Geology topics

Eugenie S. Euskirchen

Publications and source records attributed to Eugenie S. Euskirchen.

15 recordsLinked to original sources

USGS2023

On the relationship between aquatic CO2 concentration and ecosystem fluxes in some of the world’s key wetland types

To understand patterns in CO 2 partial pressure (P CO2 ) over time in wetlands’ surface water and porewater, we examined the relationship between P CO2 and land–atmosphere flux of CO 2 at the ecosystem scale at 22 Northern Hemisphere wetland sites synthesized through an open call. Sites spanned 6 major wetland types (tidal, alpine, fen, bog, marsh, and prairie pothole/karst), 7 Köppen climates, and 16 different years. Ecosystem respiration (R eco ) and gross primary production (GPP), components of vertical CO 2 flux, were compared to P CO2 , a component of lateral CO 2 flux, to determine if photosynthetic rates and soil respiration consistently influence wetland surface and porewater CO 2 concentrations across wetlands. Similar to drivers of primary productivity at the ecosystem scale, P CO2 was strongly positively correlated with air temperature (T air ) at most sites. Monthly average P CO2 tended to peak towards the middle of the year and was more strongly related to R eco than GPP. Our results suggest R eco may be related to biologically driven P CO2 in wetlands, but the relationship is site-specific and could be an artifact of differently timed seasonal cycles or other factors. Higher levels of discharge do not consistently alter the relationship between R eco and temperature normalized P CO2 . This work synthesizes relevant data and identifies key knowledge gaps in drivers of wetland respiration.

Wetlands↗

USGS2023

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions

Wetlands are the largest natural source of methane (CH 4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH 4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH 4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model—iPEACE—reasonably reproduced CH 4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH 4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH 4 emissions. Based on a sensitivity analysis, CH 4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH 4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH 4 production and CH 4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH 4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH 4 emissions across biomes. These processes and associated parameters for CH 4 emissions among and within the wetlands provide useful insights for interpreting observed net CH 4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH 4 fluxes.

Global Change Biology↗

USGS2022

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH 4 emissions are among the most uncertain components of the global CH 4 budget. The complex nature of wetland CH 4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH 4 emissions. In this study, we used the flux measurements of CH 4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH 4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH 4 emissions in all studied wetland types. Ecosystem respiration (CO 2 ) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH 4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH 4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH 4 emissions within earth system land models.

Agricultural and Forest Meteorology↗

USGS2022

Permafrost and climate change: Carbon cycle feedbacks from the warming Arctic

Rapid Arctic environmental change affects the entire Earth system as thawing permafrost ecosystems release greenhouse gases to the atmosphere. Understanding how much permafrost carbon will be released, over what time frame, and what the relative emissions of carbon dioxide and methane will be is key for understanding the impact on global climate. In addition, the response of vegetation in a warming climate has the potential to offset at least some of the accelerating feedback to the climate from permafrost carbon. Temperature, organic carbon, and ground ice are key regulators for determining the impact of permafrost ecosystems on the global carbon cycle. Together, these encompass services of permafrost relevant to global society as well as to the people living in the region and help to determine the landscape-level response of this region to a changing climate.

Annual Review of Earth Science↗

USGS2021

FLUXNET-CH4: A global, multi-ecosystem database and analysis of methane seasonality from freshwater wetlands

Methane (CH 4 ) emissions from natural landscapes constitute roughly half of global CH 4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH 4 flux are ideal for constraining ecosystem-scale CH 4 emissions due to quasi-continuous and high-temporal-resolution CH 4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH 4 Version 1.0, the first open-source global dataset of CH 4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/ , last access: 7 April 2021). FLUXNET-CH 4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH 4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH 4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH 4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH 4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH 4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH 4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20 ∘ S to 20 ∘ N) the spring onset of elevated CH 4 emissions starts 3 d earlier, and the CH 4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH 4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH 4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH 4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH 4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH 4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH 4 peak). FLUXNET-CH 4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH 4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH 4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

Earth System Science Data↗

USGS2021

The biophysical role of water and ice within permafrost nearing collapse: Insights from novel geophysical observations

The impact of permafrost thaw on hydrologic, thermal, and biotic processes remains uncertain, in part due to limitations in subsurface measurement capabilities. To better understand subsurface processes in thermokarst environments, we collocated geophysical and biogeochemical instruments along a thaw gradient between forested permafrost and collapse-scar bogs at the Alaska Peatland Experiment (APEX) site near Fairbanks, Alaska. Ambient seismic noise monitoring provided continuous high-temporal resolution measurements of water and ice saturation changes. Maps of seismic velocity change identified areas of large summertime velocity reductions nearest the youngest bog, indicating potential thaw and expansion at the bog margin. These results corresponded well with complementary borehole nuclear magnetic resonance measurements of unfrozen water content with depth, which showed permafrost soils nearest the bog edges contained the largest amount of unfrozen water along the study transect, up to 25% by volume. In situ measurements of methane within permafrost soils revealed high concentrations at these bog-edge locations, up to 30% soil gas. Supra-permafrost talik zones were observed at the bog margins, indicating talik formation and perennial liquid water may drive lateral bog expansion and enhanced permafrost carbon losses preceding thaw. Comparison of seismic monitoring with wintertime surface carbon dioxide fluxes revealed differential responses depending on time and proximity to the bogs, capturing the controlling influence of subsurface water and ice on microbial activity and surficial emissions. This study demonstrates a multidisciplinary approach for gaining new understanding of how subsurface physical properties influence greenhouse gas production, emissions, and thermokarst development.

Alaska↗

USGS2021

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH 4 ) to the atmosphere, they represent a large source of uncertainty in the global CH 4 budget due to the complex biogeochemical controls on CH 4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH 4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH 4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH 4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH 4 emissions.

Global Change Biology↗

USGS2021

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Wetland methane (CH 4 ) emissions ( FCH 4 ) are important in global carbon budgets and climate change assessments. Currently, FCH 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH 4 and temperature using observations from the FLUXNET-CH 4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH 4 and temperature, suggesting larger FCH 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH 4 production are thus needed to improve global CH 4 budget assessments.

Nature Communications↗

USGS2021

Carbon fluxes and microbial activities from boreal peatlands experiencing permafrost thaw

Permafrost thaw in northern ecosystems may cause large quantities of carbon (C) to move from soil to atmospheric pools. Because soil microbial communities play a critical role in regulating C fluxes from soils, we examined microbial activity and greenhouse gas production soon after permafrost thaw and ground collapse (into collapse-scar bogs), relative to the permafrost plateau or older thaw features. Using multiple field and laboratory-based assays at a field site in interior Alaska, we show that the youngest collapse-scar bog had the highest CH 4 production potential from soil incubations, and, based upon temporal changes in porewater concentrations and 13 C-CH 4 and 13 C-CO 2 , had greater summer in situ rates of respiration, methanogenesis, and surface CH 4 oxidation. These patterns could be explained by greater C and N availability in the young bog, while alternative terminal electron accepting processes did not play a significant role. Field diffusive CH 4 fluxes from the young bog were 4.1 times greater in the shoulder season and 1.7–7.2 times greater in winter relative to older bogs, but not during summer. Greater relative CH 4 flux rates in the shoulder season and winter could be due to reduced CH 4 oxidation relative to summer, magnifying the importance of differences in production. Both the permafrost plateau and collapse-scar bogs were sources of C to the atmosphere due in large part to winter C fluxes. In collapse scar bogs, winter is a critical period when differences in thermokarst age translates to differences in surface fluxes.

Alaska↗

USGS2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and 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 wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed 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 realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was 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 wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

Agricultural and Forest Meteorology↗

USGS2020

Co-producing knowledge: The Integrated Ecosystem Model for resource management in Arctic Alaska

Assessments of climate-change effects on ecosystem processes and services in high-latitude regions are hindered by a lack of decision-support tools capable of forecasting possible future landscapes. We describe a collaborative effort to develop and apply the Integrated Ecosystem Model (IEM) for Alaska and northwestern Canada to explore how climate change influences interactions among disturbance regimes, permafrost integrity, hydrology, and vegetation, and how these dynamics in turn influence resource management decisions. This process emphasizes co-production of knowledge among decision makers, scientists, major funders, partners, and stakeholders. We highlight research findings based on IEM applications in Arctic Alaska, as well as successes and challenges of the co-production process. The overall framework and lessons from our work with the IEM are relevant to other collaborative efforts outside the Arctic that aim to develop a decision-support tool or an undertaking of equivalent scope.

Alaska↗

USGS2019

Large loss of CO2 in winter observed across pan-arctic permafrost region

Recent warming in the Arctic, which has been amplified during the winter 1 , 2 , 3 , greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO 2 ) 4 . However, the amount of CO 2 released in winter is not known and has not been well represented by ecosystem models or empirically based estimates 5 , 6 . Here we synthesize regional in situ observations of CO 2 flux from Arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1,662 TgC per year from the permafrost region during the winter season (October–April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (−1,032 TgC per year). Extending model predictions to warmer conditions up to 2100 indicates that winter CO 2 emissions will increase 17% under a moderate mitigation scenario—Representative Concentration Pathway 4.5—and 41% under business-as-usual emissions scenario—Representative Concentration Pathway 8.5. Our results provide a baseline for winter CO 2 emissions from northern terrestrial regions and indicate that enhanced soil CO 2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions.

northern permafrost region↗

USGS2019

FLUXNET-CH4 synthesis activity: Objectives, observations, and future directions

This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH 4 ) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH 4 flux measurements globally, initial results comparing CH 4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH 4 fluxes across sites ranged from −0.2 ± 0.02 g C m –2 yr –1 for an upland forest site to 114.9 ± 13.4 g C m –2 yr –1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m –2 yr –1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH 4 flux across wetland sites globally. Water table position was positively correlated with annual CH 4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH 4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH 4 estimates due to gap-filling and random errors were on average ±1.6 g C m –2 yr –1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH 4 flux database, the controls on ecosystem CH 4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH 4 emissions.

Bulletin of the American Meteorological Society↗

USGS2018

The role of environmental driving factors in historical and projected carbon dynamics of wetland ecosystems in Alaska

Wetlands are critical terrestrial ecosystems in Alaska, covering ~177,000 km 2 , an area greater than all the wetlands in the remainder of the United States. To assess the relative influence of changing climate, atmospheric carbon dioxide (CO 2 ) concentration, and fire regime on carbon balance in wetland ecosystems of Alaska, a modeling framework that incorporates a fire disturbance model and two biogeochemical models was used. Spatially explicit simulations were conducted at 1‐km resolution for the historical period (1950–2009) and future projection period (2010–2099). Simulations estimated that wetland ecosystems of Alaska lost 175 Tg carbon (C) in the historical period. Ecosystem C storage in 2009 was 5,556 Tg, with 89% of the C stored in soils. The estimated loss of C as CO 2 and biogenic methane (CH 4 ) emissions resulted in wetlands of Alaska increasing the greenhouse gas forcing of climate warming. Simulations for the projection period were conducted for six climate change scenarios constructed from two climate models forced under three CO 2 emission scenarios. Ecosystem C storage averaged among climate scenarios increased 3.94 Tg C/yr by 2099, with variability among the simulations ranging from 2.02 to 4.42 Tg C/yr. These increases were driven primarily by increases in net primary production (NPP) that were greater than losses from increased decomposition and fire. The NPP increase was driven by CO 2 fertilization (~5% per 100 parts per million by volume increase) and by increases in air temperature (~1% per °C increase). Increases in air temperature were estimated to be the primary cause for a projected 47.7% mean increase in biogenic CH 4 emissions among the simulations (~15% per °C increase). Ecosystem CO 2 sequestration offset the increase in CH 4 emissions during the 21st century to decrease the greenhouse gas forcing of climate warming. However, beyond 2100, we expect that this forcing will ultimately increase as wetland ecosystems transition from being a sink to a source of atmospheric CO 2 because of (1) decreasing sensitivity of NPP to increasing atmospheric CO 2 , (2) increasing availability of soil C for decomposition as permafrost thaws, and (3) continued positive sensitivity of biogenic CH 4 emissions to increases in soil temperature.

Alaska↗

USGS2014

Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost

Changes in vegetation and soil properties following permafrost degradation and thermokarst development in peatlands may cause changes in net carbon storage. To better understand these dynamics, we established three sites in Alaska that vary in permafrost regime, including a black spruce peat plateau forest with stable permafrost, an internal collapse scar bog formed as a result of thermokarst, and a rich fen without permafrost. Measurements include year-round eddy covariance estimates of carbon dioxide (CO 2 ), water, and energy fluxes, associated environmental variables, and methane (CH 4 ) fluxes at the collapse scar bog. The ecosystems all acted as net sinks of CO 2 in 2011 and 2012, when air temperature and precipitation remained near long-term means. In 2013, under a late snowmelt and late leaf out followed by a hot, dry summer, the permafrost forest and collapse scar bog were sources of CO 2 . In this same year, CO 2 uptake in the fen increased, largely because summer inundation from groundwater inputs suppressed ecosystem respiration. CO 2 exchange in the permafrost forest and collapse scar bog was sensitive to warm air temperatures, with 0.5 g C m −2 lost each day when maximum air temperature was very warm (≥29°C). The bog lost 4981 ± 300 mg CH 4 m −2 between April and September 2013, indicating that this ecosystem acted as a significant source of both CO 2 and CH 4 to the atmosphere in 2013. These results suggest that boreal peatland responses to warming and drying, both of which are expected to occur in a changing climate, will depend on permafrost regime.

Alaska↗