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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Human impacts on soil carbon dynamics of deep-rooted Amazonian forests and effect of land use change on the carbon cycle in Amazon soils

The main objective of these NASA-funded projects is to improve our understanding of land-use impacts on soil carbon dynamics in the Amazon Basin. Soil contains approximately one half of tropical forest carbon stocks, yet the fate of this carbon following forest impoverishment is poorly studied. Our mechanistics approach draws on numerous techniques for measuring soil carbon outputs, inputs, and turnover time in the soils of adjacent forest and pasture ecosystems at our research site in Paragominas, state of Para, Brazil. We are scaling up from this site-specific work by analyzing Basin-wide patterns in rooting depth and rainfall seasonality, the two factors that we believe should explain much of the variation in tropical soil carbons dynamics. In this report, we summarize ongoing measurements at our Paragominas study site, progress in employing new field data to understand soil C dynamics, and some surprising results from our regional, scale-up work.

Nepstad, Daniel↗
NASA NTRS1994

Human impacts on soil carbon dynamics of deep-rooted Amazonian forests

Deforestation and logging degrade more forest in eastern and southern Amazonia than in any other region of the world. This forest alteration affects regional hydrology and the global carbon cycle, but our current understanding of these effects is limited by incomplete knowledge of tropical forest ecosystems. It is widely agreed that roots are concentrated near the soil surface in moist tropical forests, but this generalization incorrectly implies that deep roots are unimportant in water and C budgets. Our results indicate that half of the closed-canopy forests of Brazilian Amazonic occur where rainfall is highly seasonal, and these forests rely on deeply penetrating roots to extract soil water. Pasture vegetation extracts less water from deep soil than the forest it replaces, thus increasing rates of drainage and decreasing rates of evapotranspiration. Deep roots are also a source of modern carbon deep in the soil. The soils of the eastern Amazon contain more carbon below 1 m depth than is present in above-ground biomass. As much as 25 percent of this deep soil C could have annual to decadal turnover times and may be lost to the atmosphere following deforestation. We compared the importance of deep roots in a mature, evergreen forest with an adjacent man-made pasture, the most common type of vegetation on deforested land in Amazonia. The study site is near the town of Paragominas, in the Brazilian state of Para, with a seasonal rainfall pattern and deeply-weathered, kaolinitic soils that are typical for large portions of Amazonia. Root distribution, soil water extraction, and soil carbon dynamics were studied using deep auger holes and shafts in each ecosystem, and the phenology and water status of the leaf canopies were measured. We estimated the geographical distribution of deeply-rooting forests using satellite imagery, rainfall data, and field measurements.

Nepstad, Daniel C.↗
NASA NTRS2020

Satellite Monitoring of Global Surface Soil Organic Carbon Dynamics Using the SMAP Level 4 Carbon Product

Soil organic carbon (SOC) is an important metric of soil health and the terrestrial carbon balance. Short‐term climate variations affect SOC through changes in temperature and moisture, which control vegetation growth and soil decomposition. We evaluated a satellite data‐driven carbon model, operating under the NASA Soil Moisture Active‐Passive (SMAP) mission, as a means of monitoring global surface SOC dynamics. The SMAP Level 4 Carbon (L4C) product estimates a daily global carbon budget including surface (0‐ to 5‐cm depth) SOC. We found that the L4C mean latitudinal SOC distribution is generally consistent with alternative assessments from static soil inventory records and dynamic global vegetation models (r ≥ 0.89). Within forest systems, based on inventory data, L4C SOC is most similar in magnitude to litterfall but is correlated with coarse woody debris ( urn:x-wiley:jgrg:media:jgrg21790:jgrg21790-math-0001) and total SOC ( urn:x-wiley:jgrg:media:jgrg21790:jgrg21790-math-0002). L4C SOC is sensitive to seasonal and annual climate variability, with mean residence times that range from 1.5 years in the wet tropics to 17 years in the cold tundra. Incorporating soil moisture retrievals from the SMAP L‐band (1.4 GHz) microwave radiometer within the L4C algorithm provides enhanced soil moisture sensitivity under low‐to‐moderate vegetation cover (<5 kg/sq.m vegetation water content). The L‐band soil moisture had the greatest impact on the L4C carbon budget in semiarid regions, which span almost 60% of the globe and account for substantial variability in the terrestrial carbon sink. The L4C operational product enables prognostic investigations into effects of recent climate trends and anomalies (e.g., droughts and pluvials) on shallow soil carbon dynamics.

K. Arthur Endsley↗
NASA NTRS2005

Carbon Dynamics in Vegetation and Soils

The overall goals of CD-08 team in Phase I were to quantify the contributions of different components of the carbon cycle to overall ecosystem carbon balance in Amazonian tropical forests and to undertake process studies at a number of sites along the eastern LBA transect to understand how and why these fluxes vary with site, season, and year. We divided this work into a number of specific tasks: (1) determining the average rate (and variability) of tree growth over the past 3 decades; (2) determining age demographics of tree populations, using radiocarbon to determine tree age; (3) assessing the rate of production and decomposition of dead wood debris; (4) determining turnover rates for organic matter in soils and the mean age of C respired from soil using radiocarbon measurements; and (5) comparing our results with models and constructing models to predict the potential of tropical forests to function as sources or sinks of C. This report summarizes the considerable progress made towards our original goals, which have led to increased understanding of the potential for central Amazon forests to act as sources or sinks of carbon with altered productivity. The overall picture of tropical forest C dynamics emerging from our Phase I studies suggests that the fraction of gross primary production allocated to growth in these forests is only 25-30%, as opposed to the 50% assumed by many ecosystem models. Consequent slow tree growth rates mean greater mean tree age for a given diameter, as reflected in our measurements and models of tree age. Radiocarbon measurements in leaf and root litter suggest that carbon stays in living tree biomass for several years up to a decade before being added to soils, where decomposition is rapid. The time lags predicted from 14C, when coupled with climate variation on similar time scales, can lead to significant interannual variation in net ecosystem C exchange.

Trumbore, Susan↗
NASA NTRS2022

Modeling Demographic-Driven Vegetation Dynamics and Ecosystem Biogeochemical Cycling in NASA GISS’s Earth System Model (ModelE-BiomeE v.1.0)

We developed a demographic vegetation model, BiomeE, to improve the modeling of vegetation dynamics and ecosystem biogeochemical cycles in the NASA Goddard Institute of Space Studies' ModelE Earth system model. This model includes the processes of plant growth, mortality, reproduction, vegetation structural dynamics, and soil carbon and nitrogen storage and transformations. The model combines the plant physiological processes of ModelE's original vegetation model, Ent, with the plant demographic and ecosystem nitrogen processes that have been represented in the Geophysical Fluid Dynamics Laboratory's LM3-PPA. We used nine plant functional types to represent global natural vegetation functional diversity, including trees, shrubs, and grasses, and a new phenology model to simulate vegetation seasonal changes with temperature and precipitation fluctuations. Competition for light and soil resources is individual based, which makes the modeling of transient compositional dynamics and vegetation succession possible. Overall, the BiomeE model simulates, with fidelity comparable to other models, the dynamics of vegetation and soil biogeochemistry, including leaf area index, vegetation structure (e.g., height, tree density, size distribution, and crown organization), and ecosystem carbon and nitrogen storage and fluxes. This model allows ModelE to simulate transient and long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems. Furthermore, BiomeE also allows for the eco-evolutionary modeling of community assemblage in response to past and future climate changes with its individual-based competition and demographic processes.

Biogeochemical cycles↗
NASA NTRS2004

The Net Carbon Flux due to Deforestation and Forest Re-Growth in the Brazilian Amazon: Analysis using a Process-Based Model

We developed a process-based model of forest growth, carbon cycling, and land cover dynamics named CARLUC (for CARbon and Land Use Change) to estimate the size of terrestrial carbon pools in terra firme (non-flooded) forests across the Brazilian Legal Amazon and the net flux of carbon resulting from forest disturbance and forest recovery from disturbance. Our goal in building the model was to construct a relatively simple ecosystem model that would respond to soil and climatic heterogeneity that allows us to study of the impact of Amazonian deforestation, selective logging, and accidental fire on the global carbon cycle. This paper focuses on the net flux caused by deforestation and forest re-growth over the period from 1970-1998. We calculate that the net flux to the atmosphere during this period reached a maximum of approx. 0.35 PgC/yr (1PgC = 1 x 10(exp I5) gC) in 1990, with a cumulative release of approx. 7 PgC from 1970- 1998. The net flux is higher than predicted by an earlier study by a total of 1 PgC over the period 1989-1 998 mainly because CARLUC predicts relatively high mature forest carbon storage compared to the datasets used in the earlier study. Incorporating the dynamics of litter and soil carbon pools into the model increases the cumulative net flux by approx. 1 PgC from 1970-1998, while different assumptions about land cover dynamics only caused small changes. The uncertainty of the net flux, calculated with a Monte-Carlo approach, is roughly 35% of the mean value (1 SD).

Hirsch, A. I.↗
NASA NTRS2022

Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model

Soil moisture impacts the biosphere–atmosphere exchange of CO2 and CH4 and plays an important role in the terrestrial carbon cycle. A better representation of soil moisture would improve coupled carbon–water dynamics in terrestrial ecosystem models and could potentially improve model estimates of large-scale carbon fluxes and climate feedbacks. Here, we investigate using soil moisture observations from the Soil Moisture Active Passive (SMAP) satellite mission to inform simulated carbon fluxes in the global terrestrial ecosystem model LPJ-wsl. Results suggest that the direct insertion of SMAP reduces the bias in simulated soil moisture at in situ measurement sites by 40%, with a greater improvement at temperate sites. A wavelet analysis between the model and measurements from 26 FLUXNET sites suggests that the assimilated run modestly reduces the bias of simulated carbon fluxes for boreal and subtropical sites at 1–2-month time scales. At regional scales, SMAP soil moisture can improve the estimated responses of CO2 and CH4 fluxes to extreme events such as the 2018 European drought and the 2019 rainfall event in the Sudd (Southern Sudan) wetlands. The simulated improvements to land–surface carbon fluxes using the direct insertion of SMAP are shown across a variety of timescales, which suggests the potential of SMAP soil moisture in improving the model representation of carbon–water coupling.

SMAP↗
NASA NTRSDate not supplied

Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties

Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.

vegetation models↗
NASA NTRS2001

Advances in Land Data Assimilation Systems

Assimilation of remotely sensed land surface observations into regional to global scale numerical models have the potential to significantly advance our ability, to assess, understand, and predict surface water, energy, and carbon cycles. This session seeks to assess the state-of-the-art in data assimilation methods for integrating land surface remote sensing and modeling, with a focus on practical applications and techniques. Assimilated land surface variables of interest include (but are not limited to, soil moisture, surface temperature, snowpack, streamflow, vegetation dynamics, and carbon storage. Contributions describing the development of practical land surface data assimilation methods, multivariate land surface data assimilation strategies, evaluation of the required accuracy and resolution of remote sensing observations, the effects of scale, process complexity, and uncertainty on data assimilation, and the optimal treatment of model and observation errors are encouraged.

Houser, Paul R.↗
NASA NTRS2015

Variability of Phenology and Fluxes of Water and Carbon with Observed and Simulated Soil Moisture in the Ent Terrestrial Biosphere Model (Ent TBM Version 1.0.1.0.0)

The Ent Terrestrial Biosphere Model (Ent TBM) is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models (GCMs). This study describes the leaf phenology submodel implemented in the Ent TBM version 1.0.1.0.0 coupled to the carbon allocation scheme of the Ecosystem Demography (ED) model. The phenology submodel adopts a combination of responses to temperature (growing degree days and frost hardening), soil moisture (linearity of stress with relative saturation) and radiation (light length). Growth of leaves, sapwood, fine roots, stem wood and coarse roots is updated on a daily basis. We evaluate the performance in reproducing observed leaf seasonal growth as well as water and carbon fluxes for four plant functional types at five Fluxnet sites, with both observed and prognostic hydrology, and observed and prognostic seasonal leaf area index. The phenology submodel is able to capture the timing and magnitude of leaf-out and senescence for temperate broadleaf deciduous forest (Harvard Forest and Morgan- Monroe State Forest, US), C3 annual grassland (Vaira Ranch, US) and California oak savanna (Tonzi Ranch, US). For evergreen needleleaf forest (Hyytiäla, Finland), the phenology submodel captures the effect of frost hardening of photosynthetic capacity on seasonal fluxes and leaf area. We address the importance of customizing parameter sets of vegetation soil moisture stress response to the particular land surface hydrology scheme. We identify model deficiencies that reveal important dynamics and parameter needs.

Soil moisture↗
NASA NTRSDate not supplied

Vegetation Heights for Landsat Land Cover Classes in the Interior Boreal Forest Region of Alaska

Accurate determination and mapping of vegetation height is a prerequisite for regional estimations of above ground biomass, carbon pool dynamics, and improved modeling of future climate-vegetation-soil interactions. This study combined land cover class maps (at 30-m pixel resolution) derived from Landsat image bands with canopy vegetation heights detected at 1-m resolution from NASA G-LiHT Lidar flights, both from the year 2014 for the Middle Tanana Valley near Fairbanks, Alaska. The statistical distributions and structural attributes from Lidar plant heights were derived and refined for all Landsat vegetation classes. Results showed that the tallest cover classes in this region were deciduous forests (dominated by birch and aspen), followed by evergreen spruce and wooded wetlands. Open shrubland and herbaceous cover areas were determined to be 2 - 5 m shorter, on average, than these boreal forest stands. Tall shrubland cover areas were slightly shorter than most spruce-dominated evergreen forest areas. Nearly 60% of all low shrubland and herbaceous wetland pixels were detected with at least one tree crown greater than 10 m tall. This was in contrast to evergreen and deciduous forest classes that showed 88% of stands with at least one tree crown greater than 10 m tall.

Vegetation↗
NASA NTRS2023

A Multi-Satellite Framework to Rapidly Evaluate Extreme Biosphere Cascades: The Western US 2021 Drought and Heatwave

The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low-latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1–2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO 2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated 28 independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.

Causal model↗
NASA NTRS2015

Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

Seasonal Forecasting↗
NASA NTRS2010

On Simulating the Impacts of Open Water Bodies on the SMAP Passive Soil Moisture Data Product

The Soil Moisture Active and Passive (SMAP) mission is a NASA earth science mission aiming at improving our understanding of the dynamics of the cycles of energy, water, and carbon at global scales. The mission features two complementary sensors on the same low-Earth orbiting platform: an L-band synthetic aperture radar (SAR) operating at 1.26 GHz and an L-band radiometer operating at 1.41 GHz. Together these instruments will provide global mapping of soil moisture and freeze/thaw states in 2-3 days, with a tentative launch date in 2014. The work reported in this study focuses primarily on the development of the SMAP radiometer-only soil moisture data product. For passive soil moisture retrieval at satellite footprint scales, one way to improve retrieval accuracy is to correct for the microwave emission from open water bodies prior to retrieval. The accuracy of this correction will depend on not only the locations of these water bodies, but also the geolocation accuracy of the instrument. As perfect knowledge is never attainable in practice, it is important to assess the impacts of these uncertainties on the SMAP radiometer observations and hence the passive soil moisture retrieval accuracy. In this presentation, we present the results of our preliminary assessment on the impacts of these uncertainties. Our study consists of two parts: (1) a sensitivity analysis on the SMAP radiometer observations due to uncertainties in water-body classification, and (2) realistic global simulations that take into account of additional uncertainties (e.g., geolocation and ancillary data) and SMAP-specific instrument characteristics (e.g., orbit sampling and antenna pattern). The results will provide valuable prelaunch guidance to the SMAP team in identifying different error sources and their relative impacts on the passive soil moisture data product.

Chan, Steven↗
NASA NTRS1997

Comparative Model Evaluation Studies of Biogenic Trace Gas Fluxes in Tropical Forests

Simulation modeling can play a number of important roles in large-scale ecosystem studies, including synthesis of patterns and changes in carbon and nutrient cycling dynamics, scaling up to regional estimates, and formulation of testable hypotheses for process studies. Recent comparative studies have shown that ecosystem models of soil trace gas exchange with the atmosphere are evolving into several distinct simulation approaches. Different levels of detail exist among process models in the treatment of physical controls on ecosystem nutrient fluxes and organic substrate transformations leading to gas emissions. These differences are is in part from distinct objectives of scaling and extrapolation. Parameter requirements for initialization scalings, boundary conditions, and time-series driven therefore vary among ecosystem simulation models, such that the design of field experiments for integration with modeling should consider a consolidated series of measurements that will satisfy most of the various model requirements. For example, variables that provide information on soil moisture holding capacity, moisture retention characteristics, potential evapotranspiration and drainage rates, and rooting depth appear to be of the first order in model evaluation trials for tropical moist forest ecosystems. The amount and nutrient content of labile organic matter in the soil, based on accurate plant production estimates, are also key parameters that determine emission model response. Based on comparative model results, it is possible to construct a preliminary evaluation matrix along categories of key diagnostic parameters and temporal domains. Nevertheless, as large-scale studied are planned, it is notable that few existing models age designed to simulate transient states of ecosystem change, a feature which will be essential for assessment of anthropogenic disturbance on regional gas budgets, and effects of long-term climate variability on biosphere-atmosphere exchange.

Potter, C. S.↗
NASA NTRS2017

The SMAP Level-4 ECO Project: Linking the Terrestrial Water and Carbon Cycles

The SMAP (Soil Moisture Active Passive) Level-4 projects aims to develop a fully coupled hydrology-vegetation data assimilation algorithm to generate improved estimates of modeled hydrological fields and carbon fluxes. This includes using the new NASA Catchment-CN (Catchment-Carbon-Nitrogen) model, which combines the Catchment land surface hydrology model with dynamic vegetation components from the Community Land Model version 4 (CLM4). As such, Catchment-CN allows a more realistic, fully coupled feedback between the land hydrology and the biosphere. The L4 ECO project further aims to inform the model through the assimilation of Soil Moisture Active Passive (SMAP) brightness temperature observations as well as observations of Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of absorbed photosynthetically active radiation (FPAR). Preliminary results show that the assimilation of SMAP observations leads to consistent improvements in the model soil moisture skill. An evaluation of the Catchment-CN modeled vegetation characteristics showed that a calibration of the model's vegetation parameters is required before an assimilation of MODIS FPAR observations is feasible.

MODIS↗
NASA NTRS2018

The SMAP Level-4 ECO Project: Improving Terrestrial Flux Estimates Through Coupled Hydrology-Vegetation Data Assimilation

Simulations of hydrologic and vegetation states as well as water, energy and carbon fluxes from the land surface to the atmosphere are crucial for a wide range of applications, including agricultural advisories, forecasts of (short-term) atmospheric behavior and seasonal weather predictions including forecasts of extreme events, such as heatwaves or droughts. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Eco-Hydrology (L4-ECO) project aims to improve modeled estimates of the terrestrial water, energy and carbon fluxes and states by developing a fully-coupled hydrology-vegetation data assimilation system. This system is developed around the NASA Goddard Earth Observing System (GEOS) Catchment-CN land surface model, which combines land hydrology and energy balance components of the GEOS Catchment model with dynamic vegetation components of the Community Land Model version 4. Catchment-CN fully couples the terrestrial water, energy and carbon cycles, allowing feedbacks from the land hydrology to the biosphere and vice versa.Here, we implement a calibration of the Catchment-CN vegetation parameterization against observations of the fraction of absorbed photosynthetically active radiation (FPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve the model's standalone skill. Later, the DA algorithm used to produce the SMAP L4 soil moisture product will be adapted to Catchment-CN to assimilate SMAP brightness temperatures and inform the model's land hydrology component. Finally, the DA system will be further extended to assimilate MODIS FPAR observations in order to constrain the model's dynamic vegetation component.In this presentation, we demonstrate that the Catchment-CN parameter calibration leads to more realistic vegetation simulations and reduces the root mean squared error between modeled and observed vegetation states across the model's various plant functional types. We also show that the assimilation of SMAP observations is able to improve the average correlation, bias and unbiased RMSE between the modeled surface and root zone soil moisture estimates, and ground observations from the SMAP core validation sites.

Kolassa, Jana↗