63±0.22 m at Ls = 350.7° in late winter, continuously decreases to 0.45±0.06 m at Ls = 42.8° in middle spring and 0.06±0.05 m at Ls = 69.6° in late spring. These estimates are up to 0.8 m lower than the existing MOLA results during the spring, which can be mainly attributed to MOLA-related biases. In terms of interannual variations, we observe that snow in the very early spring of Mars Year 36 can be 0.36±0.13 m deeper than that in Mars Year 31.

We investigate the effect of extremely rough bathymetry on energy dissipation and mixing in a coastal region characterized by small-scale seafloor features penetrating a strongly-stratified density interface of comparable vertical scale. Our data from the non-tidal Baltic Sea include shear microstructure measurements and observations from a broadband echosounder, here used to resolve the extreme variability and intermittency of stratified turbulence in the vicinity of obstacles. Scale analysis and acoustic imaging of small-scale turbulent motions suggest that the underlying mixing mechanisms are related to topographic wake eddies and, to a smaller extent, to breaking internal waves near the bathymetric features. Vertical diffusivities exceed those at a nearby reference station with smooth bathymetry by up to two orders of magnitude. Our study emphasizes the importance of rough small-scale (< 1 km) bathymetric features for energy dissipation and vertical turbulent transport in coastal areas shaped by e.g., glacial, tectonic, or volcanic processes.

Nitrification, the stepwise oxidization of ammonia to nitrate via nitrite, is a key process in the marine nitrogen cycle. Reported nitrite oxidation rates frequently exceed ammonia oxidation rates below the euphotic zone, raising the fundamental question of whether the two steps are balanced and if alternative sources contribute to nitrite production in the dark ocean. Here we present vertically resolved profiles of ammonia, urea, and nitrite oxidation rates and their kinetic traits extending from the South China Sea to the western North Pacific Subtropical Gyre. Our results show active urea oxidation in the presence of experimental ammonium amendment, indicating direct urea oxidation. Urea oxidation rate covaries with ammonia oxidation rate, and the depthintegrated rates of urea oxidation and ammonia oxidation are comparable, demonstrating urea oxidation is a significant source of nitrite that helps to balance the two steps of nitrification in the oligotrophic ocean. Nitrifiers exhibit high affinity for their substrates, and the apparent halfsaturation constants for ammonia and nitrite oxidation decreased with depth. The apparent halfsaturation constant for urea oxidation is 1.2 to 11-fold (median 2.2) higher than that for ammonia oxidation at the corresponding depths, but with no clear vertical trend. Such kinetic traits may account for the relatively higher urea concentration compared to ammonium and nitrite concentrations in the ocean's interior. Moreover, combining our results with a review of the previous literature shows a trend of increased urea oxidation relative to ammonia oxidation, from the more eutrophic coastal zone to the oligotrophic open ocean, revealing a substrate-dependent biogeographic distribution of urea oxidation across marine environments.

Chenopodium quinoa is an important crop that is known for its salt tolerance. Salinity is an osmotic stress and accumulation of ions in the root zone causes a reduction in soil water availability, which negatively affects the uptake of essential nutrients, changes seed composition, and reduces biomass. Therefore, there is a need to develop crops that are highly productive under poor soils. This research examines the effect of salt stress on quinoa photosynthetic efficiency and chloroplast development. Sensitive and tolerant quinoa lines were examined under salt stress conditions when a concentration of 155mM NaCl was applied. Electrical soil conductivity was monitored during the experiment to assure salt stress and at approximately two months old, CropReporter images, which are used to estimate photosystem II efficiency, non-photochemical quenching (NPQ), chlorophyll content, and anthocyanin content were captured and analyzed using PlantCV. The analysis showed that the salt treatment did not negatively affect the plant photosynthetic efficiency (no changes in Fv/Fm, NPQ, Fq'/Fm') but leaf area and chlorophyll content was statistically negatively affected by the treatment when comparing genotypes. Leaf tissue was sampled and chloroplasts were imaged using super-resolution microscopy. Salt-sensitive lines showed swelling of the chloroplast lamellae, starch accumulation, and unrecognizable grana structure. A comprehensive understanding of the quinoa salt tolerant mechanisms by employing multidisciplinary approaches is necessary for their effective incorporation into salt-sensitive crops for better crop yields under stressful environments.

Soils store the largest amount of carbon (C) in the biosphere, and the C pool in soil is critical to the global C balance. Numerous microbial models have been developed over the last few decades to represent microbial processes that regulate the responses of soil organic carbon (SOC) to climate change. However, the representation of microbial processes varies, and how microbial processes are incorporated into SOC models has not been well explored. Here, we reviewed 70 microbial models to characterize the microbial processes incorporated into SOC models and analyzed variations in mechanistic complexity. We revealed that (1) four processes (microbial decomposition, mineral interaction, microbial mortality, and transition between active and dormant microbial states) are commonly incorporated in microbial models, (2) ~47% of models simulate only one (i.e., microbial decomposition) and 33% simulated two microbial processes: microbial decomposition and mineral interaction, (3) more than 80% microbial models use nonlinear mathematical equations, such as forward Michaelis-Menten kinetics, to represent SOC decomposition, (4) the concept of persistence of SOC due to its intrinsic properties has been replaced by organo-mineral interaction (~39% of microbial models) that protects SOC from decomposition, and (5) various temperature and moisture modifiers and pH effects have been used to explain the environmental effect on microbial processes. Finally, we propose a roadmap for SOC model improvement. In the future, to realistically incorporate microbial processes into Earth System Models, it is imperative to identify experimental evidence on rate limitation processes and firmly ground model structure on the field and laboratory data.

Preface Many physical processes that influence Earth's climate and weather occur on spatial (temporal) scales smaller (shorter) than typical grid sizes (time steps) of general circulation models, and thus must be parameterized. Computer models are essential tools for understanding atmospheric phenomena and for making accurate predictions of any changes in the Earth's climate, weather, and resources of renewable energy resulting from anthropogenic activities that generate greenhouse gases and particulates into the atmosphere. This book focuses on the atmospheric subgrid processes ─ collectively called fast physics ─by reviewing and synthesizing relevant physical understanding, parameterization developments, various measurement technologies, and model evaluation framework. The publication is divided into three parts, containing seventeen chapters (Chapters 2-17) to reflect and synthesize the multiple aspects involved. The first chapter briefly introduces the historical development of fast physics parameterizations and the involved complexities; the last chapter summarizes emerging challenges, new opportunities and future research directions. Part I deals with major subgrid processes, with eight chapters (Chapters 2-9) each covering different processes more or less in the conventional compartmentalized format with an emphasis on individual processes, including but, not limited to radiative transfer, aerosols, and aerosol direct & indirect effects, entrainment-mixing processes, their microphysical influences, convection & convective clouds, stratiform clouds such as stratus and stratocumulus clouds, planetary boundary layer processes, land surface and interactions with atmosphere, and gravity waves. On top of the conventional treatments, some promising ideas/approaches have recently emerged to unify the treatment of individual processes and thus allows for consideration of process interactions. Part II is devoted to such unifying efforts, with four chapters (Chapters 10-13) covering four different endeavors: the unifying parameterizations based on assumed probability density functions; the EDMF approach that combines the Eddy Diffusivity and Mass Flux approaches to unify turbulence and convection; application of machine learning techniques; and innovative top-down attempts that consider the involved totality by borrowing ideas from systems theory, statistical physics, and non-linear sciences. Part III (Chapters 14-17) is devoted to assessments, model evaluation, and model-measurement integration, with four chapters that focus on satellite and airborne remote sensing measurements, surface-based remote sensing measurements, in-situ and laboratory measurements, and model evaluation, and model-measurement integration, respectively.

The rate of rocket launches is accelerating, driven by the rapid global development of the space industry. Rocket launches emit chemically and radiatively active species into the stratosphere, where they impact ozone. We create a per-vehicle inventory of geographically-resolved stratospheric emissions for 2019, accounting for flight profiles and all major fuel types in active use. The inventory is used to simulate an intensive near-future scenario (120 launches/year at 17 current spaceports) with a chemistry-climate model. These gas-phase rocket emissions produce an overall 0.5% decrease in global annual-mean total column ozone. Compared to a reference scenario, Antarctic springtime ozone decreases by up to 9%. Arctic springtime ozone decreases by up to 5%; equivalent to half of the depletion observed over this region due to chlorofluorocarbons in the late 20th century. Our findings reiterate the need for assessment and international cooperation regarding the impact of space industrialization on Earth’s systems.

This study presents new seismic imaging of the Andean subduction zone through P-wave hybrid finite-frequency and ray-theoretical tomography. We measured both differential and absolute traveltimes using broadband seismic waveforms from stations in an array of ocean-bottom seismographs near the Chile Triple Junction (CTJ) and stations within 30° from the array. These data were combined with the global traveltime dataset to obtain a global P-wave velocity structure with a focus on central to southern South America. The new tomographic image showed the Nazca slab geometry as a continuous fast anomaly, which is consistent with seismic activity and prior slab models. Furthermore, two notable structures were observed: a broad extension of the fast anomaly beneath the Nazca slab at 26–35° S and a slow anomaly east of the CTJ. The checkerboard resolution and recovery tests confirmed the reliability of these large-scale features. The fast anomaly, isolated from the Nazca slab, was interpreted as a relic Nazca slab segment based on its strong amplitude and spatial coincidence with the current Pampean and past Payenia flat slab segments. The slow anomaly near the CTJ was consistent with the previously inferred extent of the Patagonian slab window. Moreover, the active adakitic volcanoes are aligned with the southern edge of the anomaly, and the plateau basalts are located within the anomaly. Our model showed that the slow anomaly extended to a depth of up to 250 km, suggesting a depth limit that the asthenospheric window can influence.

The impacts of climate change and increased water use for irrigation make it difficult to manage sustainable water use and food production. Sufficient research has not been conducted on how humans adapt to water risks due to climate change. One of the difficulties in considering adaptation measures is that adaptation actions in one sector conflict with the interests of other stakeholders in the basin and trade-off relationships emerge among various sectors. Here, we examined how an effective adaptation in one sector (agriculture) influences the other (water resources) by calculating the “benefits of agricultural production” and “drought risk” under current and future climate scenarios. We built a framework consisting of two process-based models of hydrology and crop science and evaluated shifting of the transplantation date as a promising measure to avoid the degradation of rice quality in Japan. Shifting the transplantation date had opposing effects on the total yield and quality of rice, with an earlier date increasing the total yield and a later date increasing the quality. Furthermore, an earlier transplantation date reduced the drought risk. Thus, in terms of the preferred adaptation options, total yield and drought were synergistic, whereas rice quality and drought were trade-offs. Our results imply that the current transplantation date has resulted from the farmers’ motivation to maximize total yield, but this motivation may change to other factors, possibly rice quality, due to climate change. Overall, this study contributes to the understanding of how interconnected systems evolve when climate or socio-economic conditions change.

Products derived from remote sensing reflectances ($R_{rs}(\lambda)$), e.g. chlorophyll, phytoplankton carbon, euphotic depth, or particle size, are widely used in oceanography. Problematically, $R_{rs}(\lambda)$ may have fewer degrees of freedom (DoF) than measured wavebands or derived products. A global sea surface hyperspectral $R_{rs}(\lambda)$ dataset has DoF=4. MODIS-like multispectral equivalent data also have DoF=4, while their SeaWiFS equivalent has DoF=3. Both multispectral-equivalent datasets predict individual hyperspectral wavelengths’ $R_{rs}(\lambda)$ within nominal uncertainties. Remotely sensed climatological multispectral $R_{rs}(\lambda)$ have DoF=2, as information is lost by atmospheric correction, shifting to larger spatiotemporal scales, and/or more open-ocean measurements, but suites of $R_{rs}(\lambda)$-derived products have DoF=1. These results suggest that remote sensing products based on existing satellites’ $R_{rs}(\lambda)$ are not independent and should not be treated as such, that existing $R_{rs}(\lambda)$ measurements hold unutilized information, and that future multi- or especially hyper-spectral algorithms must rigorously consider correlations between $R_{rs}(\lambda)$ wavebands.

V. S. Gokul 1,2,3., K. M. Sreejith4., G. Srinivasa Rao 5., M. Radhakrishna 1*., P. G. Betts 21 Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai 400 076, India.2 School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria 3800, Australia.3 IITB-Monash Research Academy, Indian Institute of Technology Bombay, Mumbai 400 076, India.4 Geosciences Division, Space Applications Centre, Ahmedabad 380 015, India.5 Department of Applied Geophysics, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad 826 004, India (*Corresponding author:[email protected])AbstractThe Indian Ocean has the largest geoid anomaly, known as the Indian Ocean Geoid Low (IOGL). This long wavelength geoid depression has a magnitude of negative 106 m, and is centered south of India. The nature and depth of the sources causing this characteristic low are poorly constrained and has been the subject of debate. In this abstract, we focus on understanding the density contributions to the geoid low from the crust and upper mantle using joint analysis of geoid and gravity data along with published tomographic models in the region. Decomposition of geoid anomalies in the spectral domain indicate that mass anomalies below the upper mantle (> 700 km) contribute to 90% of the total geoid anomaly. In order to compute the upper mantle contribution to the IOGL, we used the Moho geometry and the crustal density structure from the 3-D gravity inversion, and the SL2013sv tomography model for the upper mantle density structure. The presence of density sources, which was not resolved in the modeling within the sub-lithospheric mantle is confirmed upon comparing the crustal and upper mantle (up to 700 km) geoid response below the IOGL with n=10 residual geoid anomaly. Integrated gravity-geoid 2-D modeling of the geometries of the anomalous sources located at the base of LAB and at a depth of 320-340 km, respectively, confirms that the contribution of density structures up to 700 km explains only the ten percent of the IOGL which matches well with the spectral decomposition results. This suggests that the lower mantle sources, such as paleo-subducted slabs or plume sources from the core-mantle boundary significantly contributes to the IOGL.

Karst water resources are valuable freshwater sources for around 10 % of the world population. Nonetheless, anthropogenic factors and global changes have been seriously deteriorating the karst water quality and dependent ecosystems. Solute transport models are powerful tools to monitor, control, and manage the water quality and dependent ecosystem functioning. By representing and predicting the spatiotemporal behavior of solute migration in karst systems, the transport models enhance our understanding about the karst transport processes, thus enabling us to explore contamination risks and potential outcomes. This paper reviews the current state of knowledge on the modelling of solute transport processes in karst aquifers, thereby unveiling the fundamental challenges underlying a successful karst transport modelling. We discuss to what extent and in what ways we can handle these challenges and derive the key challenges and directions for reliable modelling of transport processes in karst systems in the present context of global changes.

J. Wei and Z. Wang made equal contributions to this work.*Corresponding authors:[email protected]; [email protected]; [email protected] imagery offers remarkable potential for various applications, including land monitoring and environmental assessment, thanks to its high spatial resolution and over 50 years of data records. However, the presence of atmospheric aerosols greatly hinders the precision of land classification and the quantitative retrieval of surface parameters. Notably, there has been no global retrieval of aerosol optical depth (AOD) from Landsat imagery that is needed for atmospheric correction, among other applications. To address this issue, this paper presents an innovative global AOD retrieval framework for Landsat imagery, propelled by atmospheric radiative transfer (ART) and enhanced GeoChronoTransformers (GCT) models incorporating multidimensional spatiotemporal sequence information and executed on the Google Earth Engine (GEE) cloud platform. We gathered all Landsat 8 and 9 images from their respective launch dates (February 2013 and September 2021) up to 2022, which were used to construct a robust ART-GCT-GEE model, and then rigorously validated the model performance across ~470 monitoring stations over land using diverse spatiotemporally independent methods. Leveraging information from multiple spectral channels, contributing to 58% according to the SHapley Additive exPlanation (SHAP) method, our results are highly consistent with observations (e.g., correlation coefficient = 0.863 and root-mean-square error = 0.096), suggesting that accurate historical and future AOD levels can be obtained. Around 81% and 50% of our AOD predictions meet the criteria of Moderate Resolution Imaging Spectroradiometer (MODIS) expected errors [±(0.05+20%)] and the Global Climate Observation System {[max(0.03, 10%)]}, respectively. Additionally, our model is less influenced by changes in surface conditions like topography and land cover. This allows us to generate spatially continuous AOD distributions with highly detailed and fine-scale information from dark to bright surfaces, especially for densely populated urban areas and expansive deserts with high aerosol loadings from both anthropogenic and natural sources.

Cover crops, plants grown during fallow periods between cash crops, are a promising solution to mitigating soil degradation induced by conventional agricultural practices and improving soil health. Cover crops can provide several beneficial ecosystem functions, such as soil structure remediation, soil microbial diversification, and nutrient recycling, depending on the plant species. Interactions between plant roots and the surrounding soil are key to the plant's ability to perform their ecosystem functions. The lack of data on cover crop roots inhibits our understanding of cover crop phenotype-ecosystem function relationships. We combine aboveground and belowground phenotyping measurements with physicochemical soil measurements to evaluate the field performance of 19 different plant species in monocultures and polycultures as winter cover crops in Missouri. Canopy cover imaging reveals significant differences in winter hardiness and weed suppression among cover crop varieties. Root biomass and root length density measured at depths up to 1 meter indicate differences in rooting behavior between cultivars suggesting the ability to breed cover crop varieties with improved root system architecture. I will also highlight our collaborative efforts utilizing remote sensing technologies (aerial RGB and hyperspectral imaging) to model carbon and nitrogen cycling in cover crop systems at a field scale. Finally, we have begun to characterize 3D root system architecture traits at the seedling stage using a gel-imaging system. Better understanding of cover crop rooting behavior will allow us to breed varieties with enhanced performance of beneficial ecosystem functions for sustainable agricultural systems.