River connectivity is defined as the water-mediated exchange of matter, energy, and biota between different elements of the riverine landscape. Connectivity is an especially important concept in large-river corridors (channel plus floodplain ) because large rivers integrate fluxes of water, sediment, nutrients, contaminants, and other transported constituents emanating from large contributing drainage basins, and thereby contribute to the complexity of large-river ecosystems. Large rivers are also highly valued for socioeconomic goods and services, which has led to historical fragmentation, lack of connectivity, and contentiousness about best policies for managing large-river corridors. The classification is intended to serve as a template for understanding geographic variation in large rivers within the Midwest, to aid in designing scientific studies of large river ecological processes, and to match specific river-management and restoration objectives to specific river reaches. The focus of the classification is on measuring river connectivity from available hydrological and geomorphic data.
We provide a multiscale assessment and classification for segments of 15 rivers that meet various criteria for largeness. All rivers are tributaries to the Mississippi River system. The 11,600 kilometers (km) that qualified as large were classified by major alterations (unimpounded, navigation pools, storage reservoir) and additionally assessed for their network continuity as a function of numbers and heights of dams. Among the 15 rivers, 55 percent of segment length was unimpounded, 30 percent was in navigation pools, and 15 percent was under storage reservoirs. Assessment of network longitudinal connectivity among river segments documented the contrast between river segments with low-head navigation dams (Upper Mississippi, Illinois, Ohio, Green, and Cumberland Rivers) and those segments with high-head dams (mostly in the Upper Missouri River). The longest unimpounded river pathways exist in the Lower Missouri River and connected tributaries where nearly 1,300 km of the Missouri River connect to an additional 1,800 km of the Middle and Lower Mississippi Rivers.
At our finest scale, we present a statistically based, component classification based on 10-km segments. Cluster analysis of hydrologic variables from 66 streamflow-gaging stations yielded 5 clusters calculated from 5 ecohydrological metrics related to lateral connectivity with the floodplain. A separate cluster analysis of 5 geomorphologic variables associated with each of the 1,172 river segments also yielded 5 clusters. When the hydrologic variables were associated with corresponding segments, the cluster analysis yielded 8 hydrogeomorphic clusters that could be explained in terms of their contribution to floodplain connectivity. Although the clusters overlap considerably in principal component space, the resulting hydrogeomorphic classification leads to a physically reasonable distribution of classes. The resulting classification is intended to increase geographic awareness of the range of variation of connectivity potential among large rivers of the Upper Midwest, to increase understanding of the extent of alteration of these rivers, and potentially to serve as a template for stratifying study designs of large-river corridor ecological processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195132","usgsCitation":"Jacobson, R.B., Rohweder, J.J., and DeJager, N.R., 2019, A hydrogeomorphic classification of connectivity of large rivers of the Upper Midwest, United States: U.S. Geological Survey Scientific Investigations Report 2019–5132, 55 p., https://doi.org/10.3133/sir20195132.","productDescription":"Report: vi, 55 p.; 2 Data Releases","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-104678","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399611,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109562.htm"},{"id":370623,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5132/sir20195132.pdf","text":"Report","size":"9.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5132"},{"id":370625,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HFYOLO","text":"USGS data release","linkHelpText":"River valley boundaries and transects generated for select large rivers of the Upper Midwest, United States"},{"id":370624,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YGOKWZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Segment-scale classification, large rivers of the Upper Midwest United States"},{"id":370622,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5132/coverthb.jpg"}],"country":"United States","state":"Colorado, Illinois, Indiana, Iowa, Kansas, Kentucky, Minnesota, Missouri, Montana, Nebraska, New York, North Carolilna,North Dakota, Ohio, Pennsylvania, South Dakota, Tennessee, Virginia, West Virginia, Wisconsin, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.8251953125,\n 35.460669951495305\n ],\n [\n -80.5078125,\n 37.020098201368114\n ],\n [\n -80.15625,\n 35.60371874069731\n ],\n [\n -79.541015625,\n 37.43997405227057\n ],\n [\n -79.365234375,\n 39.87601941962116\n ],\n [\n -77.7392578125,\n 42.74701217318067\n ],\n [\n -82.265625,\n 40.91351257612758\n ],\n [\n -86.8359375,\n 41.73852846935917\n ],\n [\n -87.5830078125,\n 41.60722821271717\n ],\n [\n -88.9453125,\n 43.61221676817573\n ],\n [\n -89.4287109375,\n 43.45291889355465\n ],\n [\n -89.7802734375,\n 46.10370875598026\n ],\n [\n -90.439453125,\n 46.37725420510028\n ],\n [\n -93.42773437499999,\n 46.86019101567027\n ],\n [\n -94.9658203125,\n 47.57652571374621\n ],\n [\n -96.85546875,\n 45.55252525134013\n ],\n [\n -100.94238281249999,\n 48.42920055556841\n ],\n [\n -104.150390625,\n 49.210420445650286\n ],\n [\n -112.0166015625,\n 49.03786794532644\n ],\n [\n -113.99414062499999,\n 45.79816953017265\n ],\n [\n -112.9833984375,\n 44.5278427984555\n ],\n [\n -111.09374999999999,\n 44.77793589631623\n ],\n [\n -106.3916015625,\n 41.47566020027821\n ],\n [\n -105.6884765625,\n 39.67337039176558\n ],\n [\n -104.150390625,\n 38.75408327579141\n ],\n [\n -94.921875,\n 38.16911413556086\n ],\n [\n -91.93359375,\n 36.98500309285596\n ],\n [\n -89.736328125,\n 36.80928470205937\n ],\n [\n -85.8251953125,\n 35.460669951495305\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
The glacial kettle ponds in the Hyannis Ponds Wildlife Management Area in Barnstable, Massachusetts, support a community of rare and endangered plants. The ponds are hydraulically connected to the unconfined aquifer that underlies Cape Cod. The plants are adapted to the rise and fall of water levels in the ponds as the water table fluctuates in response to seasonal and year-to-year natural changes in recharge. Pumping from wells for public water supply and recharge of wastewater at water pollution control facilities and septic systems also affect groundwater levels. The Hyannis Water System has proposed to install two additional wells in the Hyannis Ponds Wildlife Management Area and adjust rates of withdrawals and recharge of wastewater return flows for the municipal system that serves the village of Hyannis in the town of Barnstable. The proposal has raised concerns that pumping from the proposed wells could cause long-term average changes in pond levels that could adversely affect the critical pond-shore plant habitat.
An available three-dimensional steady-state groundwater-flow model was used to simulate the hydrologic effects of nine pumping and wastewater return-flow scenarios prepared by the Hyannis Water System. These effects were quantified by comparison of water levels simulated for the scenarios to water levels simulated for a reference condition based on 2015 withdrawal and wastewater return-flow rates. Maps of water-level responses were prepared to show the effects of pumping from a single well at different locations in the Hyannis Ponds Wildlife Management Area on the water levels of six ponds. Steady-state simulations of the nine scenarios indicated that the shapes of the simulated water-table contours near the wildlife management area changed only slightly at the regional scale, with the largest shifts near the wildlife management area and the Barnstable Water Pollution Control Facility. The simulated changes in pond levels at 10 ponds of interest for the nine scenarios relative to the simulated pond levels for the 2015 reference condition ranged from small increases (less than 0.1 foot) in one pond each in two scenarios to declines (drawdowns) of 1.03–1.11 feet at three ponds in one scenario. Water levels at the Barnstable Water Pollution Control Facility increased because part of the increase in total withdrawals from the Hyannis Water System wells was recharged as wastewater at the water pollution control facility.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195121","collaboration":"Prepared in cooperation with the Town of Barnstable","usgsCitation":"LeBlanc, D.R., McCobb, T.D., and Barbaro, J.R., 2019, Simulated water-table and pond-level responses to proposed public water-supply withdrawals in the Hyannis Ponds Wildlife Management Area, Barnstable, Massachusetts: U.S. Geological Survey Scientific Investigations Report 2019–5121, 32 p., https://doi.org/10.3133/sir20195121.","productDescription":"Report: vii, 32 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109032","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":370347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5121/sir20195121.pdf","text":"Report","size":"3.89 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5121"},{"id":370346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5121/coverthb.jpg"},{"id":370348,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U5AKLC","text":"USGS data release","linkHelpText":"MODFLOW2005 groundwater-flow model used to simulate water-supply pumping scenarios near the Hyannis Ponds Wildlife Management Area, Barnstable, Massachusetts"}],"country":"United States","state":"Massachusetts","city":"Barnstable","otherGeospatial":"Hyannis Ponds Wildlife Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.28074264526367,\n 41.67297593102651\n ],\n [\n -70.26091575622559,\n 41.67297593102651\n ],\n [\n -70.26091575622559,\n 41.68771986229327\n ],\n [\n -70.28074264526367,\n 41.68771986229327\n ],\n [\n -70.28074264526367,\n 41.67297593102651\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Constraining the magnitude of past hydrological change may improve understanding and predictions of future shifts in water availability. Here we demonstrate that water-table depth, a sensitive indicator of hydroclimate, can be quantitatively reconstructed using Kr and Xe isotopes in groundwater. We present the first-ever measurements of these dissolved noble gas isotopes in groundwater at high precision (≤0.005‰ amu−1; 1σ), which reveal depth-proportional signals set by gravitational settling in soil air at the time of recharge. Analyses of California groundwater successfully reproduce modern groundwater levels and indicate a 17.9 ± 1.3 m (±1 SE) decline in water-table depth in Southern California during the last deglaciation. This hydroclimatic transition from the wetter glacial period to more arid Holocene accompanies a surface warming of 6.2 ± 0.6 °C (±1 SE). This new hydroclimate proxy builds upon an existing paleo-temperature application of noble gases and may identify regions prone to future hydrological change.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-019-13693-2","usgsCitation":"Seltzer, A.M., Ng, J., Danskin, W.R., Kulongoski, J.T., Gannon, R., Stute, M., and Severinghaus, J.P., 2019, Deglacial water-table decline in Southern California recorded by noble gas isotopes: Nature Communications, v. 10, 5739, 6 p., https://doi.org/10.1038/s41467-019-13693-2.","productDescription":"5739, 6 p.","ipdsId":"IP-108743","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":458949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-019-13693-2","text":"Publisher Index Page"},{"id":391384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Diego","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.44384765625,\n 32.56996256044998\n ],\n [\n -116.663818359375,\n 32.56996256044998\n ],\n [\n -116.663818359375,\n 32.99484290420988\n ],\n [\n -117.44384765625,\n 32.99484290420988\n ],\n [\n -117.44384765625,\n 32.56996256044998\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2019-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Seltzer, Alan M.","contributorId":192321,"corporation":false,"usgs":false,"family":"Seltzer","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":826385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ng, Jessica","contributorId":268304,"corporation":false,"usgs":false,"family":"Ng","given":"Jessica","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":826386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danskin, Wesley R. 0000-0001-8672-5501 wdanskin@usgs.gov","orcid":"https://orcid.org/0000-0001-8672-5501","contributorId":1034,"corporation":false,"usgs":true,"family":"Danskin","given":"Wesley","email":"wdanskin@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gannon, Riley 0000-0002-1239-1083","orcid":"https://orcid.org/0000-0002-1239-1083","contributorId":205967,"corporation":false,"usgs":true,"family":"Gannon","given":"Riley","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stute, Martin","contributorId":131127,"corporation":false,"usgs":false,"family":"Stute","given":"Martin","email":"","affiliations":[{"id":7254,"text":"Columbia University - Lamont Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":826390,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Severinghaus, Jeffery P. 0000-0001-8883-3119","orcid":"https://orcid.org/0000-0001-8883-3119","contributorId":268306,"corporation":false,"usgs":false,"family":"Severinghaus","given":"Jeffery","email":"","middleInitial":"P.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":826391,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70207291,"text":"70207291 - 2019 - Response of tidal marsh vegetation to pulsed increases in flooding and nitrogen","interactions":[],"lastModifiedDate":"2020-02-25T08:11:27","indexId":"70207291","displayToPublicDate":"2019-12-13T10:09:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Response of tidal marsh vegetation to pulsed increases in flooding and nitrogen","docAbstract":"Worldwide, human activities have modified hydrology and nutrient loading regimes in coastal wetlands. Understanding the interplay between these drivers and subsequent response of wetland plant communities is essential to informing wetland management and restoration efforts. Recent restoration strategies in Louisiana proposes to use sediment diversions from the Mississippi River to build land in adjacent wetlands and reduce the rate of land to open water conversion. In conjunction with sediment delivery, diversions can increase nutrient loads and water levels in the receiving basins. We conducted a greenhouse mesocosm experiment in which we exposed three common tidal freshwater and brackish marsh plants (Panicum hemitomon, Sagittaria lancifolia, and Spartina patens) to two nitrate loading rates [high (35 g N m2 year−1) and low (0.25 g N m2 year−1)], and two flooding treatments (with and without diversion pulsing). Experimental units were set at two different elevations within the treatment tanks to simulate both a healthy and degraded marsh. Plant growth metrics and soil physicochemical properties were measured monthly. Final total biomass was determined at the study’s conclusion. Growth responses differed between species but were not significantly influenced by the treatments. Soil redox potential decreased significantly following the increase in flooding associated with the diversion pulse, but recovered to pre-diversion levels after a 3-month recovery period. Our study suggests short flooding pulses with a recovery period may be key for maintaining healthy marshes, however there remains a need for longer-term empirical studies to understand marsh response to pressures associated with river sediment diversions over time.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-019-09699-8","usgsCitation":"McCoy, M.M., Sloey, T.M., Howard, R.J., and Hester, M.W., 2019, Response of tidal marsh vegetation to pulsed increases in flooding and nitrogen: Wetlands Ecology and Management, v. 28, p. 119-135, https://doi.org/10.1007/s11273-019-09699-8.","productDescription":"17 p.","startPage":"119","endPage":"135","ipdsId":"IP-106945","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":370302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Jean Lafitte National Historical Park and Preserve, Lake Salvador Wildlife Management Area ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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M","contributorId":149516,"corporation":false,"usgs":false,"family":"Sloey","given":"Taylor","email":"","middleInitial":"M","affiliations":[{"id":17763,"text":"University of Louisiana, Lafayette","active":true,"usgs":false}],"preferred":false,"id":777557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Rebecca J. 0000-0001-7264-4364 howardr@usgs.gov","orcid":"https://orcid.org/0000-0001-7264-4364","contributorId":2429,"corporation":false,"usgs":true,"family":"Howard","given":"Rebecca","email":"howardr@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":777555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hester, Mark W.","contributorId":195572,"corporation":false,"usgs":false,"family":"Hester","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":34316,"text":"University of Louisiana at Lafayette, Lafayette, LA, USA","active":true,"usgs":false}],"preferred":false,"id":777558,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207581,"text":"70207581 - 2019 - Multiorder hydrologic position in the conterminous United States: A set of metrics in support of groundwater mapping at regional and national scales","interactions":[],"lastModifiedDate":"2020-02-06T11:28:53","indexId":"70207581","displayToPublicDate":"2019-12-11T07:33:26","publicationYear":"2019","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":"Multiorder hydrologic position in the conterminous United States: A set of metrics in support of groundwater mapping at regional and national scales","docAbstract":"The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order “n” is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2).
Director, Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
How is drought expressed in Hawai‘i & USAPI? Drought is a significant climate feature in Hawai‘i and the U.S.-Affiliated Pacific Islands (USAPI), at times causing severe impacts across multiple sectors. Below average precipitation anomalies are often accompanied by higher than average temperatures and reduced cloud cover. The resulting higher insolation and evapotranspiration can magnify the effects of rainfall deficits. These altered meteorological conditions lead to decreased soil moisture, which, depending on the persistence and severity of the conditions, can cause plant stress, affecting both agricultural and natural systems. The hydrological effects of drought include reductions in streamflow, groundwater recharge, and groundwater discharge to springs, streams, and the ocean. Drought also has socioeconomic impacts, where reduced water supply and other effects of drought have negative financial consequences. For these reasons, drought has been defined from at least five different perspectives: meteorological, ecological, agricultural, hydrological, and socioeconomic drought. In this chapter, we explore how these five faces of drought are expressed in Hawai‘i and the USAPI, and how managers operating within one or more these five perspectives address drought-related stressors to their systems. Not all droughts are the same, varying with respect to duration, frequency, extent, and severity. For example, the region receives severe episodic droughts during which an area will have little or no rainfall for months, even in areas that normally have no dry season. El Niño events fall into this category, and these moderate frequency events are typically responsible for shorter-lived but intense drought events that affect large areas. Drought can also be expressed as infrequent but long duration events of moderate severity, or long-term rainfall decline where the baseline condition appears to be changing when examined on longer time scales. From the perspective of the manager, understanding drought duration, frequency, extent, and severity is critical to understanding the duration, frequency, extent and severity of the response. For example, how an agency responds to El Niño events, with a focus on large-scale but short-lived emergency response campaigns, may differ from how an agency responds to baseline change or an increase in the frequency of extended dry periods, with a focus on longer-lived institutional, infrastructure, and personnel responses. The legislative and policy environment will also respond differently to different types of drought. Understanding and characterizing meteorological drought relies on a long-term network of climate stations. Rainfall has been extensively monitored in Hawai‘i since the early 1900s owing to the expansion of plantation agriculture (Giambelluca and others 1986), while rainfall monitoring for most of the USAPI began in earnest after World War II (Polhemus 2017). Due to prevailing winds, most of Hawai‘i’s land area is characterized by a wet season from November to April and a dry season from May to October. However, important dynamic features affect climate systems of the Pacific. For example, due to their tropical location, rainfall patterns in both Hawai‘i and the USAPI are strongly controlled by large-scale modes of climate variability, including the El Niño-Southern Oscillation (ENSO). El Niño events are typically associated with drier than average winter wet seasons and wetter dry seasons, while La Niña events often result in a wetter than average wet season and a drier dry season. Many historical drought events have been attributed to El Niño events, which produce atmospheric conditions that are unfavorable for rainfall (Chu 1995). However, not all El Niño events result in drought, and effects differ depending on whether the El Niño is classified as Central Pacific (CP) or Eastern Pacific (EP) (Bai 2017; Polhemus 2017).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Effects of drought on forests and rangelands in the United States: Translating science into management responses","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"USDA","doi":"10.2737/WO-GTR-98","usgsCitation":"Frazier, A.G., Deenik, J., Fujii, N., Funderburk, G., Giambelluca, T., Giardina, C., Helweg, D., Keener, V., Mair, D., Marra, J., McDaniel, S., Ohye, L., Oki, D.S., Parsons, E., Strauch, A., and Trauernicht, C., 2019, Managing effects of drought in Hawai’i and U.S.-affiliated Pacific Islands: General Technical Report WO-98, 27 p., https://doi.org/10.2737/WO-GTR-98.","productDescription":"27 p.","startPage":"95","endPage":"121","ipdsId":"IP-105580","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Frazier, Abby G.","contributorId":221112,"corporation":false,"usgs":false,"family":"Frazier","given":"Abby","email":"","middleInitial":"G.","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":777050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deenik, Jonathan","contributorId":221113,"corporation":false,"usgs":false,"family":"Deenik","given":"Jonathan","email":"","affiliations":[{"id":40322,"text":"East-West Center, Honolulu, HI","active":true,"usgs":false}],"preferred":false,"id":777051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fujii, Neal","contributorId":221114,"corporation":false,"usgs":false,"family":"Fujii","given":"Neal","email":"","affiliations":[{"id":40323,"text":"University of Hawai‘i at Mānoa, Department of Tropical Plant and Soil 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Center","active":true,"usgs":true}],"preferred":true,"id":777057,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marra, John ","contributorId":221119,"corporation":false,"usgs":false,"family":"Marra","given":"John ","affiliations":[{"id":40326,"text":"NOAA, National Environmental Satellite, Data, and Information Service","active":true,"usgs":false}],"preferred":false,"id":777058,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McDaniel, Sierra","contributorId":221120,"corporation":false,"usgs":false,"family":"McDaniel","given":"Sierra","email":"","affiliations":[{"id":40324,"text":"Hawai‘i Volcanoes National Park, Hawai‘i, USA","active":true,"usgs":false}],"preferred":false,"id":777059,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ohye, Lenore","contributorId":221121,"corporation":false,"usgs":false,"family":"Ohye","given":"Lenore","email":"","affiliations":[{"id":40327,"text":"State of Hawai‘i, Department of Land and Natural Resources, Commission on Water Resource Management","active":true,"usgs":false}],"preferred":false,"id":777060,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Oki, Delwyn S. 0000-0002-6913-8804","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":221122,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn","email":"","middleInitial":"S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777061,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Parsons, Elliott","contributorId":221123,"corporation":false,"usgs":false,"family":"Parsons","given":"Elliott","affiliations":[{"id":40328,"text":"State of Hawai‘i Division of Forestry and Wildlife, Pu‘u Wa‘awa‘a Forest Reserve","active":true,"usgs":false}],"preferred":false,"id":777062,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Strauch, Ayron","contributorId":221124,"corporation":false,"usgs":false,"family":"Strauch","given":"Ayron","email":"","affiliations":[{"id":40327,"text":"State of Hawai‘i, Department of Land and Natural Resources, Commission on Water Resource Management","active":true,"usgs":false}],"preferred":false,"id":777063,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Trauernicht, Clay","contributorId":221125,"corporation":false,"usgs":false,"family":"Trauernicht","given":"Clay","email":"","affiliations":[{"id":40329,"text":"University of Hawai‘i at Mānoa, Department of Natural Resources and Environmental Management","active":true,"usgs":false}],"preferred":false,"id":777064,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70206037,"text":"sir20195117 - 2019 - Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","interactions":[],"lastModifiedDate":"2019-11-27T09:54:48","indexId":"sir20195117","displayToPublicDate":"2019-11-27T06:42:07","publicationYear":"2019","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":"2019-5117","displayTitle":"Groundwater-Flow Model and Analysis of Groundwater and Surface-Water Interactions for the Big Sioux Aquifer, Sioux Falls, South Dakota","title":"Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","docAbstract":"The city of Sioux Falls, in southeastern South Dakota, is the largest city in South Dakota. The U.S. Geological Survey (USGS), in cooperation with the city of Sioux Falls, completed a groundwater-flow model to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer in the model area.
The model area includes the Big Sioux aquifer and the underlying hydrogeologic units from Dell Rapids, South Dakota, to the confluence of the Big Sioux River and the outlet of the Sioux Falls Diversion Channel in eastern Sioux Falls, S. Dak. The Big Sioux aquifer is the primary aquifer in the model area and the focus of the groundwater-flow model. The Big Sioux River is the largest stream in the model area and is in hydraulic connection with the Big Sioux aquifer.
A conceptual model for the area was constructed and includes a characterization of the hydrogeologic framework, analysis and construction of potentiometric surfaces, and summary of estimated water budget components in the model area. The primary hydrogeologic units in the model area consist of (1) the Big Sioux aquifer, (2) a glacial till confining unit, and (3) bedrock aquifers (Split Rock Creek and Sioux Quartzite aquifers). Sources of groundwater recharge included infiltration of precipitation, stream seepage, and groundwater exchanges among the hydraulically connected Big Sioux aquifer, glacial till confining unit, and bedrock aquifers. Groundwater losses included evapotranspiration, groundwater discharge to streams, and groundwater withdrawal to supply water-use needs.
A numerical groundwater-flow model (numerical model) was constructed and was used to simulate all aspects of the conceptual model for predevelopment (steady-state) and time-varying (transient) monthly conditions for 1950–2017. The numerical model was constructed using the USGS modular hydrologic simulation program, MODFLOW–6, and was calibrated using the Parameter ESTimation software, PEST++.
The transient numerical model was calibrated for steady-state and transient monthly conditions for 1950–2017. Calibration targets were observations of hydraulic head, changes in hydraulic head, monthly mean streamflow (as a rate), and cumulative monthly stream discharge (as a volume). Parameters adjusted during model calibration were horizontal and vertical hydraulic conductivity, specific storage, specific yield, recharge and evapotranspiration multipliers, and streambed hydraulic conductivity. Horizontal and vertical hydraulic conductivity were estimated at pilot points distributed within the model area; specific storage and specific yield were assigned to uniform values in each layer in the model area; recharge and evapotranspiration multipliers were assigned uniformly for every stress period in the numerical model; and streambed hydraulic conductivity values were assigned uniformly between stream confluences.
The final calibrated parameter values of horizontal and vertical hydraulic conductivity, specific yield, specific storage, streambed hydraulic conductivity, recharge, and evapotranspiration were considered reasonable for the hydrogeologic materials and conditions in the model area for 1950–2017.
Overall, simulated hydraulic head altitudes had a linear regression coefficient of determination (R2) of 0.48. Hydraulic head altitude residuals for the glacial till confining unit and bedrock aquifers were typically greater in magnitude when compared to residuals in the Big Sioux aquifer, but simulated hydraulic head altitudes in the Big Sioux aquifer compared favorably with mean observed hydraulic head altitudes and had a linear regression R2 of 0.93.
Simulated streamflow hydrographs matched the general trends of observed increases and decreases in streamflow for USGS streamgages 06482000 (Big Sioux River at Sioux Falls, S. Dak.) and 06482020 (Big Sioux River at North Cliff Avenue at Sioux Falls, S. Dak.), but larger streamflows were overestimated at the first streamgage and underestimated at the second streamgage. The numerical model reasonably estimated cumulative monthly stream discharge for the first 10–15 years of available streamflow records at both USGS streamgages. After the first 10–15 years of available streamflow record, cumulative monthly stream discharge was closely estimated for USGS streamgage 06482000 and underestimated at USGS streamgage 06482020.
Composite sensitivities without regularization were calculated by PEST++ for the calibrated numerical model parameters and were averaged by parameter group. The parameter group with the highest mean composite sensitivity was the recharge multiplier parameter group.
Model simplifications, assumptions, and limitations were necessary for construction of the conceptual and numerical models and for calibration efficiency. Spatial simplification of hydraulic properties could cause the numerical model to misrepresent reactions to changes in localized stresses, such as additional demands for groundwater withdrawal. The numerical model was temporally discretized into monthly periods and required scaling daily rates into representative monthly rates for model input and calibration targets. Based on the comparison between the observed and simulated groundwater levels, monthly mean streamflow and cumulative monthly stream discharge, and general groundwater distribution and flow, the numerical model favorably simulated the flow in the Big Sioux aquifer.
Eventual capture was calculated in the model area using a steady-state numerical groundwater-flow model. The eventual capture map shows areas of higher streamflow capture adjacent to the Big Sioux River north of the city of Sioux Falls and along the lower part of the Sioux Falls Diversion Channel, and areas of lower streamflow capture along aquifer boundaries and near the southern Sioux Quartzite barrier.
The timing of capture was determined using a transient numerical groundwater-flow model to determine the likely captured water sources for 30 years of groundwater withdrawal at three hypothetical wells using three continuous withdrawal rates (112.5, 450.0, and 900.0 gallons per minute). Supply for all three hypothetical wells became capture-dominated after only a short period of continuous withdrawal. Capture stabilized after about 10–15 years for well A, and after 20–25 years for well B, and after about 10–15 years for well C.
The groundwater-flow model is a suitable tool to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer near Sioux Falls, S. Dak. The numerical model can be used to simulate hydrologic scenarios, advance understanding of groundwater budgets, compute system response to stress, and determine likely sources of water supplied to wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195117","collaboration":"Prepared in cooperation with the city of Sioux Falls","usgsCitation":"Davis, K.W., Eldridge, W.G., Valder, J.F., and Valseth, K.J., 2019, Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5117, 86 p., https://doi.org/10.3133/sir20195117.","productDescription":"Report: xi, 86 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-105956","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":369602,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20195013","text":"SIR 2019–5013","linkHelpText":"– Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota"},{"id":369600,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3393","text":"SIM 3393","linkHelpText":"– Delineation of the hydrogeologic framework of the Big Sioux aquifer near Sioux Falls, South Dakota, using airborne electromagnetic data"},{"id":369601,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F79885XC","text":"USGS data release for SIM 3393","linkHelpText":"– Airborne electromagnetic and magnetic survey data, Big Sioux aquifer, October 2015, Sioux Falls, South Dakota"},{"id":369603,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release for SIR 2019–5013","linkHelpText":"– Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"},{"id":369535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5117/coverthb.jpg"},{"id":369536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5117/sir20195117.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5117"},{"id":369537,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O59RO0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-6 model of the Big Sioux aquifer, Sioux Falls, South Dakota"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.06146240234375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.29919735147067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Large-scale assessments of rangeland runoff and erosion require methods to extend plot-scale parameterizations to large areas. In this study, Rangeland Hydrology and Erosion Model (RHEM) parameters were developed from plot-scale foliar and ground-cover transect data for an arid, grass-shrub rangeland in southern New Mexico, and a method was assessed to upscale transect-plot parameters to a large landscape. The transect-plot data compared favorably to corresponding cell data generated from publicly available geospatial data for total foliar cover but less favorably for litter cover and poorly for rock cover. The RHEM effective hydraulic conductivity (Ke) parameter was comparable between transect-plot and geospatial-cell methods, but the splash and sheet erosion factor (Kss) had poor agreement between the two methods. Simulated runoff and erosion reflected differences in transect-plot and geospatial-cell-based RHEM parameterizations, with low error and very good agreement for runoff but high error and poor agreement for soil loss. These results demonstrate that Ke parameters developed using geospatial data calibrated to plot data can be extrapolated to large spatial areas and provide reasonable simulation of runoff using RHEM. However, these same geospatial methods do not provide reasonable estimation of Kss or simulation of soil loss. Poor representation of litter and rock cover variables, which are highly spatially heterogeneous at the plot scale, was inadequate to accurately represent Kss or soil loss using RHEM. High resolution ground cover data, such as from unmanned aerial systems, may improve parameterization of Kss, and, ultimately, arid rangeland soil erosion simulation.
","language":"English","publisher":"American Society of Agricultural and Biological Engineers","doi":"10.13031/aea.13275","usgsCitation":"Ball, G., and Douglas-Mankin, K., 2019, Geospatial scaling of runoff and erosion modeling in the Chihuahuan Desert: Applied Engineering in Agriculture, v. 5, no. 35, p. 733-743, https://doi.org/10.13031/aea.13275.","productDescription":"11 p.","startPage":"733","endPage":"743","ipdsId":"IP-104120","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":369346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Chihuahuan Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.732421875,\n 34.88593094075317\n ],\n [\n -105.13916015625,\n 33.60546961227188\n ],\n [\n -105.2490234375,\n 32.861132322810946\n ],\n [\n -105.75439453125,\n 32.491230287947594\n ],\n [\n -106.9189453125,\n 34.34343606848294\n ],\n [\n -107.1826171875,\n 33.970697997361626\n ],\n [\n -107.698974609375,\n 32.80574473290688\n ],\n [\n -109.09423828125,\n 33.19273094190692\n ],\n [\n -109.10522460937499,\n 31.325486676506983\n ],\n [\n -108.226318359375,\n 31.325486676506983\n ],\n [\n -108.204345703125,\n 31.774877618507386\n ],\n [\n -106.578369140625,\n 31.765537409484374\n ],\n [\n -106.644287109375,\n 31.970803930433096\n ],\n [\n -103.11767578124999,\n 32.01739159980399\n ],\n [\n -103.084716796875,\n 34.994003757575776\n ],\n [\n -105.732421875,\n 34.88593094075317\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"35","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ball, Grady 0000-0003-3030-055X","orcid":"https://orcid.org/0000-0003-3030-055X","contributorId":220746,"corporation":false,"usgs":true,"family":"Ball","given":"Grady","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":200849,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[],"preferred":false,"id":775598,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215106,"text":"70215106 - 2019 - Advances in quantifying streamflow variability across continental scales: 2. Improved model regionalization and prediction uncertainties using hierarchical Bayesian methods","interactions":[],"lastModifiedDate":"2020-10-07T15:26:44.598024","indexId":"70215106","displayToPublicDate":"2019-11-18T10:18:28","publicationYear":"2019","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":"Advances in quantifying streamflow variability across continental scales: 2. Improved model regionalization and prediction uncertainties using hierarchical Bayesian methods","docAbstract":"The precise estimation of process effects in hydrological models requires applying models to large scales with extensive spatial variability in controlling factors. Despite progress in large‐scale applications of hydrological models in conterminous United States (CONUS) river basins, spatial constraints in model parameters have prevented the interbasin sharing of data, complicating quantification of process effects and limiting the accuracy of model predictions and uncertainties. Hierarchical Bayesian methods enable data sharing between basins and the identification of the causes of model uncertainties, which can improve model accuracy and interpretability; however, computational inefficiencies have been an obstacle to their large‐scale application. We used a new generation of Bayesian methods to develop a hierarchical version of a previous hybrid (statistical‐mechanistic) SPAtially Referenced Regression On Watershed attributes model of long‐term mean annual streamflow in the CONUS. We identified hierarchical (regional) variations in model coefficients and uncertainties and evaluated their effects on model accuracy and interpretability across diverse environments in 16 major CONUS regions. Hierarchical coefficients significantly improved spatial accuracy of model predictions, with the largest improvements in humid eastern regions, where uncertainties were approximately one third of those in arid western regions. Half of the coefficients varied regionally, with the largest variations in coefficients associated with water losses in streams and reservoirs. Our unraveling of the causes of model uncertainties identified a small latent process component of runoff that varies inversely with river size in most CONUS regions. Our study advances the use of hierarchical Bayesian methods to improve the predictive capabilities of hydrological models.
Circumpolar lakes comprise ~ 1.4 million km2 of arctic and subarctic landscapes and are vulnerable to change in vegetation, permafrost distribution, and hydrological conditions in response to climate warming. However, the composition and cycling of dissolved organic matter (DOM) is poorly understood for these lakes because most are remote and unstudied. The goal of this study was to assess timescale and source controls on DOM composition in Canvasback Lake, a shallow, sub-Arctic lake in interior Alaska with similar hydrologic and geomorphic characteristics to about a quarter of circumpolar lake ecosystems. Lake dissolved organic carbon (DOC) concentration varied by as much as 16% from the mean (3.34 mg L−1 change) through diel cycles in spring 2016 to fall 2017 and was accompanied by minor changes in DOM composition. At the seasonal scale, DOC concentration increased from spring through fall to very high concentrations under ice in winter. Decreases in both condensed aromatic and polyphenolic compound classes and lignin carbon-normalized yield, plus increased relative abundance of aliphatic compounds, suggests that DOM composition shifts from a pulse of allochthonous DOM in the spring to more autochthonous under-ice. These changes highlight the seasonally-dynamic nature of DOM in circumpolar lakes that are poorly captured by single-visit lake surveys and underscores the need to measure DOM properties and fate consistently across multiple timescales (i.e. seasonally) to better constrain the role of DOM in lake processes. To further assess DOM sources, a suite of endmember leachates were compared to bulk lake DOM, indicating solely allochthonous inputs are not well reflected in lake DOM, highlighting the role of degradation processes or mixing with autochthonous sources. Thus, Canvasback Lake appears less well connected to terrestrial inputs compared to past studies of northern high-latitude lakes and does not behave as previous boreal lake models suggest.
Floodplain inundation poses both risks and benefits to society. In this study, we characterize floodplain inundation across the United States using 5800 stream gages. We find that between 4% and 12.6% of a river’s annual flow moves through its floodplains. Flood duration and magnitude is greater in large rivers, whereas the frequency of events is greater in small streams. However, the relative exchange of floodwater between the channel and floodplain is similar across small streams and large rivers, with the exception of the water-limited arid river basins. When summed up across the entire river network, 90% of that exchange occurs in small streams on an annual basis. Our detailed characterization of inundation hydrology provides a unique perspective that the regulatory, management, and research communities can use to help balance both the risks and benefits associated with flooding.
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2018 (October 1, 2017, through September 30, 2018), water-quality data were collected at 76 stations: 74 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Among the 76 stations in this report, 4 stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticide compounds are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed monitored by the station. Some stations have been classified based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) they monitor. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented for representative streamflow-gaging stations in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1119","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kay, R.T., 2019, Quality of surface water in Missouri, water year 2018: U.S. Geological Survey Data Series 1119, 25 p., https://doi.org/10.3133/ds1119.","productDescription":"v, 25 p.","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-107435","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":369064,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1119/ds1119.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1119"},{"id":369063,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1119/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Deposits of calcite coating the lower passages of Wind Cave in the southern Black Hills of South Dakota were precipitated under phreatic conditions. Data from samples associated with a new cave survey and hydrologic studies indicate that past water tables within Wind Cave reached a maximum height of 45 m above modern levels but were mostly confined to 25 m or less. Uranium-series ages for basal layers deposited on weathered wall rock indicate subaerial conditions in this part of the cave persisted between 1000 and 300 ka. Ages and elevations of wall coatings and cave rafts establish a 300,000 yr paleohydrograph indicating that water-table highstands occurred during interglacial or interstadial-to-early glacial periods and lowstands occurred during full-glacial and stadial episodes.
Isotopes of Sr, U, C, and O from dated calcite samples were obtained to evaluate potential shifts in paleo-groundwater composition. For comparison, Sr and U isotopic compositions were determined for modern groundwater from 18 sites previously classified into five hydrogeologic domains. Isotope data for different domains tend to cluster in separate fields, although several fields overlap. Compositions of Calcite Lake (informal name) water reflect modern recharge to shallow aquifers. In contrast, speleothem data indicate that paleo-groundwater highstands were not supported by increased infiltration associated with local recharge, or by upwelling from deeper Proterozoic sources. Instead, cave water was similar to deeper, warmer groundwater from the Madison Aquifer discharging at modern artesian springs flanking the southern Black Hills. Highstands were likely influenced by large-scale hydraulic processes associated with recharge to the Madison Aquifer under the Laurentide ice sheet on the northeast side of the Williston Basin, causing increased hydrostatic pressures in confined aquifers on the south side of the basin.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35312.1","usgsCitation":"Paces, J.B., Palmer, M.V., Palmer, A.N., Long, A.J., and Emmons, M.P., 2019, 300,000 yr history of water-table fluctuations at Wind Cave, South Dakota, USA—Scale, timing, and groundwater mixing in the Madison Aquifer: GSA Bulletin, v. 132, no. 7-8, p. 1447-1468, https://doi.org/10.1130/B35312.1.","productDescription":"22 p.","startPage":"1447","endPage":"1468","ipdsId":"IP-102435","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":385560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","city":"Rapid City, Hot Springs","otherGeospatial":"southern Black Hills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.029541015625,\n 42.98857645832184\n ],\n [\n -103.095703125,\n 42.98857645832184\n ],\n [\n -103.095703125,\n 44.33956524809713\n ],\n [\n -104.029541015625,\n 44.33956524809713\n ],\n [\n -104.029541015625,\n 42.98857645832184\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"132","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2019-11-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":215864,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":815425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmer, Margaret V.","contributorId":257970,"corporation":false,"usgs":false,"family":"Palmer","given":"Margaret","email":"","middleInitial":"V.","affiliations":[{"id":52191,"text":"State University of New York, Oneonta","active":true,"usgs":false}],"preferred":false,"id":815426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmer, Arthur N. 0000-0002-2770-0053","orcid":"https://orcid.org/0000-0002-2770-0053","contributorId":257971,"corporation":false,"usgs":false,"family":"Palmer","given":"Arthur","email":"","middleInitial":"N.","affiliations":[{"id":52191,"text":"State University of New York, Oneonta","active":true,"usgs":false}],"preferred":false,"id":815427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Emmons, Matthew P. 0000-0002-3429-396X memmons@usgs.gov","orcid":"https://orcid.org/0000-0002-3429-396X","contributorId":5023,"corporation":false,"usgs":true,"family":"Emmons","given":"Matthew","email":"memmons@usgs.gov","middleInitial":"P.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":815429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216081,"text":"70216081 - 2019 - Changes in long-term water quality of Baltimore streams are associated with both gray and green infrastructure","interactions":[],"lastModifiedDate":"2020-11-05T12:52:08.022489","indexId":"70216081","displayToPublicDate":"2019-11-04T12:16:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7348,"text":"Limnology and Oceanography journal","active":true,"publicationSubtype":{"id":10}},"title":"Changes in long-term water quality of Baltimore streams are associated with both gray and green infrastructure","docAbstract":"The steadily rising global urban population has placed substantial strain on urban water quality, and this strain is projected to increase for the foreseeable future. Considerable attention has been given to the hydrological and physico-chemical effects of urbanization on stream ecosystems. However, due to the relative infancy of the field of urban ecology, long-term water quality analyses in urban streams are sparse. Using a 15-year stream chemistry monitoring record from Baltimore, MD, USA, we quantified long-term trends in nitrate, phosphate, total nitrogen, total phosphorus, chloride, and sulfate export at several sites along a rural-urban gradient. We found no significant change in solute export at most sites, although we did find specific patterns of interest for certain solutes. For example, nitrogen export declined at the most headwater urban site, while phosphorus export declined at the most downstream urban site. Coupling long-term monitoring with data on gray and green infrastructure management throughout the landscape, we established relationships between solute export at the most downstream urban monitoring site and sanitary sewer overflows (SSOs), best management practice (BMP) implementation, and road salt application rates. Phosphorus export was correlated with BMP implementation in the watershed, whereas nitrogen export was related to SSOs. Despite highly urbanized watersheds, water quality does not appear to be declining at most of these sites, suggesting that current management may have limited further impairment. Results of our study suggest that both gray and green infrastructure are key for maintaining and improving water quality in this highly urbanized watershed.","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.10947","usgsCitation":"Reisinger, A.J., Woytowitz, E., Majcher, E.H., Rosi, E.J., Belt, K., Duncan, J.M., Kaushal, S., and Groffman, P.M., 2019, Changes in long-term water quality of Baltimore streams are associated with both gray and green infrastructure: Limnology and Oceanography journal, v. 64, no. S1, p. S60-S76, https://doi.org/10.1002/lno.10947.","productDescription":"17 p.","startPage":"S60","endPage":"S76","ipdsId":"IP-094791","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":459272,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10947","text":"Publisher Index Page"},{"id":380166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Baltimore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2835693359375,\n 39.33854604847979\n ],\n [\n -76.53076171875,\n 39.52946653645165\n ],\n [\n -76.92626953125,\n 39.52522954427751\n ],\n [\n -77.0855712890625,\n 39.317300373271024\n ],\n [\n -76.8548583984375,\n 39.095962936305476\n ],\n [\n -76.5032958984375,\n 39.036252959636606\n ],\n [\n -76.2835693359375,\n 39.33854604847979\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"64","issue":"S1","noUsgsAuthors":false,"publicationDate":"2018-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Reisinger, Alexander J. 0000-0003-4096-2637","orcid":"https://orcid.org/0000-0003-4096-2637","contributorId":203337,"corporation":false,"usgs":false,"family":"Reisinger","given":"Alexander","email":"","middleInitial":"J.","affiliations":[{"id":36601,"text":"Soil and Water Sciences Department, University of Florida, Gainesville, FL 32611","active":true,"usgs":false}],"preferred":false,"id":803950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woytowitz, Ellen L 0000-0001-9880-8160","orcid":"https://orcid.org/0000-0001-9880-8160","contributorId":244446,"corporation":false,"usgs":false,"family":"Woytowitz","given":"Ellen L","affiliations":[{"id":48912,"text":"formerly USGS Maryland WSC","active":true,"usgs":false}],"preferred":false,"id":803951,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Majcher, Emily H. 0000-0001-7144-6809","orcid":"https://orcid.org/0000-0001-7144-6809","contributorId":203335,"corporation":false,"usgs":true,"family":"Majcher","given":"Emily","middleInitial":"H.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosi, Emma J.","contributorId":201758,"corporation":false,"usgs":false,"family":"Rosi","given":"Emma","email":"","middleInitial":"J.","affiliations":[{"id":36248,"text":"Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":803953,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belt, Kenneth T.","contributorId":210142,"corporation":false,"usgs":false,"family":"Belt","given":"Kenneth T.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":803954,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duncan, Jonathan M.","contributorId":207569,"corporation":false,"usgs":false,"family":"Duncan","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":803955,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaushal, Sujay S.","contributorId":210125,"corporation":false,"usgs":false,"family":"Kaushal","given":"Sujay S.","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":803956,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Groffman, Peter M. 0000-0001-8371-6255","orcid":"https://orcid.org/0000-0001-8371-6255","contributorId":203338,"corporation":false,"usgs":false,"family":"Groffman","given":"Peter","email":"","middleInitial":"M.","affiliations":[{"id":36602,"text":"City University of New York, Advanced Science Research Center and Brooklyn College, Department of Earth & Environmental Sciences, New York, NY","active":true,"usgs":false}],"preferred":false,"id":803957,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204616,"text":"fs20193040 - 2019 - Columbia Environmental Research Center","interactions":[],"lastModifiedDate":"2019-11-01T08:55:24","indexId":"fs20193040","displayToPublicDate":"2019-10-31T12:59:26","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3040","displayTitle":"Columbia Environmental Research Center","title":"Columbia Environmental Research Center","docAbstract":"The U.S. Geological Survey Columbia Environmental Research Center performs research to solve challenging environmental problems related to contaminants and habitat alterations in aquatic and terrestrial ecosystems. The research is interdisciplinary and pursued through partnerships within the U.S. Geological Survey and with national, international, state, and local agencies; nongovernmental organizations; and universities. Research is prioritized to provide science to the U.S. Department of the Interior and other natural resource management agencies to inform rehabilitation of degraded habitats and imperiled fish and wildlife populations.
The Columbia Environmental Research Center was established in 1966 in Columbia, Missouri, as the U.S. Fish and Wildlife Service’s Fish Pesticide Research Laboratory; the Columbia Environmental Research Center was incorporated into the U.S. Geological Survey in 1996. The U.S. Geological Survey’s staff of 130 includes 90 scientists of which one-half have advanced degrees in ecology, toxicology, biology, biochemistry, chemistry, hydrology, geology, and information technology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193040","usgsCitation":"U.S. Geological Survey, 2019, Columbia Environmental Research Center: U.S. Geological Survey Fact Sheet 2019–3040, 2 p., https://doi.org/10.3133/fs20193040.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-095411","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":368747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3040/coverthb.jpg"},{"id":368748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3040/fs20193040.pdf","text":"Report","size":"650 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3040"}],"country":"United States","state":"Missouri","city":"Columbia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.6806640625,\n 38.71123253895224\n ],\n [\n -92.076416015625,\n 38.71123253895224\n ],\n [\n -92.076416015625,\n 39.155622393423215\n ],\n [\n -92.6806640625,\n 39.155622393423215\n ],\n [\n -92.6806640625,\n 38.71123253895224\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
A previously published field-experimental investigation showed that injection of nitrate in anoxic groundwater that contained aqueous and sediment-bound Fe(II) diminished concentrations of As(V) and As(III) to below drinking-water limits. In the current study, reactive transport modeling confirmed that the observed attenuation was consistent with oxidation of Fe(II) by nitrate, leading to precipitation of hydrous ferric oxide, which, in turn, sorbed both As(V) and As(III). After calibration with site-specific observations, reactive transport modeling could aid in designing effective treatment to remove arsenic using injection of nitrate to oxidize Fe(II).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Environmental Arsenic in a Changing World","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor and Francis","usgsCitation":"Kent, D.B., Smith, R.L., Jamieson, J., Bohlke, J., Repert, D.A., and Prommer, H., 2019, Reactive transport modeling to understand attenuation of arsenic concentrations in anoxic groundwater during Fe(II) oxidation by nitrate, chap. of Environmental Arsenic in a Changing World, p. 512-513.","productDescription":"2 p.","startPage":"512","endPage":"513","ipdsId":"IP-095134","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":408884,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":408883,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.taylorfrancis.com/chapters/oa-edit/10.1201/9781351046633-203/reactive-transport-modeling-understand-attenuation-arsenic-concentrations-anoxic-groundwater-fe-ii-oxidation-nitrate-kent-smith-jamieson-b%C3%B6hlke-repert-prommer"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kent, Douglas B. 0000-0003-3758-8322 dbkent@usgs.gov","orcid":"https://orcid.org/0000-0003-3758-8322","contributorId":1871,"corporation":false,"usgs":true,"family":"Kent","given":"Douglas","email":"dbkent@usgs.gov","middleInitial":"B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Richard L. 0000-0002-3829-0125 rlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3829-0125","contributorId":1592,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":856154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jamieson, James","contributorId":298646,"corporation":false,"usgs":false,"family":"Jamieson","given":"James","email":"","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":856155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":856156,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Repert, Deborah A. 0000-0001-7284-1456 darepert@usgs.gov","orcid":"https://orcid.org/0000-0001-7284-1456","contributorId":2578,"corporation":false,"usgs":true,"family":"Repert","given":"Deborah","email":"darepert@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":856157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Prommer, Henning","contributorId":298649,"corporation":false,"usgs":false,"family":"Prommer","given":"Henning","email":"","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":856158,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70205472,"text":"sir20195091 - 2019 - Summary of hydrologic testing, wellbore-flow data, and expanded water-level and water-quality data, 2011–15, Fort Irwin National Training Center, San Bernardino County, California","interactions":[],"lastModifiedDate":"2019-10-31T07:58:13","indexId":"sir20195091","displayToPublicDate":"2019-10-30T11:37:52","publicationYear":"2019","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":"2019-5091","displayTitle":"Summary of Hydrologic Testing, Wellbore‐Flow Data, and Expanded Water‐Level and Water‐Quality Data, 2011–15, Fort Irwin Training Center, San Bernardino County, California","title":"Summary of hydrologic testing, wellbore-flow data, and expanded water-level and water-quality data, 2011–15, Fort Irwin National Training Center, San Bernardino County, California","docAbstract":"In view of the U.S. Army’s historical reliance and plans to increase demands on groundwater to supply its operations at Fort Irwin National Training Center (NTC), California, coupled with the continuing water-level declines in some developed groundwater basins as a result of pumping, the U.S. Geological Survey (USGS), in cooperation with the U.S. Army, evaluated the water resources, including water quality and potential groundwater supply, of undeveloped basins in the NTC. Previous work in the three developed groundwater basins—Langford, Bicycle, and Irwin—provided information to support water-resources management of those basins. During 2009–12, the USGS installed 41 wells at the NTC; 34 wells were at 14 single- or multiple-well monitoring sites, and 7 wells were long-screen test wells. The majority of the wells were installed in previously undeveloped or minimally developed groundwater basins (Cronise, Red Pass, the Central Corridor area, Superior, Goldstone, and Nelson Basins). During 2012–15, the USGS tested hydrologic properties at 32 wells in 8 basins to help characterize the aquifer system. This report presents data and analyses from core samples; slug tests and single-well aquifer tests; coupled measurements of wellbore flow, water levels, and water-quality constituents; and results from two-dimensional numerical modeling. This information provides a basis for developing and constraining basin-scale hydrogeologic framework and groundwater-flow models to further evaluate water resources in each groundwater basin.
Core samples were tested for vertical saturated hydraulic conductivity, physical properties, and particle-size distribution. Vertical saturated hydraulic conductivities of the cores ranged from less than 0.00001 to 18.13 feet per day, and porosities ranged from 0.15 to 0.56. These physical properties and particle-size analyses indicate the high degree of heterogeneity of the hydrogeologic deposits penetrated by the boreholes. Horizontal hydraulic conductivities estimated from slug tests in 22 monitoring wells in 6 basins (Cronise, Central Corridor area, Goldstone, Langford, Bicycle, and Nelson Basins) ranged from less than 0.1 to 40 feet per day. Results of the aquifer tests at six test wells in the Goldstone, Nelson, and Superior Basins indicate hydraulic conductivities ranged from 0.37 to 66 feet per day; associated transmissivity values ranged from 130 to 28,000 feet squared per day. Wellbore-flow data, collected from the six test wells under unpumped and pumped conditions, generally showed downward movement of water. Flow data collected under unpumped conditions indicate groundwater entered the well through the upper part of the screened interval and exited to aquifer zones in the lower part of the screened interval at rates ranging from 1 to 3 gallons per minute. Flow data collected under pumping conditions show increased flow downward in the test wells, indicating higher yields from deeper aquifers.
Water levels, measured periodically between 2011 and 2015, remained stable during this period in the majority of the wells measured since 2011, except at two monitoring sites in developed basins (Bicycle and Langford). Vertical hydraulic gradients were generally low throughout the NTC, but ranged from –0.0003 to 0.27 during the summer of 2015. Multiple-well monitoring sites in Bicycle, Central Corridor area, Cronise, Goldstone, Nelson, and Superior Basins, had downward vertical gradients.
Groundwater in wells in Nelson and Superior Basins, and wells BLA5, CCT1, and GOLD2 #2, was characterized as sodium-bicarbonate water, whereas groundwater from the remaining wells in Goldstone Basin was characterized as sodium-chloride water and Cronise Basin, and well LL04 was characterized by sodium-sulfate water. Total dissolved solids (TDS) ranged from 285 to 13,400 milligrams per liter (mg/L) TDS and chloride concentrations ranged from 19 to 1,030 mg/L chloride, with lowest concentrations of each in groundwater from Superior and Nelson Basins and highest concentrations in Cronise Basin. Nitrate plus nitrite as nitrogen ranged from less than 0.040 mg/L in groundwater from Cronise and Goldstone Basins to about 20 mg/L in Nelson Basin. Groundwater from wells in Nelson Basin was isotopically light, whereas groundwater samples from wells CRTH1, CRTH2, and LL04 were isotopically heavier and plotted along an evaporative trend line. No measurable tritium was detected in groundwater from 13 wells sampled in 2015, indicating that groundwater was recharged prior to 1952. Measured carbon-14 (14C) activities in groundwater from four wells sampled in 2015 ranged from about 7.9 to 23.5 percent modern carbon and had apparent (uncorrected) ages of 11,970–20,980 years. Arsenic concentrations were above the maximum contaminant level of 10 micrograms per liter in groundwater from all wells, except those in Goldstone Basin and the two deepest wells in Langford Basin (LL04); likewise, fluoride concentrations were above the California maximum contaminant level of 2 mg/L in groundwater from most wells, except those in Goldstone and Superior Basins, the middle well in Langford Basin, middle and deep wells in two locations in Cronise Basin, and two wells in Nelson Basin.
Wellbore flow was simulated for each well by using an integrated-flow analysis tool, AnalyzeHOLE, to evaluate aquifer properties and heterogeneity. Horizontal layers in the model (hydrogeologic units) were defined by lithostratigraphic‐geophysical units, interpreted from lithologic and geophysical logs for each well, and were adjusted during calibration. The saturated hydraulic conductivities derived from the calibrated simulations ranged from less than 0.01 to 60 feet per day in Nelson, Goldstone, and Superior Basins.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Automated data-processing methods allow hydrologists to efficiently incorporate digital well-record datasets into the construction of hydrostratigraphic frameworks for groundwater-flow models. The method selected to construct the hydrostratigraphic framework can affect the extent of geologic heterogeneity that can be included in the model. The detail generated from a hydrostratigraphic framework can affect groundwater simulation results. The effects of detail on model accuracy, groundwater-flow simulations, and particle-tracking simulations are described in this study. This report compares differences in hydrostratigraphic frameworks and results of groundwater models using (1) a method that incorporates more hydrologic judgment at the expense of using limited lithologic data and (2) a method that is more automated and uses all available lithologic data. The study additionally evaluates the effect of model discretization and inclusion of more (or less) geologic detail on simulation results.
Two methods were used to create hydrostratigraphic frameworks of glacial deposits in the St. Joseph River Basin. One method, referred to as the subjective method, manually identifies stratigraphic boundaries using a sample of well logs from State databases and uses two-dimensional kriging to create three model layers of the study area. Indicator kriging is used to define aquifer extent in each layer. The second method, referred to as the objective method, uses three-dimensional kriging to automatically create a detailed heterogeneous model of the study area using all wells logs from the State database. The objective method increases detail in the vertical by greatly increasing the number of computer groundwater model layers from 3 to 30. In Elkhart County, Indiana, a previously published model represents the product of the subjective method, and a newly calibrated model of the same area represents the product of the objective method.
An automated calibration procedure was used with the objective model (derived from the objective method) for Elkhart County. The two most-sensitive parameters for the Elkhart County objective model are horizontal hydraulic conductivity of the sand and the combined sand and gravel/gravel deposits. Vertical hydraulic conductivity of the fine-grained and intermediate-sized deposits could not be estimated, possibly indicating major flow paths are along a continuously connected series of sand and gravel deposits and not through a confining layer.
The statistics measuring model calibration accuracy for the objective model were slightly better than statistics for the subjective model (model derived from the subjective method) of Elkhart County, but the hydraulic conductivities and flow rates for the two models were different. The mean absolute errors between simulated and measured groundwater levels are 2.04 and 2.16 feet for the objective and subjective models, respectively. Simulated seepage losses from and groundwater discharges to measured stream reaches in the objective model were evenly balanced in terms of over and under simulations of measured values; the subjective model tended to overpredict measured groundwater discharge to streams. The overprediction may be related to the 58 percent greater total inflow and outflow through the subjective model. The greater flow rate through the subjective model results from higher horizontal hydraulic conductivities in the subjective model than in the objective model. Horizontal hydraulic conductivity ranged from 23.9 to 111 feet per day in the objective model and generally ranged from 170 to 370 feet per day in the subjective model. The improvement in calibration statistics for the objective model relative to the subjective model may be from increased detail in how the objective model represents the distribution of fine- and coarse-grained deposits. The improvement also could be associated with the difference in methods used to represent the continuity of the confining unit.
The effect of differences in horizontal hydraulic conductivity distributions between the two models for Elkhart County is evident in the groundwater-flow paths simulated by the objective and subjective models. At a withdrawal well location, the flow lines produced by the objective model indicate a wider contributing area than that for the subjective model. The discontinuous confining unit represented in the objective model provided the opportunity for groundwater flow to split into an upper and lower path. The split in flow simulated by the objective model at one location was independently supported by bromide concentrations in groundwater; the subjective model did not duplicate the split in flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195088","collaboration":"U.S. Geological Survey Groundwater Resources Program","usgsCitation":"Arihood, L.D., Lampe, D.C., Bayless, E.R., and Brown, S.E., 2019, Comparison of groundwater-model construction methods, representations of glacial geology, model designs, and groundwater-model flow simulations within Elkhart County, Indiana: U.S. Geological Survey Scientific Investigations Report 2019–5088, 44 p., https://doi.org/10.3133/sir20195088.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-065522 ","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368474,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5088/sir20195088.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5088"},{"id":368475,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QN65RW","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"MODFLOW-2000 model used to illustrate the differences in flow paths and travel times when three-dimensional kriging is used to estimate the hydraulic conductivity distribution as compared to manual determinations of hydraulic conductivity distribution"},{"id":368473,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5088/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Elkhart County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-85.7874,41.7615],[-85.7591,41.7613],[-85.6606,41.7608],[-85.6589,41.699],[-85.6575,41.6122],[-85.6554,41.5251],[-85.6542,41.4733],[-85.6552,41.4384],[-85.7704,41.4377],[-85.8874,41.4379],[-86.0008,41.4375],[-86.059,41.4367],[-86.0594,41.4644],[-86.0593,41.474],[-86.0593,41.479],[-86.0592,41.4935],[-86.0598,41.4999],[-86.0624,41.7619],[-85.932,41.7623],[-85.7874,41.7615]]]},\"properties\":{\"name\":\"Elkhart\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278-1996