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Publications and source records attributed to Coleman, Andre.
The datasets contain wildfire risk probabilities for transmission lines in the Western Interconnect, identified only by the HIFLD dataset identifier. The dataset is generated using PNNL's WREST Tool (https://www.pnnl.gov/projects/wildfire-risk-resilience/tools/wrest). The input dataset includes HIFLD datasets on transmission lines and wildfire burn potentials and flame lengths from the PNNL RADR-FIRE Tool for 2025.
In many regions around the world, wildfires have become a growing threat, exacerbated by factors such as climate change, droughts, and urban sprawl. These wildfires often wreak havoc on power infrastructure, leading to extended power outages, which can have devastating consequences for communities, especially in socioeconomically vulnerable areas. Ensuring grid resiliency in the face of these challenges is of paramount importance. One innovative approach to mitigating the impact of wildfires on the power grid is the utilization of hydropower facilities including both powered and non-powered hydro dams. While powered hydro dams along with additional infrastructure like energy storage and solar photovoltaic can provide resilience during wildfire-related outages, non-powered dams, originally constructed for purposes other than electricity generation, also offer a unique opportunity to bolster grid resilience. In this paper, we aim to explore the efficacy of hydro-dams in general to provide resiliency during wildfire-induced outages.
Microalgae is a unique biomass resource that does not need to compete for land and water with other biomass feedstocks because it can be cultivated on low-quality unencumbered land using noncompetitive water types including saline and wastewater. It is included as a complementary resource alongside other biomass feedstocks reported in this study, albeit at higher biomass production costs reflective of more capital-intensive farming operations than typical for terrestrial biomass. Higher biomass costs can be offset by the potential to produce value-added coproducts unique to compositional constituents of microalgae. Relative to BT16, this chapter reflects the latest analysis from the 2022 Algae Harmonization Update, which uses the latest parameterized and high-performing saline algal strain, second-generation carbon capture of point-source waste CO2 and high-pressure pipeline transport resolved to specific point-source types, saline water sourcing up to 40,000 mg/L total dissolved solids for source and makeup water salinity, blowdown water treatment and recycle, and brine disposal handling. National-scale algal biomass availability potential was calculated at 152 million tons/yr ash-free dry weight (AFDW) (191 million tons/yr dry weight) at an average biomass productivity of 26.2 g/m2 -day AFDW (about 50 tons/acre/yr dry weight).1 The algal biomass was cultivated on 3.9 million acres of multi-criteria screened and potentially available land for CONUS and fixed 268 million tons of waste CO2 based on biomass uptake. The algae biomass can be produced at an average MBSP of $674/ton AFDW ($536/ton dry weight) in 2020 dollars,2 corresponding to a total energy potential of 3.3 quads/yr at an average MBSP of $31.2/MMBtu (higher-heating-value [HHV] basis).
Microalgae is a unique biomass resource that does not need to compete for land and water with other biomass feedstocks because it can be cultivated on low-quality unencumbered land using noncompetitive water types including saline and wastewater. It is included as a complementary resource alongside other biomass feedstocks reported in this study, albeit at higher biomass production costs reflective of more capital-intensive farming operations than typical for terrestrial biomass. Higher biomass costs can be offset by the potential to produce value-added coproducts unique to compositional constituents of microalgae. Relative to the 2016 Billion-Ton Report, this chapter reflects the latest analysis from the 2022 Algae Harmonization Update, which uses the latest parameterized and high-performing saline algal strain, second-generation carbon capture of point-source waste CO 2 , and high-pressure pipeline transport resolved to specific point-source types, saline water sourcing up to 40,000 mg/L total dissolved solids for source and makeup water salinity, blowdown water treatment and recycle, and brine disposal handling. National-scale algal biomass availability potential was calculated at 152 million tons/yr ash-free dry weight (AFDW) (191 million tons/yr dry weight) at an average biomass
Chapter 7.2 — Coleman, A., K. Davis, J. DeAngelo, T. Saltiel, B. Saenz, L. Miller, K. Champion, E. Harrison, and A. Otwell. 2024. “Chapter 7.2: Macroalgae.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316176.
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This report presents an updated "harmonization study" documenting the collaborative analysis of microalgae cultivation and conversion to fuels and products. Four national laboratory modeling teams reconvened to investigate the resource, economic, and environmental sustainability implications of integrated systems encompassing large-scale algae farms and conversion biorefineries. Relative to prior harmonization analyses conducted by these partners, the present effort focuses on more near-term deployment potential based on the use of nutrient-replete, high-protein algal biomass compositions (more readily achievable today without sacrificing cultivation productivity), though this also incurs challenges in lower fuel yields and accordingly higher costs and carbon intensities for the overall integrated systems. Additionally, the present assessment adds further granularity around carbon dioxide (CO 2 ) sourcing and transport via carbon capture of nearby point sources, as well as handling of high-saline cultivation media and resultant blowdown/disposal processing. Finally, this assessment focuses on conversion opportunities to produce both fuel (prioritizing sustainable aviation fuel, in this case via hydrothermal liquefaction) and protein products for the food and feed markets, recognizing growing needs for such products.
This report presents an updated “harmonization study” documenting the collaborative analysis of saline microalgae cultivation and conversion to fuels and products. Four national laboratory modeling teams reconvened to investigate the resource, economic, and environmental sustainability implications of integrated systems encompassing large-scale algae farms and conversion biorefineries. Relative to prior harmonization analyses conducted by these partners, the present effort focuses on more near-term technology potential based on the use of nutrient-replete, high-protein algal biomass compositions (more readily achievable today without sacrificing cultivation productivity) coupled with individual algae farms varying in size but generally smaller at 3,900 acres on average (more realistic in practice than a fixed 5,000-acre farm scale previously considered). Additionally, the present assessment adds further granularity around carbon dioxide (CO 2 ) sourcing and transport via carbon capture of nearby point sources, as well as handling of high-saline cultivation media and resultant blowdown/disposal processing. Finally, this assessment focuses on conversion opportunities to produce both fuel (prioritizing sustainable aviation fuel [SAF], in this case via hydrothermal liquefaction) and protein products for the food and feed markets, recognizing growing needs for such products.
The United States (US) environmental protection agency's (EPA's) climate change indicators for wildfires show a long-term trend of increased annual wildfire activity, larger wildfire size, and more variable dynamics in wildfire behavior. This has caused more frequent preemptive public safety power shutoff (PSPS) events in the regions with recognized high wildfire risk. These preemptive power shutoffs attempt to prevent the ignition of a wildfire but it nonetheless renders the transmission line non-operational, which often sheds load of downstream communities. As wildfires and PSPS events become more frequent, it is crucial to find the communities most at risk of load shedding. To that end, this paper proposes the wildfire risk evaluation of the system (WiRES) framework, which is a performance-based framework that translates extreme weather-related and PSPS event probabilities into a cumulative probability of a non-operational transmission line. This study also provides a geospatial visualization tool that breaks down the entire western electrical coordinating council (WECC) region into 50 km grid cells which can be used to 1) filter out transmission lines with higher than a threshold outage probability, and 2) graphically discover the affected regions and their biophysical and socioeconomic metrics. %such as the social vulnerability index, population density, gross domestic product, etc. Lastly, an impact assessment study is conducted which connects the results of the proposed framework to python and powerworld-based contingency analysis to highlight the applicability of the framework.
Microalgae have great potential as an energy crop. Scaling-up algal biofuel production in the United States (US) should be done with careful attention to water stress. This study evaluates the regional and seasonal water-stress impact of potential algae-pond deployments in the US. Three site-selection strategies focusing on biomass yield, water-use efficiency (WUE), and water-stress impact, respectively, are applied and compared to meet a US algae biomass production target of 30 million metric tons/yr ash-free dry weight, which converts to 20.8 billion L renewable diesel, via hydrothermal liquefaction. Ranking algae ponds based on biomass yield leads to freshwater consumption of 2.66 km3/yr, resulting in the highest water-stress impact (39.1 US equivalent km3). Under the WUE scenario, water consumption is reduced by 81%, but biomass yield is reduced by 12%. In contrast, adding a water-stress constraint to the biomass-yield ranking reduces water consumption by 50% and water-stress impact by 97%, with a small yield reduction (1.7%). Results show that pond location has a significant effect on water stress and that water stress is not proportional to water consumption or yield. Furthermore, capturing seasonal water patterns is critical for planning because sites in water-abundant regions can have short-term but significant water-stress impacts.
Civil infrastructure such as culverts and bridges are commonly designed using precipitation-based intensity-duration-frequency (PREC-IDF) curves, which assume that the occurrence of precipitation is in the form of rainfall and immediately available for the rainfall-runoff process. In snow-dominated regions, where most winter precipitation occurs as snow that melts during spring to early summer, the use of standard PREC-IDF curves may lead to substantial underestimation of design floods and high failure risk of infrastructure. In this context, we developed next-generation IDF (NG-IDF) curves that characterize the actual water reaching the land surface (i.e., rainfall plus snowmelt) to enhance standard infrastructure design in snow-dominated regions. This study evaluates the performance of NG-IDF curves coupled with U.S. Department of Agriculture Technical Release 55 hydrologic model in estimating design floods for 246 snowy locations in different hydroclimate regimes of the western United States. Design flood estimates from a well-validated continuous simulation using a physics-based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), were used as the performance benchmark. Compared with the benchmark estimates, the standard PREC-IDF curves led to substantial errors in design flood estimates while the NG-IDF curves significantly reduced these errors. For example, the averaged error in the 50-year design flood estimates over the 246 locations was reduced from 31% with the use of PREC-IDF curves to 12% with the use of NG-IDF curves. Despite the different model structures, the single-event NG-IDF approach versus the continuous simulation DHSVM did not exhibit statistically significant differences in 91% of the 246 locations for the 50-year design flood estimates. We find this indicates a satisfactory performance of NG-IDF curves to estimate design flow under the conditions tested in the snow-dominated western United States. This article also presents technical suggestions and the limitations of infrastructure design using NG-IDF curves for regulatory agencies and practicing engineers.
In this work, downscaled high-resolution climate simulations were used to provide inputs to the physics-based Distributed Hydrology Soil Vegetation Model (DHSVM), which accounts for the combined effects of snowmelt and rainfall processes, to determine spatially distributed available water for runoff (AWR). After quasi-stationary time windows were identified based on model outputs extracted for two different mountainous field sites in Colorado and California, intensity–duration–frequency (IDF) curves for precipitation and AWR were generated and evaluated at each numerical grid to provide guidance on hydrological infrastructure design. Impacts of snowmelt are found to be spatially variable due to spatial heterogeneity associated with topography according to geostatistical analyses. AWR extremes have stronger spatial continuity compared to precipitation. Snowmelt impacts on AWR are more pronounced at the wet California site than at the semiarid Colorado site. The sensitivities of AWR and precipitation IDFs to increasing greenhouse gas emissions are found to be localized and spatially variable. In subregions with significant snowfall, snowmelt can result in an AWR (e.g., 6-h 100-yr events) that is 70% higher than precipitation. For comparison, future greenhouse gas emissions may increase 6-h 100-yr precipitation and AWR by up to 50% and 80%, respectively, toward the end of this century.
Disaster response preparedness, risk mitigation, and effective post-event response are critical for national security, public health, and economic integrity. For areas impacted by extreme events, timely and accurate impact assessments of lifeline infrastructures are critical. Traditional disaster management and situational awareness platforms are not capable of handling the massive influx of available information from traditional and non-traditional sources. Next-generation analytic assessment approaches, where use of dynamically sourced, heterogeneous data with near real-time availability are integrated into a continually-updated analytics framework. The objective of this research was to develop a foundational multi-formalism modeling platform to integrate novel data sources for use in improving situational awareness after a disaster event. Specifically, to 1) provide an architecture for machine-learning based data fusion various multi-sensor datasets; 2) classify, geolocate, and temporally bin novel data sources for use in spatially-enabled machine-learning based data fusion; and 3) derive confidence-based observation data with machine-learning based prediction to fill spatial and temporal observation gaps. This project developed a reusable cloud-based pipeline architecture for ingesting, storing, and analyzing relevant data, executing a range of deep-learning models, and visualize the information geographically. The pipeline development was informed by previous PNNL investments (StreamKit) and built upon leading-edge technologies consisting of: 1) a raw datastore; 2) a data enrichment pipeline with custom and third-party analytics; 3) enriched data storage; and 4) a custom UI to display the enriched data. Machine learning/deep learning models were selected and tested to classify social media images for relevancy and presence of flood waters. Our custom trained relevance models were run on a validation dataset and the overall accuracy of the relevance classifiers ranged from 65% to 90%. We found that convolutional neural network (CNN) models were both faster and more accurate than more traditional machine learning models. We applied the relevance classifier to remove non-relevant images and then undertook a limited training of CNN models to detect flood on ~1,800 labeled images. The testing accuracy of the flood detection models were ~88%. To improve the geographic location of both social media and oblique aerial images we tested a multi-step workflow built on a pre-trained deep-learning model. To identify similarity features from test and reference scenes, we applied the Xception and Faiss models to a set of test images with inaccurate spatial locations, as well and precisely-located reference scenes from the general area. Matching reference scenes to the test scenes with the closest similarity index had limited success in improving the precision of test scene location and additional methods have been identified for testing. Data fusion between authoritative geographic data (satellite imaging) and non-authoritative crowdsourced data is an underlying theme throughout this research and methods are tested bring these data to a common geographic form through feature matching and transformations. The automated extraction of supporting information for disaster response from oblique aerial images or ground-level social media images is accomplished through semantic image segmentation and transformation. The non-authoritative data are used to validate the authoritative data collections and processing and optionally, t+0 flood models. In addition, the common form geographic data are combined with other static landscape features to develop spatially-explicit training vectors used in deep generative statistical inference models to develop complex non-linear relationships in static and dynamic data and ultimately derive a spatially continuous and probabilistic assessment of flood damage with the event domain.
Precipitation intensity-duration-frequency (PREC-IDF) curves are a standard tool to derive design floods for hydraulic infrastructure design worldwide. In snow-dominated regions where a large percentage of flood events are caused by snowmelt and rain-on-snow events, precipitation alone can be a poor predictor of flood risk. Meanwhile, anthropogenic climate change is expected to intensify hydrological cycle and alter hydrologic extremes, potentially increasing the likelihood of infrastructure failure in their design life period. To enhance future hydrologic design, we propose the next-generation IDF (NG-IDF) curves with consideration of both snow process and climate nonstationarity. The NG-IDF curves, which characterize the actual water reaching the land surface (melt + rain), use of the concept of “water available for runoff” rather than “rainfall” for hydrologic design. This paper shows that in the mountainous regions of the western United States, the standard PREC-IDF curves without considering snow process can substantially underestimate the input water intensities and the consequent peak design floods (i.e., the PREC-IDF curves may lead to underestimation of peak design flood by as much as 324%). Under a warming climate, we also observe statistically significant declining trends in both snowpack mass and extreme water available for runoff across the mountainous regions of the western United States from 1979 to 2017, confirming the proclamation that “stationarity is dead” and demonstrating the need to develop nonstationary NG-IDF curves for future hydrologic design.
We used a quality-controlled Snowpack Telemetry (SNOTEL) data set (1979–2017) combined with the nonparametric regional Kendall test (RKT) to examine changes in annual maximum water available for runoff at the land surface under four hydrometeorological conditions (snowmelt only, rain-on-snow, all melt with or without rainfall, and all melt plus rainfall on snow-free ground) over the mountainous regions of the western U.S. Our RKT analyses indicated significant declining trends at point (SNOTEL) scale under all four conditions. The annual maximum of all melt plus rainfall decreased primarily in the southwestern U.S., while the frequency of rain-on-snow events increased significantly in the northwestern U.S. The rate of annual maximum snowmelt only decreased in 10 of 11 ecological regions across the western U.S. These results suggest that under a warming climate, the four types of annual maximum water available for runoff have been substantially decreasing at local scale across the western U.S.
Waste-to-energy (WTE) technologies present an opportunity to recycle organic waste material into renewable energy while offsetting disposal and environmental costs. A key challenge to ensuring economic and environmental viability of WTE is understanding the variability of individual WTE resource characteristics, including their location, amount, and quality. The main objective of this study is to estimate the wet WTE resource potential in the United States and illustrate its geographic distribution. The wet resources considered in this study are wastewater sludge, animal manure, food waste, and FOG (fats, oils, and greases). This study is the first to achieve results below national level, at the finest geographic resolution. Our analysis indicates that about 566 teragrams (Tg) of wet WTE resources are generated annually in the United States. This amount corresponds to about 1 exajoule (EJ), which is sufficient to displace about 18% of the 2015 U.S. on-highway diesel consumption on an energy basis. About half of this potential is generated by animal manure.
To present a more unified picture of the long-term future potential for algal biofuels and the goals that must be met to reach that potential, four algae modeling groups collaborated to harmonize respective models for resource assessment (Pacific Northwest National Laboratory), techno-economic analysis (National Renewable Energy Laboratory and Pacific Northwest National Laboratory), and life-cycle analysis (Argonne National Laboratory) of algal biomass production and conversion processes. In contrast to prior harmonization studies that this group has previously conducted, which focused on establishing benchmarks attributed to current performance at the time, the primary intent of the present harmonization study was to project these models to forward-looking targets that must be achieved to reduce fuel costs and greenhouse gas emission profiles to Bioenergy Technologies Office target levels, subject to specific location constraints imposed by resource assessment modeling and thus national-scale fuel output potential (i.e., billion gallons gasoline equivalent per year, or BGGE/yr). In all, while based on a number of aspirational goals not yet demonstrated but plausible to achieve over coming years, the harmonized outputs indicate promising potential for algal biofuels to make a significant contribution to the U.S. fuels market, based on substantial quantities of biomass (104-235 MM tons/yr) projected to be available at costs below 700/ton (generally below 500/ton for freshwater cultivation). While these costs are still significantly higher than terrestrial biomass cost targets, the propensity for capturing high fuel yields and/or high-value tailored coproducts from algal biomass is shown here to provide plausible paths to achieve future targets for cost and sustainability metrics at meaningful volumes.
There is significant global interest in developing, testing, and using alternative jet fuels for both commercial and military use in an effort to create a sustainable and stable fuel supply while reducing greenhouse gas emissions. Currently, the aviation industry is entirely dependent on a finite-supply of petroleum based fuel sourced in part by politically and economically unstable regions of the world. Commercial jet fuel use within the contiguous United States (CONUS) was 17.8 billion gallons per year (BGY) in 2009, while jet fuel use in 2010 by the U.S. Air Force (USAF), Navy, and Army was 1.5 BGY, 0.6 BGY, and 0.8 BGY, respectively (Carter et al., 2011). U.S. commercial and military aviation sectors have set ambitious near-term alternative fuel and environmental performance targets. This includes a tentative Federal Aviation Administration (FAA) goal of 1 BGY alternative fuel use by commercial aircraft by 2018. The USAF has set a target of 50% for USAF domestic aviation via alternative fuels by 2016 (0.73 BGY), and 50% of the Navy’s total energy consumption afloat (0.3 BGY) will come from alternative fuels by 2020 (Carter et al., 2011). If these targets become policy, at least 2 BGY of domestically-produced alternative jet fuel will be required by 2020. The Energy Independence and Security Act (EISA) of 2007 established production requirements for domestic alternative fuels under the Renewable Fuel Standard (RFS). For example, 36 billion gallons of renewable fuel must be produced by 2022, of which 21 billion gallons shall be advanced biofuels. EISA defines advanced biofuels as non-corn starch derived biofuels having lifecycle greenhouse gas emissions 50% lower than gasoline. There a number of potential fuel pathways for meeting the RFS. One of these is biomass-based diesel, including jet fuel (Schnepf and Yacobucci, 2013). The U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) has a stated goal in its 2013 Multi-Year Program Plan (USDOE, 2013) to support the RFS through development of “…commercially viable biomass utilization technologies to encourage the creation of a new domestic bioenergy industry…”. BETO has also recognized the potential for aviation biofuels to support the bioenergy industry, seeing drop-in bio-based jet fuels one of the viable alternatives for the aviation industry and the military to meet their ambitious near-term GHG reduction targets (USDOE, 2014). One of the important Multi-year Program Plan Targets (USDOE, 2013) is to establish feedstock resource assessment models to evaluate the geographic, economic, quality and environmental criteria for which 20 million metric tons of ash free dry weight (AFDW) algal biomass can be produced by 2022. Toward meeting the EISA requirements, algal biofuels may offer a number of advantages. They can produce a range of biofuel feedstocks suitable for diesel and aviation fuels. Microalgae, on a strain-specific basis, can be cultivated using impaired water including saline, and/or brackish pumped groundwater or seawater, treated industrial wastewater, municipal sewage effluent, and produced water generated from oil and gas drilling operations. Additionally, microalgae require nitrogen and phosphates as essential nutrients and could provide water treatment co-benefits to municipalities, industry, and the environment.