SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
California relies on the Sierra Nevada snowpack for a significant portion of its water needs, yet scientists understand very little about how future changes in snowpack volume and timing will influence surface water and groundwater. Berkeley Lab researcher Erica Woodburn is working on an advanced hydrologic model to study how climate change might affect California watersheds in the future.
This presentation summarizes results from the “Modeling the Impact of Flexible CHP on the Future Electric Grid In California” report (doi:10.2172/1649545). The analysis estimated the value of flexible CHP to the gird in a somewhat decarbonized grid in California. The analysis found that advanced, flexible CHP can more than double capacity with a 6 year payback period, save site owners up to $760 million, reduce CA-ISOs operating cost by over $700 million, and eliminate hours of grid stress in CA-ISO.
As a response to mounting concerns about the consequences of unabated climate change, California established in 2018 an aspirational goal of achieving a completely carbon-neutral economy by 2045, and becoming carbon negative after that. The state has long championed climate-friendly policies such as using energy more efficiently, generating power from renewable sources, and putting cleaner cars on the road, among other emissions-reduction strategies. Unfortunately, California’s own evaluations indicate that even when these are successful—after intensive electrification of the economy and a drastic shift away from fossil fuels—there will remain substantial greenhouse gas emissions. The residuals come from widespread small sources that are hard to control, and from larger sources that cannot be controlled in time.
Previous research has examined individual factors contributing to wildfire risk, but the compounding effects of these factors remain underexplored. Here, we introduce the “Integrated Human-centric Wildfire Risk Index (IHWRI)” to quantify the compounding effects of fire-weather intensification and anthropogenic factors—including ignitions and human settlement into wildland—on wildfire risk. While climatic trends increased the frequency of high-risk fire-weather by 2.5-fold, the combination of this trend with wildland-urban interface expansion led to a 4.1-fold increase in the frequency of conditions conducive to extreme-impact wildfires from 1990 to 2022 across California. More than three-quarters of extreme-impact wildfires—defined as the top 20 largest, most destructive, or deadliest events on record—originated within 1 km from the wildland-urban interface. The deadliest and most destructive wildfires—90% of which were human-caused—primarily occurred in the fall, while the largest wildfires—56% of which were human-caused—mostly took place in the summer. By integrating human activity and climate change impacts, we provide a holistic understanding of human-centric wildfire risk, crucial for policy development.
Measurements of lead isotopic compositions in detrital K-feldspar have been increasingly used as a tool to assess sediment provenance. We compiled a database of previously published Pb isotope data from 700 bedrock K-feldspar samples and 1,423 age-corrected bedrock whole rock samples from western North American igneous and metamorphic bodies. Additionally, we report 66 new K-feldspar Pb isotope data for plutons throughout the Pacific Northwest region of the United States and British Columbia. Results show that the Pb isotope values of plutonic K-feldspar depend on the isotopically juvenile or evolved nature of underlying crust. Samples obtained from the mid Cretaceous – mid Eocene Coast Plutonic Complex, North Cascades, and Intermontane superterrane that occur west of the initial 87 Sr/ 86 Sr (Sri) = 0.706 isopleth exhibit a highly restricted 207 Pb/ 206 Pb and 208 Pb/ 206 Pb values centred upon 0.83 and 2.03, respectively. Conversely, rocks overlying older continental crust further east such as the Middle Jurassic – Late Cretaceous Omineca crystalline belt, Idaho batholith, and Boulder Batholith exhibit far greater variation of Pb isotope values that parallel the 100 Ma isochron calculated from a two-stage Pb evolution model. We demonstrate that Pb isotopic results from the Idaho and Boulder Batholith region can be used to define distinctive subregions for Pb isotopic provenance analysis, and compare these signatures to the Mojave-Salinian batholith of southern California and western Arizona, as these two areas have previously been proposed as source regions for extraregional sediment that was deposited within the Nanaimo Basin during the Campanian – Maastrichtian. Future Pb isotopic analysis of detrital K-feldspar from the Nanaimo Basin of southwestern British Columbia may effectively distinguish between potential extraregional sources separated by thousands of kilometres.
This dataset includes levoglucosan data for five coastal California (United States) streams impacted by the 2020 CZU Lightning Complex Fires which burned from August 16th through September 22nd. Levoglucosan is a highly soluble and biolabile fraction of pyrogenic carbon. The five watersheds (San Lorenzo River, Pescadero Creek, Majors Creek, Laguna Creek, and Scott Creek) were impacted by the fires with watersheds experiencing a range of burn severity and extents. Grab samples were collected from each stream between October 2020 and May 2021, targeting both baseflow and event flow hydrologic conditions. Additional biogeochemistry data (i.e., organic and black carbon concentrations) can be found in a separate data package (https://doi.org/10.4211/hs.421c0226bb38460c8393d67fe0c4f802). This data package consists of one main data folder that contains (1) readme; (2) file-level metadata; (3) data dictionary; (4) field metadata with international generic sample numbers (IGSN); (5) methods codes; and (6) levoglucosan data. All files are .csv or .pdf.
Large earthquake ground-motion simulations in 3D Earth models provide constraints on site-specific shaking intensities but have suffered from limited frequency resolution and ignored site response in soft soils. In this work we report new regional-scale 3D simulations for moment magnitude 7.0 scenario earthquakes on the Hayward Fault, northern California with SW4. Simulations resolved significantly broader band frequencies (0–10 Hz) than previous studies and represent the highest resolution simulations for any such earthquake to date. Seismic waves were excited by a kinematic rupture following Graves and Pitarka (2016) and obeyed wave propagation in a 3D Earth model with topography from the U.S. Geological Survey (USGS) assuming a minimum shear wavespeed, VSmin, of 500 m/s. We corrected motions for linear and nonlinear site response for the shear wavespeed, VS, from the USGS 3D model, using a recently developed ground-motion model (GMM) for Fourier amplitude spectra . At soft soil locations subjected to strong shaking, the site-corrected intensities reflect the competing effects of linear amplification by low V S material, reduction of stiffness during nonlinear deformation, and damping of high frequencies. Sites with near-surface V S of 500 m/s or greater require no linear site correction but can experience amplitude reduction due to nonlinear response. Averaged over all sites, we obtained reasonable agreement with empirical ergodic median GMMs currently used for seismic hazard and design ground motions (epsilon less than 1), with marked improvement at soft sedimentary sites. At specific locations, the simulated shaking intensities show systematic differences from the GMMs that reveal path and site effects not captured in these ergodic models. Results suggest how next generation regional-scale earthquake simulations can provide higher spatial and frequency resolution while including effects of soft soils that are commonly ignored in scenario earthquake ground-motion simulations.
This report summarizes a study of possible offshore wind energy locations, technologies, and levelized cost of energy in the state of California between 2015 and 2030. The study was funded by the U.S. Department of the Interior's Bureau of Ocean Energy Management (BOEM), the federal agency responsible for regulating renewable energy development on the Outer Continental Shelf. It is based on reference wind energy areas where representative technology and performance characteristics were evaluated. These reference areas were identified as sites that were suitable to represent offshore wind cost and technology based on physical site conditions, wind resource quality, known existing site use, and proximity to necessary infrastructure. The purpose of this study is to assist energy policy decision-making by state utilities, independent system operators, state government officials and policymakers, BOEM, and its key stakeholders. The report is not intended to serve as a prescreening exercise for possible future offshore wind development.
This study presents wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) Regionally Refined Models (RRM) over California at 3.25 km and 800 m horizontal resolutions, using the Python wrapper for the System Advisor Model (PySAM). The resulting wind and solar generation estimates are compared to monthly capacity factors reported to the Energy Information Administration (EIA), High-Resolution Rapid Refresh (HRRR, 3 km resolution) forecast model, and E3SM North American regionally refined model (NARRM, 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM performs better than HRRR for solar power generation in single- and dual-axis tracking systems, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of uncertainties in generation modeling.
This case study describes the Southern California Gas Company’s Industrial End User program that helps large industrial customers increase energy efficiency and reduce energy use and GHG emissions.
The 2013-14 California drought was accompanied by an anomalous high-amplitude ridge system. The anomalous ridge was investigated using reanalysis data and the Community Earth System Model (CESM). It was found that the ridge emerged from continual sources of Rossby wave energy in the western North Pacific starting in late summer, and subsequently intensified into winter. The ridge generated a surge of wave energy downwind and deepened further the trough over the northeast U.S., forming a dipole. The dipole and associated circulation pattern is not linked directly with either ENSO or Pacific Decadal Oscillation; instead it is correlated with a type of ENSO precursor. The connection between the dipole and ENSO precursor has become stronger since the 1970s, and this is attributed to increased GHG loading as simulated by the CESM. Therefore, there is a traceable anthropogenic warming footprint in the enormous intensity of the anomalous ridge during winter 2013-14, the associated drought and its intensity.
Upwelling in the California Current System (CCS) sustains a productive ecosystem and is mediated by alongshore, equatorward wind stress. A decades-old hypothesis proposes that global warming will accelerate these upwelling favorable winds. Recent analyses provide empirical support for upwelling intensification in the poleward portion of the CCS. However, these studies rely on proxies for upwelling and are limited in their ability to distinguish anthropogenic forcing from internal climate variability. Here in this paper we estimate simulated changes in CCS upwelling from 1920 to 2100 using monthly output from a single climate model ensemble, where divergences among simulations can be attributed entirely to internal climate variability. Our projections suggest that CCS upwelling will become more intense in the spring and less intense in the summer as a result of anthropogenic climate change. Anthropogenic changes in upwelling will emerge primarily in the second half of the century.
In-situ aircraft measurements of aerosol chemical and cloud microphysical properties were conducted during the CalWater campaign in February and March 2011 over the Sierra Nevada Mountains and the coastal waters of central California. The main objective was to elucidate the impacts of aerosol properties on clouds and precipitation forming processes. In order to accomplish this, we compared contrasting cases of clouds that ingested aerosols from different sources. The results showed that clouds containing pristine oceanic air had low cloud drop concentrations and started to develop rain 500 m above their base. This occurred both over the ocean and over the Sierra Nevada, mainly in the early morning when the radiatively cooled stable continental boundary layer was decoupled from the cloud base. Supercooled rain dominated the precipitation that formed in growing convective clouds in the pristine air, up to the -21°C isotherm level. A contrasting situation was documented in the afternoon over the foothills of the Sierra Nevada, when the clouds ingested high pollution aerosol concentrations produced in the Central Valley. This led to slow growth of the cloud drop effective radius with height and suppressed and even prevented the initiation of warm rain while contributing to the development of ice hydrometeors in the form of graupel. Our results show that cloud condensation and ice nuclei were the limiting factors that controlled warm rain and ice processes, respectively, while the unpolluted clouds in the same air mass produced precipitation quite efficiently. These findings provide the motivation for deeper investigations into the nature of the aerosols seeding clouds.
Background: Several epidemiological studies demonstrate associations between high summer temperatures and increased mortality. However, the quantitative implications of projected future increases in temperature have not been well characterized. Objective: This study quantifies the effects of projected future temperatures on both mortality and morbidity in California, including the potential effects of mitigation. Data and methods: We first estimated the association between temperature and mortality for populations close to weather stations throughout the state. These dose-response estimates for mortality were then combined with local measures of current and projected changes in population, and projected changes in temperature, using a baseline of average temperatures from 1961 to 1990, for the years 2025 and 2050. The latter were based on two greenhouse gas emissions scenarios (A2 and B1) developed for the Intergovernmental Panel on Climate Change. In addition, we assessed the impacts of future adaptation through use of air conditioners. Several sensitivity analyses were conducted to determine the likely range of estimates. Results: These analyses indicate that for the high emissions scenario, the central estimate of annual premature mortality ranges from 2100 to 4300 for the year 2025 and from 6700 to 11,300 for 2050. The highest estimates are from the models that use age-specific dose-response functions, while the low estimates are from the models that adjust for ozone. Estimates using the low emissions scenario are roughly half of these estimates. Mitigation based on our estimates of the effects of 10% and 20% increase in air conditioner use would generate reductions of 16% and 33% in the years 2025 and 2050, respectively. Conclusion: Our estimates suggest significant public health impacts associated with future projected increases in temperature.
Highlights: • PAHs, potential endocrine disruptors, were detected in the majority of young girls. • Multiple exposure sources predicted urinary PAH metabolites concentrations. • Most PAH metabolites had fair to moderate reproducibility over four years. Polycyclic aromatic hydrocarbons (PAHs), a class of chemicals produced as combustion by-products, have been associated with endocrine disruption. To understand exposure in children, who have been less studied than adults, we examined PAH metabolite concentrations by demographic characteristics, potential sources of exposure, and variability over time, in a cohort study of pre- and peri-pubertal girls in Northern California.
Following life-cycle assessment (LCA) methodology, this study presents a state-level estimation of embodied carbon of wood products harvested in 2019 from California and subsequently processed, manufactured, transported, used, and disposed at the end-of-life (EoL). In a conventional static approach to LCA, all GHG emissions were aggregated and considered to occur at year 0 of the given time horizon (500 years in this study) and used a static characterization factor (CF). In dynamic LCA, GHG emissions occurring in different years were considered, and their global warming impact (GWI) was determined using a time-dependent CF over the selected time horizon of 500 years. Four scenarios were developed to examine the impact of EoL choices on GWI. It was found that dynamic GWI for all scenarios ranged from 0.27 to 0.93 million tonne CO₂e, which were 45–73 % lower than those estimated with static LCA approach, indicating that the static LCA approach could lead to an underestimation of the benefits of substituting wood for non-wood products, compared to those based on dynamic LCA approach. This analysis also demonstrated that the choice of EoL treatment option is a key factor affecting the estimated GWI as it directly determines the annual emission of GHGs released into atmosphere and subsequently their warming effect depending on the time harvested wood products (HWPs) spend in the horizon of assessment. Altogether, the dynamic LCA performed in this study enabled more robust interpretations of embodied carbon by including temporal boundaries associated with the HWPs life cycle.
Extreme heat is a major cause of weather-related deaths in the United States. To address this, a heat vulnerability index (HVI) is crucial for assessing heat risk and identifying vulnerable urban areas and populations, supporting city planning and emergency response. Current HVI studies often use Principal Component Analysis (PCA) on environmental, socioeconomic, and medical data to aggregate vulnerability indicators into a single index. However, these fixed aggregation weights struggle to adapt to different use cases, which may require varying focuses. Moreover, existing tools primarily consider outdoor heat exposure, providing an incomplete picture of actual exposure, as people spend most of their time indoors. Our research introduces an HVI web mapping tool that addresses these gaps in the literature by: (1) allowing flexible weights to adapt to different use cases, and (2) uniquely integrating both outdoor and indoor heat exposure by considering building characteristics for a more comprehensive risk assessment. We demonstrated this tool in two California cities with contrasting climates: Fresno (inland, arid, hot summers) and Oakland (temperate coastal). This HVI mapping tool provides essential decision support for policymakers and stakeholders in both short-term heat mitigation and long-term urban planning for building interventions and infrastructure development.
How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $\$100$ million per year for a CAISO-size system).