Search NASA⌕ Search

SEARCH · Search NASA

Results for “Forest Modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Growing grasses in unprofitable areas of US Midwest croplands could increase species richness

The US has large potential to grow perennial energy crops, but because these crops are rarely grown in current agricultural landscapes, it is unclear how biodiversity may be affected. Over time, as agriculture has increased, many grassland species have declined. In addition, not all agricultural land is profitable for growing annual crops. Unprofitable areas were responsible for a loss of approximately $110 million USD per year from 2013 to 2016. Based on this, we want to know how converting less-profitable portions of agricultural fields to switchgrass, a native prairie grass, would influence species occurrence. To address this question, we developed an alternative landscape in which clustered corn/soy acres with a low return on investment (ROI) were replaced with grassland. We also developed and validated species distribution models to predict changes in species occurrence for 28 avian species in Iowa in response to landscape management. Furthermore, we compared results for three different models: Random forest (RF), Stochastic gradient boosting (GBM), and Neural network (Nnet) and found that all models performed well and predicted similar species distribution. Predicted species richness increased by 3.66% (RF), 2.79% (GBM), and 7.51% (Nnet) when we simulated a change in management for ~3% of Iowa's low ROI corn/soybean areas to grassland. If harvested, these areas could generate approximately 7.6 million dry tons/year of switchgrass for bioenergy, thereby increasing farmers earnings. Unprofitable areas tended to occur along streams, which suggest that incorporating partially harvested riparian buffers can benefit avian biodiversity, while improving water quality and reducing unnecessary costs for farmers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach

As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Abstract Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model‐based phenology representations fail to capture local‐ to regional‐scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground‐based observations to estimate models that better represent how community‐level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing‐based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species‐specific models in combination with species composition information to ‘upscale’ model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species‐specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.

Melaas, Eli K.↗
DOE OSTI2016

Partitioning CO2 fluxes with isotopologue measurements and modeling to understand mechanisms of forest carbon sequestration

1. Objectives This project combines automated in situ observations of the isotopologues of CO2 with root observations, novel experimental manipulations of belowground processes, and isotope-enabled ecosystem modeling to investigate mechanisms of below- vs. aboveground carbon sequestration at the Harvard Forest Environmental Measurements Site (EMS). The proposed objectives, which have now been largely accomplished, include: A. Partitioning of net ecosystem CO2 exchange (NEE) into photosynthesis and respiration using long-term continuous observations of the isotopic composition of NEE, and analysis of their dynamics ; B. Investigation of the influence of vegetation phenology on the timing and magnitude of carbon allocated belowground using measurements of root growth and indices of belowground autotrophic vs. heterotrophic respiration (via trenched plots and isotope measurements); C. Testing whether plant allocation of carbon belowground stimulates the microbial decomposition of soil organic matter, using in situ rhizosphere simulation experiments wherein realistic quantities of artificial isotopically-labeled exudates are released into the soil; and D. Synthesis and interpretation of the above data using the Ecosystem Demography Model 2 (ED2). 2. Highlights Accomplishments: • Our isotopic eddy flux record has completed its 5th full year and has been used to independently estimate ecosystem-scale respiration and photosynthesis. • Soil surface chamber isotopic flux measurements were carried out during three growing seasons, in conjunction with a trenching manipulation. Key findings to date (listed by objective): A. Partitioning of Net Ecosystem Exchange: 1. Ecosystem respiration is lower during the day than at night—the first robust evidence of the inhibition of leaf respiration by light (the “Kok effect”) at the ecosystem scale. 2. Because it neglects the Kok effect, the standard NEE partitioning approach overestimates ecosystem photosynthesis (by ~25%) and daytime respiration (by ~100%) in the first half of the growing season at our site, and portrays ecosystem photosynthetic light-use efficiency as declining when in fact it is stable until autumnal senescence. B. Vegetation Phenology and belowground allocation: Findings: 1. Autotrophic respiration (Ra) showed a seasonal pattern, peaking in mid-summer when trees were most active. 2. The effective age of the substrate for belowground respiration is less than 2 weeks. 3. Above and belowground phenology are more synchronous in deciduous hardwood stands than evergreen hemlock stands. 4. The decline in root respiration rates in the fall is related to temperature rather than acclimation of root respiration or substrate limitations. Methodological Issues: 5. The isotopic signatures of autotrophic and heterotrophic respiration are too similar for isotopic partitioning of belowground respiration into these two components at our site—in keeping with the recent findings of Bowling et al. (2015) in a subalpine conifer forest. 6. Artifacts of the trenching method, such as changes in soil moisture and increased carbon substrate from the newly severed roots, are significant and need to be quantified when determining daily to annual estimates of autotrophic and heterotrophic respiration. C. Effects of simulated exudates on priming of microbial decomposition: The stoichiometry of root exudates influences both the amount and the mechanism by which priming occurs. At low C:N, SOC loss is caused by an increase in microbial efficiency. At high C:N, SOC loss is caused by an increase in microbial biomass. D. Modeling with the Ecosystem Demography Model (ED2): 1. Incorporation of 13C tracking to create an isotopically-enabled Ecosystem Demography v2 model (ED2) 2. State-of-the-art parameter optimization methodology developed for improving ED2 model predictions and parameters. 3. Significantly improved model predictions of growth- and maintenance-related carbon fluxes and 13C fluxes

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Partitioning CO 2 fluxes with isotopologue measurements and modeling to understand mechanisms of forest carbon sequestration

1. Objectives This project combines automated in situ observations of the isotopologues of CO 2 with root observations, novel experimental manipulations of belowground processes, and isotope-enabled ecosystem modeling to investigate mechanisms of below- vs. aboveground carbon sequestration at the Harvard Forest Environmental Measurements Site (EMS). The proposed objectives, which have now been largely accomplished, include: A. Partitioning of net ecosystem CO 2 exchange (NEE) into photosynthesis and respiration using long-term continuous observations of the isotopic composition of NEE, and analysis of their dynamics ; B. Investigation of the influence of vegetation phenology on the timing and magnitude of carbon allocated belowground using measurements of root growth and indices of belowground autotrophic vs. heterotrophic respiration (via trenched plots and isotope measurements); C. Testing whether plant allocation of carbon belowground stimulates the microbial decomposition of soil organic matter, using in situ rhizosphere simulation experiments wherein realistic quantities of artificial isotopically-labeled exudates are released into the soil; and D. Synthesis and interpretation of the above data using the Ecosystem Demography Model 2 (ED2). 2. Highlights Accomplishments: • Our isotopic eddy flux record has completed its 5th full year and has been used to independently estimate ecosystem-scale respiration and photosynthesis. • Soil surface chamber isotopic flux measurements were carried out during three growing seasons, in conjunction with a trenching manipulation. Key findings to date (listed by objective): A. Partitioning of Net Ecosystem Exchange: 1. Ecosystem respiration is lower during the day than at night—the first robust evidence of the inhibition of leaf respiration by light (the “Kok effect”) at the ecosystem scale. 2. Because it neglects the Kok effect, the standard NEE partitioning approach overestimates ecosystem photosynthesis (by ~25%) and daytime respiration (by ~100%) in the first half of the growing season at our site, and portrays ecosystem photosynthetic light-use efficiency as declining when in fact it is stable until autumnal senescence. B. Vegetation Phenology and belowground allocation: Findings: 1. Autotrophic respiration (Ra) showed a seasonal pattern, peaking in mid-summer when trees were most active. 2. The effective age of the substrate for belowground respiration is less than 2 weeks. 3. Above and belowground phenology are more synchronous in deciduous hardwood stands than evergreen hemlock stands. 4. The decline in root respiration rates in the fall is related to temperature rather than acclimation of root respiration or substrate limitations. Methodological Issues: 5. The isotopic signatures of autotrophic and heterotrophic respiration are too similar for isotopic partitioning of belowground respiration into these two components at our site—in keeping with the recent findings of Bowling et al. (2015) in a subalpine conifer forest. 6. Artifacts of the trenching method, such as changes in soil moisture and increased carbon substrate from the newly severed roots, are significant and need to be quantified when determining daily to annual estimates of autotrophic and heterotrophic respiration. C. Effects of simulated exudates on priming of microbial decomposition: The stoichiometry of root exudates influences both the amount and the mechanism by which priming occurs. At low C:N, SOC loss is caused by an increase in microbial efficiency. At high C:N, SOC loss is caused by an increase in microbial biomass. D. Modeling with the Ecosystem Demography Model (ED2): 1. Incorporation of 13C tracking to create an isotopically-enabled Ecosystem Demography v2 model (ED2) 2. State-of-the-art parameter optimization methodology developed for improving ED2 model predictions and parameters. 3. Significantly improved model predictions of growth- and maintenance-related carbon fluxes and 13C fluxes

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Understanding Uncertainty of Snow Radiative Transfer Modeling Within a Mixed Deciduous and Evergreen Forest

Satellite-based passive microwave observations provide the best available continuous observational estimates of global snow water storage due to their broad geographic footprint and low sensitivity to clouds and precipitation. However, these observations are subject to substantial uncertainty due to the complex radiative properties of snow and from interference in forested areas. Physical radiative transfer models can be leveraged to improve the fidelity of these observations and as a data-assimilation tool. In this article, the Dense Media Radiative Transfer model with Multiple Layers (DMRT-ML) is used to simulate snow brightness temperatures from data collected from snow pits excavated during a two-day-long field study performed a temperate forest in the Northeast United States. The simulations are evaluated against surface-based radiometer observations collected at the snow pits. The DMRT-ML is configured with varying complexity to determine the snowpack characteristics most essential toward simulating brightness temperature within a temperate forest with complicated snow stratigraphy. In general, the single-layer configurations were not sufficiently complex to accurately simulate snow brightness temperature without significant tuning. The most accurate simulation was a two-layer configuration with a prescribed ice layer separating the snow layers. This simulation had a root-mean-square error <;15 K for the 37-GHz frequency. More complicated snowpack stratigraphy configurations did not substantively improve the results over the two-layer model configuration. The DMRT-ML was also used to examine differences between redundant datasets of density and grain size. It was determined that similar snow data collection and radiative transfer model configuration techniques are critical to ensure cross-study comparability.

Theodore Letcher↗
DOE OSTI2020

Network-Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods

Currently ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper/poster explores the use of state-of-art machine learning techniques to estimate accurate volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study

Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to non-misinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will

5g↗
DOE OSTI2016

Modeling the Swift BAT trigger algorithm with machine learning

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift/BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of ≳97% (≲3% error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6% (10.4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n{sub 0}∼0.48{sub −0.23}{sup +0.41} Gpc{sup −3} yr{sup −1} with power-law indices of n{sub 1}∼1.7{sub −0.5}{sup +0.6} and n{sub 2}∼−5.9{sub −0.1}{sup +5.7} for GRBs above and below a break point of z{sub 1}∼6.8{sub −3.2}{sup +2.8}. This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

Modeling the Swift BAT Trigger Algorithm with Machine Learning

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online.

gamma rays: general↗
NASA NTRS2016

Modeling the Swift Bat Trigger Algorithm with Machine Learning

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift / BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of greater than or equal to 97 percent (less than or equal to 3 percent error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6 percent (10.4 percent error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n (sub 0) approaching 0.48 (sup plus 0.41) (sub minus 0.23) per cubic gigaparsecs per year with power-law indices of n (sub 1) approaching 1.7 (sup plus 0.6) (sub minus 0.5) and n (sub 2) approaching minus 5.9 (sup plus 5.7) (sub minus 0.1) for GRBs above and below a break point of z (redshift) (sub 1) approaching 6.8 (sup plus 2.8) (sub minus 3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.

gamma-ray burst: general – gamma-rays: general â↗
DOE OSTI2016

Partitioning CO 2 fluxes with isotopologue measurements and modeling to understand mechanisms of forest carbon sequestration

1. Project Summary and Objectives This project combines automated in situ observations of the isotopologues of CO 2 with root observations, novel experimental manipulations of belowground processes, and isotope-enabled ecosystem modeling to investigate mechanisms of below- vs. aboveground carbon sequestration at the Harvard Forest Environmental Measurements Site (EMS). The proposed objectives, which have now been largely accomplished, include: A. Partitioning of net ecosystem CO 2 exchange (NEE) into photosynthesis and respiration using long-term continuous observations of the isotopic composition of NEE, and analysis of their dynamics ; B. Investigation of the influence of vegetation phenology on the timing and magnitude of carbon allocated belowground using measurements of root growth and indices of belowground autotrophic vs. heterotrophic respiration (via trenched plots and isotope measurements); C. Testing whether plant allocation of carbon belowground stimulates the microbial decomposition of soil organic matter, using in situ rhizosphere simulation experiments wherein realistic quantities of artificial isotopically-labeled exudates are released into the soil; and D. Synthesis and interpretation of the above data using the Ecosystem Demography Model 2 (ED2).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Partitioning CO 2 fluxes with isotopologue measurements and modeling to understand mechanisms of forest carbon sequestration

This project combines automated in situ observations of the isotopologues of CO 2 with root observations, novel experimental manipulations of below ground processes, and isotope-enabled ecosystem modeling to investigate mechanisms of below- vs. above ground carbon sequestration at the Harvard Forest Environmental Measurements Site (EMS). The proposed objectives, which have now been largely accomplished, include: (A) Partitioning of net ecosystem CO2 exchange (NEE) into photosynthesis and respiration using long-term continuous observations of the isotopic composition of NEE, and analysis of their dynamics; (B) Investigation of the influence of vegetation phenology on the timing and magnitude of carbon allocated below ground using measurements of root growth and indices of below ground autotrophic vs. heterotrophic respiration (via trenched plots andisotope measurements); (C) Testing whether plant allocation of carbon below ground stimulates the microbial decomposition of soil organic matter, using in situ rhizosphere simulation experiments wherein realistic quantities of artificial isotopically-labeled exudates are released into the soil; and (D) Synthesis and interpretation of the above data using the Ecosystem Demography Model 2 (ED2).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Evaluating the Community Land Model (CLM4.5) at a coniferous forest site in northwestern United States using flux and carbon-isotope measurements

Droughts in the western United States are expected to intensify with climate change. Thus, an adequate representation of ecosystem response to water stress in land models is critical for predicting carbon dynamics. The goal of this study was to evaluate the performance of the Community Land Model (CLM) version 4.5 against observations at an old-growth coniferous forest site in the Pacific Northwest region of the United States (Wind River AmeriFlux site), characterized by a Mediterranean climate that subjects trees to water stress each summer. CLM was driven by site-observed meteorology and calibrated primarily using parameter values observed at the site or at similar stands in the region. Key model adjustments included parameters controlling specific leaf area and stomatal conductance. Default values of these parameters led to significant underestimation of gross primary production, overestimation of evapotranspiration, and consequently overestimation of photosynthetic 13 C discrimination, reflected in reduced 13 C: 12 C ratios of carbon fluxes and pools. Adjustments in soil hydraulic parameters within CLM were also critical, preventing significant underestimation of soil water content and unrealistic soil moisture stress during summer. After calibration, CLM was able to simulate energy and carbon fluxes, leaf area index, biomass stocks, and carbon isotope ratios of carbon fluxes and pools in reasonable agreement with site observations. Overall, the calibrated CLM was able to simulate the observed response of canopy conductance to atmospheric vapor pressure deficit (VPD) and soil water content, reasonably capturing the impact of water stress on ecosystem functioning. Both simulations and observations indicate that stomatal response from water stress at Wind River was primarily driven by VPD and not soil moisture. The calibration of the Ball–Berry stomatal conductance slope ( m bb ) at Wind River aligned with findings from recent CLM experiments at sites characterized by the same plant functional type (needleleaf evergreen temperate forest), despite significant differences in stand composition and age and climatology, suggesting that CLM could benefit from a revised m bb value of 6, rather than the default value of 9, for this plant functional type. Conversely, Wind River required a unique calibration of the hydrology submodel to simulate soil moisture, suggesting that the default hydrology has a more limited applicability. Here, this study demonstrates that carbon isotope data can be used to constrain stomatal conductance and intrinsic water use efficiency in CLM, as an alternative to eddy covariance flux measurements. It also demonstrates that carbon isotopes can expose structural weaknesses in the model and provide a key constraint that may guide future model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

14 SOLAR ENERGY↗
DOE OSTI2023

Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility

Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Furthermore, our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Importance of Tree- and Species-Level Interactions With Wildfire, Climate, and Soils in Interior Alaska: Implications for Forest Change Under A Warming Climate

The boreal zone of Alaska is dominated by interactions between disturbances, vegetation, and soils. These interactions are likely to change in the future through increasing permafrost thaw, more frequent and intense wildfires, and vegetation change from drought and competition. We utilize an individual tree-based vegetation model, the University of Virginia Forest Model Enhanced (UVAFME), to estimate current and future forest conditions across sites within interior Alaska. We updated UVAFME for application within interior Alaska, including improved simulation of permafrost dynamics, litter decay, nutrient dynamics, fire mortality, and post-fire regrowth. Following these updates, UVAFME output on species-specific biomass and stem density was comparable to inventory measurements at various forest types within interior Alaska. We then simulated forest response to climate change at specific inventory locations and across the Tanana Valley River Basin on a 2 × 2 km2 grid. We derived projected temperature and precipitation from a five-model average taken from the CMIP5 archive under the RCP 4.5 and 8.5 scenarios. Results suggest that climate change and the concomitant impacts on wildfire and permafrost dynamics will result in overall decreases in biomass (particularly for spruce (Picea spp.)) within the interior Tanana Valley, despite increases in quaking aspen (Populus tremuloides) biomass, and a resulting shift towards higher deciduous fraction. Simulation results also predict increases in biomass at cold, wet locations and at high elevations, and decreases in biomass in dry locations, under both moderate (RCP 4.5) and extreme (RCP 8.5) climate change scenarios. These simulations demonstrate that a highly detailed, species interactive model can be used across a large region within Alaska to investigate interactions between vegetation, climate, wildfire, and permafrost. The vegetation changes predicted here have the capacity to feed back to broader scale climate-forest interactions in the North American boreal forest, a region which contributes significantly to the global carbon and energy budgets.

forest modeling↗
DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗