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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A comprehensive techno-eco-assessment of CO 2 enhanced oil recovery projects using a machine-learning assisted workflow

Carbon dioxide enhanced oil recovery (CO 2 -EOR) projects not only extract residual oil but also sequestrate CO 2 in the depleted reservoirs. Here, this study develops a machine-learning-based workflow to co-optimize the hydrocarbon recovery, CO 2 sequestration volume and project net present value (NPV) simultaneously. Considering the trade-off relationships among the objective functions, support vector regression with Gaussian kernel (Gaussian- SVR) proxies are coupled with multi-objective particle swarm optimization (PSO) protocol and generate Pareto optimal solutions. Taking advantage of the high computational efficacy of the proxy model, economic uncertainties introduced by tax credits, capital costs and oil price are investigated by this study. The results indicate that the tax incentive policy (Section 45Q) plays a vital role in enhancing the economic returns of CO 2 -EOR projects, especially under the depression of crude oil market. The proposed workflow has been successfully implemented to optimize a water alternative CO 2 (CO 2 -WAG) injection project in a depleted oil sand in the US. The optimization results yield an incremental oil production of 15.8 MM STB and 1.37 MM metric tons of CO 2 storage in a 20-year development strategy, with the highest project NPV to be 205.6 MM US dollars.

03 NATURAL GAS↗
DOE OSTI2020

Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes

Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. In this work, we used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. Furthermore, we compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multidisciplinary explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements have been a result of a range of factors, from biological and hydrological to political and cultural. Furthermore, project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.

13 HYDRO ENERGY↗
DOE OSTI2018

Analyses for High‐Resolution Projections Through the End of the 21st Century for Precipitation Extremes Over the United States

This study compares an ensemble of dynamically downscaled projections of extreme daily precipitation over the contiguous United States (CONUS). With a grid spacing of 12 km and a domain that encompasses most of North America, we use the Weather Research and Forecast model as a regional climate model. We incorporate initial and boundary conditions from three different Coupled Model Intercomparison Project Phase 5 Earth system models (ESMs). We focus on precipitation extremes in the future climate (2045–2054 and 2085–2094) by comparing a business-as-usual high-emissions scenario to emissions from a historical period (1995–2004). In the historical period, the Weather Research and Forecasting-downscaled simulations result in significant improvements over the ESMs for precipitation extremes. In the analyses of future climate, there is a large increase in the projected frequency of extreme precipitation events over the entire CONUS and a decrease in median precipitation days. Moreover, most regions show an increase in the number of dry days in the future scenarios. The magnitude of extreme precipitation events is projected to increase in the CONUS at all temperatures above freezing. The strongest precipitation events will increase in intensity, primarily because of a shift in precipitation distribution due to the increase in near-surface air temperatures (Clausius-Claperyon relationship). However, these events are also affected by changes in some atmospheric dynamical factors, such as stronger low level jet, especially over the Midwestern United States. In addition, compared to those calculated by the ESMs, the downscaled projections show important regional differences in the frequency and magnitude of extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Projected Changes in United States Regional Extreme Heat Days Derived From Bivariate Quantile Mapping of CMIP5 Simulations

Projections of multivariate climate extremes require methodological approaches that can maintain relationships among variables. Here we apply a piecewise multivariate quantile mapping approach to temperature and humidity projections from a subset of models from the Fifth Coupled Model Intercomparison Project and analyze the resulting climatology of extreme heat days (EHDs) with explicit consideration of the prevailing humidity. The piecewise multivariate bias correction method shows good fidelity in reproducing the frequency of different types of extreme heat events in the historical period. Projections for U.S. regions and individual cities for both the middle and end of the century are characterized by increases in the frequency of EHDs, and especially those characterized by high humidity. For many regions and individual cities, there is no overlap between the frequency of high-humidity EHDs in general circulation model ensembles from the historical and future periods, indicating that increases in extreme heat are robust. Analysis of 500-mb height, sea-level pressure, and low-level circulation composites for historical and future periods indicates that the Fifth Coupled Model Intercomparison Project models reproduce basic large-scale circulation features associated with EHDs in U.S. regions and that future changes in extreme heat are related primarily to large-scale warming rather than enhancement of regional circulation anomalies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Future Projections of the El Niño—Southern Oscillation and Tropical Pacific Mean State in CMIP6

Abstract The El Niño—Southern Oscillation (ENSO) is an important mode of tropical Pacific atmosphere‐ocean variability that drives teleconnections with weather and climate globally. However, prior studies using state‐of‐the‐art climate models lack consensus regarding future ENSO projections and are often impacted by tropical Pacific sea‐surface temperature (SST) biases. We used 173 simulations from 29 climate models participating in the Coupled Model Intercomparison Project, version 6 (CMIP6) to analyze model biases and future ENSO projections. We analyzed two ENSO indices, namely the ENSO Longitude Index (ELI), which measures zonal shifts in tropical Pacific deep convection and accounts for changes in background SST, and the Niño 3.4 index, which measures SST anomalies in the central‐eastern equatorial Pacific. We found that the warm eastern tropical‐subtropical Pacific SST bias typical of previous generations of climate models persists into many of the CMIP6 models. Future projections of ENSO shift toward more El Niño‐like conditions based on ELI in 48% of simulations and 55% of models, in association with a future weakening of the zonal equatorial Pacific SST gradient. On the other hand, none of the models project a significant shift toward La Niña‐like conditions. The standard deviation of the Niño 3.4 index indicates a lack of consensus on whether an increase or decrease in ENSO variability is expected in the future. Finally, we found a possible relationship between historical SST and low‐level cloud cover biases in the ENSO region and future changes in ELI; however, this result may be impacted by limitations in data availability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM-LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM-LIN method improved R 2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM-LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Extreme Storm Events in an Ensemble of High‐Resolution Projections

This study uses different downscaling techniques and reference observations to investigate the characteristics of extreme storm events over the conterminous United States in historical and a projected future scenario. While previous studies agree on the projected changes in intensity and frequency of precipitation extremes, there is a lack of consensus regarding how their size will change in response to an increase in radiative forcing. Moreover, the influence of different downscaling techniques on their characteristics has not been thoroughly examined. This study employs an ensemble of high‐resolution projections derived from six CMIP6 GCMs, using dynamical, statistical and artificial intelligence based downscaling techniques and two reference observations. Overall, we find noticeable differences in the size, average depth, and total precipitation volume of these storms among the climate ensembles in the historical period. Despite these differences in the historical period, we find consistent future changes across various ensembles. We find a robust projected increase in storm size during Winter and Spring but a decrease in size during Summer in the East. Nevertheless, irrespective of changes in their size, extreme storms are projected to intensify across all the ensembles and seasons.

Rastogi, Deeksha [Oak Ridge National Laboratory (O↗
DOE OSTI2021

Downscaling SSP-consistent global spatial urban land projections from 1/8-degree to 1-km resolution 2000–2100

Abstract Long-term, spatial urban land projections that simultaneously offer global coverage and local-scale empirical accuracy are rare. Recently a set of such projections was produced using data-science-based simulations and the Shared Socioeconomic Pathways (SSPs). These projections update at decadal time intervals from 2000 to 2100 with a spatial resolution of 1/8 degree, while many socio-environmental studies customarily run their analysis and modelling at finer spatial resolutions, e.g. 1-km. Here we develop and validate an algorithm to downscale the 1/8-degree spatial urban land projections to the 1-km resolution. The algorithm uses an iterative process to allocate the decadal amount of urban land expansion originally projected for each 1/8-degree grid to its constituent 1-km grids. The results are a set of global maps showing urban land fractions at the 1-km resolution, updated at decadal intervals from 2000 to 2100, under five different urban land expansion scenarios consistent with the SSPs. The data can support studies of potential interactions between future urbanization and environmental changes across spatial and temporal scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Charged particle velocity map image reconstruction with one-dimensional projections of spherical functions

Velocity map imaging (VMI) is used in mass spectrometry and in angle resolved photo-electron spectroscopy to determine the lateral momentum distributions of charged particles accelerated towards a detector. VM-images are composed of projected Newton spheres with a common centre. The 2D images are usually evaluated by a decomposition into base vectors each representing the 2D projection of a set of particles starting from a centre with a specific velocity distribution. We propose to evaluate 1D projections of VM-images in terms of 1D projections of spherical functions, instead. The proposed evaluation algorithm shows that all distribution information can be retrieved from an adequately chosen set of 1D projections, alleviating the numerical effort for the interpretation of VM-images considerably. The obtained results produce directly the coefficients of the involved spherical functions, making the reconstruction of sliced Newton spheres obsolete.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Time-dependent projected Hartree-Fock

Projected Hartree-Fock (PHF) has recently emerged as an alternative approach to describing degenerate systems where static correlation is abundant, when the spin-symmetry is projected. Here, we derive a set of linearized time-dependent equations for PHF in order to be able to access excited states. The close connection of such linear-response time-dependent PHF (TDPHF) to the stability condition of a PHF wave function is discussed. Expanding this analysis also makes it possible to give analytical expressions for the projected coupling terms of Hamiltonian and overlaps between excited Slater determinants. TDPHF with spin-projection (TDSUHF) and its Tamm-Dancoff approximation are benchmarked for several electronically degenerate molecules including the dissociating H{sub 2}, F{sub 2} and O{sub 3} at equilibrium, and the distorted ethylene. It is shown that they give consistently better descriptions of excited states than does time-dependent HF (TDHF). Furthermore, we demonstrate that they offer not only singly but also doubly excited states, which naturally arise upon spin-projection. We also address the thermodynamic limit of TDSUHF, using non-interacting He gas. While TDPHF singly excited states tend to converge to those of HF with the size of the system due to the lack of size-extensivity of PHF, doubly excited states remain reasonable even at the thermodynamic limit. We find that the overall performance of our method is systematically better than the regular TDHF in many cases at the same computational scaling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Drivers and energy justice implications of renewable energy project siting in the United States

The rapid expansion of solar and wind energy projects is raising questions of energy justice. Some scholars argue that solar and wind project development could burden under-resourced communities with negative impacts such as environmental harm and reduced access to resources. Conversely, other scholars argue that project development could be a boon to under-resourced communities, providing local economic and cultural benefits. Here, we analyze the drivers of solar and wind project siting patterns in the United States and explore their potential energy justice implications. We find that siting patterns are driven primarily by technoeconomic factors, especially resource quality and access to open undeveloped spaces. These technoeconomic factors channel projects into sparsely populated rural areas and, to a lesser extent, areas with lower income levels. Here, we avoid simplifying assumptions about the broad justice implications of these siting patterns and explore our results from multiple perspectives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

WIDE-FIELD WIDE-BAND INTERFEROMETRIC IMAGING: THE WB A-PROJECTION AND HYBRID ALGORITHMS

Variations of the antenna primary beam (PB) pattern as a function of time, frequency, and polarization form one of the dominant direction-dependent effects at most radio frequency bands. These gains may also vary from antenna to antenna. The A-Projection algorithm, published earlier, accounts for the effects of the narrow-band antenna PB in full polarization. In this paper, we present the wide-band A-Projection algorithm (WB A-Projection) to include the effects of wide bandwidth in the A-term itself and show that the resulting algorithm simultaneously corrects for the time, frequency, and polarization dependence of the PB. We discuss the combination of the WB A-Projection and the multi-term multi-frequency synthesis (MT-MFS) algorithm for simultaneous mapping of the sky brightness distribution and the spectral index distribution across a wide field of view. We also discuss the use of the narrow-band A-Projection algorithm in hybrid imaging schemes that account for the frequency dependence of the PB in the image domain.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Non-parametric projections of the net-income distribution for all U.S. states for the Shared Socioeconomic Pathways

Abstract Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. Such impacts are especially important at subnational levels, but projections of income distributions at these levels are scarce. Here, we project U.S. state-level income distributions for the Shared Socioeconomic Pathways (SSPs). We apply a non-parametric approach, specifically a recently developed principal components algorithm to generate net income distributions for deciles across 50 U.S. states and the District of Columbia. We produce these projections to 2100 for three SSP scenarios in combination with varying projections of GDP per capita to represent a wide range of possible futures and uncertainties. In the generation of these scenarios, we also generated tax adjusted historical deciles by U.S. states, which we used for validating model performance. Our method thus produces income distributions by decile for each state, reflecting the variability in state income, population, and tax regimes. Our net income projections by decile can be used in both emissions- and impact-related research to understand distributional effects at various income levels and identify economically vulnerable populations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗
DOE OSTI2023

Projections of wildfire risk and activities under 1.5 °C and 2.0 °C global warming scenarios

Wildfires are important ecosystem processes that have a significant impact on terrestrial vegetation, environment, and climate. This study investigates how future wildfire risk and activities could change under 1.5 °C and 2.0 °C warming scenarios relative to pre-industrial levels using a modified McArthur Forest Fire Danger Index (FFDIn) and the CLM4.5-BGC land surface model. Sixteen Earth System Models (ESMs) from CMIP5 and CMIP6 were employed to supply the variables of climate change under low, middle, and high greenhouse emission scenarios in the 1.5 °C and 2.0 °C scenarios. The ensemble means from the FFDIn and results from the CLM4.5-BGC with multiple forcings show that the dry areas in the southwestern US, Brazilian Highlands, and Arabian islands are projected to face higher wildfire risk with larger burned areas and more carbon emissions under a warmer climate. The Congo Basin and part of the Amazon could have a lower wildfire risk with smaller burned areas and less carbon emissions. The absolute changes in the projected FFDIn are small, although large increases are observed in boreal areas, particularly in the winter and spring. Burned area and carbon emissions are projected to increase in general in the boreal area but decrease in northeastern Asia. Compared to the 1.5 °C scenario, the wildfire risk and burned area levels are projected to increase under the 2.0 °C scenario except in the western Amazon. However, fire carbon emissions are projected to decrease more in tropical areas under the 2.0 °C scenario. The different change directions in eastern North America and eastern China produced by the FFDIn and CLM4.5-BGC suggest the potential effect of non-meteorological elements on fire activities.

1.5 °C and 2.0 °C warming↗
DOE OSTI2015

Maximum projection designs for computer experiments

Space-filling properties are important in designing computer experiments. The traditional maximin and minimax distance designs only consider space-filling in the full dimensional space. This can result in poor projections onto lower dimensional spaces, which is undesirable when only a few factors are active. Restricting maximin distance design to the class of Latin hypercubes can improve one-dimensional projections, but cannot guarantee good space-filling properties in larger subspaces. We propose designs that maximize space-filling properties on projections to all subsets of factors. We call our designs maximum projection designs. As a result, our design criterion can be computed at a cost no more than a design criterion that ignores projection properties.

97 MATHEMATICS AND COMPUTING↗