To facilitate watershed management, watershed models are often developed and used to assess the expected impact of scenarios of past and future management policies and practices and the impact of watershed conditions. However, the level of load reductions estimated using monitoring data often does not match with model predictions, leading to questions around the level of effort and investment being made in restoration activities and the reliability of the model.

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The main objective of this project is to leverage machine learning approaches -- more specifically, the combined use of hierarchical clustering and random forest (RF) classification -- to reveal regional patterns and drivers of nitrogenand phosphorus trends across the Chesapeake Bay watershed. This work involves three objectives:

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The objective of this project is to develop an extension of the flow-normalization (FN) procedure of the WRTDS (“Weighted Regressions on Time, Discharge, and Season”) method. This extension is being applied to the Chesapeake Bay Nontidal Monitoring Network to quantify water-quality trends under different flow conditions and to guide the direction of additional analysis for capturing the underlying drivers, which can inform management strategies toward improving water quality.

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The main objective of this work is to provide resource managers and estuarine scientists with a clearer perspective on a key management question: After three decades of restoration, has progress been made in reducing nitrogen, phosphorus, and sediment exports from the Bay watershed?

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The Chesapeake Bay Program and the U.S. Geologic Survey are compiling tributary basin summaries for 12 major tributaries or tributary groups in the Chesapeake Bay Watershed. These summaries summarize the following information: (1) How tidal water quality changes over time; (2) How factors that drive those changes change over time; and (3) Current state of the science on connecting change in aquatic conditions to its drivers.

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This project is aimed toward better understanding nutrient limitation of phytoplankton in Chesapeake Bay and its tidal tributaries. Leveraging novel statistical approaches and long-term monitoring data sets from the Chesapeake Bay Program Partnership, this project helps understand whether nutrient limitation patterns have changed in response to decades of nutrient reduction efforts.

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Accurate quantification of riverine water-quality concentration and flux is challenging because monitoring programs typically collect concentration data at lower frequencies than discharge data. Statistical methods are often used to estimate concentration and flux on days without observations. One recently developed approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS), which has been shown to provide among the most accurate estimates compared to other common methods.

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This project is aimed toward better understanding the current status and long-term trends of Chesapeake Bay in terms of its water quality standards attainment.

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