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DOE OSTI2019

Resilience Roadmap: A Collaborative Approach to Multi-Jurisdictional Resilience Planning

Extreme weather and aging infrastructure are creating unprecedented challenges in the United States and around the globe. Many communities have suffered damages from a variety of weather-related events, such as severe weather such as hurricanes, droughts, increasing temperatures, wildfires, and storm surges. Numerous efforts exist to improve resilience to protect national security, energy and water security, economic well-being, and quality of life. As severe weather events increase in frequency and severity, many cities, states, tribes, and federal agencies are addressing the impacts through resilience planning and measures. In an effort to support communities with resilience planning, the White House Council on Environmental Quality (CEQ) initiated preparedness pilot projects designed to showcase community resilience planning in the City of Houston and the State of Colorado. This publication is the full, technical document, for the Colorado pilot project. The process is also summarized in a user-friendly web-version on NREL's website (visit www.nrel.gov/resilience-planning-roadmap) for easier access and replicability. NASA's pilot project with the City of Houston is summarized separately (visit https://toolkit.climate.gov/case-studies/climate-resilience to learn more). water security, economic well-being, and quality of life. As severe weather events increase in frequency and severity, many cities, states, tribes, and federal agencies are addressing the impacts through resilience planning and measures. In an effort to support communities with resilience planning, the White House Council on Environmental Quality (CEQ) initiated preparedness pilot projects designed to showcase community resilience planning in the City of Houston and the State of Colorado. This publication is the full, technical document, for the Colorado pilot project. The process is also summarized in a user-friendly web-version on NREL's website (visit www.nrel.gov/resilience-planning-roadmap) for easier access and replicability. NASA's pilot project with the City of Houston is summarized separately (visit https://toolkit.climate.gov/case-studies/climate-resilience to learn more).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Wood-to-Fuel for California's Transportation Sector using Autothermal Pyrolysis (Progress Report)

We sent our updated draft Site License Agreement back to SPI on June 4 th , with redlines. Gary Blanc of SPI said he would circulate it internally to appropriate parties and get back to us. He did not return an accepted or redlined version to us before the end of the month of July, nor did he send us any questions/comments or updates, despite them being requested. He is currently traveling around visiting SPI’s various forestry locations and mills, so is difficult to reach. In working towards the commercialization strategy, during the month of July, further research was completed on understanding and evaluating the availability and potential availability of fire clearing forestry residue in California. In July, a meeting with Stephen Kaffka and Rob Williams who are both authors of the report “An Assessment of Biomass Resource in California, 2013” was attended. These individuals provided us with guidance and information about how to identify the availability and potential availability of forestry/wood resource biomass in California. Also, in the month of July, a meeting with Cal-Fire was attended in order to understand what Cal-Fire plans to do in terms of reducing the risk of wildfires and how much potential fire clearing forestry residue biomass this will produce for California. Additionally, the potential of a novel pathway of producing H2 from pyrolysis oil, maximizing the products’ value, as well as maximizing the carbon emission reduction potential by incorporating carbon capture onsite, was investigated. We continued with the process of subcontracting with Iowa State University and Frontline BioEnergy. We have our Contract Administrators working with their counterparts at each partner to get the details in place. Draft contracts with statements of work were provided to each partner, and updated schedules and budgets, broken down by task in the statements of work, have been requested from each.

09 BIOMASS FUELS↗

DOE OSTI2021

Evaluation of the Large-Scale and Regional Climatic Response Across North Africa to Natural Variability in Oceanic Modes and Terrestrial Vegetation Among the CMIP5 Models

Hydrologic variability is a serious threat to the poverty-stricken regions of North Africa. Meanwhile, the scientific community struggles to attribute these extreme climatic episodes to specific oceanic and land drivers. Prior modeling studies have not assessed simulated feedbacks over North Africa against an established observational benchmark, so their results are considered as untested, model-specific findings. The Coupled Model Intercomparison Project Phase Five (CMIP5) archive represents the state-of-the-art in climate projections, with most CMIP5 models now containing interactive vegetation phenology; however, there have been no studies to date of their simulated vegetation feedbacks. The capability of CMIP5 models at accurately simulating the forcing of both sea-surface temperature (SST) and regional leaf area index (LAI) anomalies on North African climate needs to be a key consideration in assessing the models’ overall credibility and determining appropriate model weighting for developing climate projections. The Generalized Equilibrium Feedback Assessment (GEFA) is a promising statistical method, which can be applied to either model output or observations, for isolating the local and remote impacts of individual oceanic or terrestrial forcings on regional climate. We performed a combined observational and modeling assessment of land-ocean-atmosphere interactions across the distinct ecological and moisture gradients of North Africa. We evaluated and demonstrated the reliability of the GEFA statistical method over North Africa using the Community Earth System Model (CESM), applied GEFA to observational data to quantify the observed forcing of ocean basin SST anomalies and regional LAI anomalies on North African climate, evaluated the CMIP5 models’ performance in terms of representing these key observed feedbacks, and formulated CMIP5 feedback performance metrics for weighting North African climate projections. Our study represented the first attempt to separate the observed roles of oceanic and vegetation feedbacks across North Africa, the first systematic assessment and intercomparison of land-ocean-atmosphere feedbacks in CMIP5, and the first exploration of vegetation feedbacks among CMIP5 models. The following work was accomplished. (1) The team generated 14 scientific publications and 30 presentations based on the DOE-funded research. (2) A stepwise version of the GEFA statistical method was developed in which unimportant forcings were dropped to increase the reliability of the results. (3) SGEFA was successfully validated through experiments with CESM in terms of its ability to isolate the atmospheric responses to individual oceanic or land forcings. (4) Observational evidence was revealed for the Sahel’s positive vegetation-rainfall feedback on the seasonal to interannual time scale, and it was attributed to a moisture recycling mechanism rather than an albedo mechanism. (5) An approach with SGEFA was developed in which the individual contributions of soil moisture versus vegetation forcings could be separated, revealing that the former forcing outweighed the latter for sub-Saharan Africa. (6) Tropical ocean temperatures were found to be key regulators of pan-tropical vegetation variability, especially for arid and semi-arid regions, including sub-Saharan Africa. (7) The CMIP5 models largely underestimated the importance of land feedbacks across the Sahel. (8) The general consensus among CMIP5 models indicates, for the late 21st century, a diminished seasonal predictability of sub-Saharan African regional climate and an elevated role of the land surface compared to oceanic drivers in regulating regional climate variability. (9) Land-ocean-atmosphere interactions were demonstrated to be key contributors to the seasonal predictability of African wildfire activity. (10) The El Djouf was determined to be of greater importance than the Bodélé depression in terms of providing trans-Atlantic dust transport to the Americas.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Financing Microgrids in the Federal Sector

Recent high-impact events such as hurricanes and wildfires highlight the need for energy system resilience. One potential impact of these events is the loss of utility grid power to federal sites, negatively affecting the ability to carry out their missions. Depending on the need to sustain critical functions, it may be important to continue operations during an outage of the electric grid, or at least have power restored quickly after the event. One approach to addressing this need is through a microgrid. Microgrids can provide a more reliable solution than the traditional approach of using a backup generator at individual buildings. A microgrid can enhance a site’s electrical power system during normal operations, as well as provide reliable backup power to critical loads when electric utility power is interrupted. Many times, certain components of a microgrid (and potentially the entire system) can be justified economically and paid for out of energy savings or avoided costs. In cases where savings can cover the project costs, one of several privately financed procurement mechanisms may be used to implement the project. Procurement options available to all federal agencies include utility energy services contracts (UESC), energy savings performance contracts (ESPC), and utility services contracts (USC); other mechanisms such as a power purchase agreement (PPA), enhanced use lease (EUL), and utility privatization (UP) may also be useful to agencies that have the authority to use them. Implementing a microgrid in a comprehensive effort that also includes energy efficiency can provide an important savings component, while reducing power requirements for critical loads, thus reducing the cost of the microgrid and associated distributed energy resources (DER). However, it can be difficult to achieve enough savings and avoided costs to cover the entire microgrid system, so leveraging these avoided costs in combination with appropriated funds may be necessary to implement the full project. When a site is deciding if a microgrid is the best solution, activities early in the process include identifying critical loads and existing DERs, conversations with the local electric utility, and implementation options that work best for the agency and site. This paper explores procurement options and agreements that may be useful to federal agencies interested in implementing microgrids at their sites. Microgrid complexities, implementation considerations, and suggestions to get started are also discussed. This information will be most useful to agencies when they are exploring electric energy system resilience options and/or considering different procurement options to meet their needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

QUIC-Fire: 3D Fire-Atmosphere Feedback Model for Wildland Fire Management

Los Alamos National Laboratory, U.S. Forest Service, and Tall Timbers Research Station developed QUIC-Fire, the first laptop-capable, real-time wildland fire prediction software that captures the critical influences of 3D vegetation structure, interactions between multiple fires, variable winds, and complex topography at meter-scale resolutions. Prescribed burns are needed to prevent catastrophic wildfires in the U.S. QUIC-Fire transforms a prescribed fire manager’s ability to assess risk, optimize fuel treatments, and plan prescribed burns. Los Alamos, U.S. Forest Service, Tall Timbers Research Station, U.S. Fish and Wildlife Service, Spatial Information Group, and Applied Interagency Prescribed Training Center have used QUIC-Fire.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Amanzi–ATS: Modeling Environmental Systems across Scales [Brief]

Department of Energy national labs (Los Alamos, Oak Ridge, Lawrence Berkeley, and Pacific Northwest) designed Amanzi-ATS to model complex environmental systems across multiple scales. The open-source software includes the most complete suite of surface/subsurface processes, allowing users to select physical processes and their coupling interactions without rewriting software. Amanzi–ATS has been used to analyze pristine local watersheds, wildfire impact on watersheds, subsurface contaminant transport at legacy waste sites, the effect of a warming climate on the Arctic tundra, and groundwater in fractured porous media. Department of Energy national labs, U.S. Geological Survey, academia, and industry have applied the software.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Financing Microgrids in the Federal Sector

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Development of a mineralogy-based anthropogenic combustion-iron emission inventory

Atmospheric supply of iron can modulate ocean biogeochemistry, due to its key role in global nitrogen and carbon cycles. Using a detailed technology-based methodology we revise total and soluble anthropogenic iron emissions and resolve iron into its mineral components, which allows modeling mineral-specific atmospheric reactions. The inclusion of metal smelting as an iron source, increases fine iron aerosol emissions (smaller than 1 µm) compared with most previous inventories. Different assumptions about solubility lead to estimates of 40-450 Gg/year deposition of soluble anthropogenic iron. When combined with dust and wildfires, the global total anthropogenic soluble iron deposition is 1900-2300 Gg/yr, so assumptions in anthropogenic inventories can affect estimates of global soluble iron supply by about 30%. In regions where marine primary productivity is iron-limited, anthropogenic combustion-iron contributes up to half of the atmospheric soluble iron flux to North Pacific Ocean.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Large Game Movement between Los Alamos National Laboratory and Pueblo de San Ildefonso

Movement by wildlife is a fundamental process that allows for the distribution of animal populations. The level and scale of movement varies by species, time of year, and whether long-distance migration plays a role in a species’ life history. Many species do not have regular migration patterns, but still have movement patterns essential to their survival. Changes in the environment such as wildfires, drought, and anthropogenic activities have the potential to influence the movements of animals.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

A Fire Community Observatory: Interdisciplinary, AI-informed Post-Fire Rapid Response for Improved Water Cycle Science at Watershed Scale

Wildfire is an ecological disturbance that disrupts the hydrological cycle. In the past few years, a record number of multiple-and-compounding fires have occurred across urban-wildland gradients in the Western United States. Changes to watershed hydrological partitioning in response to fires (infiltration, runoff, evapotranspiration) presents unprecedented challenges to “Water-in-the-West” through negative impacts to water supply and its quality, and is a direct threat to downstream communities, groundwater, and drinking water supply infrastructure. While much work is being done to advance Artificial Intelligence and Machine Learning (AI/ML) use during fires for emergency response (i.e. predict fire movement, direct evacuations), significant potential exists to use AI/ML to address three scientific grand challenges that are rarely addressed in a convergent science context: 1) how to enhance the potential resiliency of a landscape before fire(s), 2) how to cost-effectively and optimally monitor watershed changes after fires, and 3) how to predict future hydrological and biogeochemical trajectories in fire-impacted watershed given climate change. This whitepaper addresses DOE Focal Area 1: Data acquisition and assimilation enabled by machine learning, AI, and advanced methods.

58 GEOSCIENCES↗

DOE OSTI2021

Autonomous reinforcement learning agents for improving predictions and observations of extreme climate events

Primary Focus Area: This proposal addresses focus area 2, “Predictive modeling through the use of AI techniques.” Science Challenge: Extreme climate events associated with severe weather, coastal and inland flooding, droughts, heat waves and wildfires are expected to increase in frequency and severity in the future. Due to the complexity and chaotic behavior of the climate system, accurately predicting and observing extreme climate events requires a tremendous amount of human intervention to run predictive climate simulations and deploy measurement systems. Extreme events often unfold very quickly, leaving little time to iterate on simulations or re-position instruments. Through reinforcement learning, autonomous AI agents can be designed to make real-time decisions to characterize extreme climate events more efficiently through adaptive models and targeted observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics

Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Multi-scale Multi-physics Scientific Machine Learning for Water Cycle Extreme Events Identification, Labelling, Representation, and Characterization

Impacts of climate are usually felt through extreme events such as droughts, floods, thunderstorms, windstorms, wildfires, and so on, that are intimately tied to the water cycle. Predicting the frequency and severity of extreme events under climate change remains a significant challenge; meanwhile, the mechanisms and impacts of these extremes are far from well understood. There are several major science challenges: (1) Lack of labelled extreme events data and missing standards in defining extremes; (2) Computational demand of high-resolution ensemble climate modeling; (3) Modeling the multiscale multi-physics hierarchical structure of compound extremes; (4) Lack of understanding of mechanisms of extreme events; (5) Large uncertainty in extreme events impacts on infrastructure; (6) Subjective assessment of weather-related risk from seasonal to multi-decadal time scales and lack of metrics for risk assessment and mitigation control.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Transforming ESM Physical Parameterization Development Using Machine Learning Trained on Global Cloud-Resolving Models and Process Observations

ESMs robustly predict that 21st century greenhouse warming will slowly increase global mean precipitation, rapidly increase extreme precipitation, and increase subtropical drought. ESMs agree less about precipitation trends and extremes over particular land regions critical to human societies, e. g. in semi-arid regions such as California or the Sahel, or in wetter climates prone to monsoonal rainfall (e. g. southeast Asia) or to tropical cyclones and flooding from mesoscale convective systems (e. g. the southeastern U.S.) Deep convective parameterizations and poor representation of orography and complex vegetated land surfaces contribute to this inter-model spread; clouds, aerosols and sea-surface temperature biases are also key. Reducing regional precipitation projection uncertainty has enormous planning value for water supplies, land use, wildfire, hydropower, flood control, etc. IPCC-class ESMs are making painfully slow progress on this.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Represent precipitation-induced geological hazards in Earth system models using artificial intelligence

Precipitation-induced geological hazards, such as debris flow, landslides, mudflow and rockfalls (hereafter referred to as landslides), pose serious threats to public safety in many areas through the world. As residential properties and infrastructure in the US have increasingly expanded into landslide-prone areas and the drivers of landslides (e.g., wildfires and hurricanes) are predicted to intensify under climate warming, losses and fatalities from landslides are likely to increase in the future. Although our understanding of geoenvironmental factors and mechanisms contributing to landslides has greatly improved, only moderate progress has been made in predicting landslides out of well-studied watersheds. Furthermore, explicitly representing various landslide-related processes in Earth system models (ESMs), from the buckling of local bearing elements in granular materials, to frictional sliding between grains, formation of microcracks in the soil matrix, rupture of capillary bridges, or breakage of plant roots3 is still very unlikely within the next decade, even with the help of exascale computers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

America Resilient Climate Conference

On April 14, 2021, scientists, policymakers, and other interested parties from research institutes, academia, and other organizations gathered together virtually at the America Resilient Climate Conference to discuss one of the most pressing challenges of the 21st century: building resilience to climate change. Climate change affects the security and health of all Americans. Coastal areas are enduring more frequent and severe flooding due to sea level rise and storm surge; western states and Alaska have experienced increasingly devastating wildfires, driven in part by hotter, drier, and longer fire seasons; and communities across the nation have suffered through extreme precipitation events and heat waves. Even if emissions are reduced aggressively in the near future, the world—and the United States—will continue to feel the impacts of climate change for decades to come, due to the continued accumulation of greenhouse gasses in the atmosphere. Consequently, it is essential to act now to protect natural and human assets from the gradual—as well as extreme—impacts of a changing climate. To build resilient communities, leaders and community members need science-based information about the potential impacts climate change will have decades into the future and for specific regions. Therefore, it is essential to develop high-resolution climate models that can project both various climate impacts and the interactions between earth system variables and humans down to regional and local scales. Collecting and curating data for such models and their computational requirements poses large challenges. In the future, artificial intelligence will be needed to increase their accuracy and reduce associated uncertainties. The investments in Earth system science and artificial intelligence made by the U.S. Department of Energy and other federal entities will be essential in addressing these challenges.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Studying Rate of Fire Spread Using Landscape Statistics and FIRETEC

Wildfires are multi-system and multi-scale processes that are influenced by the land and atmosphere. Here, we study the influence of land, in terms of topography and vegetation, on uphill and downhill fire spread using data of the state of California as a testbed. The California landscape is first quantified by reclassifying its dominant vegetation types into grass, trees, and shrubs. Next, statistics of slope, vegetation height, and live fuel moisture percentage for each dominant vegetation type are gathered to create ensembles of idealized computational fluid dynamics (CFD) model simulations representative of the landscape. Rate of spread values for each CFD simulation are recorded to observe the differences in perturbing slope and fuel moisture for each vegetation type. We have found that all vegetation types prefer slopes of less than 30 degrees. Trees in the California data are 10 to 40 meters high, whereas shrub heights are less than 3 meters and grass heights are less than 1 meter. The live fuel moisture percentage of both trees and shrubs fluctuates around approximately 100% over one year (2019-2020). Using these parameters as inputs into the CFD model, fire spread rate is found to be fastest in the grass ensemble (maximum 12 m s -1 ), followed by shrubs (maximum 10 m s -1 ) and lastly by trees (maximum 7 m s -1 ). Increasing slope increases the spread rate of the uphill fire but the downhill spread decreases for slopes greater than 15 degrees. Increasing fuel moisture decreases the spread rate both up and down the hill. This study highlights the impacts that topography and vegetation have on fire spread.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Megafires: A New Fire Paradigm [Slides]

Twenty-five years ago LANL started a wildfire modeling effort with Rodman Linn to help educate firefighters. This work led to the development of HIGRAD-FIRETEC that has been used to examine a range of fires, controlled burns, and forest management. Rodman Linn and Mike Brown have recently developed QUIC-FIRE for DoD applications and wildland firefighters. HIGRAD-FIRETEC is currently being used to examine a variety of large fires including megafires.

54 ENVIRONMENTAL SCIENCES↗