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Getting lost tracking the carbon footprint of hydropower
In the transition to low-carbon electricity, well-quantified estimates of carbon dynamics are needed to ensure that emissions reduction targets are achieved. Here we review the state of the science on carbon accounting for hydropower reservoirs and identify limitations and future solutions. Nearly all research on reservoir greenhouse-gas (GHG) emissions has focused on individual reservoirs in isolation without considering their position in a freshwater network draining organic matter from upstream watersheds or the coordinated operation of reservoir cascades. Second, carbon inventories have extrapolated from a small, non-probabilistic sample of highly variable measurements of GHG emissions to unsampled reservoirs. A stronger statistical foundation is needed to estimate a global inventory and its uncertainty. Third, attribution to hydropower is based on ranks assigned to reservoir purpose. Instead, the physical influence of hydropower on carbon dynamics could be directly measured. Fourth, current carbon-accounting practices neglect time. A time-varying approach would quantify variation in emissions for electricity portfolios from changes in the fuel mix at different times and account for ancillary services, i.e., the ability to support the grid when variable renewables are not available without using natural gas. Reservoirs also sequester a significant portion of inflowing carbon in sediments and slow the carbon cycle by delaying the return of carbon to the atmosphere for decades to centuries. Together, these refinements would help to illuminate pathways toward meeting energy demand with the longest-possible delay in returning carbon to the atmosphere and without adding ancient sources to the pool of carbon cycling through aquatic ecosystems.
Delayed fire mortality has long‐term ecological effects across the Southern Appalachian landscape
Abstract Fire is a critical ecological process to the forests of the Southern Appalachians. Where fire was excluded from forest types that historically burned frequently, unanticipated changes can occur when fire is reintroduced. For example, the development of new fuel characteristics can change the patterns of fire mortality and associated ecological responses. To test the fire effects of delayed fire mortality (mortality initiated by fire that occurs subsequent to the fire year) in the Southern Appalachians, USA, we developed a fire‐effects model using both field studies and remote sensing. We then simulated these effects at a landscape scale to estimate broader ecological effects. Fire‐effects models that accounted for delayed mortality increased landscape biomass removed annually (~23%) and increased the number of sites with high light conditions (leaf area index < 4) when compared to simulations that only account for immediate mortality. While delayed mortality occurred across species and age classes, it was especially prevalent among older trees (>100 years old) and fire‐resistant species ( Quercus spp.). Overall, regeneration (trees <20 years old) changed very little, even with the inclusion of delayed mortality. This evidence suggests that, even when accounting for delayed mortality, individual fires are unlikely to shift the landscape composition toward the conditions of forests prior to fire exclusion and may even increase mesophication long term due to the loss of overstory dominant xeric trees.
Seasonal variation in methane ebullition in a temperate hydropower reservoir impacts annualized estimates
Estimates of ebullitive methane (CH4) emissions from reservoirs are highly uncertain, often based on spatial and temporal upscaling from a limited number of sampling sites and periods. While some of these estimates may adjust for seasonal variation in temperature, they rarely account for drawdown and the resulting changes in site depth and the areal extent of the reservoir. In Douglas Reservoir (Tennessee, USA), we conducted monthly sampling of 20 sites over 1 year to capture temporal and spatial variation in CH4 ebullition. We then compared various approaches for obtaining annualized, whole-reservoir ebullition estimates. CH4 ebullition fluxes were zero-inflated and highly skewed, with fluxes rising appreciably above zero in July and peaking in autumn. Estimates that did not account for drawdown overestimated annualized rates by 20%, though with high uncertainty. Seasonal variability and hotspots strongly influence annualized ebullition estimates and are important to account for in inventories and global upscaling.
Hydraulic constraints to stomatal conductance in flooded trees
Stomatal closure is a pervasive response among trees exposed to flooded soil. We tested whether this response is caused by reduced hydraulic conductance in the soil-to-leaf hydraulic continuum (k total ), and particularly by reduced root hydraulic conductance (k root ), which has been widely hypothesized. We tracked stomatal conductance at the leaf level (g s ) and canopy scale (G s ) along with physiological conditions in two temperate tree species, Magnolia grandiflora and Quercus virginiana, that were subjected to flood and control conditions in a greenhouse experiment. Flooding reduced g s , G s , k root and k total . Path analysis showed strong support for direct effects of k total on g s and for flood duration on k total , but not k root on k total . A process-based model that accounted for the k total reduction predicted the timeseries of G s in flood and control treatment trees reasonably well (predicted versus observed G s R 2 = 0.80 and 0.51 for M. grandiflora and Q. virginiana, respectively). However, accounting only for k root reduction in flooded trees was insufficient for predicting observed G s reduction. Together, these results suggest that hydraulic constraints were not limited to roots and highlight the need to account for flooding effects on k total when projecting forest ecosystem function using process-based models.
Propagation of Input Uncertainties in Numerical Simulations of Laser Powder Bed Fusion
Laser powder bed fusion has the potential of redefining state-of-the-art processing and production methods, but defect formation and inconsistent build quality have limited the implementation of this process. Numerical models are widely used to study this process and predict the formation of these defects. Presently, the uncertainties of model input parameters and thermophysical properties used by these numerical simulations have not been investigated. In the present study, the uncertainty in these input parameters and material properties are quantified for laser powder bed fusion, with and without a simulated powder bed, to determine their influence on the predictive accuracy of an experimentally validated numerical model. Accounting for all possible sources of uncertainty quickly becomes computationally expensive on account of the curse of dimensionality. Uncertainty in laser absorption, solid, and liquid specific heat of the metal were found to have the largest effect on model prediction reliability with or without the use of a powder bed. Results also illustrate that accounting for these three uncertain parameters still captures the majority of model prediction uncertainty. Furthermore, the methodology of this study may be used to understand the uncertainty in as-built microstructure through propagation to microstructure prediction models, or applied under processing conditions where high Péclet numbers are observed and the thermal convection and fluid flow within the molten pool are substantial.
Leveraging Extremal Dependence to Better Characterize the 2021 Pacific Northwest Heatwave
Abstract In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western Canada, breaking numerous all-time temperature records by large margins and directly causing hundreds of fatalities. The observed 2021 daily maximum temperature across much of the U.S. Pacific Northwest exceeded upper bound estimates obtained from single-station temperature records even after accounting for anthropogenic climate change, meaning that the event could not have been predicted under standard univariate extreme value analysis assumptions. In this work, we utilize a flexible spatial extremes model that considers all stations across the Pacific Northwest domain and accounts for the fact that many stations simultaneously experience extreme temperatures. Our analysis incorporates the effects of anthropogenic forcing and natural climate variability in order to better characterize time-varying changes in the distribution of daily temperature extremes. We show that greenhouse gas forcing, drought conditions and large-scale atmospheric modes of variability all have significant impact on summertime maximum temperatures in this region. Our model represents a significant improvement over corresponding single-station analysis, and our posterior medians of the upper bounds are able to anticipate more than 96% of the observed 2021 high station temperatures after properly accounting for extremal dependence. Supplementary materials accompanying this paper appear online.
Aboveground and belowground contributions to ecosystem respiration in a temperate deciduous forest
In this study, we developed a three-way carbon dioxide (CO 2 ) flux-partitioning algorithm that separates net ecosystem exchange (NEE) into aboveground plant respiration (R above ), belowground root and soil respiration (R below ), and gross primary production (GPP). We applied this algorithm to a coupled dataset of continuous chamber-measured soil respiration and eddy covariance (EC)-measured NEE of CO 2 in an oak-hickory (Quercus-Carya) deciduous broadleaf forest from 2006 to 2015. We found that on annual time scale, R below dominated over R above with the former accounting for 66.9–86.4% and the latter 13.6–33.1%, of the total ecosystem respiration (R eco ). The ratio of R below to R above varied seasonally, ranging from 1.77 to 7.25 in growing season, and 1.02 to 4.57 in non-growing season. The temperature sensitivity (E 0 ) of R below was significantly higher than that of R above , and E 0 of R eco responded differently to air and soil temperature. Over the whole study period, annual mean R above , R below , and GPP were 243, 806, and 1170 g C m –2 , respectively, with annual R eco accounting for 89.6% of GPP, of which 68.8% was lost as R below and 20.8% lost as R above , and leaving only 10% of the carbon fixation in ecosystems. Furthermore, these estimates, however, did not consider potential light inhibition of leaf respiration. If we accept the presence of light inhibition, then the daytime three-way partitioning method would underestimate annual R above by 20.4% whereas the nighttime method would overestimate R above by 23.9% and GPP by 4.7%, compared with estimates accounting for light inhibition in leaves.
A MPET 2 -mPBPK model for subcutaneous injection of biotherapeutics with different molecular weights: From local scale to whole-body scale
Subcutaneous injection of biotherapeutics has attracted considerable attention in the pharmaceutical industry. However, there is limited understanding of the mechanisms underlying the absorption of drugs with different molecular weights and the delivery of drugs from the injection site to the targeted tissue. Here, we propose the MPET 2 -mPBPK model to address this issue. This multiscale model couples the MPET 2 model, which describes subcutaneous injection at the local tissue scale from a biomechanical view, with a post-injection absorption model at injection site and a minimal physiologically-based pharmacokinetic (mPBPK) model at whole-body scale. Utilizing the principles of tissue biomechanics and fluid dynamics, the local MPET 2 model provides solutions that account for tissue deformation and drug absorption in local blood vessels and initial lymphatic vessels during injection. Additionally, we introduce a model accounting for the molecular weight effect on the absorption by blood vessels, and a nonlinear model accounting for the absorption in lymphatic vessels. The post-injection model predicts drug absorption in local blood vessels and initial lymphatic vessels, which are integrated into the whole-body mPBPK model to describe the pharmacokinetic behaviors of the absorbed drug in the circulatory and lymphatic system. We establish a numerical model which links the biomechanical process of subcutaneous injection at local tissue scale and the pharmacokinetic behaviors of injected biotherapeutics at whole-body scale. With the help of the model, we propose an explicit relationship between the reflection coefficient and the molecular weight and predict the bioavalibility of biotherapeutics with varying molecular weights via subcutaneous injection. The considered drug absorption mechanisms enable us to study the differences in local drug absorption and whole-body drug distribution with varying molecular weights. This model enhances the understanding of drug absorption mechanisms and transport routes in the circulatory system for drugs of different molecular weights, and holds the potential to facilitate the application of computational modeling to drug formulation.
Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo
Functional data registration is a necessary processing step for many applications. The observed data can be inherently noisy, often due to measurement error or natural process uncertainty; which most functional alignment methods cannot handle. A pair of functions can also have multiple optimal alignment solutions, which is not addressed in current literature. In this paper, a flexible Bayesian approach to functional alignment is presented, which appropriately accounts for noise in the data without any pre-smoothing required. Additionally, by running parallel MCMC chains, the method can account for multiple optimal alignments via the multi-modal posterior distribution of the warping functions. To most efficiently sample the warping functions, the approach relies on a modification of the standard Hamiltonian Monte Carlo to be well-defined on the infinite-dimensional Hilbert space. In this work, this flexible Bayesian alignment method is applied to both simulated data and real data sets to show its efficiency in handling noisy functions and successfully accounting for multiple optimal alignments in the posterior; characterizing the uncertainty surrounding the warping functions.
The impact of behavioral and geographic heterogeneity on residential-sector carbon abatement costs
Analyses of monetary and emissions savings from residential efficiency upgrades usually neglect behavioral differences between consumers. Variations in behavior are frequently large: 13% of the U.S. population sets thermostats for cooling at 20 °C (68 °F) or lower and 15% at 25.5 °C (78 °F) or higher. Efficiency analyses should account for behavioral heterogeneity as well as variations in climate and housing characteristics. We model energy and economic savings of efficiency upgrades for U.S. single-family detached houses, accounting for differences in thermostat settings, climate zone, fuel prices, and home characteristics. We consider five efficiency interventions: wall insulation, attic insulation, air sealing, high-efficiency furnaces, and high-efficiency air conditioners. Energy and economic savings vary widely by consumer; the average net present value for wall insulation is $3,020 United States Dollar (USD), but considering heterogeneity, it has a 10th percentile value of -$231 and 90th percentile value of $5,000. Neglecting heterogeneity, technology upgrades can be prioritized from lower to higher carbon abatement costs: air conditioners, wall insulation, furnace, air sealing, and attic insulation. Accounting for heterogeneity reorders this prioritization, and consumer-specific conditions affect mitigation costs such that technologies are mixed in with one another and are no longer arranged one after another. These results indicate that interventions to improve efficiency should consider differences between consumers.
A method for predicting failure statistics for steady state elevated temperature structural components
This paper presents the initial development of a high temperature life prediction method that accounts for the variability in the material properties of Grade 91 steel. The method accounts for material variability by fitting a variable 3-parameter Weibull distribution to experimental rupture data and accounts for the variability of creep deformation on the steady-state stresses via a Monte Carlo approach. To ensure reasonable computational times, the model represents the material as an extremely viscous Stokes fluid with a non-Newtonian viscosity, therefore solving the stress relaxation problem with a steady, static, instead of transient, analysis. Furthermore, the complete statistical analysis combines this model for creep deformation with a probabilistic model for creep rupture to evaluate the probability of premature failure for a set of sample problems, comparing the predicted failure statistics to the design life predicted by the ASME Boiler and Pressure Vessel Code rules.
Determination of permeability for hydrocarbon release due to excavation-induced stress redistribution in rock salt
Due to a stress redistribution after the excavation of an underground tunnel for radioactive waste disposal, an Ed/DZ (excavation disturbed/damaged zone) will be generated in the near field of the opening, resulting in significant changes in the hydraulic and mechanical properties of the rock mass in the zone. Initially more or less randomly distributed hydrocarbons at grain boundaries in rock salt, which sometimes can only be observed with ultraviolet light, can then be mobilised and migrate at a potentially significant rate towards the excavation. Within the international cooperative project DECOVALEX 2019, the migration mechanism of such fluid inclusions in rock salt is being studied intensively. Specifically, a multi-scale modelling strategy has been developed. A macroscale coupled hydro-mechanical modelling of an underground excavation was performed to determine hydraulic and time-dependent deviatoric stress conditions, by taking into account the rock salt creep behaviour. Under the obtained macro-scale constraints, micro-scale modelling of a pathway dilation along halite grain boundaries was performed using different model strategies: a) coupled hydromechanical modelling with a consideration of hydraulic pressure-induced dilatant deformation, b) nonlinear dynamic model taking account of fluid migration, stress-dependent grain boundary wetting and shear-induced dilatancy of salt, and c) phase-field modelling of flow pathway propagation. The permeability increase resulting from the pathway dilation is estimated to be as high as two orders of magnitude. Based on the permeability determined, a series of pressure buildups measured from a borehole with a high hydrocarbon release rate, a total of 430 build-ups within a monitoring time of 938 days, can be simulated with a macro-scale compressible flow model accounting for different zones around the opening.
Energy based fracture initiation criterion for strain-crystallizing rubber-like materials with pre-existing cracks
Fracture prediction is indispensable for polymers, like rubbers, which have a broad range of applications mainly due to their high extensibility. The phenomenon known as strain-induced crystallization further contributes to the fracture toughness of certain rubbers. Here in this study, a criterion based on internal bond energy, incorporating the effects of crystallization, is proposed to predict fracture initiation in rubber-like materials with pre-existing cracks. First, a multi-scale mechanical model is developed for characterizing the behavior of rubber when subjected to both uniaxial and biaxial deformation states. At the microscale, both the amorphous and crystalline chain segments are modeled as elastic in order to consider the energy contribution by the molecular bond distortions. This internal energy is considered along with the entropic and crystalline free energy for each chain. In the chain model, the effects of loading condition and the relative orientation of a chain on its crystallinity are taken into account. At the macroscopic scale, an existing crystallinity distribution function is adapted and a mixed finite element formulation with an augmented Lagrangian multiplier is utilized to impose the incompressibility constraint. A non-affine maximal advance path constraint based homogenization model is utilized for bridging the two scales. Its potential to account for anisotropy in the stretched network compels the model to be preferable due to its physical significance, for the purpose of fracture modeling. The rigidity of the crystallites is accounted for by proposing a crystallite distortion energy in addition to the critical bond dissociation energy, for fracture initiation to occur. The model is validated by comparison with existing experimental results for both crystallizing and non-crystallizing rubbers. In addition to its potential to predict the material behavior when subjected to uniaxial and biaxial loading, the capability of the model to quantitatively estimate the effect of crystallization on fracture initiation is also verified.
Statistical characterization of twin transmission across grain boundaries in magnesium
Here, a detailed statistical analysis of twin transmission (TT) across grain boundaries is performed in rolled, commercial purity magnesium compressed along the rolling direction. EBSD images are acquired from two different cuts: a section containing the rolling (RD) and normal (ND) directions; and a section at 35° to the ND which contains transverse direction (TD). An automated twinning analysis software, METIS, is used to obtain the statistical correlations between deformation twins and other microstructural features by analyzing EBSD microstructures comprising thousands of grains and twins. This detailed statistical analysis reveals that the TT propensity is sensitive to the grain boundary (GB) misorientation angle but not to the GB misorientation axis. Specifically, TT propensity decreases with increasing GB misorientation angle; however, the decreasing trend is not monotonic. Further, the detailed analysis of TT events combining macroscopic Schmid factor and the geometric measures (m’: accounts for the alignment of both twin plane normal and shear directions; m”: accounts for the alignment of only twin shear directions; and m”’: accounts for the alignment of only twin plane normal) helps in understanding and identifying the process of twin-pair formation, i.e., co-nucleation versus transmission, also to identify the role of local stresses at GBs induced by inter-grain interaction versus twinning shear transformation processes. In addition, Bayesian inference is used to draw statistically meaningful conclusions as to the likelihood of transmission given misorientation angles. Lastly, twin chain frequency as a function of chain length is predicted using the probability distribution obtained from the Bayesian inference and compared with the actual data from the EBSD microstructures.
Design of a prototypical natural circulation water-based reactor cavity cooling system (RCCS) for a pebble-bed generic FHR
A prototypical natural circulation water-based reactor cavity cooling system (RCCS) for the UC Berkeley Mark-1 pebble-bed gFHR is designed based on one-dimensional thermal hydraulics modeling and optimization implemented in an in-house MATLAB code. The model employs a lumped core consisting of fuel pebble and graphite pebble regions, while the graphite reflector, vessel, and RCCS are represented as separate but energy-coupled regions. The model is derived based on steady state energy balance equations accounting for conductive heat transfer from the lumped core to the vessel and subsequent radiative heat transfer to the RCCS and convective heat transfer in the water. Further, mass flow rate is calculated based on momentum and thermal energy balance in the RCCS. Starting with ANL’s water NSTF as baseline, effects of RCCS design parameters such as size of the plate between pipes, pipe diameter, source-to-sink distance, surface emissivity, and pitch from the core are examined in an integral effects framework which accounts for subsequent variation of the mass flow rate, number of pipes, convection coefficient, mean region and surface temperatures, radiative view factors, and water outlet temperature. The importance of including a prototypical reactor design in RCCS design calculations is emphasized as the calculations show that temperature drops from the core to the RCCS fluid to transfer the heat tend to be the limitation not the heat removal capacity of the fluid. A closed-loop design is obtained with capability to safely remove up to 0.72% of the nominal reactor power at maximum estimated peak conditions and 0.40% at shutdown. The design is based on physics calculations and does not account for economic optimization.
Leptodermic corrections to the TOV equations and nuclear astrophysics within the effective surface approximation
The macroscopic model for a neutron star (NS) as a liquid drop at the equilibrium is used to extend the Tolman-Oppenheimer-Volkoff (TOV) equations taking into account the gradient terms responsible for the system surface. The parameters of the Schwarzschild metric in the spherical case are found with these surface corrections to the known leading (zero) order of the leptodermic approximation a/R << 1, where a is the NS effective-surface (ES) thickness, and R is the effective NS radius. The energy density $\mathscr{E}$ is considered in a general form including the functions of the particle number density and of its gradient terms. The macroscopic gravitational component $Φ$(ρ) of the energy density is taken into account in the simplest form as expansion in powers of $ρ$ – $\overline{ρ}$, where $\overline{ρ}$ is the saturation density, up to second order, in terms of its contributions to the separation particle energy and incompressibility. Density distributions ρ across the NS ES in the normal direction to the ES, which are derived in the simple analytical form at the same leading approximation, was used for the derivation of the modified TOV (MTOV) equations by accounting for their NS surface corrections. As a result, the MTOV equations are analytically solved at first order and the results are compared with the standard TOV approach of the zero order.
A review of water valuation metrics: Supporting sustainable water use in manufacturing
In the manufacturing sector, water has been often considered too cheap to conserve. Such thinking relies on water valuations that limit the value of water to the price paid. Using such simple methods, the share of water cost to total manufacturing cost is significantly small, <3%. As a result, conserving water and enabling technology uptake is difficult to justify economically and slow to advance, hindering progress toward sustainable water use. However, the value of water to a manufacturer is far greater than the price paid. Valuations such as the true cost of water consider the additional in-plant treatment and energy costs and have been gaining greater traction in the manufacturing sector. However, true cost alone still undervalues water by not accounting for economic and social costs related to scarcity and environmental externalities. This paper makes the case and presents a framework for valuing manufacturing water beyond the price paid and the true cost. The proposed fuller valuation of manufacturing water takes into account the internal and opportunity costs associated with the realization of water risks. The paper follows with a review of a wide range of water valuation metrics, both at the specific industry level and regional/economy-wide level. The use of various valuation metrics incorporating the relationship between the change in value with change in water use, such as marginal value of water, shadow price, and elasticity at the specific industry level, has been limited in the U.S. manufacturing sector. Further, a limited number of studies exist on data-intensive subjective evaluation techniques such as computable general equilibrium modeling and input-output modeling for regional water valuation. After reviewing water value metrics, several recent case studies from manufacturers from the literature are presented to illustrate both the promise and challenges of a fuller valuation of water as proposed here. Some large multinational corporations have moved toward assessing the value of water via supply chain sustainability initiatives, environmental profit and loss accounting, estimating risk-adjusted values of water, hydro-economic modeling, natural capital asset valuation, and developing value chain indices. This paper provides policymakers and technology developers a framework for monetizing water value beyond its true cost and current metrics. If adopted, such fuller water valuations can help make the business case for the development and deployment of cost-effective water-conserving technologies, thereby improving the sustainability of the manufacturing sector with respect to water.