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NASA NTRS1991

The role of the EOS SAR in Mission to Planet Earth

The Earth Observing System (EOS) synthetic aperture radar (SAR) is a multifrequency, multipolarization radar which will be capable of the global monitoring of key quantitative measurements of geophysical and biophysical parameters. Scheduled to fly on its own dedicated spacecraft, it is one of three platforms that will make up the space-based component to EOS. The role of the SAR in the EOS mission is discussed, wtih emphasis on the geophysical product variables which will be generated with the SAR and used in global hydrologic, biogeochemical and energy cycle models. The latest concepts for processing the data, including calibration and geophysical processing strategies, are presented.

Holt, Benjamin↗

NASA NTRS2001

Overview and Scientific Agenda of Global Precipitation Mission

This paper addresses the status of the Global Precipitation Mission (GPM) currently planned for launch in the 2007-2008 time frame. The GPM notional design involves a 9-member satellite constellation, one of which wilt be an advanced TRMM-like "core" satellite carrying a dual-frequency Ku-Ka band radar (DFPR) and a TMI-like radiometer. The other eight members of the constellation will be considered daughters of the core satellite, each carrying some type of passive microwave radiometer measuring across the 10.7 - 85 GHz ,frequency range - likely to include a combination of lightweight satellites and co-existing operational/Experimental satellites carrying passive microwave radiometers (i.e., SSM/I and AMSR-E & -F). The constellation is designed to provide no worse than 3-hour sampling at any spot on the globe using sun-synchronous orbit architecture for the daughter satellites, with the core satellite providing relevant measurements on internal cloud-precipitation microphysical processes and the "training-calibrating" information for retrieval algorithms used on daughter satellite measurements. The GPM is organized internationally, currently involving a partnership between NASA in the US, NASDA in Japan, and ESA in Europe (representing the European community nations). The mission is expected to involve additional international participants, sister agencies to the mainstream space agencies, and a diverse collection scientists from academia, government, and the private sector, A critical element in understanding the scientific thinking which has motivated the GPM project is an understanding of what scientific problems TRMM has and has not been able to address and at what scales. The TRMM satellite broke important scientific ground because it carried to space an array of rain-sensitive instruments, two of which were specifically designed for physical precipitation retrieval. These were the 9-channel TRMM Microwave Imager (TMI) and the 13.8 GHz Precipitation Radar (PR). By the same token, because TRMM is a single satellite in a low inclination, low altitude, non-sun-synchronous orbit, it cannot provide global coverage or regular diurnal sampling. These features are essential for many current scientific inquiries involving physical processes of climate and the global water cycle, the modeling of hydrometeorological-biogeochemical cycling, and coupled land-atmosphere/ocean-atmosphere exchanges. Moreover, TRMM has not been able to retrieve explicit properties of the drop size distribution (DSD), a final major barrier to making accurate rain measurements, because the single frequency TRMM radar cannot measure differential reflectivity. which is a minimal requirement for attacking rain retrieval within the framework of extinction cross-section-dependency. GPM is expected to surmount much of the DSD retrieval problem because its core satellite wilt have the capacity to make differential reflectivity measurements with its Ku-Ka band radar (13.6 - 35 GHz) called DFPR - being developed by NASDA/CRL in Japan. This paper will provide an overview of the above issues as well as present a discussion on the expected measurement improvements.

Smith, Eric A.↗

NASA NTRS2018

Carbon-Nitrogen Coupling Under Three Schemes of Model Representation: A Traceability Analysis

The interaction between terrestrial carbon (C) and nitrogen (N) cycles has been incorporated into more and more land surface models. However, the scheme of C-N coupling differs greatly among models, and how these diverse representations of C-N interactions will affect C-cycle modeling remains unclear. In this study, we explored how the simulated ecosystem C storage capacity in the terrestrial ecosystem (TECO) model varied with three different commonly used schemes of C-N coupling. The three schemes (SM1, SM2, and SM3) have been used in three different coupled C-N models (i.e., TECO-CN, CLM 4.5, and O-CN, respectively). They differ mainly in the stoichiometry of C and N in vegetation and soils, plant N uptake strategies, down regulation of photosynthesis, and the pathways of N import. We incorporated the three C-N coupling schemes into the C-only version of the TECO model and evaluated their impacts on the C cycle with a traceability framework. Our results showed that all three of the C-N schemes caused significant reductions in steady-state C storage capacity compared with the C-only version with magnitudes of −23%, −30%, and −54% for SM1, SM2, and SM3, respectively. This reduced C storage capacity was mainly derived from the combined effects of decreases in net primary productivity (NPP; −29%, −15%, and −45%) and changes in mean C residence time (MRT; 9%, −17%, and −17%) for SM1, SM2, and SM3, respectively. The differences in NPP are mainly attributed to the different assumptions on plant N uptake, plant tissue C : N ratio, down regulation of photosynthesis, and biological N fixation. In comparison, the alternative representations of the plant vs. microbe competition strategy and the plant N uptake, combined with the flexible C : N ratio in vegetation and soils, led to a notable spread in MRT. These results highlight the fact that the diverse assumptions on N processes represented by different C-N coupled models could cause additional uncertainty for land surface models. Understanding their difference can help us improve the capability of models to predict future biogeochemical cycles of terrestrial ecosystems.

Du, Zhenggang↗

DOE OSTI2025

A Performant, Scalable Processing Pipeline for High‐Quality and FAIR Environmental Sensor Data

High-resolution environmental monitoring is necessary to record, understand, and predict biogeochemical and ecological changes particularly in coastal systems but brings significant challenges in processing and making rapidly available the resulting data. The COMPASS-FME project established a network of coastal observational sites across the Chesapeake Bay and western Lake Erie regions extensively instrumented with soil, vegetation, and weather sensors logging data every 15 min. Our data processing framework, written in R and completely open source, prioritizes rapid model-experiment iteration and makes biogeochemical data rapidly available for quality assurance/quality control, analysis, and model ingestion. This pipeline is distinguished by a standardized and modular approach to data curation, extensive metadata and documentation, and its high performance. These attributes combine to make biogeochemical data rapidly accessible across COMPASS-FME and the broader community. Flexible, powerful, and reproducible approaches to handling high-volume environmental data are crucial for accelerating biogeosciences research.

Pennington, Stephanie C. [Pacific Northwest Nation↗

DOE OSTI2025

Foliar element determination from field survey in association with the National Ecological Observatory Network Airborne Observation Platform survey, East River, Colorado 2018

The purpose of this dataset is to support research aimed at understanding the coupling between hydrologic and biogeochemical processes at watershed scale, particularly the relationship between aboveground vegetation characteristics and subsurface soil properties. These data are intended to inform and calibrate models of catchment-scale biogeochemical fluxes, including rock-derived nutrient cycling, and they were procured to address the following questions: (1) What is the distribution of vegetation characteristics across the study catchments? (2) Are foliar concentrations of rock-derived nutrients related to underlying lithology and soil availability, or are these signals masked by biotic nutrient cycling and retention processes?This data package contains foliar elemental data collected during the 2018 National Ecological Observatory Networks (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. Folair samples were collected across the East River, Washington Gulch, Slate River, and Coal Creek watersheds and contain a mixture of vegetation including meadow, shrub, and tree foliar samples. The samples were processed using aqua regia digestion and analyzed for elemental determination on inductively coupled plasma optical emission spectrometry (ICP-OES).The data package includes: (1) raw foliar elemental data files in CSV and PDF formats, (2) quality control certificates in PDF format, and (3) an aggregated CSV file containing all elemental measurements compiled across samples. No specialized software is required to access or use these files.

2018 National Ecological Observatory Network Campa↗

DOE OSTI2020

Leveraging the signature of heterotrophic respiration on atmospheric CO 2 for model benchmarking

Spatial and temporal variations in atmospheric carbon dioxide (CO 2 ) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange, but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO 2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS, and CORPSE) that simulate HR fluxes within a biogeochemical test bed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil test-bed models) on atmospheric CO 2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO 2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger-than-observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO 2 , in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1990

Field testing of thermal canopy models in a spruce-fir forest

Recent advances in remote sensing technology allow the use of the thermal infrared region to gain information about vegetative surfaces. Extending existing models to account for thermal radiance transfers within rough forest canopies is of paramount importance. This is so since all processes of interest in the physical climate system and biogeochemical cycles are thermally mediated. Model validation experiments were conducted at a well established boreal forest; northern hardwood forest ecotone research site located in central Maine. Data was collected to allow spatial and temporal validation of thermal models. Emphasis was placed primarily upon enhancing submodels of stomatal behavior, and secondarily upon enhancing boundary layer resistance submodels and accounting for thermal storage in soil and vegetation.

Source record↗

DOE OSTI2026

ELM‐MOSART‐DOC: A Large‐Scale Riverine Dissolved Organic Carbon Model and Its Application Over the United States

Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning‐based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM‐MOSART‐DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)—the Model for Scale Adaptive River Transport (MOSART). ELM‐MOSART‐DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM‐MOSART‐DOC to accurately capture long‐term average DOC concentrations, with Kling‐Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long‐term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM‐MOSART‐DOC integrates process‐based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.

Li, Lingbo [Univ. of Houston, TX (United States); ↗

DOE OSTI2026

Finding the missing pieces: filling gaps that impede the translation of omics data into models

High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

"Ames Research Center: Linking our Origins to our Future"

Our research traces a path from interstellar materials to inhabited worlds and beyond. We examine how protoplanetary disks evolve and form terrestrial planets, the evolutionary paths of habitable planets, and how external factors (e.g., orbital eccentricity) and internal factors (atmospheric circulation) affect habitability. We trace, spectroscopically and chemically, the evolution of organic molecules from the interstellar medium onto habitable bodies. We examine how membranes might form under prebiotic planetary conditions. We evolve proteins capable of sustaining early metabolism, such as synthesis of biopolymers and transport of ions across membranes. We estimate the frequency of finding a functional prebiotic protein that formed spontaneously. We characterize the formation of diagnostic microbial biosignatures in rock-hosted ecosystems in ophiolite springs as an analog for subsurface life within our solar system, and photosynthetic microbial mats as biota that could be detected on extrasolar planets. We develop quantitative models that simulate energy relationships, biogeochemical cycling, trace gas exchange, and biodiversity. We examine the effects of climate variability on a vegetation-rich biosphere over intermediate time scales, using South American ecosystems as a model. We address natural transport of life beyond its planet of origin, such as on a meteorite, where survivors must withstand radiation, desiccation, and time in transit. We fly organisms and ecosystems in low Earth orbit to test their resistance to space. The Ames E&PO program disseminates these themes to national- and international-scale audiences through partnerships with the California Academy of Sciences, Yellow stone National Park, New York Hall of Science, and several K-14 educational organizations.

DesMarais, David J.↗

DOE OSTI2021

On the Representation of Hyporheic Exchange in Models for Reactive Transport in Stream and River Corridors

Efforts to include more detailed representations of biogeochemical processes in basin-scale water quality simulation tools face the challenge of how to tractably represent mass exchange between the flowing channels of streams and rivers and biogeochemical hotspots in the hyporheic zones. Multiscale models that use relatively coarse representations of the channel network with subgrid models for mass exchange and reactions in the hyporheic zone have started to emerge to address that challenge. Two such multiscale models are considered here, one based on a stochastic Lagrangian travel time representation of advective pumping and one on multirate diffusive exchange. The two models are formally equivalent to well-established integrodifferential representations for transport of non-reacting tracers in steady stream flow, which have been very successful in reproducing stream tracer tests. Despite that equivalence, the two models are based on very different model structures and produce significantly different results in reactive transport. In a simple denitrification example, denitrification is two to three times greater for the advection-based model because the multirate diffusive model has direct connections between the stream channel and transient storage zones and an assumption of mixing in the transient storage zones that prevent oxygen levels from dropping to the point where denitrification can progress uninhibited. By contrast, the advection-based model produces distinct redox zonation, allowing for denitrification to proceed uninhibited on part of the hyporheic flowpaths. These results demonstrate that conservative tracer tests alone are inadequate for constraining representation of mass transfer in models for reactive transport in streams and rivers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Quantifying Drivers of Methane Hydrobiogeochemistry in a Tidal River Floodplain System

The influence of coastal ecosystems on global greenhouse gas (GHG) budgets and their response to increasing inundation and salinization remains poorly constrained. In this study, we have integrated an uncertainty quantification (UQ) and ensemble machine learning (ML) framework to identify and rank the most influential processes, properties, and conditions controlling methane behavior in a freshwater floodplain responding to recently restored seawater inundation. Our unique multivariate, multiyear, and multi-site dataset comprises tidal creek and floodplain porewater observations encompassing water level, salinity, pH, temperature, dissolved oxygen (DO), dissolved organic carbon (DOC), total dissolved nitrogen (TDN), partial pressure of carbon dioxide (pCO 2 ), nitrous oxide (pN 2 O), methane (pCH 4 ), and the stable isotopic composition of methane (δ 13 CH 4 ). Additionally, we incorporated topographical data, soil porosity, hydraulic conductivity, and water retention parameters for UQ analysis using a previously developed 3D variably saturated flow and transport floodplain model for a physical mechanistic understanding of factors influencing groundwater levels and salinity and, therefore, CH 4 . Principal component analysis revealed that groundwater level and salinity are the most significant predictors of overall biogeochemical variability. The ensemble ML models and UQ analyses identified DO, water level, salinity, and temperature as the most influential factors for porewater methane levels and indicated that approximately 80% of the total variability in hourly water levels and around 60% of the total variability in hourly salinity can be explained by permeability, creek water level, and two van Genuchten water retention function parameters: the air-entry suction parameter α and the pore size distribution parameter m. These findings provide insights on the physicochemical factors in methane behavior in coastal ecosystems and their representation in local- to global-scale Earth system models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Accelerated Carbon and Water Cycles in the Amazon and Congo Basins Revealed From TRENDY Models and Remote Sensing Products

Tropical forests play a vital role in the global carbon cycle and land–atmosphere interactions. Estimating tropical forest carbon–water dynamics is challenging due to observational and modeling uncertainties. This study leverages the “Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide” (TRENDY) project models and satellite observations to assess changes (2003–2021) in vegetation carbon, gross primary production (GPP), evapotranspiration (ET), and net biosphere production (NBP) in the Amazon and Congo. Atmospheric CO 2 , climate variability, and land use and land cover changes constrain these variables between 1700 and 2021 with the overall increasing trends of carbon stock and fluxes. The models overestimate vegetation carbon and GPP, while ET and NBP are consistent with observations. Fire-activated models predict lower values for vegetation carbon and GPP, ET, and NBP, aligning more closely with observations. The higher ET from fire-activated models may result from enhanced soil evaporation due to increased canopy openings. Fire-inactivated models could well estimate the magnitudes of NBP. The high vegetation carbon in nitrogen-enabled models points to simulation uncertainties and imbalance in model numbers regarding the nitrogen cycle. Although the nitrogen cycle enhances water use efficiency in both the Amazon and Congo, the models show a higher sensitivity to the nitrogen cycle in the Congo. This study highlights the challenges in accurately representing tropical biogeochemical cycles and the values of satellite products in model evaluations, underscoring the need for standard modeling protocols that address biogeochemical components (e.g., nutrient cycles) to better resolve process-based representations.

Shi, Mingjie [Pacific Northwest National Laborator↗

DOE OSTI2020

Improving collaborations between empiricists and modelers to advance grassland community dynamics in ecosystem models

Climate change, increasing atmospheric CO 2 , and land use change have altered biogeochemical and hydrologic cycles world-wide, with grassland systems being particularly vulnerable to resulting vegetation shifts (Komatsu et al., 2019). Therefore, incorporating plant community dynamics into ecosystem models is critical for accurate forecasting of ecosystem responses to global change (Levine, 2016). Process-based ecosystem models, which simulate the biogeochemical transfers of mass and energy among biota, the subsurface, and atmosphere, require representation of dynamic composition of organisms within ecosystems. For example, these models simulate leaf and plant-level characteristics, such as electron transport rate and allometry of carbon (C) allocation, to predict how net primary productivity and other ecosystem processes respond to abiotic drivers. These models are particularly useful in scaling from organismal to ecosystem levels but are still underdeveloped in their ability to capture community change, especially in grassland ecosystems. To represent compositional changes, these models must simulate competition, mortality, establishment, and reproduction of plant populations within communities. Yet, current ecosystem modeling approaches to forecast plant community change have derived from studies of forested systems and are either too coarse to capture fine-scale community dynamics (e.g. dynamic global vegetation models (DGVMs)) or too complex to be used at large spatial scales (e.g. forest gap models).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

High-Performance Simulation of Dynamic Hydrologic Exchange and Implications for Surrogate Flow and Reactive Transport Modeling in a Large River Corridor

Hydrologic exchange flows (HEFs) have environmental significance in riverine ecosystems. Key river channel factors that influence the spatial and temporal variations of HEFs include river stage, riverbed morphology, and riverbed hydraulic conductivity. However, their impacts on HEFs were often evaluated independently or on small scales. In this study, we numerically evaluated the combined interactions of these factors on HEFs using a high-performance simulator, PFLOTRAN, for subsurface flow and transport. The model covers 51 square kilometers of a selected river corridor with large sinuosity along the Hanford Reach of the Columbia River in Washington, US. Three years of spatially distributed hourly river stages were applied to the riverbed. Compared to the simulation when riverbed heterogeneity is not ignored, the simulation using homogeneous riverbed conductivity underestimated HEFs, especially upwelling from lateral features, and overestimated the mean residence times derived from particle tracking. To derive a surrogate model for the river corridor, we amended the widely used transient storage model (TSM) for riverine solute study at reach scale with reactions. By treating the whole river corridor as a batch reactor, the temporal changes in the exchange rate coefficient for the TSM were derived from the dynamic residence time estimated from the hourly PFLOTRAN results. The TSM results were evaluated against the effective concentrations in the hyporheic zone calculated from the PFLOTRAN simulations. Our results show that there is potential to parameterize surrogate models such as TSM amended with biogeochemical reactions while incorporating small-scale process understandings and the signature of time-varying streamflow to advance the mechanistic understanding of river corridor processes at reach to watershed scales. However, the assumption of a well-mixed storage zone for TSM should be revisited when redox-sensitive reactions in the storage zones play important roles in river corridor functioning.

Fang, Yilin↗

NASA NTRS2019

ESD Reviews: Climate Feedbacks in the Earth System and Prospects for Their Evaluation

Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help to quantify and to constrain the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).

Climate feedbacks↗

DOE OSTI2024

Subsurface Redox Interactions Regulate Ebullitive Methane Flux in Heterogeneous Mississippi River Deltaic Wetland

Abstract As interfaces connecting terrestrial and ocean ecosystems, coastal wetlands develop temporally and spatially complex redox conditions, which drive uncertainties in greenhouse gas emission as well as the total carbon budget of the coastal ecosystem. To evaluate the role of complex redox reactions in methane emission from coastal wetlands, a coupled reactive‐transport model was configured to represent subsurface biogeochemical cycles of carbon, nitrogen, and sulfur, along with production and transport of multiple gas species through diffusion and ebullition. This model study was conducted at multiple sites along a salinity gradient in the Barataria Basin at the Mississippi River Deltaic Plain. Over a freshwater to saline gradient, simulated total flux of methane was primarily controlled by its subsurface production and consumption, which were determined by redox reactions directly (e.g., methanogenesis, methanotrophy) and indirectly (e.g., competition with sulfate reduction) under aerobic and/or anaerobic conditions. At fine spatiotemporal scales, surface methane fluxes were also strongly dependent on transport processes, with episodic ebullitive fluxes leading to higher spatial and temporal variability compared to the gradient‐driven diffusion flux. Ebullitive methane fluxes were determined by methane fraction in total ebullitive gas and the frequency of ebullitive events, both of which varied with subsurface methane concentrations and other gas species. Although ebullition thresholds are constrained by local physical factors, this study indicates that redox interactions not only determine gas composition in ebullitive fluxes but can also regulate ebullition frequency through gas production.

03 NATURAL GAS↗

DOE OSTI2020

Assessing impacts of selective logging on water, energy, and carbon budgets and ecosystem dynamics in Amazon forests using the Functionally Assembled Terrestrial Ecosystem Simulator

Abstract. Tropical forest degradation from logging, fire, and fragmentation not only alters carbon stocks and carbon fluxes, but also impacts physical land surface properties such as albedo and roughness length. Such impacts are poorly quantified to date due to difficulties in accessing and maintaining observational infrastructures, as well as the lack of proper modeling tools for capturing the interactions among biophysical properties, ecosystem demography, canopy structure, and biogeochemical cycling in tropical forests. As a first step to address these limitations, we implemented a selective logging module into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) by mimicking the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events, splitting the logged forest patch into disturbed and intact patches; determine the survivorship of cohorts in the disturbed patch; and modifying the biomass and necromass (total mass of coarse woody debris and litter) pools following logging. We parameterized the logging module to reproduce a selective logging experiment at the Tapajós National Forest in Brazil and benchmarked model outputs against available field measurements. Our results suggest that the model permits the coexistence of early and late successional functional types and realistically characterizes the seasonality of water and carbon fluxes and stocks, the forest structure and composition, and the ecosystem succession following disturbance. However, the current version of FATES overestimates water stress in the dry season and therefore fails to capture seasonal variation in latent and sensible heat fluxes. Moreover, we observed a bias towards low stem density and leaf area when compared to observations, suggesting that improvements are needed in both carbon allocation and establishment of trees. The effects of logging were assessed by different logging scenarios to represent reduced impact and conventional logging practices, both with high and low logging intensities. The model simulations suggest that in comparison to old-growth forests the logged forests rapidly recover water and energy fluxes in 1 to 3 years. In contrast, the recovery times for carbon stocks, forest structure, and composition are more than 30 years depending on logging practices and intensity. This study lays the foundation to simulate land use change and forest degradation in FATES, which will be an effective tool to directly represent forest management practices and regeneration in the context of Earth system models.

54 ENVIRONMENTAL SCIENCES↗