In 2010, tetrachloroethylene (PCE), a chlorinated volatile organic compound, was detected in groundwater from deep (more than 300 feet below land surface) fractures in monitoring wells tapping a crystalline-rock aquifer. The aquifer underlies the Milford-Souhegan glacial-drift aquifer, a high water-producing aquifer, and the Savage Municipal Water-Supply Well Superfund site in Milford, New Hampshire. Between 30 and 40 residential water-supply wells are near (0.25 mile north of) the PCE-contaminated monitoring wells. Some of the residential water-supply wells are likely installed in similar rock types and formations as those of the monitoring wells installed as part of the Superfund site. As of 2020, periodic sampling by the U.S. Environmental Protection Agency and New Hampshire Department of Environmental Services (cooperative partners for this study) since 1996 had not detected PCE in groundwater from the residential water-supply wells. Nevertheless, understanding the vulnerability of the residential water wells to capture PCE contaminated groundwater was of concern.
A numerical groundwater flow model was developed by the U.S. Geological Survey to assess groundwater flow and advective transport of PCE-contaminated groundwater in the crystalline-rock aquifer of the Milford area. The model (called the area-wide model) encompasses a 26.5-square mile area to allow for more accurate computation of water fluxes near the PCE-contaminated monitoring wells and the residential water wells. Simulations with the area-wide model show that, with the 2016 configuration of residential wells, capture of PCE by the residential water wells appears unlikely for hydrologic conditions typical of 2010 based on steady-state, advective transport modeling. However, simulations also show that adding residential water wells to the north of the PCE-contaminated monitoring wells could affect the transport of PCE. Groundwater withdrawals at other adjacent wells in the overlying Milford-Souhegan glacial-drift aquifer affect advective transport in the crystalline-rock aquifer. Therefore, the potential for future changes in withdrawals in the area, as well as changes in hydrologic conditions, including groundwater recharge and streamflow amounts, should be considered in the remedial assessment process. The development of the area-wide model and linkages established by this study with previously developed Milford-Souhegan glacial-drift aquifer transport models will help facilitate the development of remedial strategies for this Superfund site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205137","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the New Hampshire Department of Environmental Services","usgsCitation":"Harte, P.T., 2021, Numerical modeling of groundwater flow in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund site, Milford, New Hampshire: U.S. Geological Survey Scientific Investigations Report 2020–5137, 47 p., https://doi.org/10.3133/sir20205137.","productDescription":"Report: ix, 47 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-036649","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":391937,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205137/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":391330,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5137/sir20205137.XML"},{"id":391326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5137/coverthb.jpg"},{"id":391329,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5137/images/"},{"id":391328,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J102FK","text":"USGS data release","linkHelpText":"MODFLOW -2005, MODPATH, and MOC3D used for groundwater flow simulation, pathlines analysis, and solute transport in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund site, Milford, New Hampshire"},{"id":391327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5137/sir20205137.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5137"}],"country":"United States","state":"New Hampshire","city":"Milford","otherGeospatial":"Savage Municipal Water-Supply Well Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.78741455078125,\n 42.798675589844414\n ],\n [\n -71.57524108886719,\n 42.798675589844414\n ],\n [\n -71.57524108886719,\n 42.938328528472546\n ],\n [\n -71.78741455078125,\n 42.938328528472546\n ],\n [\n -71.78741455078125,\n 42.798675589844414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Non-native species having high per capita impacts in invaded communities are those that modulate resource availability and alter disturbance regimes in ways that are biologically incompatible with the native biota. In areas where it has been introduced by humans, American beaver (Castor canadensis) is an iconic example of such species due to its capacity to alter trophic dynamics of entire ecosystems and create new invasional pathways for other non-native species. The species is problematic in several watersheds within the Southern California-Northern Baja California Coast Ecoregion, a recognized hotspot of biodiversity, due to its ability to modify habitat in ways that favor invasive predators and competitors over the region's native species and habitat. Beaver was deliberately introduced across California in the mid-1900s and generally accepted as non-native to the region up to the early 2000s; however, articles promoting the idea that beaver may be a natural resident have gained traction in recent years, due in large part to the species' charismatic nature rather than by presentation of sound evidence. Here, we discuss the problems associated with beaver disturbance and its effects on conserving the region's native fauna and flora. We refute arguments underlying the claim that beaver is native to the region, and review paleontological, zooarchaeological, and historical survey data from renowned field biologists and naturalists over the past ~160 years to show that no evidence exists that beaver arrived by any means other than deliberate human introduction. Managing this ecosystem engineer has potential to reduce the richness and abundance of other non-native species because the novel, engineered habitat now supporting these species would diminish in beaver-occupied watersheds. At the same time, hydrologic functionality would shift toward more natural, ephemeral conditions that favor the regions' native species while suppressing the dominance of the most insidious invaders.
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synthetase genes, and cyanotoxins at 11 river sites throughout the conterminous United States in a multiyear pilot study during 2017–19 through the National Water Quality Assessment Project to better understand the occurrence of cyanobacteria and cyanotoxins in large inland and coastal rivers. This report focuses on the first 2 years of data collection (2017 and 2018) and describes occurrence of anatoxin-, cylindrospermopsin-, microcystin-, and saxitoxin-producing cyanobacteria, cyanotoxin synthetase genes (anaC, cyrA, taxa specific mcyE, and sxtA), and cyanotoxins (anatoxins, cylindrospermopsins, microcystins, and saxitoxins). Study findings demonstrate that cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins are present in large U.S rivers under ambient conditions and show that downstream transport and flushing likely affect relative abundance of potential cyanotoxin-producing cyanobacteria. Additionally, the results agree with existing literature that support the importance of water temperature, light, and nutrients—as moderated by hydrologic conditions—in shaping the structure of riverine cyanobacterial communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215121","programNote":"National Water Quality Program","usgsCitation":"Zuellig, R.E., Graham, J.L., Stelzer, E.A., Loftin, K.A., and Rosen, B.H., 2021, Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5121, 22 p., https://doi.org/10.3133/sir20215121.","productDescription":"Report: v, 22 p.; 4 Data Releases; Related Work","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122244","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
A previously constructed numerical model simulating the regional groundwater flow system in the vicinity of the Wright-Patterson Air Force Base near Dayton, Ohio, was updated to incorporate current hydrologic stresses and conditions and improve the usefulness of the model for water-supply planning and protection. The original model, which simulated conditions from 1997 to 2001, was reconstructed with the most recently available U.S. Geological Survey groundwater modeling software and recalibrated to represent average groundwater flow conditions for the period of October 2018.
The steady-state, three-dimensional, three-layer MODFLOW model of the aquifer encompasses about 241 square miles in Montgomery, Greene, and Clark Counties. The Great Miami buried-valley aquifer consists of glacial sands and gravels in a buried bedrock valley. The shale bedrock in the area is poorly permeable, but the glacial deposits can yield as much as 2,000 gallons per minute to wells. As groundwater is the primary source of drinking water in the heavily populated study area, groundwater pumping from the buried-valley aquifer represents the largest time-varying stress in the groundwater flow model. The model simulated 228 pumped wells. Hydraulic conductivities in the model ranged from less than 1 foot per day to 450 feet per day. Simulated recharge rates ranged from 6 inches per year to 12.2 inches per year. Boundary conditions and aquifer properties were unchanged from the previous model. Model grid spacing and orientation also were not modified from the previous model.
Parameter estimation software was used to optimize model input parameters by matching simulated values to observed (estimated or measured) values. Calibrated parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, riverbed conductance, and recharge. Model calibration used measured water levels (hydraulic heads) from 124 observation wells, and streamflow gain/loss measurements from select reaches of the Mad River and its tributaries were compared with simulated streamflow gain/loss. Performance of the updated model is similar to previous studies. Eighty-one percent of simulated hydraulic heads were within 10 feet of the measured hydraulic heads, but comparison of the simulated streamflow gain/loss with the measured gain/loss indicates that streamflow gain/loss is not well represented by the updated model.
The particle tracking program MODPATH was used to calculate groundwater flow paths from recharge areas to selected existing and proposed groundwater withdrawal sites that service Wright-Patterson Air Force Base. Areas contributing groundwater to withdrawal sites were delineated based on 1-, 5-, and 10-year groundwater travel times. In addition, groundwater flow paths were calculated to simulate a groundwater release at eight sites near Wright-Patterson Air Force Base.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215115","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineering Center, Wright-Patterson Air Force Base","usgsCitation":"Riddle, A.D., 2021, Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5115, 36 p., https://doi.org/ 10.3133/ sir20215115.","onlineOnly":"Y","ipdsId":"IP-119316","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":391514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5115/sir20215115.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5115"},{"id":391515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FN1JK4","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used for the update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio"},{"id":391513,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5115/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Droughts are disproportionately impacting global dryland regions where ecosystem health and function are tightly coupled to moisture availability. Drought severity is commonly estimated using algorithms such as the standardized precipitation-evapotranspiration index (SPEI), which can estimate climatic water balance impacts at various hydrologic scales by varying computational length. However, the performance of these metrics as indicators of soil moisture dynamics at ecologically relevant scales, across soil depths, and in consideration of broader scale ecohydrological processes, requires more attention. In this study, we tested components of climatic water balance, including SPEI and SPEI computation lengths, to recreate multi-decadal and periodic soil-moisture patterns across soil profiles at 866 sites in the western United States. Modeling results show that SPEI calculated over the prior 12-months was the most predictive computation length and could recreate changes in moisture availability within the soil profile over longer periods of time and for annual recharge of deeper soil moisture stores. SPEI was slightly less successful with recreating spring surface-soil moisture availability, which is key to dryland ecosystems dominated by winter precipitation. Meteorological drought indices like SPEI are intended to be convenient and generalized indicators of meteorological water deficit. However, the inconsistent ability of SPEI to recreate ecologically relevant patterns of soil moisture at regional scales suggests that process-based models, and the larger data requirements they involve, remain an important tool for dryland ecohydrology
Tropical mangrove forests have been described as “coastal kidneys,” promoting sediment deposition and filtering contaminants, including excess nutrients. Coastal areas throughout the world are experiencing increased human activities, resulting in altered geomorphology, hydrology, and nutrient inputs. To effectively manage and sustain coastal mangroves, it is important to understand nitrogen (N) storage and accumulation in systems where human activities are causing rapid changes in N inputs and cycling. We examined N storage and accumulation rates in recent (1970 – 2016) and historic (1930 – 1970) decades in the context of urbanization in the San Juan Bay Estuary (SJBE, Puerto Rico), using mangrove soil cores that were radiometrically dated. Local anthropogenic stressors can alter N storage rates in peri-urban mangrove systems either directly by increasing N soil fertility or indirectly by altering hydrology (e.g., dredging, filling, and canalization). Nitrogen accumulation rates were greater in recent decades than historic decades at Piñones Forest and Martin Peña East. Martin Peña East was characterized by high urbanization, and Piñones, by the least urbanization in the SJBE. The mangrove forest at Martin Peña East fringed a poorly drained canal and often received raw sewage inputs, with N accumulation rates ranging from 17.7 to 37.9 g m–2 y–1 in recent decades. The Piñones Forest was isolated and had low flushing, possibly exacerbated by river damming, with N accumulation rates ranging from 18.6 to 24.2 g m–2 y–1 in recent decades. Nearly all (96.3%) of the estuary-wide mangrove N (9.4 Mg ha–1) was stored in the soils with 7.1 Mg ha–1 sequestered during 1970–2017 (0–18 cm) and 2.3 Mg ha–1 during 1930–1970 (19–28 cm). Estuary-wide mangrove soil N accumulation rates were over twice as great in recent decades (0.18 ± 0.002 Mg ha–1y–1) than historically (0.08 ± 0.001 Mg ha–1y–1). Nitrogen accumulation rates in SJBE mangrove soils in recent times were twofold larger than the rate of human-consumed food N that is exported as wastewater (0.08 Mg ha–1 y–1), suggesting the potential for mangroves to sequester human-derived N. Conservation and effective management of mangrove forests and their surrounding watersheds in the Anthropocene are important for maintaining water quality in coastal communities throughout tropical regions.
Sustaining the quality of the Nation’s water resources and the health of our diverse ecosystems depends on the availability of sound water-resources data and information to develop effective, science-based policies. Effective management of water resources also brings more certainty and efficiency to important economic sectors. Taken together, these actions lead to immediate and long-term economic, social, and environmental benefits that will make a difference to the lives of the almost 400 million people projected to live in the United States by 2050.
The Chesapeake Bay is the largest and most productive estuary in the United States and is a vital environmental and economic resource. Approximately half of the water volume of the Chesapeake Bay originates from streams and rivers that drain the 64,243 mi2 Chesapeake Bay watershed. The Bay and its tributaries have been degraded by excessive nutrients, such as nitrogen, from contributing watersheds. Inputs of nitrogen to the Bay lead to increased algal growth, decreased dissolved oxygen, and declining fisheries. In 2000, the Chesapeake Bay was listed as impaired under the Clean Water Act and Total Maximum Daily Loads (TMDLs) for nutrients and sediment have been established to assist with management actions aimed at nutrient reductions. Effective nutrient management requires an understanding of past, present, and future nutrient sources, fate, and transport in the watershed.
The Chesapeake Bay community has been a pioneer in science, management, and regulation to improve water quality. Factors like climate, hydrology, source inputs, and management controls play a vital role in determining the delivery and magnitude of nitrogen inputs to the Bay. Science in the form of monitoring data, predictive tools, and interpretive reports can help inform decisions to better balance the use and control of nitrogen in coastal areas. The findings in this report can contribute to effective management of the Bay and its watershed by providing a synthesis of the understanding of how human activities and environmental change in the watershed in the past, present, and future will influence the export of nitrogen to the Bay.
We hope this publication will provide you with insights and information to meet your water resource needs and will foster increased civilian awareness and involvement in the protection and restoration of our Nation’s waters. The information in this report is intended primarily for those interested or involved in resource management and protection, conservation, regulation, and policymaking at the regional and national levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1486","programNote":"National Water-Quality Program","usgsCitation":"Clune, J.W., and Capel, P.D., eds., 2021, Nitrogen in the Chesapeake Bay watershed—A century of change, 1950–2050 (ver. 1.2, 2024): U.S. Geological Survey Circular 1486, 168 p., https://doi.org/10.3133/cir1486.","productDescription":"vi, 168 p.","numberOfPages":"168","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-109208","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science 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U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
The Buenos Aires National Wildlife Refuge is located in the southern part of Altar Valley, southwest of Tucson in southeastern Arizona. The primary water-supply well at the Buenos Aires National Wildlife Refuge has experienced a two-decade decrease in groundwater levels in the well, as have other wells in the southern part of Altar Valley. In part to understand this trend, a study was undertaken by the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, to summarize what is known about the geohydrologic system on the refuge and analyze groundwater-level trends and precipitation-groundwater correlations. In addition, available data were compiled where possible on the climate, land cover, soils, geology, and hydrology to provide a foundation for future modeling of the system.
Altar Valley is a sedimentary basin bounded by a mixture of Paleozoic to Tertiary sedimentary, volcanic, granitic, and metamorphic rocks. The valley fill is undifferentiated Tertiary to Quaternary sediments underlain by middle Miocene to Pliocene rocks that consist of moderately to strongly consolidated conglomerate and sandstone. Surface water, when present in the predominantly ephemeral streams of the valley, flows from south to north. Arivaca Creek has a cienega (or wetland) where groundwater surfaces before it flows as a short perennial reach out of Arivaca Basin. Groundwater maps compiled between 1934 and 2016 showed groundwater flowing from south to north. Before the 1980s, temporal patterns of groundwater levels in wells in Altar Valley varied substantially from one well to another. In the mid-1980s, comparatively high levels of precipitation occurred: the 1980s median value was 15.3 inches, whereas the median for the period of record was 13.2 inches. In addition, apparently corresponding groundwater level increases were seen in nearly all wells studied. After this initial increase, two different groundwater-level trends began to be observed in two spatially distinct sets of wells: in the northern part, groundwater levels were relatively steady, whereas in the southern part, groundwater levels declined from 10 to 20 feet between 1990 and 2019. Annual groundwater pumpage declined substantially in the northern part of the valley beginning in the early 1980s, but it began to increase again in the 1990s. Pumpage in the southern part has remained low and relatively steady compared to the northern part. Although the precise reasons for the declining groundwater levels in the southern part remain unclear, groundwater levels may be affected by factors such as climate cycles, long-term drought, and temperature-induced declines in recharge, resulting in increased evapotranspiration.
Preliminary analyses of two wells, one selected from each part of the valley, using linear regression and lag correlation to investigate correlation between annual precipitation and groundwater levels, showed a maximum correlation at a lag of about 17 years in the southern part of the valley and about 25 years in the northern part, indicating that, although variable sources and traveltimes of recharged water may be needed to propagate to each location, the strongest correlation at each well is with precipitation that was recharged 17 and 25 years prior to the groundwater response in that well. Assuming a constant flow of groundwater from the southern to the northern part of the valley, a decrease in recharge is expected to lead to a decrease in aquifer storage. As to the comparatively stable groundwater levels in the northern part, pumpage is still only about one-half what it was in the early 1980s, even though pumpage has increased there since the 1990s. Water levels in most wells in the northern part were drawn down prior to the decrease in pumping in the early 1980s, possibly owing to a combination of pumping and the nearly 20-year midcentury drought that occurred between 1940 and 1960. Water levels were in the process of recovering when the increase in pumping occurred in the 1990s. Because the water levels were recovering (increasing) instead of remaining static, the increased pumping may have only limited the recovery rather than causing a decrease in water levels, as a new quasi-equilibrium state may have been reached. Additional possible causes for the stable groundwater levels include (1) upgradient aquifer transmissivity that was high enough to offset pumping, (2) a low-permeability barrier, such as bedrock or clay, at the north end of the valley that caused groundwater pooling, (3) higher lateral inflow of groundwater in the northern part of the valley, (4) a delay in the effect of storage declines propagating from the south, or (5) some combination thereof.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215050","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Owen-Joyce, S.J., Callegary, J.B., and Rosebrough, A.E., 2021, Preliminary geohydrologic assessment of Buenos Aires National Wildlife Refuge, Altar Valley, southeastern Arizona: U.S. Geological Survey Scientific Investigations Report 2021–5050, 29 p., https://doi.org/10.3133/sir20215050.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-118417","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":391517,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5050/sir20215050.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391518,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QST8OX","linkHelpText":"Groundwater well data and annual groundwater pumpage data (1984–2019) in Altar Valley, Arizona"},{"id":391516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5050/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Altar Valley, Buenos Aires National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.56341552734375,\n 31.459125370764387\n ],\n [\n -111.34780883789062,\n 31.459125370764387\n ],\n [\n -111.34780883789062,\n 31.81864727496152\n ],\n [\n -111.56341552734375,\n 31.81864727496152\n ],\n [\n -111.56341552734375,\n 31.459125370764387\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Wildfire-induced changes to soil and vegetation promote runoff-generated debris flows in steep watersheds. Postfire debris flows are most commonly observed in steep watersheds during the first wet season following a wildfire, but it is unclear how long the elevated threat of debris flow persists and why debris-flow potential changes in recovering burned areas. This work quantifies how rainfall intensity-duration (ID) thresholds for debris-flow initiation change with time since burning and provides a mechanistic explanation for these changes. We constrained a hydrologic model using field and remotely sensed measurements of soil-infiltration capacity, vegetation cover, runoff, and debris-flow activity. We applied this model to estimate rainfall ID thresholds for debris-flow initiation within three burned areas in the southwestern United States over a postfire recovery period of three to four years. Modeling suggests ID thresholds are lowest immediately following the fire (below a one-year recurrence interval [RI] storm) and increase with time, such that a 10- to 25-year RI storm would be required to generate a debris flow after three years of recovery. Modeled changes in rainfall ID thresholds result from increases in soil infiltration capacity, canopy interception, hydraulic roughness, and median grain size of sediment entrained in an incipient debris flow. The relative importance of each of these factors varied among our three sites. Results improve our ability to assess temporal changes in postfire debris-flow potential, highlight how site-specific factors may alter the persistence of postfire debris-flow hazards, and provide additional constraints on the timescale of recovery following wildfire.
Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-13-5127-2021","usgsCitation":"Olefeldt, D., Hovemyr, M., Kuhn, M., Bastviken, D., Bohn, T., Connolly, J., Crill, P., Euskirchen, E., Finkelstein, S., Genet, H., Grosse, G., Harris, L., Heffernan, L., Helbig, M., Hugelium, G., Hutchins, R., Juutinen, S., Lara, M., Malhotra, A., Manies, K.L., McGuire, A., Natali, S., O’Donnell, J.A., Parmentier, F., Rasanen, A., Schaedel, C., Sonnentag, O., Strack, M., Tank, S., Treat, C., Varner, R., Virtanen, T., Watts, J., and Warren, R., 2021, The Boreal-Arctic Wetland and Lake Dataset (BAWLD): Earth System Science Data, v. 13, p. 5127-5149, https://doi.org/10.5194/essd-13-5127-2021.","productDescription":"23 p.","startPage":"5127","endPage":"5149","ipdsId":"IP-129170","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":450274,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5194/essd-13-5127-2021","text":"External Repository"},{"id":400379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2021-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Olefeldt, David","contributorId":169408,"corporation":false,"usgs":false,"family":"Olefeldt","given":"David","affiliations":[{"id":32365,"text":"Department of Renewable Resources, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":842473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hovemyr, Mikael","contributorId":291509,"corporation":false,"usgs":false,"family":"Hovemyr","given":"Mikael","email":"","affiliations":[],"preferred":false,"id":842474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuhn, M.A.","contributorId":291510,"corporation":false,"usgs":false,"family":"Kuhn","given":"M.A.","email":"","affiliations":[],"preferred":false,"id":842475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bastviken, D","contributorId":264953,"corporation":false,"usgs":false,"family":"Bastviken","given":"D","affiliations":[{"id":54595,"text":"Department of Thematic Studies - Environmental Change, Linköping University, Linköping, Sweden","active":true,"usgs":false}],"preferred":false,"id":842476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bohn, T.J.","contributorId":291513,"corporation":false,"usgs":false,"family":"Bohn","given":"T.J.","email":"","affiliations":[],"preferred":false,"id":842477,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Connolly, J.","contributorId":291515,"corporation":false,"usgs":false,"family":"Connolly","given":"J.","email":"","affiliations":[],"preferred":false,"id":842478,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crill, P.M.","contributorId":248742,"corporation":false,"usgs":false,"family":"Crill","given":"P.M.","affiliations":[{"id":49996,"text":"Stockholm University, Department of Geological Sciences and Bolin Centre for Climate Research, Stockholm, Sweden","active":true,"usgs":false}],"preferred":false,"id":842479,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Euskirchen, E.S.","contributorId":216778,"corporation":false,"usgs":false,"family":"Euskirchen","given":"E.S.","email":"","affiliations":[{"id":36971,"text":"University of Alaska","active":true,"usgs":false}],"preferred":false,"id":842480,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Finkelstein, S.A.","contributorId":257296,"corporation":false,"usgs":false,"family":"Finkelstein","given":"S.A.","email":"","affiliations":[],"preferred":false,"id":842481,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Genet, H.","contributorId":291521,"corporation":false,"usgs":false,"family":"Genet","given":"H.","affiliations":[],"preferred":false,"id":842482,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Grosse, G.","contributorId":192805,"corporation":false,"usgs":false,"family":"Grosse","given":"G.","email":"","affiliations":[],"preferred":false,"id":842483,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Harris, L.I.","contributorId":291522,"corporation":false,"usgs":false,"family":"Harris","given":"L.I.","email":"","affiliations":[],"preferred":false,"id":842484,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Heffernan, L.","contributorId":291524,"corporation":false,"usgs":false,"family":"Heffernan","given":"L.","email":"","affiliations":[],"preferred":false,"id":842485,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Helbig, M.","contributorId":169378,"corporation":false,"usgs":false,"family":"Helbig","given":"M.","email":"","affiliations":[{"id":25485,"text":"Université de Montréal, Canada","active":true,"usgs":false}],"preferred":false,"id":842486,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hugelium, G.","contributorId":291527,"corporation":false,"usgs":false,"family":"Hugelium","given":"G.","email":"","affiliations":[],"preferred":false,"id":842487,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Hutchins, R.","contributorId":291530,"corporation":false,"usgs":false,"family":"Hutchins","given":"R.","email":"","affiliations":[],"preferred":false,"id":842488,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Juutinen, S.","contributorId":257303,"corporation":false,"usgs":false,"family":"Juutinen","given":"S.","affiliations":[],"preferred":false,"id":842489,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Lara, M.J.","contributorId":291534,"corporation":false,"usgs":false,"family":"Lara","given":"M.J.","email":"","affiliations":[],"preferred":false,"id":842490,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Malhotra, A.","contributorId":291536,"corporation":false,"usgs":false,"family":"Malhotra","given":"A.","affiliations":[],"preferred":false,"id":842491,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":842492,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"McGuire, A.D.","contributorId":199633,"corporation":false,"usgs":false,"family":"McGuire","given":"A.D.","email":"","affiliations":[],"preferred":false,"id":842493,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Natali, S.M.","contributorId":291541,"corporation":false,"usgs":false,"family":"Natali","given":"S.M.","email":"","affiliations":[],"preferred":false,"id":842494,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"O’Donnell, J. 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C.","affiliations":[{"id":51984,"text":"University of Finland","active":true,"usgs":false}],"preferred":false,"id":842502,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Varner, R.K.","contributorId":291557,"corporation":false,"usgs":false,"family":"Varner","given":"R.K.","affiliations":[],"preferred":false,"id":842503,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Virtanen, T.","contributorId":291558,"corporation":false,"usgs":false,"family":"Virtanen","given":"T.","email":"","affiliations":[],"preferred":false,"id":842504,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Watts, J.D.","contributorId":291559,"corporation":false,"usgs":false,"family":"Watts","given":"J.D.","email":"","affiliations":[],"preferred":false,"id":842505,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Warren, R.K.","contributorId":291562,"corporation":false,"usgs":false,"family":"Warren","given":"R.K.","email":"","affiliations":[],"preferred":false,"id":842506,"contributorType":{"id":1,"text":"Authors"},"rank":34}]}} ,{"id":70225636,"text":"sir20215038 - 2021 - Groundwater/surface-water interactions in the Partridge River Basin and evaluation of hypothetical future mine pits, Minnesota","interactions":[],"lastModifiedDate":"2022-03-23T13:15:47.763523","indexId":"sir20215038","displayToPublicDate":"2021-11-04T10:55:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5038","displayTitle":"Groundwater/Surface-Water Interactions in the Partridge River Basin and Evaluation of Hypothetical Future Mine Pits, Minnesota","title":"Groundwater/surface-water interactions in the Partridge River Basin and evaluation of hypothetical future mine pits, Minnesota","docAbstract":"The Partridge River Basin (PRB) covers 156 square miles in northeastern Minnesota with headwaters in the Mesabi Iron Range. The basin is characterized by extensive wetlands, lakes, and streams in poorly drained and often thin glacial material overlying Proterozoic bedrock. To better understand the interaction between these extensive surface water features and the groundwater system, a three-dimensional, steady-state, groundwater-flow model of the PRB was developed by the U.S. Geological Survey in cooperation with the Great Lakes Indian Fish & Wildlife Commission using the finite-difference computer code MODFLOW-NWT. The model simulates steady-state base flow in streams and groundwater interactions using the streamflow routing (SFR2) package. Existing mining features including tailings basins, stockpiles, pumped mine pits, and flooded mine pits were simulated using either high hydraulic conductivity zones or the drain (DRN) package. The unsaturated zone flow (UZF) package was used to better represent the groundwater system in areas with a high water table and for wetlands often associated with such areas. UZF typically is used to represent unsaturated zone processes but also can simulate the rejection of recharge and groundwater discharge to the land surface when the water table is near land surface. The steady-state model used data from the 2011 to 2013 period when 2011 high-resolution land surface (light detecting and ranging [lidar]) data were available that reflected land-surface and water elevations from mining activity in the basin. The parameter-estimation software suite PEST_HP was used to obtain a best fit of the modeled to measured groundwater levels, streamflow, pit inflow rates, and mapped peat deposits. The PEST calibration used the target residuals from two models with the same model parameters and targets from two separate periods: (1) a 1995–2015 calibration model, which provided a larger number of calibration targets, and (2) a 2011–2013 mining conditions model, which included calibration targets that reflected conditions consistent with the modeled mine-workings topography.
Calibration of the PRB model resulted in ranges of glacial horizontal hydraulic conductivity parameters that generally agreed with literature values and other models of the region. Horizontal hydraulic conductivity of the bedrock was higher in the upper bedrock layers where numerous and continuous fractures have been observed and lower in the deeper bedrock layers. Average basin-wide calibrated infiltration was 5.3 inches per year. An average of 4.6 inches per year of infiltration crosses the water table and becomes recharge and 0.7 inch per year is rejected by UZF due to saturated conditions at the land surface. Simulated groundwater runoff (the sum of rejected recharge and groundwater seepage to the land surface) can either be routed to streams or removed from the model as evapotranspiration. The calibrated model indicates relatively shallow groundwater-flow paths dominating and approximately 50 percent of the stream base flow coming from groundwater runoff.
The 2011–2013 mining conditions model was then used to develop five model scenarios simulating the response of the groundwater and surface-water system to potential hydrologic stress. The purpose of these mine pit scenarios is to present a possible workflow to quantify a model’s uncertainty for a given model forecast and serve as a possible guide for initial data collection that may improve a future model’s ability to make such a forecast. The scenarios included one scenario with the currently existing Peter Mitchell pit at final buildout and flooded to an elevation of 1,500 feet, and four scenarios with a hypothetical, new mine pit plus the flooded Peter Mitchell at final buildout. The five model scenarios were used to forecast streamflow at six locations in the PRB, pit inflow rates for the new mine pits and the flooded Peter Mitchell pit, and the average depth to water in 12 wetlands. A linear uncertainty analysis was performed using information from the PEST calibration and tools in the PyEMU python package to assess model uncertainty propagation to the model forecasts. Streamflows generally were reduced with future mining and the greatest streamflow reductions occurred from the flooded Peter Mitchell Pit, probably due to its large size. Average depth to groundwater in wetlands was most affected the closer the wetland was to a new mine pit.
Linear uncertainty methods were also used to evaluate data worth, which is the ability for potential new groundwater elevation observations to reduce the uncertainty in scenario forecasts. Data worth was performed for a grid of new hydraulic head observations. Overall, areas with nonnegligible data worth generally corresponded to wetland areas with no groundwater seepage to land surface from UZF. These model behaviors indicated that the land-surface boundary condition simulated by the UZF package was pinning the groundwater elevations to the land surface in areas with groundwater seepage (33 percent of the 2011–2013 base conditions model) such that the sensitivity to new observations in these areas was minimal. Therefore, representing wetlands as boundary conditions minimized the usefulness of data worth calculations because wetland areas were present over a large part of the model domain.
Probabilistic capture zones were estimated for each of the mines in the model scenarios. A capture zone represents the area contributing recharge to a model feature, like a well or a mine pit, and can be calculated by forward tracking particles from the water table. By using Monte Carlo techniques, it is possible to generate estimated capture zones that include the probability of recharge capture given the uncertainty present in the model. Monte Carlo techniques use randomly generated model parameter sets sampled from a plausible parameter range to create many possible realizations. The resulting capture zone arrays were calculated by tallying the total number of realizations in which a particle from a model cell was captured by the feature. Probabilities from the Monte Carlo runs ranged from 1 (captured in 100 percent of the runs) near the pits to 0 (captured in 0 percent of the runs) at the edges of the capture zone. Capture zones were not always spatially continuous; for example, the capture zone for the proposed mine pits south of the flooded Peter Mitchell pit was discontinuous with capture surrounding the proposed mine pit and north of the flooded Peter Mitchell pit. This northern section represents deeper groundwater flow paths that originate in the topographic high, move under the flooded pit, and discharge into the proposed pit. This pattern of capture indicates the possibility of some deeper flow through the upper fractured bedrock when the shallow groundwater flow system is modified. These results underscore that future site-specific applications of the base condition model require the input of site-specific data and recalibration to focus on the site of interest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215038","collaboration":"Prepared in cooperation with the Great Lakes Indian Fish & Wildlife Commission","usgsCitation":"Haserodt, M.J., Hunt, R.J., Fienen, M.N., and Feinstein, D.T., 2021, Groundwater/surface-water interactions in the Partridge River Basin and evaluation of hypothetical future mine pits, Minnesota: U.S. Geological Survey Scientific Investigations Report 2021–5038, 94 p., https://doi.org/10.3133/sir20215038.","productDescription":"Report: ix, 87 p.; Data Release; Dataset","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-123210","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391131,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5038/sir20215038.xml","text":"Report xml","size":"277 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5038 xml"},{"id":391130,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":391132,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5038/images"},{"id":391129,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VODOU8","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT and MODPATH models, capture zones and uncertainty data analysis for the Partridge River Basin, Minnesota"},{"id":391127,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5038/coverthb.jpg"},{"id":391128,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5038/sir20215038.pdf","text":"Report","size":"69.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5038"}],"country":"United States","state":"Minnesota","otherGeospatial":"Partridge River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.25,\n 47.4\n ],\n [\n -91.75,\n 47.4\n ],\n [\n -91.75,\n 47.8\n ],\n [\n -92.25,\n 47.8\n ],\n [\n -92.25,\n 47.4\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive,
Madison, WI 53726
As the site of the first permanent English settlement in North America in 1607, Jamestown Island, Colonial National Historical Park (COLO), Virginia, contains a rich archaeological record that extends from the Paleoindian period (15,000 to 8,000 years ago) through the 20th century. The island is located on the lower James River near the mouth of Chesapeake Bay. Jamestown Island vegetation is dominated by upland forests surrounded by tidal, freshwater-to-oligohaline marshes. Along the Virginia coast, relative sea-level rise was more than 2.5 times the global average during the 20th century. Consequently, the National Park Service (NPS) has identified COLO as one of the 25 national parks most threatened by climate change.
Surface waters across the island are hydraulically connected to the laterally continuous Surficial aquifer. The land-surface altitude of the island is low, with two-thirds of the island less than 5 feet (ft) above the North American Vertical Datum of 1988 (NAVD 88). Consequently, sea-level rise, combined with tides and storm surges, threatens the island and its resources as surface-water and groundwater levels rise, saltwater enters the Surficial aquifer, and groundwater chemistry changes. The impact of sea-level rise on the island’s surface-water resources has been well studied, but groundwater effects have been largely ignored. Quantifying the effects of tides, storm surges, and sea-level rise on groundwater levels and chemistry is essential to developing an effective strategy for managing climate-induced changes. The first step in developing a response strategy includes a parkwide general risk assessment for archaeological sites on the island, so that sites can be prioritized for management actions. The U.S. Geological Survey and the NPS began a study in 2015 to develop a long-term groundwater-monitoring program to evaluate this risk and to develop an updated management strategy.
The groundwater-monitoring program consists of 45 wells and piezometers in two individual clusters and three transects across the island in different hydrologic and chemical settings. Samples for water quality were collected from the wells and piezometers from October 2015 through September 2018 at variable time intervals. Results of the monitoring identified disparate hydrologic and chemical responses to saltwater intrusion across the island. Specific conductance (an indicator of salinity) of groundwater beneath several marshes responded differently to changes in James River salinity. Groundwater response to changes in James River specific conductance appeared to be controlled by land-surface altitude and slope, differences in lateral and vertical sediment characteristics, distance from surface waters, and the degree of surface water/groundwater connectivity between channels and the aquifer.
Groundwater chemistry data from monitoring wells at Black Point, a low-altitude, upland setting, are in contrast with conditions observed in Island House observation wells, a high-altitude, upland setting. Specific conductance (less than 200 microsiemens per centimeter [μS/cm]) and pH (greater than 5.0) of groundwater beneath much of the uplands that characterize the Island House observation wells are typical of groundwater in noncarbonate sedimentary aquifers recharged by precipitation. At Black Point, specific conductance ranged from 2,490 to 15,200 μS/cm, and pH ranged from 3.1 to 6.6 standard units. At the Black Point observation wells, the most saline and dense water was at the water table rather than deeper in the aquifer, causing a density inversion that persisted throughout the study. The density inversion likely resulted from differences in permeability between the shallow clay and fine-grained sands and the deeper coarse-grained sand and gravel. Groundwater with the lowest pH was at the water table. As saline groundwater flows through organic sediment beneath the marshes, bacterial biodegradation of organic matter creates anoxic conditions. Continued biodegradation concomitantly reduces iron-oxide minerals in the sediment and sulfate in saline water. When oxygen is reintroduced into groundwater, iron and sulfur can reoxidize to form sulfuric acid, locally lowering the pH of the water.
This report describes the groundwater monitoring network design, rationale for site selection, monitoring approach, and results of monitoring from October 2015 through September 2018. Maps of inundation at selected water-level altitudes are included to identify the risk to archaeological, cultural, and ecological resources. The monitoring results of the hydrology and chemistry data are interpreted, and the different hydrologic and chemical settings are described. The implications of the study results for management decisions are presented, and suggestions for improving the monitoring network are included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215117","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"McCoy, K.J., Rice, K.C., Rickles, E., Frederick, D., Cramer, J., and Geyer, D., 2021, Groundwater hydrology and chemistry of Jamestown Island, Virginia—Potential effects of tides, storm surges, and sea-level rise on archaeological, cultural, and ecological resources: U.S. Geological Survey Scientific Investigations Report 2021–5117, 50 p., https://doi.org/10.3133/sir20215117.","productDescription":"Report: x, 50 p.; Data Release","numberOfPages":"50","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-115948","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":391337,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K7X61F","text":"USGS data release","linkHelpText":"Field parameters and water levels from monitoring sites at Jamestown Island, Virginia, 2016 - 2018"},{"id":391336,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5117/sir20215117.pdf","text":"Report","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5117"},{"id":391335,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5117/coverthb2.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Jamestown Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.86309814453125,\n 37.16797725379289\n ],\n [\n -76.48544311523436,\n 37.16797725379289\n ],\n [\n -76.48544311523436,\n 37.36033397019125\n ],\n [\n -76.86309814453125,\n 37.36033397019125\n ],\n [\n -76.86309814453125,\n 37.16797725379289\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
The Anza-Cahuilla groundwater basin located mainly in the semi-arid headwaters of the Santa Margarita River watershed in southern California is the principle source of groundwater for a rural disadvantaged community and two Native American Tribes, the Ramona Band of Cahuilla and the Cahuilla. Groundwater in the study area is derived entirely from precipitation and managing groundwater sustainably requires an accurate assessment of the water balance components, yet long-term estimates do not exist. Demand for groundwater in the region has increased and groundwater quality has decreased due to population growth and increased irrigated cropland. To characterize monthly long-term natural recharge and runoff estimates, a physically-based water balance model (Basin Characterization Model) was locally calibrated and validated using nearby streamgages and published estimates of climatic and hydrologic variables. The average modeled annual recharge and runoff from 1981 to 2010 was 5.4 × 106 and 1.2 × 107 m3, respectively, for the study area. Recharge and runoff do not reliably occur in large amounts every year and recharge rarely occurs in the groundwater basin footprint. These long-term estimates can be used by water managers, stakeholders, and Native American Tribes to develop plans for sustainable management of future water resources, and as inputs to a three-dimensional groundwater model.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12971","usgsCitation":"Stern, M.A., Flint, L.E., Flint, A.L., and Christensen, A.H., 2021, A basin-scale approach to estimating recharge in the desert: Anza-Cahuilla groundwater basin, CA: Journal of the American Water Resources Association, v. 57, no. 6, p. 990-1003, https://doi.org/10.1111/1752-1688.12971.","productDescription":"14 p.","startPage":"990","endPage":"1003","ipdsId":"IP-119217","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":450287,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12971","text":"Publisher Index Page"},{"id":436125,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BAMCP4","text":"USGS data release","linkHelpText":"Basin Characterization Model (BCMv8) monthly recharge and runoff for the Anza-Cahuilla Groundwater Basin, California"},{"id":391385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Anza-Cahuilla groundwater basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.9166,\n 33.3333\n ],\n [\n -116.25,\n 33.3333\n ],\n [\n -116.25,\n 33.75\n ],\n [\n -116.9166,\n 33.75\n ],\n [\n -116.9166,\n 33.3333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":826394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christensen, Allen H. 0000-0002-7061-5591 ahchrist@usgs.gov","orcid":"https://orcid.org/0000-0002-7061-5591","contributorId":1510,"corporation":false,"usgs":true,"family":"Christensen","given":"Allen","email":"ahchrist@usgs.gov","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826395,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226454,"text":"70226454 - 2021 - Enhancing marsh elevation using sediment augmentation: A case study from southern California, USA","interactions":[],"lastModifiedDate":"2021-11-18T12:55:54.616791","indexId":"70226454","displayToPublicDate":"2021-11-01T06:54:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3385,"text":"Shore & Beach","printIssn":"0037-4237","active":true,"publicationSubtype":{"id":10}},"title":"Enhancing marsh elevation using sediment augmentation: A case study from southern California, USA","docAbstract":"Tidal marshes are an important component of estuaries that provide habitat for fish and wildlife, protection from flooding, recreation opportunities, and can improve water quality. Critical to maintaining these functions is vertical accretion, a key mechanism by which tidal marshes build elevation relative to local sea level. The beneficial use of dredged material to build marsh elevations in response to accelerating sea level rise has gained attention as a management action to prevent habitat loss over the coming decades. In January 2016, a sediment augmentation project using local dredged material was undertaken at Seal Beach National Wildlife Refuge in Anaheim Bay, California, USA, to benefit tidal marsh habitat and the listed species it supports. The application process added 12,900 cubic meters of sediment with an initial, average 22-cm gain in elevation over a 3.2-hectare site. Due to sediment characteristics and higher than anticipated elevations in some areas, vegetation colonization did not occur at the expected rate; therefore, adaptive management measures were undertaken to improve hydrology of the site and facilitate vegetation colonization. More case studies that test and monitor sea level adaptation actions are needed to assist in the planning and implementation of climate-resilient projects to prevent coastal habitat loss over the coming century.
Numerous hydropower facilities are under construction or planned in tropical and subtropical rivers worldwide. While dams are typically designed considering historic river discharge regimes, climate change may induce large-scale alterations in river hydrology. Here we analyze how future climate change will affect river hydrology, electricity generation, and economic viability of > 350 potential hydropower dams across the Amazon, Earth’s largest river basin and a global hotspot for future hydropower development. Midcentury projections for the RCP 4.5 and 8.5 climate change scenarios show basin-wide reductions of river discharge (means, 13 and 16%, respectively) and hydropower generation (19 and 27%). Declines are sharper for dams in Brazil, which harbors 60% of the proposed projects. Climate change will cause more frequent low-discharge interruption of hydropower generation and less frequent full-capacity operation. Consequently, the minimum electricity sale price for projects to break even more than doubles at many proposed dams, rendering much of future Amazon hydropower less competitive than increasingly lower cost renewable sources such as wind and solar. Climate-smart power systems will be fundamental to support environmentally and financially sustainable energy development in hydropower-dependent regions.
","language":"English","publisher":"Elseiver","doi":"10.1016/j.gloenvcha.2021.102383","usgsCitation":"Almeida, R., Fleischmann, A., Breda, J., Cardoso, D., Angarita, H., Collischonn, W., Forsberg, B.R., García-Villacorta, R., Hamilton, S., Hannam, P., Paiva, R., Poff, N.L., Sethi, S., Shi, Q., Gomes, C.P., and Flecker, A., 2021, Climate change may impair electricity generation and economic viability of future Amazon hydropower: Global Environmental Change, v. 71, 102383, 10 p., https://doi.org/10.1016/j.gloenvcha.2021.102383.","productDescription":"102383, 10 p.","ipdsId":"IP-125313","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467222,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research.wur.nl/en/publications/climate-change-may-impair-electricity-generation-and-economic-via","text":"External Repository"},{"id":466413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bolivia, Brazil, Columbia, Ecuador, Peru","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.57122569097338,\n 2.4107002353517686\n ],\n [\n -81.9109724474083,\n -5.429778165105471\n ],\n [\n -78.26516746011178,\n -12.916145556642341\n ],\n [\n -73.57398965219471,\n -18.62226237131766\n ],\n [\n -50.28564803197031,\n -17.244232060021176\n ],\n [\n -53.53360812090195,\n -0.4560420725219885\n ],\n [\n -61.27942669326315,\n 0.881532799542029\n ],\n [\n -80.57122569097338,\n 2.4107002353517686\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"71","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Almeida, Rafael M.","contributorId":348451,"corporation":false,"usgs":false,"family":"Almeida","given":"Rafael M.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923457,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleischmann, Ayan S.","contributorId":348452,"corporation":false,"usgs":false,"family":"Fleischmann","given":"Ayan S.","affiliations":[{"id":83365,"text":"Federal University of Rio Grande do Sul","active":true,"usgs":false}],"preferred":false,"id":923458,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breda, Joao P.F.","contributorId":348453,"corporation":false,"usgs":false,"family":"Breda","given":"Joao P.F.","affiliations":[{"id":83365,"text":"Federal University of Rio Grande do Sul","active":true,"usgs":false}],"preferred":false,"id":923459,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cardoso, Diego S.","contributorId":348454,"corporation":false,"usgs":false,"family":"Cardoso","given":"Diego S.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923460,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Angarita, Hector","contributorId":348455,"corporation":false,"usgs":false,"family":"Angarita","given":"Hector","affiliations":[{"id":83366,"text":"Stockholm Environment Institute Latin America","active":true,"usgs":false}],"preferred":false,"id":923461,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collischonn, Walter","contributorId":348456,"corporation":false,"usgs":false,"family":"Collischonn","given":"Walter","affiliations":[{"id":83365,"text":"Federal University of Rio Grande do Sul","active":true,"usgs":false}],"preferred":false,"id":923462,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Forsberg, Bruce R.","contributorId":269690,"corporation":false,"usgs":false,"family":"Forsberg","given":"Bruce","email":"","middleInitial":"R.","affiliations":[{"id":28218,"text":"National Institute of Amazonian Research","active":true,"usgs":false}],"preferred":false,"id":923578,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"García-Villacorta, Roosevelt","contributorId":348457,"corporation":false,"usgs":false,"family":"García-Villacorta","given":"Roosevelt","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923463,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hamilton, Stephen K.","contributorId":348458,"corporation":false,"usgs":false,"family":"Hamilton","given":"Stephen K.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":923464,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hannam, Phillip M.","contributorId":348459,"corporation":false,"usgs":false,"family":"Hannam","given":"Phillip M.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":923465,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Paiva, Rodrigo","contributorId":348564,"corporation":false,"usgs":false,"family":"Paiva","given":"Rodrigo","affiliations":[],"preferred":false,"id":923579,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Poff, N. LeRoy","contributorId":261271,"corporation":false,"usgs":false,"family":"Poff","given":"N.","email":"","middleInitial":"LeRoy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":923580,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923456,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Shi, Qinru","contributorId":287220,"corporation":false,"usgs":false,"family":"Shi","given":"Qinru","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923581,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Gomes, Carla P.","contributorId":177112,"corporation":false,"usgs":false,"family":"Gomes","given":"Carla","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":923582,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Flecker, Alexander S.","contributorId":287016,"corporation":false,"usgs":false,"family":"Flecker","given":"Alexander S.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923583,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70226212,"text":"70226212 - 2021 - Concentration-discharge relationships of dissolved rhenium in Alpine catchments reveal its use as a tracer of oxidative weathering","interactions":[],"lastModifiedDate":"2021-12-10T17:44:08.477064","indexId":"70226212","displayToPublicDate":"2021-10-29T07:27:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Concentration-discharge relationships of dissolved rhenium in Alpine catchments reveal its use as a tracer of oxidative weathering","docAbstract":"Oxidative weathering of sedimentary rocks plays an important role in the global carbon cycle. Rhenium (Re) has been proposed as a tracer of rock organic carbon (OCpetro) oxidation. However, the sources of Re and its mobilization by hydrological processes remain poorly constrained. Here we examine dissolved Re as a function of water discharge, using samples collected from three alpine catchments that drain sedimentary rocks in Switzerland (Erlenbach, Vogelbach) and Colorado, USA (East River). The Swiss catchments reveal a higher Re flux in the catchment with higher erosion rates, but have similar [Re]/[Na+] and [Re]/[SO42-] ratios, which indicate a dominance of Re from OCpetro. Despite differences in rock type and hydro-climatic setting, the three catchments have a positive correlation between river water [Re]/[Na+] and [Re]/[SO42-] and water discharge. We propose that this reflects preferential routing of Re from a near-surface, oxidative weathering zone. The observations support the use of Re as a proxy to trace rock-organic carbon oxidation, and suggest it may be a hydrological tracer of vadose zone processes. We apply the Re proxy, and estimate CO2 release by OCpetro oxidation of 5.7 +6.6/-2.0 tC km-2 yr-1 for the Erlenbach. The overall weathering intensity was ∼40%, meaning that the corresponding export of un-weathered OCpetro in river sediments is large, and the findings call for more measurements of OCpetro oxidation in mountains and rivers as thet cross floodplains.
While a stimulating effect of plant primary productivity on soil carbon dioxide (CO2) emissions has been well documented, links between gross primary productivity (GPP) and wetland methane (CH4) emissions are less well investigated. Determination of the influence of primary productivity on wetland CH4 emissions (FCH4) is complicated by confounding influences of water table level and temperature on CH4 production, which also vary seasonally. Here, we evaluate the link between preceding GPP and subsequent FCH4 at two fens in Wisconsin using eddy covariance flux towers, Lost Creek (US-Los) and Allequash Creek (US-ALQ). Both wetlands are mosaics of forested and shrub wetlands, with US-Los being larger in scale and having a more open canopy. Co-located sites with multi-year observations of flux, hydrology, and meteorology provide an opportunity to measure and compare lag effects on FCH4 without interference due to differing climate. Daily average FCH4 from US-Los reached a maximum of 47.7 ηmol CH4 m−2 s−1 during the study period, while US-ALQ was more than double at 117.9 ηmol CH4 m−2 s−1. The lagged influence of GPP on temperature-normalized FCH4 (Tair-FCH4) was weaker and more delayed in a year with anomalously high precipitation than a following drier year at both sites. FCH4 at US-ALQ was lower coincident with higher stream discharge in the wet year (2019), potentially due to soil gas flushing during high precipitation events and lower water temperatures. Better understanding of the lagged influence of GPP on FCH4 due to this study has implications for climate modeling and more accurate carbon budgeting.
Baseflow is critical to sustaining streamflow in the Upper Colorado River Basin. Therefore, effective water resources management requires estimates of baseflow response to climatic changes. This study provides the first estimates of projected baseflow changes from historical (1984 – 2012) to thirty-year periods centered around 2030, 2050, and 2080 under warm/wet, median, and hot/dry climatic conditions using a hybrid statistical-deterministic baseflow model. Total baseflow supplied to the Lower Colorado River Basin may decline by up to 33%, although this value may increase in the near future by 6% under warm/wet conditions. The percentage of baseflow lost during in-stream transport is projected to increase by 1 - 5% relative to historical conditions. Results highlight that climate driven changes in high elevation hydrology have impacts on basin-wide water availability. Study results have implications for human and ecological water availability in one of the most heavily managed watersheds in the world.
In the deserts of the Southwestern United States, increased off-highway vehicle use can lead to widespread vehicular damage to desert ecosystems. As the popularity and intensity of vehicle use on public lands continues, the Bureau of Land Management (BLM) is challenged to manage the routes used by recreationists while minimizing activity beyond designated routes and mitigating environmental impacts. Ecosystem function and habitat quality can be degraded by vehicle activities, especially when the activities are occurring outside authorized routes or authorized open areas. Restoration mitigates damage to soils and vegetation; however, methods vary across the desert, results appear to be inconsistent, and standardized monitoring plans do not exist. The Desert Renewable Energy Conservation Plan Land Use Plan Amendment to the California Desert Conservation Area Land Use Plan identified the need for, and directed implementation of, standardized monitoring of restoration, which includes minimizing surface disturbance to agency prescribed levels in areas of critical environmental concern and on California Desert National Conservation Lands. To assist the BLM in implementing the Desert Renewable Energy Conservation Plan Land Use Plan Amendment, we define ecological restoration as the process of halting or minimizing future degradation while simultaneously assisting the recovery of ecosystem function and community composition in relation to intact reference sites. The monitoring strategies provided in this protocol are used to restore degraded ecosystems after use of non-routes has ceased (non-designated routes or vehicle-caused linear disturbances) by applying techniques to improve edaphic properties, hydrologic function, and biotic community composition. This protocol also provides criteria that can be used to distinguish the status of non-routes and land parcels as “restored” or “disturbed.” This protocol was developed by the U.S. Geological Survey, in collaboration with BLM restoration practitioners, to identify standard restoration methods and establish criteria to determine when restoration is achieved. This protocol also develops new methods to increase restoration rates and successes on public lands in the southern California deserts. BLM’s long-term implementation plan for the evaluation of road restoration described in this report is to transition toward managing the work, including developing the workforce and long-term storage and management of the data during the next several years. This report is intended to be regularly updated as the program develops.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A17","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Esque, T.C., Jackson, K.R., Rice, A.M., Childers, J.K., Woods, C.S., Fesnock-Parker, A., Johnson, A.C., Price, L.J., Forgrave, K.E., Scoles-Sciulla, S.J., and DeFalco, L.A., 2021, Protocol for route restoration in California’s desert renewable energy conservation plan area: U.S. Geological Survey Techniques and Methods 2-A17, 60 p., https://doi.org/10.3133/tm2A17.","productDescription":"viii, 60 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126835","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":390844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a17/covrthb.jpg"},{"id":390845,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a17/tm2a17.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390846,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/a17/tm2a17.xml"},{"id":390847,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/a17/images"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.09204101562501,\n 35.137879119634185\n ],\n [\n -116.03759765625,\n 35.137879119634185\n ],\n [\n -116.03759765625,\n 36.4566360115962\n ],\n [\n -118.09204101562501,\n 36.4566360115962\n ],\n [\n -118.09204101562501,\n 35.137879119634185\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Alluvial aquifers in Iowa have more wells with nitrate exceeding drinking-water standards than other aquifers; are susceptible to contamination by organic contaminants; and have high concentrations of naturally occurring iron and manganese in depositional areas that contain abundant organic matter. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, Iowa, studied the Cedar River alluvial aquifer in Linn County, Iowa, from 1990 to 2019 to understand the effect of municipal pumping on spatial and temporal hydrologic and water-quality variability. The Cedar River alluvial aquifer is the source of water for the city of Cedar Rapids, Iowa. Withdrawal of large quantities of water for municipal and industrial supply has altered the normal flow of water in the alluvial aquifer. Pumping induces flow from the Cedar River and the underlying bedrock aquifer into the alluvial aquifer.
Water quality in the alluvial aquifer varies along the Cedar River. Changes in nitrate, ammonia, manganese, and iron in the alluvial aquifer are seen as the upstream free-flowing reach of the Cedar River transitions to a partially regulated downstream reach, likely because of differences in reduction-oxidation conditions in the aquifer, which are controlled by infiltration from the Cedar River under normal conditions and when wells are being pumped. Nitrate, normally found in oxygenated environments, had the highest concentrations in the most upstream wells in the Seminole well field and the lowest concentrations in the most downstream wells in the East well field. In contrast, ammonia, manganese, and iron, normally found in greatest abundance in anoxic (reducing) conditions, had the greatest concentrations in the most downstream wells. Additionally, dissolved nitrate plus nitrite nitrogen concentrations in wells were substantially less and manganese concentrations were greater in production wells near backwater wetlands in contrast to wells near the Cedar River.
Temporal variability in water quality in the alluvial aquifer was driven by pumping that increased flow from the Cedar River into the alluvial aquifer and ultimately led to changes in reduction-oxidation conditions of the aquifer. Increasing dissolved nitrate plus nitrite nitrogen concentrations in the Cedar River from 1990 to 2019 were mirrored in the alluvial aquifer. Anoxic conditions are prevalent in the alluvial aquifer next to the Cedar River when the aquifer is not under pumping stress. However, production well pumping caused induced infiltration of oxygenated river water into the aquifer resulting in increased dissolved nitrate plus nitrite nitrogen concentrations and pesticides and decreased naturally occurring dissolved iron and manganese.
Hydrologic and water-quality conditions in the Cedar River alluvial aquifer from 1990 to 2019 provide baseline conditions needed to evaluate the effects of current and future nutrient reduction efforts and land-use changes in the Cedar River Basin on water quality of the Cedar River alluvial aquifer and its source water, the Cedar River. This summary and analysis provide information that can assist the City of Cedar Rapids Utilities Water Department in managing groundwater resources, and provides information that could be used develop a groundwater-quality model to characterize variability over larger areas of the alluvial aquifer, allowing water providers to plan for future water needs of their users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215110","usgsCitation":"Kalkhoff, S.J., 2021, Hydrologic and water-quality conditions in the Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019: U.S. Geological Survey Scientific Investigations Report 2021–5110, 61 p., https://doi.org/10.3133/sir20215110.","productDescription":"Report: ix, 61 p.; Data Release; Dataset","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-121189","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5110/coverthb.jpg"},{"id":390748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5110/sir20215110.pdf","text":"Report","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5110"},{"id":390749,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z7VKOU","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Hydrologic and water quality data from the Cedar River and Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019"},{"id":390750,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Iowa","county":"Linn County","otherGeospatial":"Cedar River Alluvial Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.3649,42.2964],[-91.3651,42.2082],[-91.3653,42.1215],[-91.3661,42.0343],[-91.3669,41.948],[-91.3677,41.8603],[-91.4836,41.8608],[-91.5989,41.8612],[-91.716,41.862],[-91.8318,41.8617],[-91.8329,41.9485],[-91.8338,42.0366],[-91.8342,42.1242],[-91.8328,42.2087],[-91.8319,42.2987],[-91.7153,42.2971],[-91.5969,42.2959],[-91.4809,42.296],[-91.3649,42.2964]]]},\"properties\":{\"name\":\"Linn\",\"state\":\"IA\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The Great Dismal Swamp is a peat wetland in the Coastal Plain of southeastern Virginia and northeastern North Carolina. Timber harvesting and the construction of ditches to drain the swamp and facilitate the harvesting are collectively implicated in changes that altered the wetland forests, caused subsidence and decomposition of the peat, and increased the risk of fire. In response to these changes, managers have implemented strategies to control water levels and rewet the swamp using a network of 64 adjustable-height, water-control structures on the ditches. Rewetting the swamp is intended to re-establish the original wetland-forest types, reduce the risk of fire, reduce subsidence and decomposition of the peat, enhance peat accretion, and reduce the risk of fire. Knowledge of responses of the swamp to hydrologic controls, however, is critical to developing and implementing effective management goals and strategies. Because the 2008 South One fire reemphasized the need for this knowledge, the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service began studies in 2009 to identify critical hydrologic controls and responses to these controls.
These studies identified water sources, topography, the two-layered hydraulic characteristics of the peat, the absence of peat in some areas, the ditch and road network, water-control structures on the ditches, the Dismal Swamp Canal and associated infrastructure, and wetland forests as the primary hydrologic controls. Precipitation is the only water source across much of the swamp. The eastward flow of streams and groundwater from the Isle of Wight Plain, across the Suffolk scarp, and into the swamp are additional water sources to the western part of the swamp. Vertical differences in the hydraulic characteristics of the peat reflect an upper peat having a high hydraulic conductivity and specific yield overlying a lower peat and sand having lower hydraulic conductivity and specific yield. The upper peat forms the main aquifer for the storage, flow, and release of water from the swamp. Maintaining water in the upper peat is critical to water availability to the wetland forests because of these properties.
Groundwater flows from the swamp into the ditches and the Dismal Swamp Canal where it discharges into nearby streams. Discharge typically is to the closest ditch except where a spoil-pile road that impedes flow intervenes between the swamp and the ditch. When groundwater levels in a ditch are about 2 feet lower than levels in the other three ditches surrounding a part of the swamp, however, most groundwater typically discharges to the ditch having the lower level. This occurs even if a spoil-pile road intervenes between the swamp and the ditch having the lower level. Flow to a single ditch shifts watershed boundaries and groundwater divides toward the ditches having higher water levels and demonstrates how flow and discharge are controlled by ditch water levels. Consequently, managing water levels based on these and other hydrologic controls and responses is critical to achieving management objectives.
The chemistry of water across the swamp shows the effects of the peat. Dissolved organic carbon concentrations in the groundwater are among the highest reported globally, ranging from 55 to 195 milligrams per liter. The pH of groundwater and ditch water is commonly less than 4.0 standard units because of organic acids. A relation between the pH and specific conductance of groundwater and ditch water reflects water sources, flow paths, and the chemical evolution, as waters from the different sources mix and flow along the paths.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205100","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Speiran, G.K., and Wurster, F.C., 2021, Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies: U.S. Geological Survey Scientific Investigations Report 2020-5100, 104 p., https://doi.org/10.3133/sir20205100.","productDescription":"xii, 104 p.","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108950","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":436139,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZVW9C8","text":"USGS data release","linkHelpText":"Hydrologic, water-quality, fire, forest-cover, and other data, the Great Dismal Swamp, Virginia and North Carolina"},{"id":390256,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205100/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2020-5100"},{"id":390255,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5100/images"},{"id":390252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5100/coverthb.jpg"},{"id":390253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5100/sir20205100.pdf","text":"Report","size":"20.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5100"},{"id":390254,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5100/sir20205100.XML"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Great Dismal Swamp","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.651611328125,\n 36.575835338491736\n ],\n [\n -76.65710449218749,\n 36.41244153535644\n ],\n [\n -76.5142822265625,\n 36.32397712011261\n ],\n [\n -76.3714599609375,\n 36.36822190085109\n ],\n [\n -76.25061035156251,\n 36.4345419190089\n ],\n [\n -76.2835693359375,\n 36.85325222344016\n ],\n [\n -76.4483642578125,\n 36.87522650673951\n ],\n [\n -76.61865234374999,\n 36.84006462037767\n ],\n [\n -76.651611328125,\n 36.575835338491736\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Study Region: Rocky Mountains, United States
Study Focus: Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer.
New hydrological insights for region: In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100949","usgsCitation":"Newman, C.P., Kisfalusi, Z.D., and Holmberg, M.J., 2021, Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA: Journal of Hydrology: Regional Studies, v. 38, p. 1-20, https://doi.org/10.1016/j.ejrh.2021.100949.","productDescription":"100949, 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":450390,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100949","text":"Publisher Index Page"},{"id":436141,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W7DHLY","text":"USGS data release","linkHelpText":"Water-level and well-discharge data related to aquifer testing in Wet Mountain Valley, Colorado, 2019"},{"id":390678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains, Wet Mountain Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.85052490234375,\n 38.31149091244452\n ],\n [\n -105.50033569335938,\n 37.79676317682161\n ],\n [\n -105.08010864257812,\n 37.95394377350263\n ],\n [\n -105.47012329101562,\n 38.449286817153556\n ],\n [\n -105.85052490234375,\n 38.31149091244452\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kisfalusi, Zachary D. 0000-0001-6016-3213","orcid":"https://orcid.org/0000-0001-6016-3213","contributorId":222422,"corporation":false,"usgs":true,"family":"Kisfalusi","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmberg, Michael J. 0000-0002-1316-0412 mholmber@usgs.gov","orcid":"https://orcid.org/0000-0002-1316-0412","contributorId":190084,"corporation":false,"usgs":true,"family":"Holmberg","given":"Michael","email":"mholmber@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225524,"text":"70225524 - 2021 - Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","interactions":[],"lastModifiedDate":"2023-11-08T16:34:39.150126","indexId":"70225524","displayToPublicDate":"2021-10-20T08:25:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","docAbstract":"Study region: The study was conducted in the Northern Atlantic Coastal Plain aquifer system, eastern USA, an important water supply in a densely populated region.
Study focus: Manganese (Mn), an emerging health concern and common nuisance contaminant in drinking water, is mapped and modeled using the XGBoost machine learning method, predictions of pH and redox conditions from previous models, and other explanatory variables that describe the groundwater flow system and surface characteristics. Methods to address the imbalanced occurrence of elevated and low Mn concentrations are compared and used to more accurately predict concentrations of interest for human health and drinking water quality.
New hydrological insights for the region: Elevated Mn concentrations were more likely in shallow groundwater, close to recharge areas and in topographically low areas where soil or unsaturated processes influence groundwater quality. Predicted concentrations greater than the health threshold of 300 micrograms per liter extended across 17 % of the surficial aquifer area, but across <1% of the areas of underlying aquifers. pH and variables related to flow-system position and near-surface processes were more important predictors than the probability of low dissolved oxygen (DO). Mapped variable influence (SHAP values) showed that both pH and DO variables were related to hydrogeologic conditions. Class weights, which improved the predictive ability for elevated Mn without altering the data, was the preferred method to address class imbalance.
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