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At least 469 records · Page 26

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NASA NTRS1987

Modeling energy flow and nutrient cycling in natural semiarid grassland ecosystems with the aid of thematic mapper data

Energy flow and nutrient cycling were modeled as affected by herbivory on selected intensive sites along gradients of precipitation and soils, validating the model output by monitoring selected parameters with data derived from the Thematic Mapper (TM). Herbivore production was modeled along the gradient of soils and herbivory, and validated with data derived from TM in a spatial data base.

Lewis, James K.↗

DOE OSTI2024

Soil incubation methods lead to large differences in inferred methane production temperature sensitivity

Abstract Quantifying the temperature sensitivity of methane (CH 4 ) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q 10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q 10 values, with a large portion of this variation remaining unexplained. Here we applied observations in a thawing permafrost peatland (Stordalen Mire) and a well-tested process-rich model ( ecosys ) to interpret incubation observations and investigate controls on inferred CH 4 production temperature sensitivity. We developed a field-storage-incubation modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season, refrigerated storage, and laboratory incubation, followed by model evaluation. We found that CH 4 production rates during incubation are regulated by substrate availability and active microbial biomass of key microbial functional groups, which are affected by soil storage duration and temperature. Seasonal variation in substrate availability and active microbial biomass of key microbial functional groups led to strong time-of-sampling impacts on CH 4 production. CH 4 production is higher with less perturbation post-sampling, i.e. shorter storage duration and lower storage temperature. We found a wide range of inferred Q 10 values (1.2–3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. We also show that Q 10 values of CH 4 production are controlled by interacting biological, biochemical, and physical processes, which cause the inferred Q 10 values to differ substantially from those of the component processes. Terrestrial ecosystem models that use a constant Q 10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Environmental controls on the light use efficiency of terrestrial gross primary production

Abstract Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf‐level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy‐level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C 3 species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed‐effects model. The same model design was fitted to site‐based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today, as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017 in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating the rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Son Do↗

NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗

NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

NASA is preparing for the retirement of the ISS and transition of LEO activities to one or more Commercial LEO Destinations (CLDs) by 2030. This transition necessitates new models for connecting NASA and other government-funded users of the LEO environment to platforms and opportunities. This paper describes for consideration six models for facilitating government-funded activities in the post-ISS LEO ecosystem. These six models are illustrative and represent a wide trade space of potential options, each relying on unique mechanisms for facilitating activities on one or more commercial LEO platforms or vehicles. We assessed each model across three possible future scenarios varying in number and diversity of LEO activities and commercial offerings, and across five stakeholder-driven model evaluation criteria. We present the highlights of the analysis, including ways to modify and strengthen each model. The Government Research Broker model performs best across all future scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibit positive aspects, these models perform most favorably in future scenarios with well-established communities and markets. While each model has strengths and weaknesses, no single model in its current form performs well across all criteria in all three future scenarios. NASA leadership can adjust models as desired to align closer to their priorities using combinations of unique model mechanisms. A model that meets leadership priorities is likely a combination of features from multiple models.

Erica Rodgers↗

NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

Erica Rodgers↗

NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

Erica Rodgers↗

DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM-FATES-CNP)

We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre-existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant-soil dynamics for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth system models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

A toy terrestrial carbon flow model

A generalized carbon flow model for the major terrestrial ecosystems of the world is reported. The model is a simplification of the Century model and the Forest-Biogeochemical model. Topics covered include plant production, decomposition and nutrient cycling, biomes, the utility of the carbon flow model for predicting carbon dynamics under global change, and possible applications to state-and-transition models and environmentally driven global vegetation models.

Parton, William J.↗

DOE OSTI2021

Quantifying the role of moss in terrestrial ecosystem carbon dynamics in northern high latitudes

Mosses are ubiquitous in northern terrestrial ecosystems, and play an important role in regional carbon, water and energy cycling. Current global land surface models that do not consider mosses may bias the quantification of regional carbon dynamics. Here we incorporate mosses as a new plant functional type into the process-based Terrestrial Ecosystem Model (TEM 5.0), to develop a new model (TEM_Moss). The new model explicitly quantifies the interactions between vascular plants and mosses and their competition for energy, water, and nutrients. Compared to the estimates using TEM 5.0, the new model estimates that the regional terrestrial soils currently store 132.7 Pg more C and will store 157.5 and 179.1 Pg more C under the RCP8.5 and RCP2.6 scenarios, respectively, by the end of the 21st century. Ensemble regional simulations forced with different parameters for the 21st century with TEM_Moss predict that the region will accumulate 161.1±142.1 Pg C under the RCP2.6 scenario and 186.7±166.1 Pg C under the RCP8.5 scenario over the century. Our study highlights the necessity of coupling moss into Earth system models to adequately quantify terrestrial carbon–climate feedbacks in the Arctic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

Coupled biogeochemical cycles drive ecosystem ecology by influencing individual-to-community scale behaviors; yet the development of process-based models that accurately capture these dynamics remains elusive. Soil organic matter (SOM) decomposition in particular is influenced by resource stoichiometry that dictates microbial nutrient acquisition (‘ecological stoichiometry’). Despite its basis in biogeochemical modeling, ecological stoichiometry is only implicitly considered in high-resolution microbial investigations and the metabolic models they inform. State-of-science SOM decomposition models in both fields have advanced largely separately, but they agree on a need to move beyond seminal pool-based models. This presents an opportunity and a challenge to maximize the strengths of various models across different scales and environmental contexts. To address this challenge, we contend that ecological stoichiometry provides a framework for merging biogeochemical and microbiological models, as both explicitly consider substrate chemistries that are the basis of ecological stoichiometry as applied to SOM decomposition. We highlight two gaps that limit our understanding of SOM decomposition: (1) understanding how individual microorganisms alter metabolic strategies in response to substrate stoichiometry and (2) translating this knowledge to the scale of biogeochemical models. We suggest iterative information exchange to refine the objectives of high-resolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models. Assimilating theoretical and modelling frameworks from different scientific domains is the next frontier in SOM decomposition modelling; advancing technologies in the context of stoichiometric theory provides a consistent framework for interpreting molecular data, and further distilling this information into tractable SOM decomposition models.

Carbon cycling↗

DOE OSTI2021

Multi-scale assessment of a grassland productivity model

Grasslands provide many important ecosystem services globally, and projecting grassland productivity in the coming decades will provide valuable information to land managers. Productivity models can be well calibrated at local scales but generally have some maximum spatial scale in which they perform well. Here we evaluate a grassland productivity model to find the optimal spatial scale for parameterization and thus for subsequently applying it in future productivity projections for North America. We also evaluated the model on new vegetation types to ascertain its potential generality. We find the model most suitable when incorporating only grasslands, as opposed to also including agriculture and shrublands, and only in the Great Plains and eastern temperate forest ecoregions of North America. The model was not well suited to grasslands in North American deserts or northwest forest ecoregions. It also performed poorly in agriculture vegetation, likely due to management activities, and shrubland vegetation, likely because the model lacks representation of deep water pools. This work allows us to perform long-term projections in areas where model performance has been verified, with gaps filled in by future modeling efforts.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

The “Gearamid” Model: Transforming NASA Langley’s Role in the Aerospace Technology Ecosystem

NASA’s operating environment is evolving: numerous new emerging technologies are converging to create breakthrough solutions, many nontraditional players are partnering to create those solutions, and a diverse array of new public-private funding models are being employed. A team at NASA’s Langley Research Center (LaRC) developed a model to describe the challenges that must be addressed for an emerging breakthrough technology to penetrate the appropriate market sector, and how all the players in the relevant ecosystem can collaborate to accelerate the market infusion process. This model, which the team dubbed the “Gearamid,” originated from a narrower study to determine what LaRC should do to capitalize on and advance autonomous technology as a “game changer” in the civil aerospace domain. The elements in the original version of the Gearamid depict the various challenges that need to be addressed as an autonomous technology proceeds from initial development to market infusion. In addition, the Gearamid indicates the “actors” best suited to address each challenge element. The study team noted that all challenge elements in the Gearamid must be worked concurrently to assure successful infusion of autonomous solutions. The team also found that NASA expertise naturally positions Agency organizations—including LaRC—to lead contributions that address certain challenge elements and to play a supplemental role in other areas where entities external to NASA are actively working and investing to solve challenges. Furthermore, the team concluded that NASA can play a leadership role in coordinating efforts of the diverse entities across the civil aerospace community. After determining the center’s optimal role in the autonomous technology development ecosystem, the study team then extrapolated the Gearamid model for autonomous technologies into a broader, more general model depicting the challenge elements that must be overcome to develop and infuse any emerging technology. LaRC has embraced the Gearamid model and is using it to drive changes that will transform the center and allow it to function optimally in the evolving landscape. Given LaRC’s successes, the study team suggests that other organizations may also be able to use the Gearamid model to inform future planning/strategy efforts.

Jill M Marlowe↗

NASA NTRS1998

Biomarkers as tracers for life on early earth and Mars

Biomarkers in geological samples are products derived from biochemical (natural product) precursors by reductive and oxidative processes (e.g., cholestanes from cholesterol). Generally, lipids, pigments and biomembranes are preserved best over longer geological times and labile compounds such as amino acids, sugars, etc. are useful biomarkers for recent times. Thus, the detailed characterization of biomarker compositions permits the assessment of the major contributing species of extinct and/or extant life. In the case of the early Earth, work has progressed to elucidate molecular structure and carbon isotropic signals preserved in ancient sedimentary rocks. In addition, the combination of bacterial biochemistry with the organic geochemistry of contemporary and ancient hydrothermal ecosystems permits the modeling of the nature, behavior and preservation potential of primitive microbial communities. This approach uses combined molecular and isotopic analyses to characterize lipids produced by cultured bacteria (representative of ancient strains) and to test a variety of culture conditions which affect their biosynthesis. On considering Mars, the biomarkers from lipids and biopolymers would be expected to be preserved best if life flourished there during its early history (3.5-4 x 10(9) yr ago). Both oxidized and reduced products would be expected. This is based on the inferred occurrence of hydrothermal activity during that time with the concomitant preservation of biochemically-derived organic matter. Both known biomarkers (i.e., as elucidated for early terrestrial samples and for primitive terrestrial microbiota) and novel, potentially unknown compounds should be characterized.

Non-NASA Center↗

NASA NTRS1996

Lipid biomarkers for bacterial ecosystems: studies of cultured organisms, hydrothermal environments and ancient sediments

This paper forms part of our long-term goal of using molecular structure and carbon isotopic signals preserved as hydrocarbons in ancient sediments to improve understanding of the early evolution of Earth's surface environment. We are particularly concerned with biomarkers which are informative about aerobiosis. Here, we combine bacterial biochemistry with the organic geochemistry of contemporary and ancient hydrothermal ecosystems to construct models for the nature, behaviour and preservation potential of primitive microbial communities. We use a combined molecular and isotopic approach to characterize lipids produced by cultured bacteria and test a variety of culture conditions which affect their biosynthesis. This information is then compared with lipid mixtures isolated from contemporary hot springs and evaluated for the kinds of chemical change that would accompany burial and incorporation into the sedimentary record. In this study we have shown that growth temperature does not appear to alter isotopic fractionation within the lipid classes produced by a methanotropic bacterium. We also found that cultured cyanobacteria biosynthesize diagnostic methylalkanes and dimethylalkanes with the latter only made when growing under low pCO2. In an examination of a microbial mat sample from Octopus Spring, Yellowstone National Park (USA), we could readily identify chemical structures with 13C contents which were diagnostic for the phototrophic organisms such as cyanobacteria and Chloroflexus. We could not, however, find molecular evidence for operation of a methane cycle in the particular mat samples we studied.

NASA Discipline Exobiology↗

DOE OSTI2022

Quantifying the drivers of ecosystem fluxes and water potential across the soil-plant-atmosphere continuum in an arid woodland

Dryland ecosystems occupy a vast swath of the terrestrial land surface and exert a sizeable impact on the cycling of carbon and water globally. These biomes are characterized by tightly coupled carbon and water cycles that respond rapidly to transient pulses in water availability. However, there exist many mechanistic uncertainties regarding the environmental drivers of, and linkages between, plant and ecosystem processes. Thus, drylands are often poorly represented in many vegetation and land surface models. An enhanced understanding of dryland ecosystem function is limited by the lack of long-term, co-located, and frequent measurements of plant and ecosystem processes. At a piñon-juniper woodland in southeastern Utah, USA, we collected a continuous dataset of meteorological conditions, soil water potential from surface to bedrock, tree water potential, and ecosystem carbon and water fluxes from eddy covariance. We found that predawn and midday tree water potential and daily ecosystem fluxes were highly sensitive to fluctuations in soil water availability, particularly in shallower layers, and that daytime variability in atmospheric drivers only loosely controlled these processes. The strong connections between shallow soil water potential, tree water potential, and ecosystem fluxes occurred because of the dominant role of precipitation pulses in driving vegetation activity, as even small pulses of moisture stimulated shallow soil water potential, tree water potential, and evapotranspiration for between 1 and 2 weeks. Carbon fluxes (net ecosystem exchange and gross primary productivity) were sensitive to precipitation pulses for longer, up to 3 weeks. Our results highlight that improved monitoring and sensing of shallow soil moisture can greatly enhance our understanding of dryland ecosystem function. Here, a better mechanistic understanding of the impacts of precipitation pulses is also needed to improve vegetation modeling of dryland ecosystems.

54 ENVIRONMENTAL SCIENCES↗