Electron backscatter diffraction (EBSD) is an advanced materials characterization technique that has been used to determine the crystallographic orientation of glacial ice specimens, along with a plethora of other inorganic crystalline materials. However, due to the specific sample preparation requirements necessary for performing EBSD (i.e., flat, smooth, clean surfaces), characterizing porous and ephemeral materials such as seasonal snow and/or firn has always been a challenge. In this work, we demonstrate a methodology for performing EBSD on naturally-collected and laboratory grown snow specimens of depth hoar and surface hoar snow grains. These grain types were chosen because characterizing the primary growth orientation of these snow crystal types may allow for greater insight into many fundamental physical snow processes including mechanical behavior, metamorphism, and radiative transfer properties. Thus far, our results show that plate-shaped surface hoar grains grow preferentially along the primary prism axis, the most favorable and lowest energy face in the Ice 1h crystal structure. Depth hoar crystals were found to be more complex, such that growth may vary greatly depending on the temperature gradient, vapor flux, and supersaturation within the snowpack. EBSD results show that depth hoar can grow along each of the growth planes (basal, primary, and secondary planes), while secondary electron images and optical microscopy reveals the complex step-like features associated with depth hoar crystals grown along the basal growth plane due to vapor deposition. Continued research aims at growing depth hoar crystals in a controlled laboratory environment from ice substrates with known crystal orientations to further investigate the growth rates along each of the different growth planes.

Geologists need help classifying microscope rock images of sigma clasts; a type of mantled porphyroclasts widely used as kinematic indicators in rocks. Knowledge about the shear sense of sigma clast during formation (either CCW or CW shearing) gives insights into rock formation history. This work reports on early investigation of machine learning techniques for automatic detection and classification of sigma clasts and their rotation from photomicrographs. Convolutional Neural Networks (CNNs) are used to extract and leverage defining features of sigma clasts, such as shape, color, texture, and tail direction to improve accuracy. We leverage existing models that are pre-trained on very large collections of images, and use transfer learning techniques to apply them to microstructure images. We used YOLOv3 to identify different sigma clasts in a given image. We also experimented with other large pre-trained models such as ResNet50, VGG19, InceptionV3 with two additional layers trained specifically on our dataset. In order to facilitate exploration of different models with different settings, we are developing a computational experimentation environment to visualize different CNN network layers, classification heatmaps, and comparative metrics. Finally, since models perform better when more data are available, we are developing a web application to collect additional data from geoscientists and incentivize their participation in open science. The website allows researchers to upload images of rock microstructures, showing them the classification of the images based on the best models available, and allows them to correct any errors which can be used to improve the models.

Abstract It has been debated globally that the COVID-19 lockdown had significantly diminished the emission levels of anthropogenic greenhouse gases (GHGs). However, different countries possess different footprints of GHGs emission. In regions with inconsistent air quality observation, spaceborne sensors can provide synoptic assessment of air quality with time-based environmental decision making. In this study, we utilised satellite data to quantify the temporal dynamics of carbon monoxide (CO) and nitrogen dioxide (NO2) between the pre-lockdown (January–March 2020), lockdown (April–July 2020) and post-lockdown (August–September 2020) periods in Nigeria. Periodic TROPOspheric Monitoring Instrument (TROPOMI) datasets were acquired from the Google Earth Engine Sentinel-5 Explorer and the Copernicus Open Access Hub. The Population-Weighted Mean Concentration (PWEC) of CO and NO2 was computed using raster-based population data and place-based air quality estimates. The associated economic correlates were computed using data mined from TROPOMI and available health records of Nigeria. Satellite data analysis showed that aggregate CO reduced by 35.1% (25.32⋅105 tons) and 9.06% (6.54⋅105 tons) and NO2 plummeted by 32.81% (22,500 tons) and 11.63% (5,360 tons) during the lockdown and post-lockdown periods across the 36 States of the country. While mobility rate dwindled substantially, mortality rate savings from the exposure to damaging effects of the GHGs were roughly $ 14 million (CO) and $10 million (NO2). The fluxes in CO and NO2 suggest that anthropogenic interference in air quality accounting can aid the understanding of the convoluted human–nature relationships for sustainable environmental management.

Biochar is the carbon rich product of organic waste pyrolysis under oxygen-free conditions. Biochar properties are inherited from the raw materials used and can be enhanced with production methods. The versatility of the raw materials, along with the tweaks in production methods, enable biochars to have a widely varied range of properties, that make it suitable for a multitude of applications. This versatility, in turn, makes characterization, and performance monitoring challenging especially in large scale applications, typical of biochar use. Biochars have unique electrical properties that could be used to fill this gap. The complex electrical properties of biochar remain largely unstudied; this work explores them for different types and concentrations of biochar in porous media. Early measurements on the complex conductivity signatures of biochars, show promising results for both characterization and mass/volume estimation. Biochar surface properties appear have a profound effect on complex conductivity signals, suggesting the potential use of such methods as characterization and monitoring tools, even in large scale operations.

Due to the high spectral resolution goals (R > 2x10^4) for the upcoming Full-sun Ultraviolet Rocket Spectrograph (FURST), instrument calibration will be particularly important. The Solar Physics groups at NASA MSFC and Montana State University (MSU) have been developing the tools necessary to achieve this goal. These include improved tracking of error propagation, in-situ monitoring of the camera gain with a radioactive Fe-55 source, and even better wavelength calibration. This presentation will focus on the latter. We will highlight the development of a calibration method which uses a two-dimensional second-order polynomial to map pixels to wavelength under a simulated noisy diagnostic lamp signal. Additionally, we have introduced a tilted CCD in order to overcome the Nyquist limit. With this as the background, we have been investigating an effect known well among ground-based imaging: geocoronal absorption. We have been looking into how much this effect will be present in the atmosphere at sounding-rocket altitudes (~100-200km). Many studies have found ways to correct for these so-called “Telluric” lines. However, it may be that these lines can in fact be a useful tool to further improve our calibration, rather than simply a nuisance to be corrected for!

The Beijing-Tianjin-Hebei Metropolitan Region is the biggest urbanized megalopolis in North China. The region has one the lowest water resources availability per capita in China and around the world. Rapid economic development in the past 40 years has resulted in substantially increased water consumption in the region which led to various water security problems including huge burden on water supply, groundwater overdraft, serious damage of river and lake ecosystems etc. Water scarcity has now become one of the largest limiting factors that hinders the further development of the region. The objective of water resources management in this region is thus to ensure healthy balances between the natural and the societal water circulation. This means we need to protect the natural hydrological flow to maintain water's service functions, and in the meantime fulfill the increasing water demand for economic growth. In the present study, we inspected past evolution and future trend of the region's water budget, and subsequently proposed methods for restricting water use and promoting multi-source water supply optimization. This research also reviewed some new concepts and technologies which can potentially help us overcome the water security challenges in the Beijing-Tianjin-Hebei region.

How do formal and informal land use changes have the potential to create unique, and potentially synergistic, risks to communities? Traditionally environmental geochemists privilege certain analytical approaches to evaluate the risk in a system, but when using a participatory approach, the emphasis is instead placed on using the “just right” tools co-discovered with members of the community. In this study we partnered with the LEAD Agency, an environmental advocacy group with a long history of co-designing research agendas to address community concerns to study trace legacy metals in floodplain soils at the Tar Creek Superfund Site in Ottawa County, Oklahoma. At Tar Creek, large mine waste (chat) piles and acid mine seepage have contaminated surrounding communities with zinc, lead, and cadmium. Heavy metal contamination of floodplains at mining sites like Tar Creek involve a complex set of biogeochemical interactions that are controlled by land use patterns (e.g. reworking chat piles and downstream dams), transport pathways, and changing climate making it difficult to prioritize interventions aimed at reducing exposure. Using a participatory research approach, this study integrates (1) community initiated geochemical investigation of wind transportable Pb, (2) monitoring of nutrient loading to assess potential for eutrophication, which would increase metal transport and mobility in Tar Creek, and (3) examines the connections between social and political issues at Tar Creek and how these affect both scientific research and what remediation strategies are tenable. Our action based research aims to support our community partners in their goals:(1) to establish the Rights of Tar Creek through LEAD’s Clean Water Protection Ordinance, which would establish the right to clean water and legally recognize the rights of Tar Creek to exist, regenerate, and flourish, and (2) to expand the EPA’s definition of OU5, which would increase funding for remediation in Ottawa County.

A heat danger day is defined as an extreme when the heat stress index (a combined temperature and humidity measure) exceeding 41 ℃, warranting public heat alerts. This study assesses future heat risk (i.e., heat danger days times the population at risk) based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) projections. In recent decades (1995-2014) China’s urban agglomerations (Beijing-Tianjin-Hebei, Yangtze River Delta, Middle Yangtze River, Chongqing-Chengdu, and Pearl River Delta) experienced no more than 3 heat danger days per year, but this number is projected to increase to 3-13 days during the population explosion period (2041-2060) under the high-emission pathways (SSP3-7.0 and SSP5-8.5). This increase will result in approximately 260 million people in these agglomerations facing more than 3 heat danger days annually, accounting for 19% of the total population of China, and will double the current level of overall heat risk. During the period 2081-2100, there will be 8-67 heat danger days per year, 60-90% of the urban agglomerations will exceed the current baseline number, and nearly 310 million people (39% of the total China population) will be exposed to the danger, with the overall heat risk exceeding 18 times the present level. The greatest risk is projected in the Pearl River Delta region with 67 heat danger days to occur annually under SSP5-8.5. With 65 million people (68% of the total population) experiencing increased heat danger days, the overall heat risk in the region will swell by a factor of 50. Conversely, under the low-emission pathways (SSP1-2.6 and SSP2-4.5), the annual heat danger days will remain similar to the present level or increase slightly. The result indicates the need to develop strategic plans to avoid the increased heat risk of urban agglomerations under high emission-population pathways.

Droughts are particularly disastrous in South Africa and other arid regions, that are water-scarce by nature due to low rainfall and water sources. According to some studies, droughts are not uncommon in Africa’s drylands and have been rising in dry African terrain. South Africa’s provinces were severely affected recently by drought events. This study aimed at evaluating drought disaster and climate trends in the Free State Province of South Africa and to identify drought drivers using regression-based algorithms. The study used high-resolution downscaled climate change projections under three Representative Concentration Pathways (RCP). Three future periods comprising the short (the 2030s), medium (2040s) and long term (2050s) compared to the current period are analysed to understand the potential magnitude of projected climate change-related drought. The study revealed that the year 2001 and 2016 witnessed extreme drought conditions where drought index is between 0 and 20% across the entire province during summer, while the year 2003, 2004, 2007 and 2015 observed severe drought conditions across the region with variation from one part to the other. The result shows that from -24.5 to -25.5 latitude, the area witnessed a decrease in precipitation (80 to 120mm) across the time slice and an increase in the latitude -26° to -28° S for summer seasons, which is more prominent in the year 2041 to 2050. More so, findings from this study showed that agricultural lands, cultivated grasslands, and barren surfaces were influenced or impacted by drought disaster, especially in 2015, one of the drought years in Free State Province. From the feature selection results, the influence of climate proxies and anthropogenic factors on EVI shows the ecological situation within the study area.

Crystalline basement aquifers are characterized by complex flow pathways controlled by varying overburden stratigraphy and thickness as well as fracture network and connectivity within the crystalline rocks. Understanding the hydraulic connection within the fracture network and the overburden regolith is critical to predicting recharge/discharge and contaminant transport pathways. In this study, we combined geophysical imaging with multiple hydraulic testing to quantify hydraulic connectivity within the crystalline basement aquifers at the Ibadan Hydrogeophysical Research Site (IHRS) in Ibadan, Nigeria. The 50 m × 50 m field experimental site is first of its kind established in 2019 to investigate hydrological dynamics within these complex crystalline basement aquifers in sub–Saharan Africa. We acquired multiple parallel 2D electrical resistivity profiles which were also jointly inverted to obtain multiple 2D and 3D electrical resistivity tomograms of the subsurface. The resistivity tomograms were later constrained with lithological profiles from 4 test wells installed down to depths of 30 m at the site to create a conceptual model elucidating potential flow pathways. We also performed a series of 12 hours pumping tests and a NaCl tracer test to estimate flow and transport parameters including hydraulic conductivity, aquifer storage, yield, and groundwater travel time and to assess connection between the four test wells. The resistivity tomograms show 3 major resistivity zones interpreted as a clay-rich topsoil, a saturated weathered overburden, and a fractured basement rock. The delineated fractured bedrock shows an undulating topography with several primary fracture successions at 9, 14, 16 and 22 m. Hydraulic conductivities from pumping tests range from 2.6 x 10-7 to 1.2 x 10-5 m/s for the fractures and 1.7 x 10-10 to 6.4 x 10-6 m/s for the matrix while specific storage range from 3.5 x 10-8 to 1.8 x 10-3. Preferential flow is also observed with stronger connection between wells A and C. Results of this study provide a basis for detailed numerical study which will be focused on predicting recharge and solute transport under different flow and climate regime. This work will provide a scalable framework for a sustainable management of groundwater resources within the crystalline basements of Nigeria.

Does river topography have stage thresholds for maintaining fluvial landforms, and if so how can they be quantified? Geomorphic covariance structure analysis offers a novel, systematic framework for evaluating nested topographic patterns in river corridors. In this study, a threshold in mountain river stage was hypothesized to exist; above this stage landform structure is organized to be freely self-maintaining via flow convergence routing morphodynamics. A 13.2 km segment of the canyon-confined Yuba River, California, was studied using 2944 cross-sections. Geomorphic covariance structure analysis was carried out on a meter-resolution topographic model to test the hypothesis. A critical stage threshold governing flow convergence routing morphodynamics was evident in several metrics. Below this threshold, narrow/high “nozzle” and wide/low “oversized” landforms that are out-of-phase with flow convergence routing morphodynamics dominated (excluding “normal channel”), while above it wide/high “wide bar” and narrow/low “constricted pool” landforms consistent with the flow convergence mechanism were dominant. Three-level nesting of co-located base-bankfull-flood stage landforms was dictated by canyon confinement, with nozzle-nozzle-nozzle nesting as the top permutation, excluding normal channel. In conclusion, this study demonstrates a significantly different and highly effective approach to finding process-based fluvial thresholds that can complement pre-existing methods, such as estimating incipient sediment motion, to get at more powerful dynamics controlling fluvial landforms structure.

Volcanic flood basalt eruptions have covered 1000s of km2 with basalt deposits up to kilometers thick. The massive size and extended duration result in enormous releases of climactically-relevant gases such as SO2 and CO2. However, it is still unknown precisely how flood basalt eruptions influence climate via eruption rates and cadence, height of the volcanic plumes, and relative degassing abundance of species like SO2. Once eruptions occur, the complex interplay of photochemistry, greenhouse gas warming, changes to the atmospheric circulation, and aerosol-cloud interactions can only be properly simulated with a comprehensive global climate model (GCM). We created an eruption scenario for the Goddard Chemistry Climate Model (GEOSCCM) that emits SO2 in the near-surface atmosphere constantly and four times per year an explosive eruption that emits much more SO2 in the upper troposphere/lower stratosphere. The eruption lasts for 4 years and emits 30 Gt of SO2 total. This corresponds to ~1/10th of what may have been emitted during the Wapshilla Ridge eruption phase of the Columbia River flood basalt eruption 15-17 Ma. We use a pre-industrial atmosphere and otherwise modern initial and boundary conditions. The massive flux of SO2 into the atmosphere is quickly converted to H2SO4 aerosols. Global area-weighted mean visible band sulfate aerosol optical depth reaches 220 near the end of the eruption, comparable to cumulonimbus clouds. This reduces the surface shortwave radiative flux by 85% and top-of-atmosphere outgoing longwave flux by 70%. Contrary to our expectations, we find that the climate warms during and immediately following the eruption after a very brief initial cooling. Global mean surface temperature peaks 3-4 years after the eruption ends with a +6 K anomaly relative to a baseline simulation without the eruption. Post-eruption regional temperatures, particularly near-equatorial continental areas, see drastic rises of summertime temperatures with monthly mean temperatures equaling or exceeding 40°C. These temperature responses are radiative- and circulation-driven. The eruption warms and raises the tropical tropopause, allowing a massive flux of water vapor into the stratosphere. Stratospheric water vapor, usually ~3 parts per million reaches 1-2 parts per thousand.

Due to its substantial role on the Earth’s biogeochemical cycles and human health, nitrogen is recognized as one of the major water quality indicators of Sustainable Development Goal 6.3.2. Quantifying these potential impacts in large spatial scales still appears to be a grand challenge because of the high computational demand required by the distributed physically based global models and their intensive data requirements for calibration and validation. The former prevents a comprehensive analysis of the full spectrum of the model behavior under different conditions, and the latter impinges on the reliability of model-based inference. To tackle this problem, we developed a data-driven model using a spatio-temporal Random Forest algorithm to predict levels of nitrogen at 0.5-degree spatial resolution from 1992 to 2010 across the world. Several variables representing livestock, climate, hydrology, topography, etc. have been selected as predictors. The response variable of interest was nitrate–nitrite, which is responsible for the high risk of infant methemoglobinemia. Our results indicate that changes in the nitrogen concentration is mainly driven by cattle and sheep population, fertilizer application, precipitation, and temperature variability, implying livestock population, climate change, and anthropogenic forces can be important risk factors for global water quality deterioration. Furthermore, using the predicted levels of nitrogen, we characterized large-scale water quality patterns, and thus identified a few major ‘hot spots’ of water quality. The proposed model can also help assess potential impacts of future scenarios (e.g., livestock production or land use change) on global water quality conditions for better development of effective policy strategies.

It is reported that metallic iron may exist under shallow lower mantle conditions (Frost et al., 2004). If metallic iron exists, it has an important chemical and physical impact on lower mantle materials. In our previous High-pressure High-temperature experiments, metallic iron was successfully formed in (Mg,Fe)O. However, when we used different metal-oxygen fugacity buffers, it was found that the generated metallic iron existed in the form of alloy. Iron-rich alloys with different compositions even have different physical forms, such as granular and dendritic shapes. In the actual lower mantle, metallic iron produced from (Mg,Fe)O may also form alloys when encountering ferrous metals, and the physical and chemical properties of metallic iron are very different from those of alloys. Therefore the formation of iron-rich alloys is worth our attention. In this study, different metal-oxygen fugacity buffers (Cr, Ti, TIC) were used to study the formation of iron-rich alloy in (Mg, Fe) O under high temperature (1573k, 1773K) and high pressure (3GPa, 15GPa). When the oxygen fugacity is lower than a certain level, metallic iron is formed, and due to the existence of metal-oxygen fugacity buffers, the formed metallic iron will be alloyed with the around metal-oxygen fugacity buffers. At the same time, a reaction zone appears between the sample zone and oxygen fugacity buffers. Martirosyan et al. (2016) also found similar experimental results. We measured the alloy composition and content, the left composition of ((Mg, Fe) O) and oxygen fugacity buffers around the sample area to understand the formation process of iron-rich alloys in our experiments. Preliminary results will be displayed and their implications discussed.

The COVID-19 pandemic has increased the risk of global public health and has the potential to cause severe food and water insecurity due to economic recession during lockdown for people living in low-middle income countries like Bangladesh where capital resources are scarce. There is growing evidence that household food and water insecurity has been associated with poor psychological outcomes. The objective of this study was to determine the association between household food and water insecurity with mental health and whether these differed among urban-rural households. A cross-sectional online survey was conducted with 545 participants immediately after the COVID-19 lockdown period in Bangladesh (August 1-September 30, 2020). Household food and water security were determined using a 9-item Household Food Insecurity Access Scale (HFIAS) (score range 0-27) and a 12-item Household Water Insecurity Experiences (HWISE) scale (score range 0-36), respectively. The Perceived Stress Scale (PSS) was used to evaluate mental health. Multivariable logistic regression examined the association between household food and water insecurity with perceived stress, adjusting socioeconomic characteristics. An urban-rural stratified analysis was also performed. About 72.84% (397) respondents reported high stress and more than 70% of households suffered from food and water insecurity during the lockdown period. After adjusting covariates, logistic regression model results show that food insecurity was associated with a 1.07-point increase in high perceived stress (OR=1.07, 95% CI=1.01-1.11, p<0.01) while water insecurity was associated with 1.03 times greater odds of high perceived stress (OR=1.03, 95% CI=0.93-1.23, p<0.05). In stratified analysis, only food insecurity was associated with high perceived stress in the urban household (OR=1.08, 95% CI=1.00-1.11, p<0.05). However, none of the household insecurity was associated with perceived stress in rural households. Interventions that promote equal access to resources for low-income individuals will likely to be more effective to alleviate economic burden of pandemic.

In light of the increased availability of massive point cloud data, acquired by advanced remote sensing techniques, software tools for their efficient representation and processing are needed. Triangulated Irregular Networks (TINs) are an effective way to represent point clouds without the need to interpolate them into raster-based terrain models. However, GISs tools have limited support for TINs due to large storage costs. For this reason, we present the Terrain Trees Library (TTL), a library for terrain analysis based on a new scalable data structure named Terrain trees. A Terrain tree relies on a hierarchical spatial index where each leaf block encodes the minimum amount of connectivity information for the TIN. Connectivity relations among the elements of the TIN are extracted locally within each leaf block at run-time and discarded when no longer needed. Moreover, the hierarchical domain decomposition makes the library well-suited for parallel processing. TTL contains a kernel for connectivity and spatial queries, and modules for extracting morphological features, including edge and triangle slopes, roughness, curvature. It also contains modules for extracting topological structures, like critical point, critical net, watershed segmentation, based on the discrete Morse gradient, and a technique for multivariate data visualization, which enables the analysis of multiple scalar fields defined on the same terrain. To evaluate the effectiveness and scalability of TTL, we compared it against the most compact state-of-the-art data structure for TINs, the IA data structure. When encoded by Terrain trees, a TIN requires 36% less storage than when encoded by the IA data structure. Beyond this storage reduction, Terrain trees also show better performance than the IA data structure in most terrain analysis operations. This speedup is obtained since Terrain trees enable 57% to 72% faster extraction of the triangles incident in a vertex. Extracting the triangles incident in vertices as well as the adjacent vertices on the mesh is a key task in most terrain feature extraction operations on a TIN. Using Terrain trees, we achieved 36% to 55% less time consumption computing morphological features and 20% less time consumption computing the discrete Morse gradient than using the IA data structure.

Methane’s high heat trapping potential has made it a priority for quantification and mitigation efforts worldwide. Ground-based surveys and in-situ measurement techniques to quantify natural and fugitive methane emission sources are time-consuming, expensive, and often lead to sparse measurements. Failure to accurately quantify emissions at the point-source scale have thus led to poorly constrained emission estimates. Airborne imaging spectrometers such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and the Global Airborne Observatory (GAO) have been employed to map the often stochastic and intermittent point-source emissions from a diverse set of source types including oil and gas, dairy, etc. A matched filter is applied to the methane-absorption relevant spectral features of the instrument’s radiance cube. Machine learning models are then trained to recognize methane plumes from these column-matched filter methane maps. However, current Convolutional Neural Network (CNN) models suffer from a high false-positive rate and poorly generalize to new scenes. False-positive detections are primarily due to methane absorption-mimicking surface spectroscopic features, as well as a lack of training data. To supplement the available training data, we utilize Large Eddy Simulations (LES) of methane point-source emissions to train a Convolutional Neural Network (CNN) on a plume-classification task. We observe a significant distribution shift between LES and AVIRIS-NG plumes, primarily caused by high LES plume enhancements. Through a series of image transforms verified through an adversarial approach using a discriminator network, we minimize the distribution shift between synthetic LES plumes and plumes observed by AVIRIS-NG and GAO. CNNs trained on a mixture of LES and real-world plumes, and tested on flightlines from multiple campaigns exhibit an error reduction compared to previous models. The reduction in false-positive plume detections demonstrates that supplementing the limited training data of real methane plumes with LES provides an avenue to make automatic detection more robust for future airborne and spaceborne missions such as SBG, EMIT, and Carbon Mapper.

Due to the potential risks that space weather (SW) events associated with solar-wind disturbances pose on modern technology and infrastructure, there has been increasing interest in physics-based forecasts of the solar wind and related phenomena, such as coronal mass ejections. Computational models of heliospheric space plasmas and space weather are generally based on the equations of ideal magnetohydrodynamics (MHD) that describe the conservation of plasma mass, momentum, and energy, as well as the time evolution of the magnetic field. Over the last few decades significant effort has been devoted to the development of efficient numerical schemes for solving the ideal MHD equations, especially in the context of space plasmas. More recently, there has been increasing interest in incorporating observational data within SW simulations via data assimilation (DA) to produce improved space weather forecasts. While the use of DA methods is a mature field that has proved to be vastly successful in meteorological applications, its use has seen limited application in heliospheric space weather forecasting, or MHD modeling in general. In this study, the results of the assimilation of synthetic plasma observations in one-dimensional ideal MHD initial value problems are considered. DA methods are generally divided into two families of approaches: variational and sequential methods. Both categories of approaches are examined here with assimilation results presented for the 4DVar and Ensemble Kalman Filter (EnKF) methods, respectively. Observing system simulation experiments are performed and the simulation errors obtained using the 4DVar and EnKF methods, as well as, without the use of DA are compared. The sensitivity of error reduction to temporal and spatial observation availability is explored, and the computational costs of each method are reported. Finally, the challenges associated with extensions to 3D models are discussed.