Geology topics

Research about Maine, New Hampshire, Vermont

Source-linked reports with geographic coverage including Maine, New Hampshire, Vermont.

14 recordsLinked to original sources

USGS2026

Predictions of anthropogenic background PFAS concentrations in soil and relation to bedrock lithology and groundwater quality

Detectable concentrations of per- and polyfluoroalkyl substances (PFAS) have been observed in soils in remote areas and presumably originate from atmospheric deposition. These anthropogenic background concentrations may enable some PFAS to leach to groundwater at levels that exceed regulatory criteria for drinking water. However, anthropogenic background soil concentrations and their connection to groundwater are not well characterized. We developed a boosted regression tree model to predict perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) concentrations in shallow soils across northern New England. Low soil pH was the most important predictor of elevated anthropogenic PFOS and PFOA concentrations in background soils, rather than potential PFAS sources, land use, or population density. Total organic carbon (TOC) was also an important predictor for PFOS soil concentrations. Model predictions indicate that 73% of the shallow soils within Maine, Vermont, and New Hampshire exceed New Hampshire’s Soil Remediation Standard for PFOS (0.5 ng/g) and 41% exceed the PFOA standard (0.4 ng/g). Analysis of soil model results and groundwater data suggests that areas with high soil pH are associated with higher groundwater detection frequencies, illustrating how areas with less retention in soil are, conversely, also areas with potentially greater groundwater vulnerability. Further analysis indicates that groundwater may be more vulnerable in calcareous lithologies.

Maine, New Hampshire, Vermont↗

USGS2023

Groundwater recharge in northern New England: Meteorological drivers and relations with low streamflow

Meteorological drivers of groundwater recharge for spring (February–June), fall (October–January), and recharge-year (October–June) recharge seasons were evaluated for northern New England and upstate New York from 1989 to 2018. Monthly groundwater recharge was computed at 21 observation wells by subtracting the water levels at the end of each month from the level of the previous month; only positive monthly values were used to compute seasonal recharge. Precipitation, temperature, sea-level pressure, 500-mb geopotential heights, and various teleconnection indices were tested as explanatory variables for the interannual variability of recharge using random forest machine learning models. Precipitation within recharge seasons was positively correlated with groundwater recharge for most wells in all seasons. In general, whilst groundwater recharge in the study area was generally highest during the months of March and April, October precipitation was an important month for explaining the interannual groundwater recharge variability. This is likely because the variability in recharge in October may be high or low for given years. Sea-level pressure and 500-mb heights were typically inversely correlated with groundwater recharge during the recharge-year and fall recharge seasons, as higher sea-level pressure and heights are usually associated with clearer skies and less precipitation. The North Atlantic Oscillation, Pacific-North American pattern, and Pacific Decadal Oscillation teleconnections affected groundwater recharge differently by well and season. The influence of groundwater recharge on minimum daily streamflows during the subsequent summer/fall was also analysed. Summer precipitation was the most important explanatory variable for study streams whilst groundwater recharge and summer air temperature were significant variables for a few streams.

Maine, New Hampshire, Vermont↗

USGS2021

Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands

Forests are often affected by management that could influence demographics of breeding and post-breeding birds that reside within. Numerous studies have focused on immediate effects from management on wildlife soon after forestry treatment (e.g., 0–5 years), however, fewer studies have examined changes in focal species abundance over longer durations as a forest regenerates after disturbance. We examined how forest management influenced 18 conifer-associated birds during breeding and post-breeding over the forest regeneration period in a landscape dominated by forestry. To achieve this, we paired avian detection data from point count surveys in lowland conifer and mixed-wood forests with Bayesian distance-removal models and an information-theoretic framework. We estimated abundance and associations with seven common forestry treatment categories applied at the stand scale, years-since-harvest (YSH; 5–120+), and seven vegetation variables measured within stands. Forestry treatment categories and YSH were poor predictors of abundance, and none of the 14 species with good-fitting models had associations with these covariates. Twelve of 13 species with good-fitting models had important associations between abundance and vegetation variables. All vegetation variables were associated with abundance of some species, irrespective of the forestry treatment in which the site occurred, including spruce-fir tree composition (seven species), tree basal area (six species), midstory cover (five species), live crown ratio (three species), shrub cover (three species), tree diameter at breast height (two species), and shrub composition (one species). In a companion study, several species assemblages were associated with vegetation variables (i.e., spruce-fir tree composition, tree basal area, and tree diameter at breast height) that varied with YSH and forestry treatments, suggesting that some forestry treatments may indirectly influence avian abundance when certain vegetation outcomes are achieved. Our results suggest that managers should target species-specific vegetation outcomes rather than more broadly categorized forestry treatment types when managing for individual focal species because of large variations in vegetative outcomes across stands within a forest treatment category. Our study informs management and conservation of biodiversity in regions such as the Atlantic Northern Forest where commercial forestry is the dominant human land use.

Maine, New Hampshire, Vermont↗

USGS1990