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This is the AmeriFlux version of the carbon flux data for the site US-PLo Plum Island Oligohaline Marsh. Site Description - The site is a tidal oligohaline marsh located within the Plum Island Sound estuary, which is characterized by a tidal range of 2.5-3m and a vegetation cover of Typha angustifolia. Porewater salinity changes seasonally as influenced by freshwater discharge. Elevation is about 1.4m NAVD88.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PLo Plum Island Oligohaline Marsh. This is the FLUXNET version of the carbon flux data for the site US-PLo Plum Island Oligohaline Marsh produced by applying the standard ONEFlux (1F) software. Site Description - The site is a tidal oligohaline marsh located within the Plum Island Sound estuary, which is characterized by a tidal range of 2.5-3m and a vegetation cover of Typha angustifolia. Porewater salinity changes seasonally as influenced by freshwater discharge. Elevation is about 1.4m NAVD88.
Field-measured Arctic vegetation cover data is essential for creating accurate, high-quality vegetation structure and composition maps. Extrapolating field data into high-resolution cover maps provides detailed, function-specific information for use in Earth System Models, vegetation classifications, and monitoring vegetation change over time and space. However, field campaigns that collect plant cover vary substantially in scope, method, and purpose, which makes them difficult to unify across data stores, and they are often not designed to meet remote sensing needs. In this work, we synthesized and harmonized field-based fractional cover data from various data stores to create a high-quality, consistent repository schema for remote sensing-based vegetation cover mapping applications. We developed a reproducible workflow for synthesizing visual estimate and point-intercept fractional cover data. The resultant Pan-Arctic Vegetation Cover (PAVC) database contains synthesized fractional cover at both the species and plant functional type levels. The latter includes absolute foliar cover for deciduous shrubs and trees, evergreen shrubs and trees, forbs, graminoids, lichen, bryophytes, and “other” vegetation, as well as absolute cover for litter and top cover for water and bare ground.
The increasing atmospheric carbon dioxide (CO 2 ) mole fraction affects global climate through radiative (trapping longwave radiation) and physiological effects (reduction of plant transpiration). We use the simulations of the Community Earth System Model (CESM1-BGC) forced with Representative Concentration Pathway 8.5 to investigate climate-vegetation feedbacks from 1850 to the year 2300. Human-induced land use and land cover change (LULCC), through biogeochemical and biogeophysical processes, alter the climate and modify photosynthetic activity. The changing characteristics of extreme anomalies in photosynthesis, referred to as carbon cycle extremes, increase the uncertainty of terrestrial ecosystems to act as a net carbon sink. However, the role of LULCC in altering carbon cycle extremes under business-as-usual (continuously rising) CO 2 emissions is unknown. Here we show that LULCC magnifies the intensity, frequency, and extent of carbon cycle extremes, resulting in a net reduction in expected photosynthetic activity in the future. We found that large temporally contiguous negative carbon cycle extremes are due to a persistent decrease in soil moisture, which is triggered by declines in precipitation. With LULCC and global warming, vegetation exhibits increased vulnerability to hot and dry environmental conditions, increasing the frequency of fire events and resulting in considerable losses in photosynthetic activity. While most regions show strengthening of negative carbon cycle extremes, a few locations show a weakening effect driven by declining vegetation cover or benign climate conditions for photosynthesis. Increasing hot, dry, and fire-driven carbon cycle extremes are essential for improving carbon cycle modeling and estimation of ecosystem responses to LULCC and rising CO 2 mole fractions. Moreover, large aberrations in vegetation productivity represent potential and growing threats to human lives, wildlife, and food security.
Increasing wildfire severity is of growing concern in the western United States, with consequences for the production, composition, and mobilization of dissolved organic matter (DOM) from terrestrial to aquatic systems. Our current understanding of wildfire impacted DOM (often termed pyrogenic DOM) composition is largely built from temperature-based studies that can be difficult to extrapolate to field conditions, which are often defined by ‘burn severity’, or the post-wildfire impact observed at a site. Thus, burn severity can encapsulate a broader range of fire and environmental conditions not exclusive to temperature. Biogeochemical studies that describe DOM along burn severity continuums remain limited but are needed to better link DOM composition with field conditions post-fire. Here, in this study, we addressed this need with an experimental open air burn simulation that generated chars from vegetation representative of major land cover types in the western United States. The chars were leached to simulate DOM mobilization potential. The DOM composition was characterized by ultra-high resolution mass spectrometry (HR-MS) and UV/VIS absorbance and fluorescence. Our results indicated that the shifts of DOM production and composition along a burn-severity gradient depends on the land cover type that was burned, with the degree of change dependent on the composition of the starting parent vegetation material. Fluorescence signatures indicated a strong convergence across land cover types to more aromatic DOM with increasing severity, while HR-MS indicated an increase in the production of aromatic nitrogen containing DOM with increasing severity. Results from this study enhance our ability to describe DOM composition in a framework that can be more directly related with field and remote-sensing based metrics.
The increased occurrence of flood events resulting from urbanization and global climate change is a great threat to water security. To systematically evaluate the impacts of urbanization on floods, here we applied a paired catchments approach to two adjacent river basins in south-central Texas — the San Antonio River Basin (SARB), with fast urbanization; and the Guadalupe River Basin (GRB), with little land cover change. A physics-based distributed hydrological model — the Distributed Hydrology Soil Vegetation Model, embedded with a multi-purpose reservoir module (DHSVM-Res) — was used to simulate streamflow and reservoir storage. The simulations were conducted under different land cover scenarios, including a newly developed continuous land cover series (CLCS). Holistic analyses were then conducted for the paired basins using three methods: analyzing the selected flood events, detecting change points (CP) of monthly floods, and testing the elasticity of long-term flood regimes. The results suggest that: (1) urbanization may reduce lag time and elevate flood peaks significantly by 3–30% in our study area; (2) when there is little land cover change, changing climate is the major driver of variations in the monthly maximum streamflow (MMS); (3) fast urbanization can amplify streamflow variability, increase MMS significantly, and thus alter the timing of CP; and (4) the mean MMS of observed streamflow in the SARB has increased by as much as 75.7% from the pre-CP to post-CP periods. This comprehensive study fills in a gap in our current understanding of the isolated impacts of urbanization on flooding and is expected to support future explorations of anthropogenic influences on floods.
Abstract Vegetation seasonality in the northern extratropical latitudes (NEL) has changed dramatically, but our understanding of how it responds to climate change (e.g. temperature, soil moisture, shortwave radiation) and human activities (e.g. elevated CO 2 concentration) remains insufficient. In this study, we used two remote-sensing-based leaf area index and factorial simulations from the TRENDY models to attribute the changes in the integrated vegetation seasonality index ( S ), which captures both the concentration and magnitude of vegetation growth throughout the year, to climate, CO 2 , and land use and land cover change (LULCC). We found that from 2003 to 2020, the enhanced average S in the NEL (MODIS: 0.0022 yr −1 , p < 0.05; GLOBMAP: 0.0018 yr −1 , p < 0.05; TRENDY S3 [i.e. the scenario considering both time-varying climate, CO 2 , and LULCC]: 0.0011 ± 7.5174 × 10 −4 yr −1 , p < 0.05) was primarily determined by the elevated CO 2 concentration (5.3 × 10 −4 ± 3.8 × 10 −4 yr −1 , p < 0.05) and secondly controlled by the combined climate change (4.6 × 10 −4 ± 6.6 × 10 −4 yr −1 , p > 0.1). Geographically, negative trends in the vegetation growth concentration were dominated by climate change (31.4%), while both climate change (47.9%) and CO 2 (31.9%) contributed to the enhanced magnitude of vegetation growth. Furthermore, around 60% of the study areas showed that simulated major climatic drivers of S variability exhibited the same dominant factor as observed in either the MODIS or GLOBMAP data. Our research emphasizes the crucial connection between environmental factors and vegetation seasonality, providing valuable insights for policymakers and land managers in developing sustainable ecosystem management strategies amidst a changing climate.
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.
This dataset includes processed organic matter chemistry data from an experimental study designed to compare how the chemical composition of organic matter changes across different burn conditions and vegetation materials representative of major land cover types of the Pacific Northwest, USA. Chars were created in a closed muffle furnace or on an open burn table from four different feedstock species representing vegetation commonly impacted by fire regimes across the Pacific Northwest, USA. Source data and associated metadata (including methods and geospatial information) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1894135 (Grieger et al. 2022). This dataset provides processing scripts and processed data for both solid and dissolved phase organic matter characterization data from experimentally generated chars. These processed data can be used to compare how different burn conditions may influence resultant organic matter chemistry and help further our understanding of potential biogeochemical impacts on river corridors post-fire. The processed data were subsequently analyzed; and the results and ecological implications of the findings were published in peer-reviewed manuscripts. The scripts and workflows used to develop the manuscripts are also included in this data package.This data package was originally published June 2024. It was updated September 2024 (new and modified files) and in January 2025 (modified files). See the change history section in the readme for more details.This dataset is comprised of one data package readme, one data dictionary (dd), one file level metadata (flmd), and folders containing (A) processed data; (B) general processing scripts; and (C) additional folders with specific manuscript analysis scripts and processed data. Step-by-step instructions to assist the user in recreating the workflow used to generate the results in the manuscripts is also provided. The processed data folder includes (1) a folder of processed Parallel Factor Analysis (PARAFAC) and spectra indices outputs from excitation emissions matrix (EEM) fluorescence and absorbance data; (2) a folder of processed solid state carbon-13 (13-C NMR) integrals; (3) folder of high resolution characterization of organic matter via 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) processed data outputs from Formultitude (https://github.com/PNNL-Comp-Mass-Spec/Formultitude), blank corrections and data aggregation, and calculated molecular indices. All files are .pdf, .csv, .html, .Rmd, .R, or .RData.
Abstract The distributions of vegetation and fire activity are changing rapidly in response to climate warming. In many regions, climate effects on dead fuel moisture content (FMC) are expected to increase future wildfire activity. However, forest FMC is largely driven by microclimate conditions, which are moderated from open weather by vegetation canopies. As shifts in vegetation increase under climate warming, the extent to which future fire activity will be driven by climate directly or associated vegetation shifts remains unresolved. Here, we present a study aimed at quantifying the relative magnitudes of (i) direct climate warming, and (ii) vegetation change, on FMC. Field sites to evaluate these effects were established in a natural laboratory of altered forest states to mature wet temperate forest in south‐eastern Australia. FMC was estimated using a process‐based model and 48 years of reconstructed climate data. Canopy effects on microclimate were captured by transferring inputs from climate to microclimate using models parameterised with field observations. To evaluate the relative magnitude of climate and vegetation effects, we calculated the maximum difference in mean annual FMC across annual climate replicates and compared this to FMC differences across reorganising forest sites. Our results show vegetation effects on FMC can exceed those related to expected climate change. Changes to forest structure and composition increased (+15.7%) and decreased (−12.3%) mean annual FMC, with a larger negative effect when forest cover was completely removed (−18.5%). In contrast, the largest climate effect on FMC was −6.6% across 48‐years of data. Our study demonstrates that the magnitude of vegetation effects on FMC can exceed expected climate change effects. Models of future fire activity that do not account for changing vegetation effects on microclimate are omitting a key biophysical control on FMC and therefore may not be accurately predicting future fire activity.
Abstract. Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC yr−1 for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC yr−1). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).
Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.
The Baltimore Ecosystem Study (BES) established a network of long-term permanent biogeochemical study plots in 1998. These plots provide long-term data on vegetation, soil and hydrologic processes in the key ecosystem types within the urban ecosystem. The network of study plots includes forest plots (upland and riparian), chosen to represent the range of forest conditions in the area and grass plots (to represent home lawns). Plots are instrumented with lysimeters (drainage and tension) to sample soil solution chemistry, time domain reflectometry probes to measure soil moisture, dataloggers to measure and record soil temperature, and trace gas flux chambers to measure the flux of carbon dioxide, nitrous oxide and methane from soil to the atmosphere. Measurements of in situ nitrogen mineralization, nitrification and denitrification were made at approximately monthly intervals from Fall 1998 - Fall 2000. Detailed vegetation characterization (all layers) was done in summer 1998 and 2015. Data from these plots has been published in Groffman et al. (2006, 2009), Groffman and Pouyat (2009), Savva et al. (2010), Costa and Groffman (2013), Duncan et al. (2013), Waters et al. (2014), Ni and Groffman (2018), Templeton et al. (2019). Literature Cited Costa, K.H. and P.M. Groffman. 2013. Factors regulating net methane flux in urban forests and grasslands. Soil Science Society of America Journal 77:850 - 855. Duncan, J. M., L. E. Band, and P. M. Groffman. 2013. Towards closing the watershed nitrogen budget: Spatial and temporal scaling of denitrification. Journal of Geophysical Research Biogeosciences 118:1-5; DOI: 10.1002/jgrg.20090 Groffman PM, Pouyat RV, Cadenasso ML, Zipperer WC, Szlavecz K, Yesilonis IC,. Band LE and Brush GS. 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. Forest Ecology and Management 236:177-192. Groffman, P.M., C.O. Williams, R.V. Pouyat, L.E. Band and I.C. Yesilonis. 2009. Nitrate leaching and nitrous oxide flux in urban forests and grasslands. Journal of Environmental Quality 38:1848-1860. Groffman, P.M. and R.V. Pouyat. 2009. Methane uptake in urban forests and lawns. Environmental Science and Technology 43:5229-5235. DOI: 10.1021/es803720h. Ni, X. and P.M. Groffman. 2018. Declines in methane uptake in forest soils. Proceedings of the National Academies of Science of the United States of America 115:8587-8590. Savva, Y., K. Szlavecz, R. V. Pouyat, P. M. Groffman, and G. Heisler. 2010. Effects of land use and vegetation cover on soil temperature in an urban ecosystem. Soil Science Society of America Journal 74:469-480. Templeton, L., M.L. Cadenasso, J. Sullivan, M. Neel and P.M. Groffman. 2019. Changes in vegetation structure and composition of urban and rural forest patches in Baltimore from 1998 to 2015. Forest Ecology and Management. In press. Waters, E.R., J.L. Morse, N.D. Bettez and P.M. Groffman. 2014. Differential carbon and nitrogen controls of denitrification in riparian zones and streams along an urban to exurban gradient. Journal of Environmental Quality 43:955–963.
Dryland precipitation depends on upwind and local moisture sources via moisture recycling. How upwind moisture variations affect interannual variations of downwind precipitation and vegetation in China's drylands remains unclear. We used high-resolution moisture tracking data sets and found terrestrial moisture (93%) was the dominant moisture source for China's drylands, especially from drylands themselves (46%). In most dryland grids, we observed strong correlations between precipitation and upwind moisture sources from 2003 to 2022 (median r = 0.55), with a more significant effect in drier areas. These demonstrated the upwind moisture control on interannual variations of dryland precipitation, in which internal moisture from drylands exceeds the influence of external terrestrial sources. The upwind moisture variations, especially the recycled moisture of drylands, propagate to influence downwind vegetation greenness in precipitation-sensitive dryland areas. Our findings revealed that upwind moisture variations induced by climate or land-cover changes have important implications for water and food security in China's drylands.
The health and well-being of plants and soil is crucial for all life on Earth. It is well-known that vegetation cover follows climatic zones, and plants respond to climatic drivers such as temperature and precipitation. It is also well-known that plant health depends on the properties and health of the soil, and that strong interactions among biota above and belowground dictate the functioning of both realms. Yet, soils and the processes occurring belowground are often considered a “black box,” and are treated very simplistically in our efforts to understand, quantify, and model the future of the planet. Our understanding of the interactions between plants and soils is also far from complete and offers some of the most important research frontiers in community ecology, biogeochemistry, and global change science.
Grass-dominated ecosystems cover wide areas of the land surface yet have received far less attention from the Earth System Model (ESM) community. This limits model projections of ecosystem dynamics in response to global change and coupled vegetation–climate dynamics. We used the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a dynamic vegetation demography model, to determine ecosystem sensitivity to alternate, observed grass allometries and biophysical traits, and evaluated model performance in capturing California C 3 annual grasslands structure and fire regimes. Grass allometry, leaf physiology, plant phenology, and plant mortality all drove the seasonal variation in matter and energy exchange and fire dynamics in California annual grasslands. Allometry influenced grassland structure and function mainly through canopy architecture-mediated space and light competition instead of through carbon partitioning strategy. Regional variation in grassland annual burned area was driven by variation in ecosystem productivity. Our study advances the modeling of grassy ecosystems in ESMs by establishing the importance of grass allometry and plant phenology and mortality in driving C 3 annual grassland seasonal dynamics and fire regime. The calibrated annual grass allometry and biophysical traits presented can be applied in future studies to project climate–vegetation–fire feedbacks in annual grass-dominant ecosystems under global change.
Abstract Observations of the annual cycle of atmospheric CO 2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape‐level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat‐derived land cover time series to characterize plant functional types (PFTs), and solar‐induced fluorescence (SIF) from the Orbiting Carbon Observatory‐2 (OCO‐2) as a proxy for photosynthesis. We analyze these datasets to characterize post‐fire changes in plant succession and photosynthetic activity using a space‐for‐time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid‐succession yield enhancements in SIF by 8–40% during June and July for 2‐ to 59‐year stands relative to pre‐fire controls. From the analysis of post‐fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long‐term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early‐ and mid‐summer. Second, fire appears to facilitate a long‐term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO 2 exchange without a close coupling to long‐term increases in carbon storage.
The Arctic tundra biome occupies Earth’s northernmost lands, covering a 5.1 million km 2 area that encircles the Arctic Ocean and is bound to the south by the boreal forest biome. Arctic tundra ecosystems are experiencing profound changes as vegetation and underlying permafrost soils are strongly influenced by rising air temperatures and the rapid decline of sea ice (see essays Surface Air Temperature and Sea Ice). By the late 1990s, an increase in the productivity of tundra vegetation became evident in global satellite observations, a phenomenon that continued and soon became known as “the greening of the Arctic.” Arctic greening is dynamically linked with Earth’s changing climate, seasonal snow, permafrost, and sea-ice cover, and remains a focus of multidisciplinary scientific research.