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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Influence of plateau, slope, and valley on soil hydrology during the dry season in a Central Amazon old‐growth forest

Soil moisture regulates plant water supply and drought sensitivity in tropical forests, yet its vertical and topographic variation remains poorly characterized. We combined high-frequency time-domain reflectometry measurements from 5 to 100 cm across plateau, slope, and valley landforms at the Zona Florestal 2 research site north of Manaus, Central Amazonia, to quantify how soil moisture memory, timing of responses to rainfall, dry-down rates (τ), and soil–water depletion vary across these contrasting landforms. Landform-specific soil moisture calibration curves ensured accurate volumetric water content estimates in these highly weathered soils. During the 2023 dry-to-wet transition (August–November), soil moisture memory showed strong topographic contrasts, with valley profiles increasing from ∼47 h at 5 cm to ∼154 h at 100 cm, while plateaus exhibited higher near-surface persistence (∼124 h at 5 cm) but weaker memory at depth. Dry-down behavior reinforced these differences as valley soils exhibited τ values exceeding ∼200 h, more than double the characteristic τ of plateau soils (∼90 h). Rainfall–soil moisture correlations indicated immediate responses at shallow depths in valleys and progressively longer lags with depth on plateaus and slopes. These hydrologic patterns were mirrored in depletion profiles, which declined sharply below 30 cm on plateaus but remained high and sustained throughout the upper meter in slopes and valleys. Together, these findings provide the first depth-resolved field measurements of soil moisture memory, rainfall coupling, dry-down constants, and depletion dynamics across major upland landforms in Central Amazonia and offer clear observational benchmarks for improving land-surface and ecosystem model representations of soil–water processes.

Hillslope↗
DOE OSTI2024

Carbon accumulation rate peaks at 1,000-m elevation in tropical planted and regrowth forests

Tropical planted and regrowth forests (TPRFs) are one of the most low-cost components for recovering biomass-stored carbon in the tropics. Nevertheless, challenges persist in pinpointing which elevational ranges exhibit the largest carbon accumulation rate ($γ$ rapid ) due to the highly inconsistent previous assessments. This prevents the selection of optimal locations for implementing large-scale reforestation in the tropics. Here, in this study, we proposed a refined approach that used a carbon accumulation threshold (<80% of the maximum value) to quantify $γ$ rapid in TPRFs at various elevations. We find that $γ$ rapid increases with elevations from 300 to 1,000 m and declines at elevations >1,000 m. TPRFs at elevations ~1,000 m exhibit three times more $γ$ rapid than lowland TPRFs. This optimal elevation, highly dependent on background temperatures, varies slightly but significantly across different mountains. These findings provide guidelines for policymakers to determine the optimal elevations from regional to continental scales when implementing reforestation initiatives in the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mycorrhiza Better Predict Soil Fungal Community Composition and Function than Aboveground Traits in Temperate Forest Ecosystems

Forests in the northeastern US are experiencing shifts in community composition due to the northward migration of warm-adapted tree species and certain species’ declines (for example, white ash and eastern hemlock) due to invasive insects. Changes in belowground fungal communities and associated functions will inevitably follow. Therefore, we sought to investigate the relative importance of two important tree characteristics—mycorrhizal type [ectomycorrhizal (EcM) or arbuscular mycorrhizal (AM)] and leaf habit (deciduous or evergreen) on soil fungal community composition and organic matter cycling. We sampled soil in the organic and mineral horizons beneath two AM-associated (Fraxinus americana and Thuja occidentalis) and two ECM-associated tree species (Betula alleghaniensis and Tsuga canadensis), with an evergreen and deciduous species in each mycorrhizal group. To characterize fungal communities and organic matter decomposition beneath each tree species, we sequenced the ITS1 region of fungal DNA and measured the potential activity of carbon- and nitrogen-targeting extracellular enzymes. Each tree species harbored distinct fungal communities, supporting the need to consider both mycorrhizal type and leaf habit. However, between tree characteristics, mycorrhizal type better predicted fungal communities. Across fungal guilds, saprotrophic fungi were the most important group in shaping fungal community differences in soils beneath all tree species. The effect of leaf habit on carbon- and nitrogen-targeting hydrolytic enzymes depended on tree mycorrhizal association in the organic horizon, while oxidative enzyme activities were higher beneath EcM-associated trees across both soil horizons and leaf habits.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Inversion of Moisture Content of Forest Canopy and Floor from SAR Data

Several scattering mechanisms contribute to the total radar backscatter cross section measured by the synthetic aperture radar. These are volume scattering, trunk-ground double-bounce scattering, branch-ground double-bounce scattering, and surface scattering. All of these mechanisms are directly related to the dielectric constant of forest components responsible for that mechanism and their moisture.

sensing↗
NASA NTRS1996

Estimation of Moisture Content of Forest Canopy and Floor from SAR Data Part II: Trunk-Ground Double-Bounce Case

Several scattering mechanisms contribute to the total radar backscatter cross section measured by the synthetic aperture radar. These are volume scattering, trunk-ground double-bounce scattering, branch-ground double-bounce scattering, and surface scattering. All of these mechanisms are directly related to the dielectric constant of forest components responsible for that mechanism and their moisture.

Forest Canopy moisture content forest floor synthe↗
NASA NTRS1996

Estimation of Moisture Content of Forest Canopy and Floor from SAR Data Part I: Volume Scattering Case

To understand and predict the functioning of forest biomes, their interaction with the atmosphere, and their growth rates, the knowledge of moisture content of their canopy and the floor soil is essential. The synthetic aperture radar on airborne and spaceborne platforms has proven to be a flexible tool for measuring electromagnetic back- scattering properties of vegetation related to their moisture content.

Forest Canopy moisture content forest floor synthe↗
NASA NTRS2022

Estimating Species-Specific Leaf Area Index and Basal Area Using Optical and SAR Remote Sensing Data in Acadian Mixed Spruce-Fir Forests, USA

This study combined Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variable datasets to model leaf area index (LAI) and basal area per ha (BAPH) of two economically important tree species in Northeast, USA; red spruce (Picea rubens Sarg.; RS), and balsam fir (Abies balsamea (L.) Mill.; BF). We used Random Forest (RF), and Multi-Layer Perceptron (MLP) algorithms for LAI and BAPH modeling. The results showed that RF outperformed MLP by reducing the normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. The final variables selected for modeling of both LAI and BAPH indicated the superiority of Sentinel-2 variables over the Sentinel-1 SAR with minor contributions of site variables (mainly elevation). The red-edge spectral vegetation indices played a significant role in both LAI and BAPH estimation. We attained the lowest nRMSEs of 0.12, and 0.16 for the final LAI model of RS, and BF, respectively using Sentinel-2 and site variables. The lowest nRMSE for both RS and BF BAPH models was 0.12. As RS and BF are the primary host species for a cyclically occurring and most destructive pest of the region, eastern spruce budworm (Choristoneura fumiferana; SBW), these estimations will be useful to evaluate SBW dynamics in the region.

Forest inventory↗
DOE OSTI2021

Plant functional types broadly describe water use strategies in the Caatinga, a seasonally dry tropical forest in northeast Brazil

In seasonally dry tropical forests, plant functional type can be classified as deciduous low wood density, deciduous high wood density, or evergreen high wood density species. While deciduousness is often associated with drought-avoidance and low wood density is often associated with tissue water storage, the degree to which these functional types may correspond to diverging and unique water use strategies has not been extensively tested. We examined (a) tolerance to water stress, measured by predawn and mid-day leaf water potential; (b) water use efficiency, measured via foliar δ 13 C; and (c) access to soil water, measured via stem water δ 18 O. We found that deciduous low wood density species maintain high leaf water potential and low water use efficiency. Deciduous high wood density species have lower leaf water potential and variable water use efficiency. Both groups rely on shallow soil water. Evergreen high wood density species have low leaf water potential, higher water use efficiency, and access alternative water sources. These findings indicate that deciduous low wood density species are drought avoiders, with a specialized strategy for storing root and stem water. Deciduous high wood density species are moderately drought tolerant, and evergreen high wood density species are the most drought tolerant group. Our results broadly support the plant functional type framework as a way to understand water use strategies, but also highlight species-level differences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Temporal patterns of fine‐root dynamics have little influence on seasonal soil CO 2 efflux in a mixed, mesic forest

Among the contributors to soil CO 2 efflux, there remains uncertainty about the contribution of root activity to the overall soil efflux. Soil water and temperature frequently have been used to predict a large portion of the variation in soil CO 2 efflux. We hypothesized that fine-root dynamics explain most of the remaining variability in soil CO 2 efflux that cannot be explained by soil temperature and water content. We anticipated that seasonal increases in root production, mortality via decomposition, and standing crop would result in corresponding increases in soil CO 2 efflux. We tested our hypotheses by collecting and analyzing two years of minirhizotron and soil chamber CO 2 flux data from plots distributed throughout the Shale Hills Catchment of the Susquehanna-Shale Hills Critical Zone Observatory in Central Pennsylvania, USA. Here we showed that: (1) seasonal fluctuations in fine-root dynamics yielded only a very small increase in the predictability of soil CO 2 efflux; (2) fine-root mortality effects on soil CO 2 efflux were strongly tied to soil temperature; (3) fluctuations in fine-root presence or standing mass independent of temperature and moisture had little effect on soil CO 2 efflux; and (4) new fine-root length and root length mortality had limited impacts on soil CO 2 efflux rates. We conclude that, at least in temperate forests on rocky soils, characterizing fine-root dynamics may provide only limited improvement in the estimation of soil CO 2 efflux.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A mathematical assessment of the isolation random forest method for anomaly detection in big data

We present the mathematical analysis of the Isolation Random Forest Method (IRF Method) for anomaly detection, proposed by Liu F.T., Ting K.M. and Zhou Z. H. in their seminal work as a heuristic method for anomaly detection in Big Data. We prove that the IRF space can be endowed with a probability induced by the Isolation Tree algorithm (iTree). In this setting, the convergence of the IRF method is proved, using the Law of Large Numbers. Here, a couple of counterexamples are presented to show that the method is inconclusive and no certificate of quality can be given, when using it as a means to detect anomalies. Hence, an alternative version of the method is proposed whose mathematical foundation is fully justified. Furthermore, a criterion for choosing the number of sampled trees needed to guarantee confidence intervals of the numerical results is presented. Finally, numerical experiments are presented to compare the performance of the classic method with the proposed one.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

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

Abstract Topography and canopy cover influence ground temperature in warming permafrost landscapes, yet soil temperature heterogeneity introduced by mesotopographic slope positions, microtopographic differences in vegetation cover, and the subsequent impact of contrasting temperature conditions on 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 microscale 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 m) 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, whereas backslope soils had more aliphatic and condensed aromatic compounds as determined using FT‐ICR‐MS. The effect of open versus closed tree canopy cover varied with the slope position. On the backslope, we found higher oxidation of molecules under open cover than closed cover, indicating an effect of warmer soil temperature on decomposition. Little to no effect of the canopy was observed in 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 canopies. 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 the higher oxidation of SOC molecules and a lower contribution of simple molecules under open cover than 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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Identifying Nuclear Data Correlated Through Predicting Bias in Integral Experiments via Applying Principal Component Analysis to Random Forest

ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Forest Structural Complexity and Biomass Predict First-Year Carbon Cycling Responses to Disturbance

The pre-disturbance vegetation characteristics that predict carbon (C) cycling responses to disturbance are not well known. To address this gap, we initiated the Forest Resilience Threshold Experiment, a manipulative study in which more than 3600 trees were stem girdled to achieve replicated factorial combinations of four levels (control, 45, 65, and 85% gross defoliation) of disturbance severity and two disturbance types (targeting upper or lower canopy strata). Applying a standardized stability framework in which initial C cycling resistance to disturbance was calculated as the first-year natural log response ratio of disturbance and control treatments, we investigated to what extent pre-disturbance levels of species diversity, aboveground woody biomass, leaf area index, and canopy rugosity—a measure of structural complexity—predict the initial responses of subcanopy light-saturated leaf CO 2 assimilation (A sat ), aboveground wood NPP (ANPP w ), and soil respiration (R s ) to phloem-disrupting disturbance. In the year following stem girdling, we found that above-ground C cycling processes, A sat and ANPP w , were highly resistant to increases in disturbance severity, while Rs resistance declined as severity increased. Disturbance type had no effect on first-year resistance. Pre-disturbance aboveground woody biomass, and canopy rugosity were positive predictors of ANPP w resistance and, conversely, negatively related to R s resistance. Subcanopy A sat resistance was not related to pre-disturbance vegetation characteristics. Stability of C uptake processes along with R s declines suggest the net C sink was sustained in the initial months following disturbance. Therefore, we conclude that biomass and complexity are significant, but not universal, predictors of initial C cycling resistance to disturbance. Moreover, our findings highlight the utility of standardized stability measures when comparing functional responses to disturbance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (Φ PSIImax ), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R 2 = 0.86, RMSE=1.99 μmol m -2 s -1 ) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. Further, the seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the Φ PSIImax . These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in Φ PSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests

Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Resilience in soil bacterial communities of the boreal forest from one to five years after wildfire across a severity gradient

Wildfires can represent a major disturbance to ecosystems, including soil microbial communities belowground. Furthermore, fire regimes are changing in many parts of the world, altering and often increasing fire severity, frequency, and size. The boreal forest and taiga plains ecoregions of northern Canada are characterized by naturally-occurring stand-replacing wildfires on a 40–350 year basis. We previously studied the effects of wildfire on soil microbial communities one year post-fire across 40 sites, spanning a range of burn severity. Here, we return to the same sites five years post-fire to test a series of hypotheses about the effects of fire on bacterial community composition. We ask questions on two themes: which factors control bacterial community composition during post-fire recovery, and how does the importance of different fire-responsive traits change during post-fire recovery? We find the following: Five years post-fire, vegetation community, moisture regime, pH, total carbon, texture, and burned/unburned all remained significant predictors of bacterial community composition with similar predictive value (R 2 ). Bacterial communities became more similar to unburned sites five years post-fire, across the range of severity, suggesting resilience, while general structure of co-occurrence networks remained similar one and five years post-fire. Fast growth potential, as estimated using predicted 16S rRNA copy numbers, was no longer significantly correlated with burn severity five years post-fire, indicating the importance of this trait for structuring bacterial community composition may be limited to relatively short timescales. Many taxa that were enriched in burned sites one year post-fire remained enriched five years post-fire, although the degree to which they were enriched generally decreased. Specific taxa of interest from the genera Massilia, Blastococcus, and Arthrobacter all remained significantly enriched, suggesting that they may have traits that allow them to continue to flourish in the post-fire environment, such as tolerance to increased pH or ability to degrade pyrogenic organic matter. This hypothesis-based work expands our understanding of the post-fire recovery of soil bacterial communities and raises new hypotheses to test in future studies.

59 BASIC BIOLOGICAL SCIENCES↗