Search Results (101)

Many degraded streams in the Chesapeake Bay watershed have been structurally modified over the last two decades in an effort to reduce nutrient and sediment loads from urban catchments and contribute to the pollutant reduction goals of the Chesapeake Bay Total Maximum Daily Load (TMDL). The step-pool streamwater conveyance (SPSC) is a stream restoration design that has been extensively implemented in Maryland and the District of Columbia. In the summer of 2019, an SPSC was constructed in a degraded 800 m stream reach on the University of Maryland campus (i.e., Campus Creek). Precipitation, baseflow and stormflow runoff, and nutrient (nitrogen and phosphorus) and total suspended solid (TSS) concentrations were measured throughout pre- and post-restoration periods (~2 and 5 years, respectively) to determine the extent to which the SPSC structure reduced pollutant loads. A comparison of pre- (2018) versus post-restoration (2020) years with similar total annual rainfall volumes indicates that total annual runoff was 13% lower in the post-restoration period. Area yields of total nitrogen (TN), total phosphorus (TP) and TSS were 33, 39 and 59% lower, respectively, in the same pre- versus post-restoration comparison. Full article

In order to set nutrient and sediment load targets for the Chesapeake Bay, projections of changing environmental conditions through 2055 have been previously considered. This article expands the analysis through 2085. Under future ensemble scenarios of General Circulation Models (GCMs), temperature and precipitation trends for the Chesapeake Bay watershed prior to midcentury have a rate of change more than twice that of the post-midcentury trend. Prior to midcentury, runoff and nutrient loading to the Bay estuary are projected to increase. In this analysis, model simulations for post-midcentury suggest the trend of increasing runoff may be reduced. The combined effect of a reduced trend in temperature and precipitation increases post-midcentury with continued sea level rise in the ensemble scenarios leads to a decreasing trend in Chesapeake hypoxia post-midcentury, resulting in a leveling off of dissolved oxygen water quality degradation. Full article

This study presents the first application of machine learning (ML) to detect and map mowed tidal wetlands in the Chesapeake Bay region of Maryland and Virginia, focusing on emergent estuarine intertidal (E2EM) wetlands. Monitoring human disturbances like mowing is essential because repeated mowing stresses wetland vegetation, reducing habitat quality and diminishing other ecological services wetlands provide, including shoreline stabilization and water filtration. Traditional field-based monitoring is labor-intensive and impractical for large-scale assessments. To address these challenges, this study utilized 2021 and 2022 Sentinel-2 satellite imagery and a time-series analysis of the Normalized Difference Vegetation Index (NDVI) to distinguish between mowed and unmowed (control) wetlands. A bidirectional Long Short-Term Memory (BiLSTM) neural network was created to predict NDVI patterns associated with mowing events, such as rapid decreases followed by slow vegetation regeneration. The training dataset comprised 204 field-verified and desktop-identified samples, accounting for under 0.002% of the research area’s herbaceous E2EM wetlands. The model obtained 97.5% accuracy on an internal test set and was verified at eight separate Chesapeake Bay locations, indicating its promising generality. This work demonstrates the potential of remote sensing and machine learning for scalable, automated monitoring of tidal wetland disturbances to aid in conservation, restoration, and resource management. Full article

Climate change and sea-level change (SLC) are intensifying flooding in U.S. coastal communities, with disproportionate impacts on Black and minority neighborhoods that face displacement, economic hardship, and heightened health risks. In Norfolk, Virginia, sea levels are projected to rise by at least 0.91 m (3 ft) by 2100, placing underserved neighborhoods such as Oakleaf Forest at particular risk. This study investigates the compounded impacts of flooding at both the building and urban scales, situating the work within the framework of the UN Sustainable Development Goals (UN SDGs). A mixed-method, community-based approach was employed, integrating literature review, field observations, and community engagement to identify flooding hotspots, document lived experiences, and determine preferences for adaptation strategies. Community participants contributed actively through mapping sessions and meetings, providing feedback on adaptation strategies to ensure that the process was collaborative, place-based, and context-specific. Preliminary findings highlight recurring flood-related vulnerabilities and the need for interventions that address both environmental and social dimensions of resilience. The study proposes multi-scale, nature-based solutions (NbS) to mitigate flooding, restore ecological functions, and enhance community capacity for adaptation. Ultimately, this work underscores the importance of coupling technical strategies with participatory processes to strengthen resilience and advance climate justice in vulnerable coastal neighborhoods. Full article

The federally threatened Puritan tiger beetle (Ellipsoptera puritana; PTB) inhabits Upper Chesapeake Bay bluffs, beaches and Connecticut River point bars. This study focuses on Maryland’s Chesapeake Bay population (Calvert County and Sassafras River), where adult PTBs prey on beach arthropods but establish larval habitat on the adjacent bluffs. A combination of panoramic photography, GIS mapping, and field and laboratory measurements of sedimentological and ecological characteristics were measured across 17 high- and low-density Maryland beetle sites to identify the geologic and biological controls on population distribution and abundance. Results indicate that temporal and spatial fluctuations in PTB abundance are governed by bluff face quality, which in turn, is shaped by antecedent geology (medium-compacted, fine-to-medium, well-sorted sands) and bluff dynamics. We present a four-stage, multi-decadal geomorphodynamic conceptual model in which long-term bluff recession and short-term storm-driven colluvium removal periodically expose fresh bluff surfaces required for larval establishment. By integrating geomorphic, geologic, and ecological perspectives, this study highlights the role of sedimentary processes in maintaining critical estuarine habitats and provides a framework for predicting species persistence in dynamic coastal landscapes. Full article

This study reconstructs and projects relative sea-level change (RSLC) along Chesapeake Bay, a global hotspot for sea-level rise, from 1900 to 2100 by statistically extrapolating observed tide gauge trends, rather than employing climate model-based scenarios. The approach integrates global mean sea-level rise (GMSLR), regional sea-level rise (RSLR), and local land subsidence (LS) to evaluate both past and future behavior. Tide gauge data reveal that Chesapeake Bay’s sea level has accelerated at 0.099 ± 0.013 mm/year² since 1992, with a linear rate of 1.26 mm/year since 1900, slightly outpacing global averages. LS, primarily driven by glacial isostatic adjustment (GIA) and sediment compaction, has been the dominant contributor to RSLC since the early 20th century, accounting for up to 71% of the RSLC prior to 1992 across 15 tide gauge stations. However, with GMSLR accelerating at 0.120 ± 0.025 mm/year², the relative contribution of LS to RSLC is projected to decline to 31–43% by 2100. The reconstructed RSLC for the 20th century ranges between 32 and 44 cm, while extrapolated projections for the 21st century indicate a further increase of 53–99 cm. By 2100, GMSLR is expected to contribute to 60–70% of total RSLC. Spatial variability in RSLC across 15 tide gauge stations reflects differing geological conditions and anthropogenic influences such as groundwater withdrawal and construction-induced subsidence. These findings highlight the critical need for adaptive strategies to mitigate the impact of rising sea levels on coastal communities and infrastructure in the Chesapeake Bay region. Continued monitoring, improved modeling, and targeted resilience planning are essential to address the accelerating threats posed by sea-level rise and to ensure the sustainability of vulnerable coastal areas. Full article

The Chesapeake Bay (CB) region faces significant risks from relative sea-level change (RSLC), driven by global mean sea-level rise (GMSLR), regional sea-level rise (RSLR), and local land subsidence (LS). This study introduces a methodology to decipher RSLC trends in the CB area by integrating these components. We develop trend equations spanning 1900–2100, incorporating acceleration for GMSLR and RSLR since 1992, with linear LS estimation using tide gauge, satellite altimetry, and InSAR data. Our approach employs dynamic RSLC equations, Maclaurin series expansions, and inverse simulations to project RSLC trends through 2100. Stable RSLC rates require over 122 years of data for reliable linear trend estimation, with the Baltimore tide gauge providing the necessary long-term dataset. Similarity in monthly mean sea-level variations within a coastal region enables a new method to identify LS from short-term tide gauge data by correlating it with corresponding long-term data at Baltimore. LS is categorized into bedrock-surface subsidence (BSS) and compaction subsidence (CS), with methods proposed to map BSS contours and estimate CS. CS is further classified into primary consolidation, secondary consolidation, construction-induced, and negative subsidence to determine specific compaction types. The projection model highlights the dominant influence of GMSLR acceleration since 1992, with local LS and RSLR influenced by ocean circulation, density changes, and gravitational, rotational, and deformational (GRD) effects. This integrated approach enhances understanding and predictive reliability for RSLC trends, supporting resilience planning and infrastructure adaptation in coastal CB communities. Full article

Cyanobacterial blooms have been increasingly detected in estuaries and freshwater tidal rivers. To enhance detailed monitoring, an efficient approach to detecting algal blooms through remote sensing is needed to focus more detailed monitoring focused on cyanobacteria. In this study, we compared different remote sensing processing methods to determine an efficient approach to mapping chlorophyll-a. Using a subset of paired chlorophyll-a observations with Sentinel-2 imagery (2015–2022), with sites located in the Chesapeake Bay and Indian River selected along gradients of salinity, turbidity, and trophic status, we compared the combined performance of two different atmospheric processing methods (Acolite, Polymer) and a suite of empirical (band ratio, spectral shape indices) and machine learning algorithms for chlorophyll-a prediction. Acolite outperformed Polymer, resulting in 176 observation points, compared to 106 observation points from Polymer, and a greater range in chlorophyll-a values (0–74 μg/L from Acolite compared to 0–36 μg/L from Polymer), although Polymer showed more responsiveness at lower chlorophyll-a levels. Two algorithms performed best in predicting chlorophyll-a, as well as trophic state and HABs risk classes: the machine learning mixture density network (MDN) approach and the one band-ratio approach (Mishra). Full article

This article examines the migration patterns that shaped the early settlement of Dorchester County, Maryland. Dorchester County is located on Maryland’s Eastern Shore, an area distinctive in terms of its geography, history, and culture. In U.S. history, migration has generally proceeded from eastern areas to western ones and from northern areas to southern ones, a pattern dating back to the earliest colonial settlements. Settlement in Dorchester County proceeded primarily from east to west and south to north, with additional migration streams coming from the north out of Delaware and from the west out of Somerset County. This gave Dorchester County an unusual historical dynamic because of the different socio-cultural and religious backgrounds and settlement patterns from the regions in which those migrants came. The Eastern Shore’s geography, shaped by an extensive coastline and major riverways, contributed to this settlement pattern, for the Chesapeake Bay region, with its complex network of rivers and streams, forms one of the world’s three largest natural estuaries. In terms of genealogy and family history, this mix of settlers importantly shaped the cultural dynamics of the Eastern Shore, leading to complex family histories that blended different cultural, religious, and linguistic influences. Free European-American settlers dominated migration into early Dorchester, but unfree laborers, including slaves and, early on, white indentured servants, came to Dorchester in substantial numbers along these same routes and made important contributions to the cultural development of Dorchester and surrounding areas. In later years, out-migration from the Eastern Shore took settlers of all backgrounds throughout the growing United States and carried the influence of the Eastern Shore to the south and west as well as into the urban areas of the northeast. Full article

Microplastic pollution in the Chesapeake Bay is of critical concern as estuaries serve as habitats and nurseries for diverse aquatic organisms and offer vital ecological services. However, quantitative analysis of microplastics, especially those smaller than 300 µm, in the natural aquatic environment is very challenging due to a lack of efficient sampling methods. This study takes a novel approach to quantify the abundance, size distribution, and morphological characteristics of microplastics, as small as 20 µm, in the surface waters of the Chesapeake Bay. Water samples (10 L) were collected monthly from July 2023 to October 2023 at four locations along the Chesapeake Bay. The samples were digested with a 10% potassium hydroxide solution and subjected to density separation using sodium chloride (ρ = 1.2 g/cc). Microplastic particles were examined using a Shimadzu AIM–9000 FTIR microscope for enumeration and chemical identification. Overall, the mean microplastic concentration observed was 766.16 ± 302.59 MP/L, significantly higher than previously estimated in the Chesapeake Bay. Microplastic abundance exhibited a significant (p = 0.02) spatial variation across the four sampling locations. Most abundant were particles less than 100 µm (60.65%), followed by particles between 100 µm and 300 µm (23.19%), and particles exceeding 300 µm (16.16%). Morphological analysis identified fragments as the dominant shape (86.02%), followed by fibers (11.87%), and beads (2.10%). This study underscores the importance of standard and efficient sampling methods in microplastics research. By sampling microplastics as small as 20 µm, this research demonstrated that the abundance of microplastics in the Chesapeake Bay is significantly higher than previously estimated and dominated by smaller–sized particles. These small microplastics are more likely to enter the food web where human exposure may occur. Therefore, microplastic pollution in the Chesapeake Bay ecosystem has the potential to impose environmental and public health risks. Full article

Bats are a taxa of high conservation concern and are facing numerous threats including widespread mortality due to White-Nose Syndrome (WNS) in North America. With this decline comes increasing difficulty in monitoring imperiled bat species due to lower detection probabilities of both mist-netting and acoustic surveys. Lure technology shows promise to increase detection while decreasing sampling effort; however, to date research has primarily focused on increasing physical captures during mist-net surveys using sound lures. Because much bat monitoring is now performed using acoustic detection, there is a similar need to increase detection probabilities during acoustic surveys. Ultraviolet (UV) lights anecdotally have been shown to attract insects and thereby attract foraging bats for observational studies and to experimentally provide a food source for WNS-impacted bats before and after hibernation. Therefore, we constructed a field-portable and programmable UV lure device to determine the value of lures for increasing acoustic detection of bats. We tested if the lure device increased both the echolocation passes and feeding activity (feeding buzzes) across a transect of bat detectors. There was an increase in feeding activity around the UV light, with a nuanced, species-specific and positionally dependent effect on echolocation passes received. The UV light lure increased echolocation passes for the eastern red bat (Lasiurus borealis), little brown bat (Myotis lucifugus), and evening bat (Nycticeius humeralis), but decreased passes of the North American hoary bat (Lasiurus cinereus). The northern long-eared bat (Myotis septentrionalis) showed a negative response within the illuminated area but increased echolocation activity outside the illuminated area during lure treatment and activity was elevated at all positions after the lure was deactivated. Our study demonstrates some potential utility of UV lures in increasing the feeding activity and acoustic detection of bats. Additional research and development of UV lure technology may be beneficial, including alternating on and off periods to improve detection of light-averse species, and improving echolocation call quality along with the increase in received passes. Full article

Oyster aquaculture in the U.S. faces severe inefficiencies due to the absence of precise path planning tools, resulting in environmental degradation and resource waste. Current dredging techniques lack trajectory planning, often leading to redundant seabed disturbance and suboptimal shell distribution. Existing vessel models—such as the Nomoto or Dubins models—are not designed to map steering inputs directly to spatial coordinates, presenting a research gap in maneuver planning for underactuated boats. This research fills that gap by introducing a novel hybrid vessel kinetics model that integrates the Nomoto model with Dubins motion primitives. Our approach links steering inputs directly to the vessel motion, enabling Cartesian coordinate path generation without relying on intermediate variables like yaw velocity. Field trials in the Chesapeake Bay demonstrate consistent trajectory following performance across varied path complexities, with average offsets of 0.01 m, 1.35 m, and 0.42 m. This work represents a scalable, efficient step toward real-time, constraint-aware automation in oyster harvesting, with broader implications for sustainable aquaculture operations. Full article

A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R² = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article