Publications

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Integrating Sustainable Water Resource Management and Land Use Decision-Making

Human uses of land and water are directly linked and must, therefore, be managed with each other in mind. This paper puts forward an approach for integrating sustainable water resource management into local land use decision-making in the Potomac basin. The approach includes developing a clear understanding of the current regulatory, programmatic, and financial approaches to land use management; identifying opportunities from innovation; and developing a flexible, stakeholder-based framework for moving forward. Four opportunities for innovation were identified in the Potomac basin utilizing this approach, including enhancing coordination and access to information, promoting incentives to achieve desired outcomes, encouraging and promoting innovation, and integrating programs to achieve multiple objectives. The successful integration of land and water decision-making requires a sustained, long-term commitment to improvement rather than a one-time fix mentality. Initial steps for implementation include identifying and engaging diverse partners, as well as establishing channels for information dissemination. The lessons learned from this work may prove valuable to decision-makers in other regions to holistically manage diverse land and water resources.

The article was published in Water 202012(8), 2282; https://doi.org/10.3390/w12082282.

Creating a stream health baseline for the Chesapeake basin from monitoring and model data

This report describes how monitoring and model data were analyzed and combined to generate a preliminary estimate of acceptable stream health in the Chesapeake Bay basin for the 2006 – 2011 baseline period. Streams in about 73% of the basin’s 64,020 sq. miles of drainage area were evaluated with monitoring results, and output from a predictive model was used to estimate stream health in the remaining 27%. Stream health was measured with the bioregion, family-level version of the “Chessie BIBI,” a multi-metric index for stream macroinvertebrate communities. Index scores are normally expressed as one of five index ratings: Excellent or Good (well-functioning), Fair (considered satisfactory), and Poor or Very Poor (stressed or poorly-functioning). Four versions of the predictive model were developed and tested, and the selected version outputs results as three-ratings: Excellent/Good, Fair, and Poor/Very Poor. The five ratings in the monitoring data were re-grouped to match the three ratings of the selected predictive model. The monitoring- and model-based ratings were then area-weighted to reduce bias caused by uneven sample densities and aggregated to the Chesapeake basin scale, with monitoring results given preference. The combined results suggest approximately 60% of the basin’s area had acceptable stream ratings (Excellent, Good, or Fair) during 2006 – 2011. This estimate is a preliminary baseline for the Chesapeake Bay Program’s stream health goal. A final baseline estimate will be produced after a higher resolution stream layer becomes available and acceptable stream health can be estimated as a percent of the basin’s stream miles.

The Influence of Jennings Randolph Lake and Dam Operations on River Flow and Water Quality in the North Branch Potomac River

A multi-year study began in 2018 to determine if an update of the Army Corps’ 1997 Water Control Plan for Jennings Randolph Lake is needed. Watershed and river conditions have improved significantly since the turn of the century, an outcome of regulatory enforcement, mine runoff mitigation, wastewater treatment,  infrastructure improvements, forest regrowth and the abatement of acid rain. The Commission, in partnership with the Corps, has produced a draft Scoping Study report that reviews the dam’s long-running operational objectives and procedures, and assesses the current importance of these procedures in achieving the four mandates. It also reviews various modeling approaches that incorporate modern science and technology for better future management. Learn more…