Sangamon watershed is recognized as one of the most worth noting regions for water and environmental supply planning and management purposes according to its intensively management for soybean and corn production. It is also a representative area with limited geological and hydraulic measurement data, in which sustainable ground water and environmental management is essential. To better understand the hydraulic properties of the entire watershed, a multi-fidelity Gaussian Processes (Kriging) model was applied to predict the hydraulic conductivity of the upper Sangamon watershed, using previous multi-sources of field observation data (Electrical Earth Resistivity and pumping test data). The model also provided a quantification of uncertainty of the predicted values, which helps us to make reliable suggestions for the future design of hydraulic observations. The data fidelity effect to the model was discussed by comparing multi-fidelity and single-high-fidelity Kriging results. The model predicted values suggest that the accuracy of multi-fidelity Kriging depends on the locations and the distribution of both the high- and low-fidelity data. When high-fidelity data points are sparse and far away from the low-fidelity data points, the information provided from the low-fidelity data becomes extremely important, which can greatly enhance the model performance and accuracy. This study has paved the way to a more efficient parameter estimation in under-sampled sites by effectively estimating large-scale parameter maps using small-scale measurements and by applying uncertainty quantification method to a real watershed observation case. It will also draw upon and contribute to advances in Bayesian experimental design, and will optimally result in financial savings.

To manage potential risks due to H2 leaks into the near-surface geosphere from H2 underground storages (e.g. salt caverns, aquifer), reliable monitoring methods along with a precise knowledge of the geochemical environmental impacts are necessary. Thus, the evolution of some prominent parameters in soil and aquifers can be determined: gas concentrations, redox potential, ionic balance and trace elements. As part of the ROSTOCK’H project, Ineris simulated H2 leakage by injection of dissolved H2 into a shallow aquifer (~20 m deep) in an experimental site within the Paris basin. This experiment aimed to testing advanced monitoring techniques and studying hydrogeochemical impacts at shallow depths. The aquifer water has calcium-bicarbonate facies and a neutral pH. Eight piezometers were aligned over 80 m according to the aquifer main flow (west-east). Hydrogeochemical monitoring devices were set up. One of the piezometers was equipped with a completion connected to a Raman probe and a specific Mid-IR cell for continuous measurement of aqueous gases. At the experiment outset, 5 m3 of water were extracted from the aquifer to be saturated with H2 under atmospheric conditions, before being reinjected through the injection well. About 100 LSTP of dissolved H2 (concentration of 1,8 mg/L) was injected in the aquifer. The H2 injection was preceded by the injection of underground water containing tracers (He(aq), uranine and LiCl) in order to warn the H2 plume arrival in the piezometers located downstream of the injection well. The concentrations of aqueous gases (He, H2, N2, O2, CO2, H2S and CH4) were measured in a control piezometer (20 m upstream) and in six piezometers up to 60 m downstream. Thus, the maximum H2 contents were detected up to 20 m downstream of the injection well: 0.6 mg/L at 5 m, 0.17 mg/L at 7 m then 1.8 µg/L of H2 at 10 and 20 m during the first week. Following the H2(aq) addition, the aquifer physico-chemistry has been modified: low increase in pH, high decrease in redox potential and O2(aq). These results confirm the feasibility of detecting and monitoring H2 in shallow aquifers in very low concentration conditions and highlight the potential impacts. This is of first importance for establishing the surveillance and security aspects related to with H2 storage.

The São Francisco Craton (SFC) and its marginal Araçuaí and Brasília orogens exhibit a significant diversity in their lithospheric architecture. These orogens were shaped during the Neoproterozoic–Cambrian amalgamation of West Gondwana. The rigid cratonic lithosphere of the SFC and the relatively weak lithosphere of the Araçuaí Orogen were disrupted during the Cretaceous opening of the South Atlantic Ocean, whereas the Brasília Orogen remained in the continental hinterland. In earlier research, the thermal effects of the Phanerozoic reactivations in the shallow crust of the Araçuaí Orogen have been revealed by low-temperature thermochronology, mainly by apatite fission track (AFT) analysis. However, analyses from the continental interior are scarce. We present new AFT data from forty-three samples from the Brasília Orogen, the SFC and the Araçuaí Orogen, far from the passive margin of the Atlantic coast (~150 to 800 km). Three main periods of basement exhumation were identified: (i) Paleozoic, recorded both by samples from the SFC and Brasília Orogen; (ii) Early Cretaceous to Cenomanian, recorded by samples from the Araçuaí Orogen; and (iii) Late Cretaceous to Paleocene, inferred in samples from all domains. We compare the differential exhumation pattern of the different geotectonic provinces with their lithospheric strengths. We suggest that the SFC likely concentrated the Meso-Cenozoic reactivations in narrow weak zones while the Araçuaí Orogen displayed a far-reaching Meso-Cenozoic deformation. The Brasília Orogen seems to be an example of a stronger orogenic lithosphere, inhibiting reworking, confirmed by our new AFT data. Understanding the role of the lithosphere rigidity may be decisive to comprehend the processes of differential denudation and the tectonic–morphological evolution over Phanerozoic events.

Advances in remote sensing technology, notably in UAV Light Detection and Ranging (Lidar), may yield to better predictions of the implications of land cover alterations on greenhouse gas (GHG) exchanges by facilitating the acquisition of high-resolution information on surface topography and vegetation structure. Although surface morphology is fundamentally related to the magnitude of aerodynamic roughness length (zo) and zero place displacement height (d), assigning appropriate values to estimate energy and GHG fluxes remains challenging. In this study, we evaluate the effectiveness of a workflow for processing small-footprint point clouds from a UAV-Lidar system, multispectral and thermal infrared data in order to obtain necessitated parameters for calculation of water vapor fluxes over a mixed canopy, populated by agricultural vegetation and evergreen trees in Denmark. Point cloud data are classified into ground and vegetation using the progressive triangulated irregular network densification algorithm, and are interpolated with the kriging method to generate canopy height models (CHMs) with 0.5 m pixel resolution. CHMs are then delineated using the watershed algorithm to extract geometrical characteristics, orientation and spacing of the low and high vegetation and assign them to four morphometric roughness models that calculate zo and d. The rasterized aerodynamic resistance maps are rectified with thermal and multispectral orthomosaics to obtain the spatial distribution of available energy, sensible heat and water vapor fluxes by incorporating these terms into a surface energy balance model. The Lidar-derived geometric attributes are validated with selected ground truth data and the modeled water vapor fluxes are compared with eddy covariance measurements. Derivation of more precise high-resolution aerodynamic parameters and reflectance characteristics from UAV-based instrumentation can increase the accuracy of water flux estimates of a canopy under surface heterogeneity conditions and may confine the uncertainty in describing the propagation of their long-term effects on ecosystem’s resilience.

The accuracy of streamflow forecasts is important for efficient monitoring and mitigation of flood events. Unfortunately, the uncertainty in the model control variable which includes model parameters, initial and boundary conditions, propagates through the model, resulting in the degradation of streamflow forecast. Various studies in the past have shown the potential of soil moisture assimilation in hydrological models resulting in the improved forecast. Further, the efficiency of assimilation is based on the number and the distribution of observations used. This study proposes a new approach called Forward sensitivity method (FSM), which operates in two phases. By running the model and forecast sensitivity dynamics forward in time, the first phase places the observations at or near where the square of the forecast sensitivity with respect to the control takes maximum values. Then using only this subset of observations, the second phase estimates the unknown elements of the control by solving a resulting weighted least squares problem. The power of this approach is demonstrated by assimilating ASCAT soil moisture observations into a conceptual Two Parameter Model in a medium sized watershed lying in the Krishna River Basin, India. The model run extends for four monsoon years from June 2007 to June 2011 and two assimilation scenarios were tested. The first scenario uses all the observations, whereas, the second uses only sensitive observations during assimilation and the results were then compared against open loop simulation (model run without assimilation). Sensitivity results indicate that observations during monsoon time alone are sufficient for assimilation purpose, which accounts for only 37.42 percent of total observations. Also, the estimation and forecast results show improved streamflow performance when using only sensitive observations. From the results, it is concluded that FSM based assimilation can help in reducing the computation time greatly. Further, this study will be critically helpful in the places where data availability remains a major problem.

Solar energy development is land intensive and recent studies have demonstrated the negative impacts of large-scale solar deployment on vegetation and soil. Co-locating vegetation with managed grazing on utility scale solar PV sites could provide a sustainable solution to meeting the growing food and energy demands, along with providing several co-benefits. However, the impacts of introducing grazing on soil properties at vegetated solar PV sites are not well understood. To address this knowledge gap, we investigated the impacts of episodic sheep grazing on soil properties (micro and macro nutrients, carbon storage, soil grain size distribution) at six commercial solar PV sites (MN, USA) and compared that to undisturbed control sites. Results indicate that implementing managed sheep grazing significantly increased total carbon storage (10-80%) and available nutrients, and the magnitude of change correlated with the grazing frequency (1-5 years) at the study sites. Furthermore, it was found that sites that experienced consecutive annual grazing treatments benefitted more than intermittently grazed sites. The findings will help in designing resource conserving integrated solar energy and food/fodder systems, along with increasing soil quality and carbon sequestration.

At least 37 studies demonstrate some degree of short-term influence of CO2 on human cognition, broadly considered, at CO2 concentrations frequently observed in buildings (>1000ppm). Ambient concentrations of CO2 in some cities can exceed global average values by several hundred ppm due to multiple large sources and idiosyncrasies of atmospheric transport, diffusion, and dispersion. In those few cities with extensive CO2 monitoring systems, local variations exceed 50ppm along transport corridors or close to point sources such as power plants. Scenarios of future CO2 concentrations project global average values up to 950ppm by 2100. Combining these various influences suggests that some locations in cities may regularly experience CO2 concentrations of 1300ppm by 2100. In occupied enclosed spaces such as schoolrooms, CO2 concentrations can rise several thousand ppm above ambient values. ASHRAE sets indoor air standards relative to ambient levels, not as absolute levels. Highly energy efficient buildings reduce air leakage and may have lower ventilation air exchange rates to reduce energy loss. LEED standards do not address effects of certification standards on CO2 concentrations. Cabs of vehicles and other enclosed spaces in the transport sector also can be several thousand ppm above ambient levels. Long-term exposure to elevated CO2 at these levels has not been studied in humans but limited studies of mouse models demonstrate respiratory impairments after three-month exposure to 890ppm CO2 for newborn mice. We are unaware of any studies of health impacts of CO2 on mice or humans that use pre-industrial concentrations of CO2 as baseline values. Thus, experimental methodologies must be reformed and standardized before we can fully appreciate how living in an elevated CO2 world is already affecting human health. Connecting the dots between these various influences suggests that exposure (particularly long-term exposure) to CO2 concentrations that affect cognition may vary significantly depending upon distance from active sources (power plants, roadways) and occupation (e.g. truck driver), such effects will grow to serious levels, may already exert a toll on human cognitive outcomes, and could implicate environmental justice concerns.

Measurements of reflected solar radiation by imaging spectrometers allow to quantify water in different states (solid, liquid, gas) thanks to the discriminative absorption lines in the solar spectrum. We developed a retrieval method to quantify the amount of water in each of the three states from spaceborne imaging spectroscopy data, such as those from the German EnMAP mission. Our retrieval couples atmospheric radiative transfer simulations from the MODTRAN5 radiative transfer code to a surface reflectance model based on the Beer-Lambert law. The model is inverted on a per-pixel basis using a maximum likelihood estimation formalism. Based on a unique coupling of the canopy reflectance model HySimCaR and the EnMAP end-to-end simulation tool EeteS, we performed a sensitivity analysis by comparing the retrieved values with the simulation input leading to an R2 of 0.991 for water vapor and 0.965 for liquid water. Furthermore, we applied our algorithm to airborne AVIRIS-C data to demonstrate the ability to map snow/ice extents as well as to a CHRIS-PROBA dataset for which concurrent field measurements of canopy water content were available. The comparison between our retrievals and the ground measurements showed an overall R2 of 0.80 for multiple crop types and a remarkable clustering in the regression analysis indicating a dependency of the retrieved water content from the physical structure of the vegetation. In addition, our algorithm is able to produce smoother and more physically-plausible water vapor maps than the ones from the band ratio approaches used for multispectral data, since biases due to background reflectance are reduced. The demonstrated potential of imaging spectroscopy to provide accurate quantitative measures of water from space will be further exploited using upcoming spaceborne imaging spectroscopy missions like PRISMA or EnMAP.

Soil-bentonite slurry cutoff walls are used to prevent the movement of contaminated groundwater. Defects which reduce the overall efficacy of the wall may become present during construction or the aging of the wall. In this research, we use ground penetrating radar (GPR) and electrical resistivity (ER) as non-invasive and minimally invasive geophysical techniques, respectively, in an attempt to characterize the shallow portions of a soil bentonite slurry cutoff wall and detect defects within the wall. Sandbags and a limestone block were placed within the northernmost section of an experimental soil-bentonite slurry cutoff wall in Montandon, PA to represent defects that could lead to high hydraulic conductivity within the wall. GPR surveys were collected along the wall and surrounding area to investigate the unsaturated zone and determine if GPR could reliably observe the elevation difference of the water table across the wall. GPR estimates of the soil water content (SWC) differences were also evaluated as a possible way to determine the location of more permeable zones (e.g., fractures) within the unsaturated zone of the cutoff wall. The apparent resistivity of both defect and non-defect locations in the wall were collected using long point-source electrodes in Wenner and dipole-dipole arrays at various depths, as well as non-traditional geometries. The point electrodes inserted into the wall at a 1 meter a-spacing indicate a higher apparent resistivity associated at defect locations relative to at the non-defect locations. Additionally, the mise-a-la-masse method with in-wall electrodes and surface electrodes was used to measure the change in voltage along the wall and the surrounding areas in both defect and non-defect locations. We will discuss how data from the individual or concurrent use of these two methods may help locate potential defects within soil-bentonite slurry cutoff walls. The ability to reliably detect defects within slurry walls using non-invasive or minimally invasive techniques will help retain the integrity of the wall, allow for quality assurance for construction companies, and monitor walls throughout their life at a low cost.

Based on four reanalyses or gridded data sets (ERA5, 20CR, APHRODITE and REGEN), we provide an overview of 23 Historical and 7 HighResMIP experiments’ performance from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (for short, 6-Hist, HighRes) in simulating seven extreme precipitation indices over Asia defned by the Expert Team on Climate Change Detection and Indices (ETCCDI). We compare them with 28 Historical experiments in CMIP5 (5-Hist). CMIP5 and CMIP6 models are generally able to reproduce extreme precipitation’s spatial distribution and their trend patterns in comparison to the benchmark data set (APHRODITE). The overall performance of individual model is summarized by a “portrait” diagram based on four statistics for each index. We divide all 58 models into three groups (A, the top 20%; B, the median 60% and C group, the last 20%) according to MR rankings (the comprehensive ranking measure). Based on the “portrait” diagram and MR rankings, models that perform relatively well for all seven extreme precipitation indices include HadCM3, HadGEM2-AO, HadGEM2-CC and HadGEM2-ES from 5-Hist, EC-Earth3, EC-Earth3-Veg from 6-Hist and ECMWF-IFSHR, ECMWF-IFS-LR, ECMWF-IFS-MR from HighRes. The simulated performance of CMIP6 is polarized, for the top four and the last fve ranking models are both from CMIP6. Compared with the counterpart models in CMIP6 and CMIP5, the improvement of PCC (pattern correlation coefcient) is more obvious. Furthermore, the dry biases of CMIP6 (both 6-Hist and HighRes) in Southern China and India and the wet biases of CMIP6 in Tibet are reduced compared to CMIP5. This may beneft from the improvement that CMIP6 models can capture the characteristics of meridional moisture fux convergence, and improve the overestimation or underestimation of meridional and zonal specifc humidity eddies compared to CMIP5.

Exotic plant invasions alter ecosystem properties and threaten ecosystem functions globally. Interannual climate variability (ICV) influences both plant community composition (PCC) and soil properties, and interactions between ICV and PCC may influence nitrogen (N) and carbon (C) pools. We asked how ICV and non-native annual grass invasion covary to influence soil and plant N and C in a semiarid shrubland undergoing widespread ecosystem transformation due to invasions and altered fire regimes. We sampled four progressive stages of annual grass invasion at 20 sites across a large (25,000 km2) landscape for plant community composition, plant tissue N and C, and soil total N and C in 2013 and 2016, which followed two years of dry and two years of wet conditions, respectively. Multivariate analyses and ANOVAs showed that in invasion stages where native shrub and perennial grass and forb communities were replaced by annual grass-dominated communities, the ecosystem lost more soil N and C in wet years. Path analysis showed that high water availability led to higher herbaceous cover in all invasion stages. In stages with native shrubs and perennial grasses, higher perennial grass cover was associated with increased soil C and N, while in annual-dominated stages, higher annual grass cover was associated with losses of soil C and N. Also, soil total C and C:N ratios were more homogenous in annual-dominated invasion stages as indicated by within-site standard deviations. Loss of native shrubs and perennial grasses and forbs coupled with annual grass invasion may lead to long-term declines in soil N and C and hamper restoration efforts. Restoration strategies that use innovative techniques and novel species to address increasing temperatures and ICV and emphasize maintaining plant community structure – shrubs, grasses, and forbs – will allow sagebrush ecosystems to maintain C sequestration, soil fertility and soil heterogeneity.

Equatorial Guinea’s Bioko Island is located in the Atlantic Ocean off the west coast of Cameroon. Bioko is a volcanic island and the first off-shore expression of the Cameroon Volcanic Line. It is home to three shield volcanoes: Pico de Basile, Pico Biao, and San Carlos. Eruptive histories are not known for Pico Biao or San Carlos. Pico de Basile erupted within the past 100 years, and steam vents were observed as recently as 2012. Malabo, the capital city of Equatorial Guinea, sits in the shadow of Pico de Basile. There is no permanent seismic monitoring; the closest seismic stations are in Cameroon and have not reported data since 2015. In November 2017 Drexel University researchers, supported by the Bioko Biodiversity Protection Program (BBPP) and the Universidad Nacional de Guinea Ecuatorial (UNGE), installed 4 broadband seismometers. In February 2018, the data were retrieved, and stations serviced. Preliminary earthquake detection and location was completed using an automated STA/LTA algorithm. S wave arrivals were added manually. The initial locations use the global IASP91 model and events were relocated using a local model. The events detected cluster into two areas: those near Bioko Island and those near Cameroon. Between 12-Dec-2017 and 17-Feb-2018, 77 events were recorded. Local magnitudes range between 0.16 and 2.61. Of these events, 49 are located near Cameroon and 28 are near Bioko. Most of the depths are crustal, mostly upper to mid crust. Our preliminary results show there is seismicity associated with Bioko Island as well as Cameroon. The locations match well with events recorded by a local network installed in Cameroon in 2007. The four stations were serviced again in November 2018. One station failed due to water infiltration and one was vandalized within a week of the previous service. One station was still operational at service with only a few days down and the last station was operational until the height of the rainy season when power failed.

Properly designed, calibrated, and operated weighing lysimeters are recognized as accurate tools for measuring changes of soil water storage (ΔS) in the soil profile contained in the lysimeter. The neutron probe, NP, also is recognized as an accurate tool for determining soil profile water storage, S, and ΔS over the depth range of the neutron probe readings, again when properly calibrated and operated. Both methods have been used to calculate evapotranspiration (ET) using the soil water balance equation (ET = ΔS + I + P + R + F) applied to a control volume, where I is irrigation, P is precipitation, R is the sum of runon and runoff, and F is flux into or out of the control volume. However, weighing lysimeters are expensive to install and operate and are not portable, and the neutron probe faces regulatory pressures and cannot be used unattended, limiting its use. Past attempts to use electromagnetic soil water sensors based on capacitance principles to accurately determine profile water content have not met with success. Recent advances in soil water senor technology have led to accurate, low power, and relatively inexpensive electronic soil water sensors based on time domain reflectometry (TDR) theory, which can be installed in situ. Assuming that accurate values or controls were available for I, P, R, and F, we concentrated on evaluating S and ΔS as determined by lysimeter, NP, and TDR sensors. Over a cropping season at Bushland, Texas, USA we compared S and ΔS in a 2.3-m deep profile of silty clay loam soil as assessed by a large, precision weighing lysimeter, the NP in two access tubes in the lysimeter, and three profiles of TDR soil water sensors installed in the lysimeter, each profile consisting of 15 sensors. Weighing lysimeter mass was recorded every 5 minutes and TDR sensors were read every 15 minutes, both automatically using dataloggers, while the neutron probe readings were done manually at approximately one-week intervals. Comparing TDR sensors with neutron probe, coefficients of determination for profile water content and for ΔS were 0.97 and 0.91, respectively, when one-week intervals were considered. Coefficients of determination for comparisons of TDR sensors to lysimeter were 0.95 for S and 0.92 for ΔS, while values for comparison of neutron probe to lysimeter were 0.91 for water storage and 0.83 for ΔS, again for one-week intervals. Poorer performance of the NP was likely due to the fact that it could not be read to depths greater than 1.90 m, which limited the profile sensed to the top 2.0 m of soil.

California’s Central Valley is responsible for $17 billion of annual agricultural output, producing 1/4 of the nation’s food. However, land in the Central Valley is sinking at a rapid rate (as much as 20 cm per year) due to continued groundwater pumping. Land subsidence has a significant impact on infrastructure resilience and groundwater sustainability. It is important to understand subsidence and groundwater depletion in a consistent framework using improved models capable of simulating in-situ well observations and observed subsidence. Currently, groundwater well data is sparse and sampled irregularly, compromising our understanding of groundwater changes. Moreover, groundwater pumping data is a major missing piece of the puzzle. Limited data availability and spatial/temporal uncertainty in the available data have hampered understanding the complex dynamics of groundwater and subsidence. To address this limitation, we first integrated multimodal data including InSAR, groundwater, precipitation, and soil composition by interpolating data with the same spatial and temporal resolutions. We then identified regions with different temporal dynamics of land displacement, groundwater depth, and precipitation. Some areas (e.g., Helm) with coarser grain soil compositions exhibited potentially reversible land transformations (elastic land compaction). Finally, we fed the integrated data into the deep neural network of a gated recurrent unit-based sequence-to-sequence generation model. We found that the combination of InSAR, groundwater depth, and precipitation data had predictive power for soil composition using deep neural networks (correlation coefficient R=0.83, normalized Nash-Sutcliffe model efficiency NNSE=0.84). A random forest model was tested as baseline (R=0.65, NNSE=0.69). We also achieved significant accuracy with only 40% of the training data (NNSE=0.8), suggesting that the model can be generalized to other regions for indirect estimation of soil composition. Our results indicate that soil composition can be estimated using InSAR, groundwater depth and precipitation data. In-situ measurements of soil composition can be expensive and time consuming and may be impractical in some areas. The generalizability of the model sheds light on high spatial resolution soil composition estimation utilizing existing measurements.

Climate projections suggest that snowfall-dominated areas will decline substantially in the coming decades. Such climate impacts are already being observed in Colorado where the dominant source of annual peak discharge is shifting from snowmelt to rainfall, altering the paths by which water flows through a landscape and is ultimately delivered to streams. Observed climate driven shifts in stream flow dynamics and permanence highlight the increasing importance of understanding the hydrologic connectivity of uplands to streams in lower elevation, montane ecoregions. We collected geochemical and hydrometric data over three years to quantify hydrologic connectivity of uplands to a montane headwater stream at the Manitou Experimental Forest in central Colorado. We use a combined approach of concentration-discharge relationships and end-member mixing analysis, paired with high resolution measurements of soil moisture, precipitation, and groundwater levels to characterize source areas to the stream in 3-dimensions: longitudinal, lateral, and vertical. Samples were collected and measurements were recorded along the stream profile (longitudinal), from groundwater wells and soil lysimeters installed with increasing distance from the stream (lateral), and from shallow versus deep groundwater wells and soil moisture measured at different depths (vertical). Results indicate distinct differences in stream chemistry along the longitudinal stream profile, with highest concentrations at the most upstream sites and lowest concentrations at the most downstream sites. Stream solute concentrations increased with decreasing stream discharge values from spring to late summer. However, the stream remained chemostatic during all recorded rain storms, suggesting a difference in flow pathways during individual summer storm pulses. End member mixing analysis suggests spatiotemporal differences in shallow and deep vertical source areas, and between riparian and upland sources to the stream. These results provide a promising step towards quantifying the expansion and contraction of runoff source areas to a montane headwater stream.

The mid-Piacenzian (Pliocene) climate represents the most geologically recent interval of long-term average warmth, relative to the last million years, sharing similarities with the climate projected for the end of the 21st century. Therefore, this period has been studied by both geoscientists and climate modelers for many years. A better understanding of regional late Pliocene conditions can provide insight into potential climate change impacts, enabling more informed policy decisions for mitigation and adaptation. Previous work comparing climate model results with geologic data highlighted key regional and dynamic situations where there was discord between mean annual SST estimates generated by climate model simulations and paleoenvironmental reconstructions. One key area identified was the mid- to high-latitude North Atlantic. Here, we present a comparison between alkenone-based North Atlantic PRISM4 (Pliocene Research, Interpretation and Synoptic Mapping Project, Phase 4) mean annual SST estimates and an ensemble of ten climate model simulations produced as part of PlioMIP2 (Pliocene Model Intercomparison Project, Phase 2). Our latest research demonstrates that improved experimental design incorporating temporal refinement of the paleoenvironmental reconstruction, and inclusion of new PRISM4 boundary condition data sets, significantly reduces discord between data and models.

Large-scale injection of carbon dioxide (CO2) into the earth started in the 1980’s for enhanced oil recovery (EOR). Geological sequestration (injection and storage) of industrial CO2 to reduce greenhouse gas emissions began in the 1990’s using saline aquifers and depleted hydrocarbon reservoirs. Today the two processes are being co-optimized as Carbon Capture, Use and Storage (CCUS). As a result, the time-lapse seismology community has gathered about 20-30 years of experience monitoring CO2 injection projects of various types and sizes, primarily using controlled active seismic sources and Large-N receiver arrays. To help achieve IPCC projections of 2C global temperature change versus CO2 emissions, society would need to scale up current CO2 injection rates by a factor of 250x from 40 Megatons to 10 Gigatons, per year. CCUS regulations for Monitoring and Verification requirements are in various stages of development around the world, including the EU CCS Directive, US EPA, and international ISO standards. A typical commercial-scale CCUS project injects > 1 Mt CO2/yr for > 20 years. After CO2 injection ceases, the project operator must further monitor the post-injection CO2 plume behavior to establish regulatory compliance for 20-50 years in order to “handover” the project to the regulator. After handover, the regulator is then responsible for monitoring the post-injection plume stability for another 20-30 years (in the US there is no handover, the operator maintains all project and monitoring responsibilities). These CCUS requirements imply that we will need to monitor CO2 projects, both during and after injection, for 50-100 years or more. We simply do not have experience in the time-lapse seismology community with such long-term monitoring periods, in terms of data acquisition, processing, imaging, and minimizing environmental footprint and costs. It would certainly not be practical nor affordable to conduct a full-scale 4D seismic survey every year, for 100 years. These long-term monitoring requirements thus present both challenges and new research opportunities. I will present some experimental results I have obtained over the past decade to help develop long-term, near real-time, continuous monitoring of CO2 injection projects using ambient seismic noise (ASN).

Over the past 60+ years, an enormous amount of exploration geophysics survey data has been collected around the globe, the majority of which is high-quality 2D and 3D seismic data acquired by the petroleum energy industry. Much of this ‘legacy’ data still has significant commercial value today and in the future, for hydrocarbon exploration, gas storage (methane, helium, hydrogen…), groundwater, minerals, CO2 sequestration, geothermal, and other purposes. However, there is likely a subset of this exploration data that is of little further commercial value, but may be of immense value to academic, government and industry researchers, for example. This may include very long 2D seismic lines recorded in frontier exploration areas which turned out to be non-prospective; for example along convergent margin subduction zones or major continental tectonic fault zones, which are absent of major sedimentary basins. Shared access to this subset of legacy data would provide an extremely useful opportunity and resource for academic researchers and others, much as shared earthquake data via the IRIS network has revolutionized our understanding of earthquakes, faults and tectonics. There are several challenges that would need to be overcome before such legacy data can be shared widely among the broader geophysical community. Much of this legacy data is archived on old magnetic tape media that is now physically degrading, making it difficult to recover the data. The data would need to be collected, recovered, QC’ed, archived with associated metadata, and stored on modern digital data storage systems like the IRIS DMC, newer cloud-based systems like OSDU, or other options. The data archival, maintenance and support for a shared data distribution system would require a sustainable business model and funding from all of the data stakeholders, including government agencies like NSF, academic universities, and industry users. Data use agreements would need to be structured to ensure that data is used for non-commercial purposes as appropriate, data users respect various legal terms and conditions, and data is not shared freely among individual recipients without the approval of the host data center.