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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Insights on Simulating Summer Warming of the Great Lakes: Understanding the Behavior of a Newly Developed Coupled Lake-Atmosphere Modeling System

The Laurentian Great Lakes are the world's largest freshwater system and regulate the climate of the Great Lakes region, which has been increasingly experiencing climatic, hydrological, and ecological changes. An accurate mechanistic representation of the Great Lakes thermal structure in Regional Climate Models (RCMs) is paramount to studying the climate of this region. Currently, RCMs have primarily represented the Great Lakes through coupled one-dimensional (1D) column lake models; this approach works well for small inland lakes but is unable to resolve the realistic hydrodynamics of the Great Lakes and leads to inaccurate representations of lake surface temperature (LST) that influence regional climate and weather patterns. This work overcomes this limitation by developing a fully two-way coupled modeling system using the Weather Research and Forecasting model and a three-dimensional (3D) hydrodynamic model. The coupled model system resolves the interactive physical processes between the atmosphere, lake, and surrounding watersheds; and validated against a range of observational data. The model is then used to investigate the potential impacts of lake-atmosphere coupling on the simulated summer LST of Lake Superior. By evaluating the difference between our two-way coupled modeling system and our observation-driven modeling system, we find that coupled-lake atmosphere dynamics can lead to a higher LST during June-September through higher net surface heat flux entering the lake in June and July and a lower net surface heat flux entering the lake in August and September. The unstratified water in June distributes the entering surface heat flux throughout the water column leading to a minor LST increase, while the stratified waters of July create a conducive thermal structure for the water surface to warm rapidly under the higher incoming surface heat flux. This research provides insight into the coupled modeling system behavior, which is critical for enhancing our predictive understanding of the Great Lakes climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Lake Huron Enhances Snowfall Downwind of Lake Erie: A Modeling Study of the 2010 New Year’s Lake Effect Snowfall Event

In the Northeast and Great Lakes regions of the United States, the influence of multiple lakes on the overlying air can greatly affect lake-effect snowfall over downwind communities. To assess the impact of Lake Huron on snowfall downwind of Lake Erie, we simulated a lake-effect snow event which occurred from 1 to 6 January 2010 using the Regional Atmospheric Modeling System (RAMS). We found that the presence of Lake Huron enhances snowfall downwind of Lake Erie by almost 20% and leads to much heavier local snowfall totals than when Lake Huron is not present. This increase in snowfall is due to a lake-to-lake (L2L) convective band, as secondary circulations associated with lake-effect convection form over Lake Huron and persist overland between the lakes before reintensifying over Lake Erie. As these secondary circulations move over Lake Erie, lifting within the upward branch of the circulations accelerates the development of convection within the L2L band. Surface fluxes and convection over Lake Huron deepen the boundary layer, promoting deeper vertical development of this L2L band over Lake Erie. However, although this boundary layer modification strengthens the L2L band, we found that it actually reduced lake-effect snowfall produced by wind-parallel bands (WPB) in other parts of the lake. This indicates that boundary layer modification from upstream lakes may impact L2L bands differently than WPB which are not connected to upstream secondary circulations.

Lake effects↗
DOE OSTI2025

Water quality data collected from the Muskegon River (MI, USA) using AquaBOT June-July 2024.

This dataset contains processed output from AquaBOT, which combines 30-second interval measurements of GPS location and YSI water quality parameters (temperature (degrees Celsius), dissolved oxygen (mg/l), specific conductance(microSiemens/cm at 25 degrees Celsius), and turbidity (NTU)), for 3 sections of the Muskegon River in Michigan, USA. The file aquabot2024_MuskegonRiver.csv has information on locations, dates and times, and observations. The data were processed by removing observations recorded before and after AquaBOT was in the water and observations where no data values were recorded. No other QA/QC was done. This research was performed as part of the DOE Research Development and Partnership Pilot award “Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region”, which aims to expand collaborations between researchers at Central Michigan University and U.S. Department of Energy labs and projects focused on enhancing climate resilience in Great Lakes communities and ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Regional biogeography versus intra-annual dynamics of the root and soil microbiome

Abstract Background Root and soil microbial communities constitute the below-ground plant microbiome, are drivers of nutrient cycling, and affect plant productivity. However, our understanding of their spatiotemporal patterns is confounded by exogenous factors that covary spatially, such as changes in host plant species, climate, and edaphic factors. These spatiotemporal patterns likely differ across microbiome domains (bacteria and fungi) and niches (root vs. soil). Results To capture spatial patterns at a regional scale, we sampled the below-ground microbiome of switchgrass monocultures of five sites spanning > 3 degrees of latitude within the Great Lakes region. To capture temporal patterns, we sampled the below-ground microbiome across the growing season within a single site. We compared the strength of spatiotemporal factors to nitrogen addition determining the major drivers in our perennial cropping system. All microbial communities were most strongly structured by sampling site, though collection date also had strong effects; in contrast, nitrogen addition had little to no effect on communities. Though all microbial communities were found to have significant spatiotemporal patterns, sampling site and collection date better explained bacterial than fungal community structure, which appeared more defined by stochastic processes. Root communities, especially bacterial, were more temporally structured than soil communities which were more spatially structured, both across and within sampling sites. Finally, we characterized a core set of taxa in the switchgrass microbiome that persists across space and time. These core taxa represented < 6% of total species richness but > 27% of relative abundance, with potential nitrogen fixing bacteria and fungal mutualists dominating the root community and saprotrophs dominating the soil community. Conclusions Our results highlight the dynamic variability of plant microbiome composition and assembly across space and time, even within a single variety of a plant species. Root and soil fungal community compositions appeared spatiotemporally paired, while root and soil bacterial communities showed a temporal lag in compositional similarity suggesting active recruitment of soil bacteria into the root niche throughout the growing season. A better understanding of the drivers of these differential responses to space and time may improve our ability to predict microbial community structure and function under novel conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”

The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions

Abstract. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) is a state-of-the-art land surface model that simulates the intricate interactions between the terrestrial land surface and other components of the Earth system. Originating from the Community Land Model (CLM) version 4.5, ELM has been under active development, with added new features and functionality, including plant hydraulics, radiation–topography interaction, subsurface multiphase flow, and more explicit land use and management practices. This study integrates ELM v2.1 with the Weather Research and Forecasting (WRF; WRF-ELM) model through a modified Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) framework, enabling affordable high-resolution regional modeling by leveraging ELM's innovative features alongside WRF's diverse atmospheric parameterization options. This framework includes a top-level driver for variable communication between WRF and ELM and Earth System Modeling Framework (ESMF) caps for the WRF atmospheric component and ELM workflow control, encompassing initialization, execution, and finalization. Importantly, this LILAC–ESMF framework demonstrates a more modular approach compared to previous coupling efforts between WRF and land surface models. It maintains the integrity of ELM's source code structure and facilitates the transfer of future developments in ELM to WRF-ELM. To test the ability of the coupled model to capture land–atmosphere interactions over regions with a variety of land uses and land covers, we conducted high-resolution (4 km) WRF-ELM ensemble simulations over the Great Lakes region (GLR) in the summer of 2018 and systematically compared the results against observations, reanalysis data, and WRF-CTSM (WRF coupled with the Community Terrestrial Systems Model). In general, the coupled WRF-ELM model has reasonably captured the spatial distribution of surface state variables and fluxes across the GLR, particularly over the natural vegetation areas. The evaluation results provide a baseline reference for further improvements in ELM in the regional application of high-resolution weather and climate predictions. Our work serves as an example to the model development community for expanding an advanced land surface model's capability to represent fully-coupled land–atmosphere interactions at fine spatial scales. The development and release of WRF-ELM marks a significant advancement for the ELM user community, providing opportunities for fine-scale regional representation, parameter calibration in coupled mode, and examination of new schemes with atmospheric feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Wild bees and natural enemies prefer similar flower species and respond to similar plant traits

Designing wildflower habitats to support beneficial insects providing pollination and pest control services is important for supporting sustainable crop production. It is often desirable to support both groups of insects, making the selection of resource plants for insect conservation programs more challenging. Moreover, the process of selecting resource plants is complicated by the array of possible options in each region, and the need to provide resources over the entire growing season. Identifying traits shared by resource plants that are attractive to both bees and natural enemies can reduce the need to evaluate new plants in each region, by providing a guide for the types of plants expected to be rewarding to these insects. Using insect visitation data collected from replicated common garden plantings of native wildflower and shrub species from the Great Lakes region of the United States, we found a high degree of correlation between the abundance of bees and natural enemies visiting native plant species. These results were used to identify a set of 15 plant species that can provide resources for these insects throughout the summer. Across all tested species, pollen quantity per flower and the week of bloom were positively correlated with some, but not all, taxonomic groupings of beneficial insects. In contrast, floral area was consistently positively associated with visitation of both natural enemies and wild bees. This trait is easy to document and can allow for efficient local testing of potential resource plants, providing a faster path to implementing insect conservation in working landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A census of fish passage facilities at US hydropower developments across the conterminous United States

Hydropower provides reliable and secure electricity and contributes significantly to the flexibility and stability of the US electricity grid. Hydropower dams generally are aquatic barriers and hazards to migratory fish in rivers. Fish passage facilities mitigate risks from hydropower to migratory fish, but information on these facilities is incomplete for the conterminous United States (CONUS). Here, we present the first CONUS-scale dataset of fish passage facilities at US hydropower developments in over 30 years. The existence of fish passage facilities (presence or absence) was specified for 1909 hydropower features, 390 of which had at least one facility. Most features had a single passage facility that provided passage in only one direction, with downstream being more common than upstream passage. Bypasses and ladders accounted for 60 % of the fish passage facility types. While we documented 659 fish passage facilities across CONUS, facilities were most common in the New England, Pacific Northwest, Mid-Atlantic, and Great Lakes regions. In general, fish passage facilities were more common at features that were closer to the ocean, at lower elevations, and at shorter dams, but not related to installed electrical generation capacity. Hydrologic sub-basins containing salmonids also contained the largest number of hydropower features, but the proportion of features with passage was generally higher in sub-basins containing multiple migratory taxa. This census provides valuable information on existing fish passage mitigation and is a benchmark to gauge progress toward a modernized hydropower fleet that provides affordable, reliable energy while protecting fishery resources and river ecosystems.

13 HYDRO ENERGY↗
DOE OSTI2020

Synoptic Meteorology Explains Temperate Forest Carbon Uptake

Abstract While substantial attention has been paid to the effects of both global climate oscillations and local meteorological conditions on the interannual variability of ecosystem carbon exchange, the relationship between the interannual variability of synoptic meteorology and ecosystem carbon exchange has not been well studied. Here we use a clustering algorithm to identify a summertime cyclonic precipitation system northwest of the Great Lakes to determine (a) the association at a daily scale between the occurrence of this system and the local meteorology and net ecosystem exchange at three Great Lakes region forested eddy covariance sites and (b) the association between the seasonal prevalence of this system and the summertime net ecosystem exchange of these sites. We find that temperature, in addition to precipitation and cloud cover, is an important explanatory factor for the suppression of net ecosystem productivity that occurs during these cyclonic events in this region. In addition, the prevalence of this cyclonic system can explain a significant proportion of the interannual variability in summertime forest ecosystem exchange in this region. This explanatory power is not due to a simple accumulation of low‐productivity days that cooccur with this meteorological event, but rather a broader association between the frequency of these events and several aspects of prevailing seasonal conditions. This work demonstrates the usefulness of conceptualizing meteorology in terms of synoptic systems for explaining the interannual variability of regional carbon fluxes.

Randazzo, Nina A.↗
DOE OSTI2023

Multi–year analysis of rain–snow levels at Marquette, Michigan

This study uses observations from a ground-based instrument suite to investigate the rain-snow level in stratiform rainfall from January 2014 to April 2020 in the Upper Great Lakes Region. The height above the surface where ice melts to rain, the rain-snow level (RSL), influences microphysical assumptions in remote sensing precipitation retrievals and the ability of space-based radar to discriminate surface precipitation phase because of ground clutter. The instrument suite is installed at the Marquette, MI (MQT) National Weather Service station adjacent to Lake Superior. Rain events and the RSL are studied through a ground-based vertically profiling radar (Micro Rain Radar), a custom NASA-developed video disdrometer (Precipitation Imaging Package), and reanalyses products from ECMWF ERA5 and NASA MERRA-2. Further, distinct macro and micro physical characteristics are observed in precipitation events with shallow RSL ( < 1.8 km above ground level [AGL]) and intermediate RSL ( > 1.8 km AGL). Intermediate RSL correspond to rain events with relatively higher rain rates and a higher concentration of small drops in the drop size distributions (DSDs). Shallow RSL DSDs contain relatively higher concentrations of large drops with lower fall speeds suggesting that partially melted snowflakes may be reaching the surface. Reflectivity-rain-rate relationships are also impacted by microphysical differences associated with RSL regimes. Radar-detected RSL agree with reanalysis-derived melt levels- especially at wet-bulb isotherms of 0.5°C and 1°C. Seasonal differences such as shallow rain-snow levels in winter, fall, and spring have subsequent implications for satellite detectability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO 2 ) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO 2 increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Efficient Super-Resolution of Near-Surface Climate Modeling Using the Fourier Neural Operator

Downscaling methods are critical in efficiently generating high-resolution atmospheric data. However, state-of-the-art statistical or dynamical downscaling techniques either suffer from the high computational cost of running a physical model or require high-resolution data to develop a downscaling tool. Here, we demonstrate a recently proposed zero-shot super-resolution method, the Fourier neural operator (FNO), to efficiently perform downscaling without the need for high-resolution data. Because the FNO learns dynamics in Fourier space, FNO is a resolution-invariant emulator; it can be trained at a coarse resolution and produces emulation at any high resolution. We applied FNO to downscale a 4-km resolution Weather Research and Forecasting (WRF) Model simulation of near-surface heat-related variables over the Great Lakes region. The FNO is driven by the atmospheric forcings, and topographic features used in the WRF model at the same resolution. We incorporated a physics-constrained loss in FNO by using the Clausius-Clapeyron relation to better constrain the relations among the emulated states. Trained on merely 600 WRF snapshots at 4-km resolution, the FNO shows comparable performance with a widely used convolutional network, U-Net, achieving averaged modified Kling-Gupta Efficiency of 0.88 and 0.94 on the test dataset for temperature and pressure, respectively. We then employed the FNO to produce 1-km emulations to reproduce the fine climate features. Further, by taking the WRF simulation as ground truth, we show consistent performances at the two resolutions, suggesting the reliability of FNO in producing high-resolution dynamics. Our study demonstrates the potential of using FNO for zero-shot super-resolution in generating first-order estimation on atmospheric modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Stormwater Storage and Retention Within an Urban Prairie Wetland Complex

Climate change is expected to increase the frequency and severity of flooding in the Great Lakes region. In many cities, flood-control infrastructure is insufficient to protect against future climate conditions. Consequently, there is increasing focus on stormwater storage provided by urban greenspace, such as wetlands and prairies, but the ecohydrological behavior of these ecosystems is not well understood when they are embedded within cities. To improve understanding of hydrological connectivity between urban areas and natural greenspaces, we deployed a sensor network in Gensburg Markham Prairie (GMP), a large intact prairie-wetland complex in south suburban Chicago. We used the resulting high-frequency time-series data to assess surface-subsurface hydrologic dynamics between upland and low-lying wetland areas, interactions between the prairie and surrounding environment, and stormwater storage provided by the prairie. Rapid infiltration within the prairie during and after storm events provides subsurface flow that stores considerable water, flattens storm hydrographs, and increases the wetland hydroperiod. Much of the stormwater input to GMP derives from the surrounding cityscape. Consequently, storage within the prairie-wetland system reduces and slows stormwater discharge to downstream urban communities. For a typical 5-year 24-hr storm with 10.9 cm of rain, GMP stores 77,100 m 3 , 64% greater than the estimated direct rainfall volume onto the prairie, yielding 30,000 m 3 of offsite stormwater storage. This improved understanding of ecohydrological dynamics in urban prairies and wetlands informs the design and implementation of green infrastructure to meet growing needs for stormwater management.

Rivera, Vivien Anne [Northwestern University, Evan↗
DOE OSTI2025

Rising Water Levels and Vegetation Shifts Drive Substantial Reductions in Methane Emissions and Carbon Dioxide Uptake in a Great Lakes Coastal Freshwater Wetland

ABSTRACT Coastal freshwater wetlands are critical ecosystems for both local and global carbon cycles, sequestering substantial carbon while also emitting methane (CH 4 ) 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 CH 4 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 eddy covariance flux measurements during the peak growing season (June–September) of both periods, we observed a 60% decrease in CH 4 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 CH 4 residence time, enhancing oxidation potential in the water column; and (2) reduced CH 4 conductance through plants. Net carbon dioxide (CO 2 ) 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 CH 4 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 CH 4 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 CH 4 and CO 2 fluxes in coastal freshwater wetlands, underscoring the critical role of hydrological shifts in driving carbon dynamics in these ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting soil carbon changes in switchgrass grown on marginal lands under climate change and adaptation strategies

Abstract The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass ( Panicum virgatum ) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha −1 year −1 ), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha −1 year −1 ). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha −1 year −1 under projected climate. On average, this was 0.3 Mg C ha −1 year −1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.

09 BIOMASS FUELS↗
DOE OSTI2022

Switchgrass cropping systems affect soil carbon and nitrogen and microbial diversity and activity on marginal lands

Abstract Switchgrass ( Panicum virgatum L.), as a dedicated bioenergy crop, can provide cellulosic feedstock for biofuel production while improving or maintaining soil quality. However, comprehensive evaluations of how switchgrass cultivation and nitrogen (N) management impact soil and plant parameters remain incomplete. We conducted field trials in three years (2016–2018) at six locations in the North Central Great Lakes Region to evaluate the effects of cropping systems (switchgrass, restored prairie, undisturbed control) and N rates (0, 56 kg N ha −1 year −1 ) on biomass yield and soil physicochemical, microbial, and enzymatic parameters. Switchgrass cropping system yielded an aboveground biomass 2.9–3.3 times higher than the other two systems (Jayawardena et al., unpublished data) but our study found that this biomass accumulation did not reduce soil dissolved organic C, total dissolved N (TDN), or bacterial diversity. The annual aboveground biomass removal for bioenergy feedstock, however, reduced soil microbial biomass C (MBC) and microbial biomass N (MBN) and bacterial richness in the second and third years; despite this, continuous monocropping of switchgrass improved soil TDN, inorganic N, bacterial diversity, and shoot biomass in the second and/or third years compared with the first year. N fertilization increased aboveground biomass yield by 1.2 times and significantly increased soil TDN, MBN, and the shoot biomass of switchgrass compared with the unfertilized control. Locations with higher C and N contents and lower C:N ratio had higher aboveground biomass, MBC, MBN, and the activity of BG, CBH, and UREA enzymes; by contrast, locations with higher pH had higher soil TDN and activity of NAG and LAP enzymes. Our research demonstrates that switchgrass cultivation could improve or maintain soil N content and N fertilization can increase plant biomass yield. The comprehensive data also can inform future biogeochemical models to successfully implement switchgrass for bioenergy production.

09 BIOMASS FUELS↗