Stefano Potter’s research while affiliated with Woodwell Climate Research Center and other places

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Publications (33)


Spatial variability in Arctic–boreal CO2 fluxes
a,b, Maps showing the mean annual terrestrial NEE (a) and its trends (b) based on site-level data, our upscaling, the atmospheric inversion ensemble and the CMIP6 process model ensemble. The in situ trends in b are based on sites that have more than seven years of data. Supplementary Fig. 2 shows the uncertainty in upscaled NEE and the significance of the trends. While the average upscaled NEE values go up to 116 g C m⁻² yr⁻¹, most of the values are below 60 g C m⁻² yr⁻¹.
Trends in CO2 budgets
a,b, Terrestrial CO2 budgets for 1-km (blue; 2001–2020) and 8-km (grey; 1990–2016) NEE as well as 1-km NEE + fire emissions (red; 2002–2020) across the ABZ (a) and the permafrost region (b). c, An overlay analysis of NEE, GPP and Reco trend maps identifying how trends in GPP and Reco relate to trends in NEE over 2001–2020 (includes significant and non-significant trends). d, Pixels burned during 2002–2020. The central values (that is, annual budgets; solid lines) in a and b are derived from the outputs of the final model using the complete training dataset. The standard deviations (shaded areas) are calculated from the outputs of 20 different models, each trained on a unique bootstrapped sample of the original training data. The magnitude of each trend was computed using the Theil–Sen approach, and the P value determined using the Mann–Kendall test.
Seasonal shifts in CO2 flux dynamics
a–c, Average upscaled monthly NEE (a), GPP (b) and Reco (c) in boreal and tundra biomes during the past two decades. Negative NEE values represent net uptake, and positive values indicate net release. The central mean values in the figures are derived from the outputs of the final model using the complete training dataset. The standard deviations (error bars) are calculated from the outputs of 20 different models, each trained on a unique bootstrapped sample of the original training data. Error bars are shown only for the 2011–2020 period but are similar for the 2001–2010 period. Note that NEE was 1.4 g C m⁻² month⁻¹ lower in September in 2011–2020 than in 2001–2010 in the boreal biome, but this is not shown in the figure.
Regional variability in CO2 budget trends
a,b, Terrestrial CO2 budgets for NEE and NEE + fire in key regions across the boreal (a) and tundra (b). Terrestrial CO2 budgets are shown for 1-km (blue; 2001–2020) and 8-km (grey; 1990–2016) NEE as well as 1-km NEE + fire emissions (red; 2002–2020). The inset in the Alaskan boreal plot in a shows the time series with a narrower y axis than that in the main figure to better depict interannual variability. The central lines in the figures are derived from the outputs of the final model using the complete training dataset. The standard deviations (shaded areas) are calculated from the outputs of 20 different models, each trained on a unique bootstrapped sample of the original training data. The magnitude of the trend was computed using the Theil–Sen approach, and the P value determined using the Mann–Kendall test.
Wildfires offset the increasing but spatially heterogeneous Arctic–boreal CO2 uptake
  • Article
  • Full-text available

January 2025

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230 Reads

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1 Citation

Nature Climate Change

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Jennifer D. Watts

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The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO2 fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO2 sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr⁻¹; trend, −14 Tg C yr⁻¹; P < 0.001), more than 30% of the region was a net CO2 source. Tundra regions may have already started to function on average as CO2 sources, demonstrating a shift in carbon dynamics. When fire emissions are factored in, the increasing Arctic–Boreal Zone sink is no longer statistically significant (budget, −319 ± 140 Tg C yr⁻¹; trend, −9 Tg C yr⁻¹), and the permafrost region becomes CO2 neutral (budget, −24 ± 123 Tg C yr⁻¹; trend, −3 Tg C yr⁻¹), underscoring the importance of fire in this region.

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Regional changes over North Amerca and Eurasia, and their tundra and boreal sub‐regions. Indicators are: (a) near‐surface air temperature (AT; °C); (b) non‐frozen season (NFS; days per year); (c) active‐layer thickness (ALT; m); (d) precipitation (PPT; mm); (e) non‐winter vapor pressure deficit (VPD; kPa); (f) sum of winter snow cover (SC; million km²); (g) soil moisture (SM; cm³ cm⁻³); (h) surface fractional water coverage (FW; %); (i) Non‐frozen season normalized difference vegetation index (NDVI; unitless); (j) Non‐frozen season vegetation optical depth (VOD; unitless); (k) pixel‐percent tree cover (TC; %); (l) pixel‐percent non‐tree cover (NTC; %).
Percent of total regional area showing a significant increasing (red) or decreasing (blue) trend in indicator status over the ~1997–2020 period, according to boreal forest (BF), boreal wetland (BWL), boreal non‐forest (including shrubland and grassland, G/S), and tundra (TUN) for Eurasia and North America. Indicators include: (a) annual average AT (°C); (b) annual total NFS (days); (c) annual maximum ALT (m); (d) annual total PPT (mm); (e) annual total winter SC (Mkm²); (f) annual average VPD (kPa); (g) annual average non‐frozen season FW (% pixel coverage) ; (h) annual non‐frozen season SM (cm³/cm³); (i) annual non‐frozen season NDVI (unitless); (j) annual non‐frozen VOD (unitless).
Multivariate change hotspot maps for the 1997–2020 period (unless otherwise indicated), according to select directional changes in thermal, moisture, and vegetation indicators. Thermal includes: average increase in annual AT (°C); increase in annual NFS (days); and increase in annual ALT (m). Moisture includes: decrease in annual PPT (mm); increase in annual VPD (kPa); decrease in annual non‐frozen season SM (cm³ cm⁻³). Vegetation includes decreases in annual non‐frozen season average NDVI (unitless; 1998–2020) and VOD (unitless; 1998–2017). Panels (a), (c), (e) show identified change “hotspots” based on pixel‐level analyses; (b), (d), (f) show indicator change across individual ecoregions.
Regional Hotspots of Change in Northern High Latitudes Informed by Observations From Space

January 2025

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144 Reads

The high latitudes cover ∼20% of Earth's land surface. This region is facing many shifts in thermal, moisture and vegetation properties, driven by climate warming. Here we leverage remote sensing and climate reanalysis records to improve understanding of changes in ecosystem indicators. We applied non‐parametric trend detections and Getis‐Ord Gi* spatial hotspot assessments. We found substantial terrestrial warming trends across Siberia, portions of Greenland, Alaska, and western Canada. The same regions showed increases in vapor pressure deficit; changes in precipitation and soil moisture were variable. Vegetation greening and browning were widespread across both continents. Browning of the boreal zone was especially evident in autumn. Multivariate hotspot analysis indicated that Siberian ecoregions have experienced substantial, simultaneous, changes in thermal, moisture and vegetation status. Finally, we found that using regionally‐based trends alone, without local assessments, can yield largely incomplete views of high‐latitude change.


Visualisation of the distribution of RTS-present digitisations in the ARTS data set. Inset 1,2 shows the highly heterogeneous RTS density distribution where RTS occurrence tend to be hihgly clustered in hot-spot regions.
Visualisation of the distribution of RTS-absent digitisations in the ARTS data set. The RTS absent data is relatively homogeneously sampled across the Arctic permafrost regions compared with the RTS-present data.
Selected examples of digitised RTS features from the source data sets. The underlying base map is Maxar 0.5 m high-resolution satellite imagery. This shows a range of RTS types, appearances and development stages from various locations across the Arctic.
A Collaborative and Scalable Geospatial Data Set for Arctic Retrogressive Thaw Slumps with Data Standards

January 2025

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159 Reads

Scientific Data

Arctic permafrost is undergoing rapid changes due to climate warming in high latitudes. Retrogressive thaw slumps (RTS) are one of the most abrupt and impactful thermal-denudation events that change Arctic landscapes and accelerate carbon feedbacks. Their spatial distribution remains poorly characterised due to time-intensive conventional mapping methods. While numerous RTS studies have published standalone digitisation datasets, the lack of a centralised, unified database has limited their utilisation, affecting the scale of RTS studies and the generalisation ability of deep learning models. To address this, we established the Arctic Retrogressive Thaw Slumps (ARTS) dataset containing 23,529 RTS-present and 20,434 RTS-absent digitisations from 20 standalone datasets. We also proposed a Data Curation Framework as a working standard for RTS digitisations. This dataset is designed to be comprehensive, accessible, contributable, and adaptable for various RTS-related studies. This dataset and its accompanying curation framework establish a foundation for enhanced collaboration in RTS research, facilitating standardised data sharing and comprehensive analyses across the Arctic permafrost research community.


The distribution of terrestrial Hg release from wildfires during summer 2015 in the Izaviknek and Kingaglia Uplands (IKU) study area. Hg release was predicted using a machine learning (Random Forest) model, which was developed using measurements of soil and vegetation Hg stores, soil organic carbon stores, burn depth, and environmental indices derived from satellite remote sensing data. Inset maps show locations of wildfire perimeters in the IKU study area (black lines), and the extents for which atmospheric transport and deposition of wildfire Hg were modeled in Alaska (gray box), the Yukon-Kuskokwim Delta (hatched box), and the IKU. Base imagery obtained from Copernicus Sentinel-2a data (European Space Agency, https://sentinel.esa.int/).
Total mercury (Hg) deposition from background sources (brown) and from the IKU fires (blue) over three domains: Alaska (top), the Yukon-Kuskokwim Delta (YKD; middle) and the Izaviknek and Kingaglia Uplands (IKU; bottom). Fire-attributable deposition is only shown for months where deposition occurred. The shaded blue area represents the range of uncertainty for fire-attributable deposition associated with uncertainties in plume injection altitude, Hg emission speciation, and Hg emission magnitude. The left axis shows areal deposition (μg m-2 mo-1) and the right axis show cumulative deposition over each domain (kg mo-1). Deposition was simulated using the GEOS-Chem global atmospheric Hg model.
The distribution of Hg deposition during the period of active fires in the Izaviknek and Kingaglia Uplands (IKU; June – July, 2015) simulated using the GEOS-Chem global atmospheric Hg model. (a) Monthly mean Hg deposition from background sources and (b) from the IKU fires. (c) the IKU fire-attributable fraction of annual total Hg deposition (background and fire-attributable). YKD = Yukon-Kuskokwim Delta.
Annual peatland wildfire Hg emissions in the northern tundra-boreal region (>50˚N) (black solid line) and the fraction of total annual wildfire Hg emissions in the northern tundra-boreal region that originate from peatlands (blue dashed line). Shaded regions represent the range of annual total and fractional peatland Hg emissions, estimated from the uncertainty in emissions from this study.
Substantial Mercury Releases and Local Deposition from Permafrost Peatland Wildfires in Southwestern Alaska

November 2024

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110 Reads

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1 Citation

Environmental Science and Technology

Increasing wildfire activity at northern high latitudes has the potential to mobilize large amounts of terrestrial mercury (Hg). However, understanding implications for Hg cycling and ecosystems is hindered by sparse research on peatland wildfire Hg emissions. In this study, we used measurements of soil organic carbon (SOC) and Hg, burn depth, and environmental indices derived from satellite remote sensing to develop machine learning models for predicting Hg emissions from major wildfires in permafrost peatland of the Yukon-Kuskokwim Delta (YKD) in southwestern Alaska. Wildfire Hg emissions during summer 2015 – estimated as the product of Hg:SOC (0.38 ± 0.17 ng Hg / g C), predicted SOC stores (mean [5th–95th] = 9.1 [5.3–11.2] kg C / m2), and burn depth (11.3 [8.2–13.9] cm) – were 556 [164–1138] kg Hg, or approximately 6% of Hg emissions from wildfire activity >60˚N. Modeling estimates suggest that wildfire nearly doubled summertime Hg deposition within 10 kilometers, despite advection of more than 75% of total emissions beyond Alaska. YKD areal emissions combined with remote sensing estimates of burned area suggest that wildfire Hg emissions from northern peatlands (25.4 [14.9–33.6] Mg/y) are an important component of the northern Hg budget. Additional research is needed to refine these estimates and understand implications for Arctic and global Hg cycling.


Permafrost Region Greenhouse Gas Budgets Suggest a Weak CO2 Sink and CH4 and N2O Sources, But Magnitudes Differ Between Top‐Down and Bottom‐Up Methods

October 2024

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452 Reads

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3 Citations

Large stocks of soil carbon (C) and nitrogen (N) in northern permafrost soils are vulnerable to remobilization under climate change. However, there are large uncertainties in present‐day greenhouse gas (GHG) budgets. We compare bottom‐up (data‐driven upscaling and process‐based models) and top‐down (atmospheric inversion models) budgets of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) as well as lateral fluxes of C and N across the region over 2000–2020. Bottom‐up approaches estimate higher land‐to‐atmosphere fluxes for all GHGs. Both bottom‐up and top‐down approaches show a sink of CO2 in natural ecosystems (bottom‐up: −29 (−709, 455), top‐down: −587 (−862, −312) Tg CO2‐C yr⁻¹) and sources of CH4 (bottom‐up: 38 (22, 53), top‐down: 15 (11, 18) Tg CH4‐C yr⁻¹) and N2O (bottom‐up: 0.7 (0.1, 1.3), top‐down: 0.09 (−0.19, 0.37) Tg N2O‐N yr⁻¹). The combined global warming potential of all three gases (GWP‐100) cannot be distinguished from neutral. Over shorter timescales (GWP‐20), the region is a net GHG source because CH4 dominates the total forcing. The net CO2 sink in Boreal forests and wetlands is largely offset by fires and inland water CO2 emissions as well as CH4 emissions from wetlands and inland waters, with a smaller contribution from N2O emissions. Priorities for future research include the representation of inland waters in process‐based models and the compilation of process‐model ensembles for CH4 and N2O. Discrepancies between bottom‐up and top‐down methods call for analyses of how prior flux ensembles impact inversion budgets, more and well‐distributed in situ GHG measurements and improved resolution in upscaling techniques.


Annual and Seasonal Patterns of Burned Area Products in Arctic-Boreal North America and Russia for 2001–2020

September 2024

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73 Reads

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1 Citation

Boreal and Arctic regions have warmed up to four times quicker than the rest of the planet since the 1970s. As a result, boreal and tundra ecosystems are experiencing more frequent and higher intensity extreme weather events and disturbances, such as wildfires. Yet limitations in ground and satellite data across the Arctic and boreal regions have challenged efforts to track these disturbances at regional scales. In order to effectively monitor the progression and extent of wildfires in the Arctic-boreal zone, it is essential to determine whether burned area (BA) products are accurate representations of BA. Here, we use 12 different datasets together with MODIS active fire data to determine the total yearly BA and seasonal patterns of fires in Arctic-boreal North America and Russia for the years 2001–2020. We found relatively little variability between the datasets in North America, both in terms of total BA and seasonality, with an average BA of 2.55 ± 1.24 (standard deviation) Mha/year for our analysis period, the majority (ca. 41%) of which occurs in July. In contrast, in Russia, there are large disparities between the products—GFED5 produces over four times more BA than GFED4s in southern Siberia. These disparities occur due to the different methodologies used; dNBR (differenced Normalized Burn Ratio) of short-term composites from Landsat images used alongside hotspot data was the most consistently successful in representing BA. We stress caution using GABAM in these regions, especially for the years 2001–2013, as Landsat-7 ETM+ scan lines are mistaken as burnt patches, increasing errors of commission. On the other hand, we highlight using regional products where possible, such as ABoVE-FED or ABBA in North America, and the Talucci et al. fire perimeter product in Russia, due to their detection of smaller fires which are often missed by global products.


A Comparison of Satellite Imagery Sources for Automated Detection of Retrogressive Thaw Slumps

June 2024

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79 Reads

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1 Citation

Retrogressive thaw slumps (RTS) are a form of abrupt permafrost thaw that can rapidly mobilize ancient frozen soil carbon, magnifying the permafrost carbon feedback. However, the magnitude of this effect is uncertain, largely due to limited information about the distribution and extent of RTS across the circumpolar region. Although deep learning methods such as Convolutional Neural Networks (CNN) have shown the ability to map RTS from high-resolution satellite imagery (≤10 m), challenges remain in deploying these models across large areas. Imagery selection and procurement remain one of the largest challenges to upscaling RTS mapping projects, as the user must balance cost with resolution and sensor quality. In this study, we compared the performance of three satellite imagery sources that differed in terms of sensor quality and cost in predicting RTS using a Unet3+ CNN model and identified RTS characteristics that impact detectability. Maxar WorldView imagery was the most expensive option, with a ground sample distance of 1.85 m in the multispectral bands (downloaded at 4 m resolution). Planet Labs PlanetScope imagery was a less expensive option with a ground sample distance of approximately 3.0–4.2 m (downloaded at 3 m resolution). Although PlanetScope imagery was downloaded at a higher resolution than WorldView, the radiometric footprint is around 10–12 m, resulting in less crisp imagery. Finally, Sentinel-2 imagery is freely available and has a 10 m resolution. We used 756 RTS polygons from seven sites across Arctic Canada and Siberia in model training and 63 RTS polygons in model testing. The mean IoU of the validation and testing data sets were 0.69 and 0.75 for the WorldView model, 0.70 and 0.71 for the PlanetScope model, and 0.66 and 0.68 for the Sentinel-2 model, respectively. The IoU of the RTS class was nonlinearly related to the RTS Area, showing a strong positive correlation that attenuated as the RTS Area increased. The models were better able to predict RTS that appeared bright on a dark background and were less able to predict RTS that had higher plant cover, indicating that bare ground was a primary way the models detected RTS. Additionally, the models performed less well in wet areas or areas with patchy ground cover. These results indicate that all imagery sources tested here were able to predict larger RTS, but higher-quality imagery allows more accurate detection of smaller RTS.


Evidence of Reburning in Boreal Forest Ecosystems from Landsat Observations

May 2024

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119 Reads

Rising temperatures and drier conditions associated with climate change have intensified the occurrence of wildfires, presenting a growing risk to the integrity and function of boreal forests. To assess where and when reburning occurs and determine the consequences of reburning for post-fire successional trajectories we are synthesizing foundational remote sensing data with existing field data throughout the ABoVE domain. Here we have created a new high-resolution (30m x 30m) mapping product, quantifying the annual areas burned and reburned from 2010 to 2022 using Landsat observations.


The BAWLD‐RECCAP2 region defined as the northern permafrost extent (data from Obu et al., 2021) restricted to the Boreal Arctic Wetlands and Lakes Data set area (BAWLD, Olefeldt et al., 2021). The Figure overlays the extents of the Boreal Forests and the Tundra on the BAWLD‐RECCAP2 region as well as the observation sites used for upscaling GHGs.
Circumpolar percentage coverage of the five adapted Boreal‐Arctic Wetland and Lake Dataset (BAWLD) terrestrial land cover types (Boreal Forests, Non‐permafrost Wetlands, Permafrost Bogs, Dry Tundra, and Tundra Wetlands) used for ecosystem‐based upscaling of greenhouse gas flux budgets in this study. Note that these maps show the distributions across the full BAWLD domain as presented by Olefeldt et al. (2021), not the more limited extent of the RECCAP2 permafrost BAWLD domain used in this study.
Scheme of annual atmospheric greenhouse gases (GHGs) exchange (CO2, CH4, and N2O) for the five terrestrial land cover classes (Boreal Forests (9.0 × 10⁶ km²), Non‐permafrost Wetlands (1.6 × 10⁶ km²), Dry Tundra (5.2 × 10⁶ km²), Tundra Wetlands (0.4 × 10⁶ km²) and Permafrost Bogs (0.9 × 10⁶ km²)); inland water classes (Rivers (0.1 × 10⁶ km²) and Lakes (1.3 × 10⁶ km²)). Annual lateral fluxes from coastal erosion and riverine fluxes are also reported in Tg C yr⁻¹ and Tg N yr⁻¹. Symbols for fluxes indicate high (x > Q3), medium (Q1 < x < Q3), and low (<Q1) fluxes, in comparison with the quartile (Q). Note that the magnitudes across three different GHG fluxes within each land cover class cannot be compared with each other.
The Net GHG Balance and Budget of the Permafrost Region (2000–2020) From Ecosystem Flux Upscaling

April 2024

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490 Reads

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20 Citations

The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here, we construct the first comprehensive bottom‐up budgets of CO2, CH4, and N2O across the terrestrial permafrost region using databases of more than 1000 in situ flux measurements and a land cover‐based ecosystem flux upscaling approach for the period 2000–2020. Estimates indicate that the permafrost region emitted a mean annual flux of 12 (−606, 661) Tg CO2–C yr⁻¹, 38 (22, 53) Tg CH4–C yr⁻¹, and 0.67 (0.07, 1.3) Tg N2O–N yr⁻¹ to the atmosphere throughout the period. Thus, the region was a net source of CH4 and N2O, while the CO2 balance was near neutral within its large uncertainties. Undisturbed terrestrial ecosystems had a CO2 sink of −340 (−836, 156) Tg CO2–C yr⁻¹. Vertical emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. When including lateral fluxes for a complete GHG budget, the permafrost region was a net source of C and N, releasing 144 (−506, 826) Tg C yr⁻¹ and 3 (2, 5) Tg N yr⁻¹. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory.


The predictability of near‐term forest biomass change in boreal North America

January 2024

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112 Reads

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2 Citations

Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce ( Picea mariana ), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.


Citations (23)


... The Hg accumulated in soils and biomass can be re-emitted back to the atmosphere during open biomass burning, especially in boreal forest fires (Dastoor et al., 2022). Dastoor et al. (2022) estimated that Arctic fires contributed to 8.8 ± 6.4 Mg yr − 1 Hg atmospheric emissions, for the period 2001-2019, but a more recent study by Zolkos et al. (2024) suggest that this value could be underestimated. In fact, Zolkos et al. (2024) suggest that wildfire Hg emissions from northern peatlands contributed to 25.4 Mg yr − 1 (14.9-33.6 ...

Reference:

Recent advances in the study of mercury biogeochemistry in Arctic permafrost ecosystems
Substantial Mercury Releases and Local Deposition from Permafrost Peatland Wildfires in Southwestern Alaska
  • Citing Article
  • November 2024

Environmental Science and Technology

... Current evidence on recent ABZ CO 2 budget trends and their main drivers is limited to few in situ data-driven synthesis and modelling studies without a regional perspective on where and why CO 2 budgets are changing 1,[8][9][10] . These studies have focused primarily on ecosystem CO 2 fluxes (that is, not incorporating fire emissions), coarse annual or seasonal CO 2 fluxes (that is, overlooking the intra-annual dynamics), and spatial patterns in CO 2 fluxes with data from only one to two decades. ...

Permafrost Region Greenhouse Gas Budgets Suggest a Weak CO2 Sink and CH4 and N2O Sources, But Magnitudes Differ Between Top‐Down and Bottom‐Up Methods

... In related studies, ground-based imagery is commonly used for grape bunch detection [29], while others used UAV imagery and NDVI to identify live and dry vines [30]. Some works also compared the performance of different satellite image sources, such as WorldView, PlanetScope, and Sentinel-2, concluding that high-resolution imagery, such as WorldView (i.e., <1 m), is essential for detecting small features [81]. This could explain why neither Sentinel nor PlanetScope imagery has shown any relationship with the grape cluster number in the present study. ...

A Comparison of Satellite Imagery Sources for Automated Detection of Retrogressive Thaw Slumps

... based models) and top-down (derived from atmospheric inverse modeling) emission estimates, and each 49 has significant uncertainties (Saunois et al. 2020; Ramage et al. 2024). One approach to the former is 50 landcover-class scaling: developing landcover products (maps using geospatial data and remote-sensing 51 data from satellites; e.g. ...

The Net GHG Balance and Budget of the Permafrost Region (2000–2020) From Ecosystem Flux Upscaling

... Areas that have undergone firemediated forest loss and species dominance changes have generally failed to return to a composition and structure like that prior to fire, suggesting these changes are persistent (Asselin et al. 2006;Walker et al. 2023). Climate-driven advances in the treeline have been limited and variable Trant & Hermanutz 2014), and do not compensate for fire-mediated forest loss (Burrell et al. 2024;. ...

The predictability of near‐term forest biomass change in boreal North America
  • Citing Article
  • January 2024

... Climate warming has been amplified in northern latitudes, contributing to intensified tundra wildfires regimes (Rocha et al 2012, French et al 2016. Snowpacks are disappearing earlier in the spring, leaving terrestrial environments vulnerable to increased occurrences of high latitude lightning ignitions (Hessilt et al 2024). Furthermore, warmer summers and changing precipitation patterns are leading to extended and drier summer periods (Euskirchen et al 2009). ...

Geographically divergent trends in snow disappearance timing and fire ignitions across boreal North America

... As one of several carbon cycle-climate feedbacks, the permafrost carbon feedback increases global temperatures through the emission of GHGs following the decomposition of freshly thawed permafrost organic carbon that was preserved from decomposition under permafrost conditions over millennia 6,7,47,96,103 . Field studies indicate an acceleration of permafrost region carbon emission in recent decades 104,105 , but overall there is low confidence and high uncertainty associated with the direction and magnitude of recent biogeochemical changes over the pan-Arctic region [106][107][108] , and no evidence of a nonlinear response to increasing temperature levels. Model projections range from an increase in soil carbon in the permafrost region under future climate warming to a dramatic loss of soil carbon under the same warming scenarios 4 , unfolding over decades to centuries 4,7 . ...

Two decades of permafrost region CO2, CH4, and N2O budgets suggest a small net greenhouse gas source to the atmosphere
  • Citing Preprint
  • September 2023

... For example, a retreat of the snow cover at the pan-Arctic scale (Mudryk et al. 2021;Robinson 2021) and an earlier onset of melting (Mudryk et al. 2017) have been observed in spring. Moreover, the snow water equivalent (SWE) in winter has decreased, particularly in North America (Pulliainen et al. 2020) where snow cover variations exhibited regional differences, with a decreasing trend in Alaska but an increasing trend in eastern Canada (Hessilt et al. 2023). These changes are connected to modifications of the circumpolar Arctic river hydrograph, because its spring peak-flow regime is strongly related to snowpack melt. ...

Geographically divergent trends in snowmelt timing and fire ignitions across boreal North America

... For example, images obtained from global-coverage satellites utilized to detect burned areas typically have a spatial resolution of several hundred meters 14 , implying a systematic underestimation bias due to undetected small fires, especially over the tropics [15][16][17][18] . Moreover, highintensity fires burn litter and organic horizons of soil, which poses challenges 19 for remote sensing detection and accurate estimation of fuel consumption. Further, peat burning from smoldering processes occurs in natural and disturbed peat in the Arctic and tropics, which is extremely difficult to detect via burned area observations. ...

Burned area and carbon emissions across northwestern boreal North America from 2001–2019