65 Search Results

Arctic warming is causing substantial compositional, structural, and functional changes in tundra vegetation including shrub and tree-line expansion and densification. However, predicting the carbon trajectories of the changing Arctic is challenging due to interacting feedbacks between vegetation composition and structure, and surface characteristics. We conduct a sensitivity analysis of the current-date to 2100 projected surface energy fluxes, soil carbon pools, and CO₂ fluxes to different shrub expansion rates under future emission scenarios (intermediate—RCP4.5, and high—RCP8.5) using the Arctic-focused configuration of E3SM Land Model (ELM). We focus on Trail Valley Creek (TVC), an upland tundra site in the western Canadian Arctic,more » which is experiencing shrub densification and expansion. We find that shrub expansion did not significantly alter the modeled surface energy and water budgets. However, the carbon balance was sensitive to shrub expansion, which drove higher rates of carbon sequestration as a consequence of higher shrubification rates. Thus, at low shrub expansion rates, the site would become a carbon source, especially under RCP8.5, due to higher temperatures, which deepen the active layer and enhance soil respiration. At higher shrub expansion rates, TVC would become a net CO₂ sink under both Representative Concentration Pathway scenarios due to higher shrub productivity outweighing temperature-driven respiration increase. Our simulations highlight the effect of shrub expansion on Arctic ecosystem carbon fluxes and stocks. We predict that at TVC, shrubification rate would interact with climate change intensity to determine whether the site would become a carbon sink or source under projected future climate.« less

Coastal freshwater wetlands are critical ecosystems for both local and global carbon cycles, sequestering substantial carbon while also emitting methane (CH₄) due to anoxic conditions. Estuarine freshwater wetlands face unique challenges from fluctuating water levels, which influence water quality, vegetation, and carbon cycling. However, the response of CH₄ fluxes and their drivers to altered hydrology and vegetation remains unclear, hindering mechanistic modeling. To address these knowledge gaps, we studied an estuarine freshwater wetland in the Great Lakes region, where rising water levels led to a vegetation shift from emergent Typha dominance in 2015–2016 to floating-leaved species in 2020–2022. Using eddymore » covariance flux measurements during the peak growing season (June–September) of both periods, we observed a 60% decrease in CH₄ emissions, from 81 ± 4 g C m^-2 in 2015–2016 to 31 ± 3 g C m^-2 in 2020–2022. This decline was driven by two main factors: (1) higher water levels, which suppressed ebullitive fluxes via increased hydrostatic pressure and extended CH₄ residence time, enhancing oxidation potential in the water column; and (2) reduced CH₄ conductance through plants. Net carbon dioxide (CO₂) uptake decreased by 90%, from -267 ± 26 g C m^-2 in 2015–2016 to -27 ± 49 g C m^-2 in 2020–2022. Additionally, diel CH₄ flux patterns shifted, with a distinct morning peak observed in 2015–2016 but absent in 2020–2022, suggesting changes in plant-mediated transport and a potential decoupling from photosynthesis. The dominant factors influencing CH₄ fluxes shifted from water temperature and gross primary productivity in 2015–2016 to atmospheric pressure in 2020–2022, suggesting an increased role of ebullition as a primary transport pathway. Our results demonstrate that changes in water levels and vegetation can substantially alter CH₄ and CO₂ fluxes in coastal freshwater wetlands, underscoring the critical role of hydrological shifts in driving carbon dynamics in these ecosystems.« less

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clusteringmore » as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.« less

We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for deep domain knowledge, and cutting down on extensive coding requirements. A notable feature of BOA is its language-agnostic architecture, which facilitates broader application in various fields and to a wider audience. We showcase BOA's application through three examples: a high-dimensionalmore » optimization with parameters of the SWAT+ watershed model, a highly parallelized optimization of this intrinsically non-parallel model, and a multi-objective optimization of the FETCH Tree-Crown Hydrodynamics model. Furthermore, these test cases illustrate BOA's effectiveness in addressing complex optimization challenges in diverse scenarios.« less

Wetlands are the largest emitters of biogenic methane (CH₄) and represent the highest source of uncertainty in global CH₄ budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH₄ flux predictions. We develop an updated version, ELM-Wet, where we activate a separate landunit for wetlands that handles multiple wetland-specific eco-hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site-level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if datamore » exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO₂ and CH₄ fluxes in the developed wet-landunit. Site-level simulations of a coastal non-tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO₂ and CH₄ fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch-specific chamber flux observations and observations of CH₄ concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM-Wet reduces the model's CH₄ emission root mean squared error by up to 33% and is able to represent inter-daily CO₂ and CH₄ flux variability across the wetland's eco-hydrological patches, including during periods of extreme dry or wet conditions.« less

Despite decades of progress, much remains unknown about successional trajectories of carbon (C) cycling in north temperate forests. Drivers and mechanisms of these changes, including the role of different types of disturbances, are particularly elusive. To address this gap, we synthesized decades of data from experimental chronosequences and long-term monitoring at a well-studied, regionally representative field site in northern Michigan, USA. Our study provides a comprehensive assessment of changes in above- and belowground ecosystem components over two centuries of succession, links temporal dynamics in C pools and fluxes with underlying drivers, and offers several conceptual insights to the field ofmore » forest ecology. Our first advance shows how temporal dynamics in some ecosystem components are consistent across severe disturbances that reset succession and partial disturbances that slightly modify it: both of these disturbance types increase soil N availability, alter fungal community composition, and alter growth and competitive interactions between short-lived pioneer and longer-lived tree taxa. Further, these changes in turn affect soil C stocks, respiratory emissions, and other belowground processes. Second, we show that some other ecosystem components have effects on C cycling that are not consistent over the course of succession. For example, canopy structure does not influence C uptake early in succession but becomes important as stands develop, and the importance of individual structural properties changes over the course of two centuries of stand development. Third, we show that in recent decades, climate change is masking or overriding the influence of community composition on C uptake, while respiratory emissions are sensitive to both climatic and compositional change. In synthesis, we emphasize that time is not a driver of C cycling; it is a dimension within which ecosystem drivers such as canopy structure, tree and microbial community composition change. Changes in those drivers, not in forest age, are what control forest C trajectories, and those changes can happen quickly or slowly, through natural processes or deliberate intervention. Stemming from this view and a whole-ecosystem perspective on forest succession, we offer management applications from this work and assess its broader relevance to understanding long-term change in other north temperate forest ecosystems.« less

Current estimates of wetland contributions to the global methane budget carry high uncertainty, particularly in accurately predicting emissions from high methane-emitting wetlands. Microorganisms drive methane cycling, but little is known about their conservation across wetlands. To address this, we integrate 16S rRNA amplicon datasets, metagenomes, metatranscriptomes, and annual methane flux data across 9 wetlands, creating the Multi-Omics for Understanding Climate Change (MUCC) v2.0.0 database. This resource is used to link microbiome composition to function and methane emissions, focusing on methane-cycling microbes and the networks driving carbon decomposition. We identify eight methane-cycling genera shared across wetlands and show wetland-specific metabolic interactionsmore » in marshes, revealing low connections between methanogens and methanotrophs in high-emitting wetlands. Methanoregula emerged as a hub methanogen across networks and is a strong predictor of methane flux. In these wetlands it also displays the functional potential for methylotrophic methanogenesis, highlighting the importance of this pathway in these ecosystems. Collectively, our findings illuminate trends between microbial decomposition networks and methane flux while providing an extensive publicly available database to advance future wetland research.« less

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Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describemore » the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.« less

Terrestrial evapotranspiration is the second-largest component of the land water cycle, linking the water, energy, and carbon cycles and influencing the productivity and health of ecosystems. The dynamics of ET across a spectrum of spatiotemporal scales and their controls remain an active focus of research across different science disciplines. Here, we provide an overview of the current state of ET science across in situ measurements, partitioning of ET, and remote sensing, and discuss how different approaches complement one another based on their advantages and shortcomings. We aim to facilitate collaboration among a cross-disciplinary group of ET scientists to overcome themore » challenges identified in this paper and ultimately advance our integrated understanding of ET.« less