Xavier University of Louisiana, in collaboration with the Institute of Earth Science Research and Education (IESRE) run the ECoSTEM project. This project aims to improve the quality of undergraduate STEM education by introducing a computational, community-oriented component into STEM programs at Xavier University. Specifically, undergraduate students develop microcontroller-based systems for collecting airborne particulate data. They work with public school teachers (and their students) and Louisiana Department of Environmental Quality (LDEQ) officers to deploy the sensors at locations around New Orleans and to analyze the resulting data. Airborne particulates are often the pollutant of greatest concern in minority and other underserved communities. This project provide undergraduates at Xavier and the local teachers (and their students) with learning experiences that are relevant to them and their communities. ECoSTEM uses a tiered mentoring system in which the project team members mentor undergraduates, who in turn work with public school teachers and mentor their students. Research has shown positive effects from student involvement in such real-world, personally relevant STEM projects, including increases in student interest in STEM-related careers after graduation. As a result, the project has the potential to broaden student interest in STEM and increase pursuit of STEM-related majors.

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the ‘signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. Although this procedure originated in LiDAR-based estimation of mean tree and canopy height, ground noise filtering has remained prevalent in LiDAR pre-processing, even as modelers have shifted focus to forest aboveground biomass (AGB) and related characteristics for which ground returns may actually contain useful information about stand density and openness. In particular, ground returns may be helpful for making accurate biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. We applied several ground noise filtering thresholds while mapping two regions within New York State, one a forest-dominated area and the other a mixed-use landscape. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling. By fitting random forest models to each of these predictor sets, we found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds. The relative inferiority of AGB models based on filtered LiDAR returns was much greater for the mixed land-cover study area than for the contiguously forested study area. Our results suggest that ground filtering should be avoided when mapping biomass, particularly when mapping heterogeneous and highly patchy landscapes, as ground returns are more likely to represent useful ‘signal’ than extraneous ‘noise’ in these cases.

Lahars, or volcanic mudflows, are one of the most devastating natural, volcanic hazards. We present an analysis of several rain-triggered lahar events at Volcán Fuego in Guatemala with an infrasound array and time-lapse camera network deployed along the Las Lajas river channel. While infrasound detects the passage of low frequency sound waves associated with these mudflows, the time-lapse cameras can be used to verify lahar passages and channel responses as these flows move downstream. Twenty-three infrasound microphones and five time-lapse cameras were deployed during the 2021 rainy season (May-October) in the Las Lajas drainage on the southeasterly side of Volcán de Fuego. With the data collected over this field campaign, we hope to quantify flow parameters such as volumes, velocities, and the frequency of these rain-triggered mudflows, as well as characterize flow behaviors in our infrasound signals by correlating data to the time-lapse imagery. This study allows us to identify the occurrence of several lahars, quantify important characteristics, and use a multi-faceted approach to verify and delineate important flow behaviors related to these volcanic hazards.

Slow slip events have previously been observed along the Hikurangi subduction zone beneath the North Island of New Zealand. These slow slip episodes occur both on the shallow plate interface (< 15km depth) and at the deeper end of the seismogenic zone (> 30km depth). We present the first catalog of low-frequency earthquakes (LFEs) in the Hikurangi subduction zone, located beneath the Kaimanawa Range on the central Hikurangi margin, downdip of a region that regularly (every 4-5 years) hosts M7 slow slip events. To systematically detect LFEs using continuous seismic data recorded by GeoNet, we developed a matched-filter technique with template waveforms derived from previous observations of tectonic tremor. The workflow presented in this work is composed of two iterations of a matched-filter search. In each iteration, the detections were gathered into families and their common waveforms postprocessed with machine-learning methods to extract high-quality waveforms, allowing us to pick seismic phase arrivals with which to locate the LFEs. We found that LFEs occur in episodes of intense activity during the neighboring updip M7 slow slip events. We also observe a recurrence time of 2 years between other large bursts of LFEs, suggestive of a shorter cycle of slow slip. We hypothesize that these and other frequent LFE episodes highlight smaller slow transients that have not yet been geodetically observed.

The North Polar Layered Deposits (NPLD) of Mars are ice-rich sedimentary layers that formed under the influence of Mars’ modern climate and thus record the recent climatic history of Mars, analogous to terrestrial ice sheets. The 2013-2023 Planetary Science Decadal Survey recommends a lander mission to sample the NPLD for climatic records; however, linking the geologic record to the climatic history will require quantitative dating of the NPLD. In this study we use orbital reflectance spectroscopy to show for the first time that dateable mafic lithics are present throughout the NPLD. We find significant glass as well as diverse crystalline minerals, which suggests that surface processes like impacts and volcanism were active during the late Amazonian and transported sediments from across the planet to the north pole. In situ investigation of the NPLD will thus provide critical quantitative constraints on both the recent geologic and climatic histories of Mars.

Recent inversions of InSAR ground surface deformation in the Delaware Basin have revealed aseismic slip on conjugate, semi-optimally oriented normal faults. Slip, occurring over 3-5 years, extends approximately 1 km down-dip, over 10 km along strike, and reaches 23 cm. Several disposal wells are located nearby, suggesting that pressure changes from injection might have induced slip. We develop and calibrate 2D and pseudo-3D coupled pore pressure diffusion and rate-state models with velocity-strengthening friction. Pore pressure diffusion is limited to a high-permeability fault damage zone, and the net influx of fluid is adjusted to match the observed slip. A pressure increase of 1-2 MPa is required to initiate slip. Most aseismic slip occurs at approximately constant friction and ~5 MPa additional pressure increase is needed to reach ~20 cm slip. Our work suggests that models of the type developed here can be used to operationally manage injection-induced aseismic slip.

The Southwest Watershed Research Center (SWRC) of the United States Department of Agriculture-Agricultural Research Service has been conducting arid and semiarid (dryland) watershed research since 1953. This included establishment and continuous operation of the Walnut Gulch Experimental Watershed (WGEW) in southeast Arizona. The 149 km2 ephemeral watershed is one of the most densely instrumented dryland research catchments in the world with a drainage area greater than 10 km2. This instrumentation captures many aspects of the hydrological cycle including how precipitation is partitioned into soil moisture, runoff and evapotranspiration and its subsequent effects on sediment transport, vegetation productivity, carbon sequestration, and groundwater recharge. The long-term, high-resolution record of observations on the WGEW enables understanding of the mean and variability of the hydrological processes, not well characterized with shorter term records, that fail to capture the large variability common to dryland regions. This presentation will highlight trends in temperature, precipitation, and runoff over the WGEW observation period. Additional research findings made by the SWRC and collaborators on erosion; plant productivity and carbon sequestration; the facilitation of soil water redistribution by plant roots; and groundwater recharge will also be presented.

The International Monitoring System (IMS) infrasound network has been established to detect nuclear explosions and other signals of interest embedded in the station specific ambient noise. The ambient noise can be separated into coherent infrasound (e.g. real infrasonic signals) and incoherent noise (such as that caused by wind turbulence). Previous work statistically and systematically characterizing coherent infrasound recorded by the IMS. This paper expands on this analysis of the coherent ambient infrasound by including updated IMS datasets up to the end of 2020, for all 53 of the currently certified IMS infrasound stations using an updated configuration of the Progressive Multi-Channel Correlation (PMCC) method. This paper presents monthly station dependent reference curves for the back azimuth, apparent speed, and root-mean squared amplitude, which provide a means to determine the deviation from nominal monthly behaviour. In addition, a daily Ambient Noise Stationarity (ANS) factor based on deviations from the reference curves is determined for a quick reference to the data quality compared to the nominal situations. Newly presented histograms provide a higher resolution spectrum, including the observations of the microbarom peak, as well as additional peaks reflecting station dependent environmental noise. The aim of these reference curves is to identify periods of sub-optimal operation (e.g. non-operational sensor) or instances of strong abnormal signals of interest.

Detailed analysis of volcanic thermal and gas emissions over time can constrain subsurface processes throughout the pre- and post-eruption phases. Time series analyses are commonly applied to high temporal datasets like the Moderate Resolution Imaging Spectroradiometer (MODIS); however, this is the first study using the entire Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) twenty-plus year archive. The ASTER archive presents a unique opportunity to quantify volcanic precursors and processes. The spatial, spectral, and radiometric resolution of its thermal infrared (TIR) subsystem allows detection of very low-magnitude surface temperature anomalies and passively emitted small gas plumes. We developed a new statistical algorithm to automatically detect these subtle anomalies and applied it to five recently active volcanoes with well-documented eruptions: Taal (Philippians), Popocatépetl (Mexico), Mt. Etna (Italy), Fuego (Guatemala), and Kluichevskoi (Russia). More than 3,300 ASTER level-1 terrain corrected (L1T), registered, radiance-at-sensor images were downloaded from the NASA EARTHDATA website. These were screened for significant summit cloud coverage, which removed approximately 25% of scenes. The remaining were converted to brightness temperature and a median background temperature per scene was determined from an annulus around the active crater to produce the temperature above background. The algorithm creates a rejection criterion value defined by the median absolute deviation to identify the thermal anomalies. The size and intensity of these anomalies as well as the detection, composition, emission rate of small plumes are retrieved one year prior to the known eruptions for each volcano to identify all precursory signals. The results of this study have the dual purpose of constraining volcanological processes that lead to eruptions as well as providing training data for machine learning modeling. Machine learning is an effective and well-established technique that provides rapid classification of volcanic activity such as thermal anomalies that exceed a certain size and/or intensity. The comparison of these two approaches is documented in a companion abstract in this session.

As it is known, Space Weather (SWx) phenomena can have dramatic impact on satellite navigation and HF radio communication systems, being also responsible for increases on radiation levels at flight altitudes. For this reason, in recent years the International Civil Aviation Organization (ICAO) has been showing great interest in operational SWx services for aviation purposes in these three domains. Four global SWx centers have been then appointed since November 2019 by ICAO to provide real-time SWx advisories for aviation users. In particular, HF COM conditions are assessed by monitoring the F2-layer critical frequency foF2 or the MUF(3000) ionospheric characteristic (MUF = Maximum Usable Frequency), the latter representing the highest HF radio frequency that can be used for communications over a standard distance of 3000 km via F2-layer ionospheric reflection. As one of the designed SWx centers, several key operational 24/7 products for HF COM conditions assessment have been developed within PECASUS (Partnership for Excellence in Civil Aviation Space weather User Services). Nowcasting and forecasting (1-24hr) maps over Europe of MUF(3000) and its ratio with respect to a background level are then developed by INGV, as a PECASUS partner. The MUF(3000) nowcasting uses all the available real-time ionosonde measurements in different locations in order to upgrade IRI-CCIR-based background maps, and Ordinary Kriging method for spatial interpolation. The MUF(3000) modeling performance was assessed comparing predicted values to measured ones over two test stations during strong geomagnetic storm periods, obtaining an overall RMSE < 2 MHz at both stations. The MUF(3000) predicted 1-24 hours ahead depends on foF2 and M(3000) ionospheric parameters: EUROMAP forecasting model and IRI model are used for the former and the latter, respectively. The method has been applied to Europe where there are ionospheric stations with long (for some solar cycles) historical data and current real-time foF2 observations. A mapping procedure applied to the European stations provides MUF(3000) short-term prediction over the whole area. The application of these methods to storm events occurred after November 2019 is here presented, in order to study the ionospheric conditions they provide when HF COM advisories are expected to be issued.

Satellite observations are widely used to investigate, monitor, and forecast volcanic activity. Spaceborne thermal infrared (TIR) measurements of high-temperature volcanic features improve our understanding of the underlying processes and our ability to identify reactivation of activity, forecast eruptions, and assess hazards. In particular, thermal changes, indicative of subtle pre-eruptive volcanic thermal activity, have been observed. Over the last several decades, different approaches have been explored to detect and estimate the temperature above background of these thermal anomalies. The most common approach relies on a spatial statistical analysis based on a scene by scene choice of the background temperature region. Satisfactory results have also been shown by using time series anomaly detection algorithms based on statistical profiling approach. Artificial intelligence (AI) is growing sharply in different remote sensing fields because of its capability to automatically learn patterns from the data. Here, we develop an AI approach to automatically detect volcanic thermal features by using spatiotemporal information from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), LANDSAT-8 Thermal Infrared Sensor (TIRS) and MODerate-resolution Imaging Spectroradiometer (MODIS) data acquired over several decades. Our goal is to exploit AI techniques using a combination of high temporal and high spatial resolution satellite data to improve thermal volcanic monitoring and detect very low-level anomalies caused by pre-eruptive activity. We use both low-spatial, high-temporal resolution MODIS data to detect hotter thermal features at short time scales; as well as high-spatial low-temporal resolution ASTER TIR and TIRS data to detect more subtle thermal changes otherwise missed by MODIS. Our analysis is conducted in Google Earth Engine (GEE), a cloud computing platform with fast access and processing of satellite data. The comparison with a new statistical algorithm is documented in a companion abstract in this session.

Modern research methods enable unfolding the structure of the water column with higher resolution than ever revealing the importance of submesoscale. Submesoscale processes have intermediate space and time scales of <5 km and a few days in the Baltic Sea. A glider mission was conducted in the Gulf of Finland in May 2018. During the formation of the seasonal thermocline characterized by the increase of the maximum temperature gradient from 0.5 to 3.1 °C dbar-1, the structure of the water column changed constantly. A horizontal buoyancy gradient was captured and the vertical flows at a lateral scale of a km were apparent in isotherms intersecting with isopycnals. We suggest that frontal submesoscale processes manifested as smaller-scale tracer patterns on the mesoscale background demonstrate an ageostrophic secondary circulation. While the front appeared because of the persisting NE–E winds, the decrease in wind stress promoted the non-forced dynamics that, in turn, appeared to enhance the development of the thermocline. The spatial spectra of isopycnal tracer variance revealed the slopes a bit gentler than -2 between the lateral scales 2–10 km in the upper part of the water column. The discrepancy from the deeper layers where the slope closed to -1 suggests the contribution of the ageostrophic frontal effects in the energy cascade.

Discrete aurora at Mars, characterized by their small spatial scale and tendency to form near strong crustal magnetic fields, are emissions produced by particle precipitation into the Martian upper atmosphere. Since 2014, Mars Atmosphere and Volatile EvolutioN’s (MAVEN’s) Imaging Ultraviolet Spectrograph (IUVS) has obtained a large collection of nightside UV discrete aurora observations. Initial analysis of these observations has shown that, near the strong crustal field region (SCFR) in the southern hemisphere, the aurora detection frequency is highly sensitive to the interplanetary magnetic field (IMF) clock angle. However, the role of other solar wind properties in controlling the aurora detection frequency has not yet been determined. In this work, we use IUVS discrete aurora observations, and MAVEN solar wind observations, to determine how the discrete aurora detection frequency varies with solar wind dynamic pressure, IMF strength, and IMF cone angle. We find that, outside of the SCFR, the detection frequency is relatively insensitive to the IMF orientation, but significantly increases with solar wind dynamic pressure and moderately increases with IMF strength. Interestingly, the auroral emission brightness outside the SCFR is insensitive to the dynamic pressure. Inside the SCFR, the detection frequency is moderately dependent on the dynamic pressure and is much more sensitive to the IMF clock and cone angles. In the SCFR, aurora are unlikely to occur when the IMF points near the radial or anti-radial directions. Together, these results provide the first comprehensive characterization of how upstream solar wind conditions affect the formation of discrete aurora at Mars.

Seismic wave propagation in porous rocks that are saturated with a liquid exhibits significant dispersion and attenuation due to fluid flow at the pore scale, so-called squirt flow. This phenomenon takes place in compliant flat pores such as microcracks and grain contacts that are connected to stiffer isometric pores. Accurate quantitative description is crucial for inverting rock and fluid properties from seismic attributes such as attenuation. Up to now, many analytical models for squirt flow were proposed based on simplified geometries of the pore space. These models were either not compared with a numerical solution or showed poor accuracy. We present a new analytical model for squirt flow which is validated against a three-dimensional numerical solution for a simple pore geometry that has been classically used to explain squirt flow; that is why we refer to it as classical geometry. The pore space is represented by a flat cylindrical (penny-shaped) pore whose curved edge is fully connected to a toroidal (stiff) pore. Compared with correct numerical solutions, our analytical model provides very accurate predictions for the attenuation and dispersion across the whole frequency range. This includes correct low-and high-frequency limits of the stiffness modulus, the characteristic frequency, and the shape of the dispersion and attenuation curves. In a companion paper (Part 2), we extend our analytical model to more complex pore geometries. We provide as supplementary material Matlab and symbolic Maple routines to reproduce our main results.

Antarctica has been proposed as the dominant source of the meltwater that entered the oceans during Meltwater Pulse 1b (MWP1b) that occurred approximately 11,500 years ago. The deglaciation of heavily glaciated fjords off the coast of Antarctica at approximately this time has provided support for this hypothesis. Further support for this scenario was provided by the fact that the highly non-monotonic relative sea level histories recorded at sites on the coast of Scotland, which had been heavily glaciated at last glacial maximum, could be explained by the inter-hemispheric sea level teleconnection associated with a significant deglaciation of Antarctic ice sheets at this time. That the magnitude of grounded ice loss from Antarctica at MWP1b time was adequate to provide the necessary RSL rise along the coast of Scotland has not been demonstrated. Furthermore, there exist implicit suggestions to the effect that a significant contribution to MWP1b must have also been delivered to the oceans by the abrupt northern hemisphere warming that occurred at the end of the Younger Dryas (YD) cold reversal, which also occurred approximately 11,500 years ago. This warming event occurred due to the rapid intensification of the Atlantic Meridional Overturning Circulation (AMOC) when it recovered after the YD. We present a fingerprinting analysis of the contribution of all major ice sheets to MWP1b using the ICE7G_NA (VM7) model of ice loading history and find that the best agreement between calculated sea level curves and observations is obtained with a minimal Antarctic contribution.

Inland waters are an important component of the global carbon budget, emitting CO2 to the atmosphere. However, our ability to predict carbon fluxes from stream systems remains uncertain as small scales of pCO2 variability within streams (100-102 m), which makes efforts relying on monitoring data uncertain. We incorporate CO2 input and output fluxes into a stream network advection-reaction model, representing the first process-based representation of stream CO2 dynamics at watershed scales. This model includes groundwater (GW) CO2 inputs, water column (WC), and benthic hyporheic zone (BHZ) respiration, downstream advection, and atmospheric exchange. We evaluate this model against existing statistical methods including upscaling and multiple linear regressions through comparisons to high-resolution stream pCO2 data collected across the East River Watershed in the Colorado Rocky Mountains (USA). The stream network model accurately captures topography-driven pCO2 variability and significantly outperforms multiple linear regressions for predicting pCO2. Further, the model provides estimates of CO2 contributions from internal versus external sources suggesting that streams transition from GW- to BHZ-dominated sources between 3rd and 4th Strahler orders, with GW, BHZ, and WC accounting for 49.3, 50.6, and 0.1% of CO2 fluxes from the watershed, respectively. Lastly, stream network model CO2 fluxes are 4-12x times smaller than upscaling technique predictions, largely due to inverse correlations between stream pCO2 and atmosphere exchange velocities. Taken together, this stream network model improves our ability to predict stream CO2 dynamics and efflux. Furthermore, future applications to regional and global scales may result in a significant downward revision of global flux estimates.

We perform Observation System Simulation Experiments (OSSEs) with the GEOS-Chem adjoint model to test how well methane emissions over North America can be resolved using measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and similar high-resolution satellite sensors. We focus analysis on the impacts of i) spatial errors in the prior emissions, and ii) model transport errors. Along with a standard scale-factor (SF) optimization we conduct a set of inversions using alternative formalisms that aim to overcome limitations in the SF-based approach that arise for missing sources. We show that 4D-Var analysis of the TROPOMI data can improve monthly emission estimates at 25 km even with a spatially biased prior or model transport errors (42–93% domain-wide bias reduction; R increases from 0.51 up to 0.73). However, when both errors are present, no single inversion framework can successfully improve both the overall bias and spatial distribution of fluxes relative to the prior on the 25 km model grid. In that case, the ensemble-mean optimized fluxes have a domain-wide bias of 77 Gg/d (comparable to that in the prior), with spurious source adjustments compensating for the transport errors. Increasing observational coverage through longer-timeframe inversions does not significantly change this picture. An inversion formalism that optimizes emission enhancements rather than scale factors exhibits the best performance for identifying missing sources, while an approach combining a uniform background emission with the prior inventory yields the best performance in terms of overall spatial fidelity—even in the presence of model transport errors. However, the standard SF optimization outperforms both of these for the magnitude of the domain-wide flux. For the common scenario in which prior errors are non-random, approximate posterior error reduction calculations for the inversions reflect the sensitivity to observations but have no spatial correlation with the actual emission improvements. This demonstrates that such information content analysis can be used for general observing system characterization but does not describe the spatial accuracy of the posterior emissions or of the actual emission improvements. Findings here highlight the need for careful evaluation of potential missing sources in prior emission datasets and for robust accounting of model transport errors in inverse analyses of the methane budget.

Modeling of the electrical conductivity (EC) of icy moon oceans has previously assumed that chloride, sulfate, and other ions released from rock leaching are the main solutes and carriers of electrical conductivity. Here, we show that accreted volatiles, such as carbon dioxide and ammonia, can add a significant fraction of solutes in bodies whose volatile content was in part supplied from cometary materials. These volatiles can increase the EC of aqueous solutions above 1 S/m. Our salinity and EC estimates can serve as a basis for planning future magnetometer investigations at icy moons and dwarf planets. In particular, oceans expected in some of the Uranian satellites and Neptune’s satellite Triton could have EC above 3 S/m as a result of accretion of large abundances of carbon dioxide and ammonia, even if rock leaching during water-rock separation was limited, and chlorine and sulfur abundances may be at CI carbonaceous chondritic levels.