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At least 1,495 records · Page 83

Applicability of Sentinel-1 Terrain Observation by Progressive Scans multitemporal interferometry for monitoring slow ground motions in the San Francisco Bay Area

Data from the Sentinel-1 satellite have already proven useful for investigating seismic and volcanic events since its launch in April 2014. The requirement of ultrahigh coregistration accuracy and the current relatively short time of Sentinel-1 acquisitions make its application challenging for studying slow deformation processes, such as fault creep and land subsidence. Here in this paper we analyze a set of 14 SAR images over the San Francisco Bay Area spanning 1 year from early 2015 to 2016. We show that implementing an existing Enhanced Spectral Diversity algorithm or using precise orbits together with a reference digital elevation model both yield the required coregistration accuracy for making use of the phase measurements in time series analysis of ground deformation. Following a thorough validation test, we update our estimates of Hayward Fault creep rate and confirm uplift due to recharge of the Santa Clara Valley aquifer system during the final summer of 4 year drought.

58 GEOSCIENCES↗

Spatiotemporal Patterns of Precipitation-Modulated Landslide Deformation From Independent Component Analysis of InSAR Time Series

We present that long-term landslide deformation is disruptive and costly in urbanized environments. We rely on TerraSAR-X satellite images (2009–2014) and an improved data processing algorithm (SqueeSAR™) to produce an exceptionally dense Interferometric Synthetic Aperture Radar ground deformation time series for the San Francisco East Bay Hills. Independent and principal component analyses of the time series reveal four distinct spatial and temporal surface deformation patterns in the area around Blakemont landslide, which we relate to different geomechanical processes. Two components of time-dependent landslide deformation isolate continuous motion and motion driven by precipitation-modulated pore pressure changes controlled by annual seasonal cycles and multiyear drought conditions. Two components capturing more widespread seasonal deformation separate precipitation-modulated soil swelling from annual cycles that may be related to groundwater level changes and thermal expansion of buildings. Lastly, high-resolution characterization of landslide response to precipitation is a first step toward improved hazard forecasting.

54 ENVIRONMENTAL SCIENCES↗

Reformulation of dicamba herbicide: Impacts on offsite transport and soybean damage

Abstract The herbicide dicamba (3,6‐dichloro‐2‐methoxybenzoic acid) is commonly used to control broadleaf weeds in soybeans. Dicamba, however, is susceptible to volatilization and drift, thereby causing significant plant damage to nontarget crops downwind. Dicamba was reformulated to reduce volatility and off‐target movement. The effectiveness of the dicamba reformulation was assessed by quantifying dicamba emissions following spray application and investigated how meteorological factors influenced the off‐target movement. The experiments were conducted at the University of Minnesota Agricultural Experiment Station (UMORE Park) during the growing season of 2018, 2019, 2021, and 2022. Multiple high‐flow polyurethane foam air samplers were used to measure dicamba concentrations downwind from a 4‐ha soybean field sprayed with dicamba. Dicamba emissions were estimated using backward Lagrangian modeling constrained by the air sample observations. The results indicate that dicamba emissions and downwind transport were significant for several days following application. Further, non‐traited soybeans located within 15–45 m showed substantial dicamba‐related damage. In warmer, drier seasons, increased dicamba emissions caused more severe damage to downwind soybeans, likely worsened by drought stress preventing recovery. Favorable atmospheric conditions that reduced potential drift can be difficult to achieve in terms of the typical weather experienced over agricultural sites in the Upper Midwest. These results indicate that the dicamba reformulation has not adequately prevented significant post‐spray volatilization losses and downwind transport.

Agriculture↗

Multi‐model analysis of the Atlantic influence on Southern Amazon rainfall

Abstract Amazon rainfall is subject to year‐to‐year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the sea surface temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational data sets were used to support the Atlantic influence on Amazon rainfall. Here, it is found that multiple global climate models do reproduce the Atlantic–Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

54 ENVIRONMENTAL SCIENCES↗

The role of carbonaceous aerosols on short-term variations of precipitation over North Africa

Northern Africa has been subject to extensive droughts in the late 20th century, which are frequently linked to changes in the Sea Surface Temperature (SST) in both the Atlantic and Indian Oceans. However, climate models forced by observed Sea Surface Temperatures have been unable to reproduce the magnitude of rainfall reduction over the last several decades. In this study, we propose that aerosol indirect effects (AIE) may be an important feedback mechanism to contribute this recent reduction. The climate model used here has a fully predictive aerosol life cycle. Results are presented for a set of sensitivity experiments designed to distinguish the role of aerosol direct/semi-­direct and indirect effects on regional precipitation. Changes in cloud lifetime due to the presence of carbonaceous aerosols are proposed as a key mechanism to explain the reduced rainfall over the tropical and North Africa.

54 ENVIRONMENTAL SCIENCES↗

Machine learning to predict biomass sorghum yields under future climate scenarios

Crop yield modeling is critical in the design of national strategies for agricultural production, particularly in the context of a changing climate. Forecasting yields of bioenergy crops at fine spatial resolutions can help to evaluate near-term and long-term pathways for scaling up bio-based fuel and chemical production, and for understanding the impacts of abiotic stressors such as severe droughts and temperature extremes on potential biomass supply. In this work we used a large dataset of 28,364 Sorghum bicolor yield samples (uniquely identified by county and year of observation), environmental variables, and multiple approaches to analyze historical trends in sorghum productivity across the USA. We selected the most accurate machine learning approach (a variation of the random forest approach) to predict future trends in sorghum yields under four greenhouse gas (GHG) emission scenarios and two irrigation regimes. We identified irrigation practices, vapor pressure deficit, and time (a proxy for technological improvement) as the most important predictors of sorghum productivity. Our results showed a decreasing trend of sorghum yields over future years (on average 2.7% from 2018 to 2099), with greater decline under a high GHG emissions scenario (3.8%) and in the absence of irrigation (4.6%). Geographically, we observed the steepest predicted declines in the Great Lakes (8.2%), Upper Midwest (7.5%), and Heartland (6.7%) regions. Our study demonstrates the use of machine learning to identify environmental controllers of sorghum biomass yield and predict yields with reasonable accuracy. These results can inform the development of more realistic biomass supply projections for bioenergy if sorghum production is scaled up. (c) 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

09 BIOMASS FUELS↗

An overview of switchgrass phenotypes variability across diverse populations and their implications for conversion to fuels

There have been substantial changes to the human lifestyle over the past two centuries, which are reflected in the amount of fuel we consume to power our day-to-day needs. The way we use these resources has indeed manifested in an overdependence on non-renewable energy sources, such as coal and petroleum, for generating electricity and powering our transportation needs. There is a pressing need to explore alternative ways of fueling our current lifestyle without impacting the environment. Biofuels have long been touted as a sustainable solution for use as drop-in fuels in aviation and maritime applications. Still, they have yet to establish themselves as a competitive commercial alternative, necessitating further research and development. Lignocellulosic biomass is an underutilized resource that is widely accessible for the commercial processing of renewable biofuels. Bioenergy crops, such as switchgrass (Panicum virgatum L.), which can be cultivated on marginal lands with minimal competition for agricultural land, are an ideal and promising candidate for bulk-scale biofuel synthesis. Over the past 30 years, significant progress has been made in breeding and genetically modifying these grasses to enhance their drought resilience and subsequent yields. However, discrepancies in biomass composition can lead to irregular feedstocks for downstream operations, which in turn affect overall production targets for biofuels. Here, this review examines the variability in switchgrass (P. virgatum L.) biomass phenotypes across diverse populations and plant components, and their implications for biofuel conversion. The study highlights significant variations in biomass yield, composition, and cell wall chemistry both between switchgrass genotypes and within individual cultivars. Key findings include differences in cellulose, hemicellulose, and lignin content between leaves and stems, which affect biomass digestibility and ethanol yield. The review also discusses the impact of lignin chemistry, particularly the syringyl/guaicyl (S/G) ratio, on the efficiency of biomass saccharification. Furthermore, it explores how these variations respond differently to various pretreatment techniques, affecting overall biofuel production. We conclude that understanding and quantifying this variability is crucial for optimizing switchgrass as a feedstock for commercial biofuel production, thereby potentially addressing the pressing need for sustainable energy sources in sectors such as aviation.

Kousika, Rohit [Univ. of Tennessee, Knoxville, TN ↗

Coping with cold: Sorghum cold stress from germination to maturity

Abstract Sorghum [ Sorghum bicolor (L.) Moench] is an important crop that is highly drought tolerant but susceptible to low temperatures. Many studies have begun to explore the genetic basis of variation in chilling sensitivity in the sorghum germplasm in an effort to improve sorghum's chilling tolerance. However, differences in genetic maps and updates to the sorghum reference genome have made comparing studies of chilling in sorghum challenging. Here, we review the current state of research on chilling tolerance and susceptibility in sorghum during germination and emergence, vegetative growth, and reproduction and harvest stages. Using the most recent sorghum reference genome (v3.1), we have standardized the locations of quantitative trait loci and marker‐trait associations for chilling tolerance traits across the literature. This revealed substantial overlap between quantitative trait loci/marker‐trait associations identified for similar traits across studies of different sorghum populations. Chromosomes 2, 3, and 6 contained particularly concentrated regions of markers associated with chilling tolerance traits. Although many studies have uncovered genetic variation for chilling responses in the sorghum germplasm, follow‐up studies are needed to confirm and characterize the molecular mechanisms responsible for variation in chilling tolerance in sorghum. We discuss potential molecular mechanisms for cold stress tolerance based on agreements between studies and address the challenges and opportunities for increasing chilling tolerance in sorghum and other next‐generation crops.

59 BASIC BIOLOGICAL SCIENCES↗

Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape

A central challenge to understanding how climate anomalies, such as drought and heatwaves, impact the terrestrial carbon cycle, is quantification and scaling of spatial and temporal variation in ecosystem gross primary productivity (GPP). Existing empirical and model-based satellite broadband spectra-based products have been shown to miss critical variation in GPP. We evaluate the potential of high spectral resolution (10 nm) shortwave (400–2,500 nm) imagery to better detect spatial and temporal variations in GPP across a range of ecosystems, including forests, grassland-savannas, wetlands, and shrublands in a water-stressed region. Estimates of GPP from eddy covariance observations were compared against airborne hyperspectral imagery, collected across California during the 2013–2014 HyspIRI airborne preparatory campaign. Observations from 19 flux towers across 23 flight campaigns (102 total image-flux tower pairs) showed GPP to be strongly correlated to a suite of spectral wavelengths and band ratios associated with foliar physiology and chemistry. A partial least squares regression (PLSR) modeling approach was then used to predict GPP with higher validation accuracy (adjusted R 2 = 0.71) and low bias (0.04) compared to existing broadband approaches (e.g., adjusted R 2 = 0.68 and bias = -5.71 with the Sims et al. model). Significant wavelengths contributing to the PLSR include those previously shown to coincide with Rubisco (wavelengths 1,680, 1,740, and 2,290 nm) and V cmax (wavelengths 1,680, 1,722, 1,732, 1,760, and 2,300 nm). These results provide strong evidence that advances in satellite spectral resolution offer significant promise for improved satellite-based monitoring of GPP variability across a diverse range of terrestrial ecosystems.

47 OTHER INSTRUMENTATION↗

N and P constrain C in ecosystems under climate change: Role of nutrient redistribution, accumulation, and stoichiometry

Abstract We use the Multiple Element Limitation (MEL) model to examine responses of 12 ecosystems to elevated carbon dioxide (CO 2 ), warming, and 20% decreases or increases in precipitation. Ecosystems respond synergistically to elevated CO 2 , warming, and decreased precipitation combined because higher water‐use efficiency with elevated CO 2 and higher fertility with warming compensate for responses to drought. Response to elevated CO 2 , warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO 2 and climate change simulations, all ecosystems gain C. The contributions of these four attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C‐nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO 2 and climate change.

54 ENVIRONMENTAL SCIENCES↗

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross-validation skill indicates extrapolative skill.We compared projected vulnerability for 29 wetland-dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross-validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross-validation results were correlated with extrapolation results, the use of cross-validation performance metrics to guide modeling choices where knowledge is limited was supported.

thresholds↗

Tropical understory herbaceous community responds more strongly to hurricane disturbance than to experimental warming

The effects of climate change on tropical forests may have global consequences due to the forests’ high biodiversity and major role in the global carbon cycle. In this study, we document the effects of experimental warming on the abundance and composition of a tropical forest floor herbaceous plant community in the Luquillo Experimental Forest, Puerto Rico. This study was conducted within Tropical Responses to Altered Climate Experiment (TRACE) plots, which use infrared heaters under free-air, open-field conditions, to warm understory vegetation and soils + 4°C above nearby control plots. Hurricanes Irma and María damaged the heating infrastructure in the second year of warming, therefore, the study included one pretreatment year, one year of warming, and one year of hurricane response with no warming. We measured percent leaf cover of individual herbaceous species, fern population dynamics, and species richness and diversity within three warmed and three control plots. Results showed that one year of experimental warming did not significantly affect the cover of individual herbaceous species, fern population dynamics, species richness, or species diversity. In contrast, herbaceous cover increased from 20% to 70%, bare ground decreased from 70% to 6%, and species composition shifted pre to posthurricane. The negligible effects of warming may have been due to the short duration of the warming treatment or an understory that is somewhat resistant to higher temperatures. Our results suggest that climate extremes that are predicted to increase with climate change, such as hurricanes and droughts, may cause more abrupt changes in tropical forest understories than longer-term sustained warming.

54 ENVIRONMENTAL SCIENCES↗

Contrasting the hydrologic response due to land cover and climate change in a mountain headwaters system

Land cover change due to drought and insect-induced tree mortality or altered vegetation succession is one of the many consequences of anthropogenic climate change. While the hydrologic response to land cover change and increases in temperature have been explored independently, few studies have compared these two impacts in a systematic manner. These changes are particularly important in snow-dominated, headwaters systems that provide streamflow for continental river systems. Here in this paper we study the hydrologic impacts of both vegetation change and climate warming along three transects in a mountain headwaters watershed using an integrated hydrologic model. Results show that while impacts due to warming generally outweigh those resulting from vegetation change, the inherent variability within the transects provides varying degrees of response. The combination of both vegetation change and warming results in greater changes to streamflow amount and timing than either impact individually, indicating a nonlinear response from these systems to multiple perturbations. In conclusion, the complexity of response underscores the need to integrate observational data and the challenge of deciphering hydrologic impacts from proxy studies.

54 ENVIRONMENTAL SCIENCES↗

Thawing seasonal ground ice: An important water source for boreal forest plants in Interior Alaska

Little is known about the ecological impacts of permafrost degradation on water fluxes in boreal ecosystems, such as those in Interior Alaska. Low plant water stress suggests a reliance on a diversity of water sources. In addition to rainfall, we hypothesize that deep soil water derived from thawing seasonal ground ice (TSGI) supports plants during dry periods. We analyzed water stable isotopes from soils, plants, ice, and rain collected from stable and unstable permafrost sites. We found that TSGI provides a background water source for plants during wet years (at least 10–20%) and a stable source during dry years (at least 30–50%) and early in the growing season (60–80% in wet and dry years). Plant water uptake patterns “track” the soil thawing front, using deep and shallow layers in wet years and deep layers during dry years. This plasticity allows boreal plants to cope with seasonal drought and exploit available water sources. The availability of TGSI depends on the amount of rainfall the prior year and on permafrost stability. Thawing permafrost may reduce the buffering capacity of TGSI due to less seasonal ice from greater drainage and/or a deeper active layer. This study demonstrates the importance of two buffering mechanisms for plants to cope with rainfall variability within boreal forest underlain by permafrost—availability of TSGI and plasticity in water uptake patterns. We suggest that plant utilization of stored water may be why evapotranspiration in northern latitudes can exceed growing season precipitation.

54 ENVIRONMENTAL SCIENCES↗

Hydrodynamic trait coordination and cost–benefit trade‐offs throughout the isohydric–anisohydric continuum in trees

Abstract Whole‐plant hydraulic performance depends on the integrated function of trait complexes, such as embolism resistance, stomatal closure mechanisms, and root properties. The diversity of such traits produces a wide range of response strategies to both short‐term variation of environmental conditions and long‐term changes to climate and hydrological cycles. This study aims to assess the role of different emergent hydraulic trait combinations in trees' vulnerability to drought using a quantitative modelling framework. This modelling framework may be helpful in studying the influence of plant hydraulic traits independently. It will also be used to assess how different groups of traits interact to form viable hydraulic strategies in response to different environmental conditions. We use the advanced plant hydrodynamic model, finite‐difference ecosystem‐scale tree crown hydrodynamics model version 2, to resolve plant functional traits of roots, stems, and leaves and simulate the transpiration. We define a multidimensional hydraulic “strategy space” by considering a continuum of these traits to test the outcomes of different hypothetical strategies in response to environmental conditions as observed in Northern Michigan, USA. We evaluate the degree to which simulated trees suffer hydraulic failure due to cavitation or carbon deficiency in response to reduced stomatal conductance. Our results demonstrate how the relationship between plant hydraulic strategy and hydraulic safety margin emerges from trait combinations along different tissue levels. Our findings suggest that hydrodynamic models present an exciting new possibility to define and study plant traits and hydrodynamics.

Mirfenderesgi, Golnazalsadat↗

Allometric relationships for Quercus gambelii and Robinia neomexicana for biomass estimation following disturbance

Abstract In the southwestern USA, increases in size, frequency, and severity of wildfire are driving the conversion of forests to shrub‐dominated ecosystems. Increases in drought extent and severity, coupled with the way that shrub‐dominated systems are perpetuated by high‐severity fire, predisposes these post‐disturbance landscapes to remain in a non‐forest condition. Consequently, understanding the distribution of aboveground biomass in post‐disturbance, shrub‐dominated ecosystems is central to constraining the uncertainty surrounding how these ecosystems interact with light and water to sequester carbon. Here we present allometric regressions for Quercus gambelii (Gambel oak) and Robinia neomexicana (New Mexico locust), two species that dominate post‐fire landscapes in the southwestern USA. Our allometric regressions are designed to be driven by either field plot or high‐resolution remote sensing data, using either shrub area or shrub volume to estimate biomass.

Krofcheck, Dan J.↗

State changes: insights from the U.S. Long Term Ecological Research Network

Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long-term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER)network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in50 or 100 yr based on long-term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and cascading effects. Here, we report on the “state change” theme, which includes examples from the Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo(tropical forest), Sevilleta (arid grassland), and Virginia Coastal (coastal grassland) sites. Ecological thresholds(the point at which small changes in an environmental driver can produce an abrupt and persistent state change in an ecosystem quality, property, or phenomenon) were most commonly predicted. For example, in coastal ecosystems, sea-level rise and climate change could convert salt marsh to mangroves and coastal barrier dunes to shrub thicket. Reduced fire frequency has converted grassland to shrubland in mesic prairie, whereas overgrazing combined with drought drive shrub encroachment in arid grasslands. Lastly, tropical cloud forests are susceptible to climate-induced changes in cloud base altitude leading to shifts in species distributions. Overall, these examples reveal that state change is a likely outcome of global environmental change across adverse range of ecosystems and highlight the need for long-term studies to sort out the causes and consequences of state change. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about state changes as an important driver of ecosystem structure, function, services, and futures.

54 ENVIRONMENTAL SCIENCES↗