Sources Of Uncertainty Research Articles

Investigation of effects of hazard geometry and mitigation strategies on community resilience under tornado hazards using an Agent-based modeling approach

A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions. The effect of the hazard geometry (center and angle of tornado path as well as the tornado width) is studied herein on how it influences the recovery of physical and social systems within the community. Given that pre-disaster preparedness including mitigation strategies (e.g., retrofits) and policies (e.g., insurance) is crucial for increasing the resilience of the community and facilitating a faster recovery process, in this study, the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties. The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling (ABM) approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies. The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems (lifeline networks, schools, healthcare, businesses, and households). The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.

A Developed Model and Fuzzy Multi-Criteria Decision-Making Method to Evaluate Supply Chain Nervousness Strategies

Nervousness is thought to be a source of confusion, instability, or uncertainty in SC systems due to disruptions and frequent changes in decisions. Nervousness persists even with consistent SCs, which arise from planning flexibility in response to changes, where responsiveness and customer satisfaction balance. Although the term “nervousness” is well known, to our knowledge no prior research has examined and explored supply chain nervousness strategies (SCNSs). This research explores supply chain nervousness strategies, factors, reduction methods, and recent trends in the supply chain’s relationship with nervousness. The main purpose of this research is to determine the comprehensive and relevant nervousness strategies in the supply chains, especially in light of the unprecedented development and change in business, economics, and technology and the fierce competition. SCN strategies are introduced in a developed model to designate SCN measurements and indicators, mitigation strategies and stages, and management strategies. The fuzzy PROMETHEE method is employed to rank the strategies based on their importance and order of implementation. The suggested method for managing nervousness is then presented with a numerical case, along with the results. The research outcomes indicate that the top five strategies for managing nervousness include planning continuity, utilizing technology, managing nervousness, improving the SC cyber system, and managing supplies. The findings assist decision makers, practitioners, and managers in focusing on SC improvement, resilience, and sustainability.

Digital twin-enabled robust production scheduling for equipment in degraded state

Technological advancements are leading to a world where digital twins will become integral to manufacturing operations management. While wide-ranging applications of digital twins are being researched, robust production scheduling remains an enduring challenge, especially considering the numerous sources of uncertainty in a complex manufacturing system that can affect the validity of the obtained solution. Thus, the article proposes a Prognostics and Health Management (PHM)-enabled digital twin framework to perform job scheduling for a flow shop scheduling problem considering real-time equipment health state. The framework combines the adoption of a Genetic Algorithm optimizer with data-driven modelling leveraging algorithms like Principal Component Analysis and models like Discrete Event Simulation with the purpose to solve the engineering task of scheduling at hand. Building on such a technological blend, the framework incorporates the degradation and fault detection and diagnosis of failure modes across multiple components. The effect of degradation and faults on job processing times is learned as a distribution from the field data. The proposed framework is then validated in a laboratory environment where degradation is produced by inducing degradation and faults in the equipment. By means of various experiments, the optimized makespan is compared for the output schedules in different configurations. When equipment is degraded, production scheduling with PHM-enabled field-synchronized digital twin results in better makespan estimation, if compared to the digital twin without PHM. This shows the superiority of the framework in terms of more realistic makespan estimations, which finally corresponds to improved production schedule optimization, sensitive also to degraded states.

Considerations and challenges in support of science and communication of fish consumption advisories for per- and polyfluoroalkyl substances.

Federal, state, tribal, or local entities in the United States issue fish consumption advisories (FCAs) as guidance for safer consumption of locally caught fish containing contaminants. Fish consumption advisories have been developed for commonly detected compounds such as mercury and polychlorinated biphenyls. The existing national guidance does not specifically address the unique challenges associated with bioaccumulation and consumption risk related to per- and polyfluoroalkyl substances (PFAS). As a result, several states have derived their own PFAS-related consumption guidelines, many of which focus on one frequently detected PFAS, known as perfluorooctane sulfonic acid (PFOS). However, there can be significant variation between tissue concentrations or trigger concentrations (TCs) of PFOS that support the individual state-issued FCAs. This variation in TCs can create challenges for risk assessors and risk communicators in their efforts to protect public health. The objective of this article is to review existing challenges, knowledge gaps, and needs related to issuing PFAS-related FCAs and to provide key considerations for the development of protective fish consumption guidance. The current state of the science and variability in FCA derivation, considerations for sampling and analytical methodologies, risk management, risk communication, and policy challenges are discussed. How to best address PFAS mixtures in the development of FCAs, in risk assessment, and establishment of effect thresholds remains a major challenge, as well as a source of uncertainty and scrutiny. This includes developments better elucidating toxicity factors, exposures to PFAS mixtures, community fish consumption behaviors, and evolving technology and analytical instrumentation, methods, and the associated detection limits. Given the evolving science and public interests informing PFAS-related FCAs, continued review and revision of FCA approaches and best practices are vital. Nonetheless, consistent, widely applicable, PFAS-specific approaches informing methods, critical concentration thresholds, and priority compounds may assist practitioners in PFAS-related FCA development and possibly reduce variability between states and jurisdictions. Integr Environ Assess Manag 2024;00:1-20. © 2024 SETAC.

Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment

Abstract. Experiments offer incredible value to science, but results must always come with an uncertainty quantification to be meaningful. This requires grappling with sources of uncertainty and how to reduce them. In wind energy, field experiments are sometimes conducted with a control and treatment. In this scenario uncertainty due to bias errors can often be neglected as they impact both control and treatment approximately equally. However, uncertainty due to random errors propagates such that the uncertainty in the difference between the control and treatment is always larger than the random uncertainty in the individual measurements if the sources are uncorrelated. As random uncertainties are usually reduced with additional measurements, there is a need to know the minimum duration of an experiment required to reach acceptable levels of uncertainty. We present a general method to simulate a proposed experiment, calculate uncertainties, and determine both the measurement duration and the experiment duration required to produce statistically significant and converged results. The method is then demonstrated as a case study with a virtual experiment that uses real-world wind resource data and several simulated tip extensions to parameterize results by the expected difference in power. With the method demonstrated herein, experiments can be better planned by accounting for specific details such as controller switching schedules, wind statistics, and postprocess binning procedures such that their impacts on uncertainty can be predicted and the measurement duration needed to achieve statistically significant and converged results can be determined before the experiment.

Not all sources of uncertainty are worth resolving: A value of information approach for uncertainty reduction in flood risk management

AbstractFlood risk management faces challenging decisions to balance between reducing disastrous flood consequences and different societal goals such as development. The inherent complexity and limited data often lead to significant uncertainties in decision‐making, potentially resulting in suboptimal resource allocation. Consequently, there may be value in aiming to reduce uncertainty, minimizing the possibility of selecting deemed efficient decisions because of deficiencies in the current knowledge. To address this, a novel methodology is proposed, integrating Bayesian uncertainty with value of information concepts, commonly employed in healthcare economics. This methodology assesses the implications of current uncertainty and identifies worthwhile sources for resolution prior making decisions. Validation in a synthetic case study and application in a real case (Zapayan wetland in the Magdalena River, Colombia) demonstrate the method's efficacy. Results show that the proposed method can help apprising if the available information is enough to make a decision, or if more information should be obtained. For example, for the synthetic case, resolving the sources of uncertainty with extra information does not significantly improve the expected utility, so a decision could be made based on existing information. For the real case, reducing the uncertainty related to the exposed assets should be targeted first, by an information gathering activity, before deciding.

Factors controlling spatiotemporal variability of soil carbon accumulation and stock estimates in a tidal salt marsh

Abstract. Tidal salt marshes are important contributors to soil carbon (C) stocks despite their relatively small land surface area. Although it is well understood that salt marshes have soil C burial rates orders of magnitude greater than those of terrestrial ecosystems, there is a wide range in accrual rates among spatially distributed marshes. In addition, wide ranges in C accrual rates also exist within a single marsh ecosystem. Tidal marshes often contain multiple species of cordgrass due to variations in hydrology and soil biogeochemistry caused by microtopography and distance from tidal creeks, creating distinct subsites. Our overarching objective was to observe how soil C concentration and dissolved organic carbon (DOC) vary across four plant phenophases and across three subsites categorized by unique vegetation and hydrology. We also investigated the dominant biogeochemical controls on the spatiotemporal variability of soil C and DOC concentrations. We hypothesized that subsite biogeochemistry drives spatial heterogeneity in soil C concentration, and this causes variability in total soil C and DOC concentrations at the marsh scale. In addition, we hypothesized that soil C concentration and porewater biogeochemistry vary temporally across the four plant phenophases (i.e., senescence, dormancy, green-up, maturity). To test these interrelated hypotheses, we quantified soil C and DOC concentrations in 12 cm sections of soil cores (0–48 cm depth) across time (i.e., phenophase) and space (i.e., subsite), alongside several other porewater biogeochemical variables. Soil C concentration varied significantly (p < 0.05) among the three subsites and was significantly greater during plant dormancy. Soil S, porewater sulfide, redox potential, and depth predicted 44 % of the variability in soil C concentration. There were also significant spatial differences in the optical characterization properties of DOC across subsites. Our results show that soil C varied spatially across a marsh ecosystem by up to 63 % and across plant phenophase by 26 %, causing variability in soil C accrual rates and stocks depending on where and when samples are taken. This shows that hydrology, biogeochemistry, and plant phenology are major controls on salt marsh C content. It is critical to consider spatiotemporal heterogeneity in soil C concentration and porewater biogeochemistry to account for these sources of uncertainty in C stock estimates. We recommend that multiple locations and sampling time points are sampled when conducting blue C assessments to account for ecosystem-scale variability.

An overlooked source of uncertainty in the mass of the Milky Way

Abstract In the conventional approach to decomposing a rotation curve into a set of contributions from mass model components, the measurements of the rotation curve at different radii are taken to be independent. It is clear, however, that radial correlations are present in such data, for instance (but not only) because the orbital speed depends on the mass distribution at all (or, minimally, inner) radii. We adopt a very simple parametric form for a covariance matrix and constrain its parameters using Gaussian process regression. Applied to the rotation curve of the Milky Way, this suggests the presence of correlations between neighbouring rotation curve points with amplitudes of <10 km s−1 over length scales of 1.5–2.5 kpc regardless of the assumed dark halo component. We show that accounting for such covariance can result in a ∼50 per cent lower total mass estimate for the Milky Way than when it is neglected, and that the uncertainty in model parameters increases such that it seems more representative of the uncertainty in the rotation curve measurement. The statistical uncertainty associated with the covariance is comparable to or exceeds the total systematic uncertainty budget. Our findings motivate including more detailed treatment of rotation curve covariance in future analyses.

Boundary-layer instability on a highly swept fin on a cone at Mach 6

The growth and characteristics of linear, oblique instabilities on a highly swept fin on a straight cone in Mach 6 flow are examined. Large streamwise pressure gradients cause doubly inflected cross-flow profiles and reversed flow near the wall, which necessitates using the harmonic linearized Navier–Stokes equations. The cross-flow instability is responsible for the most-amplified disturbances, however, not all disturbances show typical cross-flow characteristics. Distinct differences in perturbation structure are shown between small ( $\sim$ 3–5 mm) and large ( $\sim$ 10 mm) wavelength disturbances at the unit Reynolds number $Re' = 11 \times 10^6$ m $^{-1}$ . As a result, amplification measurements based solely on wall quantities bias a most-amplified disturbance assessment towards larger wavelengths and lower frequencies than would otherwise be determined by an off-wall total-energy approach. A spatial-amplification energy-budget analysis demonstrates (i) that wall-normal Reynolds-flux terms dictate the local growth rate, despite other terms having a locally larger magnitude and (ii) that the Reynolds-stress terms are responsible for large-wavelength disturbances propagating closer to the wall compared with small-wavelength disturbances. Additionally, the effect of free-stream unit Reynolds number and small yaw angles on the perturbation amplification and energy budget is considered. At a higher Reynolds number ( $Re' = 22 \times 10^6$ m $^{-1}$ ), the most-amplified wavelength shrinks. Perturbations do not behave self-similarly in the thinner boundary layer, and the shift in most-amplified wavelength is due to decreased dissipation relative to the lower-Reynolds-number case. Small yaw angles produce a streamwise shift in the boundary layer and disturbance amplification. The yaw results quantify a potential uncertainty source in experiments and flight.

Threshold-impeded stochastic production: how noise interacts with disruptive thresholds to affect the production output in fluctuating environments

Introduction: Production systems are bound to operate in stochastic conditions. Prominent sources of performance-reducing uncertainty are constituted by machine failures, decision errors, and fluctuating supplies. This article offers a novel approach to uncertainty through modelling and simulation of nonlinear production systems. In particular, the authors consider production systems where the output is drastically reduced when a resource of fluctuating input values reaches an upper threshold.Methods: The article introduces minimal models of such hreshold-impeded stochastic production (TISP) systems and the system performance (i.e., the output) is analyzed as a function of system parameters (e.g., the type of nonlinearity) and noise input features (e.g., the distribution width or time correlations). Applications to steel manufacturing via continuous casting and power generation through wind turbines are discussed in detail.Results and Discussion: The simulation experiments illustrate that especially the extent of the input fluctuations affects the output performance which is why the authors recommend that TISP system operators counterbalance such fluctuations if possible.

A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B

Abstract. Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence of CH4 point source emissions from coal mines in Shanxi, China, which is an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced Hyperspectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in centre wavelength and change in full width at half-maximum (FWHM) from the nominal design values are estimated for all spectral channels, which are used as inputs for retrieving the enhancement of the column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter-based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of the emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 761.78 ± 185.00 to 12 729.12 ± 4658.13 kg h−1. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing them with a ground-based meteorological station. We found a large discrepancy, especially in wind direction, suggesting that incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi.

Steady but model dependent Arctic amplification of the forced temperature response in 21st century CMIP6 projections

We examine sources of uncertainty in projections of Arctic amplification (AA) using the CMIP6 multi-model (MM) ensemble and single model initial-condition large ensembles of historical and future scenario simulations. In the CMIP6 MM mean, the annual mean AA ratio is steady at approximately 2.5, both in time and across scenarios, resulting in negligibly small scenario uncertainty in the magnitude of AA. Deviations from the steady value can be found at the low and high emission scenarios due to different root causes, with the latter being mostly evident in the summer and autumn seasons. Best estimates of model uncertainty are at least an order of magnitude larger than scenario uncertainty in CMIP6. The large ensembles reveal that irreducible internal variability has a similar magnitude to model uncertainty for most of the 21st century, except in the lowest emission scenario at the end of the 21st century when it could be twice as large.

A comprehensive investigation of uncertainties in cavitating waterjet experiments

This paper systematically examines the sources of uncertainty in cavitating waterjet test setups and provides illustrative case studies to conduct thorough uncertainty analyses for erosion tests using cavitating waterjet method. Within this study, utilizing examples of uncertainty analysis presented by the International Towing Tank Conference (ITTC) for various testing techniques, a novel uncertainty analysis implementation suitable for cavitating waterjet tests has been conducted. Firstly, cavitation erosion experiments were carried out on samples aluminum (Al 6063) material under cavitation number conditions (σ = 0.02 to σ = 0.033 range) as part of the validation study. Subsequently, within the scope of uncertainty analysis calculations, five erosion tests were conducted for each of the two different cavitation numbers (σ = 0.03 and σ = 0.05). The study aims to examine the bias and precision uncertainty components separately for the test results of erosion rate and establish a reliable and repeatable procedure for predicting the uncertainty level of cavitating waterjet tests. For the first time in the literature, the uncertainty components of cavitating waterjet tests were investigated in detail and the uncertainty level in cavitating waterjet tests, including bias and precision errors, was predicted in accordance with specific ITTC guidelines and procedures. Based on the results, the primary bias error source was identified as the pressure inside the cavitation chamber, with the nozzle pressure being the second most significant bias error source. Consequently, it is recommended to perform the bias error prediction based on the ratio of these two, known as the cavitation number. Precision error, crucial for uncertainty, was estimated using five sets of erosion rate values, emphasizing its importance in cavitating waterjet tests. It is anticipated that the suggested methodology will serve as a benchmark for predicting uncertainty levels in cavitating waterjet tests.

A comprehensive analysis of uncertainties in warm rain parameterizations in climate models based on in situ measurements

Abstract Because of the coarse grid size of Earth system models (ESM), representing warm-rain processes in ESMs is a challenging task involving multiple sources of uncertainty. Previous studies evaluated warm-rain parameterizations mainly according to their performance in emulating collision-coalescence rates for local droplet populations over a short period of a few seconds. The representativeness of these local process rates comes into question when applied in ESMs for grid sizes on the order of 100 kilometers and time steps on the order of 20-30 minutes. We evaluate several widely used warm-rain parameterizations in ESM application scenarios. In the comparison of local and instantaneous autoconversion rates, the two parameterization schemes based on numerical fitting to stochastic collection equation (SCE) results perform best. However, because of Jessen’s inequality, their performance deteriorates when grid-mean, instead of locally-resolved, cloud properties are used in their simulations. In contrast, the effect of Jessen’s inequality partly cancels the overestimation problem of two semi-analytical schemes, leading to an improvement in the ESM-like comparison. In the assessment of uncertainty due to the large time step of ESMs, it is found that the rain-water tendency simulated by the SCE is roughly linear for time steps smaller than 10 minutes, but the nonlinearity effect becomes significant for larger time steps, leading to errors up to a factor of 4 for a time step of 20 minutes. After considering all uncertainties, the grid-mean and time-averaged rain-water tendency based on the parameterization schemes are mostly within a factor of 4 of the local benchmark results simulated by SCE.

Unravelling uncertainty in trajectory prediction using a non-parametric approach

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty caused by partial observability of all factors that may affect a driver’s near-future decisions, the so-called aleatoric uncertainty, and the uncertainty of deploying a model in new scenarios that are possibly not present in the training set, the so-called epistemic uncertainty. They reflect the trade-off between data collection and model improvement In this paper, we propose a new framework to systematically quantify both sources of uncertainty. Specifically, to approximate the spatial distribution of an agent’s future position, we propose a 2D histogram-based deep learning model combined with deep ensemble techniques for measuring aleatoric and epistemic uncertainty by entropy-based quantities. The proposed Uncertainty Quantification Network (UQnet) employs a causal part to enhance its generalizability so rare driving behaviours can be effectively identified. Experiments on the INTERACTION dataset show that UQnet is able to give more robust predictions in generalizability tests compared to the correlation-based models. Further analysis presents that high aleatoric uncertainty cases are mainly caused by heterogeneous driving behaviours and unknown intended directions. Based on this aleatoric uncertainty component, we estimate the lower bounds of mean-square-error and final-displacement-error as indicators for the predictability of trajectories. Furthermore, the analysis of epistemic uncertainty illustrates that domain knowledge of speed-dependent driving behaviour is essential for adapting a model from low-speed to high-speed situations. Our paper contributes to motion forecasting with a new framework, that recasts the problem of accuracy improvement in a way that focuses on differentiating between unpredictable components and rare cases for which more and different data should be collected.

Forests high on CO2: A glimpse into how scientists study the biochemical machinery of forest ecosystems

Are forests across the planet storing more carbon as a result of rising atmospheric carbon dioxide (CO2) concentration? In this text, I propose to reflect on the work of researchers who are asking that very question. The focus is on a particular approach called free-air CO2 enrichment (FACE) experiments, in which forests in their local environment are subjected to a CO2 concentration higher than in the ambient air, to test the CO2 fertilization hypothesis. This hypothesis predicts that forests store more carbon in those conditions. FACE studies held the promise of establishing cause–effect relations and transcending the here and now. They emerged in the United States in the late 1980s, at a time when ecology scaled up to make itself relevant to the then nascent Earth System science. FACE experiments in forests are now conducted in Australia, the United Kingdom, and Brazil, and their results are closely scrutinized by a small research community. The text, first, presents the engineering feat that underpins FACE experiments in forests and discuss two problems highlighted by scientists because they impede hasty generalization from the data: the timescale needed to witness forests change and the spatial heterogeneity of forest soils. It then shows how scientists contend with their incomplete understanding of forest ecosystems as they bring together computer models and empirical data to build consensus around the sources of epistemic uncertainty. To conclude, I propose to rework the idea of “the macroscope” to conceptualize what scientists do when they seek to study forests as complex, layered, multi-scale systems. I wish to capture how researchers, who willingly endorse a planetary gaze, problematize their own predicament. This, as I suggest, has to do with the immanence of forests in the world.

Solving the waste bin location problem with uncertain waste generation rate: A bi-objective robust optimization approach.

An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

Step height measurement of monoatomic silicon crystal lattice steps with a commercial atomic force microscope

Abstract Precise control of advanced materials relies on accurate dimensional metrology at the sub-nanometre scale. At this scale, the accuracy of scanning probe microscopy (SPM) has been limited by the lack of traceable transfer standard artefacts with calibration structures of suitable dimensions. With the adoption in 2019 of the silicon crystal lattice spacing as a secondary realization of the metre in the International System of Units (SI), SPM users have direct access to a realization of the SI metre at the sub-nanometre level by means of the step height of self-assembled monatomic lattice steps that can form on the surface of silicon crystals. A key challenge of successfully adopting this pathway is establishing the need for established protocols to minimize measurement errors and artifacts in routine laboratory use.
In this study, step height measurements of monoatomic lattice steps in an ordinal/staircase structure on a Si(111) crystal surface have been derived from images acquired with a commercially available, research-level atomic force microscope (AFM). Measurement results derived from AFM images using three different SPM image processing and analysis software packages are compared. Significant sources of measurement uncertainty are identified; principally the contribution from the dependence on scan direction. The calibration of the
AFM derived from this measurment was used to traceably measure the sub-nanometre lattice steps on a silicon carbide crystal surface to demonstrate the viability of this calibration pathway.

Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit.

Atmospheric vapor pressure deficit (VPD) increases with climate warming and may limit plant growth. However, gross primary production (GPP) responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and Earth System Models (ESMs) simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2 fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

Vegetation Hydrodynamics To Inform Climate Mitigation And Adaptation

Coastal ecosystems, such as seagrass, provide many ecosystem services, including coastal protection and carbon sequestration, which make them an integral part of climate mitigation and adaptation (e.g., Fourqurean et al. 2012). Predicting the value of these ecosystem services requires an understanding of the interaction of fluid motion with vegetation. While seagrass meadows are recognized as global hotspots for carbon storage, the verification of seagrass carbon is complicated by significant heterogeneity. For example, Lavery et al. (2013) reported an 18-fold range in carbon stock across 17 different seagrass habitats (260 to 4800 g C m-2). This variability is a major source of uncertainty in assessing carbon stocks, motivating work to understand what drives it. Recent studies have highlighted how hydrodynamic conditions can be an important factor (e.g., Oreska et al. 2017, Novak et al, 2020). In this talk, we consider a combination of modeling and field measurement that explores the influence of wave and current conditions on carbon accretion in seagrass meadows.