Environmental flows in the context of unconventional natural gas development in the <scp>M</scp>arcellus <scp>S</scp>hale

Buchanan, Brian; Auerbach, Daniel A.; McManamay, Ryan A.; Taylor, Jason M.; Flecker, Alexander S.; Archibald, J. A.; Fuka, Daniel R.; Walter, M. Todd

doi:10.1002/eap.1425

Cited by 22 publications

(29 citation statements)

References 59 publications

(86 reference statements)

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“…Low power to explain variance if often a consequence of multiple (unmeasured) causative drivers, leading to the increasing use of quantile regression to define the upper bounds of flow–ecology relationships (e.g. Buchanan et al., ; Kendy et al., ; Knight, Murphy, Wolfe, Saylor, & Wales ).…”

Section: Shape and Defining Attributes Of Flow–ecology Relationshipsmentioning

confidence: 99%

“…Broadly speaking, flow–ecology relationships can reflect the biological response to either flow magnitude or flow variation, both of which are potentially altered by water regulation. Ecological responses can be related to a wide variety of hydrologic drivers (Olden & Poff, ; Stewart‐Koster, Olden, & Gido, ); Buchanan et al., ; for brevity this review focuses on ecological response to flow magnitude at low discharge.…”

Section: Likelihood Of Nonlinearity For Different Ecological Indicatomentioning

confidence: 99%

“…Understanding how ecological attributes respond to changing flow (henceforth “flow–ecology relationships”) is fundamental to predicting the consequences of flow alteration, and for credibly evaluating tradeoffs between instream and out‐of‐stream uses (Arthington, Bunn, Poff, & Naiman, ; Buchanan et al., ; Davies et al., ). The Ecological Limits of Hydrological Alteration (ELOHA) paradigm for environmental flows (Poff et al., ) has enshrined flow–ecology relationships as the science foundation underlying tradeoffs that ultimately determine environmental flow allocations.…”

Section: Introductionmentioning

confidence: 99%

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Developing flow–ecology relationships: Implications of nonlinear biological responses for water management

Rosenfeld

2017

Freshwater Biology

106

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Empirical relationships between stream flow and ecological responses (flow–ecology relationships) are essential for establishing environmental flows and evaluating tradeoffs between instream values and out‐of‐stream uses. Establishing the shape of flow–ecology relationships (i.e. slope, linearity versus nonlinearity) is particularly important to avoid crossing ecological thresholds in water management. This review focuses on ecological responses to discharge at low summer flows when out‐of‐stream water demand is often highest, and identifying ecological contexts where nonlinearities are most likely. Most physical attributes (temperature, dissolved oxygen, available habitat) and ecological responses (energy flow, fish survival, recruitment, community structure) show at least some evidence of nonlinear relationships with flow, although assumptions of linearity may be reasonable across limited discharge ranges which may include low flows. Nonlinearities are most likely in systems that are near existing thresholds (e.g. cold‐water transitional fish communities that are close to upper thermal tolerances). The probability of nonlinearities is likely to increase under future landuse and climate change scenarios, particularly in combination with other stressors, such as eutrophication, which may greatly accelerate temperature‐related decline in dissolved oxygen under climate warming. Managers need to anticipate changes in flow–ecology relationships and develop management systems that are robust to change. Field programmes to establish the slope and linearity of local flow–ecology relationships are essential for regional management, but developing generalisable flow–ecology relationships that are transferrable to regions with limited resources also needs to be a priority. Generalised relationships can be generated through meta‐analysis of empirical flow–ecology relationships, and may prove especially useful if they can capture how environmental and ecological context (channel size and morphology, landuse, flow regime, antecedent conditions, habitat or taxonomic guild) affect flow–ecology relationships. For instance, linking empirical data from flow–ecology relationships to available habitat predicted by physical habitat simulation models (e.g. PHABSIM) may provide a better mechanistic basis for modelling ecological responses, while providing much needed validation for habitat simulation approaches. This would also help bridge the gap between emerging holistic environmental flow modelling approaches and more traditional habitat simulation methods.

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Section: Shape and Defining Attributes Of Flow–ecology Relationshipsmentioning

confidence: 99%

Section: Likelihood Of Nonlinearity For Different Ecological Indicatomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Developing flow–ecology relationships: Implications of nonlinear biological responses for water management

Rosenfeld

2017

Freshwater Biology

106

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“…This indicates that a stationary metric such as a percent of some flow condition (e.g. annual median, seasonal median) could provide a range of variation depending on streams hydrological regimes (Buchanan et al., ). In managed streams, low‐flows showed slight variability reflecting, among others, the climatic conditions in the drainage area and the seepage from impoundments.…”

Section: Discussionmentioning

confidence: 99%

Streamlined eco‐engineering approach helps define environmental flows for tropical Andean headwaters

et al. 2019

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Applying the environmental flows concept to human‐altered lotic ecosystems continues to face many practical challenges and barriers. Here, we modify a previously proposed framework, the Eco‐Engineering Decision Scaling, for application to part of the water supply system of Quito, Ecuador. Specifically, we used feedback from engineers and water managers to develop a common set of metrics for defining flow‐ecology relationships and assess managed‐flow impacts on stream ecology. At 12 sites over 3 years, we collected flow and benthic invertebrate data (taxonomic richness, taxa important for fish, functional feeding groups, and water quality‐sensitive taxa) during wet and dry seasons. We then used these data to identify flow thresholds (relative to unmanaged flows) where flow withdrawal caused visible ecological impacts. For this system, reduction of flow to 20% of the annual median was detrimental to benthic communities, while reductions to 40% of the annual median flow caused a variety of responses in the system. A trade‐off analysis of weighted metrics showed that a 50% benthic fauna richness could be sustained if dry season flows were maintained between 28% and 40% of the unmanaged median annual flow. This study provides a roadmap for bridging between eco‐engineering theoretical frameworks and the adoption of the environmental flows concept as actionable management thresholds.

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“…For biological condition prediction, common explanatory variables comprise geospatial datasets including land cover, land use, topography, climate, soils, societal infrastructure, and hydrologic modification. Additionally, there are studies that have analysed potential effects of climate change (Dhungel et al, 2016), environmental flow, and flow-ecology relationships, for environmental impact assessment (Buchanan et al, 2017). For instance, Patrick and Yuan (2017) errors range from 15% to 40% (Carlisle, Falcone, Wolock, et al, 2009).…”

Section: Random Forestsmentioning

confidence: 99%

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Hernandez‐Suarez

Nejadhashemi

2018

Ecohydrology

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During the last three decades, explaining cause-effect relationships between natural and anthropogenic disturbances with measures of stream health have motivated the growing application of statistical, machine learning, and soft computing methods.The aim of this review is to provide insight into the most widely used methods for predicting biological variables based on macroinvertebrate and fish species in riverine ecosystems. Therefore, we describe several methods including multiple linear regression, generalized linear models, generalized additive models, boosted regression trees, random forests, artificial neural networks, fuzzy logic-based, and Bayesian belief networks along with recent applications of these. Moreover, issues regarding variable selection, model interpretability, ensemble modelling, and model evaluation and overfitting are discussed. Recent advances have suggested the need for integrated modelling systems to enhance the predictive ability and improve interpretability.However, trade-offs between model complexity and accuracy demand research efforts in uncertainty quantification/propagation in model ensembles. Additionally, models should be perceived as complementary tools that require further validation with field measurements. Therefore, a consensus regarding monitoring and modelling practices for stream health applications is recommended.

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Environmental flows in the context of unconventional natural gas development in the Marcellus Shale

Cited by 22 publications

References 59 publications

Developing flow–ecology relationships: Implications of nonlinear biological responses for water management

Developing flow–ecology relationships: Implications of nonlinear biological responses for water management

Streamlined eco‐engineering approach helps define environmental flows for tropical Andean headwaters

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

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