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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication. More information in the related references. The dataset is a model (ecosys) output used in a study that examined the potential future shifts in high-latitude vegetation types (PFTs) driven by changes in 21st century climate and fire. The study highlights the importance of mechanistically representing differences in plant functional traits on current and possible 21st century trajectories of Alaskan PFTs. This dataset package includes 13 files in pdf, csv, and nc formats.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Dynamic subcanopy leaf traits drive resistance of net primary production across a disturbance severity gradient

Across the globe, the forest carbon sink is increasingly vulnerable to an expanding array of low- to moderate-severity disturbances. However, some forest ecosystems exhibit functional resistance (i.e., the capacity of ecosystems to continue functioning as usual) following disturbances such as extreme weather events and insect or fungal pathogen outbreaks. Unlike severe disturbances (e.g., stand-replacing wildfires), moderate severity disturbances do not always result in near-term declines in forest production because of the potential for compensatory growth, including enhanced subcanopy production. Community-wide shifts in subcanopy plant functional traits, prompted by disturbance-driven environmental change, may play a key mechanistic role in resisting declines in net primary production (NPP) up to thresholds of canopy loss. However, the temporal dynamics of these shifts, as well as the upper limits of disturbance for which subcanopy production can compensate, remain poorly characterized. In this study, we leverage a 4-year dataset from an experimental forest disturbance in northern Michigan to assess subcanopy community trait shifts as well as their utility in predicting ecosystem NPP resistance across a wide range of implemented disturbance severities. Through mechanical girdling of stems, we achieved a gradient of severity from 0% (i.e., control) to 45, 65, and 85% targeted gross canopy defoliation, replicated across four landscape ecosystems broadly representative of the Upper Great Lakes ecoregion. We found that three of four examined subcanopy community weighted mean (CWM) traits including leaf photosynthetic rate (p = 0.04), stomatal conductance (p = 0.07), and the red edge normalized difference vegetation index (p < 0.0001) shifted rapidly following disturbance but before widespread changes in subcanopy light environment triggered by canopy tree mortality. Surprisingly, stimulated subcanopy production fully compensated for upper canopy losses across our gradient of experimental severities, achieving complete resistance (i.e., no significant interannual differences from control) of whole ecosystem NPP even in the 85% disturbance treatment. Additionally, we identified a probable mechanistic switch from nutrient-driven to light-driven trait shifts as disturbance progressed. Our findings suggest that remotely sensed traits such as the red edge normalized difference vegetation index (reNDVI) could be particularly sensitive and robust predictors of production response to disturbance, even across compositionally diverse forests. The potential of leaf spectral indices to predict post-disturbance functional resistance is promising given the capabilities of airborne to satellite remote sensing. We conclude that dynamic functional trait shifts following disturbance can be used to predict production response across a wide range of disturbance severities.

resistance↗
DOE OSTI2024

The key role of forest disturbance in reconciling estimates of the northern carbon sink

Northern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr –1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr –1 ). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr –1 , and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr –1 ), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr –1 ), and the DGVM-estimated sink from CO 2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr –1 ). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr –1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

AmeriFlux US-PFb NW1 Pine-1 CHEESEHEAD 2019

This is the AmeriFlux version of the carbon flux data for the site US-PFb NW1 Pine-1 CHEESEHEAD 2019. Site Description - This tower (32m, trailer-based, telescoping) is the northwestern most tower in the 10 x 10km study domain. It is located in a red pine forest (canopy height ~25m). There is a regrowing/trap layer consisting of broadleaf aspen and other deciduous vegetation.

Desai, Ankur↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-PFb NW1 Pine-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFb NW1 Pine-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFb NW1 Pine-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is the northwestern most tower in the 10 x 10km study domain. It is located in a red pine forest (canopy height ~25m). There is a regrowing/trap layer consisting of broadleaf aspen and other deciduous vegetation.

Desai, Ankur↗
DOE OSTI2026

Land-Surface Temperature Transitions at the Bankhead National Forest (LASTT-BNF) Field Campaign Report

Land surface types that are common in the Southeast United States (e.g., forests, agriculture, urban) can play key roles in the regulation of surface-atmosphere energy exchange and boundary-layer evolution in the region (Hinkle et al. 2024). Heterogeneity in these land-surface types and associated emissivities can drive strong transitions in land surface temperature (LST), which is an important component of the surface energy balance. Within the forested canopy surrounding the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s field deployment at the Bankhead National Forest (BNF) in Alabama main site (M1) (Kuang et al. 2026), the different components of the vegetation and ground surface can create strong gradients in surface temperature that can influence the canopy thermal environment and vegetative function (e.g., stress, transpiration), both of which can have important, yet poorly characterized, controls on lower atmospheric processes (e.g., thermal turbulence, secondary circulations).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Estimating Switchgrass Biomass Yield and Lignocellulose Composition from UAV-Based Indices

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha –1 ). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R 2 = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R 2 = 0.88). Cellulose was best predicted using the NDRE (R 2 = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R 2 = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration.

09 BIOMASS FUELS↗
DOE OSTI2025

PAVC Gridded 20m Alaska NGEE Tier3 PFTs v1.0

These 20-meter spatial resolution gridded products provide per-pixel fractional cover (%) of Next Generation Ecosystem Experiments (NGEE) Arctic Plant Functional Types (PFTs) Tier 3 across Alaska, north of the boreal treeline. The products were developed for the NGEE Arctic project, which is improving Arctic vegetation representation and parameterization of the E3SM Land Model. This dataset includes 8 files containing fractional cover for NGEE Tier 3 PFTs (https://data.ess-dive.lbl.gov/view/doi:10.15485/2529470): (1) bryophytes; (2) lichens; (3) non-vascular plants, i.e., the sum of lichens and bryophytes; (4) deciduous shrubs, (5) evergreen shrubs, (6) forbs, (7) graminoids, and a non-PFT class, (8) litter. Each pixel contains the percent cover (expressed as a fraction of total ground cover) that was predicted by random-forest regression models. The random-forest models were trained on cover data collected at 978 plots from 2010 to 2021, of which are archived in the Pan-Arctic Vegetation Cover (PAVC) database (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557). The plot cover was linked to 20-meter spatial resolution, satellite-derived predictor variables: Sentinel-2 spectra and Sentinel-1 polarizations averaged over the 2019 growing season, as well as topographical features derived from ArcticDEM. Then, spatio-temporally anomalous plot data that introduced large variability to the regression outcomes were dropped using the Cook’s distance outlier detection method, and the models were re-created using high-quality plots and their associated satellite derived explanatory variables per each PFT. The correlations between plot-observed and satellite-derived fractional cover for all PFTs were well correlated (R2 = 0.69–0.95 and 0.5 for litter) and had low RMSE bias (0.02–0.11). This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Tropical forest dynamics play a crucial role in the global carbon, water, and energy cycles. However, realistically simulating the dynamics of competition and coexistence between different plant functional types (PFTs) in tropical forests remains a significant challenge. This study aims to improve the modeling of PFT coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations and (2) whether machine learning (ML)-based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted three ensembles of ELM-FATES experiments at a tropical forest site near Manaus, Brazil. By comparing the ensemble experiments without (Exp-CTR) and with (Exp-OBS) consideration of observed trait relationships, we found that accounting for these relationships slightly improves the simulations of water, energy, and carbon variables when compared to observations but degrades the simulation of PFT coexistence. Using ML-based surrogate models trained on Exp-CTR, we optimized the trait parameters in ELM-FATES and conducted another ensemble of experiments (Exp-ML) with these optimized parameters. The proportion of PFT coexistence experiments significantly increased from 21 % in Exp-CTR to 73 % in Exp-ML. After filtering the experiments that allow for PFT coexistence to agree with observations (within 15 % tolerance), 33 % of the Exp-ML experiments were retained, which is a significant improvement compared to the 1.4 % in Exp-CTR. Exp-ML also accurately reproduces the annual means and seasonal variations in water, energy, and carbon fluxes and the field inventory of aboveground biomass. This study represents a reproducible method that utilizes machine learning to identify parameter values that improve model fidelity against observations and PFT coexistence in vegetation demography models for diverse ecosystems. Our study also suggests the need for new mechanisms to enhance the robust simulation of coexisting plants in ELM-FATES and has significant implications for modeling the response and feedbacks of ecosystem dynamics to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Trends and Drivers of Terrestrial Sources and Sinks of Carbon Dioxide: An Overview of the TRENDY Project

The terrestrial biosphere plays a major role in the global carbon cycle, and there is a recognized need for regularly updated estimates of land-atmosphere exchange at regional and global scales. An international ensemble of Dynamic Global Vegetation Models (DGVMs), known as the “Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide” (TRENDY) project, quantifies land biophysical exchange processes and biogeochemistry cycles in support of the annual Global Carbon Budget assessments and the REgional Carbon Cycle Assessment and Processes, phase 2 project. DGVMs use a common protocol and set of driving data sets. A set of factorial simulations allows attribution of spatio-temporal changes in land surface processes to three primary global change drivers: changes in atmospheric CO 2 , climate change and variability, and Land Use and Land Cover Changes (LULCC). Here, we describe the TRENDY project, benchmark DGVM performance using remote-sensing and other observational data, and present results for the contemporary period. Simulation results show a large global carbon sink in natural vegetation over 2012–2021, attributed to the CO 2 fertilization effect (3.8 ± 0.8 PgC/yr) and climate (–0.58 ± 0.54 PgC/yr). Forests and semi-arid ecosystems contribute approximately equally to the mean and trend in the natural land sink, and semi-arid ecosystems continue to dominate interannual variability. The natural sink is offset by net emissions from LULCC (–1.6 ± 0.5 PgC/yr), with a net land sink of 1.7 ± 0.6 PgC/yr. Despite the largest gross fluxes being in the tropics, the largest net land-atmosphere exchange is simulated in the extratropical regions.

58 GEOSCIENCES↗
DOE OSTI2023

A neotropical perspective on the uniqueness of the Holocene among interglacials

Abstract Understanding how tropical systems have responded to large-scale climate change, such as glacial-interglacial oscillations, and how human impacts have altered those responses is key to current and future ecology. A sedimentary record recovered from Lake Junín, in the Peruvian Andes (4085 m elevation) spans the last 670,000 years and represents the longest continuous and empirically-dated record of tropical vegetation change to date. Spanning seven glacial-interglacial oscillations, fossil pollen and charcoal recovered from the core showed the general dominance of grasslands, although during the warmest times some Andean forest trees grew above their modern limits near the lake. Fire was very rare until the last 12,000 years, when humans were in the landscape. Here we show that, due to human activity, our present interglacial, the Holocene, has a distinctive vegetation composition and ecological trajectory compared with six previous interglacials. Our data reinforce the view that modern vegetation assemblages of high Andean grasslands and the presence of a defined tree line are aspects of a human-modified landscape.

Science & Technology - Other Topics↗
DOE OSTI2023

Topography and canopy cover influence soil organic carbon composition and distribution across a forested hillslope in the discontinuous permafrost zone

This dataset contains data used for the paper "Topography and canopy cover influence soil organic carbon composition and distribution across a forested hillslope in the discontinuous permafrost zone". The Related References field will be updated with a full citation when available. Topography and canopy cover influence ground temperature in warming permafrost landscapes, yet soil temperature heterogeneity introduced by meso-topographic slope positions, microtopographic differences in vegetation cover, and the subsequent impact of contrasting temperature conditions to soil organic carbon (SOC) dynamics are understudied. Buffering of permafrost-affected soils against warming air temperatures in boreal forests can reflect surface soil characteristics (e.g., thickness of organic material) as well as the degree and type of canopy cover (e.g., open cover vs closed cover). Both landscape and soil properties interact to determine meso- and micro-scale heterogeneity of ground warming. We sampled a hillslope catena transect in a discontinuous permafrost zone near Fairbanks, Alaska to test the small-scale (1 to 3 meter) impacts of slope position and cover type on soil organic matter composition. Mineral active layer samples were collected from backslope, low backslope, and footslope positions at depths spanning 19 to 60 cm. We examined soil mineralogical composition, soil moisture, total carbon and nitrogen content, and organic mat thickness in conjunction with an assessment of SOC composition using Fourier-transform ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS). Soils in the footslope position had a higher relative contribution of lignin-like compounds while backslope soils had more aliphatic and condensed aromatic compounds as determined by FT-ICR-MS. The effect of open versus closed tree canopy cover varied with slope position. On the backslope, we found higher oxidation of molecules under open cover compared with closed cover, indicating an effect of warmer soil temperature on decomposition. Little to no effect of canopy was observed for soils at the footslope position, which we attributed, in part, to the strong impact of soil moisture content in SOC dynamics in the water-gathering footslope position. The thin organic mat under open cover on the backslope position may have contributed to differences in soil temperature and thus SOC oxidation under open and closed canopy. Here, the thinner organic mat did not appear to buffer the underlying soil against warm season air temperatures and thus increased SOC decomposition as indicated by higher oxidation of SOC molecules and a lower contribution of simple molecules under open cover compared with the closed canopy sites. Our findings suggest that the role of canopy cover in SOC dynamics varies as a function of landscape position and soil properties, namely organic mat thickness and soil moisture. Condition-specific heterogeneity of SOC composition under open and closed canopy cover highlights the protective effect of canopy cover for soils on backslope positions. This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/Erin-Rooney/Y1_fairbanks (DOI: 10.5281/zenodo.8071247).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Asymmetric response of Amazon forest water and energy fluxes to wet and dry hydrological extremes reveals onset of a local drought-induced tipping point

Understanding the effects of intensification of Amazon basin hydrological cycling—manifest as increasingly frequent floods and droughts—on water and energy cycles of tropical forests is essential to meeting the challenge of predicting ecosystem responses to climate change, including forest “tipping points”. Here, we investigated the impacts of hydrological extremes on forest function using 12+ years of observations (between 2001–2020) of water and energy fluxes from eddy covariance, along with associated ecological dynamics from biometry, at the Tapajós National Forest. Measurements encompass the strong 2015–2016 El Niño drought and La Niña 2008–2009 wet events. We found that the forest responded strongly to El Niño-Southern Oscillation (ENSO): Drought reduced water availability for evapotranspiration ( ET ) leading to large increases in sensible heat fluxes ( H ). Partitioning ET by an approach that assumes transpiration ( T ) is proportional to photosynthesis, we found that water stress-induced reductions in canopy conductance ( G s ) drove T declines partly compensated by higher evaporation ( E ). By contrast, the abnormally wet La Niña period gave higher T and lower E , with little change in seasonal ET . Both El Niño-Southern Oscillation (ENSO) events resulted in changes in forest structure, manifested as lower wet-season leaf area index. However, only during El Niño 2015–2016, we observed a breakdown in the strong meteorological control of transpiration fluxes (via energy availability and atmospheric demand) because of slowing vegetation functions (via shutdown of G s and significant leaf shedding). Drought-reduced T and G s , higher H and E , amplified by feedbacks with higher temperatures and vapor pressure deficits, signaled that forest function had crossed a threshold, from which it recovered slowly, with delay, post-drought. In conclusion, identifying such tipping point onsets (beyond which future irreversible processes may occur) at local scale is crucial for predicting basin-scale threshold-crossing changes in forest energy and water cycling, leading to slow-down in forest function, potentially resulting in Amazon forests shifting into alternate degraded states.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Resist! : Sustaining forest carbon sequestration and wood production after insect disturbance

Disturbances from insect pests threaten ecologically and economically important goods and services supplied by forests, including wood production and carbon sequestration. We highlight the factors that influence these services’ resistance, a term quantifying the initial response to disturbance. Insects inflict damage through a range of mechanisms, prompting distinct plant physiological responses that scale to influence ecosystem processes and, with time, goods and services. The degree and timing of tree mortality and defoliation affect the amount of residual vegetation available to support compensatory wood production and influence carbon sequestration by changing rates of detritus-fueled decomposition. Compounding, or sequential, insect attacks may prime a forest for additional disturbance, further eroding wood production and carbon sequestration. Forest management practices that promote biological and structural diversity, and augment or retain limiting biological and nutrient resources, may buffer against the effects of insect pests on wood production and carbon sequestration.

Gough, Christopher M. [Virginia Commonwealth Univ.↗
DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multi-cyclone analysis and machine learning model implications of cyclone effects on forests

Past studies of cyclones (hurricanes, typhoons, tropical cyclones) disturbance showed that meteorological, topographical, and biological factors affect the patterns of forest disturbance intensity but left open the extent to which these findings were representative across different global cyclone regions. Using remote sensing data and machine learning models, we examined how these factors change over spatial scales and assessed their consistency across four major cyclones: Katrina (August 2005), Rita (September 2005), Yasi (February 2011), and María (September 2017). Our results revealed that the factors which best explained forest disturbance intensity pattern varied across these regions. Wind speed and precipitation were the dominant factors contributing to the variation in impacts of Katrina; terrain features, especially elevation, explained most of the variation in disturbance intensity of Rita; pre-disturbance vegetation condition was significant predictors of effects of Yasi; these factors played equal roles in explaining the disturbance intensity variation of María. A 40 m/s (144 km/h) wind speed threshold was proposed to split low- and high-level forest disturbance intensity. Other than wind speed, few generalizations can be made on features across multiple regions. We built several generalized hurricane impact models, which worked well with the test data from cyclones used for model development (R 2 = 0.89). However, these models did not have good predictions on other cyclones, such as Michael (October 2018) and Laura (August 2020). This study showed that each cyclone interacted with the landscape in a unique way and the challenges remained in building a generalized cyclone impact model.

54 ENVIRONMENTAL SCIENCES↗