The purpose of this study is to characterize streamflow availability under natural (unregulated) low-flow conditions for streams in southeast Kaua‘i, Hawai‘i. The nine main study-area basins, from north to south, include Wailua River, Hanamā‘ulu, Nāwiliwili, Pūʻali, Hulēʻia, Waikomo, Lāwaʻi, and Wahiawa Streams, and Hanapēpē River. The results of this study can be used by water managers to develop technically sound instream-flow standards for the study-area streams.
Low-flow characteristics for natural streamflow conditions were represented by flow-duration discharges that are equaled or exceeded between 95 and 50 percent of the time. Short-term continuous-record stream-gaging stations that monitored low flows on Waiahi and right branch Lāwaʻi Streams were established to serve as potential index stations for partial-record sites in the study area. Continuous-record stream-gaging station on Hanapēpē River monitored natural flow during calendar year 2017 and the streamflow record during that period was used to estimate low-flow characteristics at the station. Partial-record sites were established on 3 main streams and 15 tributary streams, upstream from existing surface-water diversions. Low-flow characteristics were determined using historical and current streamflow data from continuous-record stream-gaging stations and miscellaneous sites, as well as additional data collected as part of this study. Low-flow-duration discharges for the following streams were estimated for the 59-year base period (water years 1961–2019) using two record-augmentation techniques: right branch ʻŌpaekaʻa Stream, North Fork Wailua River, north and south fork Waikoko Streams, ‘Ili‘ili‘ula Stream, north and south fork Hanamāʻulu Streams, Kamo‘oloa Stream, Pāohia Stream, Ku‘ia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River. The 95-percent flow-duration discharges (Q95) ranged from 0.018 to 42 cubic feet per second (ft3/s). The 50-percent flow-duration discharges (Q50) ranged from 1.1 to 69 ft3/s. Upper-bound estimates of low-flow duration discharges at partial-record sites on south fork Hanamāʻulu, Hanamāʻulu tributary, ʻŌmaʻo, and Pōʻeleʻele Streams were estimated based on the highest discharges measured as part of this study during Q95 to Q50 flow conditions, which were 0.44, 0.40, 0.19, and 0.22 ft3/s, respectively. Measured discharges on Nāwiliwili, Pū‘ali, and left branch Wahiawa Streams do not correlate with data at any active long-term continuous-record stream-gaging stations (10 or more complete water years of natural-flow record) and therefore low-flow duration discharges could not be estimated.
This study also estimated streamflow gains and losses using seepage-run discharge measurements in eight of the nine study basins (Pūʻali Stream basin was excluded). A majority of the streams gained flow downstream from the uppermost diversions. Measured seepage-gain rates ranged between 0.03 and 24.3 ft3/s per mile of stream reach. Seepage gains are presumed to originate mainly from groundwater discharge in the Wailua River, Hanamā‘ulu Stream, Nāwiliwili Stream, Hulēʻia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River basins. Under natural-flow conditions and flow conditions of the seepage runs, a majority of the study-area streams flow continuously from the mountains to the ocean. Where a stream discharges into a reservoir––Hanamā‘ulu and Wahiawa Streams––a dry reach may occur immediately downstream from the reservoir to the point of seepage gain in the stream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205128","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Cheng, C.L., 2020, Low-flow characteristics of streams from Wailua to Hanapēpē, Kauaʻi, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2020–5128, 57 p., https://doi.org/10.3133/sir20205128.","productDescription":"viii, 57 p.","onlineOnly":"Y","ipdsId":"IP-119175","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":380936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5128/sir20205128.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5128"},{"id":380935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5128/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.35806274414062,\n 22.02072633149476\n ],\n [\n -159.43496704101562,\n 22.051277605102463\n ],\n [\n -159.554443359375,\n 22.071641456092383\n ],\n [\n -159.6258544921875,\n 22.03345683012737\n ],\n [\n -159.6533203125,\n 21.963424936844223\n ],\n [\n -159.65744018554688,\n 21.923937190109623\n ],\n [\n -159.59838867187497,\n 21.872969071537096\n ],\n [\n -159.43222045898438,\n 21.857675083878423\n ],\n [\n -159.33334350585935,\n 21.930306923001126\n ],\n [\n -159.31823730468747,\n 21.97106645968614\n ],\n [\n -159.33059692382812,\n 22.01945321869661\n ],\n [\n -159.35806274414062,\n 22.02072633149476\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pacific Islands Water Science Center
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
The persistence of high nitrate concentrations in shallow groundwater has been well documented in the shallow glacial aquifer of Mason County, Illinois. Nitrates in groundwater can be a concern when concentrations exceed 10 milligrams per liter in drinking water. Additionally, nitrate in groundwater can contribute to surface water nitrogen loads that can cause increased algal growth. Algal growth increases oxygen consumption causing anoxic conditions as observed in the Gulf of Mexico Hypoxic Zone.
From March 8, 2017, to March 31, 2019, groundwater level, continuous nitrate, dissolved oxygen, specific conductance, water temperature, and pH data were collected in a monitoring well to temporally assess changes in water quality using high frequency data. During this same period, instantaneous field measurements of water quality and groundwater levels were made in surface water and groundwater in and near Quiver Creek in the presumed groundwater flow path about 0.6 mile from the continuous monitoring well. Groundwater nitrate concentrations continuously measured in the aquifer during this time ranged from 14.7 to 23.2 milligrams per liter, whereas instantaneously measured nitrate concentrations in Quiver Creek ranged from 0.9 to 6.4 milligrams per liter. Nitrate concentrations measured by piezometer varied laterally and vertically in the Quiver Creek floodplain and beneath the stream. Irrigation and fertigation for agriculture is widely practiced in Mason County. This may seasonally affect the groundwater flow and movement as well as the persistence of nitrate in this area. Continuously and instantaneously measured nitrate concentrations and groundwater levels indicate that during the irrigation season, discharge to Quiver Creek from the shallow groundwater system may be limited. During and following periods when estimated irrigation use is highest, the low-nitrate deeper groundwater may be the dominant contributor to the Quiver Creek surface water, whereas during recharge events and when the system is not under the stress of irrigation, there is more contribution from the local and higher-nitrate shallow groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205108","collaboration":"Prepared in cooperation with the Illinois Environmental Protection Agency","usgsCitation":"Gruhn, L.R., and Morrow, W.S., 2020, Use of real-time sensors to temporally characterize water quality in groundwater and surface water in Mason County, Illinois, 2017–19: U.S. Geological Survey Scientific Investigations Report 2020–5108, 26 p., https://doi.org/10.3133/sir20205108.","productDescription":"viii, 26 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-108958","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5108/sir20205108.pdf","text":"Report","size":"19.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5108"},{"id":380810,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5108/coverthb.jpg"}],"country":"United States","state":"Illinois","county":"Mason 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Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 7 bridges at 6 highway crossings of the Missouri and Mississippi Rivers on the periphery of the State of Missouri from July 16 to August 13, 2018. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were present at most piers for which bathymetry could be obtained, except those on banks, on bedrock, or surrounded by riprap. Occasionally, the scour hole near a pier was difficult to discern from nearby bed features. The observed scour holes at the surveyed bridges were generally examined with respect to shape and depth.
Although partial exposure of substructural support elements was observed at several piers, at most sites the exposure likely can be considered minimal compared to the overall substructure that remains buried in bed material at these piers. The notable exceptions are piers 12 and 13 at structure L0135 on State Highway 51 at Chester, Illinois, at which the bedrock material was fully exposed around the piers.
The presence of riprap blankets, pier size and nose shape, and alignment to flow had a substantial effect on the size of the scour hole observed for a given pier. Piers that were surrounded by riprap blankets had scour holes that were substantially smaller (to nonexistent) compared to piers at which no rock or riprap were present. Narrow piers having round or sharp noses that were aligned with flow often had scour holes that were difficult to discern from nearby bed features, whereas piers having wide or blunt noses resulted in larger, deeper scour holes. Several of the structures had piers that were skewed to primary approach flow, and scour holes near these piers generally displayed deposition on the leeward side of the pier and greater depth on the side of the pier with impinging flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205088","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2020, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, July–August 2018: U.S. Geological Survey Scientific Investigations Report 2020–5088, 100 p., https://doi.org/10.3133/sir20205088.","productDescription":"Report: vii, 100 p.; Data Release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-115831","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5088/coverthb.jpg"},{"id":380761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5088/sir20205088.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5088"},{"id":380762,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDI9YF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, December 2008 through August 2018"}],"country":"United States","state":"Missouri","otherGeospatial":"Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.68017578125,\n 39.257778150283364\n ],\n [\n -94.72412109375,\n 39.07890809706475\n ],\n [\n -94.603271484375,\n 38.993572058209466\n ],\n [\n -94.28466796874999,\n 39.00211029922515\n ],\n [\n -93.88916015625,\n 39.00211029922515\n ],\n [\n -93.526611328125,\n 39.06184913429154\n ],\n [\n -93.09814453125,\n 39.155622393423215\n ],\n [\n -93.05419921875,\n 39.00211029922515\n ],\n [\n -92.92236328125,\n 38.87392853923629\n ],\n [\n -92.65869140625,\n 38.805470223177466\n ],\n [\n -92.59277343749999,\n 38.659777730712534\n ],\n [\n -92.30712890625,\n 38.53097889440024\n ],\n [\n -92.054443359375,\n 38.46219172306828\n ],\n [\n -91.42822265625,\n 38.51378825951165\n ],\n [\n -91.01074218749999,\n 38.436379603\n ],\n [\n -90.802001953125,\n 38.41916639395372\n ],\n [\n -90.3515625,\n 38.66835610151506\n ],\n [\n -90.164794921875,\n 38.77978137804918\n ],\n [\n -90.054931640625,\n 38.950865400919994\n ],\n [\n -90.19775390625,\n 38.95940879245423\n ],\n [\n -90.54931640625,\n 38.84826438869913\n ],\n [\n -90.90087890624999,\n 38.69408504756833\n ],\n [\n -91.16455078125,\n 38.831149809348744\n ],\n [\n -91.593017578125,\n 38.85682013474361\n ],\n [\n -92.10937499999999,\n 38.7283759182398\n ],\n [\n -92.373046875,\n 38.96795115401593\n ],\n [\n -92.7685546875,\n 39.27478966170308\n ],\n [\n -93.09814453125,\n 39.470125122358176\n ],\n [\n -93.955078125,\n 39.317300373271024\n ],\n [\n -94.22973632812499,\n 39.33429742980725\n ],\n [\n -94.68017578125,\n 39.257778150283364\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Regional aquifers in the Mississippi embayment are the principal sources of water used for public and domestic supply, irrigation, and industrial uses throughout the region. An understanding of how water quality varies spatially, temporally, and with depth are critical aspects to ensuring long-term sustainable use of these resources. A boosted regression tree (BRT) model was used by the U.S. Geological Survey (USGS) to map water quality in the three regional aquifers with the largest groundwater withdrawals in the embayment: the Mississippi River Valley alluvial (MRVA) aquifer, middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ).
The BRT model was used to predict pH to 1-kilometer raster grid cells for seven aquifer layers (one MRVA, four MCAQ, two LCAQ) following the hydrogeologic framework of the Mississippi embayment aquifer system regional MODFLOW model. The methods and approach used for pH predictions are the same as those used recently by the USGS to predict specific conductance and chloride in the aquifers. Explanatory variables for the BRT models included variables describing well location and construction, surficial variables such as soil properties and land use, and variables extracted from the groundwater flow model, such as groundwater levels and ages. The primary source of pH data was the USGS National Water Information System database. Additional data from State ambient groundwater monitoring programs and the Safe Drinking Water Information System also were used. For wells sampled multiple times, the most recent sample was used. Because groundwater residence times are long (greater than 100 years) throughout much of the study area, the possible effects of changes in water quality over time were considered small compared to the improvement in overall model accuracy by using available historical data. Values of pH from 3,362 wells for samples collected between 1960 and 2018 were used as training data for the BRT model. An additional 839 samples were used as holdout data to evaluate model performance. The predictive performance of the pH model is lower than for the training dataset, as indicated by an r-squared value of 0.89 for the training data and an r-squared of 0.71 for the holdout data. The root mean squared errors for the training and holdout data are 0.32 and 0.50 standard pH units, respectively. Data generated during this study and the model output are available from the companion data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3465","usgsCitation":"Kingsbury, J.A., Knierim, K.J., and Haugh, C.J., 2020, Predicted pH of groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, South-Central United States: U.S. Geological Survey Scientific Investigations Map 3465, 1 sheet, https://doi.org/10.3133/sim3465.","productDescription":"1 Sheet: 34.60 x 28.70 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-111848","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":380668,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXX7LN","text":"USGS data release","linkHelpText":"Prediction grids of pH for the Mississippi River Valley alluvial and Claiborne aquifers"},{"id":380666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3465/coverthb2.jpg"},{"id":380667,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3465/sim3465.pdf","text":"Report","size":"3.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3465"}],"country":"United States","state":"Alabama, Arkansas, Louisiana, Mississippi, Missouri","otherGeospatial":"Mississippi River Valley alluvial, Claiborne aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.296875,\n 37.020098201368114\n ],\n [\n -90.1318359375,\n 36.66841891894786\n ],\n [\n -91.93359375,\n 35.28150065789119\n ],\n [\n -93.33984375,\n 33.65120829920497\n ],\n [\n -94.04296874999999,\n 33.100745405144245\n ],\n [\n -93.91113281249999,\n 31.952162238024975\n ],\n [\n -93.1640625,\n 31.090574094954192\n ],\n [\n -91.7578125,\n 30.939924331023445\n ],\n [\n -91.0986328125,\n 31.952162238024975\n ],\n [\n -90.703125,\n 32.24997445586331\n ],\n [\n -89.3408203125,\n 32.175612478499325\n ],\n [\n -88.0224609375,\n 31.57853542647338\n ],\n [\n -87.4951171875,\n 31.80289258670676\n ],\n [\n -86.748046875,\n 32.99023555965106\n ],\n [\n -87.4072265625,\n 33.211116472416855\n ],\n [\n -88.9892578125,\n 33.94335994657882\n ],\n [\n -89.7802734375,\n 34.74161249883172\n ],\n [\n -90,\n 35.24561909420681\n ],\n [\n -89.56054687499999,\n 36.13787471840729\n ],\n [\n -89.3408203125,\n 36.421282443649496\n ],\n [\n -89.2529296875,\n 36.84446074079564\n ],\n [\n -89.296875,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The U.S. Geological Survey monitored dissolved nitrate plus nitrite as nitrogen (N) and dissolved organic carbon (DOC) concentrations and calculated loads of these constituents at the W.P. Franklin Lock and Dam (S-79) from April 2014 to December 2017. Flows from Lake Okeechobee controlled by S-77, S-78 and S-79 affect water quality in the downstream Caloosahatchee River Estuary, where increased nutrients and dissolved organic matter are of concern. Numerous algal blooms have occurred in the Caloosahatchee River and downstream estuaries in recent years (2005–18) and are often attributed to eutrophication. Dissolved nitrate plus nitrite as N (hereafter, referred to as nitrate) data were collected at 15-minute intervals using a submersible ultraviolet optical nitrate sensor. The instrument data were corrected for interferences, as determined by the relation between instrument measurements and 20 concurrent laboratory values. A surrogate model, based on 36 concurrent measurements of DOC, fluorescence of chromophoric dissolved organic matter, and specific conductance, was developed to calculate DOC at 15-minute intervals.
Mean and median calculated nitrate concentrations for the study period (2014–17) were both 0.21 milligram per liter (mg/L). Monthly mean nitrate concentrations ranged from 0.04 mg/L in April 2017 to 0.48 mg/L in November 2015. Monthly mean nitrate concentrations and the proportion of water that was attributed to Lake Okeechobee discharge, released through S-79, were weakly correlated and indicate that the nitrate concentrations typically decreased as the percentage of water released from the lake increased. Annual nitrate loads were 278 metric tons in 2015, 782 metric tons in 2016, and 525 metric tons in 2017. Monthly mean nitrate loads ranged from 1.2 metric tons in April 2017 to 171.3 metric tons in February 2016. Nitrate loads increased linearly with an increase in flow and typically increased during the wet season, May to October. Monthly loads of nitrate were strongly correlated with flow at S-77 and S-79.
Mean and median calculated DOC concentrations for the study period were 18.3 mg/L and 18.9 mg/L, respectively. Monthly mean DOC concentrations ranged from 12.6 mg/L in May 2017 to 21.5 mg/L in September 2015. Generally, DOC concentrations were lower during the dry season months (November to April) and higher during the wet season months. Monthly mean DOC concentrations were moderately correlated with monthly mean flow volumes at S-79. There was a strong correlation between monthly mean DOC concentrations and the proportion of water released at S-79 that can be attributed directly to Lake Okeechobee, indicating that contributions between Moore Haven Lock and Dam (S-77) and S-79 have a higher DOC concentration than water released from Lake Okeechobee. Monthly mean nitrate concentrations and monthly mean DOC concentrations were strongly correlated. Annual loads of DOC were 23,960 metric tons in 2015 and 65,610 metric tons in 2016 (2014 and 2017 data were incomplete). Monthly loads of DOC ranged from 284 metric tons in May 2017 to 15,122 metric tons in September 2017, the latter corresponding to the effects from Hurricane Irma. Monthly loads of DOC were strongly correlated with flow at S-77 and S-79.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201094","collaboration":"USGS Greater Everglades Priority Ecosystem Science Program","usgsCitation":"Booth, A., 2020, Measured and calculated nitrate and dissolved organic carbon concentrations and loads at the W.P. Franklin Lock and Dam, S-79, south Florida, 2014-17: U.S. Geological Survey Open-File Report 2020-1094, 37 p., https://doi.org/10.3133/ofr20201094.","productDescription":"Report: vi, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091619","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":380478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1094/coverthb.jpg"},{"id":380479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1094/ofr20201094.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1094"},{"id":380480,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V4ZGWU","text":"USGS data release","linkHelpText":"Calculated carbon concentrations, Franklin Lock and Dam (S-79) southern Florida, 2014-2017"}],"country":"United States","state":"Florida","otherGeospatial":"W.P. Franklin Lock and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.7437744140625,\n 26.701452590314368\n ],\n [\n -81.47735595703125,\n 26.701452590314368\n ],\n [\n -81.47735595703125,\n 26.74683674289727\n ],\n [\n -81.7437744140625,\n 26.74683674289727\n ],\n [\n -81.7437744140625,\n 26.701452590314368\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Urbanization substantially alters the landscape in ways that can impact stream hydrology, water chemistry, and the health of aquatic communities. Stormwater best management practices (BMPs) are the primary tools used to mitigate the effects of urban stressors such as increased runoff, decreased baseflow, and increased nutrient and sediment transport. To date, Fairfax County Virginia’s stormwater management program has made substantial investments into the implementation of both structural and nonstructural BMPs aimed at restoring and protecting watersheds. The U.S. Geological Survey (USGS), in cooperation with Fairfax County, Virginia, established a long-term water-resources monitoring program to evaluate the watershed-scale effects of these investments. Monitoring began at 14 stations in 2007 and was expanded to 20 stations in 2013. This report utilized the first 10 years of data collection to (1) assess water quantity and quality, as well as ecological condition; (2) compute annual nutrient and sediment loads; and (3) evaluate trends in streamflow, water quality, and ecological condition. Efforts are underway to link the biotic and abiotic patterns described herein to watershed management practices as well as factors such as land use change, public works infrastructure, and climate.
Hydrologic, chemical, and benthic macroinvertebrate community conditions in the streams monitored were similar to those observed in other studies of urban streams. Multidecadal trends in baseflow indices and runoff ratios at long-term Chesapeake Bay Non-tidal Network streamgages (CB-NTN) indicate a decrease in groundwater recharge and increase in storm runoff as a result of urbanization. Streamflow yields varied spatially with land cover, geology, and soil characteristics, whereas flashiness was positively related to impervious area. Dissolved oxygen typically was lowest in the Coastal Plain and across all Triassic Lowlands streams, and highest in the Piedmont. Dissolved oxygen concentrations generally were above Virginia’s minimum criterion of 4.0 milligrams per liter (mg/L), most violations occurred at Paul Spring Branch in the Coastal Plain during the warmest months of the year owing to increased chemical and biological oxygen demand. Typical pH values of the monitored streams centered on neutrality (pH = 7); however, diurnal fluctuations were most prevalent in the continuous pH data at Flatlick Branch (FLAT; a Triassic Lowlands station), as a result of increased photosynthesis catalyzed by phosphorus-rich geology. Specific conductance (SC) varied spatially owing to geology (highest at Triassic Lowlands stations) and anthropogenic disturbance (watersheds with high impervious land cover). Specific conductance typically was inversely related to streamflow except in winter months following deicing road salt applications, when values increased by several orders of magnitude. A significant increase in SC of about 2 percent per year was observed from the combined trend result of all monitoring stations over the 10-year period. Significant SC increases occurred at nearly all monitoring stations. Increasing trends were observed during winter and nonwinter months, which suggests that salts applied to deice roadways and other impervious surfaces are stored in the environment and released year-round.
Suspended-sediment (SS) concentrations in monthly samples did not vary significantly between most stations, but typically were highest in the spring and lowest in the fall as a result of seasonal differences in streamflow and climate. Suspended-sediment yields ranged from 62 to 1,428 tons per square mile (ton/mi2), with a median of 302 ton/mi2. Annual loads were greatest during the wettest water years (October 1-September 30; 2008, 2011, and 2014), with the greatest interannual variability occurring at Difficult Run above Fox Lake (DIFF) and South Fork Little Difficult Run (SFLIL). Suspended sediment was primarily composed of silts and clays; however, the proportion of sand in suspended sediment was related positively to streamflow. Cross-correlation analyses suggested the dominant sources of SS were streambank erosion and resuspension of in-channel material at DIFF and FLAT; whereas, upland sources and erosion of upper streambanks were more common at Dead Run (DEAD), Long Branch (LONG), and SFLIL.
Median total phosphorus (TP) concentrations ranged from 0.016 to 0.077 mg/L, with a networkwide median of 0.022 mg/L, were highest in the warm season (April-September), and were composed primarily of dissolved phosphorous. Although TP concentrations were relatively low across the network, the highest concentrations were consistently at stations located in the Triassic Lowlands, owing to phosphorous-rich geology, and in the Coastal Plain, owing to the low-phosphorous sorptive capacity of those soils. A significant increase in TP concentration occurred in a few stations, but the combined trend results from all stations demonstrated a significant increase of about 4 percent per year. Networkwide increases were also observed in total dissolved phosphorus, orthophosphate, and total particulate phosphorus. The composition of TP shifted from dissolved to particulate as streamflow increased and for this reason loads primarily were composed of particulate phosphorous. Median annual TP loads were highest at FLAT and DEAD and ranged from 247 to 642 pounds per square mile (lbs/mi2) networkwide. Interannual variability in phosphorous yields was apparent at most stations; the highest loading years were also the wettest years during the study period and coincident with the highest peak annual flows.
Total nitrogen (TN) concentrations typically were low throughout the network with exceptions occurring at stations located in watersheds with a high density of septic infrastructure. Elevated TN concentrations also were observed in some watersheds without a high density of septic systems and may be attributable to geologic and soil properties that limit denitrification as well as other unknown anthropogenic inputs. Total nitrogen typically was dominated by nitrate during baseflows; however, the proportion of particulate nitrogen increased during stormflows. Total nitrogen yields were similar across stations, with medians ranging from about 3,600 to 6,300 lbs/mi2 and were related to annual streamflow volume. Total nitrogen concentrations and flow-normalized concentrations decreased over the 10-year period at 7 stations, with median reductions of about 2.5 percent. Increasing trends were observed at the two stations with the highest median TN concentration (Captain Hickory Run and SFLIL, 3–5 mg/L), both watersheds contain a high density of septic infrastructure. The combined trend results from all stations revealed no trend in TN and a declining trend in nitrate of about 2 percent per year.
Overall, benthic community metrics indicated that streams throughout Fairfax County were initially of poor health; however, many metrics show an improving trend (from poor to fair based on the Fairfax County Index of Biological Integrity [IBI]). Significant increasing trends in IBI occurred at the network-scale and at 4 individual stations; additionally, scores improved by at least 1 qualitative category (for example, poor to fair, fair to good) at 11 of the 14 stations between 2009 (the first year all 14 stations were sampled) and 2017. Changes in all metrics suggest that the biodiversity, function, and condition of streams in Fairfax County are improving, but some of these improvements are driven by increased diversity and percent composition of organisms that are tolerant of the urban environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205061","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Porter, A.J., Webber, J.S., Witt, J.W., and Jastram, J.D., 2020, Spatial and temporal patterns in streamflow, water chemistry, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007–18: U.S. Geological Survey Scientific Investigations Report 2020–5061, 106 p., https://doi.org/10.3133/sir20205061.","productDescription":"Report: xii, 106 p.; Data Release","numberOfPages":"106","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-113872","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":378258,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95S9RFV","text":"USGS data release","linkHelpText":"Inputs and selected outputs used to assess spatial 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 E. Parham Road
Richmond, VA 23228
This report provides an overview of existing hydrologic information describing the quality, quantity, and extent of the major surface-water and groundwater resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma. Hydrologic information is provided for five major river systems (Cimarron River, North Canadian River, Canadian River, Washita River, and North Fork Red River), two reservoirs (Foss Reservoir and Canton Lake), and eight aquifers consisting of the alluvial aquifers associated with each of the five major river systems and three major bedrock aquifers (Ogallala aquifer, Elk City aquifer, and Rush Springs aquifer).
Types of information provided about rivers and reservoirs for the Cheyenne and Arapaho Tribal jurisdictional area include diversion sites and amounts of water allocated and diverted for permitted uses in 2015; treated wastewater discharge sites and amounts discharged in 2015; and characteristics describing water-quality field properties, major ions, nutrients, and selected trace elements. Major ions, nutrients, and selected trace elements are compared to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water. Additionally, statistics are provided describing daily, monthly, and annual streamflow characteristics at 12 U.S. Geological Survey streamgages. Streamflow statistics include the magnitudes and frequencies of floods, base-flow characteristics, and long-term streamflow trends.
Types of information provided about the aquifers include amounts of water allocated and pumped for permitted uses in 2015; characteristics of groundwater describing water-quality field properties, major ions, nitrate (measured as nitrogen), and selected trace elements with comparisons to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water; groundwater levels and long-term changes in water levels; and ranges of hydraulic conductivity, aquifer recharge, specific yield, transmissivity, and well yields from reports and groundwater-flow models.
Surface water is used primarily for irrigation and mining and other nonconsumptive uses in the Cheyenne and Arapaho Tribal jurisdictional area, except from the Washita and North Fork Red Rivers, where water is treated for use as a public-water supply. Large concentrations of dissolved solids are the primary limiting factor affecting the use of surface water. Median concentrations of dissolved solids in surface water range from less than 1,000 milligrams per liter (mg/L) in samples from the North Canadian River to greater than 9,000 mg/L in samples from the Cimarron River. Large dissolved solids concentrations are correlated with hard water. Median hardness as calcium carbonate concentrations in surface water ranges from 427 mg/L in samples from Canton Lake to 1,000 mg/L in samples from the Washita River.
In 2015, groundwater was used at more than twice the rate of surface water in the Cheyenne and Arapaho Tribal jurisdictional area. Although the alluvial aquifers are considered reliably good sources of water in the Cheyenne and Arapaho Tribal jurisdictional area, concentrations of nitrate (measured as nitrogen) exceed the maximum contaminant level of 10 mg/L established by the U.S. Environmental Protection Agency for finished drinking water in parts of all of the alluvial aquifers. Water from the three major bedrock aquifers is used for irrigation, mining, public-water supply, and other uses; however, large concentrations of dissolved solids, nitrate (measured as nitrogen), and naturally occurring trace elements such as arsenic and uranium may limit the use of groundwater as a source of public-water supply in some areas. As of 2015, the depletion of groundwater from the major aquifers in west-central Oklahoma is a minor concern to the Oklahoma Water Resources Board. Groundwater levels and other hydrologic information show that recharge rates exceed the rates of water pumped from aquifers, except in areas that may be affected locally by groundwater depletions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205105","collaboration":"Prepared in cooperation with the Cheyenne and Arapaho Tribes of Oklahoma and the Bureau of Indian Affairs","usgsCitation":"Becker, C.J., and Varonka, M.S., 2020, Water resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma, with an analysis of data gaps through 2015 (ver. 1.1, January 2021): U.S. Geological Survey Scientific Investigations Report 2020–5105, 158 p., 1 app., https://doi.org/10.3133/sir20205105..","productDescription":"xi, 158 p.","numberOfPages":"175","onlineOnly":"Y","ipdsId":"IP-109610","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":382059,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5105/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5105 Version History"},{"id":379749,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5105/sir20205105.pdf","text":"Report","size":"36.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5105"},{"id":379748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5105/coverthb2.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Cheyenne and Arapaho Tribal Jurisdictional Area","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.3595,35.1163],[-99.4067,35.1161],[-99.409,35.1148],[-99.4129,35.1134],[-99.4163,35.1134],[-99.4196,35.1143],[-99.4219,35.1138],[-99.4258,35.1129],[-99.4281,35.1111],[-99.4337,35.1097],[-99.4393,35.111],[-99.4432,35.1124],[-99.4449,35.116],[-99.7833,35.1161],[-99.7839,35.1151],[-99.7852,35.028],[-99.8897,35.0277],[-100.0008,35.0295],[-100.0014,35.1818],[-100.0015,35.2034],[-100.0013,35.2689],[-100.0012,35.293],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-100.0015,35.9478],[-100.002,36.0539],[-100.0025,36.1891],[-100.003,36.3134],[-100.0031,36.3348],[-100.0038,36.4998],[-100.0044,36.5849],[-100.0045,36.5917],[-99.6193,36.5916],[-99.605,36.5917],[-99.6043,36.506],[-99.6038,36.3051],[-99.6034,36.2457],[-99.5954,36.2457],[-99.5976,36.1639],[-99.382,36.1645],[-98.9565,36.1587],[-98.7878,36.1613],[-98.7428,36.1625],[-98.7251,36.1634],[-98.6362,36.1636],[-98.634,36.1636],[-98.3177,36.1645],[-98.2122,36.1656],[-98.1057,36.1658],[-97.6759,36.1663],[-97.6765,36.0715],[-97.6763,35.984],[-97.6761,35.8973],[-97.6757,35.7253],[-97.6753,35.5506],[-97.6719,35.5506],[-97.6729,35.4639],[-97.6733,35.3763],[-97.6729,35.335],[-97.6836,35.3351],[-97.6898,35.3338],[-97.6948,35.3339],[-97.7016,35.3353],[-97.7033,35.3353],[-97.7073,35.334],[-97.7118,35.3313],[-97.7181,35.3287],[-97.7231,35.3278],[-97.7288,35.3274],[-97.7367,35.328],[-97.7423,35.3298],[-97.749,35.3326],[-97.7546,35.3354],[-97.7635,35.3414],[-97.7759,35.3434],[-97.7917,35.3408],[-97.829,35.3348],[-97.838,35.3354],[-97.8464,35.3368],[-97.8565,35.341],[-97.8582,35.3428],[-97.8614,35.3551],[-97.8636,35.3588],[-97.8669,35.3615],[-97.8703,35.3629],[-97.8754,35.3625],[-97.8811,35.3608],[-97.8845,35.3572],[-97.8885,35.3545],[-97.8936,35.3513],[-97.9009,35.3514],[-97.9082,35.3528],[-97.9105,35.3533],[-97.915,35.3556],[-97.925,35.3612],[-97.9351,35.3649],[-97.9368,35.3672],[-97.9378,35.3744],[-97.9395,35.3763],[-97.9429,35.3772],[-97.9474,35.3777],[-97.9491,35.3764],[-97.9502,35.3745],[-97.9492,35.3668],[-97.951,35.3596],[-97.9527,35.3555],[-97.955,35.3528],[-97.9562,35.3519],[-97.9663,35.3525],[-97.9697,35.3529],[-97.9736,35.3543],[-97.9843,35.3599],[-97.9994,35.365],[-98.0184,35.3765],[-98.0985,35.3767],[-98.305,35.3744],[-98.3051,35.5437],[-98.3085,35.541],[-98.3119,35.5401],[-98.3142,35.5406],[-98.3193,35.5429],[-98.3193,35.5438],[-98.3187,35.5479],[-98.3181,35.5502],[-98.6199,35.552],[-98.6209,35.4639],[-98.6216,35.2038],[-98.616,35.2038],[-98.6177,35.0994],[-98.621,35.0981],[-98.6255,35.1035],[-98.6294,35.1167],[-98.6355,35.1231],[-98.6406,35.1231],[-98.6429,35.1145],[-98.6451,35.1113],[-98.6496,35.1141],[-98.6513,35.1177],[-98.649,35.1195],[-98.6485,35.1213],[-98.6485,35.1231],[-98.6513,35.125],[-98.6575,35.1236],[-98.6665,35.1209],[-98.6738,35.1187],[-98.6778,35.1082],[-98.6822,35.1101],[-98.6856,35.1078],[-98.6985,35.1115],[-98.7042,35.111],[-98.7109,35.1065],[-98.7132,35.1065],[-98.721,35.1138],[-98.7255,35.1115],[-98.7317,35.1129],[-98.7351,35.1029],[-98.7379,35.102],[-98.7401,35.107],[-98.748,35.1166],[-98.8244,35.1176],[-98.9312,35.1168],[-98.9807,35.1173],[-99.0425,35.1168],[-99.2555,35.1161],[-99.3595,35.1163]]]},\"properties\":{\"name\":\"Beckham\",\"state\":\"OK\"}}]}","edition":"Version 1.0: October 27, 2020; Version 1.1: January 11, 2021","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The U.S. Geological Survey, in cooperation with the Connecticut Department of Transportation, updated flood-frequency estimates with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively) for 141 streamgages in Connecticut and 11 streamgages in adjacent States using annual peak-flow data through water year 2015. Peak-flow regression equations were derived for estimating flows at ungaged stream sites with annual exceedance probabilities from 50 to 0.2 percent. Methods for estimating prediction intervals for the peak-flow regression equations are presented. The regression equations are applicable for basins in Connecticut with drainage areas ranging from 0.69 to 325 square miles that are not affected by flood-control regulation or flow diversions.
The flood discharges for select annual exceedance probabilities were estimated following new (2018) national guidelines for flood-frequency analyses. New guidelines have improved statistical methods for flood-frequency analysis including (1) the expected moments algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record and (2) the generalized multiple Grubbs-Beck test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Additionally, a new regional skew (0.37) derived for New England was used in the flood-frequency analysis for the streamgages.
Annual peak flows were analyzed for trends for four time periods (30, 50, 70, and 90 years) through 2015. Trend results show some statistical evidence of increasing peak flows in each of the time periods analyzed; however, multidecadal climate cycles may be influencing the number and magnitude of the trends. Historical peak-flow trends in and near Connecticut do not offer clear and convincing evidence for incorporating trends into flood-frequency analyses. For this study, the traditional assumption of stationarity is used with no adjustment for trends.
Generalized least squares regression techniques were used to develop the final set of multivariable regression equations for estimating flood discharges with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. The standard error of prediction for the regional regression equations ranged from 26.3 to 45.0 percent. The standard error of prediction was slightly smaller in the current study compared to the 2004 study, indicating an improvement in the predictive ability of the equations (6 percent smaller at the 50-percent annual exceedance probability to about 1 percent smaller at the 1-percent annual exceedance probability). Generalized least squares regression techniques also were used to develop a one-variable (drainage-area-only) equation. Drainage-area-only equations can be used as an alternative to the multiexplanatory variable statewide regression equations if decreased accuracy is acceptable.
The revised statistical procedures and additional streamgage data applied in the current study result in a more accurate representation of peak-flow conditions in Connecticut than was previously available. The regional regression equations will be integrated in the U.S. Geological Survey StreamStats program, which estimates basin and climatic characteristics and streamflow statistics at user-selected ungaged stream sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205054","collaboration":"Prepared in cooperation with the Connecticut Department of Transportation","usgsCitation":"Ahearn, E.A., and Hodgkins, G.A., 2020, Estimating flood magnitude and frequency on streams and rivers in Connecticut, based on data through water year 2015: U.S. Geological Survey Scientific Investigations Report 2020–5054, 42 p., https://doi.org/10.3133/sir20205054.","productDescription":"Report: v, 42 p.; 2 Tables; 2 Data Releases","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108818","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":378387,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EWHAYW","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Worksheet for computing annual exceedance probability flood discharges and prediction intervals at stream sites in Connecticut"},{"id":378386,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4F751","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Flood frequency and source data used in regional regression analysis of annual peak flows in Connecticut"},{"id":378381,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5054/coverthb.png"},{"id":378382,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054.pdf","text":"Report","size":"6.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5054"},{"id":378383,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054_table01.xlsx","text":"Table 1","size":"35.3 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Descriptions of U.S. Geological Survey streamgages in Connecticut and adjacent States used in the flood-frequency analysis and regionalization of peaks flows in Connecticut"},{"id":378384,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054_table01.csv","text":"Table 1","size":"33.5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Descriptions of U.S. Geological Survey streamgages in Connecticut and adjacent States used in the flood-frequency analysis and regionalization of peaks flows in Connecticut"}],"country":"United 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
A long history of mining and widespread metals contamination in the Coeur d’Alene River watershed and downstream into the Spokane River has led to the area’s designation as a Superfund site and to extensive, ongoing (as of 2020) remedial actions. Long-term water-quality and streamflow data, collected by the U.S. Geological Survey for up to 29 years at 20 sampling sites in the Coeur d’Alene, Spokane and St. Joe River watersheds, were analyzed to evaluate the impact of remedial actions on metals in surface water. Analyses focused on total and dissolved cadmium, zinc and lead. Trends in total phosphorus, total nitrogen and dissolved orthophosphate were also evaluated; although these nutrients are not constituents of concern for the Superfund site, they are important to the health of Coeur d’Alene Lake.
Dissolved cadmium, zinc and lead concentrations were compared to ambient water-quality criteria at 20 sample sites. For the 12 sites with the most extensive data records, Weighted Regressions on Time, Discharge and Season (WRTDS) models were developed to estimate flow-normalized annual mean concentrations and flow-normalized annual total loads; these results were used to evaluate trends because flow-normalization dampens the impact of interannual streamflow variability on concentrations and loads. WRTDS models with Kalman filtering (WRTDS_K) were developed to estimate annual mean concentrations and annual total loads; these results were used to evaluate spatial patterns in constituent sources. Models were developed for total and dissolved cadmium, lead, and zinc; total phosphorus and nitrogen; and dissolved orthophosphate, although not all constituents were modeled for all sites due to limited sample sizes. Bootstrapped confidence intervals were constructed to determine the statistical likelihood of trends and the slope of trends in flow-normalized concentrations and loads during the period of record (13–29 years, depending on the site), water years 1999–2009, and water years 2009–18.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205096","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Zinsser, L.M., 2020, Trends in concentration, loads, and sources of trace metals and nutrients in the Spokane River Watershed, northern Idaho, water years 1990-2018: U.S. Geological Survey Scientific Investigations Report 2020–5096, 58 p., https://doi.org/10.3133/sir20205096.","productDescription":"Report: vii, 58 p.; Appendix 1-2; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-116912","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":378922,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5096/coverthb.jpg"},{"id":378923,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096"},{"id":378924,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096_appendix1.pdf","text":"Appendix 1","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096 Appendix 1"},{"id":378925,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096_appendix2.pdf","text":"Appendix 2","size":"35.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096 Appendix 2"},{"id":378926,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91LNE8J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"WRTDS annual concentrations, loads and statistical trend likelihoods for sites in the Spokane River watershed, water years 1990-2018"}],"country":"United States","state":"Idaho","otherGeospatial":"Spokane River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.18017578125,\n 46.98025235521883\n ],\n [\n -114.80712890625,\n 46.98025235521883\n ],\n [\n -114.80712890625,\n 48.29781249243716\n ],\n [\n -117.18017578125,\n 48.29781249243716\n ],\n [\n -117.18017578125,\n 46.98025235521883\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
The goal of this research was to examine the impacts of Grade Control Structure (GCS) installations at the Heard Scout Pueblo (HSP) study site in the City of Phoenix, Arizona, USA. The study site is around a high-use trail system and is comprised of eroded and incised channels that conduct high flows and associated sediments into a residential neighborhood downstream, a noted stormwater control problem. We established baseline conditions associated with rainfall/runoff response before structures were installed so we could have some data for comparison afterwards.
Innovative monitoring equipment, including video cameras and pressure transducers (to calculate discharge); digital terrain models, sediment samplers and sediment chains (to measure erosion and deposition); soil moisture sensors in monitoring wells (to document infiltration and potential recharge); and weather stations (to track temperature and relative humidity) were established and a small Unmanned Aircraft System (sUAS) survey was completed by July, 11, 2017, in time for the typical summer monsoon season which officially runs from June 15th to September 30th. Only one pre-GCS installation rain event incurred a significant flow event (October 13, 2018).
Natural Channel Design (NCD), a landscape restoration company with decades of experience, was hired through a competitive bid process to develop a novel layout of ~30 GCS installations (sills, modified one-rock dams (ORD), and plugs, as well as a modified Zuni-bowl). The American Conservation Experience (ACE) hand-built the structures based on these designs in the main channel from November 13, 2018 through December 1, 2018. ACE built another ten structures in locations adjacent to the channel from January 15 through January 18, 2019. NCD worked with the landscape forensics to identify a historic channel and reinstate it using GCS.
A surface-water model was also applied, using some of the baseline measurements (terrain and hydraulic conductivity) to track the flows of water and potential infiltration associated with rainfall events before GCS installation, to assist NCD in their design. The same model was applied using the installed GCS locations to simulate impacts of the structures on flow and infiltration. Our model was able to predict the slight reduction and delay in peak flows for small events and simulate infiltration, which was measured and occurred in the channel. Results demonstrated that structures could increase infiltration by ~15% over time. More data describing geomorphology and hydrology after repeated rainfall events will allow for increased analyses.
Innovative monitoring, including the large‐scale particle image velocimetry (LSPIV) were invaluable to this research. Given the arid-land location and added drought conditions, the water levels were not high enough to compute, even using the continuous slope-area method, so discharge was calculated solely using the LSPIV. The careful redundancy of data acquisition is extremely important when studying dryland hydrology.
Weather data indicated that the HSP GCS installations created roughly a three-degree microclimate cooling effect for at least two days following rainfall events, as compared with the untreated channel. The cooling was attributed to increased moisture, evaporation, and latent heat expulsion from the evaporation.
","language":"English","publisher":"Bureau of Reclamation","usgsCitation":"Tosline, D., Norman, L., Greimann, B.P., Cederberg, J., Huang, V., and Ruddell, B., 2020, Impacts of grade control structure installations on hydrology and sediment transport as an adaptive management strategy: Final Report ST-2017-1751-01, iv, 65 p.","productDescription":"iv, 65 p.","ipdsId":"IP-121918","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":382021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382013,"type":{"id":15,"text":"Index Page"},"url":"https://data.usbr.gov/catalog/4414/item/6298"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.09899902343749,\n 33.293803558346596\n ],\n [\n -111.9784927368164,\n 33.293803558346596\n ],\n [\n -111.9784927368164,\n 33.38529959859565\n ],\n [\n -112.09899902343749,\n 33.38529959859565\n ],\n [\n -112.09899902343749,\n 33.293803558346596\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tosline, Deborah","contributorId":247510,"corporation":false,"usgs":false,"family":"Tosline","given":"Deborah","affiliations":[{"id":49564,"text":"Reclamation, Hydrologist / Program Manager","active":true,"usgs":false}],"preferred":false,"id":807809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":807810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greimann, Blair P.","contributorId":247511,"corporation":false,"usgs":false,"family":"Greimann","given":"Blair","email":"","middleInitial":"P.","affiliations":[{"id":49565,"text":"Reclamation, Hydraulic Engineer","active":true,"usgs":false}],"preferred":false,"id":807811,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cederberg, Jay 0000-0001-6649-7353","orcid":"https://orcid.org/0000-0001-6649-7353","contributorId":219724,"corporation":false,"usgs":true,"family":"Cederberg","given":"Jay","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807812,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huang, Victor","contributorId":247512,"corporation":false,"usgs":false,"family":"Huang","given":"Victor","email":"","affiliations":[{"id":49565,"text":"Reclamation, Hydraulic Engineer","active":true,"usgs":false}],"preferred":false,"id":807813,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruddell, Benjamin L.","contributorId":247513,"corporation":false,"usgs":false,"family":"Ruddell","given":"Benjamin L.","affiliations":[{"id":49567,"text":"Northern Arizona University, Professor","active":true,"usgs":false}],"preferred":false,"id":807814,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70214034,"text":"ofr20201092 - 2020 - Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D, Florida Everglades, 2013–17","interactions":[],"lastModifiedDate":"2020-09-22T16:11:54.099952","indexId":"ofr20201092","displayToPublicDate":"2020-09-22T09:39:11","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1092","displayTitle":"Observed and Modeled Mercury and Dissolved Organic Carbon Concentrations and Loads at Control Structure S-12D, Florida Everglades, 2013–17","title":"Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D, Florida Everglades, 2013–17","docAbstract":"Mercury (Hg) has been a contaminant of concern for several decades in South Florida, particularly in the Florida Everglades. The transport and bioavailability of Hg in aquatic systems is intimately linked to dissolved organic carbon (DOC). In aquatic systems, Hg can be converted to methylmercury (MeHg), which is the form of Hg that bioaccumulates in food webs. The bioaccumulation of MeHg poses significant health risks to wildlife and humans. Fish consumption advisories triggered by elevated Hg levels first appeared in the 1980s in South Florida. Multiple structures regulate freshwater distribution to Everglades National Park, including S-12D. This report summarizes seasonal and annual concentration and load data from late September 2013 to April 2017 for the total of (1) filter-passing total mercury (FTHg), (2) filter-passing methylmercury (FMeHg), (3) particulate total mercury (PTHg), (4) particulate methylmercury (PMeHg) and, (5) DOC discharged through control structure S-12D. The loads of Hg fractions and DOC at control structure S-12D were determined by pairing discharge data with constituent concentrations estimated by empirical models based on surrogate in situ water-quality measurements.
Calculated concentrations of DOC ranged from 12.8 milligrams per liter (mg/L) to 27.9 mg/L with a mean of 18.8 mg/L during the study period. Annual loads of DOC ranged from 3,950 tons in 2015 to 10,900 tons in 2016. DOC loads increased linearly with an increase in flow, and the highest monthly DOC load of 1,630 tons was observed in February 2016.
Calculated concentrations of FTHg ranged from 0.35 to 1.55 nanograms per liter (ng/L) with a mean of 0.85 ng/L during the study period. Calculated concentrations of FMeHg ranged from 0.06 ng/L to 0.24 ng/L with a mean of 0.14 ng/L during the study period. Generally, FTHg and FMeHg concentrations were lower during periods of decreased flow and higher during periods of increased flow. Calculated PTHg concentrations ranged from 0.09 ng/L to 4.19 ng/L with a mean of 0.58 ng/L during the study period. Calculated PMeHg concentrations ranged from below the limit of detection <0.01 ng/L to 0.29 ng/L with a mean of 0.03 ng/L during the study period.
Loads of Hg were often zero or lowest from November to May, owing to the lack of flow or low-flow conditions. FTHg and FMeHg loads increased linearly with an increase in flow and typically were highest from June to October. During periods of increasing flow or following changes in gate operations, PTHg and PMeHg constituted a greater percentage of the total Hg load. Annual loads of total Hg (filter-passing and particulate) ranged from 254 grams in 2015 to 658 grams in 2016. FTHg was the predominant contributor to the total Hg load. Information presented herein provides the first assessment of DOC and Hg loads to Everglades National Park through control structure S-12D using continuous in situ measurements of discharge and constituent surrogates and compares the surrogate model approach to loads calculated from monthly sampling. Analysis of calculated and observed loads demonstrates the significance of flow data on calculating constituent loads.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Biological conservation often requires an understanding of how environmental conditions affect species occurrence and detection probabilities. We used a hierarchical framework to evaluate these effects for several Appalachian stream fish species of conservation concern: Chrosomus cumberlandensis (BSD; blackside dace), Etheostoma sagitta (CAD; Cumberland arrow darter), and Etheostoma spilotum (KAD; Kentucky arrow darter). Etheostoma susanae (Cumberland darter) also is present in the study area but was too rare to model in this analysis. In this study, conducted by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, fish and habitat data were collected from 205 randomly selected stream sites in the upper Cumberland and Kentucky River Basins (120 and 85 sites, respectively) of Kentucky and Tennessee. Sites were sampled with 10 spatial replicates (2 meter x 5 meter electrofishing zones) to enable estimation of detection probabilities and environmental effects. The best models (that is, lowest Akaike information criterion scores) showed the effects of agriculture (negative) on occurrence of BSD and stream conductivity (negative) on occurrence of CAD and KAD. These effects were statistically more important than measures of basin area, elevation, and substrate size. Conductivity and agriculture showed nonlinear effects on species occurrence, and effects of conductivity were more precise above 400 microsiemens per centimeter than below this threshold. Models incorporated detection-level effects of electrofishing time (positive), flow velocity (negative), sand substrate (positive), and gravel/cobble substrate (negative). Models accounting for detection of BSD estimated occupancy rates similar to the observed proportion of occupied sites (0.10), but the best-supported models for CAD and KAD increased expected occupancy by about 4 percent for each species (from 0.17 to 0.21 for CAD and from 0.07 to 0.11 for KAD). Results of this study provide new inferences for modeling stream fish occurrence and detection processes and highlight the importance of continued monitoring and assessment of rare fish species in Appalachian headwater streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201100","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Hitt, N.P., Rogers, K.M., Kessler, K., and Macmillan, H., 2020, Modeling occupancy of rare stream fish species in the upper Cumberland and Kentucky River Basins: U.S. Geological Survey Open-File Report 2020–1100, 22 p., https://doi.org/10.3133/ofr20201100.","productDescription":"vi, 22 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118746","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":378605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1100/ofr20201100.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1100"},{"id":378604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1100/coverthb.jpg"}],"country":"United States","state":"Kentucky, Tennessee, Virginia","otherGeospatial":"Cumberland River basin, Kentucky River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1875,\n 35.88905007936091\n ],\n [\n -81.39770507812499,\n 35.88905007936091\n ],\n [\n -81.39770507812499,\n 38.77121637244273\n ],\n [\n -87.1875,\n 38.77121637244273\n ],\n [\n -87.1875,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Streamflow data and statistics are vitally important for proper protection and management of the water quality and water quantity of Alabama streams. Such data and statistics are generally available at U.S. Geological Survey streamflow-gaging stations, also referred to as streamgages or stations, but are often needed at ungaged stream locations. To address this need, the U.S. Geological Survey, in cooperation with numerous Alabama State agencies and organizations, developed regional regression equations for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama that are not substantially affected by tides, regulation, diversions, or other anthropogenic influences. A small percentage of the streamgages included in this study experience zero flows during certain periods; thus, the final low-flow frequency regression equations were developed by using weighted left-censored regression analyses to analyze the flow data in an unbiased manner, with weights based on number of years of record.
The equations developed include the annual minimum 1- and 7-day average streamflows with a 10-year recurrence interval (referred to as the 1Q10 and 7Q10 flows), the annual minimum 7-day average streamflow with a 2-year recurrence interval (referred to as the 7Q2 flow), and the mean annual flow using data from 174 streamgages from Alabama and surrounding States. For the 1Q10, 7Q2, and 7Q10 low-flow frequency statistics, the regional regression equations are functions of drainage area, streamflow-variability index, mean annual precipitation, and percentage of the drainage basin located in the Piedmont and Southeastern Plains ecoregions. The mean annual flow regression equation is a function of drainage area, mean annual precipitation, and percentage of the drainage basin located in the Southeastern Plains ecoregion. For the mean annual flow regression equation, the average standard error of estimate was 11.2 percent. For the selected low-flow frequency equations, the average standard errors of estimate ranged from 18.1 to 38.8 percent.
The regional regression equations developed from this investigation have been incorporated into the U.S. Geological Survey StreamStats application for Alabama. StreamStats (https://streamstats.usgs.gov/ss/) is a web-based geographic information system application that delineates drainage basins at selected stream locations and then generates the needed basin characteristics for available regional regression equations. Along with the low-flow frequency equations developed in this investigation, the StreamStats application also has regional regression equations for estimating flood-frequency statistics at locations on rural and urban streams in Alabama.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205099","collaboration":"Prepared in cooperation with Alabama Power; Alabama Farmers Federation; Alabama Association of Conservation Districts; Alabama Association of Resource Conservation and Development Councils; Alabama Department of Agriculture and Industries; Alabama Department of Conservation and Natural Resources— Wildlife and Freshwater Fisheries Division; Alabama Department of Economic and Community Affairs—Office of Water Resources; Alabama Department of Environmental Management; Alabama Soil and Water Conservation Committee; Choctawhatchee, Pea and Yellow Rivers Watershed Management Authority; Geological Survey of Alabama; and The University of Alabama—Alabama Water Institute","usgsCitation":"Feaster, T.D., Kolb, K.R., Painter, J.A., and Clark, J.M., 2020, Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on 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2020–5099 Version History"},{"id":378593,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5099/sir20205099_appendix2.xlsx","text":"Appendix 2","size":"49.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5099 Appendix 2","linkHelpText":"— U.S. Geological Survey streamgages and independent and dependent variables used in the low-flow frequency and mean annual flow regression analyses for ungaged locations on streams in Alabama"},{"id":378592,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5099/sir20205099_appendix2.csv","text":"Appendix 2","size":"27.6 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5099 Appendix 2","linkHelpText":"— U.S. Geological Survey streamgages and independent and dependent variables used in the low-flow frequency and mean annual flow regression analyses for ungaged locations on streams in Alabama"},{"id":378832,"rank":6,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5099/coverthb3.jpg"},{"id":378591,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5099/sir20205099_appendix1.xlsx","text":"Appendix 1","size":"18.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5099 Appendix 1","linkHelpText":"— U.S. Geological Survey streamgages that were excluded from the regional regression analysis for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations in Alabama"},{"id":378833,"rank":7,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5099/sir20205099.pdf","text":"Report","size":"3.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5099"},{"id":378590,"rank":1,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5099/sir20205099_appendix1.csv","text":"Appendix 1","size":"12.7 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\"}}]}","edition":"Version 1.0: September 21, 2020; Version 1.1: September 29, 2020; Version 1.2: November 20, 2020","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, Tennessee 37211
Water-quality conditions (including temperature) in the Willamette River and many of its adjacent off-channel features, such as alcoves and side channels, were monitored between river miles 67 (near Salem, Oregon) and 168 (near Eugene, Oregon) during the summers of 2015 and 2016. One or more parameters (water temperature, dissolved oxygen, pH, specific conductance, and [or] water depth) were continuously measured at sites in the main channel (9 sites in 2015; 5 sites in 2016) and select off-channel features (20 features in 2015; 22 features in 2016). This study was initiated in reaction to the unusually warm, dry weather and resulting low streamflows that occurred in the Pacific Northwest in 2015 and the need for flow managers to understand the effects of streamflow on water-quality conditions in off-channel features of the Willamette River. Field monitoring was focused on documenting water-quality conditions during low summer streamflows and during fluctuations in streamflow, including when side channels became alcoves and reconnected to become side channels again.
Water in the main channel of the Willamette River upstream from river mile 50 near Newberg typically is well mixed during summer, with warm water temperatures (greater than 18 degrees Celsius) and high dissolved-oxygen concentrations (often greater than 7.7 milligrams per liter). During low summer flows, a diverse suite of off-channel features exists adjacent to the main channel of the Willamette River. Despite temporal and spatial variability within individual features, comparison of continuous water-temperature data between the main channel and off-channel features indicated that some off-channel features were consistently cooler than the main channel, some were consistently warmer than the main channel, and others frequently fluctuated between warmer or cooler than the main channel. Site-specific characteristics including upstream connection, depth, and presence or absence of aquatic or riparian vegetation were factors that seemed to affect the water quality of a feature.
Results from this study showed a relation between the geomorphology, hydrology, ecology, and water quality of an off-channel feature. Data confirmed that many features that can be classified as cold-water refuges based on water-temperature standards also contained low concentrations of dissolved oxygen that may not be suitable for sensitive fish species. A simplified site classification scheme is proposed that links water-quality conditions in measured off-channel features with site-specific characteristics and summer streamflows. The site classification scheme was extended to create a theoretical process matrix that relates measured water-quality conditions to a list of the processes and site-specific characteristics that could create those conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205068","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Smith, C.D., Mangano, J.F., and Rounds, S.A., 2020, Temperature and water-quality diversity and the effects of surface-water connection in off-channel features of the Willamette River, Oregon, 2015–16: U.S. Geological Survey Scientific Investigations Report 2020–5068, 70 p., https://doi.org/10.3133/sir20205068.","productDescription":"Report: viii, 70 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-102289","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":378475,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73T9FPK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Continuous temperature measurements to assess upstream connection of off-channel features of the middle and upper Willamette River, Oregon, summer, 2016"},{"id":378473,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ315D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Point measurements of temperature and water quality in main-channel and off-channel features of the Willamette River, 2015 -16"},{"id":378472,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5068/sir20205068.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5068"},{"id":378471,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5068/coverthb.jpg"},{"id":378474,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77M06DV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water surface elevations recorded by submerged water level loggers in off-channel features of the middle and upper Willamette River, Oregon, summer, 2016"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46435546875,\n 44.133333\n ],\n [\n -122.43713378906249,\n 44.133333\n ],\n [\n -122.43713378906249,\n 45.216667\n ],\n [\n -123.46435546875,\n 45.216667\n ],\n [\n -123.46435546875,\n 44.133333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Power consumption coefficients (PCCs) and dedicated flowmeter records for irrigation wells in three Utah groundwater basins were analyzed to develop a method to better characterize the accuracy of annual groundwater withdrawal estimates. The PCC method has been used by the U.S. Geological Survey in Utah since 1963 as a way to estimate groundwater withdrawal. As a result, most irrigation wells in Utah have historic records consisting of multiple PCCs. Over time, numerous wells have been retrofitted with dedicated flowmeters to more accurately describe groundwater use for irrigation. The combination of historical PCCs and flowmeter data was examined to classify wells as simple, complex, or borderline. The PCCs for each well were statistically analyzed for each period of record to determine the PCC coefficient of variation (CV). Variance, standard deviation, and CV also were calculated for each well, yielding similar results. The CV was selected as the best statistical method for classifying wells. Through field verification and examination of records, CV thresholds were established, allowing wells to be classified as simple, complex, or borderline. This well classification provides information on the uncertainty and best methods for quantifying annual groundwater withdrawals from irrigation wells in a basin.
Annual irrigation groundwater withdrawals in Tooele, Parowan, and Goshen Valleys were calculated by using various combinations of historical PCC records and data from dedicated flowmeters. Differences between annual groundwater withdrawal using the most recent measurements, and historic minimum, maximum, mean, and median PCCs were compared. The smallest percent difference between annual groundwater withdrawal calculated using the most recently measured PCCs, which is the current method for calculating withdrawal in most basins, in Tooele and Parowan Valleys, was 7 and 9 percent respectively, using historical median and mean.
In Goshen Valley, most wells have dedicated flowmeters, and there is a subset of wells that have 2016 power usage data, historical PCC records, and 2016 reported dedicated flowmeter withdrawal. Using this subset of irrigation wells, the smallest percent different between withdrawal from dedicated flowmeters and withdrawal calculated by using other methods was 5 percent (using withdrawal calculated with historical mean PCCs for each well). Annual groundwater withdrawal calculated using the most recently measured PCCs was 9-percent less than dedicated flowmeter reported withdrawal. So, if withdrawal from dedicated flowmeters is as close to reality as possible, then in the case of Goshen Valley, using historical mean PCCs to calculate withdrawal is closer to reality than using the most recently measured PCCs to calculate withdrawal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201106","collaboration":"Water Availability and Use Science ProgramDirector, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
The U.S. Geological Survey, in cooperation with the Village of East Hampton, New York, conducted a 1-year study from August 2017 to August 2018 to provide data necessary to improve understanding of the sources of nutrients and pathogens to Hook Pond watershed to allow for possible mitigation or reduction of loads. Chronic eutrophication and recent concern over harmful cyanobacteria in Hook Pond are the result of past and present land uses and a changing climate that have prompted the Village of East Hampton and local businesses to study and remediate factors contributing to the persistent loading of nutrients, organic contaminants, and pathogens. This assessment of Hook Pond, Hook Pond Dreen, and shallow groundwater provides the most comprehensive set of water-quality data to date. Interpretations presented in this study and the data on which they are based can be used to support management decisions, inform and contribute to modeling, and serve as a baseline for future assessments.
Results from continuous monitoring of water temperature, specific conductance, and elevation at Hook Pond site 10 (Maidstone Club golf cart bridge), as well as ancillary weather and tidal data from nearby stations, were used to help explain seasonal and storm-related concentration variation of nitrogen, phosphorus, wastewater-indicator compounds, and pathogens. Data collected were also compared to existing historical data. Physicochemical constituents measured on a routine basis throughout the pond and along the tributary showed the spatial variability in water temperature, specific conductance, dissolved oxygen, pH, turbidity, and chlorophyll a and phycocyanin fluorescence. A lakebed survey was compiled based on the year-round sampling throughout the pond for future comparisons. Water-quality data from shallow groundwater at points around Hook Pond and adjacent to Hook Pond Dreen were interpreted and quantified to estimate relative contributions and species of nutrients, wastewater-indicator compounds, and microbial source tracking (MST) markers to base flow. To supplement the continuous water-surface elevation data, a single set of discharge measurements was collected under normal (nonstorm) conditions to better understand the relative contributions and dilution of surface waters by contaminated groundwater.
The nutrient and physicochemical data from this study can be used in conjunction with current and future models and decision support tools to guide planned and ongoing restoration efforts, such as dredging to reduce sediment accumulation, opening a pathway to the ocean (which would change the salinity and flow dynamics of the pond and adjacent groundwater), and addressing growing concerns over cyanobacterial blooms, while serving as a baseline for measuring changes resulting from sea-level rise, climate change, and changes in nutrient loading. The microbial source tracking and indicator bacteria results can help direct efforts to reduce runoff and direct contributions of fecal contamination from dogs and waterfowl along Hook Pond Dreen. The results can also be used to assess the current state of wastewater infrastructure surrounding and contributing to Hook Pond Dreen, based on detection of human markers throughout the year and with both Bacteroides and coliphage methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205071","collaboration":"Prepared in cooperation with the Village of East Hampton","usgsCitation":"Fisher, S.C., McCarthy, B.A., Kephart, C.M., and Griffin, D.W., 2020, Assessment of water quality and fecal contamination sources at Hook Pond, East Hampton, New York: U.S. Geological Survey Scientific Investigations Report 2020–5071, 58 p., https://doi.org/10.3133/sir20205071.","productDescription":"Report: viii, 58 p.; Dataset","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103528","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":378179,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5071/coverthb.jpg"},{"id":378180,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5071/sir20205071.pdf","text":"Report","size":"3.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5071"},{"id":378181,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"New York","otherGeospatial":"Hook Pond","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.20489501953125,\n 40.94360177170972\n ],\n [\n -72.17124938964844,\n 40.94360177170972\n ],\n [\n -72.17124938964844,\n 40.95656702665609\n ],\n [\n -72.20489501953125,\n 40.95656702665609\n ],\n [\n -72.20489501953125,\n 40.94360177170972\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
We assessed the history of flow and riparian ecosystem change along the Wind River using cottonwood tree-ring data, streamgage records, historical temperature and precipitation data, drought indices, and local observations and Traditional Ecological Knowledge from elders of the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming. This assessment identified impacts that have occurred to riparian resources of concern to the Tribes, which will assist in prioritizing drought planning efforts. Impacts included reduced abundance, reduced regeneration, and increased mortality in cottonwoods (Populus deltoides and P. angustifolia); an increase in invasive species, especially Russian olive (Elaeagnus angustifolia), that are gradually replacing cottonwoods and other native woody plants; decreased abundance of native and culturally important plants; reduced abundance of culturally important fish; reduced volume and changes to the timing of flows; and changes in river course. This assessment documented the biophysical and social factors that have contributed to riparian ecosystem change and to reduced water availability and flows, including agricultural diversion, drought, and fire. Cottonwoods along the Wind River are as much as 300 years old. By relating tree-ring width to recorded streamflows, we were able to reconstruct streamflows confidently back to the 1850s and speculatively back to the mid-1700s. Extending the historical record of streamflows allows for a more-complete understanding of hydroclimatic variability and provides a foundation for developing preparedness and response strategies for drought management. Ring width of cottonwood trees at the Boysen Site was more strongly correlated to river flow than to local precipitation or temperature, indicating that growth of trees is controlled more by montane snowmelt than by local weather. Therefore, tree rings are a better indicator of water supply than of the local conditions controlling water demand. The extended flow record from tree rings revealed the occurrence of a major period of low flow from 1870 to 1910 that was not evident in the shorter instrumental records of flow and weather. Information from tree rings, streamflow measurements, drought indices, and elder observations all suggest that the early 2000s drought was the most severe, sustained drought in the last century. Our results illustrate how drought is experienced in different ways across locations and sectors, which underscores the importance of using multiple indicators for drought management. These results will contribute to ongoing assessment, monitoring, and planning efforts at the Wind River Reservation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201072","collaboration":"Prepared in cooperation with the Eastern Shoshone Tribe of the Wind River Reservation, Wyoming, the Northern Arapaho Tribe of the Wind River Reservation, Wyoming, and Colorado State University","usgsCitation":"McNeeley, S.M., Friedman, J.M., Beeton, T.A., and Thaxton, R.D., 2020, Cottonwoods, water, and people—Integrating analysis of tree rings with observations of elders from the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming: U.S. Geological Survey Open-File Report 2020–1072, 33 p., https://doi.org/10.3133/ofr20201072.","productDescription":"Report: iv, 33 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-113563","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":378034,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S1UIAL","text":"USGS data release","linkHelpText":"Tree-Ring Data Collected in 2017 and 2018 From Cottonwood Trees Along the Wind River in Wind River Indian Reservation, Wyoming"},{"id":377898,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1072/coverthb.jpg"},{"id":377899,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1072/ofr20201072.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1072"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.44442749023438,\n 42.83569550641452\n ],\n [\n -108.160400390625,\n 42.83569550641452\n ],\n [\n -108.160400390625,\n 43.54456658436357\n ],\n [\n -109.44442749023438,\n 43.54456658436357\n ],\n [\n -109.44442749023438,\n 42.83569550641452\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Avenue, Bldg. C
Fort Collins, CO 80526
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Streamflow estimates for floods with an annual exceedance probability of 0.001 or lower are needed to accurately portray risks to critical infrastructure, such as nuclear powerplants and large dams. However, extrapolating flood-frequency curves developed from at-site systematic streamflow records to very low annual exceedance probabilities (less than 0.001) results in large uncertainties in the streamflow estimates. Traditionally, methods for statistically estimating flood frequency have relied on the systematic streamflow record, which provides a time series of annual maximum flood peaks, often including some historical peaks. However, most peak-flow records are less than 100 years, and uncertainties are large when trying to extrapolate magnitudes of very low annual exceedance probability events.
Other data may be available that extend the record beyond the systematic dataset. Historical data are defined as data from outside the period of systematic records but within the period of human records. Examples of historical information include flood estimates from other agencies and newspaper accounts that can be translated to flood magnitude point estimates, interval estimates, or perception thresholds (such as a statement that an 1880 flood was the largest since 1869). Paleoflood data, which may also extend the dataset, include a broad range of information about flood occurrence or magnitude from sources like sediment deposits or tree rings.
Several assumptions are made in flood-frequency analysis, and an understanding of whether the data conform to these assumptions is desired. A particularly difficult assumption to evaluate for flood-frequency analysis is the underlying assumption that the flood series is stationary—the assumption that a time series of peak flow varies around a constant mean within a particular range of values (constant variance). As the hydrologic community’s understanding of natural systems and anthropogenic effects on streamflows has evolved, the community has come to understand that many surface-water systems exhibit one or more forms of nonstationarity, and thus the stationarity assumption is often violated to some degree. However, there is currently (2020) no consensus among hydrologists regarding the most appropriate flood-frequency-analysis methods for nonstationary systems, and this topic remains an active area of research.
A literature review was completed to summarize the state of the science of flood frequency. The literature review highlights tools available to detect nonstationarities and identifies approaches that include external information to inform flood-frequency analysis. To demonstrate methods for initial data analysis and for incorporating historical and paleoflood information in flood-frequency analysis, five sites were selected: the Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada; lower reach, Rapid Creek, South Dakota; Spring Creek, South Dakota; Cherry Creek near Melvin, Colorado; and Escalante River near Escalante, Utah. The sites were chosen for the availability of published historical and paleoflood data and for their geographic diversity and unique characteristics, which highlighted issues such as autocorrelation, change points, trends, outlier peaks, or short periods of record.
An initial data analysis that involved examining records for autocorrelation, change points, and trends was completed for all sites. The flood-frequency analysis completed for this study used version 7.2 of the U.S. Geological Survey PeakFQ program. Multiple analyses were done on each site documenting the change in the flood-frequency curve when additional historical or paleoflood data were added. When other flood-frequency studies were available, their results were compared to the results here. The comparisons in some cases simply show the effect of additional years of data, whereas other comparisons show results from probability distributions or fitting methods other than those used in PeakFQ.
For the Red River of the North, flood-frequency analysis shows that paleoflood data appear necessary to reasonably estimate very low annual exceedance probabilities. For the analysis of the lower reach of Rapid Creek and Spring Creek, paleoflood information helped put a high outlier from the systematic period in context; however, very low annual exceedance probabilities at these sites still had extraordinarily large confidence bounds. These sites also showed that paleoflood information might be transferred from one site to another, with the caveat that this is a case where we had existing paleoflood data to test the transfer of paleoflood information—this is not the case at many sites, and transferring paleoflood information requires assumptions about the comparability of floods at the sites. The Cherry Creek analysis affirmed the result of an earlier study that showed that the generalized Pareto distribution was not a good distribution for estimating very low annual exceedance probabilities. The Escalante River analysis showed that adding paleoflood information might increase uncertainty for very low annual exceedance probabilities, compared to analysis with the systematic period of record information only, when the paleoflood peaks are of much larger magnitudes than the systematic record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205065","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Ryberg, K.R., Kolars, K.A., Kiang, J.E., and Carr, M.L., 2020, Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity: U.S. Geological Survey Scientific Investigations Report 2020–5065, 89 p., https://doi.org/10.3133/sir20205065.","productDescription":"Report: xii, 89 p.; 5 Tables; Appendix; Dataset","numberOfPages":"105","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088812","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":377559,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_appendix.zip","text":"Appendix 1. Data, Settings, and Output for Each Site and Scenario","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5065 Appendix 1","linkHelpText":"— Each zipped file represents the analysis for a particular site and scenario"},{"id":377557,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_7.pdf","text":"Table 7","size":"114 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 7","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under two different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 06712500 Cherry Creek near Melvin, Colorado, with comparisons to other distributions and fitting methods."},{"id":377553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065.pdf","text":"Report","size":"5.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065"},{"id":377554,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_4.pdf","text":"Table 4","size":"139 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 4","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under 10 different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 05OJ015 Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada, as well as results from flood-frequency studies by Burn and Goel (2001) and Harden (1999)."},{"id":377697,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":377555,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_5.pdf","text":"Table 5","size":"122 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 5","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for the lower reach of Rapid Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377556,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_6.pdf","text":"Table 6","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 6","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for Spring Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5065/coverthb.jpg"},{"id":377558,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_8.pdf","text":"Table 8","size":"116 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 8","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 09337500 Escalante River near Escalante, Utah, with comparisons to Webb and others (1988), Webb and Rathburn (1988), and Kenney and others (2008)."}],"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
Municipal and domestic water users in Douglas County, Colorado, rely on groundwater from the bedrock aquifers in the Denver Basin aquifer system as part of their water supply. The four principal Denver Basin bedrock aquifers are, from shallowest to deepest, the Dawson aquifer (divided administratively into “upper” and “lower” Dawson aquifers in Douglas County), the Denver aquifer, the Arapahoe aquifer, and the Laramie-Fox Hills aquifer. Increased groundwater pumping in response to rapid population growth and development has led to declining groundwater levels in Douglas County, where groundwater is a primary water source for densely populated and rural communities. The U.S. Geological Survey, in cooperation with the Rural Water Authority of Douglas County, began a study in 2011 to assess the groundwater resources of the Denver Basin bedrock aquifers within the county. The primary purpose of this report is to present a summary of groundwater levels measured during the study period (2011–19) and present results from statistical analyses of changes in groundwater-level elevations, reported above the land-surface datum, North American Vertical Datum of 1988, through time. During the study period, January 2011 through June 2019, discrete groundwater levels were routinely measured at 36 wells producing from Denver Basin bedrock aquifers within Douglas County. Of the 36 wells, 15 are instrumented with pressure transducers that record groundwater-level measurements at hourly intervals, and these data were temporally aggregated into time-series records. During 2011, wells were added to the monitoring network in phases, so that the start dates of the well records are noncontemporaneous. To keep temporal analysis among wells consistent, the periods of record used in statistical analyses were from February 2012 through February 2019 for the discrete data and from January 2012 through June 2019 for the time-series data.
The upper Dawson, lower Dawson, Denver, and Arapahoe aquifers had some wells with rises in calculated groundwater-level elevations, but most wells showed declines on the basis of statistically significant trends and the relative differences in static groundwater-level elevations between the February 2012 and February 2019 measurements. Neither of the two wells in the Laramie-Fox Hills aquifer showed significant trends in groundwater-level elevations, and these wells had few static discrete measurements, precluding a comparison between 2012 and 2019 static groundwater-level elevations. Of the 13 wells in the upper Dawson, lower Dawson, Denver, and Arapahoe aquifers with significant trends in discrete groundwater-level elevation measurements, the records of 12 wells demonstrated negative trends during the study period. The upper Dawson, lower Dawson, Denver, and Arapahoe aquifers had median significant trends of −0.23, −0.31, −0.92, and −2.26 feet per year, respectively. Although the Arapahoe aquifer had the greatest negative median trend, this median only represents one well with significant trends. Otherwise, the Denver aquifer had the next greatest negative trend, with a median trend of −0.92 foot per year. Significant trends in time-series groundwater-level elevations agreed with significant trends in discrete groundwater-level elevations; for all wells with statistically significant trends in discrete and in time-series groundwater-level elevation data, trend estimates from the two records were within 0.1 foot per year of each other. Potentiometric-surface maps of the upper Dawson, lower Dawson, and Denver aquifers, created using discrete static groundwater levels measured in February 2019, show that groundwater flow direction for the upper Dawson, lower Dawson, and Denver aquifers is generally from south to north. Results of this study could guide future groundwater monitoring in the county and aid in long-term planning of water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205076","collaboration":"Prepared in cooperation with the Rural Water Authority of Douglas County","usgsCitation":"Malenda, H.F., and Penn, C.A., 2020, Groundwater levels in the Denver Basin bedrock aquifers of Douglas County, Colorado, 2011–19: U.S. Geological Survey Scientific Investigations Report 2020–5076, 44 p., https://doi.org/10.3133/sir20205076.","productDescription":"Report: vii, 44 p.; Dataset","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-112835","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":377656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5076/coverthb.jpg"},{"id":377657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5076/sir20205076.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5076"},{"id":377658,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Colorado","county":"Douglas County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.6627,39.5665],[-104.6626,39.4762],[-104.663,39.3892],[-104.664,39.3026],[-104.6638,39.2165],[-104.6642,39.1308],[-104.8303,39.1311],[-104.9175,39.131],[-104.9371,39.1312],[-105.032,39.1311],[-105.0503,39.1312],[-105.1607,39.1306],[-105.274,39.1309],[-105.3232,39.1307],[-105.322,39.1343],[-105.3213,39.1407],[-105.3195,39.1434],[-105.3171,39.1443],[-105.3148,39.1461],[-105.3136,39.1493],[-105.3117,39.1542],[-105.3069,39.161],[-105.3051,39.1624],[-105.3015,39.1632],[-105.2985,39.1673],[-105.2961,39.1705],[-105.2908,39.1741],[-105.2872,39.1772],[-105.2841,39.1863],[-105.2817,39.1935],[-105.2768,39.2016],[-105.2744,39.2048],[-105.272,39.2052],[-105.2649,39.2061],[-105.2619,39.2074],[-105.2601,39.2097],[-105.2595,39.2156],[-105.2582,39.2283],[-105.2557,39.2332],[-105.2533,39.2378],[-105.2527,39.2396],[-105.2491,39.2395],[-105.2432,39.2395],[-105.2396,39.2399],[-105.2348,39.2431],[-105.2259,39.248],[-105.2217,39.2534],[-105.2216,39.2575],[-105.2204,39.2589],[-105.2168,39.2593],[-105.2144,39.2606],[-105.2162,39.2643],[-105.2167,39.2683],[-105.2143,39.2729],[-105.2058,39.29],[-105.2046,39.295],[-105.2016,39.2982],[-105.1938,39.3018],[-105.1938,39.304],[-105.1955,39.3081],[-105.1948,39.3126],[-105.1919,39.3131],[-105.1877,39.3158],[-105.187,39.3194],[-105.1846,39.3239],[-105.184,39.328],[-105.1815,39.3352],[-105.1767,39.3402],[-105.1718,39.3501],[-105.1694,39.3555],[-105.1658,39.36],[-105.1663,39.3682],[-105.168,39.3732],[-105.1697,39.3809],[-105.1702,39.3845],[-105.166,39.39],[-105.1654,39.3949],[-105.1671,39.399],[-105.1671,39.4031],[-105.1664,39.4049],[-105.1586,39.4094],[-105.1527,39.4116],[-105.1419,39.417],[-105.1383,39.4197],[-105.1335,39.4233],[-105.1268,39.4296],[-105.1238,39.4336],[-105.1244,39.4368],[-105.1237,39.4409],[-105.1225,39.4468],[-105.123,39.4531],[-105.1253,39.4563],[-105.1289,39.4586],[-105.1306,39.4654],[-105.1305,39.4695],[-105.1263,39.4731],[-105.1197,39.4762],[-105.1155,39.4785],[-105.1149,39.4798],[-105.1137,39.4825],[-105.1119,39.4834],[-105.1,39.4829],[-105.0928,39.4846],[-105.0874,39.4891],[-105.0843,39.4941],[-105.0818,39.5022],[-105.08,39.5059],[-105.077,39.5095],[-105.0775,39.5126],[-105.0757,39.5194],[-105.0762,39.524],[-105.0724,39.5402],[-105.0646,39.547],[-105.0609,39.5501],[-105.0609,39.5551],[-105.0561,39.5592],[-105.0494,39.5627],[-105.0452,39.5659],[-104.9408,39.5664],[-104.8292,39.5663],[-104.7182,39.5661],[-104.6627,39.5665]]]},\"properties\":{\"name\":\"Douglas\",\"state\":\"CO\"}}]}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Digital flood-inundation maps for about an 8-mile reach of the Little Calumet River, Illinois, were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at three USGS streamgages: Little Calumet River at South Holland, Ill. (USGS station 05536290); Little Calumet River at Munster, Indiana (USGS station 05536195); and Thorn Creek at Thornton, Ill. (USGS station 05536275). Near-real-time stages at these streamgages may be obtained on the internet from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites.
Flood profiles were computed for the stream reaches using a one-dimensional unsteady flow step-backwater hydraulic model. The model performance was evaluated using historical streamflow measurements and the most current stage-discharge relations at the USGS streamgages at Little Calumet River at South Holland, Ill.; Little Calumet River at Munster, Ind.; and Thorn Creek at Thornton, Ill. The model was used to compute 24 water-surface profiles at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to about the 0.2-percent annual-exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.6-foot vertical accuracy and a 2-foot horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from USGS streamgages and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205074","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Dunn, A.P., Straub, T.D., and Manaster, A.E., 2020, Flood-inundation maps for the Little Calumet River from Lansing to South Holland, Illinois, 2020: U.S. Geological Survey Scientific Investigations Report 2020–5074, 10 p., https://doi.org/10.3133/sir20205074.","productDescription":"Report: vi, 10 p.; Data Release; Dataset","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-097182","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377581,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99L14DN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets for the flood-inundation study of Little Calumet River from Lansing to South Holland, Illinois, 2020, 2020"},{"id":377582,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":377580,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5074/sir20205074.pdf","text":"Report","size":"2.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5074"},{"id":377579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5074/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Lansing, South Holland","otherGeospatial":"Little Calumet River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.6295280456543,\n 41.54404730359805\n ],\n [\n -87.52584457397461,\n 41.54404730359805\n ],\n [\n -87.52584457397461,\n 41.62339874820646\n ],\n [\n -87.6295280456543,\n 41.62339874820646\n ],\n [\n -87.6295280456543,\n 41.54404730359805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Haskell Lake is a shallow, 89-acre drainage lake in the headwaters of the Squirrel River, on the Lac du Flambeau Reservation in northern Wisconsin. The lake has long been valued by the Lac du Flambeau Band of Lake Superior Chippewa Indians (LDF Tribe) for abundant wild rice and game fish. In recent decades, however, wild rice has mostly disappeared from the lake and the fishery has declined. A petroleum contamination plume discovered in the 1990s in the shallow aquifer upgradient from the northern end of the lake poses a threat to the ecological health of the lake and the aquifer, which is the sole drinking water source for nearby residents and businesses. Understanding of the lake’s hydrology is important to the LDF Tribe as they seek to restore wild rice and maintain the ecological health of the Haskell Lake/Tower Creek watershed. An improved understanding of lithology in the area of the contamination plume, documentation of a contamination pathway from groundwater in the plume source area to Haskell Lake, and an understanding of the plume extent beneath the lake are needed to advance remediation efforts. Evaluation of the fraction of groundwater discharge that is contaminated relative to the overall lake water budget is desired as a first step towards determining the extent of ecological effects from the plume.
A cooperative study between the U.S. Geological Survey and the LDF Tribe was initiated to quantify the lake water budget and the sources of water to the lake, to provide a rough estimate of the maximum quantity of groundwater discharge to the lake that may be contaminated, and to improve the conceptual understanding of the plume extent and subsurface materials in the area of contamination. The results of this study can help inform natural resource management of the Haskell Lake/Tower Creek watershed, including planned wild rice restoration and cleanup of the contaminant plume.
During 2016–17, field data on lake and groundwater levels, gradients, fluxes, and subsurface lithology were collected using a variety of techniques that ranged from basic measurement of water levels and streamflows to distributed temperature sensing, vertical temperature profiling, and several shallow geophysical methods. The data were used to inform a MODFLOW–NWT model that simulated the contributing groundwatershed, including the water budget for Haskell Lake and Tower Creek using the Lake, Streamflow-Routing, and Unsaturated Zone-Flow Packages. Particle tracking with the MODFLOW solution (using MODPATH 6) was used to improve understanding of the downgradient extent of the contamination plume, estimate groundwater flux through the plume area, and delineate the groundwater contributing area (groundwatershed) for the lake/creek system. Linear uncertainty estimates for model results were computed during model parameter estimation using the software package PEST++.
Results indicate groundwater discharge along the perimeter of Haskell Lake, with groundwater accounting for about 22 (± 11.5) percent of the lake water budget. Field data and particle tracking results indicate discharge of the entire contamination plume to Haskell Lake. Although the exact locations where contaminated groundwater enters the lake are unknown, the downgradient extent of the plume beneath Haskell Lake is likely limited to within about 700 feet from the shore. Groundwater flux through the plume accounts for at most about 1.4 percent of total groundwater discharge to Haskell Lake, or about 0.3 percent of the lake water budget. Most groundwater discharging to Haskell Lake and Tower Creek originates as terrestrial recharge. A lesser amount originates in or passes through neighboring lakes, including Buckskin, Crawling Stone, Broken Bow, Tippecanoe, and Jerms Lakes, as well as several unnamed kettles. The average age of simulated groundwater discharge to the lake is about 20 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205024","collaboration":"Prepared in cooperation with the Lac du Flambeau Band of Lake Superior Chippewa Indians","usgsCitation":"Leaf, A.T., and Haserodt, M.J., 2020, Hydrology of Haskell Lake and investigation of a groundwater contamination plume, Lac du Flambeau Reservation, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5024, 79 p., https://doi.org/10.3133/sir20205024.","productDescription":"Report: x, 70 p.; Appendices: 1.1-10.3; Data Release; Companion Report","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098814","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377617,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZQGGHY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW–NWT and MODPATH models, data from aquifer tests and temperature profilers, and groundwater flux estimates used to assess groundwater/surface-water interactions in Haskell Lake, Wisconsin"},{"id":377616,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table10.1_10.3.xlsx","text":"Appendix Tables 10.1 to 10.3","size":"19.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 10.1 to 10.3"},{"id":377615,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_9.1.xlsx","text":"Appendix Table 9.1","size":"12.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 9.1"},{"id":377614,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_8.1.xlsx","text":"Appendix Table 8.1","size":"17.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 8.1"},{"id":377611,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_5.1.xlsx","text":"Appendix Table 5.1","size":"12.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 5.1"},{"id":377607,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table1.1_1.12.xlsx","text":"Appendix Tables 1.1 to 1.12","size":"35.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 1.1 to 1.12"},{"id":377606,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20205005","text":"SIR 2020–5005","size":"3.67 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— A distributed temperature sensing investigation of groundwater discharge to Haskell Lake, Lac du Flambeau Reservation, Wisconsin, July 27–August 1, 2016"},{"id":377610,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_4.1.xlsx","text":"Appendix Table 4.1","size":"10.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 4.1"},{"id":377608,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_2.1.xlsx","text":"Appendix Table 2.1","size":"12.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 2.1"},{"id":377609,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_3.1_3.6.xlsx","text":"Appendix Tables 3.1 to 3.6","size":"24.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 3.1 to 3.6"},{"id":377604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5024/coverthb.jpg"},{"id":377801,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/downloads","text":"Appendix Tables","size":"47.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5024 Appendix Tables"},{"id":377612,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_6.1_6.2.xlsx","text":"Appendix Tables 6.1 to 6.2","size":"13.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 6.1 to 6.2"},{"id":377613,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_7.1.xlsx","text":"Appendix Table 7.1","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 7.1"},{"id":377605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5024"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Haskell Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.93322372436523,\n 45.89717666670996\n ],\n [\n -89.89992141723633,\n 45.89717666670996\n ],\n [\n -89.89992141723633,\n 45.920467927558576\n ],\n [\n -89.93322372436523,\n 45.920467927558576\n ],\n [\n -89.93322372436523,\n 45.89717666670996\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Digital flood-inundation maps for a 9.9-mile reach of Dardenne Creek, St. Charles County, Missouri, were created by the U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Transportation, St. Charles County, and the Cities of O’Fallon and St. Peters, Mo. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages 05514860 Dardenne Creek at Old Town St. Peters, Mo., and 05587450 Mississippi River at Grafton, Illinois. Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ ahps2/ hydrograph.php? wfo= lsx&gage= drcm7 and https://water.weather.gov/ ahps2/ hydrograph.php? wfo= lsx&gage= grfi2, which also forecasts flood hydrographs at these sites (sites DRCM7 and GRFI2).
Flood profiles were computed for the Dardenne Creek stream reach by means of a one-dimensional model for simulating water-surface profiles with steady-state flow computations. The model was calibrated by using the current stage-streamflow relation at the USGS streamgages 05514840 Dardenne Creek at O’Fallon, Mo., and 05514860 Dardenne Creek at Old Town St. Peters, Mo., and the documented high-water marks from the flood of December 2015.
The hydraulic model was then used to compute 17 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 16 ft, or near bankfull, to 32 ft at the reference streamgage 05514860. Stages in the lower Dardenne Creek can be affected by backwater from the Mississippi River; therefore, several sets of water-surface profiles were developed representing the extent of varying levels of backwater as referenced to the USGS streamgage 05587450 on the Mississippi River at Grafton, Ill. The upper stage for each map library exceeds the stage corresponding to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood) at the streamgage location. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.26-ft vertical accuracy and 0.71-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with real-time information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood mitigation, preparedness and planning, flood-response activities such as evacuations and road closures, and postflood recovery efforts.
Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The Taiya River flows through the Chilkoot Trail Unit of Klondike Gold Rush National Historical Park in southeast Alaska, which was founded to preserve cultural and historical resources and further understanding of natural processes active in the surrounding coastal-to-subarctic basin. Riverine processes exert an important influence on ecologically important boreal toad (Anaxryus boreas boreas), salmon [chum salmon (Oncorhynchus keta), pink salmon (O. gorbushca), and coho salmon (O. kisutch)], and eulachon (Thaleichthys pacificus) habitats, erosion of the historic ghost town of Dyea and other cultural and historical artifacts, and recreational opportunities in the lower 7.5 kilometers (km) of the Taiya River valley bottom. Recurrent consideration of hydroelectric development in West Creek upstream of the park since the 1980s has included proposals for damming and diverting West Creek, which could alter the delivery of water and sediment to this section of the Taiya River. To improve understanding of the hydrologic dependence of park resources for the purposes of guiding effective monitoring and conservation, this study, conducted by the U.S. Geological Survey in cooperation with the National Park Service, used a review of hydrologic data, collection of discrete suspended sediment data, geomorphic mapping, and analysis of historical aerial and ground photographs in a reconnaissance of formative geomorphic processes and hydrologic conditions in the lower 7.5 km of the Taiya River valley bottom.
Streamflow and suspended sediment data collected at the U.S. Geological Survey streamgages on the Taiya River and West Creek, combined with historical data, document conditions consistent with streams draining strongly glacierized basins in Alaska. Suspended sediment concentrations from samples collected concurrently over six varying flow levels during 2017–18 ranged from 6 to 284 milligrams per liter (mg/L) for the Taiya River and 13 to 162 mg/L for West Creek, which are similar to or slightly higher than historical values. For the common period of record (1970–77), correlation of daily mean discharge between the two streams was strongest (Pearson’s r = 0.97) during the prolonged May–October high-flow season and weakest (r = 0.90) during the November–April low-flow season, when West Creek daily mean discharge was proportionally higher. For the Taiya River, streamflow data compared between the available periods of record (1970–77 and 2004–17) showed no decadal-scale patterns in mean annual discharge but did show a shift toward an earlier spring snowmelt pulse. Notable flooding in the Taiya River Basin includes glacial lake outburst floods from the Nourse River valley prior to and during the 1897–98 Gold Rush, a 2002 glacial lake outburst flood from the West Creek valley, and a 1967 rainfall-generated flood.
Geomorphic mapping identified four categories of surfaces in the valley bottom—active main stem, abandoned main stem, alluvial fans, and emergent tidal surfaces. Using the maps, main-stem surfaces were subdivided into age categories to identify channel migration patterns from prior to 1940s to 2018. The valley bottom is dominated by active or abandoned channels of the Taiya River except at the extensive low-angle West Creek fan. The active main stem presently supports a mostly single-thread channel with bars and a few sloughs, but the channel actively moved and sometimes was braided within multiple, wider unvegetated corridors in 1894 and earlier. An inventory of 29 off-main-stem channels identified for the study indicates that abandoned main stem channels provide local topographic lows that can intercept groundwater or sustain tributary flow, facilitating the formation of most nonestuarine wetlands in the valley and sustaining important boreal toad breeding habitat.
Within the active main stem corridor, the channel has episodically built and reworked meanders and bars, eroding more than one-half of the historic Dyea townsite, in response to glacially controlled delivery of water and sediment, flooding, inputs from West Creek, local features including large woody debris and beaver dams, and rapid uplift from isostatic rebound. West Creek has constructed a large, persistent fan, provoked kilometer-scale Taiya River channel change near the confluence, constructively added to high-season streamflow that affects Taiya River channel migration capacity, disproportionately contributed early-season streamflow, and possibly contributed to groundwater levels in the valley bottom. The progressive narrowing and stability of the main stem corridor, possibly a result of reduction in the magnitude or frequency of glacial lake outburst floods or glacial sediment delivery to streams, indicates less active future reworking of abandoned main-stem surfaces or regeneration of wetland features. The fluvial history of the Taiya River valley bottom collectively indicates continued channel change within a limited corridor, relative stability in wetland locations but uncertainty in stability of groundwater supply to them, and channel incision and extension in response to uplift.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205059","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Curran, J.H., 2020, Hydrology and geomorphology of the Taiya River near the West Creek Tributary, southeast Alaska: U.S. Geological Survey Scientific Investigations Report 2020–5059, 57 p., https://doi.org/10.3133/sir20205059.","productDescription":"Report: viii, 57 p.; Data Release","numberOfPages":"57","onlineOnly":"Y","ipdsId":"IP-102183","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":376975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5059/covrthb.jpg"},{"id":376976,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5059/sir20205059.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376977,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XP1SE7","linkHelpText":"Geomorphic surface and channel boundaries for the lower 7.5 kilometers of the Taiya River Valley, southeast Alaska, 2018"}],"country":"United States","state":"Alaska","otherGeospatial":"Taiya River Near the West Creek Tributary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -137.5927734375,\n 57.71588512774503\n ],\n [\n -135,\n 57.657157596582984\n ],\n [\n -132.64892578125,\n 57.621875380195455\n ],\n [\n -132.64892578125,\n 59.877911874831156\n ],\n [\n -137.61474609375,\n 59.877911874831156\n ],\n [\n -137.5927734375,\n 57.71588512774503\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Modeling of ecosystems is a part of the U.S. Environmental Protection Agency’s protocol for developing site-specific selenium guidelines for protection of aquatic life. Selenium as an environmental contaminant is known to bioaccumulate and cause reproductive effects in fish and wildlife. Here we apply a modeling methodology—ecosystem-scale selenium modeling—to understand and document the scientific basis for predicting and validating ecological protection for Lake Koocanusa, a transboundary reservoir between Montana and British Columbia. A comprehensive set of site-specific data compiled from public databases (Federal, State, and Provincial) and reports by Teck Coal Ltd., is available in a companion U.S. Geological Survey data release. The tissue guideline used within modeling here to assess protection is the U.S. Environmental Protection Agency’s national selenium guideline for whole-body fish (dry weight); however, other numeric values for a whole-body guideline or other tissue types may be assumed if applicable tissue-to-tissue conversion factors are available.
We consider the report assembled here as a working document that presents a model that can effectively address and structure the needs of (1) scientific understanding in representing the lake’s ecosystem and selenium biodynamics and (2) policy and management development during a decision-making process, but it is open to modification and updating as more ecologically detailed data become available. The approach brings together the main concerns involved in selenium toxicity: likelihood of high exposure, inherent species sensitivity, and close connectivity of ecosystem characteristics and behavioral ecology of predators. Detailed site-specific modeling equations are provided to document the linked factors that determine the responses of ecosystems to selenium. A series of scenarios quantifies the implications of choices of site-specific variables including food-web species, bioavailability of particulate material, and partitioning between the dissolved and particulate phases at the base of food webs. A gradient mapping tool applied to Lake Koocanusa provides a precedent for ecosystem-scale modeling of lakes by recognizing the importance of lake strata and hydrodynamics as components of modeling.
Data requirements for ecosystem modeling, including ecological and hydrological process information fundamental to the dietary biodynamics of selenium in site-specific food webs, were assessed as a precursor to model validation for Lake Koocanusa. Understanding these relationships is necessary to connect modeling outcomes to reproductive effects and establish boundaries, in the case of Lake Koocanusa, for the influences of dam operation, fish-community viability, and its Clean Water Act impaired 303(d)-listing status on ecosystem function.
We find that an assemblage of conditions affects the representation of Lake Koocanusa’s ecosystem within modeling scenarios but that the constructed gradient maps, mechanistic model, and associated bioaccumulation potentials portray and quantify the variables that are determinative to protection of predator species. Ecological and hydrological sorting of compiled individual data points on a site- and species-specific basis helps identify and address model uncertainties. Sources of uncertainty include (1) the scarcity of data for some environmental media compartments across time and locations, (2) the complexity of hydrodynamic conditions that can lead to seasonal ecological disconnects such as in selenium partitioning from water into particulates, and (3) the functional status of Lake Koocanusa’s ecosystem because of cumulative effects of various environmental stresses (for example, fish-community changes, flow regime changes, parasites, gonadal dysfunction, and increasing mining input-selenium concentrations since 1984). To this last point, it is important to determine where Lake Koocanusa is in an impairment-restoration cycle so as not to base protection on survivor bias, the maintenance of a currently degraded ecosystem, or normalized toxicity. In a broader context, one of the overall consequences of revised selenium regulations is that their derivation is now dependent on being able to define and understand the status of the ecosystem on which protection is based.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201098","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Presser, T.S., and Naftz, D.L., 2020, Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada: U.S. Geological Survey Open-File Report 2020–1098, 40 p., https://doi.org/10.3133/ofr20201098.","productDescription":"Report: viii, 40 p.; 3 Tables; Data Releases","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436823,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99LM27E","text":"USGS data release","linkHelpText":"Results of Ecosystem Scale Selenium Modeling in Support of Site-Specific Guidelines Development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada, 2020"},{"id":377297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HB5S5F","text":"USGS data release","description":"USGS Data Release","linkHelpText":"USGS measurements of dissolved and suspended particulate material selenium in Lake Koocanusa in the vicinity of Libby Dam (MT), 2015–2017 (update)"},{"id":377296,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VXYSNZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selenium concentrations in food webs of Lake Koocanusa in the vicinity of Libby Dam (MT) and the Elk River (BC) as the basis for applying ecosystem-scale modeling, 2008–2018"},{"id":377295,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098.pdf","text":"Report","size":"19.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1098"},{"id":377294,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1098/coverthb.jpg"},{"id":377363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098_tables_1_and_3_to_10.xlsx","text":"Tables 1 and 3–10","size":"91.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1098 Tables"}],"country":"United States, Canada","state":"Montana, British Columbia","otherGeospatial":"Lake Koocanusa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.72998046875,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 48.33251726168281\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Water Mission Area
U.S. Geological Survey
345 Middlefield Rd.
Menlo Park, CA 94025