Multi-Channel Ground-Penetrating Radar for the Continuous Quantification of Snow and Firn Density, Depth, and Accumulation

A priority of ice sheet surface mass balance (SMB) prediction is ascertaining the surface density and annual snow accumulation. These forcing data are inputs for firn density models and can be used to inform remotely sensed ice sheet surface processes and to assess Regional Climate Model (RCM) skill. The Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) retrieved 16 shallow firn cores and dug 42 snow pits along the Western percolation zone of the Greenland Ice Sheet (GrIS) during May and June of 2016 and 2017. I deployed and maintained a multi-channel 500 MHz ground-penetrating radar in a multi-offset configuration throughout the two traverse campaigns. The multi-channel radar technique accurately and independently estimates density, depth, and annual snow accumulation -- between the firn core and snow pit sites -- by horizon velocity analysis of common midpoint radar reflections from the snow and shallow firn. I analyzed a 45 km section of the traverse in a high accumulation zone, known as the GreenTrACS Core 15 Western Spur. Deviations in surface density up to +- 15 kg/m3 from the transect mean correlate with surface elevation and surface slope angle. Spatial variation in mean annual accumulation of ~0.175 m w.e. ɑ-1 occurs across a trough in the surface topography ~5 km wide. The reported variability of density and accumulation demonstrates that RCMs must be down-scaled to resolutions within 5 km to assess subtle yet significant contributions to the GrIS SMB

Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications

Recent Precipitation Decrease Across the Western Greenland Ice Sheet Percolation Zone

The mass balance of the Greenland Ice Sheet (GrIS) in a warming climate is of critical interest in the context of future sea level rise. Increased melting in the GrIS percolation zone due to atmospheric warming over the past several decades has led to increased mass loss at lower elevations. Previous studies have hypothesized that this warming is accompanied by a precipitation increase, as would be expected from the Clausius–Clapeyron relationship, compensating for some of the melt-induced mass loss throughout the western GrIS. This study tests that hypothesis by calculating snow accumulation rates and trends across the western GrIS percolation zone, providing new accumulation rate estimates in regions with sparse in situ data or data that do not span the recent accelerating surface melt. We present accumulation records from sixteen 22–32m long firn cores and 4436 km of ground-penetrating radar, covering the past 20–60 years of accumulation, collected across the western GrIS percolation zone as part of the Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) project. Trends from both radar and firn cores, as well as commonly used regional climate models, show decreasing accumulation rates of 2:4±1:5%a-1 over the 1996–2016 period, which we attribute to shifting storm tracks related to stronger atmospheric summer blocking over Greenland. Changes in atmospheric circulation over the past 20 years, specifically anomalously strong summertime blocking, have reduced GrIS surface mass balance through both an increase in surface melting and a decrease in accumulation rates

Multisensor Geophysical Fusion for Improved Sub-surface Imaging at Historic Camptown Cemetery, Brenham, Texas

ABSTRACT Multisensor Geophysical Fusion for Improved Sub-surface Imaging at Camptown Cemetery, Brenham, Texas. (May 2014) Tate Gregory Meehan Department of Geology and Geophysics Texas A&M University Research Advisor: Dr. Mark E. Everett Department of Geology and Geophysics A non-invasive geophysical survey of the historic African American Camptown Cemetery in Brenham, Texas was undertaken to provide the local heritage museum staff with approximate locations, depths, and quantities of marked and unmarked burial sites. We constructed an intuitively understood map describing the uncertainty of our data interpretation. This map consists of three types of zones color-coded according to the confidence we ascribe to the location and depth of a grave site: red for “certain”, yellow for “unclear”, and green for “certainly not”. The completed work helps to define a current "state-of-the-practice" in 3-D historical cemetery geophysical mapping. Three sub-surface geophysical techniques were used in the mapping of Camptown Cemetery: magnetics (MAG), electromagnetic induction (EMI), and ground penetrating radar (GPR). The approach is to combine, or fuse, the MAG, EMI, and GPR information, rendering a joint interpretation in which the confidence in the fused product is greater than the confidence in the product formed by using any one of the methods working alone. Our goal was to identify burial sites within the cemetery and, wherever possible, corroborate with existing historical records and death certificates of the city of Brenham. This task is of cultural and historical importance to the families and relatives of those buried in Camptown as they seek to restore the lost African American heritage of their community

Assessing Controls on Ice Dynamics at Crane Glacier, Antarctic Peninsula, Using a Numerical Ice Flow Model

The Antarctic Peninsula\u27s widespread glacier retreat and ice shelf collapse have been attributed to atmospheric and oceanic warming. Following the initial post-collapse period of retreat, several former tributary glaciers of the Larsen A and B ice shelves have been slowly re-advancing for more than a decade. Here, we use a flowline model of Crane Glacier to gauge the sensitivity of former tributary glaciers to future climate change following this period of long-term dynamic adjustment. The glacier\u27s long-term geometry and speed changes are similar to those of other former Larsen A and B tributaries, suggesting that Crane Glacier is a reasonable representation of regional dynamics. For the unperturbed climate simulations, discharge remains nearly unchanged in 2018–2100, indicating that dynamic readjustment to shelf collapse took ~15 years. Despite large uncertainties in Crane Glacier\u27s past and future climate forcing, a wide range of future climate scenarios leads to a relatively modest range in grounding line discharge (0.8–1.5 Gt a−1) by 2100. Based on the model results for Crane, we infer that although former ice shelf tributaries may readvance following collapse, similar to the tidewater glacier cycle, their dynamic response to future climate perturbations should be less than their response to ice shelf collapse

Snowpack Relative Permittivity and Density Derived from Near-Coincident Lidar and Ground-Penetrating Radar

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive \u3e20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to \u3e 30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged \u3e 400 mm, however, model uncertainty increased to \u3e 20% when SWE averaged \u3c 300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods

Future business and the role of purchasing and supply management: Opportunities for ‘business-not-as-usual’ PSM research

The raison d'être for this article is simple: traditional ways of researching, theorizing, and practicing purchasing and supply management (PSM) are no longer sufficient to ‘meet the moment’. Scholars need to advance a “business-not-as-usual” footing approach to their work, if they are to make a meaningful contribution to addressing the current and future emergencies, as highlighted by recent extreme weather and the COVID-19 pandemic. Yet, what can this, or should this, mean for a field rooted in traditional business thinking? This article builds on the Journal of Purchasing and Supply Management's (JPSM) 25th Anniversary Special Issue editorial (2019); members of the JPSM's editorial team advance their unique perspectives on what “business-not-as-usual” means for PSM. Specifically, we advocate both thinking much more widely, in scope and ambition, than we currently do, and simultaneously building our ability to comprehend supply chains in a more nuanced and granular way. We explore whether the bias toward positivist work has omitted potentially interesting findings, and viewpoints. This leads to a call to re-think how we approach our work: should the key criteria always be to focus on theory development or testing? Should academics “think bigger”? Turning to specific research themes, illustrations of how our current thinking can be challenged or broadened by addressing the circular economy, and role of purchasing and innovation. Specifically, the focus on the PSM function as an intrapreneur within the larger organization, and the role of innovation and technology in PSM work. Taken together, we hope the ideas and arguments presented here will inform and inspire ambitious and novel approaches to PSM research with significant and enduring impact on the transformation of business

Power-based behaviors in supply chains and their effects on relational satisfaction: A fresh perspective and directions for research

Although the sources of a firm’s power vis-à-vis upstream and downstream relationships in supply chains have been studied extensively, how a firm may act or react to power-based behaviors of its partners has not been sufficiently defined and discussed. To this end, we present three power-based behaviors: dominance, egalitarian, and submissive. From a cross-disciplinary reading of the relevant literature, we conceptualize and discuss the characteristics of these behaviors as manifested by dyads within supply chains. Three power-based behaviors are proposed to describe both initiating and responding behaviors used by partners, with these behaviors affecting relational satisfaction. This results in nine potential descriptors of the state of any supply chain relationship. We then discuss the opportunities to use our approach to better research the dynamics of power in supply chain relationships

Procuring sustainably in social housing: The role of social capital

In order to explore its many complexities, scholars have called for a move beyond, descriptions of sustainable procurement. This study responds by seeking insights into sustainable procurement through the lens of social capital theory. Social capital is conceptualized as comprising cognitive, social and relational elements. Sustainable procurement is seen as a means of pursuing environmental, economic and social goals through the purchasing and supply process. The study, proposes and empirically tests the operational measures of social capital and their relationship with, sustainable procurement activity on a sample of 135 procurement professionals in organizations, providing social housing. The results indicate partial support for the study proposition; structural, social capital, rather than structural, social and relational taken together, is found to be the most robust predictor of sustainable procurement. The results highlight the importance of broadening, collaboration models for sustainable procurement beyond an exclusive focus on dyadic relations. It, also demonstrates that this broader engagement with other stakeholders focused on knowledge creation, as well as knowledge sharing, is a significant contributor to sustainable procurement activity. © 2014 Elsevier Ltd