Watershed-scale hydrologic models are frequently used to inform conservation and restoration efforts by identifying critical source areas (CSAs; alternatively 'hotspots'), defined as areas that export relatively greater quantities of nutrients and sediment. The CSAs can then be prioritized or ‘targeted’ for conservation and restoration to ensure efficient use of limited resources. However, CSA simulations from watershed-scale hydrologic models may be uncertain and it is critical that the extent and implications of this uncertainty be conveyed to stakeholders and decision makers. We used an ensemble of four independently developed Soil and Water Assessment Tool (SWAT) models and a SPAtially Referenced Regression On Watershed attributes (SPARROW) model to simulate CSA locations for flow, phosphorus, nitrogen, and sediment within the ~17,000-km2 Maumee River watershed at the HUC-12 scale. We then assessed uncertainty in CSA simulations determined as the variation in CSA locations across the models. Our application of an ensemble of models - differing with respect to inputs, structure, and parameterization - facilitated an improved accounting of CSA prediction uncertainty. We found that the models agreed on the location of a subset of CSAs, and that these locations may be targeted with relative confidence. However, models more often disagreed on CSA locations. On average, only 16%–46% of HUC-12 subwatersheds simulated as a CSA by one model were also simulated as a CSA by a different model. Our work shows that simulated CSA locations are highly uncertain and may vary substantially across models. Hence, while models may be useful in informing conservation and restoration planning, their application to identify CSA locations would benefit from comprehensive uncertainty analyses to avoid inefficient use of limited resources.
Plants vary in their hydrological and climatic niches. How these niche dimensions covary among closely related species can help identify co‐adaptations to hydrological and climatic factors, as well as predict biodiversity responses to environmental change.
Western United States.
Relationships between riparian dependence and climate niches of willows (Salix L.) were assessed, incorporating phylogenetics and functional traits to understand the adaptive nature of those relationships. The riparian dependence niche was estimated as the mean distance between georeferenced occurrence records and the nearest stream based on the National Hydrography Database. Results were compared to oaks (Quercus L.), a less riparian‐dependent clade, with the expectation of different niche relationships.
Willows generally occurred closer to streams than expected by chance, but riparian dependence varied substantially among species. Riparian dependence was positively correlated with mean annual temperature and diurnal temperature range niche, both indicators of atmospheric demand on evapotranspiration. Phylogenetic independent contrast correlations for these relationships were significant as well, and the high degree of niche convergence among species indicated evolutionarily labile co‐adaptations to riparian dependence and atmospheric demand. Plant height increased with mean annual temperature niche, and specific leaf area increased with residual variation in height, indicating underlying morphological correlates of niche variation. Oaks, on the other hand, exhibited no relationship between atmospheric demand and riparian dependence, and weaker niche relationships with riparian dependence overall.
These results support the assertion that hydric‐adapted, woody riparian plants compensate for increased atmospheric demand on transpiration with a reliable supply of water provided by riparian habitats and that this trade‐off may be unique from mesic–xeric woody plants. Conservation of warm‐adapted riparian trees and shrubs under increasing temperatures and atmospheric demand may necessitate reversal of groundwater depletion. Cool‐adapted species may be best conserved through maintenance or expansion of riparian buffers as they become more riparian obligate with warming.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13192","usgsCitation":"Butterfield, B.J., Palmquist, E.C., and Hultine, K.R., 2021, Regional coordination between riparian dependence and atmospheric demand in willows (Salix L.) of western North America: Diversity and Distributions, v. 27, no. 21, p. 377-388, https://doi.org/10.1111/ddi.13192.","productDescription":"12 p.","startPage":"377","endPage":"388","ipdsId":"IP-116352","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454296,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13192","text":"Publisher Index Page"},{"id":382510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.24609374999999,\n 32.54681317351514\n ],\n [\n -114.60937499999999,\n 32.62087018318113\n ],\n [\n -110.830078125,\n 31.353636941500987\n ],\n [\n -108.19335937499999,\n 31.57853542647338\n ],\n [\n -105.64453124999999,\n 27.994401411046148\n ],\n [\n -105.64453124999999,\n 26.03704188651584\n ],\n [\n -104.23828125,\n 25.085598897064752\n ],\n [\n -102.74414062499999,\n 27.839076094777816\n ],\n [\n -101.07421875,\n 25.085598897064752\n ],\n [\n -99.49218749999999,\n 22.268764039073968\n ],\n [\n -97.119140625,\n 26.03704188651584\n ],\n [\n -96.591796875,\n 28.69058765425071\n ],\n [\n -93.42773437499999,\n 29.6880527498568\n ],\n [\n -92.724609375,\n 38.34165619279595\n ],\n [\n -93.69140625,\n 42.293564192170095\n ],\n [\n -96.591796875,\n 46.558860303117164\n ],\n [\n -105.556640625,\n 51.069016659603896\n ],\n [\n -113.90625,\n 52.05249047600099\n ],\n [\n -118.740234375,\n 52.96187505907603\n ],\n [\n -119.091796875,\n 49.89463439573421\n ],\n [\n -122.16796875,\n 49.15296965617042\n ],\n [\n -124.98046874999999,\n 48.40003249610685\n ],\n [\n -124.45312499999999,\n 43.26120612479979\n ],\n [\n -124.541015625,\n 39.70718665682654\n ],\n [\n -121.37695312499999,\n 35.53222622770337\n ],\n [\n -117.24609374999999,\n 32.54681317351514\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"21","noUsgsAuthors":false,"publicationDate":"2020-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":801745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":801746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hultine, Kevin R. 0000-0001-9747-6037","orcid":"https://orcid.org/0000-0001-9747-6037","contributorId":23772,"corporation":false,"usgs":true,"family":"Hultine","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":801747,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217910,"text":"70217910 - 2021 - Multi-year hydroclimatic droughts and pluvials across the conterminous United States","interactions":[],"lastModifiedDate":"2021-03-19T20:26:17.083435","indexId":"70217910","displayToPublicDate":"2020-11-05T08:18:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2032,"text":"International Journal of Climatology","active":true,"publicationSubtype":{"id":10}},"title":"Multi-year hydroclimatic droughts and pluvials across the conterminous United States","docAbstract":"Time series of water‐year runoff for 2,109 hydrologic units (HUs) across the conterminous United States (CONUS) for the 1900 through 2014 period were used to identify drought and pluvial (i.e., wet) periods. Characteristics of the drought and pluvial events including frequency, duration, and severity were examined and compared. Additionally, a similar analysis was performed using gridded tree‐ring reconstructions of the Palmer Drought Severity Index (PDSI) for the period 1475 through 2005 to place the drought and pluvial characteristics determined using water‐year runoff for 1900 through 2014 in the context of multi‐century climate variability. The temporal and spatial variability of droughts and pluvials determined using runoff for the 1900 through 2014 period indicated that most drought events in the CONUS occurred before about 1970, whereas most pluvial periods occurred after about 1970. This change in the frequencies of drought and pluvial events around 1970 was largely related to an increase in fall (October through December) precipitation across much of the central United States. Also, the duration and severity of droughts and pluvials identified using runoff for the 1900 through 2014 period generally were not significantly different from the drought and pluvial characteristics identified using the PDSI for the 1475 through 2005 period.
The increasing availability of high‐frequency freshwater ecosystem metabolism data provides an opportunity to identify links between metabolic regimes, as gross primary production and ecosystem respiration patterns, and consumer energetics with the potential to improve our current understanding of consumer dynamics (e.g., population dynamics, community structure, trophic interactions). We describe a conceptual framework linking metabolic regimes of flowing waters with consumer community dynamics. We use this framework to identify three emerging research needs: (1) quantifying the linkage of metabolism and consumer production data via food web theory and carbon use efficiencies, (2) evaluating the roles of metabolic dynamics and other environmental regimes (e.g., hydrology, light) in consumer dynamics, and (3) determining the degree to which metabolic regimes influence the evolution of consumer traits and phenology. Addressing these needs will improve the understanding of consumer biomass and production patterns as metabolic regimes can be viewed as an emergent property of food webs.
Hydrodynamic processes can lead to the accumulation and/or dispersal of water column constituents, including sediment, phytoplankton, and particulate detritus. Using a combination of field observations and stable isotope tracing tools, we identified how hydrodynamic processes influenced physical habitat, pelagic communities, and food web structure in a freshwater tidal system. The pelagic habitat of a terminal channel differed spatially, likely aligning with differences in hydrodynamics. Three zones that we classified by exchange with downstream habitat had distinct water quality characteristics, supported different densities of zooplankton and nekton, and exhibited disparate support from benthic and pelagic trophic pathways to pelagic consumers. Hydrodynamically driven zones and their emergent characteristics appeared sensitive to hydrology, as elevated runoff was correlated with a shift in hydrodynamic habitat and organismal distributions. The results of our study highlight the relationship between hydrodynamic processes, biological responses, and climate, and suggest that understanding the physical process can improve understanding of pelagic habitats and communities.
","language":"English","publisher":"Wiley","doi":"10.1002/iroh.202002063","usgsCitation":"Young, M.J., Feyrer, F.V., Stumpner, P., Violette, V.L., Patton, O., and Brown, L.R., 2021, Hydrodynamics drive pelagic communities and food web structure in a tidal environment: International Review of Hydrobiology, v. 106, no. 2, p. 69-85, https://doi.org/10.1002/iroh.202002063.","productDescription":"17 p.","startPage":"69","endPage":"85","ipdsId":"IP-120317","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":454361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/iroh.202002063","text":"Publisher Index Page"},{"id":382245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75872802734375,\n 38.017803980061124\n ],\n [\n -121.497802734375,\n 38.017803980061124\n ],\n [\n -121.497802734375,\n 38.59326051987162\n ],\n [\n -121.75872802734375,\n 38.59326051987162\n ],\n [\n -121.75872802734375,\n 38.017803980061124\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806328,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806329,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stumpner, Paul 0000-0002-0933-7895 pstump@usgs.gov","orcid":"https://orcid.org/0000-0002-0933-7895","contributorId":5667,"corporation":false,"usgs":true,"family":"Stumpner","given":"Paul","email":"pstump@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Violette, Veronica L. 0000-0002-7390-4655 vviolette@usgs.gov","orcid":"https://orcid.org/0000-0002-7390-4655","contributorId":222824,"corporation":false,"usgs":true,"family":"Violette","given":"Veronica","email":"vviolette@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806331,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patton, Oliver 0000-0002-2911-7718","orcid":"https://orcid.org/0000-0002-2911-7718","contributorId":218217,"corporation":false,"usgs":true,"family":"Patton","given":"Oliver","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806332,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806333,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220102,"text":"70220102 - 2021 - Summer runoff generation in foothill catchments of the Colorado Front Range","interactions":[],"lastModifiedDate":"2021-04-21T12:06:38.659758","indexId":"70220102","displayToPublicDate":"2020-10-20T06:54:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Summer runoff generation in foothill catchments of the Colorado Front Range","docAbstract":"Climatic shifts, disturbances, and land-use change can alter hydrologic flowpaths, water quality, and water supply to downstream communities. Prior research investigating streamflow generation processes in mountainous areas has largely focused on high-elevation alpine and subalpine catchments; less is known about these processes in lower-elevation foothills and montane catchments. In these lower-elevation ecoregions, precipitation shifts seasonally from snow to rain, which can result in differing seasonal flowpaths. We analyzed stream water for electrical conductivity, SiO2, Ca, Mg, Na, Cl, SO4, K, and dissolved organic carbon on both a weekly and storm event basis from April to August 2018 in three small (<10 km2) foothill catchments, and one larger (63.2 km2) catchment extending from the foothills to the subalpine ecoregions, in the Colorado Front Range. Using two end-member hydrograph separations and concentration-runoff relationships, we inferred the dominant catchment-scale flowpaths of precipitation to the streams. We selected catchments with varying land use to investigate the relationship between these characteristics and hydrologic flowpaths. We observed that concentrations of lithogenic constituents generally increased and dissolved organic carbon decreased as seasonal runoff decreased in the three foothill catchments, reflecting a transition from shallow subsurface flowpaths to deeper subsurface flowpaths. Elevated SO4 and Cl concentrations during low-flow periods in two of our catchments suggest that historical or current anthropogenic activities, such as mining, application of road salt, and/or near-stream septic systems, affect local stream and groundwater chemistry. In a foothill catchment with anthropogenic and geologic impervious surfaces, streamflow during storm responses was sourced from faster, surficial flowpaths compared to a less disturbed neighboring catchment, highlighting the influence of anthropogenic land-use on runoff generation. This study provides insight into the fundamental hydrology of foothill catchments and how they may function in the future with human development, precipitation shifts and disturbances.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.125672","usgsCitation":"Bukoski, I.S., Murphy, S.F., Birch, A.L., and Barnard, H.R., 2021, Summer runoff generation in foothill catchments of the Colorado Front Range: Journal of Hydrology, v. 595, 125672, 13 p., https://doi.org/10.1016/j.jhydrol.2020.125672.","productDescription":"125672, 13 p.","ipdsId":"IP-117845","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":454362,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2020.125672","text":"Publisher Index Page"},{"id":385217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.083984375,\n 39.757879992021756\n ],\n [\n -104.765625,\n 39.757879992021756\n ],\n [\n -104.765625,\n 40.212440718286466\n ],\n [\n -106.083984375,\n 40.212440718286466\n ],\n [\n -106.083984375,\n 39.757879992021756\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"595","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bukoski, Isaac S.","contributorId":257521,"corporation":false,"usgs":false,"family":"Bukoski","given":"Isaac","email":"","middleInitial":"S.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":814487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":814488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birch, Andrew L.","contributorId":257522,"corporation":false,"usgs":false,"family":"Birch","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":814489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnard, Holly R.","contributorId":257523,"corporation":false,"usgs":false,"family":"Barnard","given":"Holly","email":"","middleInitial":"R.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":814490,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219196,"text":"70219196 - 2021 - Signatures of hydrologic function across the critical zone observatory network","interactions":[],"lastModifiedDate":"2021-03-30T12:05:44.485187","indexId":"70219196","displayToPublicDate":"2020-10-18T06:50:52","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":"Signatures of hydrologic function across the critical zone observatory network","docAbstract":"Despite a multitude of small catchment studies, we lack a deep understanding of how variations in critical zone architecture lead to variations in hydrologic states and fluxes. This study characterizes hydrologic dynamics of 15 catchments of the U.S. Critical Zone Observatory (CZO) network where we hypothesized that our understanding of subsurface structure would illuminate patterns of hydrologic partitioning. The CZOs collect data sets that characterize the physical, chemical, and biological architecture of the subsurface, while also monitoring hydrologic fluxes such as streamflow, precipitation, and evapotranspiration. For the first time, we collate time series of hydrologic variables across the CZO network and begin the process of examining hydrologic signatures across sites. We find that catchments with low baseflow indices and high runoff sensitivity to storage receive most of their precipitation as rain and contain clay‐rich regolith profiles, prominent argillic horizons, and/or anthropogenic modifications. In contrast, sites with high baseflow indices and low runoff sensitivity to storage receive the majority of precipitation as snow and have more permeable regolith profiles. The seasonal variability of water balance components is a key control on the dynamic range of hydraulically connected water in the critical zone. These findings lead us to posit that water balance partitioning and streamflow hydraulics are linked through the coevolution of critical zone architecture but that much work remains to parse these controls out quantitatively.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR026635","usgsCitation":"Wlostowski, A.N., Molotch, N.P., Anderson, S.P., Brantley, S.L., Chorover, J., Dralle, D., Kumar, P., Li, L., Lohse, K.A., Mallard, J., McIntosh, J.C., Murphy, S.F., Parrish, E., Safeeq, M., Seyfried, M., Shi, Y., and Harman, C., 2021, Signatures of hydrologic function across the critical zone observatory network: Water Resources Research, v. 57, no. 3, e2019WR026635, 28 p., https://doi.org/10.1029/2019WR026635.","productDescription":"e2019WR026635, 28 p.","ipdsId":"IP-117846","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":454369,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr026635","text":"Publisher Index Page"},{"id":384750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Wlostowski, Adam N. 0000-0001-5703-9916","orcid":"https://orcid.org/0000-0001-5703-9916","contributorId":191365,"corporation":false,"usgs":false,"family":"Wlostowski","given":"Adam","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":813172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Molotch, Noah P. 0000-0003-4733-8060","orcid":"https://orcid.org/0000-0003-4733-8060","contributorId":203466,"corporation":false,"usgs":false,"family":"Molotch","given":"Noah","email":"","middleInitial":"P.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":813173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Suzanne P. 0000-0002-6796-6649","orcid":"https://orcid.org/0000-0002-6796-6649","contributorId":172732,"corporation":false,"usgs":false,"family":"Anderson","given":"Suzanne","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":813174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brantley, Susan L. 0000-0003-4320-2342","orcid":"https://orcid.org/0000-0003-4320-2342","contributorId":184201,"corporation":false,"usgs":false,"family":"Brantley","given":"Susan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":813175,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chorover, Jon 0000-0001-9497-0195","orcid":"https://orcid.org/0000-0001-9497-0195","contributorId":139472,"corporation":false,"usgs":false,"family":"Chorover","given":"Jon","email":"","affiliations":[],"preferred":false,"id":813176,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dralle, David 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0000-0002-1641-3710","orcid":"https://orcid.org/0000-0002-1641-3710","contributorId":197290,"corporation":false,"usgs":false,"family":"Li","given":"Li","affiliations":[],"preferred":false,"id":813179,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lohse, Kathleen A. 0000-0003-1779-6773","orcid":"https://orcid.org/0000-0003-1779-6773","contributorId":196995,"corporation":false,"usgs":false,"family":"Lohse","given":"Kathleen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":813180,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mallard, John 0000-0002-0494-9024","orcid":"https://orcid.org/0000-0002-0494-9024","contributorId":256757,"corporation":false,"usgs":false,"family":"Mallard","given":"John","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":813181,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McIntosh, Jennifer C. 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Eric","contributorId":256760,"corporation":false,"usgs":false,"family":"Parrish","given":"Eric","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":813184,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Safeeq, Mohammad 0000-0003-0529-3925","orcid":"https://orcid.org/0000-0003-0529-3925","contributorId":77814,"corporation":false,"usgs":false,"family":"Safeeq","given":"Mohammad","email":"","affiliations":[{"id":6641,"text":"University of California at Merced","active":true,"usgs":false}],"preferred":false,"id":813185,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Seyfried, Mark 0000-0001-8081-0713","orcid":"https://orcid.org/0000-0001-8081-0713","contributorId":256763,"corporation":false,"usgs":false,"family":"Seyfried","given":"Mark","email":"","affiliations":[{"id":51849,"text":"United States Department of Agriculture - Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813186,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Shi, Yuning 0000-0003-0118-5847","orcid":"https://orcid.org/0000-0003-0118-5847","contributorId":256765,"corporation":false,"usgs":false,"family":"Shi","given":"Yuning","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":813187,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Harman, Ciaran 0000-0002-3185-002X","orcid":"https://orcid.org/0000-0002-3185-002X","contributorId":242780,"corporation":false,"usgs":false,"family":"Harman","given":"Ciaran","email":"","affiliations":[{"id":48526,"text":"Department of Environmental Health and Engineering, Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":813188,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70216960,"text":"70216960 - 2021 - Geochemical and geophysical indicators of oil and gas wastewater can trace potential exposure pathways following releases to surface waters","interactions":[],"lastModifiedDate":"2020-12-18T12:54:58.942717","indexId":"70216960","displayToPublicDate":"2020-10-14T06:48:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical and geophysical indicators of oil and gas wastewater can trace potential exposure pathways following releases to surface waters","docAbstract":"Releases of oil and gas (OG) wastewaters can have complex effects on stream-water quality and downstream organisms, due to sediment-water interactions and groundwater/surface water exchange. Previously, elevated concentrations of sodium (Na), chloride (Cl), barium (Ba), strontium (Sr), and lithium (Li), and trace hydrocarbons were determined to be key markers of OG wastewater releases when combined with Sr and radium (Ra) isotopic compositions. Here, we assessed the persistence of an OG wastewater spill in a creek in North Dakota using a combination of geochemical measurements and modeling, hydrologic analysis, and geophysical investigations. OG wastewater comprised 0.1 to 0.3% of the stream-water compositions at downstream sites in February and June 2015 but could not be quantified in 2016 and 2017. However, OG-wastewater markers persisted in sediments and pore water for 2.5 years after the spill and up to 7.2-km downstream from the spill site. Concentrations of OG wastewater constituents were highly variable depending on the hydrologic conditions. Electromagnetic measurements indicated substantially higher electrical conductivity under the bank adjacent to a seep 7.2 km downstream from the spill site. Geomorphic investigations revealed mobilization of sediment is an important contaminant transport process. Labile Ba, Ra, Sr, and ammonium (NH4) concentrations extracted from sediments indicated sediments are a long-term reservoir of these constituents, both in the creek and on the floodplain. Using the drivers of ecological effects identified at this intensively studied site we identified 41 watersheds across the North Dakota landscape that may be subject to similar episodic inputs from OG wastewater spills. Effects of contaminants released to the environment during OG waste management activities remain poorly understood; however, analyses of Ra and Sr isotopic compositions, as well as trace inorganic and organic compound concentrations at these sites in pore-water provide insights into potentials for animal and human exposures well outside source-remediation zones.
The movement of salt marshes into uplands and marsh submergence as sea level rises is well documented; however, predicting how coastal marshes will respond to rising sea levels is constrained by a lack of process-based understanding of how various marsh zones adjust to changes in sea level. To assess the way in which salt marsh zones differ in their elevation response to sea-level change, and to evaluate how potential hydrologic drivers influence the response, surface elevation tables, marker horizons, and shallow rod surface elevation tables were installed in a Virginia salt marsh in three zones that differed in elevation and vegetation type. Decadal rates of elevation change, surface accretion, and shallow subsidence or expansion were examined in the context of hydrologic drivers that included local sea-level rise, flooding frequency, hurricane storm surge, and precipitation. Surface elevation increases were fastest in the low-elevation zone, intermediate in the middle-elevation zone, and slowest in the high-elevation zone. These rates are similar to (low and middle marsh) or less than (high marsh) local rates of sea-level rise. Root zone expansion, presumably due to root growth and organic matter accumulation, varied among the three salt marsh zones and accounted for 37%, but probably more, of the increase in marsh surface elevation. We infer that, during marsh transgression, soil-forming processes shift from biogenic (high marsh) to minerogenic (low marsh) in response, either directly or indirectly, to changing hydrologic drivers.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00796-z","usgsCitation":"Blum, L.K., Christian, R., Cahoon, D., and Wiberg, P.L., 2021, Processes influencing marsh elevation change in low- and high-elevation zones of a temperate salt marsh: Estuaries and Coasts, v. 44, p. 818-833, https://doi.org/10.1007/s12237-020-00796-z.","productDescription":"16 p.","startPage":"818","endPage":"833","onlineOnly":"Y","ipdsId":"IP-111984","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":377331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","county":"Northampton County","otherGeospatial":"Phillips Creek Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.7342529296875,\n 37.91170058826019\n ],\n [\n -75.97320556640625,\n 37.56417412088097\n ],\n [\n -76.07757568359375,\n 37.25000751785145\n ],\n [\n -75.97869873046874,\n 37.083666782415534\n ],\n [\n -75.7781982421875,\n 37.21064411993447\n ],\n [\n -75.59967041015625,\n 37.55328764595765\n ],\n [\n -75.5145263671875,\n 37.80544394934271\n ],\n [\n -75.7342529296875,\n 37.91170058826019\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Blum, Linda K. 0000-0002-5252-6106","orcid":"https://orcid.org/0000-0002-5252-6106","contributorId":208046,"corporation":false,"usgs":false,"family":"Blum","given":"Linda","email":"","middleInitial":"K.","affiliations":[{"id":37559,"text":"University of Virginia, Charlottesville, VA","active":true,"usgs":false}],"preferred":false,"id":795480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christian, Robert R.","contributorId":237855,"corporation":false,"usgs":false,"family":"Christian","given":"Robert R.","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":795481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahoon, Donald R. 0000-0002-2591-5667","orcid":"https://orcid.org/0000-0002-2591-5667","contributorId":219657,"corporation":false,"usgs":true,"family":"Cahoon","given":"Donald","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiberg, Patricia L.","contributorId":237856,"corporation":false,"usgs":false,"family":"Wiberg","given":"Patricia","email":"","middleInitial":"L.","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":795483,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217892,"text":"70217892 - 2021 - Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications","interactions":[],"lastModifiedDate":"2021-02-11T17:40:25.289588","indexId":"70217892","displayToPublicDate":"2020-04-17T06:32:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications","docAbstract":"Spatial information on the distribution of ecosystem patterns and processes can be a critical component of designing and implementing effective management programs in river‐floodplain ecosystems. For example, translating how flood pulses detected within a stream gauge record are spatially manifested across a river‐valley bottom can be used to evaluate whether the current distribution of physical conditions has the potential to support priority habitats or if intervention is needed to meet desired goals. The size and complexity of large river‐floodplain systems can make mapping inundation dynamics a challenging task. We used a geospatial model to simulate 40 years (1972–2011) of daily surface‐water inundation depths for 11,331 km2 of the Upper Mississippi River System floodplain. We identified discrete inundation events at each 4‐m × 4‐m pixel in the model as sequential days of submergence. We then quantified and mapped four aspects of inundation regime – event frequency, duration, magnitude, and timing – for each pixel. The spatial distribution of inundation regime attributes varied within and among multiple levels of river organization, including navigation pools and geomorphic reaches, but only event timing exhibited a strong down‐river trend. Non‐linear relations among inundation attributes and their geospatial distributions likely reflect complex interactions among topographic, hydrologic, and anthropogenic constraints on flooding dynamics. Together, our results reveal spatial gradients in inundation dynamics not captured by hydrologic data alone. Characterizing such diversity in inundation dynamics is important for testing hypotheses about ecological processes, developing models of ecosystem functions, and informing management actions.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3628","usgsCitation":"Van Appledorn, M., De Jager, N.R., and Rohweder, J.J., 2021, Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications: River Research and Applications, v. 37, no. 2, p. 241-255, https://doi.org/10.1002/rra.3628.","productDescription":"15 p.","startPage":"241","endPage":"255","ipdsId":"IP-113745","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":383139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.3955078125,\n 38.51378825951165\n ],\n [\n -88.154296875,\n 40.245991504199026\n ],\n [\n -86.572265625,\n 41.07935114946899\n ],\n [\n -86.7919921875,\n 41.50857729743935\n ],\n [\n -88.0224609375,\n 42.45588764197166\n ],\n [\n -89.3408203125,\n 44.213709909702054\n ],\n [\n -91.7578125,\n 45.85941212790755\n ],\n [\n -92.63671875,\n 46.195042108660154\n ],\n [\n -92.59277343749999,\n 47.724544549099676\n ],\n [\n -94.8779296875,\n 47.249406957888446\n ],\n [\n -95.9326171875,\n 47.30903424774781\n ],\n [\n -95.4052734375,\n 44.84029065139799\n ],\n [\n -93.6474609375,\n 42.13082130188811\n ],\n [\n -93.515625,\n 39.605688178320804\n ],\n [\n -90.3955078125,\n 38.51378825951165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810091,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217687,"text":"70217687 - 2021 - Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data","interactions":[],"lastModifiedDate":"2021-02-08T18:00:13.597366","indexId":"70217687","displayToPublicDate":"2018-12-31T11:58:10","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data","docAbstract":"Airborne electromagnetic (AEM) dataare usedto estimate large-scale model structural geometry, i.e. the spatial distribution of different lithological units based on assumed or estimated resistivity-lithology relationships, and the uncertainty in those structures given imperfect measurements. Geophysically derived estimates of model structural uncertainty are then combined with hydrologic observations to assess the impact of model structural error on hydrologic calibration and prediction errors. Using a synthetic numerical model, we describe a sequential hydrogeophysical approach that: (1) uses Bayesian Markov chain Monte Carlo (McMC) methods to produce a robust estimate of uncertainty in electrical resistivity parameters, (2) combines geophysical parameter uncertainty estimates with borehole observations of lithology to produce probabilistic estimates of model structural uncertainty over the entire AEM survey area using geostatistical sequential indicator simulation algorithms, and (3) uses model structural estimates along with hydrologic observations to quantify both hydrologic parameter and prediction uncertainty using a second McMC sampling algorithm. Results of simulations will be presented that illustrate the complete workflow from geophysical parameter uncertainty analysis to the impact of model structural uncertainty on hydrologic parameter estimates.
","conferenceTitle":"7th International Workshop on Airborne Electromagnetics","conferenceDate":"June 17-20, 2018","conferenceLocation":"Kolding, Denmark","language":"English","publisher":"Aarhus University","usgsCitation":"Minsley, B.J., Christensen, N.K., Christensen, S., and Ley-Cooper, Y., 2021, Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data, 7th International Workshop on Airborne Electromagnetics, Kolding, Denmark, June 17-20, 2018, 4 p.","productDescription":"4 p.","ipdsId":"IP-095925","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":383107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383106,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.conferencemanager.dk/aem2018"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809258,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, Nikolaj K","contributorId":199736,"corporation":false,"usgs":false,"family":"Christensen","given":"Nikolaj","email":"","middleInitial":"K","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":809259,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Steen","contributorId":199737,"corporation":false,"usgs":false,"family":"Christensen","given":"Steen","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":809260,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ley-Cooper, Yusen","contributorId":248494,"corporation":false,"usgs":false,"family":"Ley-Cooper","given":"Yusen","email":"","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":809261,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221393,"text":"70221393 - 2021 - Streamflow, sediment transport, and geomorphic change during the 2011 flood on the Missouri River near Bismarck-Mandan, ND","interactions":[],"lastModifiedDate":"2021-06-15T10:36:19.944894","indexId":"70221393","displayToPublicDate":"2018-08-27T07:47:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2126,"text":"JAWRA","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow, sediment transport, and geomorphic change during the 2011 flood on the Missouri River near Bismarck-Mandan, ND","docAbstract":"Geomorphic change from extreme events in large managed rivers has implications for river management. A steady-state, quasi-three-dimensional hydrodynamic model was applied to a 29-km reach of the Missouri River using 2011 flood data. Model results for an extreme flow (500-year recurrence interval [RI]) and an elevated managed flow (75-year RI) were used to assess sediment mobility through examination of the spatial distribution of boundary or bed shear stress (τb) and longitudinal patterns of average τb, velocity, and kurtosis of τb. Kurtosis of τb was used as an indicator of planform channel complexity and can be applied to other river systems. From differences in longitudinal patterns of sediment mobility for the two flows we can infer: (1) under extreme flow, the channel behaves as a single-thread channel controlled primarily by flow, which enhances the meander pattern; (2) under elevated managed flows, the channel behaves as multithread channel controlled by the interaction of flow with bed and channel topography, resulting in a more complex channel; and (3) for both flows, the model reach lacks a consistent pattern of deposition or erosion, which indicates migration of areas of erosion and deposition within the reach. Despite caveats and limitations, the analysis provides useful information about geomorphic change under extreme flow and potential implications for river management. Although a 500-year RI is rare, extreme hydrologic events such as this are predicted to increase in frequency.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12678","usgsCitation":"Nustad, R.A., Benthem, A.J., Skalak, K., McDonald, R.R., Schenk, E., and Galloway, J.M., 2021, Streamflow, sediment transport, and geomorphic change during the 2011 flood on the Missouri River near Bismarck-Mandan, ND: JAWRA, v. 54, no. 5, p. 1151-1167, https://doi.org/10.1111/1752-1688.12678.","productDescription":"17 p.","startPage":"1151","endPage":"1167","ipdsId":"IP-075678","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":454576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12678","text":"Publisher Index Page"},{"id":386466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","city":"Bismarck","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.0137939453125,\n 45.94351068030587\n ],\n [\n -100.3436279296875,\n 45.94351068030587\n ],\n [\n -100.3436279296875,\n 46.98774725646568\n ],\n [\n -101.0137939453125,\n 46.98774725646568\n ],\n [\n -101.0137939453125,\n 45.94351068030587\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Nustad, Rochelle A. 0000-0002-4713-5944 ranustad@usgs.gov","orcid":"https://orcid.org/0000-0002-4713-5944","contributorId":1811,"corporation":false,"usgs":true,"family":"Nustad","given":"Rochelle","email":"ranustad@usgs.gov","middleInitial":"A.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benthem, Adam J. 0000-0003-2372-0281","orcid":"https://orcid.org/0000-0003-2372-0281","contributorId":220000,"corporation":false,"usgs":true,"family":"Benthem","given":"Adam","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":817500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":817501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schenk, Edward R.","contributorId":202017,"corporation":false,"usgs":false,"family":"Schenk","given":"Edward R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":817554,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817555,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216163,"text":"sir20205090 - 2020 - Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York","interactions":[],"lastModifiedDate":"2021-04-27T17:33:12.761031","indexId":"sir20205090","displayToPublicDate":"2021-04-27T13:40:00","publicationYear":"2020","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":"2020-5090","displayTitle":"Analysis of Remedial Scenarios Affecting Plume Movement Through a Sole-Source Aquifer System, Southeastern Nassau County, New York","title":"Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York","docAbstract":"A steady-state three-dimensional groundwater-flow model based on present conditions is coupled with the particle-tracking program MODPATH to assess the fate and transport of volatile organic-compound plumes within the Magothy and upper glacial aquifers in southeastern Nassau County, New York. Particles are forward tracked from locations within plumes defined by surfaces of equal concentration. Particles move toward ultimate well capture and discharge to the general head and drain boundaries representing natural receptors in the models. Because rates of advection within coarse-grained sediments typically exceed 0.1 foot per day, mechanisms of dispersion and diffusion were assumed to be negligible. Resulting particle pathlines are influenced by hydrogeologic framework features and the interplay of nearby hydrologic stresses. Simulated hydrologic effects include cones of depression near pumping wells and water-table mounding near points of treated water recharge; however, remedial pumping amounts are balanced by treated-water return, and net effects at distant regional boundaries, including freshwater/saltwater interfaces, are minor.
Once a steady-state model was developed and calibrated, eight hypothetical remedial scenarios were evaluated to hydraulically contain the volatile organic-compound plumes. Specifically, the remedial scenarios were optimized to achieve full containment by altering the pumping-well locations, adjusting the pumping rates, and adjusting the discharge locations and rates. Based on the results, total hypothetical extraction rates varied from about 5,462 gallons per minute during an anticipated near-future condition to about 13,340 gallons per minute during full hydraulic containment of all site-related compounds identified by the New York State standards, criteria, and guidance for environmental investigations and cleanup. Targeting of high-concentration zones of the plume increases the total amount of remedial pumpage necessary to capture all parts of the plume but may decrease the total amount of time necessary to operate a remedial system. Simulated time frames of advective transport ranged from about 12 years to capture zones with elevated concentrations of volatile organic compounds (mean particle travel time plus the standard deviation of travel time) to more than 100 years to capture all zones.
Groundwater-flow model analysis indicates that all the optimal plume-containment scenarios would have negligible effects on streams and the saltwater-freshwater interface along the south shore of Long Island. Massapequa, Bellmore, Seaman, and Seaford Creeks are represented by using MODFLOW drain-boundary conditions. Saltwater-freshwater interfaces are represented by using MODFLOW general head-boundary conditions where the Magothy aquifer discharges upward into saline groundwater across the Gardiners clay confining unit and the Lloyd aquifer discharges upward into saline groundwater across the Raritan confining unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205090","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Misut, P.E., Walter, D., Schubert, C., and Dressler, S., 2020, Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York: U.S. Geological Survey Scientific Investigations Report 2020–5090, 83 p., https://doi.org/10.3133/sir20205090.","productDescription":"Report: vi, 83 p.; Data Release; 5 Figures","numberOfPages":"83","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105143","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":380266,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5090/sir20205090_figures.zip","text":"High-resolution figures","size":"159 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Figures 16, 18, 20, 22, and 24"},{"id":380264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DOBQ8N","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 model use to analyze remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York"},{"id":380262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5090/coverthb.jpg"},{"id":380263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5090/sir20205090.pdf","text":"Report","size":"18.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5090"}],"country":"United States","state":"New York","county":"Nassau County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.87619018554688,\n 40.482470524589516\n ],\n [\n -73.289794921875,\n 40.482470524589516\n ],\n [\n -73.289794921875,\n 40.81796653313175\n ],\n [\n -73.87619018554688,\n 40.81796653313175\n ],\n [\n -73.87619018554688,\n 40.482470524589516\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
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Below average and infrequent rainfall from May through September 2020 led to an extreme hydrologic drought across much of New England, with some areas experiencing a flash drought, reflecting its quick onset. The U.S. Geological Survey (USGS) recorded record-low streamflow and groundwater levels throughout the region. In September, the U.S. Department of Agriculture (2020) declared Aroostook County in Maine and Hillsborough and Merrimack Counties in New Hampshire as crop disaster areas. By the beginning of October, 166 community water systems and 5 municipalities in New Hampshire, more than 100 municipalities in Massachusetts, and several community water supplies in Connecticut, Maine, and Rhode Island had mandatory water restrictions in place.
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DGMETA (Dissolved Gas Modeling and Environmental Tracer Analysis) is a Microsoft Excel-based computer program that is used for modeling air-water equilibrium conditions from measurements of dissolved gases and for computing concentrations of environmental tracers that rely on air-water equilibrium model results. DGMETA can solve for the temperature, salinity, excess air, fractionation of gases, or pressure/elevation of water when it is equilibrated with the atmosphere. Models are calibrated inversely using one or more measurements of dissolved gases such as helium, neon, argon, krypton, xenon, and nitrogen. Excess nitrogen gas, originating from denitrification or other sources, also can be included as a fitted parameter or as a separate calculation from the dissolved gas modeling results. DGMETA uses the air-water equilibrium models to separate measured concentrations of gases and isotopes of gases into components that are used for tracing water in the environment. DGMETA calculates atmospheric dry-air mole fractions (mixing ratios) for transient atmospheric gas tracers such as chlorofluorocarbons, sulfur hexafluoride, and bromotrifluoromethane (Halon-1301); and concentrations of tritiogenic helium-3 and radiogenic helium-4, which accumulate from the decay of tritium in water and the decay of uranium and thorium in rocks, respectively.
Sample data can be graphed to identify applicable models of excess air, samples that contain excess nitrogen gas, or samples that have partially degassed, for example. Monte Carlo analysis of errors associated with dissolved gas equilibrium model results can be carried through computations of environmental tracer concentrations to provide robust estimates of error. In addition, graphical routines for separating helium sources using helium isotopes are included to refine estimates of tritiogenic helium-3 when terrigenic helium from mantle or crustal sources is present in samples. Environmental tracer concentrations and their errors computed from DGMETA can be used with other programs, such as TracerLPM (Jurgens and others, 2012), to determine groundwater ages and biogeochemical reaction rates. DGMETA also produces output files in a format that meets the U.S. Geological Survey open data requirements for documentation of model inputs and outputs.
DGMETA is a versatile and adaptable program that allows users to add solubility data for new gases, modify the existing set of gas solubility data, modify the default set of gases used for modeling, choose calculations based on real (non-ideal) gas behavior, and select various concentration units for data entry and results to match laboratory reports and study objectives. DGMETA comes with a set of gases widely used in hydrology and oceanography and many gases include multiple solubilities from previous work. Seventeen dissolved gases are included in the default version of the program: noble gases (helium, neon, argon, krypton, and xenon), reactive gases (nitrogen, oxygen, methane, carbon dioxide, carbon monoxide, hydrogen, and nitrous oxide), and environmental tracers (chlorofluorocarbon-11, chlorofluorocarbon-12, chlorofluorocarbon-113, sulfur hexafluoride, and Halon-1301).
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Wildland fires burn millions of forested hectares annually around the world, affecting biodiversity, carbon storage, hydrologic processes, and ecosystem services largely through fire-induced tree mortality (Bond-Lamberty et al. 2007; Dantas et al. 2016). In spite of this widespread importance, the underlying mechanisms of fire-caused tree mortality remain poorly understood, (Hood et al. 2018). Post-fire tree mortality has been traditionally modeled as an empirical function of tree defenses (bark thickness) and fire injury (crown scorch, stem char) (Ryan and Amman 1996; Woolley et al. 2012). Empirical models are commonly used in fire management to predict fire effects (Reinhardt et al. 1997), from the finescale software tools for fire management planning, to process-based succession models (Keane et al. 2011), and global models of the terrestrial carbon cycle (Hantson et al. 2016). Nevertheless, many fire-caused tree mortality models have undergone little evaluation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Fire Continuum-Preparing for the future of wildland fire","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"U.S. Forest Service","usgsCitation":"Cansler, C.A., Hood, S., Varner, J., and van Mantgem, P., 2020, Evaluating and optimizing the use of logistic regression for tree mortality models in the First Order Fire Effects Model (FOFEM), in Proceedings of the Fire Continuum-Preparing for the future of wildland fire, p. 239-246.","productDescription":"8 p.","startPage":"239","endPage":"246","ipdsId":"IP-106540","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412660,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fs.usda.gov/research/treesearch/63223"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Hood, Sharon M.","contributorId":221183,"corporation":false,"usgs":false,"family":"Hood","given":"Sharon","email":"","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":863362,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Drury, Stacy","contributorId":302054,"corporation":false,"usgs":false,"family":"Drury","given":"Stacy","email":"","affiliations":[],"preferred":false,"id":863363,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Steelman, Toddi A","contributorId":169893,"corporation":false,"usgs":false,"family":"Steelman","given":"Toddi","email":"","middleInitial":"A","affiliations":[{"id":18060,"text":"School of Environment and Sustainability, University of Saskatchewan, Canada","active":true,"usgs":false}],"preferred":false,"id":863364,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Steffens, Ron","contributorId":302055,"corporation":false,"usgs":false,"family":"Steffens","given":"Ron","email":"","affiliations":[],"preferred":false,"id":863365,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Cansler, C. Alina 0000-0002-2155-4438","orcid":"https://orcid.org/0000-0002-2155-4438","contributorId":225029,"corporation":false,"usgs":false,"family":"Cansler","given":"C.","email":"","middleInitial":"Alina","affiliations":[{"id":41022,"text":"Missoula Fire Science Lab","active":true,"usgs":false}],"preferred":false,"id":863286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hood, Sharon","contributorId":147091,"corporation":false,"usgs":false,"family":"Hood","given":"Sharon","affiliations":[{"id":16786,"text":"U of Montana, Missoula, MT","active":true,"usgs":false}],"preferred":false,"id":863287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Varner, J. Morgan","contributorId":265933,"corporation":false,"usgs":false,"family":"Varner","given":"J. Morgan","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":863288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863289,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210826,"text":"70210826 - 2020 - Recent planform changes in the Upper Mississippi River","interactions":[],"lastModifiedDate":"2021-11-03T14:42:36.620726","indexId":"70210826","displayToPublicDate":"2020-12-31T09:03:48","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5000,"text":"Long Term Resource Monitoring Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"LTRM-2019GC8","title":"Recent planform changes in the Upper Mississippi River","docAbstract":"Geomorphic changes in the Upper Mississippi River (UMR) have long been a concern of river agencies charged with maintaining and restoring river habitat (GREAT 1980; Jackson et al. 1981; USFWS 1992). Large meandering alluvial rivers like the UMR are expected to constantly change and adjust their fluvial landforms within their riparian corridors as a result of the natural interaction of hydrologic processes, sediment movement, and vegetation over time. However, present geomorphic changes in the UMR reflect altered hydrologic, hydraulic, and sediment conditions caused by regulated flows, constructed agricultural levees and navigation dams, altered land use in the watershed, and climate change. Levees reduce lateral hydrologic and sediment connectivity between channels and floodplains on many tributaries and on the Mississippi River downstream of Pool 13. Between each of the dams are a repeating series of landforms associated with tailwater, intermediate, and impounded conditions. The dams maintain a minimum water level, thus creating many off-channel areas that act as sediment traps. Whereas high-head dams cut off sedimentological connectivity longitudinally through the river corridor (Skalak et al., 2013), low head dams on the UMR only slightly altered transport longitudinally. Deltaic-like sedimentation can be common in the impounded sections of dammed rivers. Erosion of relict land surfaces that remained above the raised impounded water levels has been the dominant change in UMR impounded sections due to increased wind fetch leading to increased wave action. Even though upland sources of sediment from tributaries have decreased over the middle to late 20th century, increased annual precipitation, the interplay of increased variability in flood magnitudes from year to year, and more fall and winter flooding have likely changed erosion and sedimentation patterns in the UMR (Belby, et al., 2019). Paradoxically, monitoring and research indicates that the concentration of some water column constituents like total suspended solids and phosphorous has decreased during the 1991 to 2014 time period (Kreiling and Houser, 2016). In areas prone to increased sedimentation, bed elevations rise and thereby water depths are reduced at a given discharge, resulting in loss of fish habitat. Sediment deposition or erosion further influences water exchange rates between main channel and off-channel areas in the river by increasing resistance in connecting channels or enlarging existing connecting channels. Water depth and water exchange rates are the most prominent features describing habitat quality in the UMR (De Jager et al. 2018), and in some cases, the trajectory of planform change from heightened deposition promises to threaten deep backwater habitats particularly important for overwintering fish.\n\nAlthough information on the rate of vertical change in bed elevation is needed for a complete assessment of geomorphic change associated with the loss of deep backwater habitats, mapping planform changes over time (i.e., lateral changes between the land-water boundary) provide needed information on the location, potential cause, and progressive direction of deposition, especially in the mid sections between dams where deltaic processes are the most pronounced. Several types of planform changes have been observed and identified as concerns. For example, island loss in the large impounded areas of the upper part of the UMR was one of the concerns identified by river managers in the 1980s and 90s, and subsequently island construction became a common form of restoration implemented by the Upper Mississippi River Restoration (UMRR) Program (USACE 2012). Other subtler planform changes, such as channel bank erosion and delta formation in backwaters, are perceived to be important, but have largely gone unquantified. A systemwide reconnaissance of the UMR and IWW conducted in 1998 concluded that 14-percent of the river banks were eroding (Nakato and Anderson 1998). However, stabilization of existing river banks has never been widely pursued as a restoration measure, due to the high cost and uncertain benefits. Delta formation reduces the amount of backwater habitat; however, the deltas maintain and create a mix of riparian and aquatic habitats, and that is generally considered to be beneficial for wildlife and fish. If recent hydrologic trends of more frequent and longer duration flood events continue, a better understanding of planform changes can help in describing past changes, and then be used to forecast potential future trajectories of change. If UMR resource managers determine that past and forecasted conditions are undesirable, then UMRR projects could be identified and prioritized to address those concerns.\n\nVegetative cover associations with landform changes have been used to detect and quantify planform changes in many rivers (Johnson 1985; Hiatt 2015; Volte et al. 2015). Freyer and Jefferson (2013) completed such a study in Pool 6 of the UMR using the landcover data from 12 dates over a 115-yr period, including the 1989, 2000, and 2010/2011 landcover/use (LCU) data from the UMRR Program. Planform change detected over the last 20 years represented by the UMRR Program data best reflect present-day geomorphic patterns, rates and processes. Changes occurring prior to dam construction and changes occurring soon after dam construction are likely not the same as those happening now, 50-70 years after dam construction and creation of the impoundments (McHenry et al., 1984; Bhowmik and Adams, 1986; WEST Consultants, 2000). \n\nThe LCU data from each of the 1989, 2000, and 2010/2011 imagery was developed using similar methods and is available in a Geographical Information System (GIS) for the entire UMR and therefore provides the opportunity for a more comprehensive planform change analysis. This study used GIS overlays of LCU classes to map and quantify changes in planform features over two periods, looking specifically for depositional areas where terrestrial and wetland vegetation expanded at the expense of open water. The land expansion was grouped into four possible process-based types common in large floodplain rivers, some following that used by Lewin et al. (2017). The four types include: crevasse deltas emanating from a breach from a main channel through a natural levee or narrow floodplain into backwaters (crevasse deltas), tributary deltas expanding into backwaters (tributary deltas), deltaic bars at the upstream end of impoundments (impounded deltas), and linear-like bars extending from the downstream ends of narrow levees and remnant floodplains (bar-tail limbs). The methods deployed for change detection addressed possible errors from a variety of sources.","language":"English","publisher":"US Army Corps of Engineers, Upper Mississippi River Restoration (UMRR) Program","usgsCitation":"Rogala, J.T., Fitzpatrick, F., and Hendrickson, J.S., 2020, Recent planform changes in the Upper Mississippi River: Long Term Resource Monitoring Technical Report LTRM-2019GC8, 33 p.","productDescription":"33 p.","ipdsId":"IP-113610","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391323,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/documents/publications/2020/rogala_a_2020.html"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90,\n 38.58252615935333\n ],\n [\n -91.0546875,\n 40.07807142745009\n ],\n [\n -90,\n 41.86956082699455\n ],\n [\n -90.8349609375,\n 43.29320031385282\n ],\n [\n -91.2744140625,\n 44.465151013519616\n ],\n [\n -93.55957031249999,\n 46.01222384063236\n ],\n [\n -93.4716796875,\n 46.619261036171515\n ],\n [\n -95.1416015625,\n 46.46813299215554\n ],\n [\n -94.52636718749999,\n 45.24395342262324\n ],\n [\n -93.251953125,\n 44.55916341529182\n ],\n [\n -91.93359375,\n 43.866218006556394\n ],\n [\n -91.1865234375,\n 42.4234565179383\n ],\n [\n -90.791015625,\n 42.22851735620852\n ],\n [\n -91.14257812499999,\n 41.705728515237524\n ],\n [\n -91.669921875,\n 41.07935114946899\n ],\n [\n -91.97753906249999,\n 39.842286020743394\n ],\n [\n -91.318359375,\n 38.89103282648846\n ],\n [\n -90,\n 38.58252615935333\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rogala, James T. 0000-0002-1954-4097 jrogala@usgs.gov","orcid":"https://orcid.org/0000-0002-1954-4097","contributorId":2651,"corporation":false,"usgs":true,"family":"Rogala","given":"James","email":"jrogala@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":791606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209612,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hendrickson, Jon S.","contributorId":177520,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Jon","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":791608,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216911,"text":"sir20205118 - 2020 - Hydrogeology, numerical simulation of groundwater flow, and effects of future water use and drought for reach 1 of the Washita River alluvial aquifer, Roger Mills and Custer Counties, western Oklahoma, 1980–2015","interactions":[],"lastModifiedDate":"2020-12-30T20:18:58.899472","indexId":"sir20205118","displayToPublicDate":"2020-12-30T13:15:00","publicationYear":"2020","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":"2020-5118","displayTitle":"Hydrogeology, Numerical Simulation of Groundwater Flow, and Effects of Future Water Use and Drought for Reach 1 of the Washita River Alluvial Aquifer, Roger Mills and Custer Counties, Western Oklahoma, 1980–2015","title":"Hydrogeology, numerical simulation of groundwater flow, and effects of future water use and drought for reach 1 of the Washita River alluvial aquifer, Roger Mills and Custer Counties, western Oklahoma, 1980–2015","docAbstract":"The Washita River alluvial aquifer is a valley-fill and terrace alluvial aquifer along the valley of the Washita River in western Oklahoma that provides a productive source of groundwater for agricultural irrigation and water supply. The Oklahoma Water Resources Board (OWRB) has designated the westernmost section of the aquifer in Roger Mills and Custer Counties, Okla., as reach 1 of the Washita River alluvial aquifer; reach 1 is the focus of this report. The OWRB issued an order on November 13, 1990, that established the maximum annual yield (MAY; 120,320 acre-feet per year [acre-ft/yr]) and equal-proportionate-share (EPS) pumping rate (2.0 acre-feet per acre per year [(acre-ft/acre)/yr]) for reach 1 of the Washita River alluvial aquifer. The MAY and EPS were based on hydrologic investigations that evaluated the effects of potential groundwater withdrawals on groundwater availability in the Washita River alluvial aquifer. Every 20 years, the OWRB is statutorily required to update the hydrologic investigation on which the MAY and EPS were based. Because 30 years have elapsed since the last order was issued, the U.S. Geological Survey, in cooperation with the OWRB, conducted a new hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater flow and availability in the Washita River alluvial aquifer.
The Washita River is the primary source of inflow to Foss Reservoir, a Bureau of Reclamation reservoir constructed in 1961 for flood control, water supply, and recreation. Foss Reservoir provides water for Bessie, Clinton, New Cordell, and Hobart, Okla. Nearly 98 percent of the total groundwater use from the Washita River alluvial aquifer during 1967 to 2015 was for irrigation; other uses of groundwater in the study area include public supply, mining, and agriculture.
A hydrogeologic framework was developed for the Washita River alluvial aquifer and included the physical characteristics of the aquifer, the geologic setting, the hydraulic properties of hydrogeologic units, the potentiometric surface (water table), and groundwater-flow directions at a scale that captures the regional controls on groundwater flow. The Washita River alluvial aquifer consists of alluvium and terrace deposits that were transported primarily by water and range from clay to gravel in size. The terrace includes windblown deposits of silt size and, in some cases, contains gravel laid down at several levels along former courses of present-day rivers.
A conceptual flow model is a simplified description of the aquifer system that includes hydrologic boundaries, major inflow and outflow sources of the groundwater-flow system, and a conceptual water budget with the estimated mean flows between those hydrologic boundaries. During the study period 1980–2015, mean annual groundwater withdrawals, predominantly used for agricultural irrigation, totaled 5,502 acre-ft/yr, or 14 percent of aquifer outflows. When applied across the 132-square-mile aquifer area used for modeling purposes (84,366 acres), mean annual recharge of 3.15 inches per year corresponds to a mean annual recharge volume of 22,169 acre-ft/yr, or 56 percent of aquifer inflows. The annual saturated-zone evapotranspiration outflow was 11,828 acre-ft/yr for the Washita River alluvial aquifer, or about 30 percent of aquifer outflows. For the Washita River alluvial aquifer, lateral flow was 17,157 acre-ft/yr, or 44 percent of the aquifer inflows. The conceptual flow model and hydrogeologic framework were used to conceptualize, design, and build the numerical groundwater-flow model.
A numerical groundwater-flow model of the Washita River alluvial aquifer was constructed by using MODFLOW-2005. The Washita River alluvial aquifer groundwater-model grid was spatially discretized into 350-foot (ft) cells and two layers. Layer 1 represented the undifferentiated alluvium and terrace deposits of Quaternary age, and layer 2 represented the bedrock of Permian age, which was given a uniform nominal thickness of 100 ft. The groundwater-simulation period was temporally discretized into 433 monthly transient stress periods, representing January 1980 to December 2015. An initial 365-day steady-state stress period was configured to represent mean annual inflows and outflows from the Washita River alluvial aquifer for the study period. The groundwater-flow model was calibrated manually and by automated adjustment of model inputs by using PEST++. Calibration targets for the Washita River alluvial aquifer model included groundwater-level observations and reservoir-stage observations, as well as base-flow and stream-seepage estimates.
Three groundwater-availability scenarios were used in the calibrated groundwater model to (1) estimate the EPS pumping rate that retains the saturated thickness that meets the minimum 20-year life of the aquifer, (2) quantify the effects of projected pumping rates on groundwater storage over a 50-year period, and (3) evaluate how projected pumping rates extended 50 years into the future and sustained hypothetical drought conditions over a 10-year period affect base flow and groundwater in storage. The results of the groundwater-availability scenarios could be used by the OWRB to reevaluate the established MAY of groundwater from the Washita River alluvial aquifer.
EPS scenarios for the Washita River alluvial aquifer were run for periods of 20, 40, and 50 years. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were 1.7, 1.6, and 1.6 (acre-ft/acre)/yr, respectively. Given the aquifer area used for modeling purposes (84,366 acres), these rates correspond to annual yields of 142,579, 134,986, and 134,986 acre-ft/yr, respectively. Groundwater storage at the end of the 20-year EPS scenario was about 281,000 acre-feet (acre-ft), or about 306,000 acre-ft (52 percent) less than the starting storage. Considering the land-surface area of the Washita River alluvial aquifer and using a specific yield of 0.12, this decrease in storage was equivalent to a mean groundwater-level decline of about 30 ft. The Washita River downstream from Foss Reservoir and most of the streams in the study area were dry at the end of the 20-year EPS scenario. Foss Reservoir stage was below the dead-pool stage of 1,597 ft after about 7 years of pumping in the 20-year EPS scenario.
Four projected 50-year groundwater-use scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage in the Washita River alluvial aquifer. These four scenarios used (1) no groundwater use, (2) groundwater use at the 2015 pumping rate, (3) mean groundwater use for the simulation period, and (4) increasing groundwater use. Groundwater storage after 50 years with no groundwater use was 545,249 acre-ft, or 693 acre-ft (0.1 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean groundwater-level increase of 0.1 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 543,831 acre-ft, or 723 acre-ft (0.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.1 ft. Groundwater storage after 50 years with the mean pumping rate for the study period was 543,202 acre-ft, or 1,349 acre-ft (0.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.1 ft. Groundwater storage at the end of the 50-year period with an increasing demand groundwater-pumping rate, which was 38 percent greater than the 2015 groundwater-pumping rate, was 542,584 acre-ft, or 1,967 acre-ft (0.4 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.2 ft.
A hypothetical 10-year-drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage in the Washita River alluvial aquifer and Foss Reservoir stage and storage. To simulate the hypothetical drought, recharge in the calibrated model was reduced by 50 percent during the simulated drought period (1983–1992). Groundwater storage at the end of the drought period in December 1992 was 562,000 acre-ft, or 36,000 acre-ft (6 percent) less than the groundwater storage of the calibrated groundwater model (598,000 acre-ft). At the end of the hypothetical drought, the largest changes in saturated thickness (as great as 43.5 ft) were in the area upgradient from Foss Reservoir, particularly in the terrace at the model boundary. Substantial decreases in the Foss Reservoir stage began during the fall of 1985 in conjunction with base-flow decreases of up to 100 percent at U.S. Geological Survey streamgage 07324200 Washita River near Hammon, Okla. These lake-stage declines outpaced groundwater-level declines in the surrounding aquifer. The minimum Foss Reservoir storage simulated during the drought period was 77,954 acre-ft, which was a decrease of 46 percent from the nondrought storage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205118","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Ellis, J.H., Ryter, D.W., Fuhrig, L.T., Spears, K.W., Mashburn, S.L., and Rogers, I.M.J., 2020, Hydrogeology, numerical simulation of groundwater flow, and effects of future water use and drought for reach 1 of the Washita River alluvial aquifer, Roger Mills and Custer Counties, western Oklahoma, 1980–2015: U.S. Geological Survey Scientific Investigations Report 2020–5118, 81 p., https://doi.org/10.3133/sir20205118.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-116035","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":381399,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PKMG6U","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used in simulation of groundwater flow, and analysis of projected water use for the Washita River alluvial aquifer, western Oklahoma"},{"id":381398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5118/sir20205118.pdf","text":"Report","size":"18.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5118"},{"id":381397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5118/coverthb.jpg"}],"country":"United States","state":"Oklahoma","county":"Roger Mills County, Custer County","otherGeospatial":"Washita River alluvial aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-98.6305,35.812],[-98.6308,35.6387],[-98.6307,35.552],[-98.6199,35.552],[-98.6209,35.4639],[-98.8338,35.4653],[-98.9399,35.4659],[-99.0455,35.4654],[-99.1517,35.4658],[-99.3629,35.4649],[-99.3631,35.508],[-99.5755,35.5085],[-99.576,35.42],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-99.9742,35.8921],[-99.9566,35.8959],[-99.947,35.9009],[-99.938,35.9037],[-99.9272,35.9074],[-99.9228,35.9115],[-99.9177,35.9175],[-99.9132,35.9234],[-99.911,35.928],[-99.9082,35.9325],[-99.9049,35.9371],[-99.9038,35.9462],[-99.9045,35.9562],[-99.9051,35.9589],[-99.899,35.9698],[-99.894,35.9748],[-99.8522,36.0051],[-99.8398,36.0115],[-99.829,36.0107],[-99.8227,36.0089],[-99.8152,36.0026],[-99.8078,35.9949],[-99.8019,35.9827],[-99.8019,35.9737],[-99.8051,35.9618],[-99.809,35.9518],[-99.8111,35.9364],[-99.8099,35.9287],[-99.8087,35.9246],[-99.8007,35.9174],[-99.7938,35.9102],[-99.788,35.8962],[-99.784,35.8921],[-99.7725,35.8867],[-99.76,35.885],[-99.7521,35.8824],[-99.7372,35.8738],[-99.7258,35.8653],[-99.7189,35.8626],[-99.7149,35.854],[-99.6979,35.855],[-99.6774,35.847],[-99.6615,35.847],[-99.6558,35.8457],[-99.6416,35.8444],[-99.6291,35.84],[-99.6149,35.84],[-99.6042,35.8478],[-99.6002,35.8519],[-99.5929,35.8551],[-99.5855,35.8574],[-99.577,35.8588],[-99.5623,35.8621],[-99.5578,35.8675],[-99.5562,35.8825],[-99.5416,35.903],[-99.532,35.9076],[-99.5241,35.9185],[-99.5156,35.9281],[-99.5067,35.9481],[-99.5061,35.9535],[-99.5085,35.9608],[-99.5085,35.9649],[-99.5045,35.9703],[-99.4995,35.974],[-99.4876,35.9795],[-99.4785,35.9899],[-99.4638,35.9995],[-99.4445,36.01],[-99.4303,36.016],[-99.4144,36.0169],[-99.3928,36.017],[-99.3809,36.017],[-99.3808,35.8991],[-99.374,35.8991],[-99.3736,35.8111],[-99.0571,35.8112],[-98.7366,35.8118],[-98.6305,35.812]]]},\"properties\":{\"name\":\"Custer\",\"state\":\"OK\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Environmental DNA (eDNA) can be used for early detection, population estimations, and assessment of potential spread of invasive species, but questions remain about factors that influence eDNA detection results. Efforts are being made to understand how physical, chemical, and biological factors—settling, resuspension, dispersion, eDNA stability/decay—influence eDNA estimations and potentially population abundance. In a series of field and controlled mesocosm experiments, we examined the detection and accumulation of eDNA in sediment and water and the transport of eDNA in a small stream in the Lake Michigan watershed, using the invasive round goby fish (Neogobius melanostomus) as a DNA source. Experiment 1: caged fish (average n = 44) were placed in a stream devoid of round goby; water was collected over 24 hours along 120-m of stream, including a simultaneous sampling event at 7 distances from DNA source; stream monitoring continued for 24 hours after fish were removed. Experiment 2: round goby were placed in laboratory tanks; water and sediment were collected over 14 days and for another 150 days post-fish removal to calculate eDNA shedding and decay rates for water and sediment. For samples from both experiments, DNA was extracted, and qPCR targeted a cytochrome oxidase I gene (COI) fragment specific to round goby. Results indicated that eDNA accumulated and decayed more slowly in sediment than water. In the stream, DNA shedding was markedly lower than calculated in the laboratory, but models indicate eDNA could potentially travel long distances (up to 50 km) under certain circumstances. Collectively, these findings show that the interactive effects of ambient conditions (e.g., eDNA stability and decay, hydrology, settling-resuspension) are important to consider when developing comprehensive models. Results of this study can help resource managers target representative sites downstream of potential invasion sites, thereby maximizing resource use.
","language":"English","publisher":"Public Library of Science (PLoS)","doi":"10.1371/journal.pone.0244086","usgsCitation":"Nevers, M., Przybyla-Kelly, K., Shively, D., Morris, C.C., Dickey, J., and Byappanahalli, M., 2020, Influence of sediment and stream transport on detecting a source of environmental DNA: PLoS ONE, v. 15, no. 12, e0244086, 21 p., https://doi.org/10.1371/journal.pone.0244086.","productDescription":"e0244086, 21 p.","ipdsId":"IP-120509","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454617,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0244086","text":"Publisher Index Page"},{"id":436691,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HI425V","text":"USGS data release","linkHelpText":"Environmental DNA detection and survival, influence of sediment, and stream transport in a Lake Michigan watershed, 2018"},{"id":381935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Przybyla-Kelly, Katarzyna 0000-0001-9168-3545 kprzybyla-kelly@usgs.gov","orcid":"https://orcid.org/0000-0001-9168-3545","contributorId":201534,"corporation":false,"usgs":true,"family":"Przybyla-Kelly","given":"Katarzyna","email":"kprzybyla-kelly@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shively, Dawn A.","contributorId":247309,"corporation":false,"usgs":false,"family":"Shively","given":"Dawn A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":807646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morris, Charles C.","contributorId":201532,"corporation":false,"usgs":false,"family":"Morris","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":807647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickey, Joshua","contributorId":201536,"corporation":false,"usgs":false,"family":"Dickey","given":"Joshua","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":807648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X byappan@usgs.gov","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":147923,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","email":"byappan@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807649,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217713,"text":"70217713 - 2020 - Effects of fish populations on Pacific Loon (Gavia pacifica) and Yellow-billed Loon (G. adamsii) lake occupancy and chick production in northern Alaska","interactions":[],"lastModifiedDate":"2021-02-01T14:24:20.683608","indexId":"70217713","displayToPublicDate":"2020-12-27T07:50:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":894,"text":"Arctic","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Effects of fish populations on Pacific Loon (Gavia pacifica) and Yellow-billed Loon (G. adamsii) lake occupancy and chick production in northern Alaska","title":"Effects of fish populations on Pacific Loon (Gavia pacifica) and Yellow-billed Loon (G. adamsii) lake occupancy and chick production in northern Alaska","docAbstract":"Predator populations are vulnerable to changes in prey distribution or availability. With warming temperatures, lake ecosystems in the Arctic are predicted to change in terms of hydrologic flow, water levels, and connectivity with other lakes. We surveyed lakes in northern Alaska to understand how shifts in the distribution or availability of fish may affect the occupancy and breeding success of Pacific (Gavia pacifica) and Yellow-billed Loons (G. adamsii). We then modeled the influence of the presence and abundance of five fish species and the physical characteristics of lakes (e.g., hydrologic connectivity) on loon lake occupancy and chick production. The presence of Alaska blackfish (Dallia pectoralis) had a positive influence on Pacific Loon occupancy and chick production, which suggests that small-bodied fish species provide important prey for loon chicks. No characteristics of fish species abundance affected Yellow-billed Loon lake occupancy. Instead, Yellow-billed Loon occupancy was influenced by the physical characteristics of lakes that contribute to persistent fish populations, such as the size of the lake and the proportion of the lake that remained unfrozen over winter. Neither of these variables, however, influenced chick production. The probability of an unoccupied territory becoming occupied in a subsequent year by Yellow-billed Loons was low, and no loon chicks were successfully raised in territories that were previously unoccupied. In contrast, unoccupied territories had a much higher probability of becoming occupied by Pacific Loons, which suggests that Yellow-billed Loons have strict habitat requirements and suitable breeding lakes may be limited. Territories that were occupied had high probabilities of remaining occupied for both loon species.
Floodplain forest on the upper Mississippi River (UMR), a unique habitat in the Midwest that is important for many bird species, has been reduced and is undergoing continued reduction and changes in structure and species diversity because of river engineering and invasive species. Hydrological changes are causing tree diversity to decline favoring Acer saccharinum (silver maple) and Fraxinus pennsylvanica (green ash). Invasive Phalaris arundinacea (reed canary grass, Phalaris) threatens tree regeneration, and recent Agrilus planipennis (emerald ash borer) arrival threatens to decimate the important ash component of the forest canopy. During the 1990s, virtually no information was available about breeding songbird species and abundances on the UMR floodplain forest from along many river miles and a broad range of forest situations (for example, mainland, island, edge, interior). From 1994 to 1997, we surveyed breeding birds and sampled vegetation at 391 random points on UMR floodplain forest along a latitudinal gradient from Red Wing, Minnesota, to Clinton, Iowa, to characterize bird assemblages and associations with gradients in forest structure at survey points (local scale) and land cover composition within a 200-meter radius of survey points (landscape scale).
Eighty-six bird species were detected during the study, but 28 species comprised 90 percent of all detections. Species that are typically associated with woodland edge or are tolerant of fragmentation were the most common: Setophaga ruticilla (American Redstart), Troglodytes aedon (House Wren), Turdus migratorius (American Robin), Quiscalus quiscula (Common Grackle), and Vireo gilvus (Warbling Vireo). Species typically associated with large forest patches—Setophaga cerulea (Cerulean Warbler), Hylocichla mustelina (Wood Thrush), and Dryocopus pileatus (Pileated Woodpecker)—were rare. Principal components analyses consistently described local habitat gradients related to canopy cover and Phalaris presence and described landscape gradients related to forest area and areas of open land cover types. However, nonmetric multidimensional scaling revealed no pattern in bird assemblages. Canonical correspondence analyses with local habitat variables for each year revealed that bird assemblages were affected by canopy cover, the presence of Phalaris, and the number of tree species. Four bird species were consistently associated with Phalaris presence or negatively with canopy cover, and no species were associated with the number of tree species variable. Although landscape variables were significantly related to the bird assemblage in canonical correspondence analyses, no bird species were consistently related to any landscape variable. These results indicate that there is one assemblage of forest birds on the UMR composed mainly of edge-tolerant species. Species associated with lower canopy cover and Phalaris presence may be favored to increase in abundance as canopy cover opens as trees die and Phalaris becomes more prevalent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205114","usgsCitation":"Kirsch, E.M., 2020, Breeding birds of the upper Mississippi River floodplain forest: One community in a changing forest, 1994 to 1997 (ver. 1.1, February 2021): U.S. Geological Survey Scientific Investigations Report 2020–5114, 22 p., https://doi.org/10.3133/sir20205114.","productDescription":"Report: vi, 22 p.; Data Release; Version History","numberOfPages":"32","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-096746","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":381528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5114/coverthb2.jpg"},{"id":381529,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5114/sir20205114.pdf","text":"Report","size":"4.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5114"},{"id":383313,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5114/versionHist.txt","text":"Version History","size":"667 B","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5114 Version History"},{"id":381530,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z5M7NT","text":"USGS data release","description":"USGS Data Release","linkHelpText":"1990s bird and vegetation data from upper Mississippi River floodplain forest"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Wisconsin","otherGeospatial":"Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.39501953125,\n 44.653024159812\n ],\n [\n -92.548828125,\n 44.59046718130883\n ],\n [\n -92.43896484375,\n 44.32384807250689\n ],\n [\n -92.021484375,\n 44.10336537791152\n ],\n [\n -91.58203125,\n 43.91372326852401\n ],\n [\n -91.40625,\n 43.50075243569041\n ],\n [\n -91.38427734374999,\n 43.02071359427862\n ],\n [\n -91.1865234375,\n 42.633958722673135\n ],\n [\n -90.94482421875,\n 42.32606244456202\n ],\n [\n -90.32958984375,\n 41.918628865183045\n ],\n [\n -90.10986328125,\n 41.918628865183045\n ],\n [\n -89.89013671875,\n 41.95131994679697\n ],\n [\n -89.97802734375,\n 42.261049162113856\n ],\n [\n -90.3955078125,\n 42.601619944327965\n ],\n [\n -90.76904296874999,\n 42.827638636242284\n ],\n [\n -91.03271484375,\n 43.08493742707592\n ],\n [\n -91.16455078125,\n 43.99281450048989\n ],\n [\n -91.7578125,\n 44.35527821160296\n ],\n [\n -92.39501953125,\n 44.653024159812\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 22, 2020; Version 1.1: February 18, 2021","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
The U.S. Geological Survey, in cooperation with the Albuquerque Bernalillo County Water Utility Authority (ABCWUA), measures groundwater levels continuously (hourly) and discretely (semiannually and annually) at a network of wells and piezometers (hereafter called the observation network) within the Middle Rio Grande Basin in central New Mexico. Groundwater levels that are measured in this observation network provide a long-term hydrologic dataset that is heavily relied upon to make water management decisions. The desire to upgrade and perform maintenance on this observation network initiated this study, which assesses the spatial and temporal importance of measurements towards optimization of monitoring the observation network to reduce or redirect monitoring costs. This report describes the methods and results of the optimization assessment of this observation network, which included separate spatial and temporal methodologies and an evaluation using principal component analysis (PCA).
Results from the spatial optimization assessment can be used to help identify observation network sites that do not significantly affect the generation of winter groundwater-elevation contour maps of the production zone. Results from the temporal optimization assessment and PCA can also be consulted when deciding which sites to remove from the network, especially for sites that are monitored more frequently than annually. Results from the temporal optimization assessment can be used to inform the minimum monitoring frequency at the observation network required to capture the trends shown in higher frequency monitoring. The PCA results distinguish spatially distributed characteristic water-level trends that can inform the management decisions that are made when using the spatial and temporal optimization assessment results. Reducing the temporal frequency or spatial density of monitoring is ultimately a management decision that depends on the amount of data loss or degradation that is deemed acceptable while still meeting the network objectives of the ABCWUA. This study can also serve as a starting point to a data gap analysis of local aquifer characteristics and help guide enhanced observation network design as needs arise or in advance of future water management decisions.
The results of the spatial optimization assessment indicate that as many as about 20 specified sites can be removed from the observation network with a relatively small loss in the ability to represent the kriged groundwater-elevation surfaces of the production zone that were generated by using median groundwater elevations for two periods: the 2001 time interval and 2015 time interval. This analysis also demonstrated the importance of wells at the margin of the study area and in areas where there are large hydrologic gradients. At many of the 47 hourly monitored sites analyzed in the temporal optimization assessment, temporal trends were well represented for at least one of the reduced monitoring frequencies tested, indicating that a reduced frequency may be sufficient to adequately characterize seasonal and long-term trends. PCA and k-means clustering analysis of the 15 hourly monitored sites that are screened within the production zone indicate that the sites can be categorized into four groups, or clusters, of differing groundwater-level hydrograph characteristics. Except for one cluster, all of the clusters have the potential to be well represented by fewer index monitoring sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205007","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Ritchie, A.B., and Pepin, J.D., 2020, Optimization assessment of a groundwater-level observation network in the Middle Rio Grande Basin, New Mexico (ver. 2, December 2020): U.S. Geological Survey Scientific Investigations Report 2020–5007, 113 p., https://doi.org/10.3133/sir20205007.","productDescription":"Report: vii, 113 p.; 2 Figures","numberOfPages":"125","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-102753","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":373207,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5007/sir20205007.pdf","text":"Report","size":"7.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5007"},{"id":399629,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109787.htm"},{"id":381500,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5007/versionHist.txt","text":"Version History","description":"SIR 2020–5007 Version History"},{"id":373209,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5007/sir20205007_figure23B.pdf","text":"Figure 23B","size":"170 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5007 Figure 23B","linkHelpText":"Clustered monthly median hydrographs plotted using an independent y-axis range for all plots"},{"id":373208,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5007/sir20205007_figure23A.pdf","text":"Figure 23A","size":"163 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5007 Figure 23A","linkHelpText":"Clustered monthly median hydrographs plotted using the same fixed y-axis range for all plots"},{"id":373206,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5007/coverthb2.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Middle Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8517,\n 34.9064\n ],\n [\n -106.4542,\n 34.9064\n ],\n [\n -106.4542,\n 35.4011\n ],\n [\n -106.8517,\n 35.4011\n ],\n [\n -106.8517,\n 34.9064\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: March 17, 2020; Version 2.0: December 21, 2020","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Hinkley Valley, in the central to western Mojave Desert of southeastern California, has a long historical record owing to its position as a crossroads for rail and road traffic and its position adjacent to the Mojave River. Subflow in the Mojave River provided groundwater recharge that maintained water consumption and demand by way of shallow wells for local agriculture in the valley. Its crossroads position led to construction of several power-transmission lines, pipeline, and communications cable routes that transect Hinkley Valley. One of these, a natural gas pipeline and its associated compressor station, was the locus of hexavalent chromium, Cr(VI), released into, and consequent contamination of, groundwater. Understanding the movement and fate of the contaminants is a complex hydrologic and geochemical problem. Geologic mapping of the Hinkley Valley area provides framework elements for use in resolving this problem. This report provides new information on surface and subsurface geology to better constrain the origin and geometry of hydrologically important deposits in the Hinkley Valley area and describes youthful faults that may control sediment distribution and groundwater flow. The geologic map (sheet 1) presents substantial new information on surficial geology, including Pliocene deposits, but does not contain significant new work on bedrock. Bedrock investigations were specific to identifying youthful faults and representative outcrops for rocks that were penetrated by boreholes in the valley. Special attention was placed on locating and describing youthful faults. In addition, we analyzed gravity data to (1) map horizontal gradients that we interpret to reflect long-term fault traces and to (2) estimate the depth to bedrock, which is defined as Miocene and older intrusive and metamorphic rocks for the purposes of this report. The subsurface geology of Hinkley Valley was investigated by examining borehole sediment cores and rock encountered at the base of the sediment section. We analyzed the core to determine depositional environments, provenance, and age of the sediment that infilled the valley. Valleys, mountains, and basins in the Hinkley Valley area are topographically complex and incompletely named. The nearly flat floored Hinkley Valley slopes gently northward. It is framed by Mount General and the informally named “Hinkley hills” (southeast of Mount General) on the northeast and by Iron Mountain and Lynx Cat Mountain on the southwest, although breaks in the western mountains allow stream connections between Hinkley Valley and another valley to the west that is herein referred to as Hawes valley. At its south end, Hinkley Valley is traversed by the entrenched Mojave River, which passes east out of the valley past Barstow. North of Hinkley Valley, a few low hills (including Red Hill) separate the valley from a broad west-sloping piedmont that is part of the physiographic Harper Basin (of which the Harper Lake playa is the center). The lower part of this piedmont, however, is referred to as Water Valley, although it is not a distinct valley. The name derives from groundwater sourced from subflow in the Mojave River, which caused shallow water and even artesian flow in Water Valley but not in other parts of the Harper Basin. When water filled the Harper Basin to form Pleistocene Lake Harper it not only submerged Water Valley but also northern Hinkley Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3458","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board and the State Water Resources Control Board","usgsCitation":"Miller, D.M., Langenheim, V.E., and Haddon, E.K., 2020, Geologic map and borehole stratigraphy of Hinkley Valley and vicinity, San Bernardino County, California: U.S. Geological Survey Scientific Investigations Map 3458, pamphlet 23 p., 2 sheets, scale 1:24,000, https://doi.org/10.3133/sim3458.","productDescription":"Pamphlet,: iv, 23 p.; 2 Sheets ; 2 Tables; Database; Data Release; Metadata","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102109","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":381271,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet2.pdf","text":"Sheet 2","size":"32 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381270,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet1.pdf","text":"Sheet 1","size":"40 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381269,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_database.zip","text":"Database","size":"7.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381268,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_base.zip","text":"Base","size":"1.25 GB","linkFileType":{"id":6,"text":"zip"}},{"id":381267,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_metadata.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_pamphlet.pdf","text":"Pamphlet","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381451,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FV5LG5","linkHelpText":"Gravity data of the Hinkley area, southern California"},{"id":381273,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_7.xlsx","text":"Table 7","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":381265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3458/covrthb.jpg"},{"id":381272,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_3.xlsx","text":"Table 3","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","state":"California","county":"San Bernadino County","otherGeospatial":"Hinkley Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.26257324218749,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 34.80647431931937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses.
The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations.
A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period.
These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15.
About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205091","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., Masterson, J.P., Finkelstein, J.S., Monti, J., Jr., Misut, P.E., and Fienen, M.N., 2020, Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15: U.S. Geological Survey Scientific Investigations Report 2020–5091, 75 p., https://doi.org/10.3133/sir20205091.","productDescription":"Report: ix, 75 p.; 3 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112206","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381521,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5091/images/"},{"id":381195,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5091"},{"id":381194,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5091/coverthb2.jpg"},{"id":381192,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":381191,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWQSEJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to simulate groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15"},{"id":381190,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens Counties, New York, October-November 2017"},{"id":381520,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.XML"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.102783203125,\n 40.55554790286311\n ],\n [\n -73.7017822265625,\n 40.49709237269567\n ],\n [\n -72.8778076171875,\n 40.65147128144057\n ],\n [\n -72.037353515625,\n 40.91351257612758\n ],\n [\n -71.6143798828125,\n 41.16211393939692\n ],\n [\n -71.9384765625,\n 41.178653972331674\n ],\n [\n -72.191162109375,\n 41.236511201246216\n ],\n [\n -72.65808105468749,\n 41.11660732012896\n ],\n [\n -73.4490966796875,\n 40.967455873296714\n ],\n [\n -73.751220703125,\n 40.89275342420696\n ],\n [\n -74.0203857421875,\n 40.718119379753446\n ],\n [\n -74.102783203125,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
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About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is from groundwater pumped from wells located along North Fork Eagle Creek in the National Forest System lands of the Lincoln National Forest near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the North Fork Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial loss of vegetation in the basin, the U.S. Department of Agriculture Forest Service, Lincoln National Forest, has required monitoring of a portion of North Fork Eagle Creek for short-term geomorphic change as part of the permitting decision that allows for the continued pumping of the production wells. The objective of this study is to address the geomorphic monitoring requirements of the permitting decision by conducting annual geomorphic surveys of North Fork Eagle Creek along the stream reach between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (U.S. Geological Survey [USGS] site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The monitoring of short-term geomorphic change in the stream reach began in June 2017 with surveys of select cross sections and surveys of all woody debris accumulations and pools found in the channel. In June 2018, the monitoring of short-term geomorphic change continued with another geomorphic survey of the stream reach (with some modification to the monitoring methods).
The 2017 and 2018 surveys were conducted by the USGS, in cooperation with the Village of Ruidoso, and were the first two in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey were summarized and interpreted in a previous USGS Open-File Report, and the data were published in the companion data release of that report. In this report, the results of the 2018 geomorphic survey are summarized, interpreted, and compared to the results of the 2017 survey. The data from the 2018 geomorphic survey are published in the companion data release of this report.
The study reach surveyed in June 2018 is 1.89 miles long, beginning about 260 feet upstream from the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station and ending at the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station. Large sections of the study reach are characterized by intermittent streamflow, and where streamflow is normally continuous (including at the upper and lower portions of the study reach, near the streamflow-gaging stations), the streamflow typically remains less than 2 cubic feet per second throughout the year except during seasonal high flows, which most often result from rainfall during the North American monsoon months of July, August, and September or from snowmelt runoff in March, April, and May. Between the 2017 and 2018 surveys, high-flow events resulting from both rainfall (during the North American monsoon season) and snowmelt runoff (during the winter) occurred in the study reach, and those high-flow events appeared to have caused some minor and localized geomorphic changes in the study reach, which were evaluated through comparison of the 2017 and 2018 survey results.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach, and in 2018, those same 14 cross sections were resurveyed. Comparisons of the cross-section survey results indicated that minor observable geomorphic changes had occurred in 3 of the 14 cross sections. These minor observable geomorphic changes included aggradation or degradation of surface materials by about 1–2 feet in some parts of the affected cross sections.
To further assess geomorphic changes within the study reach, other features, including woody debris accumulations and pools, were surveyed in both 2017 and 2018. During the 2018 geomorphic survey, 112 distinct accumulations of woody debris and 71 pools were identified in the study reach. Charred wood or burn-marked wood was present in at least 17 of the identified woody debris accumulations (and was present in some of the woody debris accumulations identified during the 2017 survey), indicating that some of the woody debris in the channel may have been sourced from trees or forest litter that had burned during 2012 Little Bear Fire. Only 22 of the 112 woody debris accumulations identified during the 2018 survey were certain to have also been present during the 2017 survey (when 58 woody debris accumulations were identified), indicating that most of the woody debris accumulations surveyed in 2017 were likely transported during the high-flow events between the 2017 and 2018 surveys but also indicating that the flows during those events were not high enough to remove some of the more firmly anchored woody debris accumulations. Most woody debris accumulations identified in 2018 did not appear to have substantially influenced geomorphic change in the locations where they were found. However, the formation of 10 of the 71 pools identified in the study reach in 2018 appeared to have been influenced by the presence of woody debris, indicating that some woody debris accumulations may have driven local geomorphic changes. Notably, pool totals from the 2017 survey could not be accurately compared to the pool totals from the 2018 survey because of differences between the two surveys in the methods used to identify pools.
Because the study began 5 years after the 2012 Little Bear Fire, and because the period and geomorphic scope of the study have so far been limited, it cannot be said that the geomorphic changes observed between the 2017 and 2018 surveys are representative of a pattern of geomorphic change following the 2012 Little Bear Fire. Though, once geomorphic changes between the 2017 and 2018 surveys can be compared with results from geomorphic surveys planned for 2019, 2020, and 2021, it may be possible to develop an understanding of the patterns in geomorphic change following the 2012 Little Bear Fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201121","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2020, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2018: U.S. Geological Survey Open-File Report 2020–1121, 37 p., https://doi.org/10.3133/ofr20201121.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-112737","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":381235,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1121/ofr20201121.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1121"},{"id":381236,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94ZQHKU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2018 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":381234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1121/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5621337890625,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 32.99023555965106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113