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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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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

What a difference a delay makes! CT urogram: a pictorial essay

Purpose: The aim of this pictorial essay is to demonstrate several cases where the diagnosis would have been difficult or impossible without the excretory phase image of CT urography. Methods: A brief discussion of CT urography technique and dose reduction is followed by several cases illustrating the utility of CT urography. Results: CT urography has become the primary imaging modality for evaluation of hematuria, as well as in the staging and surveillance of urinary tract malignancies. CT urography includes a non-contrast phase and contrast-enhanced nephrographic and excretory (delayed) phases. While the three phases add to the diagnostic ability of CT urography, it also adds potential patient radiation dose. Several techniques including automatic exposure control, iterative reconstruction algorithms, higher noise tolerance, and split-bolus have been successfully used to mitigate dose. The excretory phase is timed such that the excreted contrast opacifies the urinary collecting system and allows for greater detection of filling defects or other abnormalities. Sixteen cases illustrating the utility of excretory phase imaging are reviewed. Conclusions: Excretory phase imaging of CT urography can be an essential tool for detecting and appropriately characterizing urinary tract malignancies, renal papillary and medullary abnormalities, CT radiolucent stones, congenital abnormalities, certain chronic inflammatory conditions, and perinephric collections.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗
DOE OSTI2023

Changes in soil pore structure generated by the root systems of maize , sorghum and switchgrass affect in situ N 2 O emissions and bacterial denitrification

Due to the heterogeneous nature of soil pore structure, processes such as nitrification and denitrification can occur simultaneously at microscopic levels, making prediction of small-scale nitrous oxide (N 2 O) emissions in the field notoriously difficult. We assessed N 2 O+N 2 emissions from soils under maize (Zea mays L.), switchgrass (Panicum virgatum L.), and energy sorghum (Sorghum bicolor L.), three potential bioenergy crops in order to identify the importance of different N 2 O sources to microsite production, and relate N 2 O source differences to crop-associated differences in pore structure formation. The combination of isotopic surveys of N 2 O in the field during one growing season and X-ray computed tomography (CT) enabled us to link results from isotopic mappings to soil structural properties. Further, our methodology allowed us to evaluate the potential for in situ N 2 O suppression by biological nitrification inhibition (BNI) in energy sorghum. Our results demonstrated that the fraction of N 2 O originating from bacterial denitrification and reduction of N 2 O to N 2 is largely determined by the volume of particulate organic matter occluded within the soil matrix and the anaerobic soil volume. Bacterial denitrification was greater in switchgrass than in the annual crops, related to changes in pore structure caused by the coarse root system. This led to high N-loses through N 2 emissions in the switchgrass system throughout the season a novel finding given the lack of data in the literature for total denitrification. Isotopic mapping indicated no differences in N 2 O-fluxes or their source processes between maize and energy sorghum that could be associated with the release of BNI by the investigated sorghum variety. The results of this research show how differences in soil pore structures among cropping systems can determine both N 2 O production via denitrification and total denitrification N losses in situ.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Projected changes to severe thunderstorm environments as a result of twenty-first century warming from RegCM CORDEX-CORE simulations

Hazardous weather related to the occurrence of severe thunderstorms including tornadoes, high-winds, and hail cause significant damage globally to life and property every year. Yet the impact on these storms from a warming climate remains a difficult task due to their transient nature. Here, we investigate the change in large-scale environments in which severe thunderstorms form during twenty-first century warming (RCP2.6 and RCP8.5) in a group of RegCM CORDEX-CORE simulations. Severe potential is measured in terms of Convective Available Potential Energy (CAPE) and vertical wind-shear during the severe seasons in three regions which are known to currently be prone to severe hazards: North America, subtropical South America, and eastern India and Bangladesh. In every region, environments supportive for severe thunderstorms are projected to increase during the warm season months in both the RCP2.6 and RCP8.5 scenarios during the twenty-first century. The number of days supportive for severe thunderstorms increases by several days per season over the vast majority of each region by the end of the century. Analyzing the CAPE and shear trends during the twenty-first century, we find seasonally and regionally specific changes driving the increased severe potential. Twenty-first century surface warming is clearly driving a robust increase in CAPE in all regions, however poleward displacement of vertical shear in the future leads to the displacement of severe environments over North America and South America. The results found here relate that severe impacts in the future cannot be generalized globally, and that regionally specific changes in vertical shear may drive future movement of regions prone to severe weather.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems

Motor systems show an overall robustness, but because they are highly nonlinear, understanding how they achieve robustness is difficult. In many rhythmic systems, robustness against perturbations involves response of both the shape and the timing of the trajectory. This makes the study of robustness even more challenging. To understand how a motor system produces robust behaviors in a variable environment, we consider a neuromechanical model of motor patterns in the feeding apparatus of the marine mollusk Aplysia californica (Shaw et al. in J Comput Neurosci 38(1):25–51, 2015; Lyttle et al. in Biol Cybern 111(1):25–47, 2017). We established in (Wang et al. in SIAM J Appl Dyn Syst 20(2):701–744, 2021. https://doi.org/10.1137/20M1344974) the tools for studying combined shape and timing responses of limit cycle systems under sustained perturbations and here apply them to study robustness of the neuromechanical model against increased mechanical load during swallowing. Interestingly, we discover that nonlinear biomechanical properties confer resilience by immediately increasing resistance to applied loads. In contrast, the effect of changed sensory feedback signal is significantly delayed by the firing rates’ hard boundary properties. Our analysis suggests that sensory feedback contributes to robustness in swallowing primarily by shifting the timing of neural activation involved in the power stroke of the motor cycle (retraction). This effect enables the system to generate stronger retractor muscle forces to compensate for the increased load, and hence achieve strong robustness. The approaches that we are applying to understanding a neuromechanical model in Aplysia, and the results that we have obtained, are likely to provide insights into the function of other motor systems that encounter changing mechanical loads and hard boundaries, both due to mechanical and neuronal firing properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets

Recent advances in modeling large-scale, complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex systems can require significant computational resources. Similarly, acquiring experimental datasets can prove difficult. For these systems, often computationally inexpensive, but in general inaccurate models, known as the low-fidelity models, are available. Here in this paper, we propose a bi-fidelity modeling approach for complex physical systems, where we model the discrepancy between the true system's response and a low-fidelity response in the presence of a small training dataset from the true system's response using a deep operator network, a neural network architecture suitable for approximating nonlinear operators. We apply the approach to systems that have parametric uncertainty and are partially unknown. Three numerical examples are used to show the efficacy of the proposed approach to model uncertain and partially unknown physical systems.

MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Hoyle Family: The Search for Alpha-Condensate States in Light Nuclei

Our present understanding of the structure of the Hoyle state in 12 C and other near-threshold states in α-conjugate nuclei is reviewed in the framework of the α-condensate model. The 12 C Hoyle state, in particular, is a candidate for α-condensation, due to its large radius and α-cluster structure. The predicted features of nuclear α-particle condensates are reviewed along with a discussion of their experimental indicators, with a focus on precision break-up measurements. Two experiments are discussed in detail, firstly concerning the break-up of 12 C and then the decays of heavier nuclei. With more theoretical input, and increasingly complex detector setups, precision break-up measurements can, in principle, provide insight into the structures of states in α-conjugate nuclei. However, the commonly-held belief that the decay of a condensate state will result in Nα-particles is challenged. We further conclude that unambiguously characterizing excited states built on α-condensates is difficult, despite improvements in detector technology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Laboratory Evaluation of the Thermal Breakout Method for Maximum Horizontal Stress Measurement

Measuring in situ stress is essential for many problems in geomechanics, and the maximum horizontal stress is the most difficult to constrain. We are developing an extension of the breakout method to measure maximum horizontal stress in regions where natural breakouts do not occur. In the novel thermal breakout method, additional compression which leads to breakout development is induced by heating the borehole wall. In the present study, we validated the method experimentally in a true-triaxial apparatus on samples with predrilled boreholes. Two rocks were selected for laboratory testing: high-porosity Berea sandstone and low-porosity Niagaran dolomite. Prior to main true-triaxial tests, we carried out standard testing to characterize the strength, elasticity and thermal properties. The true-triaxial experiments consisted of: (1) room-temperature tests where samples were first loaded mechanically until the breakout formed, and (2) elevated-temperature tests where samples were loaded mechanically within the elastic range with additional compression induced thermally. Breakout initiation was monitored by acoustic emission sensors mounted on the pistons that applied horizontal stresses. Furthermore, the magnitude of induced thermal stress was calculated from temperature measurements around the borehole wall. In both rock types, we created thermally induced breakouts and examined analytical expressions to constrain maximum horizontal stress based on strength, elastic and thermal properties of the rocks.

58 GEOSCIENCES↗
DOE OSTI2022

Detecting impacts of surface development near weather stations since 1895 in the San Joaquin Valley of California

Abstract Temperature readings observed at surface weather stations have been used for detecting changes in climate due to their long period of observations. The most common temperature metrics recorded are the daily maximum (TMax) and minimum (TMin) extremes. Unfortunately, influences besides background climate variations impact these measurements such as changes in (1) instruments, (2) location, (3) time of observation, and (4) the surrounding artifacts of human civilization (buildings, farms, streets, etc.) Quantifying (4) is difficult because the surrounding infrastructure, unique to each site, often changes slowly and variably and is thus resistant to general algorithms for adjustment. We explore a direct method of detecting this impact by comparing a single station that experienced significant development from 1895 to 2019, and especially since 1970, relative to several other stations with lesser degrees of such development (after adjustments for the (1) to (3) are applied). The target station is Fresno, California (metro population ~ 15,000 in 1900 and ~ 1 million in 2019) situated on the eastern side of the broad, flat San Joaquin Valley in which several other stations reside. A unique component of this study is the use of pentad (5-day averages) as the test metric. Results indicate that Fresno experienced + 0.4 °C decade −1 more nighttime warming (TMin) since 1970 than its neighbors—a time when population grew almost 300%. There was little difference seen in TMax trends between Fresno and non-Fresno stations since 1895 with TMax trends being near zero. A case is made for the use of TMax as the preferred climate metric relative to TMin for a variety of physical reasons. Additionally, temperatures measured at systematic times of the day (i.e., hourly) show promise as climate indicators as compared with TMax and especially TMin (and thus TAvg) due to several complicating factors involved with daily high and low measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

MADE3D: Enabling the next generation of high-torque density wind generators by additive design and 3D printing

Direct-drive wind turbine generators are increasing in popularity, thanks to recent project developments—especially offshore, where reliability and efficiency are major cost drivers. Yet, high capital costs are forcing many original equipment manufacturers to consider lightweight, high-torque density generators for next-generation multi-megawatt turbines that may be difficult to realize by traditional design or manufacturing methods. In this study, we present a new design framework enabled by advanced machine learning and multimaterial additive manufacturing to perform a magnetic topology optimization that maximizes the torque per rotor active mass for a 15-megawatt direct-drive permanent magnet wind generator. A comparison of the proposed approach against conventional topology optimization demonstrated a significant increase in computational efficiency and accuracy in performance predictions. Results using single and multimaterial compositions for rotor core and magnets identify a wider choice of 3D printable designs for a given specification. A hybrid combination of sintered and dysprosium-free polymer-bonded magnets shows good potential for torque performance by saving material costs up to 8.75%. More than 30% improvement in rotor torque densities is identified which can marginally improve the overall generator torque density. With the rapid evolution of multipowder deposition technolgies, this study can greatly inspire a new paradigm for design-driven manufacturing with novel material compositions and lightweight, low-cost, high-strength multimaterial geometries that were previously unexplored for direct-drive generators.

17 WIND ENERGY↗
DOE OSTI2025

Mineral-Associated Organic Matter Concentration Beneath Northern Temperate Trees Varies by Mycorrhizal Type and Leaf Habit

Mycorrhizal fungi are important drivers of soil organic matter dynamics, but it can be difficult to isolate the effects of the fungi themselves from covarying traits of their host trees. For example, many trees with an evergreen leaf habit associate with ectomycorrhizal (ECM) fungi, while many deciduous tree species associate with arbuscular mycorrhizal (AM) fungi. Because leaf habit influences the quantity and quality of organic matter inputs to soil, it is often an important factor in soil carbon and nitrogen dynamics, and thus can mask the effects of mycorrhizal fungi on soil organic matter processes. We evaluated how tree mycorrhizal associations and leaf habit separately influence the amount and composition of mineral-associated organic matter (MAOM) and particulate organic matter (POM) in forest soils in New Hampshire and Vermont, USA. We measured carbon (C) and nitrogen (N) concentrations and C/N ratios of three soil density fractions beneath six tree species that vary in mycorrhizal association and leaf habit. We found lower concentrations of MAOM C and N beneath evergreen vs. deciduous trees, but only for tree species associating with AM fungi. Further, MAOM C/N was higher beneath evergreen trees and beneath trees with ECM fungi rather than AM fungi. Furthermore, these results add to the growing body of support for mycorrhizal fungi as mediators of soil organic matter dynamics, suggesting that the MAOM fraction is more sensitive to leaf habit beneath AM-associated versus ECM-associated trees. Because MAOM decomposition is thought to be less responsive than POM decomposition to changes in soil temperature and moisture, differences in the tendency of AM- and ECM-dominated forests to support MAOM formation and persistence may lead to systematic differences in the response of these forest types to ongoing climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

McCormick envelopes in mixed-integer PDE-constrained optimization

McCormick envelopes are a standard tool for deriving convex relaxations of optimization problems that involve polynomial terms. Such McCormick relaxations provide lower bounds, for example, in branch-and-bound procedures for mixed-integer nonlinear programs but have not gained much attention in PDE-constrained optimization so far. This lack of attention may be due to the distributed nature of such problems, which on the one hand leads to infinitely many linear constraints (generally state constraints that may be difficult to handle) in addition to the state equation for a pointwise formulation of the McCormick envelopes and renders bound-tightening procedures that successively improve the resulting convex relaxations computationally intractable. We analyze McCormick envelopes for a model problem class that is governed by a semilinear PDE involving a bilinearity and integrality constraints. We approximate the nonlinearity and in turn the McCormick envelopes by averaging the involved terms over the cells of a partition of the computational domain on which the PDE is defined. This yields convex relaxations that underestimate the original problem up to an a priori error estimate that depends on the mesh size of the discretization. These approximate McCormick relaxations can be improved by means of an optimization-based bound-tightening procedure. We show that their minimizers converge to minimizers to a limit problem with a pointwise formulation of the McCormick envelopes when driving the mesh size to zero. We provide a computational example, for which we certify all of our imposed assumptions. The results point to both the potential of the methodology and the gaps in the research that need to be closed. Our methodology provides a framework first for obtaining pointwise underestimators for nonconvexities and second for approximating them with finitely many linear inequalities in an infinite-dimensional setting.

Approximations and Expansions↗
DOE OSTI2022

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗
DOE OSTI2022

A multi-material platform for imaging of single cell-cell junctions under tensile load fabricated with two-photon polymerization

We previously reported a single-cell adhesion micro tensile tester (SCAμTT) fabricated from IP-S photoresin with two-photon polymerization (TPP) for investigating the mechanics of a single cell-cell junction under defined tensile loading. A major limitation of the platform is the autofluorescence of IP-S, the photoresin for TPP fabrication, which significantly increases background signal and makes fluorescent imaging of stretched cells difficult. In this study, we report the design and fabrication of a new SCAμTT platform that mitigates autofluorescence and demonstrate its capability in imaging a single cell pair as its mutual junction is stretched. By employing a two-material design using IP-S and IP-Visio, a photoresin with reduced autofluorescence, we show a significant reduction in autofluorescence of the platform. Further, by integrating apertures onto the substrate with a gold coating, the influence of autofluorescence on imaging is almost completely mitigated. With this new platform, we demonstrate the ability to image a pair of epithelial cells as they are stretched up to 250% strain, allowing us to observe junction rupture and F-actin retraction while simultaneously recording the accumulation of over 800 kPa of stress in the junction. In conclusion, the platform and methodology presented here can potentially enable detailed investigation of the mechanics of and mechanotransduction in cell-cell junctions and improve the design of other TPP platforms in mechanobiology applications.

Autofluorescence↗
DOE OSTI2024

A Study of Intermittent Turbulence in Stable Arctic Boundary Layers

Abstract Polar boundary layers are difficult to model due to the existence of intermittent turbulence within stable layers. Here we present a case study evaluation of coherent structures in a stable boundary layer observed during a series of flights with an uncrewed aircraft system (DataHawk2) on 19 October 2016 at Oliktok Point, Alaska as part of the ERASMUS (Evaluation of Routine Atmospheric Sounding Measurements using Unmanned Systems) field campaign. During a sequence of five flights over a nine-hour period, 57 profiles of atmospheric properties (0–400 m a.g.l) were collected. Turbulence was identified using derived Richardson Number, temperature structure function parameter, and turbulence kinetic energy dissipation. Throughout all flights on this strongly stable day, intermittent turbulence was observed. These turbulent layers showed well-mixed potential temperature profiles embedded within otherwise stable potential temperature profiles; often resulting in a characteristic staircase pattern. Turbulent layers ranged from 1 to 30 m deep, with most individual layers being 1–2 m deep. Vertical propagation velocities of layers in the lower atmosphere were on the order of a few cm s −1 , typical of non-convective environments. In different regions of the profile, turbulence was driven by a different balance of buoyancy and shear forces, with turbulence in the near surface environment driven by strong shear forces overcoming strong resistance to buoyancy, while turbulence in elevated layers characterized by weaker shear forces overcoming weaker resistance to buoyancy. We discuss the potential of such datasets for improving subgrid parameterizations of small-scale turbulence embedded within stable boundary layers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Exploiting chitosan to improve the interface of nanocellulose reinforced polymer composites

Cellulose nanofibrils (CNFs) have been widely used as a nanofiller for polymer composite reinforcement due to their excellent mechanical properties. However, CNF is produced in water and needs to be dried prior to use in composite materials. The presence of hydroxyl groups on the surface of CNF creates strong hydrogen bonding that makes it difficult and costly to dry. Additionally, the hydrophilicity at the fiber surface results in agglomeration of CNFs within many polymer matrices. In this study, chitosan (CS) was co-precipitated with CNF to produce a dual-bonding filler for use in poly (lactic acid) (PLA) composites. CS promotes improved interfacial interaction within the polymer matrix by forming strong hydrogen bonds with the CNF and potential covalent bonds with the PLA. Finally, the results confirmed that the addition of a small amount of CS significantly improved the mechanical properties compared to PLA + CNF composites and neat PLA. The detailed study of the PLA + CNF/CS composites reveals the synergetic effect of the hydrogen and covalent bonding mechanism for PLA reinforcement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Ground Truth Program: Simulations as Test Beds for Social Science Research Methods.

Social systems are uniquely complex and difficult to study, but understanding them is vital to solving the world’s problems. The Ground Truth program developed a new way of testing the research methods that attempt to understand and leverage the Human Domain and its associated complexities. The program developed simulations of social systems as virtual world test beds. Not only were these simulations able to produce data on future states of the system under various circumstances and scenarios, but their causal ground truth was also explicitly known. Research teams studied these virtual worlds, facilitating deep validation of causal inference, prediction, and prescription methods. The Ground Truth program model provides a way to test and validate research methods to an extent previously impossible, and to study the intricacies and interactions of different components of research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Attaining freshwater and estuarine-water soil saturation in an ecosystem-scale coastal flooding experiment

Abstract Coastal upland forests are facing widespread mortality as sea-level rise accelerates and precipitation and storm regimes change. The loss of coastal forests has significant implications for the coastal carbon cycle; yet, predicting mortality likelihood is difficult due to our limited understanding of disturbance impacts on coastal forests. The manipulative, ecosystem-scale Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m 2 ), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Transient saturation (5 h) of the entire soil rooting zone (0–30 cm) across a 2000 m 2 coastal forest was attained by delivering 300 m 3 of water through a spatially distributed irrigation network at a rate just above the soil infiltration rate. Our water delivery approach also elevated the water table (typically ~ 2 m belowground) and achieved extensive, low-level inundation (~ 8 cm standing water). A TEMPEST simulation approximated a 15-cm rainfall event and based on historic records, was of comparable intensity to a 10-year storm for the area. This characterization was supported by showing that Hurricane Ida’s (~ 5 cm rainfall) hydrologic impacts were shorter (40% lower duration) and less expansive (80% less coverage) than those generated through experimental manipulation. Future work will apply TEMPEST treatments to evaluate coastal forest resilience to changing hydrologic disturbance regimes and identify conditions that initiate ecosystem state transitions.

54 ENVIRONMENTAL SCIENCES↗