Geology topics

Matthew Baker

Publications and source records attributed to Matthew Baker.

3 recordsLinked to original sources

USGS2025

Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities

The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.

Delaware, Maryland, Virginia↗

USGS2022

Loss of phylogenetic diversity under landscape change

Habitat alteration and destruction are primary drivers of biodiversity loss. However, the evolutionary dimensions of biodiversity loss remain largely unexplored in many systems. For example, little is known about how habitat alteration/loss can lead to phylogenetic deconstruction of ecological assemblages at the local level. That is, while species loss is evident, are some lineages favored over others? Using a long-term dataset of a globally, ecologically important guild of invertebrate consumers, stream leaf “shredders,” we created a phylogenetic tree of the taxa in the regional species pool, calculated mean phylogenetic distinctiveness for >1000 communities spanning >10 year period, and related species richness, phylogenetic diversity, and distinctiveness to watershed-scale impervious cover. Using a combination of changepoint and compositional analyses, we learned that increasing impervious cover produced marked reductions in all three measures of diversity. These results aid in understanding both phylogenetic diversity and mean assemblage phylogenetic distinctiveness. Our findings indicate that, not only are species lost when there is an increase in watershed urbanization, as other studies have demonstrated, but that those lost are members of more distinct lineages relative to the community as a whole.

Science of the Total Environment↗

USGS2015

Sediment contributions from floodplains and legacy sediments to Piedmont streams of Baltimore County, Maryland

Disparity between watershed erosion rates and downstream sediment delivery has remained an important theme in geomorphology for many decades, with the role of floodplains in sediment storage as a common focus. In the Piedmont Province of the eastern USA, upland deforestation and agricultural land use following European settlement led to accumulation of thick packages of overbank sediment in valley bottoms, commonly referred to as legacy deposits. Previous authors have argued that legacy deposits represent a potentially important source of modern sediment loads following remobilization by lateral migration and progressive channel widening. This paper seeks to quantify (1) rates of sediment remobilization from Baltimore County floodplains by channel migration and bank erosion, (2) proportions of streambank sediment derived from legacy deposits, and (3) potential contribution of net streambank erosion and legacy sediments to downstream sediment yield within the Mid-Atlantic Piedmont. We calculated measurable gross erosion and deposition rates within the fluvial corridor along 40 valley segments from 18 watersheds with drainage areas between 0.18 and 155 km 2 in Baltimore County, Maryland. We compared stream channel and floodplain morphology from lidar-based digital elevation data collected in 2005 with channel positions recorded on 1:2400 scale topographic maps from 1959–1961 in order to quantify 44–46 years of channel change. Sediment bulk density and particle size distributions were characterized from streambank and channel deposit samples and used for volume to mass conversions and for comparison with other sediment sources. Average annual lateral migration rates ranged from 0.04 to 0.19 m/y, which represented an annual migration of 2.5% (0.9–4.4%) channel width across all study segments, suggesting that channel dimensions may be used as reasonable predictors of bank erosion rates. Gross bank erosion rates varied from 43 to 310 Mg/km/y (median = 114) and were positively correlated with drainage area. Measured deposition within channels accounted for an average of 46% (28–75%) of gross erosion, with deposition increasingly important in larger drainages. Legacy sediments accounted for 6–90% of bank erosion at individual study segments, represented about 60% of bank height at most exposures, and accounted for 57% (± 16%) of the measured gross erosion. Extrapolated results indicated that first- and second-order streams accounted for 62% (± 38%) of total streambank erosion from 1005 km 2 of northern Baltimore County. After accounting for estimated redeposition, extrapolated net streambank sediment yields (72 Mg/km 2 /y) constituted 70% of estimated average Piedmont watershed yields (104 Mg/km 2 /y). The results suggest that streambank sediments are a relatively large source of sediment from Piedmont tributaries to the Chesapeake Bay.

Maryland↗