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.

Pesticide seed treatments (PST) which contain fungicides and insecticides are commonly used in agriculture; however, little is known about their effect on soil microbial communities and soil health. Neonicotinoids – controversial insecticides which are common in PST – have received criticism due to potential non-target effects. While fungal pathogens need to be moderated, PST have the potential to disturb broader fungal communities which could lead to reduced nutrient cycling and poor soil health. Given the broad use of PST, their effect on soil fungi needs to be studied within the context of other agricultural management practices. For example, tillage regimes can result in distinct fungal communities which may respond differently to PST. An experimental site was established in 2013 with a corn-soy rotation under three tillage treatments: Full-till, Strip-till, and No-till. Since 2016, seeds with or without PST (fungicides and insecticides) were planted under each tillage regime in a fully factorial design. In 2018, bulk soil was collected from within rows while soy was growing. A range of soil physicochemical variables were measured, and soil function was determined with substrate-induced respiration and enzyme assays. DNA was extracted from soil and the ITS region was sequenced to determine fungal community structure and diversity. While tillage significantly affected fungal community structure (p < 0.01), there was no effect of PST on either community structure (p = 0.59) or diversity (p = 0.52). This indicates that PST does not affect bulk soil fungal communities; however, they may have an impact at different temporal or spatial scales than those studied here. Across all treatments, fungal community structure correlated with soil water holding capacity (rs = 0.23, p = 0.04) and electrical conductivity (rs = 0.26, p = 0.01). Despite not finding an effect of PST on fungal communities, we did find that PST increased potentially mineralizable nitrogen under no-till and shifted community level physiological profiles determined by substrate-induced respiration. These results suggest that while PST can affect certain aspects of soil health, there are no clear effects on the soil fungal community.

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-hour average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007–2013) was 4.91 μg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 μg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 μg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California’s population lived in a county that experienced at least one episode of high smoke exposure (“smokewave”) from 2007–2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 µg/m3 bias) and 2013 (1.6 µg/m3 bias) while underestimating for 2009 (−2.1 µg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.

Wind and solar energy technologies are, by their nature, variable. Variations in resource availability, based on weather patterns, occur on intra-day to inter-annual time scales. Many energy system models optimize over a single year of input weather and electricity demand data. Energy system planners need increased understanding of the variability in generation potential across multiple years and how this could impact model results. A system achieving 100% reliability modeled using Year A data will not necessarily achieve 100% reliability when applied to Year B data unless an overbuild safety margin is added. We demonstrate: 1) model results can vary significantly based on the year of data used, 2) adding wind and solar does not necessarily reduce the predictability of meeting reliability targets year-to-year and can improve predictability in many cases, and 3) we illustrate a method to derive safety margins to predictably meet 100% reliability year after year and find the least-cost option.

This presentation will show an ongoing freshwater health assessment stemming from a partnership between the National Aeronautical and Space Agency (NASA) and Conservation International (CI) that is dedicated to improving natural resources assessment for conservation and sustainable management. The goal of this work is to develop a calibrated satellite- and hydrologic modeling-based tool to support the assessment of hydrologic environmental health and value natural capital in the Lower Mekong River Basin. Vollmer et al., (2018) have presented the social-ecological framework named the Freshwater Health Index (FHI), which takes account of the interplay between governance, stakeholders, freshwater ecosystems and the ecosystem services they provide. The FHI framework and its accompanying indicators are oriented toward management and stakeholder engagement, and they make a significant contribution by providing a systematic, evidence-based quantitative tool that supports the integration between social and ecological nature of fresh waters at the basin level. Since the FHI is intended to be used iteratively, we leverage multiple data products and hydrological modelling capabilities specifically created to improve decision support in the Lower Mekong basin (Mohammed et al., 2018). Mohammed et al. modelling capabilities enable the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil characteristics, and land cover and land use information to drive watershed model water simulations over the Lower Mekong River Basin. Multiple dam reservoirs scenarios have been envisioned and tested based on stakeholder engagement to enhance the results of the integrative social and ecological nature of fresh waters at the Srepok, Sesan, and Sekong (3S) River Basins of the Lower Mekong. This assessment provides a comprehensive picture of freshwater ecosystem health, the services it provides and the status of its governance at the Lower Mekong.

Previous studies done for the TROPOMI mission (e.g. Meister et al (2017), Hu et al (2016), Landgraf et al (2016)) has shown that inhomogeneous illumination within the field of view causes distortions in the instrument spectral response function (ISRF). The differences in the distorted ISRF and the ISRF measured in the pre-flight calibration lead to retrieval biases. It is known through laboratory trials that these distortions increase with wavelength and various methods have been employed to mitigate these issues, including continuous within-slit scanning and temporal oversampling. Recently, a hardware solution, called a slit homogenizer, has shown promise in reducing distortions due to inhomogeneous illumination. In this work, we used an instrument model of the Geostationary Carbon Cycle Observatory, GeoCarb, to simulate observations in the 0.76um, 1.6um, 2.05um, and 2.3um spectral bands with varying illuminations. We found that the posterior scatter and bias of Greenhouse Gas retrievals grow linearly with the coefficient of variation of illumination within a footprint. We also found that the ISRF distortion effects of inhomogeneous illumination are significantly reduced through the use of a slit homogenizer, though not completely resolved.

Glaciers are major contributors to global sea level change and regional freshwater resources. In light of the worldwide societal importance of glacier change, we see a need for accessible glacier-education materials in many languages. We have developed a set of Jupyter notebooks, available in both Spanish and English, that coaches users through basic simulations with the Open Global Glacier Model (edu.oggm.org). The notebooks are freely available via GitHub (github.com/ehultee/CdeC-glaciologia), with no special software installation requirements for users. Participants in a week-long glaciology workshop at Clubes de Ciencia Peru 2019 used our notebooks to study glacier physics and make future projections for mountain glaciers of their choice. Only one participant had previously used Python, and none had experience with numerical modelling of Earth systems in any language. All were able to run the notebooks and explain their findings. Most participants were also able to modify the notebooks to conduct experiments of interest to them. We consider this a particularly successful case of science outreach, in that workshop participants are now equipped to explore their own important questions independently. The notebooks we have developed can support multilingual glaciology outreach; we hope that sharing our experience can support public geoscience more broadly.

Gas leakage through wellbore micro-channels is a major challenging problem for well cementing and integrity. One of the reason that leads to weaker cementing is the drilling fluid contamination within the cement plug, which provides weak parts for the flowing pathways of gas and fluids. The objective of this paper was to experimentally investigate the drilling fluids contamination in terms of liquid and dehydrated drilling fluid on wellbore cement in multiple scale to compare and quantify the impact of the drilling fluids on the cement plug. In the experiment, specific amount of drilling fluid was mixed with wellbore cement. Different level of contamination was introduced for each phase of the drilling fluid. Class H Portland cement slurries were then prepared using API-10 B standard for a density of 16.4 lb/gal (SG=1.97), and the control sample with 0% contamination is referred to as neat Class H cement slurry. The first part of the testing is to quantify the effect of the contamination on core scale. Samples were tested for petrophysical and mechanical properties in terms of porosity, permeability and unconfined compressive strength. Moreover, Microstructural properties and hydration process were evaluated using Optical Microscopy and Scanning Electron Microscope (SEM) on micrometer scale in order to investigate the fundamental mechanism of contamination on properties of cement matrix. The results showed that the presence of mud would highly harm the performance of the cement. Micro-fractures appeared at the interface of the cement/mud through the Optical Microscope. The porosity of the core increased from 30% porosity of the neat cement to approximately 40% porosity of contaminated samples at most. The compressive strength could drop to more than 50% of the original value. Micro-annular channel and irregularities in the structure can be observed which might explain the increased porosity and permeability. The results presented in this paper would lead to safe implementation of cementing, enable safe Oil and Gas exploration in deepwater without risks of environmental contamination, and contribute to improvement in overall wellbore construction and development of robust hydraulic barrier materials.

Physics of two-phase flows in heterogeneous rocks plays an important role in many applications such as oil and gas migration and geological sequestration of carbon dioxide. Although current pore-scale models are used to compute macroscopic properties required in reservoir simulators, most work is limited to small sample size and homogeneous rocks. There is a need for pore-scale modeling approaches that can accurately represent the 3D complex pore structure and heterogeneity of real media. Pore network modeling simplifies the geometry and flow equations at pore-scale, but can provide characteristic curves in capillary-dominated systems on fairly large samples with huge saving on computational costs compare to direct numerical simulation methods. However, there are limitations for attaining a large representative pore network for heterogeneous cores, namely the technical limits on sample size to discern void space and computational limits on network extraction algorithms. To address these issues, we propose a novel stochastic pore network stitching method in combination with network generation to provide large-enough representative pore network for a core. Our approach proposes to use micro-CT images of various reservoir rock cores in different resolutions to characterize the pore structure. Few signature parts of the core are selected and their corresponding void space and equivalent pore network are extracted. The space between pore networks is filled by using a stochastic network generator that utilizes statistics of all signature networks and a layered stitching method that glues networks based on their average properties. The output is a large network that can be used in any pore network solver. We are focusing on flow properties via quasi-static pore network modeling solver to obtain absolute permeability, relative permeability, and capillary pressure curves. Since the method is stochastic, the workflow should be run on many realizations and final results yield both average and variability of the derived properties. We have tested the developed method on various generated and extracted networks, and we have extended the stitching method to 3D heterogeneous samples.

Water table depth (WTD) is the predominant biophysical control over the occurrence of peat and forest fires in tropical peatlands. In Indonesia, prolonged droughts caused by El-Niño and/or positive Indian Ocean Dipole (IOD), exacerbated by extensive peat drainage for agriculture and plantation establishment, can promote severe peatland fires by lowering WTD and hence desiccating surface and sub-surface peats. The severe drought episode of late 2015 across Indonesia, caused by a strong El Nino and a positive IOD, led to a major and damaging increase in peatland fires, highlighting an urgent need to develop operational systems to forecast potentially severe fire events to mitigate the impacts of fire and haze. The 2002 ASEAN Agreement on Transboundary Haze Pollution, signed and ratified by a total of 10 ASEAN states, including Indonesia, identifies a critical need for such systems based on near-time climate projections. However, such systems have not yet been developed. While an operational early warning system for forecasting dangerous burning conditions in Indonesia is currently within reach using state-of-the-art modelling tools, such as the ECMWF’s System 5 seasonal forecast model (SEAS5), development is still hampered by insufficient knowledge about the influence of fluctuations in peat moisture on fire, particularly during periods of extreme drought. The main objectives of this study were: i) to deploy a process-based ecosystem model “ecosys” to study how WTD and peat moisture profiles change in tropical peatlands across Riau province, Sumatra, in response to drought and land cover change, focusing on the 2015 drought; and ii) to examine whether those changes could have been predicted using SEAS5. Model spin-up from 2008-2014 was driven by inputs from ECMWF’s climate reanalysis data (ERA5), followed by 3 parallel simulations for 2015, driven by ERA5, ERA5 climatology, and SEAS5 hindcasts. Model outputs of peat moisture profiles and WTD showed how peat moisture and WTD were significantly affected by weather and land uses during the dry season of 2015 which were corroborated well against data from Soil Moisture Active Passive satellite and site-level monitoring networks. Our work is a pioneering attempt to perform large-scale process-based modelling to predict seasonal variations in tropical peatland WTD.

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.