Growing interest in renewable and domestically produced energy motivates the evaluation of woody bioenergy feedstock production. In the southeastern U.S., woody feedstock plantations, primarily of loblolly pine (Pinus taeda), would be intensively managed over short rotations (10-12 years) to achieve high yields.
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This dataset reports the pre-treatment hydrology and pre- and post-treatment water quality data from a watershed-scale experiment that is evaluating the effects of growing short-rotation loblolly pine for bioenergy on water quality and quantity in the southeastern U.S. The experiment is taking place on the Savannah River Site, near New Ellenton, South Carolina, USA. Beginning in 2010, water quality and hydrology were measured for two years in 3 watersheds (R, B, C).
Nitrogen (N) is an important nutrient as it often limits productivity, but in excess can impair water quality. Most studies on watershed N cycling have occurred in upland forested catchments where snowmelt dominates N export; fewer studies have focused on low-relief watersheds that lack snow. We examined watershed N cycling in three adjacent, low-relief watersheds in the Upper Coastal Plain of the southeastern United States to better understand the role of hydrological flowpaths and biological transformations of N at the watershed scale.
A framework for selecting and evaluating indicators of bioenergy sustainability is presented.
This framework is designed to facilitate decision-making about which indicators are useful for assessing
sustainability of bioenergy systems and supporting their deployment. Efforts to develop sustainability
indicators in the United States and Europe are reviewed. The fi rst steps of the framework for
indicator selection are defi ning the sustainability goals and other goals for a bioenergy project or program,
In order to aid operations that promote sustainability goals, researchers and stakeholders use sustainability assessments. Although assessments take various forms, many utilize diverse sets of indicators numbering anywhere from two to over 2000. Indices, composite indicators, or aggregate values are used to simplify high dimensional and complex data sets and to clarify assessment results. Although the choice of aggregation function is a key component in the development of the assessment, there are fewliterature examples to guide appropriate