Geology topics

Eric J. Walther

Publications and source records attributed to Eric J. Walther.

3 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

Stream restoration produces transitory, not permanent, changes to fish assemblages at compensatory mitigation sites

There is inconsistent evidence that stream restoration projects lead to recovery of ecosystem attributes, especially stream biota. While some assessments have documented desired changes in fish community metrics in the first years following restoration, longer-term studies have not always corroborated these findings. In this study, we used data and monitoring reports submitted to federal regulators by stream mitigation consultants to examine whether in-stream restoration activities led to changes in fish community attributes at 23 compensatory mitigation projects representing 53 sampling sites in Georgia, United States over 7 years of post-restoration monitoring. Modeling results indicated that abundance and species richness of fishes generally increased in the first years after restoration before decreasing to baseline levels by the seventh year. This pattern was consistent for models considering sensitive fish taxa, as well as at sites across a range of agricultural and forested land cover percentages. However, the effect of restoration on species richness was dampened in larger streams and at more urbanized locations. A community trajectory analysis supported the findings that fish community change was transitory at most sites. Remote estimation of canopy cover change at restoration sites suggested that the hump-shaped response may be driven by increased light availability during the immediate-post restoration period, followed by subsequent re-shading of stream channels by riparian plantings. Our analysis indicates that reach-level manipulation of streams should not be expected to induce long-term changes in fish communities, and that publicly available monitoring reports may be leveraged to address questions of stream restoration efficacy.

Restoration Ecology↗

USGS2022

Species distributions and the recognition of risk in restoration planning: A case study of salmonid fishes

One of the risks faced by habitat restoration practitioners is whether habitats included in restoration planning will be used by the target species or, conversely, whether habitats excluded from restoration planning would have benefited the target species. With the goal of providing a quantitative decision-making approach that represented varying levels of risk tolerance, we used multiple probability decision thresholds (PDT) to predict the range of occurrence for three anadromous fishes ( Oncorhynchus spp.) in a watershed in southwestern Washington, USA. For each species, we compared the predicted range of occurrence to the distribution used for restoration planning and quantified the amount of habitat blocked by anthropogenic barriers. Coho salmon ( O. kisutch ) had the broadest predicted range of occurrence (3061.6–6357.9 km; 0.75–0.25 PDT), followed by steelhead trout ( O. mykiss ; 1828.8–2836.8 km) and chum salmon ( O. keta ; 1373.9–1629.1 km). For each species, the predicted range of occurrence was similar or greater than the distribution used for restoration planning, suggesting that the current plan may exclude habitats that would benefit each species. Coho salmon had the greatest percentage of habitat blocked by anthropogenic barriers, followed by steelhead trout and chum salmon, respectively. Modeling species distributions at multiple risk-tolerance scenarios acknowledges uncertainty in restoration planning and allows practitioners to weigh the ecological benefits and budgetary constraints when considering locations for restoration. To effectively communicate restoration science to support practitioners in decision-making, we developed an R Shiny application online user interface available at: https://shiny.wdfw-fish.us/ChehalisRiverBasinSalmonidRangeOfOccurence/ .

Washington↗