Search USGS⌕ Search

USGS · sir20065141

Effects of streambank fencing of pasture land on benthic macroinvertebrates and the quality of surface water and shallow ground water in the Big Spring Run basin of Mill Creek watershed, Lancaster County, Pennsylvania, 1993-2001

Abstract

Streambank fencing along stream channels in pastured areas and the exclusion of pasture animals from the channel are best-management practices designed to reduce nutrient and suspended-sediment yields from drainage basins. Establishment of vegetation in the fenced area helps to stabilize streambanks and provides better habitat for wildlife in and near the stream. This study documented the effectiveness of a 5- to 12-foot-wide buffer strip on the quality of surface water and near-stream ground water in a 1.42-mi2 treatment basin in Lancaster County, Pa. Two miles of stream were fenced in the basin in 1997 following a 3- to 4-year pre-treatment period of monitoring surface- and ground-water variables in the treatment and control basins. Changes in surface- and ground-water quality were monitored for about 4 years after fence installation. To alleviate problems in result interpretation associated with climatic and hydrologic variation over the study period, a nested experimental design including paired-basin and upstream/downstream components was used to study the effects of fencing on surface-water quality and benthic-macroinvertebrate communities. Five surface-water sites, one at the outlet of a 1.77-mi2 control basin (C-1), two sites in the treatment basin (T-3 and T-4) that were above any fence installation, and two sites (one at an upstream tributary site (T-2) and one at the outlet (T-1)) that were treated, were sampled intensively. Low-flow samples were collected at each site (approximately 25-30 per year at each site), and stormflow was sampled with automatic samplers at all sites except T-3. For each site where stormflow was sampled, from 35 to 60 percent of the storm events were sampled over the entire study period. Surface-water sites were sampled for analyses of nutrients, suspended sediment, and fecal streptococcus (only low-flow samples), with field parameters (only low-flow samples) measured during sample collection. Benthic-macroinvertebrate samples were collected in May and September of each year; samples were collected at the outlet of the control and treatment basins and at three upstream sites, two in the treatment basin and one in the control basin. For each benthic-macroinvertebrate sample: Stream riffles and pools were sampled using the kick-net method; habitat was characterized using Rapid Bioassessment Protocols (RBP); water-quality samples were collected for nutrients and suspended sediment; stream field parameters were measured; and multiple biological metrics were calculated. The experimental design to study the effects of fencing on the quality of near-stream shallow ground water involved a nested well approach. Two well nests were in the treatment basin, one each at surface-water sites T-1 and T-2. Within each well nest, the data from one deep well and three shallow wells (no greater than 12 ft deep) were used for regional characterization of ground-water quality. At each site, two of the shallow wells were inside the eventual fence (treated wells); the other shallow well was outside the eventual fence (control well). The wells were sampled monthly, primarily during periods with little to no recharge, for laboratory analysis of nutrients and fecal streptococcus; field parameters of water quality also were measured.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 39.833333333333336° to 40.166666666666664° latitude; -76.55° to -76° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Daniel G. Galeone, Robin A. Brightbill, Dennis J. Low, David L. O’Brien. 2006. Effects of streambank fencing of pasture land on benthic macroinvertebrates and the quality of surface water and shallow ground water in the Big Spring Run basin of Mill Creek watershed, Lancaster County, Pennsylvania, 1993-2001. https://doi.org/10.3133/sir20065141

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

Scientific Investigations Report↗

Groundwater budget for the Portneuf River Basin, southeastern Idaho

The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, developed the first groundwater budget for the upper Portneuf River Basin in southeastern Idaho, a tributary of the Snake River. A groundwater budget for the Portneuf River Basin supports water resource and administrative management decisions within the basin and for downstream users of the eastern Snake River Plain aquifer. The analysis quantifies inflows, outflows, and residuals for an aquifer system defined by the Idaho Department of Water Resources under three recent precipitation conditions: an average year (2016), a dry year (2021), and a wet year (2023). The budget was calculated for the basin and for two subbasins delineated by the U.S. Geological Survey streamgage Portneuf River at Topaz, Idaho (13073000): the above Topaz subbasin, which includes the Portneuf-Gem valley, and the below Topaz subbasin, which includes Marsh Valley. Aquifer inflows include mountain-front recharge; areal recharge; recharge from applied water, including recharge from irrigation, canal seepage, and effluent returns; groundwater flow into the basin; and reservoir seepage. Outflows include groundwater pumping for agricultural, municipal, and domestic uses; subsurface outflow through the Portneuf gap; and discharge to streams as base flow. Estimated annual inflows for the full basin ranged from 116,880 acre-feet in 2021 to 154,880 acre-feet in 2023, and outflows ranged from 132,190 acre-feet in 2016 to 158,490 acre-feet in 2023. Basinwide residuals, the change in storage plus uncertainty, were −2,410 acre-feet in 2016, −18,410 acre-feet in 2021, and −3,610 acre-feet in 2023, and each subbasin had a mixture of negative and positive residuals. These basinwide results, interpreted as change in storage plus water-budget uncertainty, indicate minor net losses that increase with dry conditions, consistent with well depth-to-water measurements indicating gradual multi-decadal declines. Patterns in subbasin residuals are driven by irrigation practices and surface-water management; for example, the above Topaz subbasin relies more on groundwater for irrigation, resulting in more negative residuals during dry years. In contrast, the below Topaz subbasin depends more on surface water, producing positive residuals during dry years because of irrigation losses and canal seepage. The analyses for both subbasins indicate decades-long groundwater declines, which agree with the overall negative residuals calculated. The largest sources of uncertainty include climate inputs (precipitation and evapotranspiration) and poorly constrained components such as groundwater inflow and tributary stream infiltration. These findings provide insight into how climatic variables and irrigation practices affect groundwater inflows and outflows within the Portneuf River Basin.

Idaho↗

World minerals outlook—Antimony, copper, graphite, niobium, potash, silicon, silver, and tungsten through 2030

Demand for mineral commodities continues to increase owing to rapid technological advancements and global economic growth. To support this increase in demand, information about current and near-term supplies of these mineral commodities is essential. Such data help evaluate the health and stability of global supply chains and protect against potential supply risks. Current production and production capacity data coupled with information regarding near-term expected changes (such as facility closures, planned expansions, or the completion of new mineral production projects) can provide insight into expected future market developments and expected available supply of mineral commodities. This report within the “World Minerals Outlook” series analyzes data for antimony, copper, natural graphite, niobium, potash, silicon metal, silver, and tungsten—eight mineral commodities listed in the 2025 List of Critical Minerals. World production data from 2019 through 2025, as well as estimated world capacity data from 2023 through 2025 were analyzed. Using these data, near-term (2026–2030) expected world capacities were estimated. For antimony, copper, niobium, silicon metal, silver, and tungsten, modest increases in production capacity of 4 to 10 percent were expected between 2025 and 2030, whereas capacities for natural graphite and potash were expected to increase by about 18 to 20 percent.

Scientific Investigations Report↗