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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Salt Diapir‐Driven Recycling of Gas Hydrate

Abstract By harnessing both hypothetical, synthetic basin and gas hydrate (GH) system models and real‐world models of well‐studied salt diapir‐associated GH sites at Green Canyon (Gulf of Mexico) and Blake Ridge (U.S. Atlantic coast), we propose and demonstrate salt movement (and in particular, diapirism) to be a new mechanism for the recycling of marine GH. At Green Canyon, for example, we show that by considering this newly proposed diapir‐driven recycling mechanism in conjunction with previously proposed lithological control on sandy‐reservoir‐hosted hydrate at the base of the GH stability zone (BGHSZ; ∼bottom‐simulating reflector, BSR), modeled GH saturations match drilling data. Overall, salt diapir movement‐induced GH recycling provides a temperature‐driven mechanism by which GH saturations at the BGHSZ may reach >90 vol. % and by which GH volumes near and free gas volumes beneath the BGHSZ may be increased significantly through time. Interestingly, comparison of salt diapir‐driven recycling and sediment burial‐driven recycling scenarios suggests notably higher rates of recycling via diapir‐driven versus burial‐driven processes. Our results suggest that GH and associated free gas accumulations above salt diapir crests represent particularly attractive targets for unconventional and conventional hydrocarbon resource exploration and for scientific and academic drilling expeditions aimed at exploiting GH systems. Salt basins containing GH systems—including passive margin basins of the Gulf of Mexico, southeastern Brazil, and southwestern Africa—are therefore compelling localities for studying salt‐driven GH recycling and for salt diapir‐associated natural gas exploration.

58 GEOSCIENCES↗
DOE OSTI2021

Insights into storm direction effect on flood response

In this study, we investigate the directional influence of storm movement on catchment flood peak response using the synthetic circular basin. Due to the complexity in defining storm movements that require meteorological modeling, we adopt a novel approach of combining the basin rotation method (BRM) with a circular basin construct. A systematic basin rotation approach provides a proxy for the storm direction relative to the river network topology to study its hydrologic response. Using Stage-IV rainfall with 4-km by 1-hour resolution for 16 years period from 2003 to 2018 in Iowa, U.S., we systematically analyze consequent flood peak response due to storm directions (basin rotations) using a distributed hydrologic model called the Hillslope-Link Model (HLM). HLM has demonstrated the ability to reproduce observed streamflow for real river basins studied in Iowa. The hydrologic simulation results show that the BRM can quantify the relationship between the rainstorm direction and catchment hydrologic response. Also, the maximum flood peak response is strongly dependent on the interaction of the peak of the rainstorm with the peak of the width function. The results indicate significant differences in runoff volume and runoff peak and their interannual variability due to rainstorm direction. The study has important implications for flood frequency predictions and developing flood resilience and watershed management strategies, particularly due to changing storm tracks under a warming climate.

42 ENGINEERING↗
DOE OSTI2025

Assessing the Role of Hydrodynamics in Enhancing Height-Above-the-Nearest-Drainage Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan

The conventional approach to generating synthetic rating curves (SRC) using the Height-Above-the-Nearest-Drainage (HAND) method typically relies on the assumption of uniform flow, such as Manning's equation, to establish stage-discharge ratings. The zero-physics application of the uniform flow equation is insufficient for capturing detailed hydraulic features (e.g., backwater effect) and neglects the hydraulic effects from adjacent channels. This lack of hydrodynamic computation can impact the accuracy and effectiveness of riverine flood risk estimation and management. To reduce this foreseeable error, we introduce the HAND-hd workflow, which integrates sophisticated hydrodynamic computations in the production of HAND-based SRC with hydrodynamic features (SRC hd ). The results indicate that SRC hd demonstrates consistent agreement with both gauge observations and benchmark solutions. Additionally, the comparative analysis suggests that SRC hd provides notable improvements in stage-discharge ratings over conventional HAND-based SRCs, particularly in channels with mild bed gradients, where it reduces water stage prediction errors and percent biases. In steeper channel segments, SRC hd maintains comparable accuracy to conventional methods. The comprehensive evaluation in this study emphasizes the potential discrepancies and inaccuracies associated with the adoption of the uniform flow assumption in the conventional HAND-SRCs and addresses the necessity of including hydrodynamic physics in the application of HAND-based SRC (e.g., inundation map) in channels with mild gradients.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Using Concentrating Solar Power to Create a Geological Thermal Energy Reservoir for Seasonal Storage and Flexible Power Plant Operation

Abstract We propose a hybrid renewable energy system—a geothermal energy storage system (GeoTES) with solar—to provide low-cost dispatchable power at various timescales from daily, to weekly, to seasonally. GeoTES with solar uses a concentrating solar power collector field to produce hot water that is injected into a sedimentary basin to create a synthetic geothermal resource. The stored geothermal heat can then be dispatched when required by the electrical grid. GeoTES is particularly valuable for a grid with a high penetration of non-flexible renewable technologies such as photovoltaic and wind power. In this work, a sophisticated hybrid model is developed to assess the technical and economic potential of GeoTES by combining IPSEpro, which is a power-cycle simulation tool, and SAM, an economic analysis tool by National Renewable Energy Laboratory (NREL). The analysis shows with proper initial charging period that the heat loss in storage is almost negligible and is a suitable technology for long-term energy storage. Various power-cycle options are evaluated, and the most suitable power cycle is selected for further study. Annual calculations of the GeoTES system indicate that a levelized cost of storage (LCOS) of 12.4 ¢/kWh e can be achieved for seasonal storage of 4000 h; this value is much lower than the existing long-term storage. The LCOS of GeoTES is insensitive to the storage duration above 8 h, unlike battery and molten-salt thermal storage systems. This result demonstrates that GeoTES can be a competitive seasonal storage technology in the future electricity market. The levelized cost of electricity of the GeoTES system is also carefully analyzed and can vary between 10.0 and 16.4 ¢/kWh e , depending on solar-collector prices.

25 ENERGY STORAGE↗
DOE OSTI2025

Synthetic Streamflow Datasets Derived from DOE 9505 for Select Texas Basins

This dataset is generated using a Bayesian Hidden Markov Model trained on the DOE 9505 streamflow projection ensemble. A set of 21,000 streamflow realizations are generated for the Colorado, Sabine, and Trinity river basins in Texas. Separate models are trained either using the full 9505 ensemble or a subset based on three hyperparameters: bias correction, downscaling, and hydrological model.

drought↗
NASA NTRS2006

Terrestrial Water Mass Load Changes from Gravity Recovery and Climate Experiment (GRACE)

Recent studies show that data from the Gravity Recovery and Climate Experiment (GRACE) is promising for basin- to global-scale water cycle research. This study provides varied assessments of errors associated with GRACE water storage estimates. Thirteen monthly GRACE gravity solutions from August 2002 to December 2004 are examined, along with synthesized GRACE gravity fields for the same period that incorporate simulated errors. The synthetic GRACE fields are calculated using numerical climate models and GRACE internal error estimates. We consider the influence of measurement noise, spatial leakage error, and atmospheric and ocean dealiasing (AOD) model error as the major contributors to the error budget. Leakage error arises from the limited range of GRACE spherical harmonics not corrupted by noise. AOD model error is due to imperfect correction for atmosphere and ocean mass redistribution applied during GRACE processing. Four methods of forming water storage estimates from GRACE spherical harmonics (four different basin filters) are applied to both GRACE and synthetic data. Two basin filters use Gaussian smoothing, and the other two are dynamic basin filters which use knowledge of geographical locations where water storage variations are expected. Global maps of measurement noise, leakage error, and AOD model errors are estimated for each basin filter. Dynamic basin filters yield the smallest errors and highest signal-to-noise ratio. Within 12 selected basins, GRACE and synthetic data show similar amplitudes of water storage change. Using 53 river basins, covering most of Earth's land surface excluding Antarctica and Greenland, we document how error changes with basin size, latitude, and shape. Leakage error is most affected by basin size and latitude, and AOD model error is most dependent on basin latitude.

Seo, K.-W.↗
DOE OSTI2024

A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

Abstract After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.

Environmental Sciences & Ecology↗
DOE OSTI2022

A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Analysis of Seismic Wave Amplification in Sedimentary Basins Using 3D Wavefield Simulations: Nenana Basin, Central Alaska

We investigate seismic wave amplification in Nenana basin, central Alaska, using 3D seismic wavefield simulations. We quantify frequency-dependent amplification by comparing synthetic seismograms among four different seismic velocity models: (a) a tomographic model, (b) a tomographic model with the uppermost 6.5 km replaced by a 1D basin profile, (c) a tomographic model with an embedded ellipsoidal basin, and (d) a tomographic model with an embedded realistic basin. For each model we perform wavefield simulations accurate up to 2 Hz for a set of 10 different earthquakes, which provide a range of frequencies and incident angles for waves interacting with the basin. Average amplification ratios are four on the horizontal components and seven on the vertical component. The dominant influence of the amplification is the 3D geometry of the basin, not the slow-velocity profile near the surface. Our synthetic amplification ratios are in general agreement with amplification estimates obtained from 14 stations that recorded the same set of earthquakes. Our approach offers a general strategy for documenting frequency-dependent basin amplification for a region with realistic basin structures and local earthquakes.

Geosciences↗
NASA NTRS2010

Proceedings of the Astrobiology Science Conference 2010. Evolution and Life: Surviving Catastrophes and Extremes on Earth and Beyond

The Program of the 2010 Astrobiology Science Conference: Evolution and Life: Surviving Catastrophes and Extremes on Earth and Beyond, included sessions on: 50 Years of Exobiology and Astrobiology: Greatest Hits; Extraterrestrial Molecular Evolution and Pre-Biological Chemistry: From the Interstellar Medium to the Solar System I; Human Exploration, Astronaut Health; Diversity in Astrobiology Research and Education; Titan: Past, Present, and Future; Energy Flow in Microbial Ecosystems; Extraterrestrial Molecular Evolution and Prebiological Chemistry: From the Interstellar Medium to the Solar System II; Astrobiology in Orbit; Astrobiology and Interdisciplinary Communication; Science from Rio Tinto: An Acidic Environment; Can We Rule Out Spontaneous Generation of RNA as the Key Step in the Origin of Life?; How Hellish Was the Hadean Earth?; Results from ASTEP and Other Astrobiology Field Campaigns I; Prebiotic Evolution: From Chemistry to Life I; Adaptation of Life in Hostile Space Environments; Extrasolar Terrestrial Planets I: Formation and Composition; Collaborative Tools and Technology for Astrobiology; Results from ASTEP and Other Astrobiology Field Campaigns II; Prebiotic Evolution: From Chemistry to Life II; Survival, Growth, and Evolution of Microrganisms in Model Extraterrestrial Environments; Extrasolar Terrestrial Planets II: Habitability and Life; Planetary Science Decadal Survey Update; Astrobiology Research Funding; Bioessential Elements Through Space and Time I; State of the Art in Life Detection; Terrestrial Evolution: Implications for the Past, Present, and Future of Life on Earth; Psychrophiles and Polar Environments; Life in Volcanic Environments: On Earth and Beyond; Geochronology and Astrobiology On and Off the Earth; Bioessential Elements Through Space and Time II; Origins and Evolution of Genetic Systems; Evolution of Advanced Life; Water-rich Asteroids and Moons: Composition and Astrobiological Potential; Impact Events and Evolution; A Warm, Wet Mars?; Titan Versus Europa - Potential for Astrobiology; Habitability Potential of Mars; Biosignatures: Tools and Development I; Origins of Molecular Asymmetry, Homochirality, and Life Detection; Deserts and Evaporite Basins and Associated Microbialite Systems; Ancient Life and Synthetic Biology: Crossroad of the Past and Future; Biosignatures: Tools and Development II; Free Oxygen: Proxies, Causes, and Consequences; Life in Modern Microbialite Systems - Function and Adaptation; Hydrothermal Systems and Organosynthesis Processes: Origin and Evolution of Life; Where Should We Go on Mars to Seek Signs of Life?; Search for Intelligent Life I. Innovative SETI Observing Programs and Future Directions; Integrating Astrobiology Research Across and Beyond the Community; Education in Astrobiology in K-12; Search for Intelligent Life II. Global Engagement and Interstellar Message Construction; Poster sessions included: Extraterrestrial Molecular Evolution and Pre-Biological Chemistry; Prebiotic Evolution: From Chemistry to Life; RNA World; Terrestrial Evolution: Implications for the Past, Present, and Future of Life on Earth; Hydrothermal Systems and Organosynthesis Processes: Origin and Evolution of Life; Virology and Astrobiology; Horizontal Genetic Transfer and Properties of Ancestral Organisms; Life in Volcanic Environments: On Earth and Beyond; Impact Events and Evolution; Evolution of Advanced Life; Evolution of Intelligent Life; Education in Astrobiology in K-12; Origins of Molecular Asymmetry, Homochirality, and Life Detection; Astrobiology and Interdisciplinary Communication; Diversity in Astrobiology Research and Education; Integrating Astrobiology Research Across and Beyond the Community; Policy and Societal Issues: Dealing with Potential Bumps in the Astrobiology Road Ahead; Results from ASTEP and Other Astrobiology Field Campaigns; Energy Flow in Microbial Ecosystems; Psychrophiles and Polar Environments; Deserts and Evaporite Basins and Associated Microbialite stems; Life in Modern Microbialite Systems - Function and Adaptation; Free Oxygen: Proxies, Causes, and Consequences; Bioessential Elements Through Space and Time; Water-rich Asteroids and Moons: Composition and Astrobiological Potential; Biosignatures: Tools and Developments; Robotics and Instrumentation for Astrobiology; State of the Art in Life Detection; Astrobiology in Orbit; Survival, Growth, and Evolution of Microrganisms in Model Extraterrestrial Evolution; Search for Intelligent Life; Habitability Potential of Mars; How and Where Should We Seek Signs of Life on Mars?; Titan: Past, Present, and Future; Extrasolar Terrestrial Planets: Formation, Composition, Diversity, Habitability and Life; Human Exploration, Astronaut Health; Science from Rio Tinto: An Acidic Environment and Adaptation of Life in Hostile Space Environments;

Source record↗
DOE OSTI2024

A Machine Learning Bias Correction on Large–Scale Environment of High–Impact Weather Systems in E3SM Atmosphere Model

Large–scale dynamical and thermodynamical processes are common environmental drivers of high–impact weather systems causing extreme weather events. However, such large–scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high–impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large–scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ~1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high–impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large–scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias–corrected large–scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large–scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large–scale storm environments simulated by low–resolution climate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Multisensor Data Integration Techniques

The availability of data from sensors operating in several different wavelength regions had led to the development of new techniques and strategies for both data management and image analysis. Work is ongoing to develop computer techniques for analysis of integrated data sets. These techniques include coregistration of multisensor images, rectification of radar images in areas of topographic relief to ensure pixel to pixel registration with planimetric data sets, calibration of data so that signatures can be applied to remote areas, normalization of data acquired with disparate sensors and determination of extended spectral signatures of surface units. In addition, software is being developed to analyze coregistrated digital terrain and image data so that automated stratigraphic and structural analyses can be performed. These software procedures include: strike and dip determination, terrain profile generation, stratigraphic column generation, stratigraphic thickness measurements, structural cross-section generation, and creation of 3-D block diagrams. These techniques were applied to coregistered LANDSAT 4 Thematic Mapper (TM), Thermal Infrared Multispectral Scanner (TIMS), and multipolarization synthetic aperture radar (SAR) data of the Wind River Basin in Wyoming.

Evans, D. L.↗
NASA NTRS1997

Interpretation of ERS-SAR images over urban surfaces

The results of a preliminary analysis of ERS synthetic aperture radar (SAR) images collected over the Los Angeles basin (CA) are reported. The observations were performed in order to explore the applicability of microwave remote sensing to the study of the energy balance at the urban surface. Large variations of backscatter intensities were observed in the SAR images. The imaging anisotropies observed mask the variations of backscatter due to other processes, illustrating the difficulties of extracting quantitative information from SAR images of urban surfaces.

Dousset, Benedicte↗
DOE OSTI2025

A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.

Khand, Kul↗
NASA NTRSDate not supplied

Harmonized Sentinel-1 SAR Global River Geometry and Inundation Database

Satellite-based observations on river geometries are sporadic in time, space, or both. Most satellite-based surface water maps, river widths, water surface elevations (WSE), slopes, and bathymetry are asynchronized in time and space. The current configuration of satellites such as Sentinel-6 measured the WSE but is missing the river width, slopes, and depths. To advance hydrological sciences research, there is a need to produce a harmonized time series of river geometry data of non-SWOT satellites in partnership with the upcoming SWOT mission. The SWOT satellite will measure river width, height, and slope but missing river depth measurements in space and time. Further, none of these current satellites measure the WSE, river width, and slopes synchronously. In this work, we use the Sentinel-1 SAR satellite data archive from 2015 to the present to create a global river width and surface water database at the reach scale. A modified version of the Sentinel SAR surface water classification algorithm from ASF is used to quantify the surface water extent on the stream approximately every six days (at the equator) at 10m spatial resolution globally. This 10m water mask is fed into a workflow to quantify the river widths, surface water inundations, slopes, and synthetic bathymetry in SWORD (SWOT River Database) stream networks. A Satellite HAND is used to address the cloud obscured surface water observations using a trained machine learning algorithm. We use WSE derived from the Global Water Monitor from NASA GSFC, Hydroweb from LEGOS, and ICESat-2 to harmonize the WSE observation. And Landsat-8/9 and Sentinel-2 water observations to fill the gaps in the Sentinel-1 SAR database. We use Congo River Basin as a test case where we have more than 500 radar altimetry-based WSE, continuous series of Sentinel-1, ICESat-2, Landsat-8/9, and Sentinel-2 observations. A Congo River hydrologic model is used to generate the streamflow discharge. The satellite observed river reaches are assimilated with the stream flows computed by the routing models. And the downstream reaches in the river network without satellite observations get optimized for discharge/river geometry at each observation cycle. Our final product is a harmonized river geometry dataset (reach's water extent, WSE, slope, synthetic bathymetry) for Congo Basin's SWORD reaches.

Chandana Gangodagamage↗
DOE OSTI2021

Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based multilayer perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic basin. In the first experiment, a 24-h forecast period was considered. To overcome sample size limitations, we adopted a leave one year out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–18 operational data using the LOYO scheme, the MLP outperformed other statistical–dynamical models by 9%–20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical–dynamical models by 5%–22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-h intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

58 GEOSCIENCES↗