Civil engineering

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/4905

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Permanence and Life Cycle Performance of Biocementation Soil Improvement
(2026-04-20) Oliveira Ribeiro, Bruna Gabrielly; Gomez, Michael M
Microbially Induced Calcite Precipitation (MICP) is a bio-mediated technology that can improve the engineering properties of granular soils through the generation of calcium carbonate minerals on soil particles surfaces and at interparticle contacts. The process can offer an environmentally beneficial alternative to conventional ground improvement methods, which have traditionally relied on energy-intensive interventions. Through nearly two decades of research, a wide array of geotechnical applications for this process have been explored ranging from liquefaction mitigation to surficial soil stabilization. While promising, there has remained limited understanding of the permanence of the generated carbonate minerals and how the engineering behaviors of biocemented soils may change over time following chemical and mechanical damage. In this research, the life-cycle performance of biocementation is investigated through experiments examining the long-term geochemical permanence of biocementation and the engineering behavior of biocemented soils following chemically induced damage. First, batch and soil column experiments were performed wherein soils were first biocemented and then subjected to acidic solutions to induce dissolution. Insights from these experiments were then used to calibrate and validate a reactive transport model capable of forward predicting calcium carbonate dissolution spatially and temporally for a variety of different geochemical conditions. Next, direct simple shear tests were employed to examine the consequences of chemical damage on the engineering behaviors of biocemented loose sands and identify new methods by which post-treatment material damage might be detected and accounted for. Results from this research will significantly improve our understanding of the long-term engineering performance of biocementation soil improvement including site-specific material longevity, changes in engineering behaviors following damage, life cycle environmental impacts, and favorable uses cases for the technology that maximize environmental benefits.
Exploring the Relationship Between Reservoir Management and Riverine Ecosystems from Space
(2026-04-20) Darkwah, George Kwabena; Hossain, Faisal
Rivers serve as the habitat for aquatic organisms and a source of resources to meet societal needs through river regulation. Despite its societal benefits, river regulation disrupts the river environment in diverse ways, including changes to river water temperature and consequently, aquatic organisms. However, historically, considerations for effective river regulation tend to focus more on the impacts on flow (quantity) than the impacts on water temperature (quality). Such a bias toward water quantity considerations distorts the overall effectiveness of reservoir management efforts for better riverine ecosystem outcomes. Therefore, the goal of this dissertation is to explore the relationship between reservoir operations and riverine ecosystems using an enhanced approach to estimating riverine water temperature through satellite remote sensing. Central to the study is the development of the Thermal History of Regulated Rivers (THORR) framework, which integrates satellite remote sensing and data-driven techniques to estimate river water temperature in the continuum of space and time. This framework enhances traditional in-situ measurements by providing estimates in areas without direct monitoring. Temperature estimates from THORR are applied to further examine how river regulation’s impact on water temperature can potentially influence the dynamics of fish abundance in regulated rivers. The findings indicate a likely connection between the rate of fish retention in a river reach and the water temperature of the reach, which is influenced by my upstream river regulation. Finally, in an effort to extend THORR’s application, the study investigates the feasibility of solely using satellite remote sensing to identify harmful algal blooms in regulated rivers. A successful identification of harmful algal blooms can open up the possible link between river regulation, water temperature, and algal blooms as another component of riverine ecosystems. Overall, this dissertation encapsulates the feasibility and need to incorporate water quality considerations in river regulation for better riverine ecosystem outcomes. The principal mode of investigation relies on satellite remote sensing, specifically Landsat and Sentinel-2 products. However, this study sets a foundation for promising explorations using recent satellite products such as the Surface Water and Ocean Topography (SWOT) for water quantity considerations and Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) for further water quality considerations.
Partitioning-Enhanced Photochemical Reductive Dehalogenation of Organohalogen Compounds in Wastewater-Associated Dissolved Organic Matter and Foams
(2026-04-20) Hooper, Jennifer Lynn; Dod, Michael; Ray, Jessica
Reactive reducing species (RRS) – including hydrated electron (eaq–), hydrogen atom (H•), and other reducing species – may form during ultraviolet irradiation of chromophoric dissolved organic matter (CDOM) in natural or engineered aquatic systems. Traditionally, RRS have been assumed as insignificant in photochemical processes due to their rapid scavenging by aqueous molecular oxygen and protons. However, within CDOM microenvironments, RRS may be shielded from scavengers, resulting in higher levels than in the bulk aqueous phase. CDOM may also sorb hydrophobic and amphiphilic trace contaminants (TCs), including those susceptible to RRS-mediated degradation, such as organohalogens. In such contexts, the extended RRS lifetimes, enriched organohalogen concentrations, and enhanced spatial proximity between RRS and organohalogens within CDOM may facilitate elevated rates of reductive dehalogenation relative to the bulk aqueous phase. To quantify the potential for organohalogen TCs to be transformed within DOM microenvironments, experimental work investigated partitioning of select organohalogen TCs into natural and wastewater-associated DOM, applying solid-phase microextraction (SPME) batch sorption isotherms to derive the organic carbon–water partitioning coefficient (KDOM). Method development addressed analytical artifacts and kinetics through targeted assessments such as SPME fiber fouling by DOM and kinetic screening tests. A key finding was that DOM sorption isotherms were linear, indicating that sorption sites were not limiting and generally contained similar free energies for association of the organohalogen TC with DOM. KDOM values derived from SPME tests revealed differences of up to three orders of magnitude in equilibrium partitioning, indicating intermolecular interactions of the organohalogen TC with DOM were substantially different. In particular, higher molecular weight organohalogen TCs with larger molar volumes and those with a greater degree of halogen substitution tended to have greater KDOM values, likely due to greater hydrophobic van der Waals interactions. Further, organohalogen TCs capable of hydrogen-accepting and - interactions had a greater association with DOM. Finally, acid/base speciation significantly impacted partitioning, where KDOM values were lower for anionic species, likely due to electrostatic repulsion due to the negative surface charge associated with DOM. Thus, key intermolecular interactions between organohalogen TCs and DOM were van der Waals hydrophobic interactions, acid/base speciation and associated electrostatic interactions, and - interactions. This research further characterized and compared the degrees to which representative CDOM macromolecules (i.e., whole natural organic matter, humic and fulvic acids, model biomolecules, and an authentic effluent organic matter (EfOM) isolate) generated RRS during exposure to (1) broadband simulated sunlight generated by a Xe arc lamp solar simulator, and (2) 254 nm UV light generated by a low-pressure Hg lamp. The sorption-enhanced effects of RRS-mediated organohalogen degradation within CDOM microenvironments were assessed relative to the bulk aqueous phase using hexachloroethane (HCE) and chloroacetate (CA) as probe compounds, respectively. HCE served as an intra-CDOM RRS probe due to its CDOM partitioning (logKDOM = 4.14), high second-order rate constant for reaction with eaq– ( = 3.9×1010 M–1 s–1), and resistance to direct photolysis or degradation by co-occurring reactive species (e.g., hydrogen atom, H● and hydroxyl radical, HO●). CA served as a bulk aqueous phase RRS probe due to its high water solubility, high second-order rate constant for reaction with eaq– ( = 1×109 M–1 s–1), and resistance to direct photolysis. A key finding was that biomolecule components, particularly those with phenolic or indole moieties, were confirmed to be capable of generating eaq–. Further, the eaq– quantum yield under low pressure irradiation was estimated as 126±48× higher within DOM microenvironments compared to the bulk aqueous phase. The eaq– quantum yield under simulated sunlight was moderately lower within DOM microenvironments but still significant at 73±41× compared to bulk aqueous measurements. Thus, this research highlighted substantial increases in eaq– quantum yields and steady state concentrations within DOM microenvironments relative to bulk water. These data provide further context that may be used to estimate the consequent extent(s) to which organohalogen TC degradation by RRS with DOM microenvironments is/are likely to occur in engineered and natural systems. Building upon the investigation of DOM partitioning, this work further explored whether foam microenvironments can enrich organohalogen TCs relative to bulk water, using a bench-scale synthetic-foam matrix representative of surface water and a foam reactor coupled with SPME to quantify sorption/partitioning metrics (e.g., logKDOM and foam/water partitioning expressed via enrichment factors). Consistent with earlier DOM partitioning behavior, the organohalogen TCs exhibited substantial association with DOM components (logKDOM values in the ~4–6 range for SRHA and the plant-based surfactant saponin), with compound- and DOM-specific behavior such as 4,4′-DDT sorbing more strongly to SRHA than saponin (associated with π–π interactions in SRHA’s aromatic fractions). However, foam fractionation produced only modest contaminant enrichment overall (EFs generally ~1 to 1.6). Mirex had the highest enrichment factor (~1.6) consistent with its high hydrophobicity. FOSA paradoxically had slightly higher foam enrichment at pH 8 despite lower DOM sorption—supporting the conclusion that air–water interfacial partitioning, rather than DOM-driven hydrophobic association, can dominate foam association for some compounds. Importantly, DOM itself was not enriched into the generated foam, contrasting with much larger DOM enrichment factors reported for natural marine/freshwater/wastewater foams. The simplified synthetic matrix tested in this work lacked suspended biomass and partially degraded biomolecules that are known to promote foaming, which may have limited the foam enrichment factors observed. Future work is recommended with real waters or amended matrices to better emulate environmental conditions conducive to foam formation and TC partitioning. This work is novel in that it explored the partitioning behavior of organohalogen TCs to DOM, the potential role of RRS-mediated organohalogen degradation within CDOM microenvironments in both natural and engineered systems (i.e., municipal wastewaters and wastewater-impacted receiving streams), and investigated the potential for enrichment within foams as an additional multiphase heterogeneous microenvironment that can influence contaminant partitioning and has potential to influence photochemical transformation. This research further highlights underrecognized but potentially important pathways for organohalogen contaminant fate and attenuation.
Advancing Urban Building Energy Modeling with Satellite-Derived Microclimate Data
(2026-04-20) Worthy, Amanda; Abbasabadi, Narjes
Building energy models typically reference aggregated, non-urban-specific weather and climate datasets that overlook urban microclimate conditions. This embedded shortfall leads to substantial differences between the modeled and actual performance of buildings, and thus to inaccurate energy demand-side calculations. Our research aims to close this gap by integrating microclimate information, derived from earth observational and remote sensing datasets, into urban building energy models. We evaluate the value of these products by conducting two complementary studies that examine the integration of satellite-derived microclimate information in both data-driven and physics-based building energy modeling workflows. First, we develop a bottom-up, data-driven urban building energy modeling framework that combines and integrates earth observational microclimate data, spatially interpolated Typical Meteorological Year data, and annual energy usage data, measured by the Seattle Energy Benchmarking Dataset, to capture the impacts of microclimates on urban building energy performance. Using machine learning techniques and Seattle, Washington, USA as a proof of concept, we compare predictive model performance across multiple climate input scenarios; ultimately validating the effectiveness of using earth-observational data inputs to address simulation-to-real modeled uncertainties in microclimate integration. Second, we conduct a hybrid study that augments physics-based residential building energy consumption insights with earth observational microclimate data using machine learning predictions. This approach produces a spatial heat map of residential energy consumption for a typical family home across Los Angeles County, USA, that is explicitly tailored to reflect urban microclimate variation. Here, we confirm current building EnergyPlus weather file (EPW) sampling sites to be in lower vulnerability areas with fewer streets and buildings than the city average. This result identifies a mismatch between the environmental conditions observed in dense urban areas and those normally simulated in building energy modeling protocols, further underscoring the structural misrepresentations that are embedded in current frameworks. Throughout this work, we emphasize the value in integrating satellite-derived microclimate products into data-driven building energy studies. However, we cite obstacles in integrating satellite-derived microclimate data directly into physics-based urban building energy models, suggesting future research to explore more spatially and temporally compatible datasets that measure EnergyPlus weather file parameters and respective downscaling opportunities. Despite these challenges, the Landsat thermal band 10, or land surface temperature products, show strong potential in being effective, scalable proxies for incorporating microclimate effects into both data-driven and physics-based urban building energy studies. This research advocates for the integration and validation of urban microclimate effects into building energy modeling frameworks, to enable more accurate, just, and precise energy policy and planning.
U.S. National Liquefaction Hazard Maps and their Implications for Engineering Practice and Policy
(2026-02-05) Zdanovski, Victoria; Maurer, Brett
Abstract: This study introduces U.S. National Liquefaction Hazard Maps (NLHMs) developed using a mechanics-informed, machine-learning geospatial liquefaction model trained on more than 37,000 cone penetration tests from the U.S. and abroad. The model surrogates state-of-practice liquefaction models, exploits a large library of geospatial predictors to infer subsurface traits, and is geostatistically updated near in-situ tests, thereby anchoring ML predictions to measured conditions. Liquefaction hazard is mapped across the contiguous U.S. at ~90 m resolution within both conditional (2475-year design event) and unconditional (return period of ground failure) formulations by convolving the liquefaction model with the 2023 U.S. national seismic hazard model, the execution and magnitude-disaggregation of which requires cloud computing. The resulting NLHMs provide useful insights for land-use policy, preliminary site assessment, simulating earthquakes and planning response across regional scales, prioritizing retrofits within portfolios of distributed infrastructure, and creating screening tools for regulatory enforcement. Beyond quantifying and visualizing liquefaction hazard, the NLHMs are used herein to examine three questions of engineering practice and policy across a continuous spatial domain (i) the effect of selecting modal versus mean magnitude in conditional analyses; (ii) the differences between conditional andunconditional hazard formulations; (iii) and the extent to which liquefaction hazard compounds with socioeconomic vulnerability. Results elucidate: where and how the choice of magnitude alters computed hazards; that unconditional maps reveal important spatial deviations in hazard suppressed by singlescenario maps, which are convenient and widely used in current building codes, but less than completely rational; and that modest but statistically significant socioeconomic gradients in exposure to liquefaction exist. The NLHMs should not be used in lieu of site-specific analyses and are not intended to supplant intensive city- or region-specific mapping efforts. Rather, the NLHMs are intended to provide a baseline prediction of liquefaction hazard, developed by a standard approach, for the entire contiguous U.S.
Detecting Breaking Waves and Measuring Bore Speeds in Optical Surf Zone Imagery using Machine Learning
(2026-02-05) LeClair, Malcolm James; Hegermiller, Christie; Thomson, Jim
A machine learning algorithm is developed to detect breaking waves in optical remote sensing data collected under visually diverse conditions along a kilometer-scale beach in Duck, NC. Bore speeds are estimated from the breaking-wave detections and are compared with theoretical models using surveyed bathymetry. Bathymetry inversion from the derived bore speeds is then explored, revealing low but systematic bias within the surf zone. Despite this limitation, a qualitative analysis of the inverted bathymetry demonstrates that the method captures morphological change over the course of the experiment. This method shows promise as a robust, low-cost approach for measuring wave-breaking patterns and dynamics across large surf zones. The results highlight important considerations for the data resolution, quality, and processing needed to achieve robust measurements of breaking waves using optical remote sensing.
Improving synthetic aperture radar measurements of surface movement and snow depth in mountain environments
(2026-02-05) Brencher, George; Shean, David
Mountainous regions store critical water resources and produce devastating natural hazards. As climate change disproportionately impacts mountainous regions, accurate and timely observations are needed for adaptive resource and hazard management and to understand changing cryospheric and geomorphic processes. Synthetic aperture radar (SAR) can provide these observations, but SAR-based measurements are subject to noise and errors that reduce their reliability. Relying on the extensive SAR archive, this dissertation develops workflows that integrate emerging data science approaches, including deep learning, with established geophysical methods to improve SAR-based measurements of surface movement and snow depth in mountainous terrain.In Chapter 1, I used a convolutional neural network (CNN) to remove atmospheric noise from interferometric synthetic aperture radar (InSAR) interferograms. The CNN was trained using thousands of Sentinel-1 interferograms and exploits differences in the spatial and topographic structure of atmospheric noise and deformation signals, without relying on external atmospheric data. This approach outperforms commonly used atmospheric correction methods and reveals previously obscured centimeter-scale deformation of rock glaciers and landslides in the Rocky Mountains. These improvements enable more reliable interpretation of subtle surface kinematics in high-relief terrain. In Chapter 2, I developed a fused InSAR and SAR feature tracking approach to quantify surface displacement of moraines damming glacial lakes. Combining InSAR and feature tracking results in improved displacement time series that are more accurate than those produced using either method alone. Application to the Imja Lake moraine dam in Nepal reveals decimeter-scale cumulative subsidence over a seven-year period and widespread buried ice. I validated these results using very-high-resolution satellite stereo digital elevation models. The observed displacement patterns are consistent with year-round ice flow and warm-season ice melt. These results provide new constraints on the processes contributing to moraine dam degradation and have direct implications for glacial lake outburst flood (GLOF) hazard assessment. In Chapter 3, I extended this approach to the 23 moraine-dammed glacial lakes in Nepal which are the highest priority for monitoring. I used seasonal change in InSAR coherence as a proxy for buried ice presence. I found that most moraine dams contain buried ice that produces surface displacement of centimeters to tens of centimeters per year. Analysis of displacement components indicates that the observed deformation reflects a combination of ice melt and ice flow, with the relative contribution of each process varying between sites. I found evidence for extensive buried ice in several moraine dams previously classified as ice-free, which substantially changes the conclusions of prior hazard assessments. These results can be used to improve GLOF hazard assessments and modelling studies. In Chapter 4, I developed a deep-learning approach for regional snow depth prediction across the Western United States. I trained a U-Net CNN using a large archive of airborne lidar snow depth measurements and multi-modal inputs including SAR backscatter, optical imagery, topography, and coarse-resolution physical model outputs. The final CNN substantially outperforms existing approaches for near real-time prediction of Western U.S. snow depth in accuracy, precision, and resolution. It can be applied to create spatially continuous maps of snow depth over the entire Western U.S. and dense snow depth time series over the past decade. This work establishes a new benchmark for regional snow depth prediction performance, with implications for future operational forecasting.
Numerical Evaluation of Code Requirements and Nonlinear Performance of Torsionally Irregular Structures
(2026-02-05) Uwaoma, Uzochukwu Daniel; Thonstad, Travis
Structures with torsionally irregular configurations – those with non-coincident centers of mass, stiffness, and strength – are vulnerable to amplified seismic demands from twisting modes of response that localize deformation and damage, increasing the likelihood of failure or collapse during strong ground shaking. Despite decades of research, design provisions for torsional irregularity remain inconsistent across international codes and are often based on studies of reduced-order models that may not adequately capture the behavior of multi-story or spatially irregular systems. To address these perceived gaps, this dissertation leverages high-performance computing to investigate the design and behavior of torsionally irregular structures through three interconnected studies: (1) Minimizing superstructure twist in irregular bridges through optimization of structural parameters (2) Evaluation of design provisions in the New Zealand seismic design standard (NZS 1170.5:2004) for the seismic assessment of torsionally irregular buildings, and (3) Improving the seismic performance of torsionally irregular buildings using force-limiting diaphragm connections. The first study investigates geometrically irregular bridges using a validated finite element model of a previously tested reinforced concrete bridge. It evaluates three modification strategies: adjusting column effective heights, altering end fixity conditions, and redistributing superstructure mass, to reduce torsional response. Numerical and optimization-based studies showed that increasing the effective stiffness of columns by reducing their effective height was the most efficient strategy. The study also demonstrated that a small subset of hazard-consistent ground motions could capture the essential behavior required for optimization, providing a practical balance between computational efficiency and accuracy. The second study examines torsionally irregular buildings within the context of the New Zealand seismic design standard (NZS 1170.5:2004). Reinforced Concrete Shear Wall (RCSW) and Steel Special Moment Frame (SSMF) buildings were designed and analyzed using nonlinear time-history simulations of site-specific ground motions derived from the 2022 New Zealand National Seismic Hazard Model. A comparative analysis of the current code provisions and proposed updates by a working task group showed that the proposed updates substantially reduced maximum drift demands and collapse probabilities, especially for highly ductile SSMF systems, while penalizing designs with excessive torsional irregularity. The third study explores the potential of deformable Inertial Force-Limiting Connections (IFLC) to reduce seismic demands in irregular buildings. By replacing conventional rigid diaphragm-to-lateral system links with deformable connections designed to dissipate energy and limit force transfer, the study established rational benchmarks for connection stiffness and strength based on diaphragm design forces and system drift constraints. Results indicated that properly tuned IFLCs can reduce lateral force demands, making them a promising design option for improving the seismic resilience of irregular structures. Overall, the findings of this dissertation improve our understanding of torsionally irregular structural systems through code-level evaluations employing advanced numerical modeling techniques and the examination of innovative connection strategies through numerical optimization. The results provide a rational basis for updating national seismic design standards and highlight the potential of force-limiting methods as a next-generation seismic design tool for irregular structures.
Advancing cold-region hydrology with large-sample data and deep learning: insights from Icelandic catchments
(2026-02-05) Helgason, Hordur Bragi; Nijssen, Bart
This thesis advances understanding of cold-region hydrology by combining large-sample data development, hydrological trend analysis, and deep learning. Iceland serves as the study domain because of its extensive hydrological observations and its unique landscape shaped by snow, glaciers, permeable volcanic terrain, and minimal human influence on rivers. These conditions allow natural hydrological processes to be examined in a rapidly warming Arctic environment.The first part introduces LamaH-Ice, a dataset that compiles streamflow records, detailed weather information, glacier mass-balance measurements, snow cover, and catchment characteristics for more than 100 Icelandic basins. It fills an important gap by providing consistent multi-basin information from a high-latitude region where observations are typically scarce. The second part examines how Icelandic streamflow has changed over the past few decades. The analysis shows higher cool-season streamflow and lower summer flows in many rivers, with baseflow acting as a buffer that dampens summer flow declines and moderates winter and spring increases. Glacier-fed systems have shifted from increasing melt-season discharge over the past fifty years to weaker or negative tendencies in the past thirty years. Streamflow variability has also declined, and baseflow now makes up a larger share of total runoff. The third part evaluates a regional Long Short-Term Memory model trained across Icelandic catchments. The model predicts streamflow with strong skill, and an examination of its internal states shows that it learned hydrologically plausible snow and ice dynamics even when trained only on streamflow. Static physiographic catchment characteristics contribute little to raw predictive accuracy but play an important role in interpretability. Multi-task experiments show that adding a cryospheric output improves the model’s internal representation of snow and ice processes, but does not improve streamflow predictions under the tested architecture. Together, these contributions provide new data resources, clarify how Icelandic hydrology is changing, and demonstrate the potential of deep learning to capture processes in snow- and glacier-influenced environments.
Snow–Atmosphere Exchange in Complex Terrain: Turbulent and Advective Controls on Snow Sublimation and Surface Energy Fluxes
(2026-02-05) Schwat, Eli; Lundquist, Jessica
This dissertation examines turbulent and advective processes controlling the snow-atmosphere exchange of water vapor, heat, momentum, and turbulent kinetic energy (TKE) in the mountainous East River Valley of Colorado. We used measurements spanning spatial and temporal scales, from eddy covariance point measurements to Doppler lidar measurements of wind fields that span the width of a mountain valley.For Chapter 2, we measured snow sublimation and found that 10% of the seasonal snowpack is lost to the process. We also found that sublimation of suspended, blowing snow induced positive water vapor flux divergence, and, as a result, sublimation estimates are sensitive to instrument deployment height. By quantifying sublimation in a Colorado River headwater catchment, we addressed sublimation’s potential role in declining streamflow efficiency. While models suggest sublimation removes 20–40% of snowpack, our observations indicated it removes less, although future measurement campaigns should investigate sublimation rates above forests and on exposed ridges. To future field campaigns, we suggest that eddy covariance systems should be deployed near the top of the blowing snow layer, which was around 10 m at our field site. For Chapter 3, we measured strong wind shear at the tops of mountain ridges and the occasional formation of rotors and vortices in the lee of a prominent ridge. During these highly turbulent events, TKE propagated down into the valley, and near-surface mixing and sublimation rates increased. Surface fluxes during these events were under-predicted by Monin-Obukhov similarity theory, the predominant method for predicting surface fluxes in weather and land surface models. Our observations suggested that similarity theory fails to predict surface fluxes in complex terrain because the theory assumes that the near-surface TKE budget involves only shear and bouyancy. In complex terrain, TKE transport can be an important term in the near-surface TKE budget. Additionally, we found that eddies transporting TKE also carried upper-atmosphere dry air to the surface. This finding indicates that future surface flux parameterizations should account for non-local exchange in complex terrain. For Chapter 4, we estimated the full surface energy balance over isolated patches of snow. We used an infrared video camera to estimate horizontal sensible heat advection, an eddy covariance system to measure vertical heat fluxes, a scanning lidar to quantify snow melt, and a radiometer to measure radiative fluxes. We found that once the fractional snow-covered area fell below ∼50%, horizontal heat advection supplied ∼50% of the energy consumed by melting snow. This result suggests that advective processes should be incorporated into snowmelt models and motivates further investigation into whether advection influences snowpack disappearance and streamflow timing. Our field measurements of sensible heat advection also diverged from the predictions of an idealized model, potentially reflecting the geometry of the observed snow patch. Future studies should therefore aim to measure sensible heat advection over snow patches situated within the complex and often concave landforms where they typically persist.
Evaluation of Commercial Sorbents for Separation of Ultrashort-, Short-, and Long-chain Per- and Polyfluoroalkyl Substances (PFAS) from Municipal Wastewater Effluent
(2026-02-05) Zygas, Kovas Saulius; Ray, Jessica
PFAS are a diverse class of synthetic compounds that, are persistent, mobile, and have been detected ubiquitously in the environment in all parts of the world. In addition to the continued detection of "legacy PFAS" such as, PFOA and PFOS, there is increasing awareness and concern over ultrashort-chain and short-chain perfluoroalkyl acids (PFAAs) in treated wastewater effluent. Traditional adsorptive media such as granular activated carbon (GAC) and ion exchange (IX) resins have demonstrated inhibited PFAS removal due to the presence of organic matter (OM), co-occurring ionic constituents, and relatively poor uptake of short- and ultrashort-chain PFAAs. This study evaluated commercially available sorbent media for the removal of ultrashort-, short-, and long-chain per- and polyfluoroalkyl substances (PFAS) from a variety of aquatic matrices including ultrapure water, synthetic wastewater effluent and treated municipal wastewater effluent – with a focus on addressing the influence of matrix interferences in the form of wastewater effluent-derived organic matter and co-occurring constituents on PFAS removal. The adsorptive media we evaluated included GACs, non-selective and "PFAS-selective" ion exchange resins (IX), and two alternative adsorbent materials that are commercially marketed as PFAS treatment technologies: an anonymized surface-modified clay (SMC), and a β-cyclodextrin-based polymeric adsorbent (DEXSORB®, Cyclopure®). Batch adsorption capacity and kinetics tests were conducted in both ultrapure (UP) and synthetic wastewater (SW) matrices to characterize PFAS removal prior to continuous-flow rapid small-scale column tests (RSSCTs) using artificially spiked tertiary treated municipal wastewater effluent from an ultrafiltration (UF) membrane pilot system. In UP water, long-chain PFAS outcompeted and displaced (ultra)short-chain PFAS across all sorbents, suppressing (ultra)short-chain uptake and driving strong preferential adsorption of long-chain PFSAs and PFCAs. While GAC and DEXSORB® exhibit pronounced chain-length–dependent sorption dominated by hydrophobic interactions, the IX resins show more balanced uptake across PFAS chain-lengths, reflecting the greater role of electrostatic interactions in their removal. Despite strong adsorption in ultrapure water, IRA910 IX performed similarly to GAC in the SW matrix, demonstrating inhibited adsorption kinetics and capacities across all PFAS chain-lengths, especially for PFCAs and short- and ultrashort-chain PFAS, while PFA694 IX and DEXSORB® were least affected by the SW matrix. In the RSSCTs, the PFA694 IX resin treated the suite of 18 PFAS to the lowest effluent concentrations for the longest operational times compared to DEXSORB, F400 GAC, and non-selective IRA910 IX resin. Although, PFA694 IX only exhibited incremental improvement over GAC for treating short- and ultrashort-chain PFCAs. Agreement between SW batch tests and column results underscores the need to evaluate PFAS treatment media under realistic wastewater conditions. Fundamentally, further characterization and elucidation of the physical and chemical characteristics of the adsorbent media, particularly the proprietary PFAS-selective IX resins, in addition to evaluations of single-PFAS solute systems in ultrapure and complex aqueous matrices, will help improve the mechanistic understanding of the removal of ultrashort-, short-, and long-chain PFAAs and other PFAS widespread in the environment and municipal wastewater effluents. The results of this study suggest that in an ideally designed treatment train scenario installation of a PFAS-selective IX resin employed as a polishing step following tertiary treatment of secondary wastewater effluent could be a feasible method to target PFAS for removal from municipal wastewater.
Behaviorally Informed Machine Learning for Human Mobility
(2026-02-05) Ugurel, Ekin; Chen, Cynthia; Huang, Shuai
Large-scale digital trace datasets hold considerable promise for long-range transportation planning, offering the potential to observe mobility at metropolitan scales with far greater temporal and spatial resolution than traditional household travel surveys while capturing the regularities in daily travel behavior that underlie trip-making. Passively-collected mobile (PCM) data (e.g., location signals from smartphones and in-vehicle GPS) are central to this promise. Their usefulness, however, is limited by discontinuities in individual trajectories, privacy constraints that restrict data sharing and integration, and representativeness biases that distort inferred patterns of regional travel demand and travel behavior across population groups. This dissertation addresses these limitations through four contributions. First, it develops a multi-task Gaussian Process-based imputation method (grounded in recurring daily, weekly, and seasonal travel behavior patterns) capable of handling both short- and long-duration gaps in GPS traces, significantly improving the completeness and usability of mobility data. Second, it introduces an individualized, physics-regularized learning framework that produces high-fidelity mobility traces reflective of observed movement patterns. These generated trajectories can be scaled to build richer, more diverse mobility datasets for developing and validating activity-based models. Third, it investigates the predictive signal linking mobility patterns as expressions of travel behavior to sociodemographic attributes that shape those behaviors, and develops imputation strategies for enriching PCM datasets with these inferred labels. This enrichment supports both more detailed planning analyses and a clearer diagnosis of representativeness biases in passively collected data. Finally, through a qualitative study of long-range transportation planners, this dissertation investigates barriers to the adoption of big data products and provides recommendations for their effective integration into planning processes. Together, these contributions bridge methodological advances in machine learning with insights from travel behavior research and the practical needs of public agencies, offering a more transparent and behaviorally coherent foundation for data-driven planning and travel behavior analysis.
Concrete Pavements in the United States and Performance of Concrete Pavements in the Washington State Department of Transportation’s Network
(2026-02-05) Basheer, Kieran Joel; Muench, Steve
This thesis consisted of two major components, a 50-state review of concrete pavement design, construction, and rehabilitation practices, and an evaluation of Washington State Department of Transportation (WSDOT) existing concrete pavement network using detailed Washington State Pavement Management System (WSPMS) data. The last major evaluation of WSDOT concrete pavement design, construction, and rehabilitation practices occurred in 2010. Significant advancements have emerged in pavement materials, design methods, sustainability considerations, and rehabilitation technologies. WSPMS has also been accumulating performance data for over 25 years. Ten states were found to have comprehensive and highly detailed concrete pavement design manuals, with 15 states having less detailed but still robust manuals. These resources served as the basis for identifying national best practices for concrete pavement design, rehabilitation methods, and testing. The WSPMS data consisted of 9055 data points and represented 2090.84 lane-miles and 795.54 center line miles of concrete pavement. Despite the age of WSDOT concrete pavements, indicators such as rutting, faulting, and IRI remain largely within acceptable limits, particularly on segments rehabilitated with dowel bar retrofits (DBR) or diamond grinding. Section specific analysis demonstrates that recent crack, seat, and overlay (CSOL) and grinding projects have greatly improved ride quality and slowed deterioration.
Modeling Liquefaction-Induced Road Disruptions in Cascadia Megathrust Earthquakes: Implications for Emergency Response and Access in the U.S. Pacific Northwest
(2026-02-05) Smith, Olyvia; Maurer, Brett; Wartman, Joseph
Large earthquakes along the Cascadia Subduction Zone (CSZ) are expected to trigger widespread soil liquefaction that could severely disrupt transportation systems across the U.S. Pacific Northwest. However, past regional assessments have relied on simple geologic screening methods and binomial shaking thresholds that are only loosely informed by liquefaction science This study introduces a mechanics-informed, data-driven framework for estimating liquefaction-induced road closures and service reductions following a magnitude-9 CSZ earthquake. Liquefaction hazard is mapped using a geospatial liquefaction model trained on more than 37,000 cone penetration tests and conditioned on regional datasets describing geomorphology, geology, climate, and hydrology. Predicted liquefaction severity is translated into segment-level probabilities of closure and reduced service using empirically derived fragility relationships. These probabilities are mapped at 90-m resolution across Washington, Oregon, and California and propagated through the National Highway System using a spatially correlated Monte Carlo simulation to estimate link-level disruption. Results show that impacts are concentrated in low-lying coastal zones, river valleys, and urban waterfronts, with major disruptions expected along critical routes including U.S. Route 101. Local mobility is further examined in Pacific and Grays Harbor Counties, Washington, where limited network redundancy, strong shaking, and high liquefaction susceptibility lead to elevated probabilities of isolation and loss of hospital access. Socioeconomic analysis reveals modest but statistically significant associations between road impacts and demographic indicators, suggesting that liquefaction impacts may compound with existing social vulnerabilities. While not a substitute for site-specific analysis, the results provide a regional baseline for emergency planning, risk communication, and prioritization of more advanced geotechnical sampling and analysis. Moreover, the methodology proposed here is not specific to the CSZ, but rather, could be applied to analogous studies of road impacts elsewhere.
From peak to planet: advancing multi-scale detection of snowmelt timing with satellite radar
(2026-02-05) Gagliano, Eric; Shean, David
Snowmelt runoff onset represents a critical parameter in mountain hydrology, marking the beginning of increased water availability for over a billion people who depend on seasonal snowmelt, as well as being a key indicator of climate change. Despite its importance, systematic high-resolution observations of snowmelt timing across the Earth’s diverse mountain regions are limited with current monitoring approaches: sparse in-situ networks, cloud-obscured optical remote sensing, and coarse passive microwave observations.This dissertation advances the detection of snowmelt runoff onset from local to global scales using Synthetic Aperture Radar (SAR) observations from the European Space Agency Sentinel-1 mission. SAR overcomes key limitations of other snow monitoring approaches through its ability to observe through clouds and in darkness, high spatial resolution, and sensitivity to liquid water content changes in snowpack that coincide with snowmelt runoff onset. Chapter 2 establishes the methodological foundation of this work by demonstrating scalable detection of snowmelt runoff onset using Sentinel-1 backscatter time-series analysis over stratovolcanoes in the Cascade Range of North America. By integrating multiple orbital geometries, the approach achieves a median temporal resolution of 3.9 days at 10-meter spatial resolution. Validation with in-situ snow pillow measurements shows a median offset of 1 day and median absolute offset of 10 days for the SAR snowmelt runoff onset timing estimates. Analysis across elevation gradients reveals strong topographic control, with median delays of 4.9 days per 100 meter elevation gain, as well as dramatic interannual variability including 25-day early runoff onset during the 2015 snow drought. Chapter 3 scales this methodology, processing over 3.9 million Sentinel-1 images to create a global snowmelt runoff onset dataset spanning the 10-year period from 2015 through 2024. To enable robust detection of snowmelt runoff onset across diverse environments, we develop a custom MODIS-derived snow phenology dataset that provides spatial and temporal constraints for runoff onset identification. Systematic analysis establishes empirically-derived recommendations for snowmelt runoff onset dataset application based on forest cover, snow accumulation, and observation frequency. Validation against runoff onset estimates from over 900 in-situ snow pillows across the Western U.S. demonstrates robust dataset performance across different mountain environments. This dataset provides an unprecedented look at annual snowmelt runoff onset on a global scale, with 80-meter spatial resolution and 9.3-day average temporal resolution. Chapter 4 presents the first comprehensive global analysis of snowmelt timing patterns and controls across 150 major mountain ranges. Continental-scale analysis reveals systemic weakening of elevation gradients from mid-latitudes toward polar regions, as well as snowmelt runoff onset timing differences between sunny and shaded areas that varies seasonally but reaches maximum values of 20-60 days around early- to mid-spring. Mountain range-scale aggregation reveals a median runoff onset delay of 3.5 days per 100 meters of elevation gain, but with substantial variability, reflecting differences in climate, topography, and snowpack characteristics. Individual mountain ranges show variable aspect differences in runoff onset timing depending on local climate and topography, with some clear-sky continental ranges exhibiting differences exceeding 40 days while nearby cloudier ranges show minimal aspect differences. The tropical Andes and Tibetan Plateau mountain ranges display the highest interannual variability of snowmelt runoff onset timing, often exceeding 30 days. Temperature sensitivity analysis reveals that 72% of mountain ranges show correlations between spring (March-May) temperature anomaly and runoff onset timing, with most mid-latitude mountain ranges exhibiting runoff onset 8 to 13 days early for every 1°C warmer spring average temperature. Chapter 5 synthesizes these findings and examines implications for water-dependent populations through basin-scale analysis in High-Mountain Asia and western North America. The analysis reveals coherent regional patterns in snowmelt runoff onset during documented anomalous weather events, including the 2015 western North American “snow drought” and 2022 High-Mountain Asia “mega-heatwave”, demonstrating how synoptic-scale weather events can produce spatially coherent 20-40 day shifts in snowmelt runoff onset timing across entire regions. Preliminary vulnerability assessment identifies high-population basins with high interannual variability in snowmelt runoff onset timing, with implications for water security. This dissertation establishes the first systematic framework for observing snowmelt runoff onset at high resolution across global mountain regions. The decade-long record provides unprecedented detail of snowmelt timing patterns, quantifies fundamental physical controls operating across diverse environments, and documents substantial interannual variability linked to average spring air temperature. The methodology, open-source tools, and open datasets from this dissertation will enable improved understanding of snow hydrology and support more effective water resource management in an era of increasing environmental change.
Initial Evaluation of Digital Twin Technology and Internet-of-Things Sensors for the Interstate-90 Homer Hadley Floating Bridge
(2026-02-05) Bernard, Timothy; Thonstad, Travis
The emergence of digital twin technology is set to reshape the management of civil infrastructure by enabling real-time monitoring, predictive maintenance, and data-driven decision-making. Advances in internet-of-things (IoT) sensors, 5G connectivity, and cloud computing allow structural health monitoring systems to collect high-resolution data from diverse sensor types, transmit it in real time, and aggregate it within accessible, cloud-based platforms. These capabilities are particularly valuable for complex structures like floating bridges, which require constant visual inspections and are highly sensitive to dynamic forces and inputs from the environment. This thesis details the design, deployment, and initial evaluation of a “proof-of-technology” digital twin for the Homer M. Hadley (I 90) floating bridge in collaboration with the Washington State Department of Transportation, the University of Washington’s Mobility Innovation Center, and industry partners. The Homer M. Hadley bridge is the only floating bridge in the world that supports light-rail transit, requires many more maintenance and operations decisions than a typical bridge, and has the potential to be uniquely benefitted from the insights that digital twins can provide. A system of IoT sensors was installed to monitor key structural and environmental parameters, including anchor cable tension, pontoon movement, pontoon freeboard, and temperature. Data was transmitted over the 5G cellular network and integrated into a cloud-based digital twin platform. Additional data, for example, lake level, lake water quality, traffic, and weather data from outside sources were also federated into the system. The digital twin was then used to assess bridge behavior in real-world conditions, focusing on anomaly detection capabilities, usefulness of real-time monitoring, and the feasibility of integrating such a system into existing maintenance programs.
Dynamics of Small-Scale River Plumes in the Surf Zone: Idealized and Realistic Modeling
(2025-10-02) Lou, Yingzhong; Horner-Devine, Alexander R; Derakhti, Morteza
River plumes transport freshwater, nutrients, and terrigenous materials (e.g., sediment, larvae, and contaminants) into the coastal ocean, significantly impacting nearshore water quality and coastal ecosystems. Despite the abundance and potentially significant combined fluxes of small-scale plumes with small discharge that are generated by narrow rivers and creeks, their dynamics and impacts remain poorly understood. Small rivers often exhibit complex behavior due to their vulnerability to morphological changes, climate changes, and human activities. Furthermore, the pollutants they carry may pose greater risks than those from larger discharges, as they are generally more difficult to monitor and tend to remain trapped within the surf zone. We use an idealized numerical model to investigate the dynamics and fate of a small river discharging into the surf zone. Our study reveals that the plume reaches a steady state, at which point the combined advective and diffusive freshwater fluxes from the surf zone to the inner shelf balance the river discharge. At steady state, the surf zone is well-mixed vertically due to wave-enhanced vertical turbulent diffusion and has a strong cross-shore salinity gradient. The horizontal gradient drives a cross-shore buoyancy-driven circulation, directed offshore at the surface and onshore near the bottom, which opposes the wave-driven circulation. Using a scaling analysis based on momentum and freshwater budgets, we determine that the steady state alongshore plume extent (Lp) and the fraction of river water trapped in the surf zone depend on the ratio of the near-field plume length to the surf zone width (Lnf/Lsz) across a wide range of discharge and wave conditions, and a limited set of tidal conditions. This scaling also allows us to predict the residence time and freshwater fraction (or dilution ratio) in the steady-state plume within the surf zone, which range from approximately 0.1 to 10 days and 0.1 to 0.3, respectively. These findings establish the basic dynamics and scales of an idealized plume in the surf zone, as well as estimates of residence times and dilution rates that may provide guidance to coastal managers. We then investigate the transport of the river plume at Los Penasquitos Lagoon (LPL), a small estuary in Southern California that discharges into an energetic surf zone. As a part of the Plumes in Nearshore Conditions (PiNC) project, we developed a quasi-realistic COAWST model incorporating in-situ bathymetry measurements and forced with realistic river discharge, tides, and waves. The model was validated by a comparison with the observed spectra of water surface elevation and the distribution of dye released from the estuary mouth, demonstrating its ability to effectively capture river transport patterns. The results show that at LPL, breaking waves generate alongshore convergent currents due to shoreline curvature, which dominate the volume budget within the surf zone. Freshwater ejection events at the surf zone edge are typically narrow (95% are less than 60 m wide) and are tidally modulated. These events are associated with bathymetry-driven rip currents during mid-tide and by alongshore convergent currents during low tide. Furthermore, sensitivity analysis suggests that uncertainty in the choice of horizontal mixing scheme significantly influences the modeled plume processes. Our findings offer a framework for understanding the wave-driven transport of small-scale river plumes discharging into the surf zone, which may contribute to improved predictions of spreading and dilution processes and help mitigate risks associated with poor coastal water quality. Finally, in order to generalize plume behavior that is complex due to an array of factors that influence nearshore currents, many of which are specific to individual river mouths, we utilized the quasi-realistic COAWST model to conduct a series of sensitivity tests examining the impacts of bathymetry, tides, waves, river discharge, and buoyancy on plume dynamics at LPL. Prior works have shown that breaking waves can fundamentally alter plume structure by preventing river water from directly discharging into the inner shelf. In this work, we find that wave-generated currents can also facilitate transport by inducing alongshore convergence in the presence of large-scale bathymetric features such as shoreline curvature. Small-scale bathymetric perturbations generate rip currents and surf zone eddies, which interact with large-scale surf zone circulation and lead to ejection events across the surf zone. Tidal forcing produces pronounced peaks and troughs in cross-shore freshwater transport, corresponding to ebbing and rising tides, because the magnitude of tidal discharge greatly exceeds that of the small-scale river, and tidal currents enhance offshore and onshore transport during ebbing and rising phases, respectively. Although buoyancy effects generally do not significantly affect freshwater transport patterns at LPL, they may shift the convergence point of alongshore currents and dampen peak export during ebbing tides. Additionally, we observe that the half-life of dye concentration at the river mouth is generally linearly related to the ratio of local Stokes and Eulerian mean velocities across a range of conditions. These findings provide a framework for assessing the relative contributions of various environmental forcings to the transport of small-scale river plumes discharging into the surf zone. The results may help improve predictions of the spreading and dilution of river water and its associated constituents under varying conditions, thereby supporting efforts to reduce human exposure to contaminated waters.
Mapping snow cover at fine resolution in complex and forested terrain
(2025-10-02) Boudreau, Emma Troy; Lundquist, Jessica; Cristea, Nicoleta
Complex topography in mountain basins allows deep snow accumulation in depressions throughout the winter season. During the melt period, snow on exposed terrain disappears faster, leaving behind a patchy mosaic of lingering snow cover. These persistent snow patches help sustain late-season streamflow during the drier summer months. In this work, we explored methods to observe heterogeneous snow cover in a complex subbasin in the Sierra Nevada, CA, using Planet Labs, Inc. 3 m resolution commercial optical satellites and an existing random forest model (Yang et al., 2023). Here, we tested two different model training configurations and a spatio-temporal post-processing approach to improve snow mapping throughout the season for three separate years, paying particular attention to snow in forested areas. In this work, we used lidar only for evaluation so that the methods used can be applied anywhere. The new model training approach and the spatio-temporal post-processing approach introduced in this work both performed well, showcased better results than prior methods, and improved F1 scores in the forest by 0.11 and 0.09, respectively. We then compared basin-wide snow-covered area from the 3 m resolution snow maps with the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain Size (STC-MODSCAG) product (Rittger et al., 2020), which is coarser resolution but more readily available. In general, they compared well, but on average our snow maps reported snow disappeared over 3 weeks later than STC-MODSCAG, and STC-MODSCAG missed ~303,000 m2 of snow cover that the PlanetScope snow maps identified. Overall, our results demonstrate the capability of high-resolution imagery for detecting snow patches relevant for ecology and the potential for improved snow cover mapping in forested basins using the model training methods or the spatial post-processing methods introduced in this work.
Measuring waves in difficult places: New approaches to observing waves in hurricanes and sea ice
(2025-10-02) Davis, Jacob; Thomson, Jim
New approaches are applied to study ocean surface wave dynamics in hurricanes and the coastal Arctic. In hurricanes, arrays of drifting buoys and airborne radar are used to characterize the wind speed dependence and spatial distribution of ocean surface roughness caused by waves. Hurricane-generated waves, and the drag imparted by their roughness, contribute to coastal flooding, cause infrastructure damage, and modify exchanges of momentum and heat between the atmosphere and ocean---important controls on storm intensification offshore. In the coastal Arctic, a novel method called Distributed Acoustic Sensing is combined with machine learning to measure waves from a submarine fiber-optic cable offshore of Oliktok Point, Alaska. This system can be used for the subsequent study of wave-ice interactions and to monitor wave action in regions susceptible to coastal change. Both approaches leverage innovative, low-cost sensing modalities to measure waves in hard-to-reach environments with exceptional spatial resolution. These measurements enhance our ability to understand, monitor, and predict the impacts of the ocean on coastlines. Drifting buoy observations in hurricanes Ian (2022) and Fiona (2022) are merged with modeled surface wind speeds to determine the evolution of wave slope at high wind speeds. Wave slope is quantified using the mean square slope, which is commonly used as proxy for ocean surface roughness. At low-to-moderate wind speeds (< 15 m/s), slopes increase linearly with wind speed. At higher winds (> 15 m/s), slopes continue to increase, but at a reduced rate. At extreme winds (> 30 m/s), slopes asymptote. The mean square slopes are directly related to the wave spectral shapes, which over the resolved frequency range (0.03 to 0.5 Hz) are characterized by an equilibrium tail (f^-4) at moderate winds and a saturation tail (f^-5) at higher winds. The asymptotic behavior of wave slope as a function of wind speed could contribute to the reduction of surface drag at high wind speeds. An airborne radar is then combined with the drifting wave buoys to provide a multiscale view of hurricane-generated waves. Wave slopes measured by the radar, which include waves 0.2 m and longer, saturate in a similar manner to the buoy-measured slopes. A method to infer the shape of the spectral tail from 0.5 Hz to 3 Hz using colocated mean square slope observations from each instrument is introduced. The method is able to recover the frequency f^-5 tail characteristic of the saturation range expected at these frequencies based on theory. Next, a dense array of buoy observations in Hurricane Idalia (2023) is used to investigate the spatial distribution and dependence of mean square slope on wind, wave, and storm characteristics. Inside Hurricane Idalia, buoy-measured mean square slopes have a secondary dependence on wind-wave alignment: at a given wind speed, slopes are higher where wind and waves are aligned compared to where wind and waves are crossing. At moderate wind speeds, differences in mean square slope between aligned and crossing conditions can vary 15% to 20% relative to their mean. These changes in wave slope may be related to the reported dependence of air-sea drag coefficient on wind-wave alignment. Lastly, in the coastal Arctic, two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning. The new models are trained on target data from pressure moorings at three sites along 27.1 km of cable and are benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform frequency-wavenumber spectra to pressure spectra outperforms the benchmark in wave height and period prediction when evaluated on the cable at Oliktok Point. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions.
Hidden versatility, competition, and cooperation: Methanogenesis and nitrification drive unexpected carbon and nitrogen cycle linkages
(2025-10-02) Abrahamson, Britt; Winkler, Mari KH
Wetlands serve as crucial nodes in the carbon (C) and nitrogen (N) cycles by sequestering ~30% of global soil organic matter (SOM) and contributing to ~20% of annual global methane emissions despite accounting for only ~6% of global land area. The degradation, release, and retention of organic carbon and nitrogen in wetlands is facilitated by the activity of microorganisms that assemble into microbial communities within the pore network of wetland sediment aggregates. However, biogeochemical nutrient gradients will impose selective pressures that control the structure and function of the microbial community responsible for greenhouse gas emissions and SOM storage. The complex feedback between microbial activity and biogeochemistry makes it difficult to predict how increasing anthropogenic activity will influence nutrient cycling in wetland ecosystems. This dissertation aims to address this critical gap by linking single-cell microbial ecophysiology and community structure to ecosystem-scale biogeochemistry while developing predictive understanding of how microbial communities respond to environmental change. This work aims to investigate both the microbial interactions governing the degradation of organic carbon to methane by methanogenic communities in wetland sediments and the ecophysiology of ammonia-oxidizing microorganisms (AOM)—major contributors to terrestrial nitrous oxide (N2O) emissions and the global nitrogen cycle. Methane (CH4) and N2O are potent greenhouse gases (GHG) with global warming potentials 27 and 273 times greater than carbon dioxide (CO2) over a 100-year timescale, respectively. These important environmental processes were studied in laboratory enrichments and isolates with a combination of culture-based, isotopic, multi-omic, and mathematical modeling approaches. Wetland sediment chemostat enrichments were used in the first two sections to investigate spatial community structure, carbon degradation, and links between the carbon and nitrogen cycle. This thesis begins with a comprehensive overview of methanogenesis and nitrification and their connections to global carbon and nitrogen cycles within wetland ecosystems (Chapter 1). Synthetic wetland sediment aggregate chemostat enrichments and mathematical models of lactate degrading methanogenic communities demonstrated spatial community structuring and highlighted the importance of spatial proximity for methanogenesis (Chapter 2). Aerobic and anaerobic necromass degrading wetland communities reverted to their prior states after oxygen perturbations demonstrating the resiliency of these functionally redundant communities, while emphasizing necromass recycling as an underappreciated link between the carbon and nitrogen cycle (Chapter 3). Next, the physiological mechanisms underlying nitrogen removal in a novel partial nitritation-anammox reactor using high affinity AOM were evaluated (Chapter 4). This physiological investigation led to the discovery of hydroxylamine-dependent nitrate reduction and the biotic mechanism of N2O production by the complete ammonia-oxidizing (comammox) bacteria Nitrospira inopinata (Chapter 5). These results highlight the spatial dimension of metabolic niche partitioning, link the carbon and nitrogen cycles through necromass degradation, and uncover previously unknown nitrogen cycling metabolic versatility.

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