Ecosystem services from partially harvested riparian buffers can offset biomass production costs.

There is a broad consensus that riparian buffers provide environmental benefits and increase resilience to climate change. In this study, we examined the potential benefits of multi-zone riparian buffers with outer layers planted in perennial crops (i.e., partially harvested buffers). This was accomplished by developing a simplified regional modeling tool, BioVEST, which was applied in the Mid-Atlantic region of the USA. Our analysis revealed that a substantial portion of variable costs to produce biomass for energy can potentially be offset by values provided by ecosystem services from partially harvested riparian buffers. Ecosystem services were monetized and found to represent a substantial fraction (median = ~42%) of variable crop production cost. Simulated water-quality improvements and carbon benefits generally occurred where buffer area was available, but hotspots occurred in different watersheds, suggesting potential trade-offs in decisions about buffer locations. A portion of buffers could be eligible for ecosystem service payments under US government incentive programs. Partially harvested buffers could represent a sustainable and climate-resilient part of multi-functional agricultural landscapes, and one that could become economically viable if farmers are able to reap the value of providing ecosystem services and if logistical challenges are resolved. Our results suggest that payments for ecosystem services can close the gap between what biorefineries are willing to pay and what landowners are willing to accept to grow and harvest perennials along streams.

Planning green infrastructure placement based on projected precipitation data.

Continued urbanization has led to tremendous changes on the landscape. These changes have exacerbated the effects of extreme climatic events such as flooding because of constrained water infiltration and increased surface flow. Typical runoff control measures involve sophisticated gray infrastructure that guide excess surface flow into storage and disposal sites. In a dynamic climate system, these measures are not sustainable since they cannot be easily modified to accommodate large volumes of runoff. Green Infrastructure (GI) is an adaptable technique that can be used to minimize runoff, in addition to offering an array of additional benefits (urban heat regulation, aesthetics, improved air quality etc.). Strategic placement of GI is key to achieving maximum utility. While physical site characteristics play a major role in determining suitable GI placement sites, knowledge of future precipitation patterns is crucial to ensure successful flood mitigation. In this paper, suitable GI sites within the city of Knoxville, Tennessee, were determined based on potential impact of an extreme flood event as indicated by site characteristics. Then, the relative potential likelihood of a flood event was determined based on projected precipitation data and knowledge of existing flood zones. By combining potential impact with likelihood information, low, medium, and high priority GI implementation sites were established. Results indicate that high priority sites are in the central parts of the city with priority decreasing outward. The GI prioritization scheme presented here, offers valuable guidance to city planners and policy makers who wish to exploit the GI approach for flood mitigation.

A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development.

The datasets described herein provide the foundation for a decision support prototype (DSP) toolkit aimed at assisting stakeholders in determining evidence of which aspects of river ecosystems have been impacted by hydropower. The DSP toolkit and its application are presented and described in the article "Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development" [1]. Development of the DSP and the output for decision support centralize around 42 river function indicators describing the dimensionality of river ecosystems through six main categories: biota and biodiversity, water quality, hydrology, geomorphology, land cover, and river connectivity. Three main tools are represented in the DSP: A science-based questionnaire (SBQ), an environmental envelope model (EEM), and a river function linkage assessment tool (RFLAT). The SBQ is a structured survey-style questionnaire whose objective is to provide evidence of which indicators have been impacted by hydropower. Based on a global literature review, 140 questions were developed from general hypotheses regarding the impacts of dams on rivers. The EEM is a model to predict the likelihood of hydropower impacting indicators based on a several variables. The intended use of the EEM is for situations of new hydropower development where results of the SBQ are incomplete or highly uncertain. The EEM was developed through the compilation of a dataset containing attributes of dams, reservoirs, and geospatial information on environmental concerns, which was combined with data on ecological indicators documented at those sites through literature review. The model operates through 247 "envelopes" and weighting factors, representing the individual effect of each variable on each indicator, all available through spreadsheets. Finally, the RFLAT is a tool to examine causal relationships amongst indicators. Inter-indicator relationships were hypothesized based on literature review and summarized into node and edge datasets to represent the structure of a graphical network. Bayes theorem was used estimate conditional probabilities of inter-indicator relationships based on the output of the SBQ. Nodes and edges were imported into R programming environment to visualize ecological indicator networks. The datasets can be expanded upon and enriched with more detailed questions for the SBQ, building upon the EEM with to develop more sophisticated models, and identifying new relationships for the RFALT. Additionally, once the tools are applied to numerous hydropower developments, the output of the tools (e.g. evidence of impacted indicators) becomes a very useful dataset for meta-analyses of hydropower impacts.

Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development.

Global expansion of hydropower resources has increased in recent years to meet growing energy demands and fill worldwide gaps in electricity supply. However, hydropower induces significant environmental impacts on river ecosystems - impacts that are addressed through environmental impact assessment (EIA) processes. The need for effective EIA processes is increasing as environmental regulations are either stressed in developing countries undertaking rapid expansion of hydropower capacity or time- and resource-intensive in developed countries. Part of the challenge in implementing EIAs lies in reaching a consensus among stakeholders regarding the most important environmental factors as the focus of impact studies. To help address this gap, we developed a weight-of-evidence approach (and toolkit) as a preliminary and coarse assessment of the most relevant impacts of hydropower on primary components of the river ecosystem, as identified using river function indicators. Through a science-based questionnaire and predictive model, users identify which environmental indicators may be impacted during hydropower development as well as those indicators that have the highest levels of uncertainty and require further investigation. Furthermore, an assessment tool visualizes inter-dependent indicator relationships, which help formulate hypotheses about causal relationships explored through environmental studies. We apply these tools to four existing hydropower projects and one hypothetical new hydropower project of varying sizes and environmental contexts. We observed consistencies between the output of our tools and the Federal Energy Regulatory Commission licensing process (inclusive of EIAs) but also important differences arising from holistic scientific evaluations (our toolkit) versus regulatory policies. The tools presented herein are aimed at increasing the efficiency of the EIA processes that engender environmental studies without loss of rigor or transparency of rationale necessary for understanding, considering, and mitigating the environmental consequences of hydropower.

A Checklist of River Function Indicators for hydropower ecological assessment.

Hydropower generation has advantages for societies that seek low-carbon, renewable energy alternatives, but sustainable hydropower production will require an explicit consideration of potential tradeoffs between socioeconomic and environmental priorities. These tradeoffs are often explored during a formal environmental impact assessment process that can be complex and controversial. The steps taken to address stakeholder concerns through impact hypotheses and field studies are not always transparent. We have created a Checklist of River Function Indicators to facilitate stakeholder discussions during hydropower licensing and to support more transparent, holistic, and scientifically informed hydropower environmental analyses. Based on a database of environmental metrics collected from hydropower project studies documented by the Federal Energy Regulatory Commission (FERC), the International Hydropower Association, the Low Impact Hydropower Institute, and peer-reviewed scientific literature, our proposed Checklist of River Function Indicators contains 51 indicators in six categories. We have tested the usefulness of the Indicators by applying them to seven hydropower projects documented by FERC. Among the case study projects, 44 of the 51 Indicators were assessed according to the FERC documentation. Even though each hydropower project presents unique natural resource issues and stakeholder priorities, the proposed Indicators can provide a transparent starting point for stakeholder discussions about which ecological impacts should be considered in hydropower planning and relicensing assessments.

Dataset of timberland variables used to assess forest conditions in two Southeastern United States׳ fuelsheds.

The data presented in this article are related to the research article entitled "How is wood-based pellet production affecting forest conditions in the southeastern United States?" (Dale et al., 2017) [1]. This article describes how United States Forest Service (USFS) Forest Inventory and Analysis (FIA) data from multiple state inventories were aggregated and used to extract ten annual timberland variables for trend analysis in two case study bioenergy fuelshed areas. This dataset is made publically available to enable critical or extended analyses of changes in forest conditions, either for the fuelshed areas supplying the ports of Savannah, Georgia and Chesapeake, Virginia, or for other southeastern US forested areas contributing biomass to the export wood pellet industry.

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.

Comparing scales of environmental effects from gasoline and ethanol production.

Understanding the environmental effects of alternative fuel production is critical to characterizing the sustainability of energy resources to inform policy and regulatory decisions. The magnitudes of these environmental effects vary according to the intensity and scale of fuel production along each step of the supply chain. We compare the spatial extent and temporal duration of ethanol and gasoline production processes and environmental effects based on a literature review and then synthesize the scale differences on space-time diagrams. Comprehensive assessment of any fuel-production system is a moving target, and our analysis shows that decisions regarding the selection of spatial and temporal boundaries of analysis have tremendous influences on the comparisons. Effects that strongly differentiate gasoline and ethanol-supply chains in terms of scale are associated with when and where energy resources are formed and how they are extracted. Although both gasoline and ethanol production may result in negative environmental effects, this study indicates that ethanol production traced through a supply chain may impact less area and result in more easily reversed effects of a shorter duration than gasoline production.

Higher trends but larger uncertainty and geographic variability in 21st century temperature and heat waves.

Generating credible climate change and extremes projections remains a high-priority challenge, especially since recent observed emissions are above the worst-case scenario. Bias and uncertainty analyses of ensemble simulations from a global earth systems model show increased warming and more intense heat waves combined with greater uncertainty and large regional variability in the 21st century. Global warming trends are statistically validated across ensembles and investigated at regional scales. Observed heat wave intensities in the current decade are larger than worst-case projections. Model projections are relatively insensitive to initial conditions, while uncertainty bounds obtained by comparison with recent observations are wider than ensemble ranges. Increased trends in temperature and heat waves, concurrent with larger uncertainty and variability, suggest greater urgency and complexity of adaptation or mitigation decisions.