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.

Crystal lattice defects in nanocrystalline metacinnabar in contaminated streambank soils suggest a role for biogenic sulfides in the formation of mercury sulfide phases.

At mercury (Hg)-contaminated sites, streambank erosion can act as a main mobilizer of Hg into nearby waterbodies. Once deposited into the waters, mercury from these soils can be transformed to MeHg by microorganisms. It is therefore important to understand the solid-phase speciation of Hg in streambanks as differences in Hg speciation will have implications for Hg transport and bioavailability. In this study, we characterized Hg solid phases in Hg-contaminated soils (100-1100 mg per kg Hg) collected from the incised bank of the East Fork Poplar Creek (EFPC) in Oak Ridge, TN (USA). The analysis of the soil samples by scanning electron microscopy-energy dispersive spectroscopy indicated numerous microenvironments where Hg and sulfur (S) are co-located. According to bulk soil analyses by extended X-ray absorption fine structure spectroscopy (EXAFS), the near-neighbor Hg molecular coordination in the soils closely resembled freshly precipitated Hg sulfide (metacinnabar, HgS); however, EXAFS fits indicated the Hg in the HgS structure was undercoordinated with respect to crystalline metacinnabar. This undercoordination of Hg-S observed by spectroscopy is consistent with transmission electron microspy images showing the presence of nanocrystallites with structural defects (twinning, stacking faults, dislocations) in individual HgS-bearing particles. Although the soils were collected from exposed parts of the stream bank (i.e., open to the atmosphere), the presence of reduced forms of S and sulfate-reducing microbes suggests that biogenic sulfides promote the formation of HgS nanoparticles in these soils. Altogether, these data demonstrate the predominance of nanoparticulate HgS with crystal lattice defects in the bank soils of an industrially impacted stream. Efforts to predict the mobilization and bioavailability of Hg associated with nano-HgS forms should consider the impact of nanocrystalline lattice defects on particle surface reactivity, including Hg dissolution rates and bioavailability on Hg fate and transformations.

U.S. national water and energy land dataset for integrated multisector dynamics research.

Understanding resource demands and tradeoffs among energy, water, and land socioeconomic sectors requires an explicit consideration of spatial scale. However, incorporation of land dynamics within the energy-water nexus has been limited due inconsistent spatial units of observation from disparate data sources. Herein we describe the development of a National Water and Energy Land Dataset (NWELD) for the conterminous United States. NWELD is a 30-m, 86-layer rasterized dataset depicting the land use of mappable components of the United States energy sector life cycles (and related water used for energy), specifically the extraction, development, production, storage, distribution, and operation of eight renewable and non-renewable technologies. Through geospatial processing and programming, the final products were assembled using four different methodologies, each depending upon the nature and availability of raw data sources. For validation, NWELD provided a relatively accurate portrayal of the spatial extent of energy life cycles yet displayed low measures of association  with mainstream land cover and land use datasets, indicating the provision of new land use information for the energy-water nexus.

Integrated watershed process model for evaluating mercury sources, transport, and future remediation scenarios in an industrially contaminated site.

We used the Soil Water Assessment Tool (SWAT) as a framework to develop an empirical Hg flux model for Upper East Fork Poplar Creek (UEFPC), a Hg-contaminated watershed in Oak Ridge, Tennessee. By integrating long-term Hg monitoring data with simulated flow and suspended solid loads in a site-specific empirical Hg transport model, we (1) quantified the spatial, temporal, and flow regime controls on daily Hg flux (adjusted R2 = 0.82) and (2) made predictions about Hg flux under future climate, land use, and management scenarios. We found that 62.79% of the average daily Hg flux in the watershed is currently driven by base flow, whereas variability in Hg flux is driven by storm and extreme flow. We estimate an average annual Hg flux of 28.82 g day-1 leaving the watershed under baseline precipitation, with an estimated 43.73% reduction in daily Hg flux under drought conditions and a 296% increase in daily Hg flux in extreme precipitation scenarios. We estimated that a new mercury treatment facility would result in a 24.7% reduction in Hg flux under baseline conditions and a 33.4% reduction under extreme precipitation scenarios. The study demonstrated the merit of this approach, which can be replicated for sites where information on flow, suspended solids, and Hg concentrations is available.

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.

Methods for integrating high-resolution land, climate, and infrastructure scenarios in a hydrologic simulation model.

Global alterations of the hydrologic cycle by humans have led to alarming rates of water shortages and irreversible ecosystem change. Our ability to manage water resources lies in accurately modeling water availability at scales meaningful to management. Although hydrologic models have been used to understand the implications of future climate and land cover change on regional water availability, many modeling approaches fail to integrate human infrastructures (HI) with bio-geophysical drivers to facilitate sustainable regional water resource management. This paper presents an integrated framework, inclusive of modeling and data needs, to quantify the effects of both bio-geophysical and HI influence on regional surface water hydrology. The framework enables the integration of high spatial and temporal anthropogenic alterations of water availability for identifying hot-spots and hot-moments of hydrological stresses within individual river-segments using a hydrologic simulation model, Soil and Water Analysis Tool (SWAT). •A high-resolution river network for the study region with a greater spatial granularity compared to contemporary SWAT applications attempted to account for HI.•The anthropogenic influence on water balance for each river segment was estimated using data on human infrastructures, such as water intakes, power production facilities, discharges, dams, and land transformation.

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.

A stream classification system for the conterminous United States.

Stream classifications are important for understanding stream ecosystem diversity while also serving as tools for aquatic conservation and management. With current rates of land and riverscape modification within the United States (US), a comprehensive inventory and evaluation of naturally occurring stream habitats is needed, as this provides a physical template upon which stream biodiversity is organized and maintained. To adequately represent the heterogeneity of stream ecosystems, such a classification needs to be spatially extensive where multiple stream habitat components are represented at the highest resolution possible. Herein, we present a multi-layered empirically-driven stream classification system for the conterminous US, constructed from over 2.6 million stream reaches within the NHDPlus V2 stream network. The classification is based on emergent natural variation in six habitat layers meaningful at the stream-reach resolution: size, gradient, hydrology, temperature, network bifurcation, and valley confinement. To support flexibility of use, we provide multiple alternative approaches to developing classes and report uncertainty in classes assigned to stream reaches. The stream classification and underlying data provide valuable resources for stream conservation and research.

A stream classification system to explore the physical habitat diversity and anthropogenic impacts in riverscapes of the eastern United States.

Describing the physical habitat diversity of stream types is important for understanding stream ecosystem complexity, but also prioritizing management of stream ecosystems, especially those that are rare. We developed a stream classification system of six physical habitat layers (size, gradient, hydrology, temperature, valley confinement, and substrate) for approximately 1 million stream reaches within the Eastern United States in order to conduct an inventory of different types of streams and examine stream diversity. Additionally, we compare stream diversity to patterns of anthropogenic disturbances to evaluate associations between stream types and human disturbances, but also to prioritize rare stream types that may lack natural representation in the landscape. Based on combinations of different layers, we estimate there are anywhere from 1,521 to 5,577 different physical types of stream reaches within the Eastern US. By accounting for uncertainty in class membership, these estimates could range from 1,434 to 6,856 stream types. However, 95% of total stream distance is represented by only 30% of the total stream habitat types, which suggests that most stream types are rare. Unfortunately, as much as one third of stream physical diversity within the region has been compromised by anthropogenic disturbances. To provide an example of the stream classification's utility in management of these ecosystems, we isolated 5% of stream length in the entire region that represented 87% of the total physical diversity of streams to prioritize streams for conservation protection, restoration, and biological monitoring. We suggest that our stream classification framework could be important for exploring the diversity of stream ecosystems and is flexible in that it can be combined with other stream classification frameworks developed at higher resolutions (meso- and micro-habitat scales). Additionally, the exploration of physical diversity helps to estimate the rarity and patchiness of riverscapes over large region and assist in conservation and management.

US cities can manage national hydrology and biodiversity using local infrastructure policy.

Cities are concentrations of sociopolitical power and prime architects of land transformation, while also serving as consumption hubs of "hard" water and energy infrastructures. These infrastructures extend well outside metropolitan boundaries and impact distal river ecosystems. We used a comprehensive model to quantify the roles of anthropogenic stressors on hydrologic alteration and biodiversity in US streams and isolate the impacts stemming from hard infrastructure developments in cities. Across the contiguous United States, cities' hard infrastructures have significantly altered at least 7% of streams, which influence habitats for over 60% of North America's fish, mussel, and crayfish species. Additionally, city infrastructures have contributed to local extinctions in 260 species and currently influence 970 indigenous species, 27% of which are in jeopardy. We find that ecosystem impacts do not scale with city size but are instead proportionate to infrastructure decisions. For example, Atlanta's impacts by hard infrastructures extend across four major river basins, 12,500 stream km, and contribute to 100 local extinctions of aquatic species. In contrast, Las Vegas, a similar size city, impacts <1,000 stream km, leading to only seven local extinctions. So, cities have local policy choices that can reduce future impacts to regional aquatic ecosystems as they grow. By coordinating policy and communication between hard infrastructure sectors, local city governments and utilities can directly improve environmental quality in a significant fraction of the nation's streams reaching far beyond their city boundaries.

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies.

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multi-faceted explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements are functions of a range of factors, from biophysical to socio-political. Project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.

Predicting fine-scale distributions of peripheral aquatic species in headwater streams.

Headwater species and peripheral populations that occupy habitat at the edge of a species range may hold an increased conservation value to managers due to their potential to maximize intraspecies diversity and species' adaptive capabilities in the context of rapid environmental change. The southern Appalachian Mountains are the southern extent of the geographic range of native Salvelinus fontinalis and naturalized Oncorhynchus mykiss and Salmo trutta in eastern North America. We predicted distributions of these peripheral, headwater wild trout populations at a fine scale to serve as a planning and management tool for resource managers to maximize resistance and resilience of these populations in the face of anthropogenic stressors. We developed correlative logistic regression models to predict occurrence of brook trout, rainbow trout, and brown trout for every interconfluence stream reach in the study area. A stream network was generated to capture a more consistent representation of headwater streams. Each of the final models had four significant metrics in common: stream order, fragmentation, precipitation, and land cover. Strahler stream order was found to be the most influential variable in two of the three final models and the second most influential variable in the other model. Greater than 70% presence accuracy was achieved for all three models. The underrepresentation of headwater streams in commonly used hydrography datasets is an important consideration that warrants close examination when forecasting headwater species distributions and range estimates. Additionally, it appears that a relative watershed position metric (e.g., stream order) is an important surrogate variable (even when elevation is included) for biotic interactions across the landscape in areas where headwater species distributions are influenced by topographical gradients.