A Checklist of River Function Indicators for hydropower ecological assessment

Assessment of flow ramping in water bodies impacted by hydropower operation in Norway - Is hydropower with environmental restrictions more sustainable?

Hydropower production is a key electricity generation technology in many parts of the world which can play a significant role in the transition towards a green and clean energy system. Hydropower can mobilize flexible energy on demand (hydropeaking) to balance out intermittent electricity from wind and photovoltaics. Adoption of hydropower as a peaking power source could lead to increased frequency of flow ramping in rivers downstream hydropower tailraces, which is one of the main stressors for riverine biota in alpine rivers. Both planned and accidental shutdowns of hydropower turbines need ecological mitigation. Our survey revealed that >3000 km of rivers downstream ca 800 hydropower plants in Norway may be ecologically impacted by non-natural flow fluctuations, and few have appropriate mitigation thresholds. A considerable eco-friendly peaking service may come from the Norwegian hydropower portfolio of over 19 GW installed capacity, with outlets into fjords, reservoirs or other large water bodies which normally dampen the ecological impacts of flow ramping. Intensive flow ramping occurs with irregular intervals from most types of hydropower. Although the highest frequency of stops were revealed in hydropower turbines not impacting river flow from storage hydropower, a significant number of turbine flow stops lasting over half a day in most types and categories of diversion hydropower. We suggest that further emerging ecosystem-based mitigations need to be adapted in hydropower licenses. This includes operational thresholds for both up and down ramping, constructional measures like by-pass valves, retention basins and increased base-flow or flow cap to ensure sustainability for hydropower operations. Our data reveal some of the most intensive hydropeaking operations from hydropower impacting longer rivers. Hence, our data underpins the potential for restoring downstream modified flow by ecosystem based measures related to both up and down ramping events in many regulated rivers.

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 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.