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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

The observation of possible reconnection events in the boundary changes of solar coronal holes

Coronal holes are large scale regions of magnetically open fields which are easily observed in solar soft X-ray images. The boundaries of coronal holes are separatrices between large-scale regions of open and closed magnetic fields where one might expect to observe evidence of solar magnetic reconnection. Previous studies by Nolte and colleagues using Skylab X-ray images established that large-scale (more than 90,000 km) changes in coronal hole boundaries were due to coronal processes, i.e., magnetic reconnection, rather than to photospheric motions. Those studies were limited to time scales of about one day, and no conclusion could be drawn about the size and time scales of the reconnection process at hole boundaries. Sequences of approximate Skylab X-ray images with a time resolution of about 90 min are here used during times of the central meridian passages of the coronal hole labelled 'Coronal Hole 1' to search for hole-boundary changes which can yield the spatial and temporal scales of coronal magnetic reconnection. It is found that 29 of 32 observed boundary changes could be associated with bright points. The appearance of the bright point may be the signature of reconnection between small-scale and large-scale magnetic fields. The observed boundary changes contributed to the quasi-rigid rotation of Coronal Hole 1.

Kahler, S. W.↗
NASA NTRS2019

Remote Sensing of Solar-Induced Chlorophyll Fluorescence (SIF) in Vegetation: 50 Years of Progress

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Review↗
DOE OSTI2021

Potential of hotspot solar‐induced chlorophyll fluorescence for better tracking terrestrial photosynthesis

Abstract Remote sensing of solar‐induced fluorescence (SIF) opens a new window for quantifying a key ecological variable, the terrestrial ecosystem gross primary production (GPP), because of the revealed strong SIF–GPP correlation. However, similar to many other remotely sensed metrics, SIF observations suffer from the sun‐sensor geometry effects, which may have important impacts on the SIF–GPP relationship but remain poorly understood. Here we used remotely sensed SIF, globally distributed tower GPP data, and a mechanistic model to provide a systematic analysis. Our results reveal that leaf physiology, canopy structure, and sun‐sensor geometries all affect the SIF–GPP relationship. In particular, we found that SIF observations in the sun‐tracking hotspot direction can be a better proxy of GPP due to the similar responses of light use efficiency and SIF escaping probability in the hotspot direction to the increasing incoming solar radiation. Such conclusions are supported by a variety of modeling simulations and satellite observations over various plant function types, at different time scales and with satellite observational modes. This study demonstrates the potential and advantage of normalizing SIF observations to the hotspot direction for better global GPP estimations. This study also demonstrates the great potentials of current and future spaceborne sun‐tracking satellite missions for a significant improvement in measuring and monitoring, at a wide range of spatial and temporal scales, the changes in terrestrial ecosystem GPP in response to anticipated changes in the Earth's environmental conditions.

Hao, Dalei↗
DOE OSTI2020

Extreme Scale Infrasound Inversion and Prediction for Weather Characterization and Acute Event Detection

Accurate and timely weather predictions are critical to many aspects of society with a profound impact on our economy, general well-being, and national security. In particular, our ability to forecast severe weather systems is necessary to avoid injuries and fatalities, but also important to minimize infrastructure damage and maximize mitigation strategies. The weather community has developed a range of sophisticated numerical models that are executed at various spatial and temporal scales in an attempt to issue global, regional, and local forecasts in pseudo real time. The accuracy however depends on the time period of the forecast, the nonlinearities of the dynamics, and the target spatial resolution. Significant uncertainties plague these predictions including errors in initial conditions, material properties, data, and model approximations. To address these shortcomings, a continuous data collection occurs at an effort level that is even larger than the modeling process. It has been demonstrated that the accuracy of the predictions depends on the quality of the data and is independent to a certain extent on the sophistication of the numerical models. Data assimilation has become one of the more critical steps in the overall weather prediction business and consequently substantial improvements in the quality of the data would have transformational benefits. This paper describes the use of infrasound inversion technology, enabled through exascale computing, that could potentially achieve orders of magnitude improvement in data quality and therefore transform weather predictions with significant impact on many aspects of our society.

58 GEOSCIENCES↗
NASA NTRS1994

Multiscale low-frequency circulation modes in the global atmosphere

In this paper, fundamental multiscale circulation modes in the global atmosphere are identified with the objective of providing better understanding of atmospheric low-frequency variabilities over a wide range of spatial and temporal scales. With the use of a combination of rotated principal component technique, singular spectrum analysis, and phase space portraits, three categories of basic multiscale modes in the atmosphere are found. The first is the interannual-mode (IAM), which is dominated by time scales longer than a year and can be attributed to heating and circulation anomalies associated with the coupled tropical ocean-atmosphere, in particular the El Nino-Southern Oscillation. The second is a set of tropical intraseasonal modes consisting of three separate multiscale patterns (ISO-1, -2, -3) related to tropical heating that can be identified with the different phases of the Madden-Julian Oscillation (MJO), including its teleconnection to the extratropics. The ISO spatial and temporal patterns suggest that the extratropical wave train in the North Pacific and North America is related to heating over the Maritime Continent and that the evolution of the MJO around the equator may require forcing from the extratropics spawning convection over the Indian Ocean. The third category represents extratropical intraseasonal oscillations arising from internal dynamics of the basic-state circulation. In the Northern Hemisphere, there are two distinct circulation modes with multiple frequencies in this category: the Pacific/North America (PNA) and the North Atlantic/Eurasia (NAE). In the Southern Hemisphere, two phase-locked modes (PSA-1 and PSA-2) are found depicting an eastward propagating wave train from eastern Australia, via the Pacific South America to the South Atlantic. The extratropical modes exhibit temporal characteristics such as phase locking and harmonic oscillations possibly associated with quadratically nonlinear dynamical systems. Additionally, the observed monthly and seasonal anomalies arise from a complex interplay of the various multiscale low-frequency modes. The relative dominance of the different modes varies widely from month to month and from year to year. On the monthly time scale, while one or two mechanisms may dominate in one year, no single mechanism seems to dominate for all years. There are indications that when the IAM, that is, ENSO heating patterns are strong, the extratropical modes may be suppressed and vice versa. For the seasonal mean, the interannual mode tends to dominate and the contribution from the PNA remains quite significant.

Lau, K.-M.↗
DOE OSTI2021

Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems

In order to achieve stringent greenhouse gas emission reductions, a transition of our entire energy system from fossil to renewable resources needs to be designed. Such an energy transition brings two main challenges: most renewables generate variable electric energy, yet most demand is currently not electric (carrier mismatch) and does not always manifest at the same time as supply (temporal mismatch). Integrating multiple energy infrastructures can address both challenges by using the synergy between different energy carriers; building on existing infrastructure, while allowing a robust and flexible integration of the new. This paper proposes an optimization framework for long-term, multi-period investment planning of urban energy systems in an integrated manner. We formulate it as a mixed-integer linear program, combining a capacitated facility location with a multi-dimensional, capacitated network design problem. It includes generation and network expansion planning as well as interconnections between networks and storage infrastructure for each energy system. It can incorporate pathway effects like techno-economic developments, policy measures, and weather variations. The intended use is to support urban decision makers with long-term investment planning, though it can be tailored to fit other geographical or temporal scales. We demonstrate the model using two cases based on an average city in The Netherlands, which wants to reduce its CO 2 -emissions with 95% by 2050. In the first case, we include explicit carbon-emission constraints to study the effects of the carrier mismatch. In the second case, we implement interannual weather variations to analyze the temporal mismatch. The results give valuable insights into the energy transition design strategy for urban decision makers. They also show the future potential, as well as the computational challenges of the optimization framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

Decorrelation Times of Photospheric Fields and Flows

We use autocorrelation to investigate evolution in flow fields inferred by applying Fourier Local Correlation Tracking (FLCT) to a sequence of high-resolution (0.3 "), high-cadence (approx = 2 min) line-of-sight magnetograms of NOAA active region (AR) 10930 recorded by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite over 12 - 13 December 2006. To baseline the timescales of flow evolution, we also autocorrelated the magnetograms, at several spatial binnings, to characterize the lifetimes of active region magnetic structures versus spatial scale. Autocorrelation of flow maps can be used to optimize tracking parameters, to understand tracking algorithms f susceptibility to noise, and to estimate flow lifetimes. Tracking parameters varied include: time interval Delta t between magnetogram pairs tracked, spatial binning applied to the magnetograms, and windowing parameter sigma used in FLCT. Flow structures vary over a range of spatial and temporal scales (including unresolved scales), so tracked flows represent a local average of the flow over a particular range of space and time. We define flow lifetime to be the flow decorrelation time, tau . For Delta t > tau, tracking results represent the average velocity over one or more flow lifetimes. We analyze lifetimes of flow components, divergences, and curls as functions of magnetic field strength and spatial scale. We find a significant trend of increasing lifetimes of flow components, divergences, and curls with field strength, consistent with Lorentz forces partially governing flows in the active photosphere, as well as strong trends of increasing flow lifetime and decreasing magnitudes with increases in both spatial scale and Delta t.

Welsch, B. T.↗
DOE OSTI2022

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA)

To explore the various couplings across space, time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past towards a unification of the range of scales inherent in the Earth System. This paper describes the forward-looking MUlti-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatio-temporal scales, coupling to other Earth System components and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders.

Pfister, Gabriele G.↗
DOE OSTI2025

A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial‐Aquatic Interfaces

Interconnected landscape features such as terrestrial‐aquatic interfaces play an outsized role in biogeochemical cycles as ecosystem control points, but it is notoriously challenging to characterize these. Here, we document a synoptic sensor network design that is (a) flexible to accommodate diverse ecosystem interfaces and gradients, (b) adaptable to monitoring and modeling needs of small and large projects alike, (c) standardized for intercomparability across sites and field experiments, and (d) adequately replicated to capture heterogeneity of each parameter monitored. This real‐time monitoring of surface water, groundwater, soil, and vegetation supports configuration and evaluation of models that span upland, wetland, open water strata, and transitions between them. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including large‐scale flood manipulation experiments in both regions. A central design element is “one data logger program to rule them all”—a collection of sensor‐specific modules deployed on 40 loggers controlling ∼2,000 sensors, with the goal of streamlining maintenance, debugging, and reproducible data processing. The network generates ∼6 M observations per month, capturing system dynamics at the broad spatial and fine temporal scales needed to initialize and benchmark models; measurement frequency can be modified remotely to capture events. This network design has also revealed behaviors not represented in Earth system models, such as transient groundwater oxygen pulses. Completely documented and open source, this standardized, flexible, and efficient sensor network design can reduce barriers to understanding environmental changes and ecosystem responses across systems and scales.

Ward, Nicholas D. [Pacific Northwest National Labo↗
DOE OSTI2021

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2007

Characterization of Polar Stratospheric Clouds With Spaceborne Lidar: CALIPSO and the 2006 Antarctic Season

The role of polar stratospheric clouds in polar ozone loss has been well documented. The CALIPSO satellite mission offers a new opportunity to characterize PSCs on spatial and temporal scales previously unavailable. A PSC detection algorithm based on a single wavelength threshold approach has been developed for CALIPSO. The method appears to accurately detect PSCs of all opacities, including tenuous clouds, with a very low rate of false positives and few missed clouds. We applied the algorithm to CALIPSO data acquired during the 2006 Antarctic winter season from 13 June through 31 October. The spatial and temporal distribution of CALIPSO PSC observations is illustrated with weekly maps of PSC occurrence. The evolution of the 2006 PSC season is depicted by time series of daily PSC frequency as a function of altitude. Comparisons with virtual solar occultation data indicate that CALIPSO provides a different view of the PSC season than attained with previous solar occultation satellites. Measurement-based time series of PSC areal coverage and vertically-integrated PSC volume are computed from the CALIPSO data. The observed area covered with PSCs is significantly smaller than would be inferred from a temperature-based proxy such as TNAT but is similar in magnitude to that inferred from TSTS. The potential of CALIPSO measurements for investigating PSC microphysics is illustrated using combinations of lidar backscatter coefficient and volume depolarization to infer composition for two CALIPSO PSC scenes.

Pitts, Michael C.↗
DOE OSTI2021

Application of remote sensing precipitation data and the $\mathrm{CONNECT}$ algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019)

In recent years, the number of floods following unprecedented rainfall events have increased in Iran during early spring (March 21st to April 20th, referred to in Iran as the month of “Farvadin”). While numerous studies have addressed changes in climate extremes and precipitation trends at different temporal scales from daily to annual across the country, analyses of short-duration and heavy precipitation, especially during recent years, are rarely considered. Furthermore, most studies investigate the variations in extremes and total precipitation using a limited number of synoptic weather stations across Iran. Here this study assesses the variations in heavy precipitation (precipitation with intensities greater than or equal to 3 mm/3 h) at 0.04° spatial and 3-hourly temporal resolution during the month of Farvardin. In addition, the effect of atmospheric river conditions over Iran and their possible link to intensifying heavy precipitation is explored. For this purpose, the CONNected-objECT (CONNECT) algorithm is applied on a precipitation dataset, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and an Integrated Water Vapor Transport (IVT) dataset from the NASA Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2). The results suggest that the increase in the number of floods in recent years is related to the increase in the intensity and volume of heavy precipitation events, although the frequency and duration of heavy precipitation events have not changed significantly. Furthermore, the results show that atmospheric river conditions over the country are present during the same window as each year’s most extreme events. It is found that 8 out of 13 of the largest ARs over Iran come from moisture plumes with pathways over the African and Red Sea.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
NASA NTRS2013

A Stochastic Fractional Dynamics Model of Space-time Variability of Rain

Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, that allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and times scales. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and in Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to the second moment statistics of radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment.

TRMM↗
DOE OSTI2025

Cyclic Background Noise Variations on Infrasound Microbarometers From Micrometeorology and Human Activity

Infrasound microbarometers deployed on the Earth's surface capture pressure fluctuations and acoustic signatures, revealing trends in surface wind speed and cycles in ambient sound. Previous studies investigated wind noise trends at quarter day resolution and urban acoustic background variations on hourly to weekly scales. Cyclic variations at sub-hourly resolution over local distances remain unaddressed. We show that topography-driven micrometeorology impacts diurnal background noise levels below 10 Hz. Anthropogenic noise occurs over daily and weekly cycles, with inputs from traffic, ventilation systems, and power lines. These noise patterns vary at stations spaced less than a kilometer apart. We observe these phenomena by using a circular spectrogram to visualize frequency trends over a periodic temporal scale. This study demonstrates that infrasound microbarometer deployments can highlight localized wind patterns and time scales of human activity. The results suggest that future microbarometer deployments may benefit from site noise surveys before selecting final sensor locations.

Malach, Amrit Kaur [Sandia National Lab. (SNL-CA),↗
DOE OSTI2025

A Multi-Model, Multi-Scale Research Program in Stressors, Responses, and Coupled Systems Dynamics at the Energy-Water-Land Nexus and for Concentrated, Interdependent Infrastructures: Toward Next Generation Capabilities in Integrated Impacts, Adaptation, and Vulnerability (I-IAV) Modeling and a Community of Practice

The goal of this research program was to build a next generation integrated suite of science-driven modeling and analytic capabilities, and a more expanded and connected community of practice, for analyses of the stressors, impacts, adaptations and vulnerabilities of global and regional change. The emphasis was on understanding energy-water-land interactions and feedbacks and interdependent infrastructures at appropriate regional and temporal scales. Although the scope spans many complex facets of data, modeling, and analysis, as well as scales appropriate for integrated impacts and adaptation research, the focus of this effort was the development of multi-model, multi-scale capabilities spanning the domains of Multi-Sector Dynamics (MSD) models; Impact, Adaptation, and Vulnerability (IAV) models; and Earth System Models (ESMs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Fire as a fundamental ecological process: Research advances and frontiers

Fire is a powerful ecological and evolutionary force that regulates organismal traits, population sizes, species interactions, community composition, carbon and nutrient cycling and ecosystem function. It also presents a rapidly growing societal challenge, due to both increasingly destructive wildfires and fire exclusion in fire–dependent ecosystems. As an ecological process, fire integrates complex feedbacks among biological, social and geophysical processes, requiring coordination across several fields and scales of study. Here, we describe the diversity of ways in which fire operates as a fundamental ecological and evolutionary process on Earth. We explore research priorities in six categories of fire ecology: (a) characteristics of fire regimes, (b) changing fire regimes, (c) fire effects on above–ground ecology, (d) fire effects on below–ground ecology, (e) fire behaviour and (f) fire ecology modelling. We identify three emergent themes: the need to study fire across temporal scales, to assess the mechanisms underlying a variety of ecological feedbacks involving fire and to improve representation of fire in a range of modelling contexts. As fire regimes and our relationships with fire continue to change, prioritizing these research areas will facilitate understanding of the ecological causes and consequences of future fires and rethinking fire management alternatives.

54 ENVIRONMENTAL SCIENCES↗