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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
DOE OSTI2024

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

42 ENGINEERING↗
DOE OSTI2022

Drought-induced decoupling between carbon uptake and tree growth impacts forest carbon turnover time

The ability of forests to withstand, and recover from, acute drought stress is a critical uncertainty regarding the impacts of climate change on the terrestrial carbon (C) cycle, but it is unclear how drought responses scale from individual trees to whole forests. Here, we assembled a dataset of tree-ring chronologies co-located within the footprint of eddy covariance towers across North America and Europe, with the aim of quantifying the sensitivity of tree radial growth versus gross primary productivity (GPP) during and following drought. We found that drought induced a large decoupling across C cycle processes, whereby GPP was relatively resistant to water stress despite large reductions in tree-ring widths. This decoupling also occurred in the year following drought (i.e., a ‘drought legacy effect’), and was similar in magnitude in response to both summer and winter droughts. By modeling whole-forest C turnover time, we show that a radial growth-GPP decoupling has important ramifications for the forest C cycle, especially if the C not used to support radial growth is instead allocated towards pools with short residence times. Furthermore, our results demonstrate that quantifications of drought impacts that rely solely on C uptake are missing this fundamental pathway through which drought alters the forest C cycle and the resulting feedbacks to the climate system.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Biomass Retrieval from L-Band Polarimetric UAVSAR Backscatter and PRISM Stereo Imagery

The forest above-ground biomass (AGB) and spatial distribution of vegetation elements have profound effects on the productivity and biodiversity of terrestrial ecosystems. In this paper, we evaluated biomass estimation from L-band Synthetic Aperture Radar (SAR) data acquired by National Aeronautics and Space Administration (NASA) Uninhabited Aerial Vehicle SAR (UAVSAR) and the improvement of accuracy by adding canopy height information derived from stereo imagery acquired by Japan Aerospace Exploration Agency (JAXA) Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) on-board the Advanced Land Observing Satellite (ALOS). Various models for prediction of forest biomass from UAVSAR data were investigated at pixel sizes of 1/4 ha (50 m x 50 m) and 1 ha. The variance inflation factor (VIF) was calculated for each of the explanatory variables in multivariable regression models to assess the multi-collinearity between explanatory variables. In addition, the t-and p-values were used to interpret the significance of the coefficients of each explanatory variables. The R(exp. 2), Root Mean Square Error (RMSE), bias and Akaike information criterion (AIC), and leave-one-out cross-validation (LOOCV) and bootstrapping were used to validate models. At 1/4-ha scale, the R(exp. 2) and RMSE of biomass estimation from a model using a single track of polarimetric UAVSAR data were 0.59 and 52.08 Mg/ha. With canopy height from PRISM as additional independent variable, R(exp. 2) increased to 0.76 and RMSE decreased to 39.74 Mg/ha (28.24%). At 1-ha scale, the RMSE of biomass estimation based on UAVSAR data of a single track was 39.42 Mg/ha with a R(exp. 2) of 0.77. With the canopy height from PRISM, R(exp. 2) increased to 0.86 and RMSE decreased to 29.47 Mg/ha (20.18%). The models using UAVSAR data alone underestimated biomass at levels above approximately 150 Mg/ha showing the saturation phenomenon. Adding canopy height from PRISM stereo imagery significantly improved the biomass estimation and elevated the saturation level in estimating biomass. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation.Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation at 1/4-ha scale, R(exp. 2) increased from 0.59 to 0.66 and RMSE reduced from 52.08 to 48.57 Mg/ha. Averaging multiple acquisitions of UAVSAR data from the same look azimuth direction did not improve biomass estimation. A biomass map derived from NASA's LVIS (Laser Vegetation Imaging System) wave-form data was used as a reference for evaluation of the biomass maps from these models. The study has also shown that the errors decreased when deciduous, evergreen, and mixed forests were modeled separately but the improvement was not significant

Zhang, Zhiyu↗
DOE OSTI2019

9Cr steel visualization and predictive modeling

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Anthropogenic Land-use Change and the Dynamics of Amazon Forest Biomass

This project was focused on assessing the effects of prevailing land uses, such as habitat fragmentation, selective logging, and fire, on biomass and carbon storage in Amazonian forests, and on the dynamics of carbon sequestration in regenerating forests. Ancillary goals included developing GIs models to help predict the future condition of Amazonian forests, and assessing the effects of anthropogenic climate change and ENS0 droughts on intact and fragmented forests. Ground-based studies using networks of permanent plots were linked with remote-sensing data (including Landsat TM and AVHRR) at regional scales, and higher-resolution techniques (IKONOS imagery, videography, LIDAR, aerial photographs) at landscape and local scales. The project s specific goals were quite eclectic and included: Determining the effects of habitat fragmentation on forest dynamics, floristic composition, and the various components of above- and below-ground biomass. Assessing historical and physical factors that affect trajectories of forest regeneration and carbon sequestration on abandoned lands. Extrapolating results from local studies of biomass dynamics in fragmented and regenerating forests to landscape and regional scales in Amazonia, using remote sensing and GIS. Testing the hypothesis that intact Amazonian forests are functioning as a significant carbon sink. Examining destructive synergisms between forest fragmentation and fire. Assessing the short-term impacts of selective logging on aboveground biomass. Developing GIS models that integrate current spatial data on forest cover, deforestation, logging, mining, highway and roads, navigable rivers, vulnerability to wild fires, protected areas, and existing and planned infrastructure projects, in an effort to predict the future condition of Brazilian Amazonian forests over the next 20-25 years. Devising predictive spatial models to assess the influence of varied biophysical and anthropogenic predictors on Amazonian deforestation.

Laurance, William F.↗
DOE OSTI2026

Accelerated Carbon and Water Cycles in the Amazon and Congo Basins Revealed From TRENDY Models and Remote Sensing Products

Tropical forests play a vital role in the global carbon cycle and land–atmosphere interactions. Estimating tropical forest carbon–water dynamics is challenging due to observational and modeling uncertainties. This study leverages the “Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide” (TRENDY) project models and satellite observations to assess changes (2003–2021) in vegetation carbon, gross primary production (GPP), evapotranspiration (ET), and net biosphere production (NBP) in the Amazon and Congo. Atmospheric CO 2 , climate variability, and land use and land cover changes constrain these variables between 1700 and 2021 with the overall increasing trends of carbon stock and fluxes. The models overestimate vegetation carbon and GPP, while ET and NBP are consistent with observations. Fire-activated models predict lower values for vegetation carbon and GPP, ET, and NBP, aligning more closely with observations. The higher ET from fire-activated models may result from enhanced soil evaporation due to increased canopy openings. Fire-inactivated models could well estimate the magnitudes of NBP. The high vegetation carbon in nitrogen-enabled models points to simulation uncertainties and imbalance in model numbers regarding the nitrogen cycle. Although the nitrogen cycle enhances water use efficiency in both the Amazon and Congo, the models show a higher sensitivity to the nitrogen cycle in the Congo. This study highlights the challenges in accurately representing tropical biogeochemical cycles and the values of satellite products in model evaluations, underscoring the need for standard modeling protocols that address biogeochemical components (e.g., nutrient cycles) to better resolve process-based representations.

Shi, Mingjie [Pacific Northwest National Laborator↗
DOE OSTI2026

TES: Modelling Microbes to Predict Post-Fire Carbon Cycling in the Boreal Forest across Burn Severities (Exploratory Proposal)

Wildfires in boreal ecosystems represent a globally significant ecological process that is expected to be sensitive to changing climate as well as forest management strategies, but is currently insufficiently understood and represented in models. We sought to determine whether linking belowground microbial community composition, size, and activity to aboveground properties of burn severity and plant community composition (upland jack pine and upland spruce) would allow us to better model post-fire soil CO 2 fluxes using the Carbon, Organisms, Rhizosphere and Protection in the Soil Environment (CORPSE) model. We used an integrated modelling experimental approach, with mechanistic laboratory experiments designed to inform models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A Comparison of the Diel Cycle of Modeled and Measured Latent Heat Flux During the Warm Season in a Colorado Subalpine Forest

Abstract Precipitation changes the physiological characteristics of an ecosystem. Because land‐surface models are often used to project changes in the hydrological cycle, modeling the effect of precipitation on the latent heat flux λE is an important aspect of land‐surface models. Here we contrast conditionally sampled diel composites of the eddy‐covariance fluxes from the Niwot Ridge Subalpine Forest AmeriFlux tower with the Community Land Model (CLM, version 4.5). With respect to measured λE during the warm season: for the day following above‐average precipitation, λE was enhanced at midday by ≈40 W m −2 (relative to dry conditions), and nocturnal λE increased from ≈10 W m −2 in dry conditions to over 20 W m −2 in wet conditions. With default settings, CLM4.5 did not successfully model these changes. By increasing the amount of time that rainwater was retained by the canopy/needles, CLM was able to match the observed midday increase in λE on a dry day following a wet day. Stable nighttime conditions were problematic for CLM4.5. Nocturnal CLM λE had only a small (≈3 W m −2 ) increase during wet conditions, CLM nocturnal friction velocity u ∗ was smaller than observed u ∗ , and CLM canopy air temperature was 2°C less than those measured at the site. Using observed u ∗ as input to CLM increased λE ; however, this caused CLM λE to be increased during both wet and dry periods. We suggest that sloped topography and the ever‐present drainage flow enhanced nocturnal u ∗ and λE . Such phenomena would not be properly captured by topographically blind land‐surface models, such as CLM.

Burns, Sean P.↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization

Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2024

Evaluating crown scorch predictions from a computational fluid dynamics wildland fire simulator

Abstract Background Crown scorch—the heating of live leaves, needles, and buds in the vegetative canopy to lethal temperatures without widespread combustion—is one of the most common fire effects shaping post-fire canopies. Despite the ability of computational fluid dynamic models to finely resolve fire activity and buoyant plume dynamics including heterogenous 3D distributions of forest canopy heating, these models have had only limited use in simulating fire effects and have not been used to evaluate crown scorch. Here, we demonstrate a method of evaluating crown scorch using a computational fluid dynamics model, FIRETEC, and validate this approach by simulating the experiments that were used to develop Van Wagner’s 1973 crown scorch model. Results The average scorch height prediction from FIRETEC compares well with the empirical model derived by Van Wagner, which is the most widely used empirical model for crown scorch. We further find that the 3D buoyant plume dynamics from a steady and homogeneous idealized heat source on the ground results in a spatially heterogenous crown scorch pattern reflecting complex heating dynamics that are best represented by percent scorch rather than height of scorch. Conclusions The ability of the computational fluid dynamics model to capture variation in crown scorch due to 3D buoyant plume dynamics provides direct links between forest structure, fire behavior, and fire effects that can be used by forest managers and researchers to better understand how fires result in crown damage under various environmental and management scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Application of predictive data analytics to model daily hydrocarbon production using petrophysical, geomechanical, fiber-optic, completions, and surface data: A case study from the Marcellus Shale, North America

Predicting gas production from stimulated unconventional reservoirs has always been a challenge to oil and gas companies because the physics of fluid flow through such reservoirs is not well understood. On the other hand, modeling gas production from shale reservoirs is a complex multi-variate problem that requires proper integration of data from multiple disciplines, such as geology, petrophysics, geomechanics, completions, and surface-based measurements, etc. Here this study demonstrates the application of data-driven machine learning algorithms, integrating geoscientific, distributed acoustic sensing (DAS), distributed temperature sensing (DTS) fiber-optic, completions, flow scanner production log, and surface data to model daily gas production from a 28-stage stimulated horizontal well drilled in the Marcellus Shale of the Appalachian basin in North America. In addition, this study aims to utilize the data from a fiber-optic monitoring system, such as DAS and DTS to evaluate the well performance. We build supervised data-driven machine learning models using Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM) algorithms to understand the well performance and forecast the daily gas production. The study compares the forecasted results, tests the level of accuracy, and addresses different issues with these machine learning algorithms. A spatio-temporal database is constructed and used to perform sensitivity analyses to identify the key drivers for gas production. The results show that RF and ANN algorithms can be used to predict daily gas production with significantly high accuracy; however, RF algorithm is the best predictor in terms of highest accuracy (96%), less computational time and cost. Based on sensitivity analysis, it appears that Poisson's ratio, minimum horizontal stress, DAS, casing pressure, gamma log are the most important parameters to predict gas production.

02 PETROLEUM↗
DOE OSTI2023

Prediction of impact sensitivity, heat of formation and heat of explosion using atomic connectivity

In these proceedings we revisit a large collection of explosives and explosive descriptors with the goal of predicting impact sensitivity using only local atomic environments that can be deciphered from molecular SMILES strings as descriptors without utilizing empirically measured values or computationally expensive electronic structure calculations. From the original database of nearly 500 descriptors, removing empirically measured and electronic structure values decreased the number of descriptors to 135, which we reduced to 18 the most important descriptors using Random Forests. The condensed model predicted impact sensitivity with essentially the same accuracy as the existing, more complex model (R 2 = 0.788 and RSME = 0.312), while remaining applicable to all types of explosives (Peroxides, azides, C-Nitros, Nitroamines, Nitrate Esters, etc.). In addition to impact sensitivity, we proposed similar models to accurately predict values heat of formation (ΔH f ) and heat of explosion (Q), with R 2 = 0.966 and 0.916, respectively. In conclusion, the work in these proceedings allows for prediction of explosive performance and sensitivity with only chemical structure information and an estimate of density.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Are there differences in public interest toward automated vehicles’ ownership in burdened and non-burdened communities?

This study examines differences in public interest toward automated vehicle (AV) adoption between burdened communities (BCs) and non-burdened communities (non-BCs). The study uniquely captures travel behavior by integrating the census tract-level community indicators with the 2017–2019 Puget Sound Household Travel Survey. Public interest in AV ownership is modeled as a five-level ordered outcome, ranging from ‘Not interested at all’ to ‘Very interested.’ Segmented ordered logit models and Random Forest are used to identify key drivers and barriers to AV adoption across BCs and non-BCs. Results show that longer commute times, lack of personal vehicles, and concerns about AV performance in adverse weather conditions are significantly associated with higher interest levels toward owning AVs in BCs. In conclusion, the findings offer insights for developing targeted policies to promote AV adoption and address differences between BCs and non-BCs in accessing emerging transportation technologies.

Automated vehicles↗
DOE OSTI2019

Critical wind speeds suggest wind could be an important disturbance agent in Amazonian forests

Abstract Recent research in the central Amazon suggests that wind is a major agent of disturbance, however, a mechanistic understanding of how wind may lead to tree mortality in Amazonian forests remains unclear. Here we estimated wind speeds necessary to topple central Amazon trees by linking both static and dynamic versions of two wind speed estimation methods (four methods total) to field data on tree failure derived from a static winching study. Static versions of these methods assumed invariant wind characteristics as more trees failed, while dynamic versions updated tree spacing, leaf area index and wind profiles progressively after each tree failure. First, we used a profile method which estimates wind force on individual trees by segments. We calculated drag on each segment and converted drag into basal turning moment, and compared the summed turning moments to the critical turning moment measured in the winching study. Estimated critical wind speeds from the static profile method varied greatly, from 10.75 m s−1 to >120.0 m s−1 with a mean of 45.70 m s−1. Critical wind speeds estimated with static approaches decreased with tree size but were not significantly different between two focal genera. Primary drivers of variation in critical wind speed were tree height and crown size. Second, we used the turning moment coefficient method of Hale, S.E., Gardiner, B., Peace, A., Nicoll, B., Taylor, P. and Pizzirani, S. 2015 Comparison and validation of three versions of a forest wind risk model. Environ. Model. Softw.68, 27–41. doi:10.1016/j.envsoft.2015.01.016.; the static version of this method yielded less-variable estimates, ranging from 18.98 to 52.01 m s−1, with a mean of 30.88 m s−1. Notably, the two static methods for estimating critical wind speeds differed in the trees they identified as having the highest and lowest critical wind speeds. Dynamic variants of the above two methods produced greatly reduced ranges in CWS estimates for our study trees, because after the early tree failures, remaining trees were subject to greater wind penetration into the stand and thus greater loading for a given above-canopy wind speed. CWS estimated with dynamic approaches differed significantly between the focal taxa. Nevertheless, both estimates suggest that wind speeds commonly observed during Amazon storms are sufficient to produce widespread tree damage and mortality.

Peterson, Chris J.↗