Michelle P Katoski
Michelle is a Physical Scientist at the Lower Mississippi-Gulf Water Science Center.
Michelle is a Physical Scientist at the Lower Mississippi-Gulf (LMG) Water Science Center, stationed at EPA’s Chesapeake Bay Program (CBP) Office in Annapolis, MD. She joined the CBP in October 2023, and now supports efforts to monitor and benchmark Chesapeake restoration progress using geospatial analysis and modelling.
Prior to joining the LMG and CBP, Michelle spent 4 years as a Physical Scientist at the MD-DE-DC Water Science Center in Baltimore, MD. During her time at MD-DE-DC, Michelle provided science and leadership support to local, regional, and national projects funded by DC DOEE, USGS Chesapeake Bay PES, USGS Water Mission Area, and USGS Community for Data Integration.
Recent project themes:
- Urban flood frequency analysis
- Water Use data acquisition, storage and management
- Anomaly detection for timeseries data
- Software development
- Cloud computing; Statistical programming
- Geospatial programming
- Sediment budgets.
- Watershed modelling
Professional Experience
Physical Scientist (September 2023 – Present) - Lower Mississippi-Gulf Water Science Center, Chesapeake Bay Program Office, Annapolis, MD
Physical Scientist (September 2019 – October 2023) - MD-DE-DC Water Science Center Baltimore, MD
Student Researcher (December 2017 – September 2019) - University of Maryland, Baltimore County Baltimore, MD
Seasonal Technician (May 2018 – November 2018) - Maryland Department of Natural Resources, Annapolis, MD
Education and Certifications
M.S. Student (2020 - Present) University of Maryland, Baltimore County
Department of Geography and Environmental Systems, Committee Chair: Matthew Baker
B.S. Environmental Science, GIS Certificate (2014 - 2017)
University of Maryland, Baltimore County
Abstracts and Presentations
Katoski, M.P., Cashman, M.J., and Lester T., Vogel K., 2023. From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making. USGS Timeseries Record Automation Focus Group. June 22, 2023.
Katoski, M.P., Baker, M.E, 2023. Characterizing urban woodlands using LiDAR derived metrics of vertical structure in Baltimore, MD. International Association of Landscape Ecology – North America Annual Meeting. Riverside, CA; March 22, 2023.
Katoski, M.P., Cashman, M.J., and Lester T., Vogel K., 2022. From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making. USGS Community for Data Integration. August 11, 2022.
Science and Products
Quantifying connectivity and its effects on sediment budgeting for an agricultural basin, Chesapeake Bay Watershed, United States
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Digital elevation model and derivative datasets to support the integration of stormwater drainage into the Washington, D.C. Stormwater StreamStats application
Basin characteristics data for the Washington, D.C. StreamStats application
Questions and responses to USGS-wide poll on quality assurance practices for timeseries data, 2021
Hydrogen and oxygen stable isotope mass balance evaluation of the National Water Model (v2.1) streamflow, runoff and groundwater flows
Sediment model inputs and outputs for the Smith Creek watershed near New Market, Virginia for 2012-2016
Visible and near-infrared spectroscopy for sediment samples collected across North-eastern U.S.
Science and Products
- Publications
Quantifying connectivity and its effects on sediment budgeting for an agricultural basin, Chesapeake Bay Watershed, United States
Excessive sediment runoff as a result of anthropogenic activities is a major concern for watershed ecologic health. This study sought to determine the sources, storage, and delivery of sediment using a sediment budget approach for the predominantly pasture and forested Smith Creek watershed, Virginia United States, a tributary to the Chesapeake Bay. Utilizing a novel combination of the Universal SAuthorsZachary Clifton, Allen C. Gellis, Matt J. Cashman, Michelle Patricia Katoski, Lucas A Nibert, Gregory B. NoeNon-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
- Science
- Data
Digital elevation model and derivative datasets to support the integration of stormwater drainage into the Washington, D.C. Stormwater StreamStats application
This dataset was created to support the Washington D.C. StreamStats project funded by the Washington D.C. Department of Energy and Environment (DOEE). The dataset contains digital elevation model (DEM), flow direction and catchment layers that were conditioned using Washingtons D.C.’s stormwater network layer. The data are hosted online as a component of the USGS StreamStats web application (httpsBasin characteristics data for the Washington, D.C. StreamStats application
This data was created to support the Washington D.C. StreamStats project funded by the Washington D.C. Department of Energy and Environment (DOEE). The data release contains high-resolution (1-meter) land cover data layers for Washington D.C. and its tributaries that represent canopy cover, canopy cover with building and road coverage removed (“pervious canopy”), impervious cover, slope (in unitsQuestions and responses to USGS-wide poll on quality assurance practices for timeseries data, 2021
This data record contains questions and responses to a USGS-wide survey conducted to identify issues and needs associated with quality assurance and quality control (QA/QC) of USGS timeseries data streams. This research was funded by the USGS Community for Data Integration as part of a project titled “From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions anHydrogen and oxygen stable isotope mass balance evaluation of the National Water Model (v2.1) streamflow, runoff and groundwater flows
This data release contains the results of an isotopic mass balance approach to provide an estimate of the long-term average isotope ratios of NWM streamflow for the summer season (JJA) between 2000 and 2019 in the Western United States. The NWM-estimated long-term average isotope ratios are compared directly to 6426 stream stable isotope observations in 995 unique catchments. Quantified similaritiSediment model inputs and outputs for the Smith Creek watershed near New Market, Virginia for 2012-2016
This data release includes data created, collected, and/or otherwise modified in the process of quantifying a sediment budget for the Smith Creek Watershed near New Market, Virginia, USA. Five raster files are included, namely a modeled index of hydrological connectivity, a raster model of modeled floodplain depositional masses and extent, a rasters of modeled gross and delivered upland erosion, aVisible and near-infrared spectroscopy for sediment samples collected across North-eastern U.S.
This data record contains a CSV file with spectral reflectance (420-2114 nm) for sediment samples collected from each of four source locations (cropland, stream bank, glaciolacustrine, and street dust) located across sites in the Northwestern US during prior studies. Data were collected in a laboratory setting using Spectrecology USB4000-VIS-NIR and NIRQuest512-2.2 spectrometers. The data contain