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This is the AmeriFlux version of the carbon flux data for the site US-BZB Bonanza Creek Thermokarst Bog. Site Description - Intermediate to old age thermokarst collapse scar boreal bog in the Tanana Lowlands of Interior Alaska. Peat soils ~1-m thick. Recharged by precipitation and runoff from the surrounding forested peat plateaus. Primary wind direction in the summer is WSW.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-BZB Bonanza Creek Thermokarst Bog. This is the FLUXNET version of the carbon flux data for the site US-BZB Bonanza Creek Thermokarst Bog produced by applying the standard ONEFlux (1F) software. Site Description - Intermediate to old age thermokarst collapse scar boreal bog in the Tanana Lowlands of Interior Alaska. Peat soils ~1-m thick. Recharged by precipitation and runoff from the surrounding forested peat plateaus. Primary wind direction in the summer is WSW.
Computational reproducibility is a difficult challenge across science. I attempted to use R 3.6.1 to reproduce linear model fits, done originally using v2.6.0 for a 2009 paper on the drivers of large-scale forest evapotranspiration after wildfire. Model outputs were largely identical, aside from minor formatting changes, except for one–out of 12 total– regression in which the median residual value changed very slightly (in the sixth decimal place). I suggest that this essentially successful reproducibility is due to the relative simplicity of the script, its use of only base R functions, and R’s historically conservative approach to breaking changes.
Here, we report ecosystem-scale CO 2 and CH 4 flux observations for an Amazonian palm swamp peatland over a two-year period in relation to hydrometeorological forcings. Seasonal and short-term variations in hydrometeorological forcing had a strong effect on carbon dioxide and methane fluxes. High air temperature and vapor pressure deficit exerted an important limitation on photosynthesis during the dry season, while latent heat flux appeared to be insensitive to these climate drivers. Evidence from light-response analyses and flux partitioning support that photosynthetic activity was downregulated during dry conditions, while ecosystem respiration was either inhibited or enhanced depending on water table position. The cumulative net ecosystem carbon dioxide exchange indicated that the peatland was a significant carbon dioxide sink ranging from –465 (–279 to –651) g C m -2 y -1 in 2018 to –462 (–277 to –647) g C m -2 y -1 in 2019. The forest was a methane source of 22 (20 to 24) g C m -2 y -1 , similar in magnitude to other tropical peatlands and larger than boreal and arctic peatlands. Furthermore, the annual carbon budget of this Amazonian palm swamp peatland appears to be a major carbon sink under current hydrometeorological conditions, but evidence indicates that the sink strength is vulnerable to future climate change.
The clumping index (CI) quantifies the non-random distribution of vegetation across space, which regulates the canopy radiative transfer processes and land surface carbon, water, and energy cycles. However, its impact on global surface energy budget, particularly sensible heat fluxes and surface temperature, is not well understood. Additionally, while there have been studies showing significant seasonal variations in CI, the impacts of these variations on surface energy fluxes remain unclear. In this study, we incorporated satellite-derived spatially and temporally explicit CI data into the Community Land Model version 5 (CLM5) to evaluate the effects of CI on global land energy fluxes. Our results showed that including CI increased the global mean sensible heat flux dissipated from ground by 3.9 W m -2 (~18%), while decreasing the global mean vegetation sensible heat flux by 4.9 W m -2 (~65%), resulting in a total sensible heat decrease of 1.0 W m -2 (~3%). In contrast, CI increased the global mean latent heat flux by 0.8 W m -2 (~2%), primarily due to increased evapotranspiration (up to 11 W m -2 ) in tropical regions. We also found considerable impacts of seasonal variations in CI, particularly on sensible heat fluxes from ground and vegetation in evergreen needleleaf forests and deciduous needleleaf forests. Using constant CI rather than considering seasonal variations resulted in significant overestimation and underestimation of the sensible heat fluxes from vegetation and ground, respectively, in boreal summer. In conclusion, these changes in surface energy fluxes caused by CI and its seasonal variations led to up to 1.7 and 0.5 K differences in simulated mean ground temperature. These findings highlight the importance of including CI and considering its seasonal variations in modeling land surface energy fluxes.
This is the AmeriFlux version of the carbon flux data for the site US-BZo Bonanza Creek Old Thermokarst Bog. Site Description - Old thermokarst collapse scar boreal bog (several hundred years old) in the Tanana Lowlands of Interior Alaska. Peat soils ~1-m thick. Recharged by precipitation and runoff from the surrounding forested peat plateaus. Primary wind direction in the summer is WSW.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-BZo Bonanza Creek Old Thermokarst Bog. This is the FLUXNET version of the carbon flux data for the site US-BZo Bonanza Creek Old Thermokarst Bog produced by applying the standard ONEFlux (1F) software. Site Description - Old thermokarst collapse scar boreal bog (several hundred years old) in the Tanana Lowlands of Interior Alaska. Peat soils ~1-m thick. Recharged by precipitation and runoff from the surrounding forested peat plateaus. Primary wind direction in the summer is WSW.
Abstract. Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, predicting the occurrence of rare and extreme BP fires proves to be challenging, and gaining a quantitative understanding of the factors, both natural and anthropogenic, inducing BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2015 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effectively addressed the unbalanced data and improved the recall rate by 26.88 %–48.62 % when using multiple datasets, and the error-correcting technique tackled the overestimation of fire sizes during fire seasons; (2) nonparametric models outperformed parametric models in predicting fire occurrences, and the random forest machine learning model performed the best, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.93 across multiple fire datasets; and (3) four sets of factor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities. Our findings demonstrate the efficiency and accuracy of ML techniques in predicting rare and extreme fire events and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.
Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data-driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH 4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ~0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4 y –1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH 4 y –1 ) and overlaps with top-down atmospheric inversion models (155–200 TgCH 4 y –1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra-tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions.
Coarse woody debris (CWD) is an important component in forests, hosting a variety of organisms that have critical roles in nutrient cycling and carbon (C) storage. We developed a process-based model using literature, field observations, and expert knowledge to assess woody debris decomposition in forests and the movement of wood C into the soil and atmosphere. The sensitivity analysis was conducted against the primary ecological drivers (wood properties and ambient conditions) used as model inputs. The analysis used eighty-nine climate datasets from North America, from tropical (14.2° N) to boreal (65.0° N) zones, with large ranges in annual mean temperature (26.5°C in tropical to -11.8°C in boreal), annual precipitation (6,143 to 181 mm), annual snowfall (0 to 612 kg m -2 ), and altitude (3 to 2,824 m above mean see level). The sensitivity analysis showed that CWD decomposition was strongly affected by climate, geographical location and altitude, which together regulate the activity of both microbial and invertebrate wood-decomposers. CWD decomposition rate increased with increments in temperature and precipitation, but decreased with increases in latitude and altitude. CWD decomposition was also sensitive to wood size, density, position (standing vs downed), and tree species. The sensitivity analysis showed that fungi are the most important decomposers of woody debris, accounting for over 50% mass loss in nearly all climatic zones in North America. The model includes invertebrate decomposers, focusing mostly on termites, which can have an important role in CWD decomposition in tropical and some subtropical regions. The role of termites in woody debris decomposition varied widely, between 0 and 40%, from temperate areas to tropical regions. Woody debris decomposition rates simulated for eighty-nine locations in North America were within the published range of woody debris decomposition rates for regions in northern hemisphere from 1.6° N to 68.3° N and in Australia.
This dataset contains bud cold hardiness measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site (Hanson et al, 2017) within the Marcell Experimental Forest in northern Minnesota, USA. Cold hardiness was quantified as the temperature at which low temperature exotherms (LTEs) were identified (i.e., the temperature at which supercooled water within a bud freezes) measured by differential thermal analysis (DTA). Buds were sampled at semi-regular intervals from September 2021 through May 2025 (2021-09-25 to 2025-05-21) across four seasons (2021-2022 through 2024-2025) for four co-occurring boreal peatland tree and shrub species: Picea mariana (PIMA), Larix laricina (LALA), Rhododendron groenlandicum (RHGR), and Chamaedaphne calyculata (CHCA). Samples were collected across the experiment's warming gradient (ambient to +9 degrees Celsius (C)) and pooled by species within each enclosure. These data were used to assess the species- and season-specific effects of experimental warming on cold hardiness and cold damage risk in boreal peatland vegetation (Campos-Arguedas et al, accepted). This dataset contains one data file in comma-separate values (*.csv) format. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.
Nonstructural carbohydrates (NSC) buffer plants against carbon imbalances, yet their partitioning between storage and soluble pools remains elusive at global scales. Here, we compiled a dataset of starch to soluble sugar ratio (St : Su) for 308 woody species across 220 sites world-wide and introduce a dimensionless index that integrates storage and demand while minimizing methodological artifacts. St : Su was strongly associated with growth, identifying it as a key axis of carbon allocation. Foliage consistently exhibited lower St : Su than lignified organs, reflecting a division between transient and conservative pools. Conifers accumulated more starch in foliage but less in stems relative to angiosperms, while leaf habits and mycorrhizal associations further modulated organ-specific strategies. Contrary to expectation, foliar and root St : Su varied little among biomes, but stems exhibited higher ratios in tropical rainforests than in boreal or arid regions, reflecting differences in species composition and adaptive storage under disturbance. Phylogeny constrained stem storage, whereas climatic variability, rather than mean conditions, dominated allocation in leaves and roots. These findings establish St : Su as a robust functional trait linking allocation strategies, growth, and resilience, which can be used to improve vegetation model prediction of forest productivity and mortality under climate variability.
Time series of methane fluxes measured by eddy-covariance require gap-filling to estimate annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for methane, with regards both to the best model algorithms and predictors. In this study, we address the need for standardization by synthesizing results of gap-filling methods applied at 17 wetland sites spanning boreal to tropical regions including all major wetlands classes and two rice paddies. We introduce new procedures for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with robust uncertainty estimates. We tested a conventional method (marginal distribution sampling) and four machine learning algorithms - penalized linear regression, artificial neural networks, random forests, and boosted decision trees - and four predictor sets, including temporal, meteorological, ecosystem carbon and energy flux, and soil predictors. We find that the conventional method can achieve similar median performance to the machine learning models but is worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. Finally, we gap-fill and provide summary evaluation metrics for all 81 sites in the FLUXNET-CH4 community dataset and publicly release the python code for model development, evaluation, and uncertainty estimation.
Effective use of solar-induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global-scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold-and-wet climates to hot-and-dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Finally, our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales.
These 20-meter spatial resolution gridded products provide per-pixel fractional cover (%) of Next Generation Ecosystem Experiments (NGEE) Arctic Plant Functional Types (PFTs) Tier 3 across Alaska, north of the boreal treeline. The products were developed for the NGEE Arctic project, which is improving Arctic vegetation representation and parameterization of the E3SM Land Model. This dataset includes 8 files containing fractional cover for NGEE Tier 3 PFTs (https://data.ess-dive.lbl.gov/view/doi:10.15485/2529470): (1) bryophytes; (2) lichens; (3) non-vascular plants, i.e., the sum of lichens and bryophytes; (4) deciduous shrubs, (5) evergreen shrubs, (6) forbs, (7) graminoids, and a non-PFT class, (8) litter. Each pixel contains the percent cover (expressed as a fraction of total ground cover) that was predicted by random-forest regression models. The random-forest models were trained on cover data collected at 978 plots from 2010 to 2021, of which are archived in the Pan-Arctic Vegetation Cover (PAVC) database (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557). The plot cover was linked to 20-meter spatial resolution, satellite-derived predictor variables: Sentinel-2 spectra and Sentinel-1 polarizations averaged over the 2019 growing season, as well as topographical features derived from ArcticDEM. Then, spatio-temporally anomalous plot data that introduced large variability to the regression outcomes were dropped using the Cook’s distance outlier detection method, and the models were re-created using high-quality plots and their associated satellite derived explanatory variables per each PFT. The correlations between plot-observed and satellite-derived fractional cover for all PFTs were well correlated (R2 = 0.69–0.95 and 0.5 for litter) and had low RMSE bias (0.02–0.11). This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.
Wildfires are important ecosystem processes that have a significant impact on terrestrial vegetation, environment, and climate. This study investigates how future wildfire risk and activities could change under 1.5 °C and 2.0 °C warming scenarios relative to pre-industrial levels using a modified McArthur Forest Fire Danger Index (FFDIn) and the CLM4.5-BGC land surface model. Sixteen Earth System Models (ESMs) from CMIP5 and CMIP6 were employed to supply the variables of climate change under low, middle, and high greenhouse emission scenarios in the 1.5 °C and 2.0 °C scenarios. The ensemble means from the FFDIn and results from the CLM4.5-BGC with multiple forcings show that the dry areas in the southwestern US, Brazilian Highlands, and Arabian islands are projected to face higher wildfire risk with larger burned areas and more carbon emissions under a warmer climate. The Congo Basin and part of the Amazon could have a lower wildfire risk with smaller burned areas and less carbon emissions. The absolute changes in the projected FFDIn are small, although large increases are observed in boreal areas, particularly in the winter and spring. Burned area and carbon emissions are projected to increase in general in the boreal area but decrease in northeastern Asia. Compared to the 1.5 °C scenario, the wildfire risk and burned area levels are projected to increase under the 2.0 °C scenario except in the western Amazon. However, fire carbon emissions are projected to decrease more in tropical areas under the 2.0 °C scenario. The different change directions in eastern North America and eastern China produced by the FFDIn and CLM4.5-BGC suggest the potential effect of non-meteorological elements on fire activities.
FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R 2 >0.94 at 1° spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr -1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R 2 ~0.75) than with GPP from DGVMs (mean global pixel-wise R 2 ~0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R 2 >0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R 2 =0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO 2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.
Increasing iron (Fe) concentrations have been reported for freshwaters across northern Europe over the last decades. This increase, together with elevated concentrations of dissolved organic carbon (DOC), leads to browning of freshwaters, which affects aquatic organisms, ecosystem functioning, biogeochemical cycles, and brings challenges to drinking water production. However, how such increasing trends in stream Fe concentrations reflect the contribution of different catchment sources remains poorly resolved. Here, we explored how catchment characteristics, that is, mires and coniferous soils, regulate spatial and temporal patterns of Fe in a boreal stream network. For this, we determined Fe speciation in riparian and mire soils, and studied temporal Fe dynamics in soil-water and stream-water over a span of 18 years. Positive Fe trends were found in the solution of the riparian soil, while no long-term trend was observed in the mire. These differences were reflected in stream-water, where three headwater streams dominated by coniferous cover also displayed positive Fe trends, whereas the mire dominated stream showed no trend. Surprisingly, the majority of higher order streams showed declining Fe trends, despite long-term increases in DOC. In addition, we found that an extreme drought event led to a prolonged release of Fe and DOC from the riparian soils, that could have long-term effects on stream Fe concentrations. Our results show that riparian forest soils can be major contributors to ongoing increases in freshwater Fe concentrations and that drought can further promote the release of Fe from organic soils.