Peter E. Schweizer

Environmental Indicators of Biofuel Sustainability: What About Context?

Indicators of the environmental sustainability of biofuel production, distribution, and use should be selected, measured, and interpreted with respect to the context in which they are used. The context of a sustainability assessment includes the purpose, the particular biofuel production and distribution system, policy conditions, stakeholder values, location, temporal influences, spatial scale, baselines, and reference scenarios. We recommend that biofuel sustainability questions be formulated with respect to the context, that appropriate indicators of environmental sustainability be developed or selected from more generic suites, and that decision makers consider context in ascribing meaning to indicators. In addition, considerations such as technical objectives, varying values and perspectives of stakeholder groups, indicator cost, and availability and reliability of data need to be understood and considered. Sustainability indicators for biofuels are most useful if adequate historical data are available, information can be collected at appropriate spatial and temporal scales, organizations are committed to use indicator information in the decision-making process, and indicators can effectively guide behavior toward more sustainable practices.

Progress toward Evaluating the Sustainability of Switchgrass as a Bioenergy Crop using the SWAT Model

Adding bioenergy to the U.S. energy portfolio requires long-term profitability for bioenergy producers and long-term protection of affected ecosystems. In this study, we present steps along the path toward evaluating both sides of the sustainability equation (production and environmental) for switchgrass (Panicum virgatum) using the Soil and Water Assessment Tool (SWAT). We modeled production of switchgrass and river flow using SWAT for current landscapes at a regional scale. To quantify feedstock production, we compared lowland switchgrass yields simulated by SWAT with estimates from a model based on empirical data for the eastern U.S. The two produced similar geographic patterns. Average yields reported in field trials tended to be higher than average SWAT-predicted yields, which may nevertheless be more representative of production-scale yields. As a preliminary step toward quantifying bioenergy-related changes in water quality, we evaluated flow predictions by the SWAT model for the Arkansas-White-Red river basin. We compared monthly SWAT flow predictions to USGS measurements from 86 subbasins across the region. Although agreement was good, we conducted an analysis of residuals (functional validation) seeking patterns to guide future model improvements. The analysis indicated that differences between SWAT flow predictions and field data increased in downstream subbasins and in subbasins with higher percentage of water. Together, these analyses have moved us closer to our ultimate goal of identifying areas with high economic and environmental potential for sustainable feedstock production.

Forecasting changes in water quality in rivers associated with growing biofuels in the Arkansas-White-Red river drainage, USA

Excess nutrients from agriculture in the Mississippi River drainage, USA have degraded water quality in freshwaters and contributed to anoxic conditions in downstream estuaries. Consequently, water quality is a significant concern associated with conversion of lands to bioenergy production. This study focused on the Arkansas‐White‐Red river basin (AWR), one of five major river basins draining to the Mississippi River. The AWR has a strong precipitation gradient from east to west, and advanced cellulosic feedstocks are projected to become economically feasible within normal‐to‐wet areas of the region. In this study, we used large‐scale watershed modeling to identify areas along this precipitation gradient with potential for improving water quality. We compared simulated water quality in rivers draining projected future landscapes with and without cellulosic bioenergy for two future years, 2022 and 2030 with an assumed farmgate price of

Modeling Regional Variation in Riverine Fish Biodiversity in the Arkansas–White–Red River Basin

50 per dry ton. Changes in simulated water quantity and quality under future bioenergy scenarios varied among subbasins and years. Median water yield, nutrient loadings, and sediment yield decreased by 2030. Median concentrations of nutrients also decreased, but suspended sediment, which is influenced by decreased flow and in‐stream processes, increased. Spatially, decreased loadings prevailed in the transitional ecotone between 97° and 100° longitude, where switchgrass, Panicum virgatum L., is projected to compete against alternative crops and land uses at

Indicators to support environmental sustainability of bioenergy systems

50 per dry ton. We conclude that this region contains areas that hold promise for sustainable bioenergy production in terms of both economic feasibility and water quality protection.

Estimation of the Risks of Collision or Strike to Freshwater Aquatic Organisms Resulting from Operation of Instream Hydrokinetic Turbines

Abstract The patterns of biodiversity in freshwater systems are shaped by biogeography, environmental gradients, and human-induced factors. In this study, we developed empirical models to explain fish species richness in subbasins of the Arkansas–White–Red River basin as a function of discharge, elevation, climate, land cover, water quality, dams, and longitudinal position. We used information-theoretic criteria to compare generalized linear mixed models and identified well-supported models. Subbasin attributes that were retained as predictors included discharge, elevation, number of downstream dams, percent forest, percent shrubland, nitrate, total phosphorus, and sediment. The random component of our models, which assumed a negative binomial distribution, included spatial correlation within larger river basins and overdispersed residual variance. This study differs from previous biodiversity modeling efforts in several ways. First, obtaining likelihoods for negative binomial mixed models, and thereby av...