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Predicted Physical Habitat Metrics for the Chesapeake Bay watershed at the 1:24k scale, 1985-2023

Published by U.S. Geological Survey | Department of the Interior | Catalog Last Checked: August 01, 2026 at 03:27 AM | Dataset Last Updated: February 17, 2026 at 12:00 AM
Degraded physical habitat is a common stressor affecting river ecosystems and a primary focus of management activities, including stream restorations. In order to assess regional conditions and help prioritize management efforts, there is an ongoing need to provide estimates of different aspects of instream physical habitat conditions at spatially continuous scales. We utilized over 16,000 unique habitat assessments from multiple jurisdictions across the Chesapeake Bay watershed and created a spatially continuous habitat assessment using predictive random-forest modeling based on landscape attributes. Through this work, we produced predictions for the twelve rapid habitat metrics contained within the EPA Rapid Habitat Protocols (Barbour and others 1999), with a climate-normalized signal at annual timesteps from 1985 to 2023, corresponding to landscape conditions. We also produced two summary habitat metrics, based on principal component analysis, that captured the majority of variability across the Chesapeake Bay Watershed, which was also produced for each year from 1985 to 2023. This data release contains tabular model inputs and outputs of the predictions for the twelve original rapid habitat metrics, plus two summary metrics, for all NHDPlus High Resolution data at 1:24,000 scale catchments/stream reaches for the Chesapeake Watershed annually from 1985 to 2023. Data are provided in both .csv format and Apache Parquet (.parquet), a free and open-source column-oriented data storage format that is well-suited for large datasets, which enables smaller file sizes, faster reading, faster queries and data manipulations, and the ability to be read in multiple languages (e.g., R, Python, Rust, C++, Java, and more). This dataset is an update to an existing dataset at the NHD 1:100,000 scale (Cashman et al., 2024a) using methods described in Cashman et al. (2024b).

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