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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #438997

Research Project: CEAP Wetlands Mid-Atlantic Coastal Plain Regional Study

Location: Hydrology and Remote Sensing Laboratory

Project Number: 8042-13610-030-042-I
Project Type: Interagency Reimbursable Agreement

Start Date: Oct 1, 2019
End Date: Sep 30, 2023

Our FY2020 plans include six main objectives: [1] Assess wetland function at National Resources Inventory (NRI) points; [2] Improve Soil Water Assessment Tool (SWAT) capacity using denitrification potential maps associated with prior converted croplands; [3] Estimate of wetland hydroperiod at the watershed scale using an improved SWAT; [4] Test the causality of multiple wetlands with downstream waters; [5] Improved stream and ditch maps for Delmarva Peninsula; and [6] Documenting methane emissions from depressional wetlands.

Objective 1. Our development will extend the applicability of the existing approach to regional or national levels. The developed approach will be tested in the Mid- Atlantic Region (MAR) as regional pilot. By using three levels of assessment, users and decision-makers can choose an appropriate level for their needs. Objective 2 will involve modifying SWAT to set up spatialized parameterization of denitrification potential and estimating a denitrification potential for individual modeling units from topographic metrics. Objective 3 will involve preparation wetland data at the watershed scale, running an improved SWAT to simulate watershed-level wetland hydroperiod. Objective 4 will involve monitoring wetland water levels is a key objective to understand wetland hydrology using high frequency long-term wetland data. Objective 5 will involve using the protocol developed for surface flow delineation in FY 2020 and combine the topographic openness with enhanced topographic relief metrics to detect wetland-stream connectivity in the Delmarva Peninsula. Objective 6 will involve comparison of the Eddy Covariant Model (ECM) estimates for CH4 emission with estimates using the Backward Lagrangian Stochastic Model (BLSM) to gain information on uncertainties associated with different CH4 emission models.