Present work deals with paleogeographic reconstruction through sequence modelling, facies and micro-facies analysis and bio-geochemical investigations of late Archaean Banded Iron Formations (BIFs) from Kushtagi-Hungund Schist Belt (KHSB) of Eastern Dharwar Craton (EDC), South Indian Shield (SIS). This comparatively less metamorphosed schist belt of EDC is correlated to the Bababudan Group of Dharwar Supergroup, Western Dharwar Craton (WDC). The lower and upper age constraints have been established from dating of 3.4-3.1 Ga old underlying Sargur Group and TTG gneisses, and 2.5 Ga old younger granites, intruding the KHSB metasediments, respectively. Further, metavolcanics intercalating with the metasediments of KHSB have been dated at 2750-2670 Ma. In this work, special emphasis has been given to the shaley-BIFs of this schist belt which is comprised of thin (few millimeters to a maximum of 1.2-1.5 cm thick) alternating units of iron rich shale, chert and hematite. Facies analysis and sequence model reveal deep water, offshore paleogeography, where proximal outer shelf is dominated by shaley siliciclastics and distal outer shelf and further deep oceanic succession is occupied by chert-hematite-dominated chemogenic sediment suits. Micro-facies analysis of the shaley-BIFs elucidated the interaction between chemogenic and deep water siliciclastic and volcaniclastic shaley sediments within micro domains. Primary chert layers contain several permineralized structures associated with carbonaceous matters. Recent RAMAN spectrometric analysis (compared to previous data in provided figure) and Carbon-isotopic values (d13C values ranging from -22.08 to -30.84 ‰ -VPDB, n= 12) of corresponding Total Organic Carbon (TOC) (ranging from 0.03 to 0.14 %) from cherts and shales indicate preservation of Archean biogenic remnants. Recent elemental (major and trace) and oxygen isotopic data, associated with the micro-facies systematics of shale-chert-iron oxide units, have been compared to previously published geochemical data sets derived from KHSB and other BIFs of SIS to provide important clues and new insights regarding late Archean ocean water chemistry, redox state, paleoclimate and control of tectonics and provenance on sedimentation pattern, prior to Great Oxygenation Event (GOE).

Sea ice leads are produced from deformational forces, which break apart the ice surface and expose open water areas or leads. Leads are the primary regulators of heat in Arctic sea ice during the polar night. Partially-frozen and re-frozen leads produce smaller heat exchanges than open leads due to the absence of a warm water surface. Thus detecting leads is important because they may be used as an indirect way of estimating air-sea heat fluxes. To quantify winter-time leads, we utilize Sentinel-1 C-Band Synthetic Aperture Radar (SAR) data to examine sea ice images through heavy cloud cover. We employ a support vector machine learning technique in a cloud computation environment (Google Earth Engine) to detect and quantify lead areas. With the use of dual-polarization data, we improve the separation of leads from other elongated features (e.g., ridges) in the Sentinel-1 dataset by adding altimetry information from ICESat-2. In addition to typical texture analysis to assess surface roughness, the ICESat-2 ATL-10 data allows us to train the algorithm by discretizing leads and ridges by their freeboard values. Performing this method in a cloud environment allows processing of a large volume of satellite data and converting it into a time series of leads properties. Overall, our method improves lead detection with dual-polarization SAR data while simultaneously providing a big data solution for SAR image processing. The interannual variability of leads and newly formed ice fractions were found for the winters of 2017-2020. Finally, we compare the results from previous studies to validate our cloud-derived sea ice lead detection maps.

One of the most important steps in any AI/ML application is the pre-processing of the data. The objective of this step is to project the original data in a new basis, or in a new latent space, where the different features of the problem are comparable and where their distribution covers a large range of values. Using the data in its natural basis can lead to under-performing AI/ML models. While almost all papers in our domain are careful to normalize or standardize the data, it is less frequent to see the use of simple linear PCA transformations, and even less frequent the use of more complex non-linear projections in latent spaces. Here we show how our research team is using autoencoder neural networks to perform non-linear transformations of images, simulations and time-series used in heliophyisical applications. Autoencoder transformations allow to parametrize any type of data by projecting it onto a latent space of higher or lower dimension. In these latent spaces the transformed data commonly presents better statistical properties allowing improvements in the AI/ML modeling. In addition, autoencoders are also known as generative techniques, i.e. they can be used to produce “artificial” or “synthetic” data. We will present three particular examples of the use of autoencoders: 1) parametrization of solar wind observations using standard feed forward autoencoders, 2) parametrization of magnetosphere simulations using convolutional autoencoders, and 3) parametrization and generation of solar active regions using variational convolutional autoencoders. We will show how these parametrizations can then be used for AI/ML classification and forecasting. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776262 (AIDA).

Data of the Earth’s magnetic field strength is diversely applicable from dating archeological artefacts or lava flows to understanding early earth evolution and the mechanisms of the geodynamo. Lava flows are commonly used as a means to obtain records of this paleointensity. Understanding the underlying paleomagnetic and rock magnetic properties and how they vary across a flow is crucial to ensure collection of good quality samples for analysis. In fact, the success of paleointensity as well as paleomagnetic analysis is strongly dependent on the rock-magnetic properties of the samples, and large variations may exist between samples even of the same unit, related mainly to varying cooling rates. The active Tungurahua volcano is one of the most prominent features in the Ecuadorian Eastern Cordillera with numerous basaltic andesite and andesitic lava flows exposed along its flanks. To appraise the relation between volcanic emplacement processes and rock-magnetic properties, we sampled a vertical transect in a ~20 m thick lava flow at Tungurahua volcano. A total of 55 oriented in situ sample from six sites distributed across Ulba Cascada lava flow were collected for this purpose. We will present petrographical analysis of each sample as a function of the depth within the lava flow, with an emphasis on the textural and structural characteristics of magnetic minerals as observed with transmitted, reflected light and scanning electron microscopes. Moreover, a detailed analysis of rock-magnetic properties such as magnetic susceptibility, hysteresis (remanent magnetization, saturation remanent magnetization, coercivity and back-field coercivity), accompanied with detailed stepwise alternating field and thermal demagnetizations will allow us to determine the direction of the magnetic field and assess the variations of magnetic properties with respect to the position within a lava flow. We will also discuss the correlations that may exist between grain size, oxidation state of the magnetic minerals and the emplacement processes of a lava flow, as well as the implications of all these results to paleointensity determinations.

We propose a new technique for energizing coronal magnetic equilibria toward eruptions. We achieve this via a sequence of MHD relaxations of small line-tied pulses of magnetic helicity, each of which is simulated by a suitable rescaling of the current-carrying part of the field. The whole procedure is ‘magnetogram-matching’ because it involves no changes to the normal component of the field at the lower boundary. The technique is illustrated by application to bipolar force-free configurations whose magnetic flux ropes (MFRs) are modeled with our regularized Biot-Savart law method. We have found that, in spite of the bipolar character of the ambient potential field in these examples, the resulting MFR eruption is generally sustained by two reconnection processes. The first, which we refer to as breakthrough reconnection, is analogous to breakout reconnection in quadrupolar configurations. It occurs at a quasi-separator field line located inside the current layer that wraps around the erupting MFR, and results from taking into account the line-tying effect at the photosphere. The second process is the classical tether-cutting reconnection that develops at the second quasi-separator inside a vertical current layer formed below the erupting MFR. Both reconnection processes work in tandem to propel the MFR through the overlying ambient field. The considered examples suggest that our technique will be beneficial for both the modeling of particular eruptive events and theoretical studies of eruptions in idealized magnetic configurations. This research was supported by NASA programs HTMS (award no. 80NSSC20K1274) and HSR (80NSSC19K0858 and 80NSSC20K1317); NASA/ NSF program DRIVE (80NSSC20K0604); and NSF grants AGS-1923377 and ICER-1854790.

Interplanetary shocks have been long known sources of suprathermal and energetic ions, and their origin will contribute in unveiling the origin of cosmic rays. Sudden ion intensity enhancements in the form of spikes that last anywhere between minutes to tens of seconds are observed during the passage of such shocks. Identification of spikes with peak intensities lasting ~1 min surveyed in over 304 shocks from January 2007 – December 2014 observed by the SEPT (Solar Electron and Proton Telescope) onboard STEREO (Solar Terrestrial Relations Observatory) A/B Spacecraft was performed using a new Python Code, followed by visual inspection. We also inspected the database of EPAM (Electron, Proton, and Alpha Monitor) onboard ACE (Advanced Composition Explorer) from 2003 to 2014. We present and discuss the statistical analysis of the shock spikes as a function of parameters such as shock normal angle and Mach number. The Python code can be used to analyse other databases such as WIND and this further paves the way for the employment of ML techniques to replace visual inspection. Such studies are vital in performing exhaustive and in-depth assessment of shock associated particle events.

During an explosive eruption, large volumes of ash and gases are ejected into the atmosphere, forming a volcanic plume which is transported by the wind. The dispersion of volcanic ash in atmosphere represents a threat for aviation safety, whereas the tephra fallout, together with gas emission, may strongly affect population health and damage to environment and infrastructure as well. Volcanic monitoring from space offers now a powerful tool to quantify hazards on both population and air traffic and gain insight into processes and mechanism of violent explosive eruptions. Here we propose a machine learning (ML) algorithm that exploits the Thermal Infrared (TIR) bands of the images acquired by the sensor Spinning Enhanced Visible and InfraRed Imager (SEVIRI), on board Meteosat Second Generation (MSG) geostationary satellite, to identify the components of ash and SO2 gas in a volcanic plume. The detection and assessment of volcanic ash clouds has been performed applying the brightness temperature difference (BTD) approach, between bands at 10.8 μm and 12.0 μm, which highlights the presence of thin volcanic ash, while the algorithm for the SO2 retrieval is based on the contributions given by the bands at 10.8 μm and 8.7 μm. Combining the latter two bands with the 10.8 μm band in the RGB channels, it is possible to create an Ash RGB image, used both day and night for the detection and monitoring of volcanic ash and sulphur dioxide gas. The advantage of the machine learning algorithm is to detect and extract automatically these features from an Ash RGB image. As test cases, we considered the sequence of explosive eruptions occurred at Etna volcano (Italy) in early 2021, which produced very long and high plume columns. Thanks to the high temporal resolution of SEVIRI (one image every 15 minutes), it was possible to visualize and to follow the plumes, from their formation to their complete dispersion in the atmosphere. The comparison of our ML algorithm with the consolidated procedure based on a RGB channels combination in the visible (VIS) spectral range showed a good agreement.

Globally available, georeferenced data from earth observing satellites and coupled earth systems models provide new opportunities to use limited field observations to infer trends in environmental processes across unsampled locations and over time. Building statistical models that are intended generalize earth surface processes across large spatial scales represents a new frontier in supervised statistical inference. For example, care must be taken to collect unbiased samples given the complexity of the earth system: surface features can appear vastly different from the perspective of multispectral and SAR imagery in different atmospheric/landscape contexts. Environmental processes occur at variable spatial/temporal scales, and sampling resolution can drastically alter the appearance of patterns, and lead to spatial and temporal autocorrelation which can bias model weights and/or parameter estimates. Multicollinearity in multivariate datasets, which is also scale dependent, can inflate variance in parameter/weight estimates. All these in tandem can undermine the robustness of models in predicting in out-of-sample contexts. To overcome this, the GRRIEn (Generalizable, Reproducible, Robust, and Interpretable Environmental) analysis framework is introduced as a standard method of training and validating supervised data-driven models at large spatial scales. The method is explained, and demonstrated with a case study detecting surface water at CONUS scale using SAR and multispectral imagery.

Documented trends in the timing and intensity of precipitation events have been met with associated hydrologic changes in water-balance partitioning. Changes in land use, initiation and length of the growing season and associated irrigation practices, timing of autumn leaf senescence, snow accumulation and melt, flooding, and drought all affect the hydrological processes of infiltration, evapotranspiration, and runoff. This study is an investigation of trends in groundwater recharge over space and time in the temperate climate of the Midwest USA. It tests the hypothesis that increasingly intense precipitation events resulting in extreme runoff events might be short-circuiting processes of infiltration and groundwater recharge by lowering the residence time of water in upland landscapes, while increasing infiltration in lowlands and riparian corridors. The climate-driven changes in precipitation events can be more easily linked to the documented increasing stream discharge trends than trends in groundwater levels. In this ongoing study, multiple methods were utilized to calculate annual and monthly, or seasonal, potential aquifer recharge amounts and trends in unconsolidated aquifers in Indiana and surrounding watersheds. Because observation wells in Indiana are sparsely distributed, estimates of groundwater recharge for selected wells were used for calibration and validation of a 40-year spatially continuous water balance (U.S. Geological Survey Soil Water Balance v 2.0, SWB2) model that solved for potential groundwater recharge (net infiltration) at a daily time step and was analyzed at annual and monthly intervals. Using estimates of annual net infiltration from the SWB2 model, 40-year temporal trends calculated on space-time cubes suggest that over 50% of the model domain is experiencing statistically significant increases (p < 0.006) in potential groundwater recharge. Trend analyses of unconfined water levels from 1980-2019 were conducted, finding that water levels in 8 (out of 9) climate divisions in the state are increasing (rising) in 6 of the 8 climate divisions. The distribution of increasing water tables reflects the same spatial distribution as the annual estimates of recharge generated by the SWB2 model.

Time-lapse borehole nuclear magnetic resonance (bNMR) relaxation is a promising method for linking water content changes in the unsaturated region of the critical zone with pore-scale properties associated with bedrock weathering. The saturation-dependance of the NMR T2 distribution is strongly controlled by pore-scale material properties and can be linked to hydraulic properties (e.g. the water retention function and hydraulic conductivity). Here, we leverage NMR’s sensitivity to pore-scale properties to investigate material controls on plant-available water storage dynamics in weathered bedrock via time-lapse bNMR relaxation measurements. To overcome slow logging speed and poor signal-to-noise (SNR) ratio typically associated with bNMR measurements in the unsaturated zone, we focus on the sum of echos (SE). We show that the advantage of using SE to characterize NMR relaxation, rather than using the full T2 distribution or the logarithmic mean of the distribution, is that it is easy to calculate, does not require inversion, has enhanced SNR, and is sensitive to both volumetric water content (VWC) and mean T2. This leads to high contrast in SE between time-lapse measurements relative to other metrics of NMR relaxation. At our hillslope study site associated with the Eel River CZO, VWC changes in weathered bedrock driven by deeply-rooted trees allow us to create “NMR characteristic curves” for different regions of the weathering profile. Analogous to a water retention function, the NMR characteristic curves describe NMR relaxation times of a material at a given VWC, and can be used to identify differences in pore-scale properties. We show that mean T2 times are typically shorter in bedrock that is more weathered for the same VWC, which is consistent with smaller pore-sizes and higher surface relaxivities associated with weathering products such as secondary clays and oxides. Our well logging indicates that changes in pore structure associated with bedrock weathering control plant-available water supply within the bedrock weathering profile. While these results illustrate the utility of bNMR, further studies that quantitatively link NMR measurements to flow properties via pore or empirical models will benefit mechanistic understanding of plant available water in the critical zone.

Marine sediments contain considerable amounts and different types of magnetic mineral particles. Magnetic minerals in sediments may be statistically aligned to the direction of the ambient geomagnetic field so that sediments potentially preserve geomagnetic intensity records in the past. However, different types of magnetic minerals should preserve the remanent magnetization in different manners. And the compositional variation of magnetic mineral assemblages in marine sediments may hinder us from extracting reliable geomagnetic paleointensity records. We studied a sediment core taken from the Ontong-Java plateau, from which relative paleointensity (RPI) variations were estimated. The magnetic mineral assemblages of the sediment core are principally a two-component mixture of terrigenous and biogenic magnetite. So it provides an opportunity to assess the influence that compositional variations in marine sediments could bring to RPI estimations and thus to distinguish different contributions of the biogenic and terrigenous components to RPI recording in marine sediments. RPI obtained by normalizing natural remanent magnetization (NRM) with anhysteretic remanent magnetization (ARM) shows downcore decreases, and it has an inverse correlation with the ratio of ARM susceptibility (kARM) to saturation isothermal remanent magnetization (SIRM) (kARM/SIRM). This indicates that the RPI signal becomes apparently weaker with increasing proportion of biogenic magnetite. Moreover, NRM-ARM demagnetization diagrams show concave-down curvature, which indicates that the coercivity distributions of NRM and ARM are different. If we assume that the magnetization of the higher coercivity interval is mainly carried by biogenic magnetite while that of the lower coercivity interval is mainly carried by the terrigenous component, RPI recording efficiency of the biogenic component may be lower than that of the terrigenous component. The validity of this assumption was investigated by first-order reversal curve (FORC) measurements, transmission electron microscope (TEM) observations, low-temperature measurements, and extraction of silicate-hosted magnetic inclusion from the sediments, and the results proved that NRM of the higher coercivity interval is carried mainly by biogenic magnetite. But our conclusion contradicts with some previous studies using a similar method, which suggested higher RPI recording efficiency of the biogenic magnetic component than the terrigenous component [Ouyang et al., 2014; Chen et al., 2017]. Different concentrations of silicate-hosted magnetic inclusions due to different sedimentary environments might be a possible reason for the contradiction. The contribution of silicate-hosted magnetic inclusions to the magnetization is minor in our sediments (less than ~7% of SIRM). This contradiction remains to be studied further. Keywords: geomagnetic paleointensity, silicate-hosted magnetic mineral inclusion, biogenic magnetite, Ontong-Java plateau

In recent decades, major growth is observed in wildfire incidents across the globe. These ecological disasters, triggered by natural and/or anthropogenic factors, can have long-lasting effects on the environment, ecosystems, and biodiversity. Advancements in remote sensing technology provided an impetus to forest fires research, enabling precise determination of the geographical locations susceptible to fire and assess fire risk. This study focuses on India, whose total forest cover (71.22 Mha) is about 21.67% of the country’s total geographical area. Around 90% of the forest fires in India are attributable to anthropogenic factors. Therefore, the generation of a forest fire risk map (FRM) is essential for devising strategies to mitigate/manage forest fires and avert their disastrous impacts. An attempt is made to develop FRM for the Budhabalanga river basin, which contains the Similipal national park, a part of the UNESCO World Network of Biosphere Reserves. For this purpose, covariates affecting forest fire are identified viz. fuel (vegetation), topographic features (elevation, aspect, and slope), and human activities. The covariates are assigned weights, depicting their relative importance in influencing the fire, byusing Analytical Hierarchical Process (AHP). The AHP is a widely used technique in multi-criteria decision-making models. The Similipal national park has experienced a prolonged dry spell and below-average monsoon in 2020. The consequent dry conditions led to a significant forest fire event in the last week of February 2021, which lasted nearly three weeks. Fine (30m) resolution satellite data (Landsat-8) are used to calculate the Normalized Difference Vegetation Index (NDVI) corresponding to the study area to assess vegetation conditions before the fire event. Furthermore, Cartosat-1 Digital Elevation Model (24m resolution) is used to extract topographic-related information. The FRM generated for the basin using the assigned weights was 80% accurate when validated with 375m resolution NASA’s VIIRS (Visible Infrared Imaging Radiometer Suite) fire point data for the analyzed fire event. Hence, the methodology considered for developing FRM appears promising. It can be extended to other river basins for identifying fire risk zones and devise timely strategies to mitigate fire risk.

The Surface Biology and Geology (SBG) mission is one of the core missions of NASA’s Earth System Observatory (ESO). SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~10 TB/day and generate products at ~75 TB/day. As the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing/re- processing, disseminating, and exploiting the SBG data presents new challenges. To address these challenges, we are developing a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight. Our science pipeline is based on the science processing operations technology developed for the Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or an on-site supercomputer. Our effort began by ingesting data and applying workflows from the EO- 1/Hyperion 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We have fully implemented the first stage of processing, from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We plan to begin reprocessing the entire 55 TB Hyperion data set by the end of 2021. Work to implement an atmospheric correction module to convert the L1R data to surface reflectance (Level 2) is also underway. Additionally, an effort to develop a hybrid High Performance Computing (HPC)/cloud processing framework has been started to help optimize the cost, processing throughput and overall system resiliency for SBG’s science data system (SDS). Separately, we have developed a method for generating full-volume synthetic data sets for SBG based on MODIS data and have made the first version of this data set available to the community on the data portal of NASA’s Advanced Supercomputing Division at NASA Ames Research Center. The synthetic data will make it possible to test parts of the pipeline infrastructure and other software to be applied for product generation.

Studies have long reported the existence of pronounced diurnal and semi-diurnal variations in near-surface winds and divergence over the tropical oceans. Diurnal cycles of convective precipitation and cloudiness in the tropics are also well recognized from in-situ and satellite observations. However, the linkages between diurnal variations in tropospheric circulation, cloudiness and precipitation over the tropical oceans remain to be fully documented and understood. Recently, global storm-resolving models, which do not require convective parameterizations, have created an unprecedented opportunity to investigate the full three-dimensional structure of the diurnal cycle over the tropical oceans. In this study, we used one such model – the Model for Prediction Across Scales (MPAS) – for two main purposes: first, to evaluate the model’s representation of semi-diurnal and diurnal variations in near-surface winds, precipitation, and cloudiness over the tropical oceans; and second, to extend the analyses to provide a full three-dimensional picture of the daily variations in tropospheric circulation and their linkage with the hydrological cycle. A 40-day MPAS simulation (the same as used for DYAMOND-1 global storm-resolving models inter-comparison project) was utilized in this study to examine the large-scale geographical patterns and vertical structures of mean daily variations of zonal and meridional wind components, wind divergence, vertical velocity, cloudiness, water vapor mixing ratio and precipitation. The model shows generally good agreement with the previously reported observational results for near-surface winds and divergence. In particular, MPAS exhibits a pronounced large-scale diurnal cycle in the local Hadley Circulation over the Tropical Pacific Ocean, with lower tropospheric divergence (convergence) relative to the daily mean, maximizing around 1000 (2200) LT. The amplitude of the diurnal variation in near-surface wind divergence at the equator is approximately 0.8×10-6 s-1, or approximately 44% of the daily mean. The vertical structure of this diurnal circulation, along with its signature in vertical velocity and its association with water vapor, cloudiness and precipitation, will be presented.

Soil moisture plays an essential role in the complex eco-hydrologic processes, such as infiltration, rainfall-evapotranspiration-runoff circulation, photosynthesis, and groundwater recharge. However, the accurate estimation of soil moisture (SM) at regional or larger scale is difficult because SM varies highly over space and time due to heterogeneous land cover and soil properties, and ground measurements are often time-consuming and expensive. Currently, Bangladesh Meteorological Department (BMD) measures SM only at twelve stations which is quite inadequate for assessing large-scale spatial and temporal variation of SM. Thus, satellite-derived soil moisture data products or Global Land Data Assimilation System simulated (GLDAS-2.2) soil moisture dataset with the Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA) can be promising alternatives to the in-situ measurement for this data-scarce region. In this study, the spatial and temporal variations of SM from GLDAS and Soil Moisture Active Passive (SMAP) satellite were compared against the in-situ measurements from seven agrometeorological stations of Bangladesh. The GLDAS and SMAP products overpredicted the in-situ SM for most of the stations and could capture the temporal dynamics of observed SM with correlation coefficient (R) of 0.36 and 0.17, respectively. Later an Artificial Neural Network model was developed based on soil moisture from both sources (SMAP and GLDAS) and terrestrial water storage from GLDAS to obtain more accurate estimation of SM for this data-scarce region. The ANN model shows an improvement in estimation and predicted SM with R = 0.63 (considering all stations). The results were more promising when separate model is developed for each study site. Incorporating additional climate data (such as precipitation with different lag times) as input improved the accuracy marginally. This study suggests that the release of daily GRACE gravity field solutions in near-real-time may provide a reasonable and continuous estimate of soil moisture in this data-scarce region.

This work reports on the design and implementation of advanced geospatial simulations using an Agent-Based Model (ABM) integrated with an augmented reality solution for interactive and immersive modeling exploration. The multi-scenario modeling framework allows for emergent phenomena and provides flexible representation of biological and physical environmental factors associated with natural and man-made systems. Augmented reality is provided by a sandbox running Tangible Landscape, based on a customization of GRASS GIS. An integrated Microsoft Kinect sensor mounted over the sandbox captures real-time topography produced by physical interactions with sand and resulting digital elevation models are ingested into the Recursive Porous Agent Simulation Toolkit (Repast) as landscape definition input. We illustrate the implementation by presenting a model system that includes a classic predator-prey relationship over a grassland habitat where sheep and wolves coexist as agents. Food sources for sheep are scattered over the landscape and are consumed as agents forage. Wolves control sheep population by actively searching for sheep and chasing individuals when their presence is detected. We simulate natural conditions by defining that the presence and movement of agents over the landscape is controlled by elevation provided by the sandbox. For instance, the presence of agents and resources can be limited to specific elevation ranges and slope is used to incorporate movement cost (energy loss) while individual agents travel over the landscape. Ecological conditions are further simulated by the consumption and regrowth of food resources. Users interact with the sandbox and the modeling effort by manually moving sand and altering landforms. This effort brings together multiple technologies and data manipulation/visualization strategies and allows for feature-rich experimentation by supporting multiple co-located and georeferenced layers (e.g., land use/land cover, soil, hydrography).

Understanding rock mechanical behavior, especially the onset and nature of yield (i.e. permanent deformation), is critical to ensure safe and sustainable operations in many geological engineering applications, including ‘low-carbon’ subsurface technologies to aid decarbonization. In porous sandstones, yield corresponds to onset of either permanent dilatation or compaction accompanied by microcracking and is defined by a ‘capped’ yield surface in differential stress vs effective mean stress (Q/P) space. Previous studies showed that compactive yield in porous sandstones corresponds to the onset of grain fragmentation and pore collapse, manifested by the deviation of the porosity reduction curve from the hydrostat. However, determining the onset of yield can be ambiguous. Here, we investigate the micromechanical basis for the compactive yield behavior of porous (25%) Hollington sandstone, which is comparable to common aquifers, hydrocarbon and geothermal reservoir rocks and other sandstone formations with potentials for underground storage of fluids (e.g. carbon dioxide and hydrogen). A suite of conventional triaxial compression experiments with concomitant pore volumometry were performed to characterize the mechanical behavior of the rock. Effective mean stress vs porosity reduction data show a slow but near-linear initial deviation from the hydrostat followed by a sharp increase in the rate of compaction for most samples that underwent inelastic porosity reduction below 243 MPa effective mean stress (P*). Synthesis of mechanical with microstructural data suggests that the initial deviation from the hydrostat is related to the collapse of local large pores while the sharp increase in the rate of compaction corresponds to the onset of grain fragmentation leading to widespread pore collapse. Plotting yield stresses in Q/P space reveal a near linear cap of negative slope, albeit with considerable scatter, for stresses related to the collapse of local large pores, evolving into an elliptical cap for stresses approximately corresponding to the onset of widespread grain fragmentation. The results highlight the enhancing influence of non-hydrostatic stresses acting around large pores on the compactive yield in rocks with porosity heterogeneities.

Hydrochemical constituents in streams may originate from currently active sources at the surface and/or legacy sources retained in soil, slow-flowing groundwater and sediments from earlier surface inputs, waste deposits, and land contamination. These source contributions need to be distinguished for effective pollution mitigation and water quality improvement. This study outlines a methodology for such distinction based on some general mechanistic differences in stream concentration and load behavior versus discharge between the contributions from these different types of sources. The methodology is applied to and tested on stream concentration data for chloride, nitrogen, phosphorous, copper, lead, and zinc, and corresponding data for water discharge, measured over recent decades (time series of 1-3 decades) in multiple Swedish hydrological catchments of different scales (10-37 catchments depending on substance, chloride/nutrient/metal, and its monitoring). Mixed sources are indicated in most (18 of 19) catchments for chloride (with average 19% active-source contribution to total load), but only in 3-4 (of 37) catchments for total nitrogen and total phosphorus (32-59% active), and 1-3 (of 11) catchments for copper, lead, and zinc (1-3% active). Only 1 catchment (of 37) is indicated to have dominant active sources for total nitrogen, and most catchments thus have dominant legacy source contributions for all substances. The legacy contributions correlate well with human activity indicators in the catchments (urban areas for chloride, agricultural land share for nitrogen, population density for phosphorus, historic mining and mine waste areas for the metals), indicating that they are largely anthropogenic. The developed and tested methodology is relatively simple and can be used to screen commonly available stream monitoring data for distinction of active and legacy contributions of any hydrochemical constituent in and across various hydrological catchment settings.