Geology topics

S. Kyle McKay

Publications and source records attributed to S. Kyle McKay.

7 recordsLinked to original sources

USGS2023

Patterns, drivers, and a predictive model of dam removal cost in the United States

Given the burgeoning dam removal movement and the large number of dams approaching obsolescence in the United States, cost estimating data and tools are needed for dam removal prioritization, planning, and execution. We used the list of removed dams compiled by American Rivers to search for publicly available reported costs for dam removal projects. Total cost information could include component costs related to project planning, dam deconstruction, monitoring, and several categories of mitigation activities. We compiled reported costs from 455 unique sources for 668 dams removed in the United States from 1965 to 2020. The dam removals occurred within 571 unique projects involving 1–18 dams. When adjusted for inflation into 2020 USD, cost of these projects totaled \$1.522 billion, with per-dam costs ranging from $1 thousand (k) to \$268.8 million (M). The median cost for dam removals was \$157k, \$823k, and \$6.2M for dams that were< 5 m, between 5–10 m, and > 10 m in height, respectively. Geographic differences in total costs showed that northern states in general, and the Pacific Northwest in particular, spent the most on dam removal. The Midwest and the Northeast spent proportionally more on removal of dams less than 5 m in height, whereas the Northwest and Southwest spent the most on larger dam removals > 10 m tall. We used stochastic gradient boosting with quantile regression to model dam removal cost against potential predictor variables including dam characteristics (dam height and material), hydrography (average annual discharge and drainage area), project complexity (inferred from construction and sediment management, mitigation, and post-removal cost drivers), and geographic region. Dam height, annual average discharge at the dam site, and project complexity were the predominant drivers of removal cost. The final model had an R 2 of 57% and when applied to a test dataset model predictions had a root mean squared error of $5.09M and a mean absolute error of \$1.45M, indicating its potential utility to predict estimated costs of dam removal. We developed a R shiny application for estimating dam removal costs using customized model inputs for exploratory analyses and potential dam removal planning.

Frontiers in Ecology and Evolution↗

USGS2023

A decision-support framework for dam removal planning and its application in northern California

Dam removals are occurring more frequently with the rising cost of maintaining aging infrastructure, public safety concerns, and growing interest in river restoration. So far, most dam-removals have been unsystematic in their approach. Given the several thousand dam removals expected over the coming decades, a systematic approach to plan future dam removals holds potential for aligning and delivering multiple benefits. Despite multi-sector factors driving decision-making, most existing prioritization frameworks tend to operate within single or related disciplines. Here we present a hierarchical, multi-disciplinary decision-support framework to prioritize dam removals based on opportunistic factors (Tier 1), hydro-ecological variables (Tier 2), and socio-cultural considerations (Tier 3). This framework integrates multiple decision criteria under data availability constraints, incorporates value-driven weights, and can be applied to a portfolio of dams at various spatial scales. The final output facilitates the identification of dam removal projects that align opportunistic, environmental, and social benefits. We recommend the application of this framework as a critical first step to identifying high-priority candidates for removal, recognizing that removal decisions will ultimately require detailed feasibility studies and stakeholder engagement. To illustrate its utility, we apply this framework to California's North Coast region and identify a small number of “good” candidates to be considered for removal. We conclude with recommendations for filling critical knowledge gaps and advancing systematic dam removal planning in the United States and beyond.

California↗

USGS2022

Habitat associations of riverine fishes among rocky shoals

Understanding species' associations with physical habitat conditions is a fundamental goal of ecology. For organisms that occupy lotic ecosystems, relationships to streamflow are of particular importance, but these associations are unstudied for most species. We tested the predictability of fish–microhabitat relationships in river shoals (shallow, rocky areas with relatively swift water flow) using a large data set from the Conasauga River in Georgia, USA. Our objective was to assess the consistency of species-specific relationships with flow-dependent variables (depth, velocity, Reynolds number and Froude number) while accounting for other microhabitat variables (e.g. vegetation). We used data from 8285 seine-sets collected during late summer or autumn at 26 sites over 12 years to relate occurrence and counts of 22 fish species to habitat variables using generalised linear multiple regression models. Results showed that microhabitat models explained a substantial amount of the variation in counts for some species, although other species were poorly predicted. We classified 16 species as velocity specialists and nine species as depth specialists, with six species specialised for depth and velocity and three species classified as depth and velocity generalists. The variability in habitat associations that we observed suggests that species will be unevenly affected by anthropogenic activities that alter flows.

Georgia↗

USGS2022

Toward improved prediction of streamflow effects on freshwater fishes

Understanding the effects of hydrology on fish populations is essential to managing for native fish conservation. However, despite decades of research illustrating streamflow influences on fish habitat, reproduction and survival, biologists remain challenged when tasked with predicting how fish populations will respond to changes in flow regimes. This uncertainty stems from insufficient understanding of the context-dependent mechanisms underlying fish responses to, for example, periods of reduced flow or altered frequency of high-flow events. We aim to address this gap by drawing on previous research to hypothesize mechanisms by which low- and high-flows influence fish populations and communities, identifying challenges that stem from data limitations and ecological complexity, and outlining research directions that can advance an empirical basis for prediction. Focusing flow-ecology research on testing and refining mechanistic hypotheses can help narrow management uncertainties and better support species conservation in changing flow regimes.

Fisheries Magazine↗

USGS2022

Community-powered urban stream restoration: A vision for sustainable and resilient urban ecosystems

Urban streams can provide amenities to people living in cities, but those benefits are reduced when streams become degraded, potentially even causing harm (disease, toxic compounds, etc.). Governments and institutions invest resources to improve the values and services provided by urban streams; however, the conception, development, and implementation of such projects may not include meaningful involvement of community members and other stakeholders. Consequently, project objectives may be misaligned with community desires and needs, and projects may fail to achieve their goals. In February 2020, the 5 th Symposium on Urbanization and Stream Ecology, an interdisciplinary meeting held every 3 to 5 y, met in Austin, Texas, USA, to explore new approaches to urban stream projects, including ways to maximize the full range of potential benefits by better integrating community members into project identification and decision making. The symposium included in-depth discussion about 4 nearby field case studies, participation of multidisciplinary urban stream experts from 5 continents, and input from the Austin community. Institutional barriers to community inclusion were identified and analyzed using real-world examples, both from the case studies and from the literature, which clarified disparities in power, equity, and values. Outcomes of the symposium have been aggregated into a vision that challenges the present institutional approach to urban stream management and a set of strategies to systematically address these barriers to improve restoration solutions. Integrating community members and other stakeholders throughout the urban restoration process, and a transparent decision-making process to resolve divergent objectives, can help identify appropriate goals for realizing both the ecological and social benefits of stream restoration.

Texas↗

USGS2019

Long-term (37 years) impacts of low-head dams on freshwater shrimp habitat connectivity in northeastern Puerto Rico

Freshwater migratory shrimp in Puerto Rico depend on watershed connectivity, from stream headwaters to the ocean, to complete their life cycle. Moreover, shrimp populations in different watersheds are known to be connected in an island-wide metapopulation. However, low-head dams paired with water intakes on streams draining the El Yunque National Forest (EYNF) reduce streamflow. Here, we examine the cumulative effects of low-head dams on shrimp habitat connectivity over 37-years across seven EYNF watersheds. We calculate total and refugia habitat connectivity (where refugia habitat is defined as predator-free upstream reaches above waterfalls > 5 m in height) at a monthly time step using a habitat-weighted index of longitudinal riverine connectivity, which incorporates location and operation of water intakes and streamflow variability. Findings indicate total and refugia habitat connectivity declined over 37 years (by 27% and 16%, respectively) as additional water intakes have been placed in lower reaches of watersheds. On a monthly time-step, the proportion of streamflow withdrawn has the largest effect on habitat connectivity, with the result that connectivity is ~17% lower during drought years than in non-drought years and ~7% lower in dry compared to wet seasons. Our analysis of this long-term dataset highlights how cumulative effects of low-head dams paired with water intakes have reduced shrimp habitat connectivity. These results underscore the importance of reducing existing withdrawal rates in EYNF, and locating intakes where effects on connectivity are minimal, if conserving shrimp habitat is a management objective.

Puerto Rico↗

USGS2016

Application of effective discharge analysis to environmental flow decision-making

Well-informed river management decisions rely on an explicit statement of objectives, repeatable analyses, and a transparent system for assessing trade-offs. These components may then be applied to compare alternative operational regimes for water resource infrastructure (e.g., diversions, locks, and dams). Intra- and inter-annual hydrologic variability further complicates these already complex environmental flow decisions. Effective discharge analysis (developed in studies of geomorphology) is a powerful tool for integrating temporal variability of flow magnitude and associated ecological consequences. Here, we adapt the effectiveness framework to include multiple elements of the natural flow regime (i.e., timing, duration, and rate-of-change) as well as two flow variables. We demonstrate this analytical approach using a case study of environmental flow management based on long-term (60 years) daily discharge records in the Middle Oconee River near Athens, GA, USA. Specifically, we apply an existing model for estimating young-of-year fish recruitment based on flow-dependent metrics to an effective discharge analysis that incorporates hydrologic variability and multiple focal taxa. We then compare three alternative methods of environmental flow provision. Percentage-based withdrawal schemes outcompete other environmental flow methods across all levels of water withdrawal and ecological outcomes.

Environmental Management↗