An understanding of the spatial and temporal extent of groundwater recharge is critical for many types of hydrologic assessments involving water quality, contaminant transport, ecosystem health, and sustainable use of groundwater. Annual potential groundwater recharge was simulated at a 1-kilometer resolution with the Soil-Water-Balance (SWB) model for the glacial aquifer system east of the Rocky Mountains, from central Montana east to Maine, for calendar years 1980–2011. The SWB model used high resolution meteorological, land cover, and soil hydrology datasets that are nationally consistent and publicly available. The SWB model computed daily potential groundwater recharge as precipitation in excess of interception, runoff, evapotranspiration, and soil-water storage capacity. Daily potential recharge values within each year of the simulation were summed to produce annual potential recharge rates. Potential recharge as described in this report is water that infiltrates vertically below the plant rooting zone and is assumed to reach the water table.
The calibrated SWB model in this report is called the glacial SWB model. Model calibration assumed that the area contributing to groundwater discharge equaled the surface watershed. The model was calibrated to stream base flows from 39 watersheds throughout the model domain that had hydrologic conditions appropriate for hydrograph separation. Base flows were calculated from daily streamflow records with the HYSEP local minimum hydrograph separation method The glacial SWB model reproduced the mean annual base-flow calibration targets well; the Nash-Sutcliffe efficiency coefficient was 0.94, and the root mean squared error was 1.28 inches per year.
The glacial SWB model provides insight into the spatial and temporal variability in potential annual recharge across the glacial aquifer system. About 20 percent of the active model area had an average potential recharge rate of less than 1 inch per year. Total precipitation, total recharge, and recharge as a percentage of precipitation increased from west to east. A substantial amount of the recharge water (39 percent) entering the glacial aquifer system travels through developed (urbanized) and agricultural landscapes, which are known to cause water-quality impairments. Regional climatic events, such as the 1988 to 1989 drought, are apparent in the potential recharge time series. Potential recharge generally increased across the glacial aquifer system between 2001 and 2011.
A comparison of the potential recharge from the glacial SWB model to previous broad-scale recharge estimates reveals several important considerations for future SWB modeling applications. Shifts in the overall distribution of potential recharge between separate models can be explained by methods used to generate base-flow calibration target datasets. Spatial patterns in potential recharge simulated by SWB models are strongly dependent on the data and assumptions used to assign model cells to hydrologic soil groups. A review of several SWB models used to estimate groundwater recharge (and not surface runoff) revealed that model results are most sensitive to input climatic data, followed by surface runoff (curve number) and root-zone depth parameters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185080","collaboration":"Prepared as part of the Glacial Aquifer System Groundwater Availability Study, a cooperative effort between the U.S. Department of the Interior’s WaterSMART Initiative and the U.S. Geological Survey’s Water Availability and Use Science Program","usgsCitation":"Trost, J.J., Roth, J.L., Westenbroek, S.M., and Reeves, H.W., 2018, Simulation of potential groundwater recharge for the glacial aquifer system east of the Rocky Mountains, 1980–2011, using the Soil-Water-Balance model: U.S. Geological Survey Scientific Investigations Report 2018–5080, 51 p., https://doi.org/10.3133/sir20185080.","productDescription":"Report: vii, 51 p.; Tables; Data Release","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088856","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":355759,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XW4HRJ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-Water-Balance (SWB) model used to simulate potential groundwater recharge for the glacial aquifer system east of the Rocky Mountains, 1980–2011"},{"id":355764,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2018/5080/sir20185080_tables.xlsx","text":"Tables 5 and 8","size":"52.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2018–5080 Tables"},{"id":355757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5080/coverthb.jpg"},{"id":355758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5080/sir20185080.pdf","text":"Report","size":"24.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5080"}],"country":"United States","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-113.51074218749999,49.05227025601607],[-113.4228515625,48.705462895790546],[-112.8515625,48.16608541901253],[-112.6318359375,47.88688085106901],[-112.7197265625,47.68018294648414],[-112.67578124999999,47.249406957888446],[-112.1484375,47.025206001585396],[-111.70898437499999,47.17477833929903],[-111.11572265625,47.44294999517949],[-110.93994140625,47.945786463687185],[-110.06103515625,47.989921667414194],[-109.86328125,48.31242790407178],[-109.00634765625,48.09275716032736],[-108.74267578125,47.931066347509784],[-108.74267578125,47.82790816919329],[-108.74267578125,47.53203824675999],[-107.57812499999999,47.487513008956554],[-106.67724609375,47.68018294648414],[-106.06201171875,47.69497434186282],[-103.90869140625,48.004625021133904],[-102.87597656249999,48.04870994288686],[-102.06298828125,47.54687159892238],[-101.5576171875,47.502358951968574],[-101.14013671875,47.47266286861342],[-100.92041015625,47.05515408550348],[-100.56884765624999,46.36209301204985],[-100.56884765624999,46.08847179577592],[-100.34912109375,45.89000815866184],[-100.45898437499999,45.22848059584359],[-100.5029296875,44.902577996288876],[-100.546875,44.5435052132082],[-100.4150390625,44.33956524809713],[-99.82177734375,44.213709909702054],[-99.5361328125,43.96119063892024],[-99.47021484375,43.70759350405294],[-99.2724609375,43.42100882994726],[-98.94287109375,43.16512263158296],[-98.76708984374999,43.02071359427862],[-97.998046875,42.58544425738491],[-98.23974609375,42.24478535602799],[-98.23974609375,41.88592102814744],[-97.93212890625,41.44272637767212],[-98.3056640625,40.83043687764923],[-98.3056640625,40.36328834091583],[-97.646484375,39.639537564366684],[-96.1962890625,38.788345355085625],[-95.537109375,38.75408327579141],[-94.50439453125,38.95940879245423],[-94.24072265625,39.06184913429154],[-92.61474609375,39.06184913429154],[-91.42822265625,39.01064750994083],[-90.5712890625,38.95940879245423],[-90.28564453124999,38.976492485539396],[-90.19775390625,38.94232097947902],[-90.15380859375,38.59970036588819],[-90.3076171875,38.44498466889473],[-90.32958984375,38.18638677411551],[-90.10986328125,37.996162679728116],[-89.7802734375,37.84015683604136],[-89.58251953125,37.68382032669382],[-89.49462890625,37.61423141542417],[-89.56054687499999,37.3002752813443],[-89.12109375,37.54457732085582],[-89.033203125,37.666429212090605],[-88.74755859375,37.56199695314352],[-88.35205078124999,37.63163475580643],[-88.22021484375,37.68382032669382],[-88.08837890625,37.70120736474139],[-87.91259765625,37.85750715625203],[-87.91259765625,37.97884504049713],[-87.42919921875,37.90953361677018],[-87.20947265625,37.85750715625203],[-87.29736328125,38.28993659801203],[-87.2314453125,38.61687046392973],[-87.2314453125,38.805470223177466],[-87.3193359375,39.198205348894795],[-87.3193359375,39.62261494094297],[-87.29736328125,39.80853604144591],[-86.6162109375,39.41922073655956],[-86.044921875,39.436192999314095],[-85.9130859375,39.16414104768742],[-86.1328125,38.788345355085625],[-86.06689453125,38.324420427006544],[-85.89111328125,38.25543637637947],[-85.6494140625,38.34165619279595],[-85.27587890625,38.65119833229951],[-85.05615234375,38.788345355085625],[-84.8583984375,38.8225909761771],[-84.90234375,39.04478604850143],[-84.83642578125,39.13006024213511],[-84.52880859375,39.06184913429154],[-84.44091796875,39.11301365149975],[-84.30908203125,38.85682013474361],[-83.95751953125,38.736946065676],[-83.60595703125,38.61687046392973],[-83.4521484375,38.61687046392973],[-83.3203125,38.59970036588819],[-82.90283203125,38.839707613545144],[-82.63916015625,39.14710270770074],[-82.59521484375,39.487084981687495],[-82.30957031249999,39.639537564366684],[-82.0458984375,39.740986355883564],[-81.67236328125,39.87601941962116],[-81.2109375,40.3130432088809],[-81.27685546875,40.697299008636755],[-81.05712890625,40.79717741518766],[-80.4638671875,40.81380923056958],[-79.541015625,41.062786068733026],[-79.29931640625,41.376808565702355],[-79.1015625,41.672911819602085],[-78.68408203124999,41.934976500546604],[-78.1787109375,42.01665183556825],[-77.6953125,41.902277040963696],[-77.18994140625,41.52502957323801],[-77.01416015625,41.11246878918088],[-76.48681640625,40.97989806962013],[-75.2783203125,40.74725696280421],[-73.98193359375,40.54720023441049],[-73.63037109375,40.59727063442024],[-72.70751953125,40.68063802521456],[-72.35595703125,40.863679665481676],[-71.806640625,40.96330795307353],[-71.30126953124999,41.062786068733026],[-70.59814453125,41.1290213474951],[-70.224609375,41.16211393939692],[-69.873046875,41.22824901518529],[-69.9609375,41.45919537950706],[-69.98291015625,41.82045509614034],[-70.048828125,42.01665183556825],[-70.24658203125,42.13082130188811],[-70.2685546875,41.934976500546604],[-70.13671875,41.72213058512578],[-70.46630859375,41.78769700539063],[-70.55419921875,41.983994270935625],[-70.64208984375,42.147114459220994],[-70.8837890625,42.24478535602799],[-70.94970703125,42.342305278572816],[-70.59814453125,42.56926437219384],[-70.68603515625,42.779275360241904],[-70.72998046875,43.03677585761058],[-70.46630859375,43.27720532212024],[-70.02685546875,43.70759350405294],[-69.54345703125,43.77109381775651],[-69.36767578124999,43.97700467496408],[-69.23583984375,43.96119063892024],[-69.14794921875,44.08758502824516],[-68.79638671875,43.97700467496408],[-68.3349609375,44.040218713142146],[-68.02734375,44.33956524809713],[-67.8076171875,44.5278427984555],[-67.34619140625,44.653024159812],[-67.08251953125,44.731125592643274],[-66.9287109375,44.62175409623324],[-66.77490234375,44.5278427984555],[-66.68701171875,44.762336674810996],[-67.08251953125,44.88701247981298],[-67.236328125,45.213003555993964],[-67.3681640625,45.13555516012536],[-67.52197265625,45.24395342262324],[-67.4560546875,45.336701909968134],[-67.47802734375,45.521743896993634],[-67.763671875,45.62940492064501],[-67.82958984375,45.81348649679973],[-67.763671875,45.920587344733654],[-67.8076171875,47.100044694025215],[-68.115234375,47.27922900257082],[-68.4228515625,47.35371061951363],[-68.66455078125,47.249406957888446],[-68.994140625,47.204642388766935],[-68.994140625,47.3834738721015],[-69.06005859375,47.45780853075031],[-69.27978515625,47.41322033016902],[-70.07080078125,46.694667307773116],[-70.0048828125,46.51351558059737],[-70.13671875,46.392411189814645],[-70.3125,46.31658418182218],[-70.13671875,46.057985244793024],[-70.20263671875,45.874712248904764],[-70.37841796875,45.78284835197676],[-70.6201171875,45.66012730272194],[-70.751953125,45.506346901083425],[-70.6640625,45.398449976304086],[-70.94970703125,45.398449976304086],[-70.90576171875,45.30580259943578],[-71.1474609375,45.336701909968134],[-71.3232421875,45.213003555993964],[-71.43310546875,45.120052841530544],[-71.60888671875,44.98034238084973],[-74.8388671875,45.042478050891546],[-75.41015624999999,44.809121700077355],[-75.8056640625,44.62175409623324],[-75.91552734375,44.402391829093915],[-76.26708984375,44.213709909702054],[-76.39892578125,44.134913443750726],[-76.35498046875,43.89789239125797],[-76.2890625,43.61221676817573],[-76.37695312499999,43.50075243569041],[-76.86035156249999,43.29320031385282],[-77.32177734375,43.229195113965005],[-77.51953125,43.213183300738876],[-77.71728515624999,43.29320031385282],[-78.0908203125,43.29320031385282],[-79.03564453124999,43.29320031385282],[-79.03564453124999,43.004647127794435],[-78.92578124999999,42.76314586689492],[-79.25537109375,42.58544425738491],[-80.37597656249999,42.01665183556825],[-81.14501953125,41.705728515237524],[-81.80419921875,41.5579215778042],[-82.41943359375,41.49212083968776],[-82.85888671875,41.60722821271717],[-83.408203125,41.72213058512578],[-83.47412109375,41.88592102814744],[-83.29833984375,42.08191667830631],[-83.16650390625,42.27730877423709],[-82.81494140625,42.391008609205045],[-82.55126953124999,42.69858589169842],[-82.4853515625,42.94033923363181],[-82.50732421875,43.18114705939968],[-82.6611328125,43.46886761482925],[-82.6611328125,43.70759350405294],[-82.72705078125,43.94537239244209],[-82.96875,44.071800467511565],[-83.29833984375,44.008620115415354],[-83.49609375,43.78695837311561],[-83.8037109375,43.628123412124616],[-84.00146484374999,43.8028187190472],[-83.84765625,43.94537239244209],[-83.6279296875,44.02442151965934],[-83.56201171875,44.19795903948531],[-83.408203125,44.308126684886126],[-83.27636718749999,44.66865287227321],[-83.4521484375,45.042478050891546],[-83.232421875,45.02695045318546],[-83.408203125,45.30580259943578],[-83.7158203125,45.44471679159555],[-84.0673828125,45.506346901083425],[-84.44091796875,45.706179285330855],[-84.74853515625,45.78284835197676],[-85.05615234375,45.67548217560647],[-85.23193359375,45.47554027158593],[-85.341796875,45.259422036351694],[-85.69335937499999,45.182036837015886],[-86.06689453125,44.84029065139799],[-86.3525390625,44.4808302785626],[-86.3525390625,44.22945656830167],[-86.5283203125,44.008620115415354],[-86.5283203125,43.75522505306928],[-86.5283203125,43.51668853502906],[-86.41845703124999,43.08493742707592],[-86.2646484375,42.48830197960227],[-86.63818359375,41.86956082699455],[-87.07763671875,41.672911819602085],[-87.51708984375,41.60722821271717],[-87.86865234374999,42.08191667830631],[-87.890625,43.27720532212024],[-87.91259765625,43.88205730390537],[-87.71484375,44.19795903948531],[-87.34130859375,44.512176171071054],[-87.0556640625,45.01141864227728],[-86.85791015625,45.42929873257377],[-87.20947265625,45.27488643704891],[-87.56103515625,44.99588261816546],[-87.64892578125,45.1510532655634],[-87.802734375,45.336701909968134],[-87.03369140625,45.767522962149876],[-86.55029296875,46.01222384063236],[-86.02294921875,45.9511496866914],[-85.10009765625,46.07323062540835],[-84.7705078125,45.90529985724799],[-83.935546875,45.90529985724799],[-84.13330078125,46.210249600187225],[-84.1552734375,46.51351558059737],[-84.48486328124999,46.604167162931844],[-85.05615234375,46.73986059969267],[-85.84716796875,46.66451741754235],[-86.5283203125,46.5286346952717],[-86.81396484375,46.49839225859763],[-87.69287109375,46.92025531537451],[-88.11035156249999,46.93526088057719],[-88.39599609375,46.81509864599243],[-88.2861328125,47.010225655683485],[-87.62695312499999,47.35371061951363],[-87.4951171875,47.45780853075031],[-87.95654296875,47.53203824675999],[-88.505859375,47.42808726171425],[-88.74755859375,47.27922900257082],[-89.18701171875,47.08508535995386],[-89.56054687499999,46.86019101567027],[-90.439453125,46.70973594407157],[-90.703125,46.694667307773116],[-90.90087890624999,46.649436163350245],[-90.703125,46.89023157359399],[-90.37353515625,47.100044694025215],[-90.966796875,46.99524110694593],[-91.51611328125,46.830133640447386],[-91.95556640625,46.7549166192819],[-92.08740234375,46.84516443029276],[-91.38427734374999,47.12995075666307],[-91.0546875,47.368594345213374],[-90.7470703125,47.5913464767971],[-90.2197265625,47.73932336136857],[-89.62646484375,47.90161354142077],[-89.47265625,47.97521412341618],[-89.8681640625,48.03401915864286],[-90.37353515625,48.10743118848039],[-90.9228515625,48.16608541901253],[-90.85693359375,48.25394114463431],[-91.16455078125,48.151428143221224],[-91.47216796875,48.019324184801185],[-91.69189453125,48.122101028190805],[-92.13134765625,48.268569112964336],[-92.1533203125,48.38544219115483],[-92.39501953125,48.19538740833338],[-92.5048828125,48.31242790407178],[-92.65869140625,48.48748647988415],[-93.14208984375,48.60385760823255],[-93.49365234375,48.531157010976706],[-94.1748046875,48.647427805533546],[-94.7021484375,48.76343113791796],[-94.74609375,49.10983779052439],[-94.76806640624999,49.36806633482156],[-95.16357421875,49.410973199695846],[-95.16357421875,49.13859653703879],[-95.1416015625,49.009050809382046],[-113.51074218749999,49.05227025601607]]]}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Groundwater and surface water collectively supply the domestic, agricultural, and industrial needs of the 895-square mile upper Chehalis River Basin upstream of Grand Mound, Washington, while providing streamflow for fish and other aquatic species in the Chehalis River and its tributaries. To support sustainable water management decision-making, a water budget (including precipitation, interception, groundwater recharge, surface runoff, and groundwater pumping) was developed for the upper Chehalis River Basin during October 2001–September 2015. Water-budget components were estimated from the U.S. Geological Survey Soil-Water-Balance (SWB) model except for groundwater pumping, which was estimated from public water purveyor records, annual system data from the Washington State Department of Health, census population data, and water-use estimates. Groundwater recharge estimated from the SWB model was compared to base flow, a proxy for groundwater recharge, independently estimated from separation of the hydrograph recorded by the U.S. Geological Survey streamgage at the outlet of the basin. Mean annual precipitation for the basin was estimated at 72.6 inches, of which 35 percent was lost to evapotranspiration, 30 percent was recharged to groundwater, 30 percent was surface runoff, and 5 percent was lost to interception. SWB model estimates of groundwater recharge were 17 percent less than estimates of base flow from hydrograph separation. Groundwater pumpage in the basin was estimated at 1 percent of groundwater recharge estimated by SWB and 0.8 percent of base flow estimated by hydrograph separation. These estimates form a baseline for understanding future changes to components of water use and may be used to inform numerical groundwater models to support sustainable management of water resources in the upper Chehalis River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185084","collaboration":"Prepared in cooperation with the City of Centrailia","usgsCitation":"Gendaszek, A.S., and Welch, W.B., 2018, Water budget of the upper Chehalis River Basin, southwestern Washington: U.S. Geological Survey Scientific Investigations Report 2018-5084, 17 p., https://doi.org/10.3133/sir20185084.","productDescription":"v, 17 p.","onlineOnly":"Y","ipdsId":"IP-096130","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":437827,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8K1F","text":"USGS data release","linkHelpText":"Soil Water Balance Model of Upper Chehalis River Basin, Southwestern Washington"},{"id":355593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5084/coverthb.jpg"},{"id":355594,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5084/sir20185084.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5084"}],"country":"United States","state":"Washington","otherGeospatial":"Chehalis River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1667,\n 46.8333\n ],\n [\n -122.3333,\n 46.8333\n ],\n [\n -122.3333,\n 46.3333\n ],\n [\n -123.1667,\n 46.3333\n ],\n [\n -123.1667,\n 46.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
A series of 15 digital flood-inundation maps was developed for a 10.2-mile reach of the Pawtuxet River in the municipalities of West Warwick, Warwick, and Cranston, Rhode Island, by the U.S. Geological Survey (USGS), in cooperation with the Rhode Island Emergency Management Agency and the U.S. Army Corps of Engineers. The coverage of the maps extends downstream from Natick Pond dam near State Route 33/Providence Street bridge in West Warwick to the mouth of the river at Pawtuxet Cove (Broad Street bridge) on the border between Cranston and Warwick, R.I. A one-dimensional step-backwater hydraulic model created and calibrated for the Federal Emergency Management Agency Flood Insurance Studies for Kent and Providence Counties in 2015 was updated for this study. The updated hydraulic model reflects the removal of the Pawtuxet Falls dam during 2011 and the raised elevation of a levee surrounding the Warwick Sewer Authority wastewater treatment facility during 2014–17. The hydraulic model was calibrated by using the current (2018) stage-discharge relation at the USGS Pawtuxet River at Cranston, Rhode Island, streamgage (01116500) and documented high-water marks from the March 31, 2010, flood, which had a peak flow greater than the estimated 0.2-percent annual exceedance probability floodflow.
The hydraulic model was used to compute water-surface profiles for 15 flood stages at 1-foot (ft) intervals referenced to the USGS Pawtuxet River at Cranston, Rhode Island, streamgage (01116500) and ranging from 8.0 ft (15.2 ft, North American Vertical Datum of 1988), which is the National Weather Service Advanced Hydrologic Prediction Service flood category “action stage,” to 22.0 ft (29.2 ft, North American Vertical Datum of 1988), which is the maximum stage of the stage-discharge relation at the streamgage and exceeds the National Weather Service Advanced Hydrologic Prediction Service flood category “major flood stage” of 13.0 ft. The simulated water-surface profiles were combined with a geographic information system digital elevation model derived from light detection and ranging (lidar) data with a 1.0-ft vertical accuracy to create flood-inundation maps. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to 15 selected flood stages at the streamgage. The flood-inundation maps depict only riverine flooding and do not depict any tidal backwater or coastal storm surge that might occur in the lower part of the river reach. The flood-inundation maps can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation. Near-real-time stages and discharges at the Pawtuxet River streamgage can be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/. The National Weather Service Advanced Hydrologic Prediction Service provides flood forecasts of stage for this site (CRAR1) at https:/water.weather.gov/ahps/.
The availability of flood-inundation maps referenced to current and forecasted water levels at the USGS Pawtuxet River at Cranston, Rhode Island, streamgage (01116500) can provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, and postflood recovery efforts. The flood-inundation maps are nonregulatory but provide Federal, State, and local agencies and the public with estimates of the potential extent of flooding during flood events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185043","collaboration":"Prepared in cooperation with the Rhode Island Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Bent, G.C., and Lombard, P.J., 2018, Flood-inundation maps for the Pawtuxet River in West Warwick, Warwick, and Cranston, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2018–5043, 16 p., https://doi.org/10.3133/sir20185043.","productDescription":"Report: vii, 16 p.; Application; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090311","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":355600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5043/sir20185043.pdf","text":"Report","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5043"},{"id":355601,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78C9V6B","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation Grids and Shapefiles for the Pawtuxet River in West Warwick, Warwick, and Cranston, Rhode Island"},{"id":355602,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html","linkHelpText":"- Flood Inundation Mapper"},{"id":355599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5043/coverthb.jpg"}],"country":"United States","state":"Rhode Island","city":"Cranston, Warwick, West Warwick","otherGeospatial":"Pawtuxet River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.630859375,\n 41.572306568724365\n ],\n [\n -71.27105712890625,\n 41.572306568724365\n ],\n [\n -71.27105712890625,\n 41.912497421968425\n ],\n [\n -71.630859375,\n 41.912497421968425\n ],\n [\n -71.630859375,\n 41.572306568724365\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Bathymetric and topobathymetric light detection and ranging (lidar) digital elevation models created for the Delaware River were provided to the National Geospatial Program and used to evaluate synthetic flowpath extraction from bathymetric/topobathymetric lidar survey data as a data source for improving the density, distribution, and connectivity of the National Hydrography Dataset High Resolution Flowline Network. As the surface-water component of The National Map, the National Hydrography Dataset maintains the Nation’s drainage network flow information and geometries for surface-water features used in hydrologic, hydraulic, and other science and engineering disciplines. The regional lidar survey for the Delaware River between Hancock, New York, and Trenton, New Jersey, was collected for the U.S. Geological Survey using the Experimental Advanced Airborne Research Lidar sensor system and processed by the Coastal National Elevation Database Applications Program.
Using 1 percent of the maximum flow accumulation value for the surveyed Delaware River corridor as the flow accumulation threshold for grid cells at 1-, 5-, and 10-meter resolution created 223 to 283 kilometers of synthetic flowpaths potentially representing the river channel thalweg, which is the deepest point in a riverbed cross-section. There was potential for improving the High Resolution National Hydrography Dataset (HR NHD) Flowline network in places where the Delaware River channel, depicted as an Artificial Path in the HR NHD, is offset from the extracted synthetic river flowpath which sometimes appeared better positioned than the Artificial Path to represent the river thalweg. For the same area, using 0.05 percent of the maximum flow accumulation at the 1-, 5-, and 10-meter resolutions extracted 744 to 1,317 kilometers of synthetic flowpaths, with extracted synthetic flowpaths representing the main river channel and additional synthetic flowpaths representing tributaries or streams adjacent to the main channel. Overlaying these results with the HR NHDFlowline Network indicates that some of the additional synthetic flowpaths are connected to or extend HR NHD stream/river feature types. Some disconnected or isolated synthetic flowpaths not included in stream/river feature types were validated in orthoimagery and U.S. Topo Maps and provide examples of how extracted synthetic flowpaths could be used to delineate new stream/river features. Other additional extracted synthetic flowpaths depict linear features such as canals, tree lines, roads, or linear topographic depressions.
For some river reaches where obstructions to flow or where low-relief topographic or bathymetric surfaces alter the flow direction, the software tool used to develop the flow direction grid did not calculate a primary flowpath for the river channel. Based on the results of this analysis, site conditions for the Delaware River corridor did not affect the quality of lidar bathymetric survey data. However, depending on the resolution of the lidar bathymetric digital elevation models (BDEMs), site conditions do have different effects on results for extracted synthetic flowpaths. We found that synthetic flowpaths extracted from 1-meter resolution lidar DEMs had more varied flow directions around in-channel landforms that obstructed flow than synthetic flowpaths extracted from 5- or 10-meter resolution lidar DEMs. As a result the 1-meter resolution DEM created some isolated or discontinuous synthetic flowpath segments where the 5- and 10-meter DEMs developed more continuous flowpaths. In this case the river bed upstream from the in-channel obstruction is shallower than the river bed downstream. Under these conditions the 1-meter resolution DEM provided synthetic flowpaths delineating a potential river thalweg. In this same area, the software solution modified (virtually raised) the river bed in the 5- and 10-meter resolution DEMs and flattened the bathymetric surface to create a continuous downstream flow direction, which caused trellis-patterned synthetic flowpaths to form. Under different site conditions and converse to the above development of synthetic flowpaths at different resolutions, at an abandoned river flood plain (terrace) with low relief that is adjacent to the river channel, the flow direction grid for the 1-meter resolution DEM developed continuous synthetic flowpath corresponding to a HR NHD Flowline network stream/river feature that connected to the main river channel but the larger resolution DEMs created isolated or disconnected synthetic flowpaths.
A project to continue an evaluation of benefits of or issues caused by extracting synthetic flowpaths to enhance the HR NHD could include a study to assess the potential for merging surface-water flowpaths extracted from lidar topobathymetry and 3D Elevation Program digital elevation models. The merged DEM approach to synthetic flowpath extraction could extend the HR NHDFlowline network and enhance flow accumulations that might develop better flow direction grids in low-relief areas. Because of the confined lateral extent of the Delaware River, the lidar DEMs were not used to create catchments or watersheds; however, the merged DEM approach could also be tested as a resource for enhancing HR NHD catchments and watersheds.
This lidar DEM synthetic flowpath extraction project supports the National Geospatial Program efforts to collect and produce high-quality lidar data to provide 3-dimensional representations of natural feature and aligns with the National Spatial Data Infrastructure to improve utilization of geospatial data. The results also can be useful for understanding strategies that can help maintain quality data in the HR NHD programs.
KEYWORDS: bathymetric, digital elevation model, extracted synthetic flowpath, lidar, High Resolution National Hydrography Dataset, topobathymetric
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181058","usgsCitation":"Miller-Corbett, C., 2018, A comparison of synthetic flowpaths derived from light detection and ranging topobathymetric data and National Hydrography Dataset high resolution flowlines: U.S. Geological Survey Open-File Report 2018–1058, 29 p., https://doi.org/10.3133/ofr20181058.","productDescription":"vii, 29 p.","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-079961","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":355596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1058/ofr20181058.pdf","text":"Report","size":"4.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1058"},{"id":355595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1058/coverthb.jpg"}],"country":"United States","state":"New Jersey","city":"Hancock Narrows, Middle River, Trenton","otherGeospatial":"Delaware River","contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Groundwater has been a part of the city of Santa Barbara’s water-supply portfolio since the 1800s; however, since the 1960s, the majority of the city’s water has come from local surface water, and the remainder has come from groundwater, State Water Project, recycled water, increased water conservation, and as needed, seawater desalination. Although groundwater from the Santa Barbara and Foothill groundwater basins only accounts for a small percentage of the long-term supply, it is an important source of supplemental water during times of surface-water shortages. During the late 1980s and early 1990s, production wells extracted additional groundwater to compensate for drought related water-delivery shortfalls from other sources; in response, water levels declined substantially in the Santa Barbara and Foothill groundwater basins (below sea level in the Santa Barbara groundwater basin).
In coastal basins that have groundwater extraction near shore, seawater intrusion is often a problem. Seawater intrusion in the Santa Barbara groundwater basin is thought to be more limited than in other coastal basins because of an offshore fault that acts as a partial barrier to groundwater flow. During the late 1980s and early 1990s, seawater intrusion was observed in the Santa Barbara groundwater basin, as indicated by increased chloride concentrations at several monitoring wells that ranged from 200 ft to 1,300 ft from the ocean and as close as 2,900 ft to the nearest pumping well. This demonstrated that seawater can intrude into the Santa Barbara groundwater basin when groundwater levels fall below sea level near the coast.
The city of Santa Barbara is interested in developing a better understanding of the sustainability of its groundwater supplies. In 2014, California adopted historic legislation to manage its groundwater: the Sustainable Groundwater Management Act (SGMA). The SGMA requires the development and implementation of “Groundwater Sustainability Plans” in 127 priority groundwater basins; although Santa Barbara was not a designated priority basin, the city is taking steps to achieve sustainability. Sustainability was defined in the SGMA in terms of avoiding undesirable results: significant and unreasonable groundwater-level declines, reduction in groundwater storage, seawater intrusion, water-quality degradation, land subsidence, and surface-water depletion.
In this project, a cooperative study between the U.S. Geological Survey (USGS) and the city of Santa Barbara, sustainable yield is defined as the volume of groundwater that can be pumped from storage without causing water-level drawdowns and the associated increases in seawater intrusion (as indicated by increases in measured chloride concentrations) at selected wells. In order to estimate the sustainability of Santa Barbara’s groundwater basins, a three-dimensional density-dependent groundwater-flow and solute-transport model (the Santa Barbara Flow and Transport Model, or SBFTM) was developed on the basis of an existing groundwater-flow model. To simulate seawater intrusion to the Santa Barbara Basin under various management strategies, the SBFTM uses the USGS code SEAWAT to simulate salinity transport and variable-density flow. The completed SBFTM was coupled with a management optimization tool, in this case a multi-objective evolutionary algorithm, to determine optimal pumping strategies that maximize the sustainable yield and at the same time satisfy user-defined drawdown and chloride-concentration constraints.
As part of this study, a three-dimensional hydrogeologic framework model was developed to quantify the extent and hydrogeologic characteristics of the Santa Barbara and Foothill groundwater basins and to help define the discretization and hydraulic properties used in the SBFTM. The development of the hydrogeologic framework model required the collection and reconciliation of geologic and geophysical data from existing maps, reports, and databases, along with geologic and hydrologic data from recently drilled wells. These data were integrated into a three-dimensional hydrogeologic framework model that defines the stratigraphy and geometry of the aquifer zones and the major geologic structures in the basin. The hydrogeologic framework model also quantifies the variation in sediment grain size within each aquifer zone as the percentage of coarse-grained sediment. Previous studies indicated that there are two principal water-producing zones in the Santa Barbara groundwater basin, the upper and lower producing zones; an additional thin, productive zone was identified as part of this study. This “middle producing zone” is not as areally extensive as the upper and lower producing zones and only exists in the coastal part of Storage Unit I. These producing zones are bounded at depth by less productive shallow, middle, and deep zones.
Two versions of the SBFTM were constructed: an initial-condition model and a modern transient model. The initial-condition model is a long-term transient model that simulates flow and solute-transport conditions during a period with limited anthropogenic influences preceeding the modern transient model. The simulation-transient model simulates flow and transport conditions from 1929 through 2013; however, because of data availability, the focus of the model calibration was 1972–2013. The SBFTM was calibrated to measured groundwater levels and drawdown, as well as measured chloride concentrations and change in concentrations, using a combination of automated and trial-and-error parameter-estimation techniques.
A sensitivity analysis indicated that, in general, the SBFTM was most sensitive to recharge- and pumping-distribution parameters, specifically those controlling the amount of small-catchment recharge and the distribution of water extraction by hydrogeologic layer for production wells. The model was also sensitive to parameters controlling stream-recharge rates, horizontal and vertical hydraulic conductivity, and porosity.
From 1929 to 1971, most of the water entering the area represented by the SBFTM was from creek and small-catchment recharge, and the majority of water leaving the SBFTM area was from pumping, discharge to creeks, and drains. In addition, about 37 percent of the total pumpage came from a net reduction in groundwater storage. From 1972 to 2013, the amount of water entering and leaving the SBFTM was fairly similar as that from 1929 to 1971, except the reduction in pumpage added about 17,000 acre-ft of water to storage. During this later period, there were also times of storage loss. For example, during July 1990, a month when approximately 705 acre-ft of groundwater was pumped in the study area, the pumpage was much greater than all sources of recharge combined, and about 382 acre-ft of water was removed from groundwater storage.
Simulated hydraulic heads replicated the observed data to an acceptable matching of the measured water-level, flow direction, and vertical gradients. Simulated hydrographs for selected wells were in good agreement with the measured data, with an average residual of -2.7 ft and a standard deviation of 14.5 ft, indicating that the simulated heads, on average, underestimated the observed water levels. An examination of the model fit indicated that most of the discrepancies were lower simulated heads at wells proximal to production well sites.
The simulated chloride concentrations reasonably matched the rising limbs of the measured breakthrough curves in terms of timing and magnitude; however, the simulation overestimated the chloride concentrations on the falling limbs. The overestimation of low chloride concentrations was attributed to the model overestimating the advance of the chloride front during periods of heavy pumping and underestimating the retreat of the chloride front during periods of low pumping. These simulation errors would result in a conservative response by local water managers to seawater intrusion.
The SBFTM was used to develop a collection of predictive simulations optimized to produce pumping schedules that maximize yield, subject to a set of constraints and competing objectives. The simulations were grouped as scenarios that differed in their time horizon, initial conditions for groundwater levels and chloride concentrations, as well as precipitation, which was incorporated into the model through simulated recharge. Overall, five scenarios were developed in a multi-objective framework to obtain optimal pumping rates for all of the wells managed by the city, while minimizing excessive drawdown and seawater intrusion.
For the current study, complexities in the simulation model and the optimization formulation required additional considerations. Incorporating the solute-transport equations to simulate chloride transport added a highly nonlinear process that is solved iteratively in each time step of the groundwater-flow model. These nonlinearities, coupled with the highly refined grid in the current model, creates challenges for many traditional optimization methods. Therefore, an optimization method was needed that could address nonlinear relationships as well as a very large problem size. Lastly, the optimization problem was reformulated to include multiple objectives without requiring convergence to a single solution. This approach, guided by the city’s objectives, allowed the maximum extraction of information from the complex simulation.
Borg, a multi-objective evolutionary algorithm, was chosen as the optimization algorithm for this study for several reasons: (1) it is very computationally efficient; (2) it can run in parallel; (3) it requires little user input; and (4) it can solve for multiple competing objectives. The first three points allow the algorithm to proceed toward the optimal solutions at the fastest possible rate. The fourth point is advantageous for large, complex optimization problems because it is difficult to formulate the optimization problem in a way that produces only one optimal solution.
The problem formulation consisted of four competing objectives and a constraint set in accordance with the main concerns of the city. The objectives were maximizing total pumpage, minimizing seawater intrusion, minimizing total drawdown in production wells, and minimizing the maximum drawdown. The constraints were pump capacity, meeting drinking-water standards for chloride, maintaining a specified minimum flowrate to a groundwater treatment plant, and maintaining minimum water levels in pumping wells. The decision variables either were quarterly pumpage by well or total pumpage by basin.
Five optimization scenarios were developed that allow the decision makers to evaluate a range of optimal solutions for a variety of water levels and chloride concentrations as well as potential future climatic conditions. Three scenarios (1, 2, and 5) were multi-objective optimization formulations that allowed for variations in management preferences and climatic conditions. The other two scenarios (3 and 4) were designed to examine the optimization results to answer specific questions. Scenario 1 described the best-case sustainable yield assuming a “full” basin (that is, high initial water levels) and typical climate conditions for 10 years. Scenario 2 also started with a “full” basin; however, this was followed by a 10-year drought. Scenario 3 determined if an “empty” basin (that is, low initial water levels) would recover to full conditions (1998 conditions) given climate assumptions and optimal pumping schedules from scenarios 1 and 2. Scenario 4 was designed to produce decision rules that can be used by water managers to help choose an optimal pumping schedule based on measured water-level or chloride data. Scenario 5 identified future pumping schedules based on short-term climate variations during a 2-year management horizon.
The results from scenarios 1 and 2 described the differences in maximum pumpage in the basin under typical and dry long-term climate projections, respectively. The scenario 1 results indicated the maximum 10-year pumpage of the basin was about 31,300 acre-ft under typical conditions and controlling simulated seawater intrusion and drawdowns. For scenario 2, less recharge over the 10-year dry climate produced a maximum pumpage estimate of 30,000 acre-ft to control seawater intrusion and drawdowns. The larger pumpage for scenario 1 resulted in more seawater intrusion, but less total drawdown, compared to that of scenario 2.
Results for scenarios 3 and 4 showed the basin’s response to management actions combined with climate projections. Both scenarios used the optimal pumping schedules and the 10-year climates from scenarios 1 and 2. The scenario 3 results showed that under minimal pumping, the basin did not fully recover to 1998 water levels within 10 years under either climate scenario. The relatively larger recharge from the typical climate resulted in less drawdown at coastal monitoring wells after the 10-year recovery period than that from the dry climate. The location of the seawater intrusion front was not appreciably different between the scenarios, however. Scenario 4 used the optimal results from scenarios 1 and 2 to produce decision-rule curves that illustrated the pumpage for each basin, given measured levels of chloride concentration or drawdown. This allowed the use of additional measurements at monitoring wells to assess future management decisions on the basis of the sensitivity of observations of drawdown and seawater intrusion to various pumping rates.
Scenario 5 allowed managers to investigate the effects of short-term climate variations on optimal pumping schedules. Three specific 2-year simulations were optimized: typical-to-dry (scenario 5A), dry-to-typical (scenario 5B), and dry-to-dry (scenario 5C). The most noteable result from scenario 5 was the overall reduction in optimal pumpage for most schedules in scenario 5C, when the climate is simulated as dry-to-dry. There are also many optimal pumping schedules that produced an overall increase in waterlevels over the two-year simulation period, regardless of climatic condition. Similar to scenario 2, the scenario 5C results represents conservative yield estimates under a minimal-precipitation climatic condition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185059","collaboration":"Prepared in cooperation with the city of Santa Barbara","usgsCitation":"Nishikawa, T., ed., 2018, Santa Barbara and Foothill groundwater basins Geohydrology and optimal water resources management—Developed using density dependent solute transport and optimization models, U.S. Geological Survey, Scientific Investigations Report 2018-5059, 4 chap. (A–D), variously paged, https://doi.org/10.3133/sir20185059.","productDescription":"xiv, 384 p.","numberOfPages":"402","onlineOnly":"Y","ipdsId":"IP-063921","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":355581,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5059/sir20185059_.pdf","text":"Report","size":"81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5059"},{"id":355580,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5059/coverthb_.jpg"},{"id":356222,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74J0DF5","text":"Data release","description":"USGS Data Release","linkHelpText":"SEAWAT model used to evaluate water management issues in the Santa Barbara and Foothill groundwater basins, California"}],"country":"United States","state":"California","city":"Santa Barbara","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.94299316406249,\n 34.134541681937364\n ],\n [\n -119.10278320312499,\n 34.134541681937364\n ],\n [\n -119.10278320312499,\n 35.10193405724606\n ],\n [\n -120.94299316406249,\n 35.10193405724606\n ],\n [\n -120.94299316406249,\n 34.134541681937364\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2016 (October 1, 2015, through September 30, 2016), data presented in this report were collected at 71 stations: 69 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Among the 71 stations in this report, 4 stations have data presented for additional cooperative efforts with the U.S. Army Corps of Engineers. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for these 71 stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows is presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1086","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Barr, M.N., and Bartels, K.A., 2018, Quality of surface water in Missouri, water year 2016: U.S. Geological Survey Data Series 1086, 25 p., https://doi.org/10.3133/ds1086.","productDescription":"v, 25 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-095490","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":355412,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1086/ds1086.pdf","text":"Report","size":"1.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1086"},{"id":355411,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1086/coverthb.jpg"}],"country":"United States","state":"Missouri","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.545006,36.336809],[-89.605668,36.342234],[-89.615841,36.336085],[-89.620255,36.323006],[-89.611819,36.309088],[-89.578492,36.288317],[-89.554289,36.277751],[-89.539487,36.277368],[-89.534507,36.261802],[-89.539229,36.248821],[-89.562206,36.250909],[-89.577544,36.242262],[-89.602374,36.238106],[-89.642182,36.249486],[-89.678046,36.248284],[-89.695235,36.252766],[-89.705328,36.239898],[-89.69263,36.224959],[-89.607004,36.171179],[-89.591605,36.144096],[-89.59307,36.129699],[-89.601936,36.11947],[-89.666598,36.095802],[-89.678821,36.084636],[-89.688577,36.029238],[-89.706932,36.000981],[-90.37789,35.995683],[-90.351732,36.025347],[-90.34909,36.040131],[-90.339343,36.047112],[-90.333261,36.067504],[-90.320746,36.071326],[-90.320662,36.087138],[-90.29991,36.098236],[-90.294492,36.112949],[-90.266256,36.120559],[-90.235585,36.139474],[-90.231386,36.147348],[-90.23537,36.159153],[-90.220425,36.184764],[-90.21128,36.183392],[-90.188189,36.20536],[-90.152497,36.215582],[-90.14224,36.227522],[-90.126366,36.229367],[-90.130114,36.240307],[-90.118219,36.253491],[-90.114922,36.265595],[-90.086471,36.271531],[-90.06398,36.303038],[-90.081961,36.322097],[-90.074074,36.342895],[-90.077695,36.348478],[-90.066297,36.3593],[-90.064514,36.382085],[-90.078671,36.399116],[-90.138512,36.413952],[-90.134231,36.422827],[-90.143743,36.424433],[-90.143798,36.428483],[-90.134136,36.436602],[-90.137323,36.455411],[-90.141101,36.461791],[-90.155804,36.463555],[-90.152888,36.47093],[-90.142222,36.470554],[-90.143683,36.476029],[-90.158838,36.479558],[-90.159305,36.492446],[-90.152481,36.497952],[-94.617919,36.499414],[-94.617975,37.722176],[-94.607354,39.113444],[-94.589933,39.140403],[-94.591933,39.155003],[-94.608834,39.160503],[-94.640035,39.153103],[-94.662435,39.157603],[-94.663835,39.179103],[-94.680336,39.184303],[-94.714137,39.170403],[-94.741938,39.170203],[-94.763138,39.179903],[-94.781518,39.206146],[-94.811663,39.206594],[-94.831679,39.215938],[-94.835056,39.220658],[-94.825663,39.241729],[-94.831471,39.256273],[-94.84632,39.268481],[-94.887056,39.28648],[-94.905329,39.311952],[-94.910017,39.352543],[-94.88136,39.370383],[-94.879281,39.37978],[-94.885026,39.389801],[-94.901823,39.392798],[-94.92311,39.384492],[-94.942039,39.389499],[-94.946293,39.405646],[-94.972952,39.421705],[-94.982144,39.440552],[-95.0375,39.463689],[-95.045716,39.472459],[-95.052177,39.499996],[-95.082714,39.516712],[-95.109304,39.542285],[-95.113077,39.559133],[-95.103228,39.577783],[-95.089515,39.581028],[-95.064519,39.577115],[-95.049277,39.589583],[-95.046361,39.599557],[-95.055152,39.621657],[-95.053367,39.630347],[-95.027644,39.665454],[-95.018318,39.672869],[-94.984149,39.67785],[-94.971317,39.68641],[-94.971206,39.729305],[-94.965318,39.739065],[-94.948726,39.745593],[-94.902612,39.724202],[-94.875643,39.730494],[-94.862943,39.742994],[-94.860743,39.763094],[-94.869644,39.772894],[-94.912293,39.759338],[-94.934262,39.773642],[-94.935206,39.78313],[-94.929654,39.788282],[-94.884084,39.794234],[-94.875944,39.813294],[-94.878677,39.826522],[-94.886933,39.833098],[-94.916918,39.836138],[-94.942567,39.856602],[-94.928466,39.876344],[-94.929574,39.888754],[-94.95154,39.900533],[-94.986975,39.89667],[-95.00844,39.900596],[-95.024389,39.891202],[-95.027931,39.871522],[-95.037767,39.865542],[-95.085003,39.861883],[-95.128166,39.874165],[-95.140601,39.881688],[-95.143802,39.901918],[-95.149657,39.905948],[-95.179453,39.900062],[-95.199347,39.902709],[-95.206326,39.912121],[-95.20069,39.928155],[-95.204428,39.938949],[-95.250254,39.948644],[-95.269886,39.969396],[-95.302507,39.984357],[-95.315271,40.01207],[-95.356876,40.031522],[-95.387195,40.02677],[-95.40726,40.033112],[-95.416824,40.043235],[-95.42164,40.058952],[-95.409856,40.07432],[-95.407591,40.09803],[-95.394216,40.108263],[-95.39284,40.115887],[-95.398667,40.126419],[-95.428749,40.135577],[-95.436348,40.15872],[-95.460746,40.169173],[-95.479193,40.185652],[-95.482757,40.197346],[-95.469718,40.227908],[-95.477501,40.24272],[-95.490333,40.248966],[-95.521925,40.24947],[-95.552473,40.261904],[-95.556325,40.267714],[-95.550966,40.285947],[-95.562157,40.297359],[-95.581787,40.29958],[-95.610439,40.31397],[-95.642262,40.306025],[-95.657328,40.310856],[-95.653729,40.322582],[-95.625204,40.334288],[-95.623728,40.346567],[-95.641027,40.366399],[-95.643934,40.386849],[-95.659134,40.40869],[-95.65819,40.44188],[-95.693133,40.469396],[-95.699969,40.505275],[-95.661687,40.517309],[-95.652262,40.538114],[-95.655848,40.546609],[-95.671754,40.562626],[-95.678718,40.56256],[-95.694147,40.556942],[-95.69505,40.533124],[-95.708591,40.521551],[-95.722444,40.528118],[-95.75711,40.52599],[-95.769281,40.536656],[-95.763366,40.550797],[-95.773549,40.578205],[-95.765645,40.585208],[-94.632035,40.571186],[-94.080463,40.572899],[-92.689854,40.589884],[-91.729115,40.61364],[-91.716769,40.59853],[-91.686357,40.580875],[-91.690804,40.559893],[-91.681714,40.553035],[-91.6219,40.542292],[-91.618028,40.53403],[-91.621353,40.510072],[-91.590817,40.492292],[-91.574746,40.465664],[-91.52509,40.457845],[-91.524053,40.448437],[-91.533623,40.43832],[-91.519935,40.433673],[-91.526555,40.419872],[-91.522333,40.409648],[-91.498093,40.401926],[-91.489816,40.404317],[-91.484507,40.3839],[-91.465116,40.385257],[-91.465009,40.376223],[-91.452458,40.375501],[-91.441243,40.386255],[-91.419422,40.378264],[-91.444833,40.36317],[-91.46214,40.342414],[-91.492727,40.278217],[-91.490524,40.259498],[-91.505828,40.238839],[-91.505495,40.195606],[-91.512974,40.181062],[-91.508224,40.157665],[-91.510322,40.127994],[-91.489606,40.057435],[-91.494878,40.036453],[-91.465315,39.983995],[-91.41936,39.927717],[-91.41988,39.916533],[-91.443513,39.893583],[-91.446922,39.883034],[-91.436051,39.84551],[-91.377971,39.811273],[-91.361571,39.787548],[-91.370009,39.732524],[-91.3453,39.709402],[-91.27614,39.665759],[-91.229317,39.620853],[-91.181936,39.602677],[-91.174651,39.593313],[-91.168419,39.564928],[-91.153628,39.548248],[-91.100307,39.538695],[-91.079769,39.507728],[-91.064305,39.494643],[-91.059439,39.46886],[-91.03827,39.448436],[-90.993789,39.422959],[-90.940766,39.403984],[-90.928745,39.387544],[-90.904862,39.379403],[-90.893777,39.367343],[-90.8475,39.345272],[-90.816851,39.320496],[-90.793461,39.309498],[-90.751599,39.265432],[-90.72996,39.255894],[-90.717113,39.213912],[-90.707902,39.15086],[-90.686051,39.117785],[-90.681086,39.10059],[-90.681994,39.090066],[-90.712541,39.057064],[-90.71158,39.046798],[-90.678193,38.991851],[-90.675949,38.96214],[-90.657254,38.92027],[-90.639917,38.908272],[-90.625122,38.888654],[-90.583388,38.86903],[-90.555693,38.870785],[-90.500117,38.910408],[-90.486974,38.925982],[-90.482419,38.94446],[-90.472122,38.958838],[-90.440078,38.967364],[-90.395816,38.960037],[-90.309454,38.92412],[-90.250248,38.919344],[-90.109407,38.843548],[-90.123107,38.798048],[-90.166409,38.772649],[-90.176309,38.754449],[-90.20991,38.72605],[-90.20921,38.70275],[-90.18641,38.67475],[-90.181325,38.660381],[-90.17801,38.63375],[-90.18451,38.611551],[-90.196011,38.594451],[-90.222112,38.576451],[-90.260314,38.528352],[-90.285215,38.443453],[-90.295316,38.426753],[-90.349743,38.377609],[-90.368219,38.340254],[-90.373929,38.281853],[-90.353902,38.213855],[-90.331554,38.18758],[-90.290765,38.170453],[-90.274928,38.157615],[-90.243116,38.112669],[-90.218708,38.094365],[-90.17222,38.069636],[-90.158533,38.074735],[-90.130788,38.062341],[-90.126612,38.043981],[-90.11052,38.026547],[-90.08826,38.015772],[-90.059367,38.015543],[-90.051357,38.003584],[-90.03241,37.995258],[-90.00011,37.964563],[-89.978919,37.962791],[-89.942099,37.970121],[-89.933797,37.959143],[-89.925085,37.960021],[-89.932467,37.947497],[-89.959646,37.940196],[-89.974918,37.926719],[-89.952499,37.883218],[-89.923185,37.870672],[-89.901832,37.869822],[-89.844786,37.905572],[-89.799333,37.881517],[-89.796087,37.859505],[-89.786369,37.851734],[-89.782035,37.855092],[-89.739873,37.84693],[-89.71748,37.825724],[-89.669644,37.799922],[-89.660227,37.781032],[-89.667993,37.759484],[-89.665546,37.752095],[-89.64953,37.745498],[-89.617278,37.74972],[-89.612478,37.740036],[-89.596566,37.732886],[-89.583316,37.713261],[-89.516685,37.692762],[-89.51204,37.680985],[-89.517718,37.641217],[-89.478399,37.598869],[-89.47603,37.590226],[-89.486062,37.580853],[-89.519808,37.582748],[-89.521925,37.560735],[-89.517051,37.537278],[-89.475525,37.471388],[-89.439769,37.4372],[-89.421054,37.387668],[-89.432836,37.347056],[-89.489005,37.333368],[-89.511842,37.310825],[-89.51834,37.285497],[-89.489915,37.251315],[-89.470525,37.253357],[-89.458827,37.248661],[-89.467631,37.2182],[-89.456105,37.18812],[-89.42558,37.138235],[-89.37871,37.094586],[-89.375712,37.080505],[-89.384681,37.048251],[-89.362397,37.030156],[-89.322982,37.01609],[-89.29213,36.992189],[-89.278628,36.98867],[-89.263527,37.00005],[-89.257608,37.015496],[-89.260003,37.023288],[-89.304752,37.047565],[-89.310819,37.057897],[-89.30829,37.068371],[-89.259936,37.064071],[-89.25493,37.072014],[-89.234053,37.037277],[-89.200793,37.016164],[-89.192097,36.979995],[-89.185491,36.973518],[-89.170008,36.970298],[-89.125069,36.983499],[-89.109498,36.976563],[-89.099594,36.964543],[-89.100762,36.944002],[-89.117567,36.887356],[-89.131944,36.857437],[-89.137969,36.847349],[-89.1704,36.841522],[-89.178888,36.831368],[-89.179229,36.812915],[-89.171069,36.798119],[-89.155891,36.789126],[-89.12353,36.785309],[-89.116563,36.767557],[-89.126134,36.751735],[-89.166888,36.759633],[-89.184523,36.753638],[-89.197808,36.739412],[-89.19948,36.716045],[-89.169522,36.688878],[-89.169467,36.674596],[-89.15908,36.666352],[-89.197654,36.628936],[-89.202607,36.601576],[-89.217447,36.576159],[-89.236542,36.566824],[-89.258318,36.564948],[-89.278935,36.577699],[-89.326731,36.632186],[-89.365548,36.625059],[-89.375453,36.615719],[-89.382762,36.583603],[-89.41977,36.493896],[-89.448468,36.46442],[-89.464153,36.457189],[-89.486215,36.46162],[-89.494248,36.475972],[-89.465888,36.529946],[-89.467761,36.546847],[-89.479093,36.568206],[-89.500076,36.576305],[-89.542459,36.580566],[-89.566817,36.564216],[-89.571241,36.547343],[-89.560344,36.525436],[-89.519501,36.475419],[-89.523427,36.456572],[-89.543406,36.43877],[-89.545255,36.427079],[-89.509722,36.373626],[-89.519,36.3486],[-89.545006,36.336809]]]},\"properties\":{\"name\":\"Missouri\",\"nation\":\"USA \"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Rd
Rolla, MO 65401
Condit Dam, at river kilometer 5.3 on the White Salmon River, Washington, was breached in 2011, and removed completely in 2012, providing anadromous salmonids with the opportunity to recolonize habitat blocked for nearly 100 years. Prior to dam removal, a multi-agency workgroup concluded that the preferred salmonid restoration alternative was to allow natural recolonization. Monitoring would assess fish recolonization efficacy, followed by management evaluation 5 years after dam removal. Limited monitoring of salmon and steelhead recolonization has occurred since 2011. The U.S. Geological Survey began juvenile salmonid monitoring in 2016 and did a second year during 2017, with sampling efforts like those of 2016. River conditions differed between the 2 years, both during (that is, high flows in 2017) and prior to (that is, 2015 summer drought conditions and December 2015 White Salmon River flood event) sampling. We operated a rotary screw trap at river kilometer 2.3 (3 kilometers downstream of the former dam site) from early April through early June to assess species diversity, and production of smolt and other migrant life stages. We also used backpack electrofishing during summer to assess juvenile salmonid distribution and abundance. Both sampling methods provided the opportunity to collect genetic samples (analysis of samples was not covered under funding received from the Mid-Columbia Fisheries Enhancement Group for the 2017 monitoring efforts) and to tag fish with passive integrated transponder (PIT) tags, which will provide life-history data through future recaptures and detections.
The screw trap captured steelhead (anadromous rainbow trout, Oncorhynchus mykiss), fry, parr, and smolts; coho salmon (O. kisutch) fry, parr, and smolts; and Chinook salmon (O. tshwaytscha) fry, parr, and one smolt. Prolonged high water and some missed trapping periods during 2017 prevented us from generating smolt estimates. Despite difficult trapping conditions, the number of coho salmon fry and parr, and steelhead fry and parr captured in 2017 exceeded those captured during 2016. The number of age-0 Chinook salmon captured in the screw trap during 2017 was much higher (n = 222) than in 2016 (n = 4).
Electrofishing in tributaries provided information on distribution and abundance of juvenile coho salmon and O. mykiss. Juvenile coho salmon were again found in Mill and Buck Creeks and, for the first time, in Rattlesnake Creek (all three creeks are upstream of the former dam site). In both Rattlesnake and Buck Creeks, age-0 O. mykiss abundance decreased between 2016 and 2017; however, age-1 and older O. mykiss and age-0 coho salmon abundance increased between years at both sites. Data on O. mykiss abundance at sites in Buck and Rattlesnake Creeks is providing the opportunity to begin to understand trends and variability post-dam removal and to compare to pre-dam removal periods.
Mean age-0 O. mykiss abundance (fish per meter [fish/m]) at the Rattlesnake Creek site has been slightly lower during post-dam removal (mean = 3.0, n = 2, range = 2.4–3.6) than pre-dam removal (mean = 3.4, n = 5, range = 1.5–5.1). However, the presence of juvenile coho salmon in Rattlesnake Creek during 2017 (0.5 fish/m) brought total age-0 salmonid abundance in 2017 to 2.9 fish/m. Mean age-1 or older O. mykiss abundance (fish/m) at the Rattlesnake Creek site has been lower post-dam removal (mean = 0.2, n = 2, range = 0.1–0.3) than pre-dam removal (mean = 0.5, n = 2, range = 0.3–0.8). Mean age-0 O. mykiss abundance (fish/m) at the Buck Creek site has been higher post-dam removal (mean = 2.1, n = 2, range = 1.2–3.0) than pre-dam removal (mean = 1.8, n = 2, range = 1.6–1.9). The addition of age-0 coho salmon to Buck Creek brings mean age-0 salmonid abundance post-dam removal to 2.7 fish/m (range = 1.9–3.4). Mean age-1 or older O. mykiss abundance (fish/m) in Buck Creek has been slightly higher post-dam removal (mean = 0.8, n = 2, range = 0.6–1.1) than pre-dam removal (mean = 0.6, n = 2, both years 0.6).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181106","collaboration":"Prepared in cooperation with the Mid-Columbia Fisheries Enhancement Group","usgsCitation":"Jezorek, I.G., and Hardiman, J.M., 2018, Juvenile salmonid monitoring following removal of Condit Dam in the White Salmon River watershed, Washington, 2017: U.S. Geological Survey Open-File Report 2018-1106, 31 p. https://doi.org/10.3133/ofr20181106.","productDescription":"vi, 31 p.","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-094796","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":355554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1106/ofr20181106.pdf","text":"Report","size":"874 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1106"},{"id":355553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1106/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Condit Dam, White Salmon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.21466064453125,\n 45.64668833372338\n ],\n [\n -121.09680175781249,\n 45.64668833372338\n ],\n [\n -121.09680175781249,\n 46.47191632087041\n ],\n [\n -122.21466064453125,\n 46.47191632087041\n ],\n [\n -122.21466064453125,\n 45.64668833372338\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
Groundwater-quality data were collected from 502 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and are included in this report. Most of the wells (500) were sampled from January through December 2015, and 2 of them were sampled in 2013. The data were collected from five types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; and vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI], and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release. Some data from environmental samples collected in 2013 and quality-control samples collected in 2014 also are included in the associated data release; these data are associated with networks described in this report and have not been published previously.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1087","usgsCitation":"Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., and Belitz, K., 2018, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014: U.S. Geological Survey Data Series 1087, 68 p., https://doi.org/10.3133/ds1087.","productDescription":"Report: ix, 67 p.; Data Release","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091701","costCenters":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":355481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1087/coverthb.jpg"},{"id":355482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1087/ds1087.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1087"},{"id":355483,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XK8DHK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January through December 2015 and Previously Unpublished Data from 2013–2014"}],"country":"United States","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 N. Goodwin
Urbana, IL 61801
The U.S. Geological Survey and Bureau of Reclamation collected water-quality data from March 2012 to March 2015 at locations in the Lost River and Klamath River Basins, Oregon, in an effort to characterize water quality and compute a nutrient budget for the Bureau of Reclamation Klamath Reclamation Project. The study described in this report resulted in the following significant findings:
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Cyanobacteria cause a multitude of water-quality concerns, including the potential to produce toxins and taste-and-odor compounds that may cause substantial economic and public health concerns, and are of particular interest in lakes, reservoirs, and rivers that are used for drinking-water supply. Extensive cyanobacterial blooms typically do not develop in the Kansas River; however, reservoirs in the lower Kansas River Basin occasionally develop blooms that may affect downstream water quality. During July 2012 through September 2016, continuous and (or) discrete water-quality data were collected at several sites (Wamego, De Soto, and three main reservoir-fed tributaries) on the Kansas River to characterize the sources, frequency and magnitude of occurrence, and causes of cyanobacteria, cyanobacterial toxins, and taste-and-odor compounds and to develop a real-time notification system of changing water-quality conditions that may affect drinking-water treatment.
Algal biomass, as estimated by chlorophyll, was consistently higher at the downstream De Soto site than the upstream Wamego site. Higher algal biomass at the De Soto site likely was caused by algal growth during downstream transport without major losses due to grazing by aquatic organisms or other processes. Algal biomass at the Wamego and De Soto sites was negatively correlated with streamflow and total and bioavailable nutrient concentrations. The negative association between algal biomass and nutrients in the Kansas River likely reflects the relatively strong positive association between nutrient concentrations and streamflows.
Cyanobacteria were relatively common in the Kansas River but rarely dominated the algal community. Like overall algal biomass, cyanobacterial abundances typically were higher at the De Soto site than the Wamego site. Cyanobacterial abundances generally peaked in late summer or early fall (July through October), with smaller peaks occasionally observed in spring (April through May). Cyanobacteria in the Kansas River rarely exceeded 20,000 cells per milliliter, the abundance at which cyanobacteria may become a concern for drinking-water treatment. Relations between cyanobacterial abundance and streamflow, turbidity, and nutrients in the Kansas River were similar to those between chlorophyll and total phytoplankton abundance, indicating the same processes that influence overall algal biomass and dynamics also are influencing cyanobacteria.
The cyanotoxin microcystin was detected in about 27 percent of the samples collected from Kansas River tributary and main-stem sites. Cylindrospermopsin was detected in one sample from the De Soto site. Microcystin occurrence and concentration were similar between the Wamego and De Soto sites. Concentrations exceeded the U.S. Environmental Protection Agency health advisory guidance values for finished drinking water of 0.3 (for bottle-fed infants and pre-school children) and 1.6 micrograms per liter (μg/L; for school-age children and adults) in 6 percent or less of samples collected. These guidance values are for finished drinking water and are not directly applicable to observed environmental concentrations but do provide a benchmark for comparison. Microcystin was detected most often and had the highest concentrations during summer. Though seasonal patterns in microcystin occurrence were generally consistent, seasonal maxima varied by an order of magnitude across years.
The taste-and-odor compounds geosmin and 2-methylisoborneol (MIB) were detected in about 78 and 43 percent of samples, respectively, collected across all sites (main stem and tributaries). Geosmin and MIB occurrence and concentration varied considerably between the Wamego and De Soto sites. Geosmin was detected in about 67 percent of Wamego samples and 81 percent of De Soto samples. The human detection threshold of 5 nanograms per liter (ng/L) was exceeded for geosmin in about 11 and 17 percent of the samples collected at the Wamego and De Soto sites, respectively. Geosmin was detected during all months of the year at both sites, and there were no clear seasonal patterns. MIB was detected less frequently in the Kansas River than geosmin and was observed in about 42 percent of Wamego samples and 33 percent of De Soto samples. Concentrations exceeded 5 ng/L in about 7 and 5 percent of samples from the Wamego and De Soto sites, respectively. As observed for geosmin, there were no clear seasonal patterns in MIB occurrence or concentration.
There seems to be a connection between microcystin detections in the Kansas River and occurrence of microcystin in upstream reservoirs (and tributary streams). Microcystin concentrations greater than 0.3 μg/L may be likely during the summer when streamflow is less than 3,000 cubic feet per second (ft3/s) and contributions from Milford Lake exceed about 30 percent of total flow in the Kansas River. Observed microcystin concentrations typically were higher at the De Soto site than the Wamego or tributary sites during 2012 through 2016, indicating cyanobacteria may continue to grow and produce microcystin once introduced to the Kansas River.
The spatial and temporal patterns in geosmin and MIB occurrence and concentration were more complex than microcystin. There were no clear connections between geosmin and MIB occurrence in the Kansas River and potential upstream reservoir (or tributary stream) sources. Likewise, there was not a clear relation between algal biomass, cyanobacteria, or actinomycetes bacteria and taste-and-odor events in the Kansas River. Geosmin and MIB were not strongly correlated with any measured environmental variable at either Kansas River site.
Continuous water-quality data may be used independently or in combination with regression models to provide information on changing water-quality conditions that may affect drinking-water treatment processes or recreational activities on the Kansas River. For example, logistic regression model outputs and continuous water-quality data may both be indicative of the potential for microcystin events. Logistic regression models that are estimating a high probability of microcystin occurrence at concentrations above 0.1 μg/L can be used as one indicator. Streamflows less than 3,000 ft3/s during upstream reservoir releases during periods with low turbidity and increased chlorophyll fluorescence, specific conductance, and pH values may also be indicative of microcystin events. Advanced or near-real-time notification may inform proactive, rather than reactive, management strategies when water-quality conditions are changing rapidly or are likely to cause cyanobacteria-related events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185089","collaboration":"Prepared in cooperation with the Kansas Water Office, the City of Lawrence, the City of Olathe, the City of Topeka, and Johnson County WaterOne","usgsCitation":"Graham, J.L., Foster, G.M., Williams, T.J., Mahoney, M.D., May, M.R., and Loftin, K.A., 2018, Water-quality conditions with an emphasis on cyanobacteria and associated toxins and taste-and-odor compounds in the Kansas River, Kansas, July 2012 through September 2016: U.S. Geological Survey Scientific Investigations Report 2018–5089, 55 p., https://doi.org/10.3133/sir20185089.","productDescription":"Report: vi, 54 p.; 6 Appendixes; 2 Data Releases","numberOfPages":"66","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-091849","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":355473,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EVITTP","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Phytoplankton data for the Kansas River and tributaries, July 2012 through February 2017"},{"id":355474,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P973V4A9","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Discrete water-quality data for the Kansas River and tributaries, July 2012 - September 2016"},{"id":355471,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix5.pdf","text":"Appendix 5","size":"239kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 5"},{"id":355465,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5089/coverthb2.jpg"},{"id":355472,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix6.pdf","text":"Appendix 6","size":"701 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 6"},{"id":355466,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089.pdf","text":"Report","size":"3.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089"},{"id":355467,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix1.pdf","text":"Appendix 1","size":"365 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 1"},{"id":355468,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix2.pdf","text":"Appendix 2","size":"370 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 2"},{"id":355469,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix3.pdf","text":"Appendix 3","size":"377 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 3"},{"id":355470,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix4.pdf","text":"Appendix 4","size":"372 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 4"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97,\n 38.5\n ],\n [\n -94.6307373046875,\n 38.5\n ],\n [\n -94.6307373046875,\n 40\n ],\n [\n -97,\n 40\n ],\n [\n -97,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The Colorado River Basin (CRB) drains 246,000 square miles and includes parts of California, Colorado, Nevada, New Mexico, Utah, and Wyoming, and all of Arizona (Basin States). This report contains water-use estimates by category of use for drainage basins (Hydrologic Unit Code 8; HUC‑8) within the CRB from 1985 to 2010, at 5-year intervals. Estimates for public supply, domestic, commercial, industrial, irrigation, livestock, mining, aquaculture, hydroelectric and thermoelectric power, and wastewater returns are tabulated as (1) water withdrawals from groundwater or surface‑water sources of fresh or saline quality, (2) water delivered for domestic use, (3) wastewater returns and instream use (hydroelectric), and (4) consumptive use, or water that is consumed (USGS definition) and not available for immediate reuse. Water transported outside of the CRB (interbasin transfers) is not included as part of withdrawals and are not accounted for in any category of use within the CRB.
Total withdrawals in the CRB (excluding interbasin transfers) averaged about 17 million acre-feet (maf) from 1985 to 2010, peaked at about 17.76 maf in 2000, and reached their lowest levels of 16.43 maf in 1990. Interbasin transfers to serve mostly public-supply and irrigation needs outside of the CRB are reported for 2000, 2005, and 2010 only, and averaged 5.40 maf. More surface water was used in the CRB than groundwater, averaging about 78 percent of total withdrawals, and its use increased less than 2 percent from 1985 to 2010, while groundwater withdrawals decreased about 12 percent. From 1985 to 2010, surface water averaged 98 percent of withdrawals in the upper CRB, and about 59 percent in the lower CRB. Nearly all withdrawals were freshwater, but some saline groundwater was used for mining and self-supplied industrial.
Interbasin transfers have a large effect on flows in the Colorado River and are listed in this report separately with no explanation of how the water is used outside of the CRB. There are 34 interbasin transfers that conveyed an estimated 5.83, 5.20, and 5.18 maf out of the CRB in 2000, 2005, and 2010, respectively. The largest interbasin transfers are in the lower CRB and convey surface water (Colorado River water) to southern California; these accounted for 80 to 84 percent of total interbasin transfers in the CRB from 2000 to 2010. Intrabasin transfers are conveyances of surface water that cross drainage basin or State boundaries in the CRB, but the water does not leave the CRB. There are many intrabasin transfers in the CRB, but this report lists 11 that are mostly in the State of Colorado. The largest is the Central Arizona Project (CAP), through which more than 1.00 maf of water was provided to irrigate nearly 1 million acres in Maricopa, Pinal, and Pima Counties, as well as provide municipal water for Phoenix and Tucson, Arizona, during 2000, 2005, and 2010. In 2010, interbasin and intrabasin transfers accounted for 24 and 11 percent of the total water withdrawals in CRB, respectively, with the larger volumes being conveyed out of the lower CRB.
Total population in the CRB increased from 4.56 to 9.44 million people from 1985 to 2010. Most of those people were in the lower CRB, with 86 percent of the total in 1985, and 90 percent of the total in 2010. Total public-supply withdrawals in the CRB provided most people with their potable water, and averaged about 1.63 maf from 1985 to 2010, ranging from about 1.07 maf in 1985 to about 2.10 maf in 2010, when it peaked. Most of public-supply withdrawals occurred in the lower CRB, ranging from 87 to 91 percent of total public-supply withdrawals in the CRB over the 25 years. Total domestic use, comprised of public-supply deliveries and self-supply domestic withdrawals, increased more than 90 percent from 1985 to 2010, from about 0.80 maf to about 1.54 maf. Domestic daily per-capita use rates in the CRB ranged from about 144 (1985) to about 121 (2000) gallons (gal) per‑capita between 1985 and 2010. When comparing domestic daily per-capita rates for the upper and lower CRB, people in the lower CRB, on average, used less water for domestic purposes at 128 gal per-capita daily (1985–2010), while those in the upper CRB for the same time period averaged 133 gal per-capita daily. The trend in daily per-capita use rates for the entire CRB fluctuated between the reporting years, but decreased overall, indicating that more people used less water in 2010 than in 1985, likely due to improved infrastructure, conservation, and improvements to water using appliances in homes and businesses.
Irrigation accounted for most total withdrawals in the CRB, excluding instream use for hydroelectric power and interbasin transfers, averaging 85 percent from 1985 to 2010. Far more surface water than groundwater was used for irrigation in both the upper and lower CRB, but in the upper CRB, it accounted for an average of more than 98 percent of the total withdrawals (1985–2010), whereas in the lower CRB, surface-water withdrawals for irrigation averaged 61 percent of total withdrawals. On average, the upper CRB accounted for 56 percent of total irrigated acres, and the irrigation systems in the upper CRB trended towards more efficient sprinkler systems from 1985 to 2010. Long-term drought in the CRB substantially decreased the amount of streamflow available for irrigation. Increases in micro-irrigation acres, which can have efficiencies that exceed 90 percent and require 20–50 percent less water than sprinkler systems, likely contributed to reduced withdrawals in the lower CRB.
For thermoelectric power, total withdrawals, including the use of reclaimed wastewater, were greater in the upper CRB from 1985 through 2005. In 2010, the lower CRB exceeded the upper by only 11,000 acre-feet. On average, thermoelectric consumptive use accounted for about 80 percent of the total withdrawals; however, consumptive-use data in the upper CRB was incomplete. Surface water was the primary source in the upper CRB and groundwater was the primary source in the lower CRB. In the CRB overall, water withdrawals for thermoelectric generation has decreased since 2000, except for groundwater withdrawals in the lower CRB. Power generation at thermoelectric plants was greater in the upper CRB from 1985 to 2000, and after 2005 the difference in power generation was small; however, the upper CRB continued to have more power generation. In both the upper and lower CRB, power generation increased from 1985 to 2005.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185049","usgsCitation":"Maupin, M.A., Ivahnenko, T., and Bruce, B., 2018, Estimates of water use and trends in the Colorado River Basin, Southwestern United States, 1985–2010: U.S. Geological Survey Scientific Investigations Report 2018–5049, 61 p., https://doi.org/10.3133/sir20185049.","productDescription":"Report: ix, 61 p.; Data release","numberOfPages":"75","onlineOnly":"Y","ipdsId":"IP-074683","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":354013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5049/sir20185049.pdf","text":"Report","size":"18.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5049"},{"id":354012,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5049/coverthb.jpg"},{"id":355338,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P84B5G","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Dataset of estimated use of water by subbasin (HUC8) in the Colorado River Basin, Southwestern United States, 1985-2010"}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.82910156249999,\n 32.45415593941475\n ],\n [\n -110.9619140625,\n 31.316101383495624\n ],\n [\n -109.072265625,\n 31.316101383495624\n ],\n [\n -108.52294921875,\n 31.466153715024294\n ],\n [\n -108.4130859375,\n 31.80289258670676\n ],\n [\n -108.3251953125,\n 31.93351676190369\n ],\n [\n -108.08349609375,\n 32.045332838858506\n ],\n [\n -107.490234375,\n 33.706062655101206\n ],\n [\n -107.7978515625,\n 34.08906131584994\n ],\n [\n -107.7978515625,\n 34.77771580360469\n ],\n [\n -107.46826171874999,\n 35.35321610123823\n ],\n [\n -106.4794921875,\n 35.460669951495305\n ],\n [\n -105.77636718749999,\n 35.460669951495305\n ],\n [\n -105.46875,\n 35.44277092585766\n ],\n [\n -105.16113281249999,\n 36.049098959065645\n ],\n [\n -105.1171875,\n 36.61552763134925\n ],\n [\n -105.1171875,\n 36.914764288955936\n ],\n [\n -105.1171875,\n 37.61423141542417\n ],\n [\n -104.87548828125,\n 38.048091067457236\n ],\n [\n -104.96337890625,\n 38.70265930723801\n ],\n [\n -104.91943359374999,\n 39.14710270770074\n ],\n [\n -105.31494140625,\n 39.9434364619742\n ],\n [\n -105.31494140625,\n 40.53050177574321\n ],\n [\n -105.27099609375,\n 40.79717741518766\n ],\n [\n -105.09521484375,\n 40.93011520598305\n ],\n [\n -105.16113281249999,\n 41.44272637767212\n ],\n [\n -105.75439453125,\n 41.49212083968776\n ],\n [\n -104.80957031249999,\n 42.342305278572816\n ],\n [\n -105.05126953124999,\n 42.53689200787315\n ],\n [\n -105.556640625,\n 42.73087427928485\n ],\n [\n -106.3037109375,\n 42.90816007196054\n ],\n [\n -107.09472656249999,\n 42.633958722673135\n ],\n [\n -107.70996093749999,\n 42.261049162113856\n ],\n [\n -108.9404296875,\n 42.61779143282346\n ],\n [\n -109.09423828125,\n 43.004647127794435\n ],\n [\n -109.35791015625,\n 43.37311218382002\n ],\n [\n -110.10498046875,\n 43.78695837311561\n ],\n [\n -110.654296875,\n 43.83452678223682\n ],\n [\n -110.830078125,\n 43.46886761482925\n ],\n [\n -110.98388671874999,\n 43.11702412135048\n ],\n [\n -111.09374999999999,\n 42.374778361114195\n ],\n [\n -111.11572265625,\n 42.16340342422401\n ],\n [\n -111.77490234375,\n 41.75492216766298\n ],\n [\n -112.03857421875,\n 41.178653972331674\n ],\n [\n -111.9287109375,\n 40.212440718286466\n ],\n [\n -111.9287109375,\n 39.791654835253425\n ],\n [\n -112.1044921875,\n 39.470125122358176\n ],\n [\n -112.2802734375,\n 39.07890809706475\n ],\n [\n -112.67578124999999,\n 38.54816542304656\n ],\n [\n -112.74169921875,\n 38.09998264736481\n ],\n [\n -112.8515625,\n 37.87485339352928\n ],\n [\n -113.04931640625,\n 37.75334401310656\n ],\n [\n -113.31298828125,\n 37.47485808497102\n ],\n [\n -113.5546875,\n 37.28279464911045\n ],\n [\n -113.88427734374999,\n 37.07271048132943\n ],\n [\n -113.99414062499999,\n 36.98500309285596\n ],\n [\n -114.47753906249999,\n 37.16031654673677\n ],\n [\n -114.71923828124999,\n 37.735969208590504\n ],\n [\n -115.00488281250001,\n 38.35888785866677\n ],\n [\n -115.15869140624999,\n 38.8225909761771\n ],\n [\n -115.51025390625,\n 39.16414104768742\n ],\n [\n -115.77392578125,\n 39.16414104768742\n ],\n [\n -116.16943359374999,\n 39.027718840211605\n ],\n [\n -116.3232421875,\n 38.35888785866677\n ],\n [\n -115.6640625,\n 37.10776507118514\n ],\n [\n -116.5869140625,\n 36.94989178681327\n ],\n [\n -116.76269531249999,\n 36.56260003738545\n ],\n [\n -116.08154296875001,\n 35.764343479667176\n ],\n [\n -115.20263671874999,\n 35.38904996691167\n ],\n [\n -114.80712890625,\n 35.08395557927643\n ],\n [\n -114.60937499999999,\n 34.70549341022544\n ],\n [\n -114.54345703125,\n 34.07086232376631\n ],\n [\n -114.80712890625,\n 33.8521697014074\n ],\n [\n -114.80712890625,\n 33.578014746143985\n ],\n [\n -114.89501953124999,\n 33.17434155100208\n ],\n [\n -114.76318359375,\n 32.93492866908233\n ],\n [\n -114.82910156249999,\n 32.45415593941475\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
We reexamine the geometry of the causative fault structure of the 1989 moment-magnitude-6.9 Loma Prieta earthquake in central California, using seismic-reflection, earthquake-hypocenter, and magnetic data. Our study is prompted by recent interpretations of a two-part dip of the San Andreas Fault (SAF) accompanied by a flower-like structure in the Coachella Valley, in southern California. Initially, the prevailing interpretation of fault geometry in the vicinity of the Loma Prieta earthquake was that the mainshock did not rupture the SAF, but rather a secondary fault within the SAF system, because network locations of aftershocks defined neither a vertical plane nor a fault plane that projected to the surface trace of the SAF. Subsequent waveform cross-correlation and double-difference relocations of Loma Prieta aftershocks appear to have clarified the fault geometry somewhat, with steeply dipping faults in the upper crust possibly connecting to the more moderately southwest-dipping mainshock rupture in the middle crust. Examination of steep-reflection data, extracted from a 1991 seismic-refraction profile through the Loma Prieta area, reveals three robust fault-like features that agree approximately in geometry with the clusters of upper-crustal relocated aftershocks. The subsurface geometry of the San Andreas, Sargent, and Berrocal Faults can be mapped using these features and the aftershock clusters. The San Andreas and Sargent Faults appear to dip northeastward in the uppermost crust and change dip continuously toward the southwest with depth. Previous models of gravity and magnetic data on profiles through the aftershock region also define a steeply dipping SAF, with an initial northeastward dip in the uppermost crust that changes with depth. At a depth 6 to 9 km, upper-crustal faults appear to project into the moderately southwest-dipping, planar mainshock rupture. The change to a planar dipping rupture at 6–9 km is similar to fault geometry seen in the Coachella Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181093","usgsCitation":"Zhang, E., Fuis, G.S., Catchings, R.D., Scheirer, D.S., Goldman, M., and Bauer, K., 2018, Reexamination of the subsurface fault structure in the vicinity of the 1989 moment-magnitude-6.9 Loma Prieta earthquake, central California, using steep-reflection, earthquake, and magnetic data: U.S. Geological Survey Open-File Report 2018–1093, 35 p., https://doi.org/10.3133/ofr20181093.","productDescription":"v; 35 p.","onlineOnly":"Y","ipdsId":"IP-097280","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":355009,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1093/coverthb.jpg"},{"id":355010,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1093/ofr20181093.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1093"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.92214965820311,\n 36.97128966642495\n ],\n [\n -121.75804138183594,\n 36.97128966642495\n ],\n [\n -121.75804138183594,\n 37.2\n ],\n [\n -121.92214965820311,\n 37.2\n ],\n [\n -121.92214965820311,\n 36.97128966642495\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
https://earthquake.usgs.gov/
As questions regarding the influence of increasing urbanization on water quality in the Edwards aquifer are raised, a better understanding of the sources, fate, and transport of compounds of concern in the aquifer—in particular, nutrients and pesticides—is needed to improve water management decision-making capabilities. The U.S. Geological Survey, in cooperation with the San Antonio Water System, performed a study from 2010 to 2016 to better understand how water quality changes under a range of hydrologic conditions and in contrasting land-cover settings (rural and urban) in the Edwards aquifer. The study design included continuous hydrologic monitoring, continuous water-quality monitoring, and discrete sample collection for a detailed characterization of water quality at a network of sites throughout the aquifer system. The sites were selected to encompass a “source-to-sink” (that is, from aquifer recharge to aquifer discharge) approach. Network sites were selected to characterize rainfall, recharging surface water, and groundwater; groundwater sites included wells in the unconfined part of the aquifer (unconfined wells) and in the confined part of the aquifer (confined wells) and a major discharging spring. Storm-related samples—including rainfall samples, stormwater-runoff (surface-water) samples, and groundwater samples—were collected to characterize the aquifer response to recharge.
Elevated nitrate concentrations relative to national background values and the widespread detection of pesticides indicate that the Edwards aquifer is vulnerable to contamination and that vulnerability is affected by factors such as land cover, aquifer hydrogeology, and changes in hydrologic conditions. Greater vulnerability of groundwater in urban areas relative to rural areas was evident from results for urban groundwater sites, which generally had higher nitrate concentrations, elevated δ15N-nitrate values, a greater diversity of pesticides, and higher pesticide concentrations. The continuum of water quality from unconfined rural groundwater sites (least affected by anthropogenic contamination) to unconfined urban groundwater sites (most affected by anthropogenic contamination) demonstrates enhanced vulnerability of urban versus rural land cover. Differences in contaminant occurrences and concentration among unconfined urban wells indicate that the urban parts of the aquifer are not uniformly vulnerable, but rather are affected by spatial differences in the sources of nutrients and pesticides. In urban areas, the shallow, unconfined groundwater sites showed greater temporal variability in both nutrient and pesticide concentrations, as well as a greater degree of contamination, than did deeper, confined groundwater sites. In comparison to that of the shallow, unconfined groundwater sites, the water quality of the deeper, confined groundwater sites was relatively invariant during this multiyear study. Although aquifer hydrogeology is an important factor related to aquifer vulnerability, land cover likely has a greater influence on pesticide contamination of groundwater. Temporal variability in hydrologic conditions for the Edwards aquifer is apparent in data for surface water as a source of groundwater recharge, water-level altitude in wells, spring discharge, and groundwater quality. This temporal variability affects recharge sources, recharge amounts, groundwater traveltimes, flow routing, water-rock interaction processes, dilution, mixing, and, in turn, water quality. Relations of land cover, aquifer hydrogeology, and changing hydrologic conditions to water quality are complex but provide insight into the vulnerability of Edwards aquifer groundwater—a vital drinking-water resource.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185060","collaboration":"Prepared in cooperation with the San Antonio Water System","usgsCitation":"Opsahl, S.P., Musgrove, M., Mahler, B.J., and Lambert, R.B., 2018, Water-quality observations of the San Antonio segment of the Edwards aquifer, Texas, with an emphasis on processes influencing nutrient and pesticide geochemistry and factors affecting aquifer vulnerability, 2010–16: U.S. Geological Survey Scientific Investigations Report 2018–5060, 67 p., https://doi.org/10.3133/sir20185060.","productDescription":"Report: viii, 67 p.; Data release","numberOfPages":"79","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-093387","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":354772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5060/sir20185060.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5060"},{"id":354771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5060/coverthb.jpg"},{"id":354786,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KP81C5","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-Quality Data Characterizing the San Antonio Segment of the Edwards Aquifer With an Emphasis on Processes Influencing Nutrient and Pesticide Concentrations and Factors Affecting Aquifer Vulnerability, 2010-2016"}],"country":"United States","state":"Texas","otherGeospatial":"Edwards Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-98.4138,29.9442],[-98.2986,30.0395],[-98.2197,30.2335],[-98.1793,30.3395],[-98.1732,30.356],[-97.7131,30.0229],[-97.7659,29.9791],[-97.7763,29.9679],[-97.7891,29.9599],[-97.7995,29.9459],[-97.8161,29.9371],[-97.8599,29.91],[-97.897,29.8819],[-97.9008,29.8554],[-97.8966,29.8558],[-97.8934,29.8566],[-97.8924,29.8575],[-97.8918,29.8584],[-97.8907,29.8598],[-97.8902,29.8612],[-97.8896,29.8616],[-97.888,29.8625],[-97.8838,29.8615],[-97.8786,29.8591],[-97.9354,29.8185],[-97.9478,29.8091],[-97.9823,29.7726],[-97.9996,29.7537],[-98.0895,29.6857],[-98.2045,29.6363],[-98.3124,29.5973],[-98.3115,29.5886],[-98.308,29.5816],[-98.303,29.5664],[-98.3,29.5613],[-98.2937,29.5599],[-98.2891,29.5571],[-98.2813,29.5514],[-98.2663,29.5429],[-98.259,29.5414],[-98.2563,29.5423],[-98.2563,29.5432],[-98.2573,29.5451],[-98.2594,29.5456],[-98.2626,29.5465],[-98.2636,29.5484],[-98.2645,29.5521],[-98.2645,29.5544],[-98.2613,29.5557],[-98.2587,29.5561],[-98.2571,29.5547],[-98.2577,29.5524],[-98.2562,29.5501],[-98.252,29.55],[-98.2509,29.5514],[-98.2503,29.5546],[-98.2471,29.5568],[-98.2393,29.5572],[-98.2341,29.5548],[-98.231,29.5502],[-98.2295,29.5488],[-98.2253,29.5487],[-98.2232,29.5473],[-98.2222,29.5454],[-98.2233,29.5445],[-98.2248,29.5441],[-98.227,29.5441],[-98.2296,29.5446],[-98.2307,29.5424],[-98.2302,29.541],[-98.2281,29.54],[-98.226,29.5377],[-98.2245,29.534],[-98.2247,29.528],[-98.2205,29.5252],[-98.2132,29.5242],[-98.2013,29.518],[-98.2008,29.5157],[-98.2019,29.5139],[-98.202,29.5121],[-98.2004,29.5098],[-98.1978,29.5097],[-98.1968,29.5097],[-98.1947,29.5092],[-98.1921,29.5078],[-98.1906,29.506],[-98.1896,29.5036],[-98.1913,29.4968],[-98.1903,29.4922],[-98.1894,29.489],[-98.1858,29.4866],[-98.1795,29.486],[-98.1794,29.4879],[-98.1799,29.4902],[-98.183,29.4921],[-98.1861,29.493],[-98.185,29.4958],[-98.1798,29.4966],[-98.174,29.4956],[-98.1704,29.4946],[-98.1699,29.4918],[-98.17,29.49],[-98.1706,29.485],[-98.1702,29.4809],[-98.1676,29.4785],[-98.1629,29.4784],[-98.1571,29.4779],[-98.1498,29.4778],[-98.1466,29.4795],[-98.146,29.4818],[-98.1455,29.4836],[-98.1439,29.4845],[-98.1413,29.4845],[-98.1408,29.4822],[-98.1372,29.4803],[-98.1314,29.4811],[-98.1298,29.4825],[-98.1261,29.4815],[-98.1252,29.4778],[-98.1232,29.4727],[-98.1202,29.4644],[-98.1193,29.4607],[-98.1214,29.4585],[-98.124,29.459],[-98.1266,29.4586],[-98.1288,29.4559],[-98.1278,29.4531],[-98.1253,29.4503],[-98.1237,29.4484],[-98.1238,29.4461],[-98.1259,29.4457],[-98.128,29.4457],[-98.1301,29.4458],[-98.1322,29.4445],[-98.1349,29.4422],[-98.1381,29.4368],[-98.4083,29.1104],[-98.8042,29.2513],[-98.8039,29.0884],[-99.4107,29.087],[-99.6813,29.0872],[-100.1119,29.0844],[-100.6686,29.0834],[-100.6704,29.0889],[-100.6713,29.0916],[-100.6771,29.1003],[-100.6798,29.1058],[-100.681,29.1072],[-100.6824,29.109],[-100.6892,29.1121],[-100.6906,29.1128],[-100.6931,29.114],[-100.695,29.1148],[-100.7012,29.1166],[-100.707,29.1189],[-100.7143,29.1221],[-100.7227,29.1284],[-100.7327,29.1343],[-100.7333,29.1348],[-100.7374,29.138],[-100.7395,29.1416],[-100.7385,29.1453],[-100.7388,29.1475],[-100.7391,29.149],[-100.7412,29.1517],[-100.7464,29.1544],[-100.7532,29.1553],[-100.7579,29.1566],[-100.7621,29.1584],[-100.7622,29.1585],[-100.7647,29.1612],[-100.766,29.1637],[-100.7663,29.1644],[-100.7685,29.1676],[-100.7711,29.1694],[-100.7719,29.1696],[-100.7753,29.1703],[-100.7779,29.1716],[-100.7789,29.1739],[-100.7769,29.1776],[-100.7738,29.1799],[-100.7707,29.1818],[-100.7681,29.1864],[-100.7682,29.1923],[-100.7687,29.1955],[-100.7689,29.1957],[-100.7713,29.1983],[-100.773,29.1985],[-100.774,29.1987],[-100.7776,29.2001],[-100.7792,29.2028],[-100.7793,29.2065],[-100.7788,29.2115],[-100.7793,29.2152],[-100.782,29.2211],[-100.7822,29.2216],[-100.7836,29.2248],[-100.7873,29.228],[-100.7904,29.2289],[-100.7936,29.2284],[-100.7946,29.2281],[-100.7972,29.2274],[-100.7993,29.2288],[-100.7993,29.232],[-100.7985,29.2351],[-100.7978,29.2375],[-100.7968,29.2398],[-100.7901,29.2449],[-100.7859,29.2468],[-100.7796,29.2473],[-100.7754,29.2432],[-100.7691,29.2396],[-100.7639,29.2368],[-100.7597,29.2387],[-100.7597,29.241],[-100.7603,29.2428],[-100.7629,29.246],[-100.764,29.2474],[-100.7635,29.2502],[-100.7614,29.2529],[-100.7588,29.2557],[-100.7567,29.2557],[-100.7531,29.2553],[-100.7494,29.2548],[-100.7474,29.2585],[-100.7453,29.2645],[-100.7454,29.2709],[-100.7428,29.2783],[-100.7403,29.2852],[-100.7409,29.2907],[-100.7467,29.2966],[-100.7488,29.2993],[-100.7488,29.3039],[-100.7473,29.308],[-100.7452,29.3126],[-100.7448,29.3186],[-100.7453,29.3227],[-100.7453,29.3264],[-100.7438,29.3296],[-100.7386,29.331],[-100.7323,29.332],[-100.724,29.333],[-100.7177,29.3362],[-100.7125,29.3399],[-100.711,29.3436],[-100.7096,29.3592],[-100.7086,29.3757],[-100.7062,29.3982],[-100.7052,29.4018],[-100.7016,29.4065],[-100.7006,29.4097],[-100.7001,29.4161],[-100.6981,29.4216],[-100.6992,29.6236],[-100.6999,30.2899],[-100.1183,30.2897],[-100.0347,30.287],[-99.7576,30.2882],[-99.3032,30.289],[-99.3034,30.1398],[-98.9217,30.139],[-98.5896,30.1375],[-98.4138,29.9442]]]},\"properties\":{\"name\":\"Bandera\",\"state\":\"TX\"}}]}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754
Sustaining the quality of the Nation’s water resources and the health of our diverse ecosystems depends on the availability of sound water-resources data and information to develop effective, science-based policies. Effective management of water resources also brings more certainty and efficiency to important economic sectors. Taken together, these actions lead to immediate and longterm economic, social, and environmental benefits that make a difference to the lives of the almost 400 million people projected to live in the United States by 2050.
In 1991, Congress established the U.S. Geological Survey National Water-Quality Assessment (NAWQA) to address where, when, why, and how the Nation’s water quality has changed, or is likely to change in the future, in response to human activities and natural factors. Since then, NAWQA has been a leading source of scientific data and knowledge used by national, regional, state, and local agencies to develop science-based policies and management strategies to improve and protect water resources used for drinking water, recreation, irrigation, energy development, and ecosystem needs. Plans for the third decade of NAWQA (2013–23) address priority water-quality issues and science needs identified by NAWQA stakeholders, such as the Advisory Committee on Water Information and the National Research Council, and are designed to meet increasing challenges related to population growth, increasing needs for clean water, and changing land-use and weather patterns.
This report is one of a series of publications, The Quality of Our Nation’s Waters, which describes major findings of the NAWQA Project on water-quality issues of regional and national concern and provides science-based information for assessing and managing the quality of our groundwater resources. Other reports in this series focus on occurrence and distribution of nutrients, pesticides, and volatile organic compounds in streams and groundwater, the effects of contaminants and stream-flow alteration on the condition of aquatic communities in streams, and on the quality of groundwater from private domestic and public supply wells. Each reports builds toward a more comprehensive understanding of the quality of regional and national water resources. All NAWQA reports are available online (https://water.usgs.gov/nawqa/bib/).
We hope this publication will provide you with insights and information to meet your water-resource needs and will foster increased citizen awareness and involvement in the protection and restoration of our Nation’s waters. The information in this report is intended primarily for those interested or involved in resource management and protection, conservation, regulation, and policymaking at the regional and national levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1433","collaboration":"National Water-Quality ProgramNational Water-Quality Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
We investigated the benthic foraminiferal faunal and stable carbon and oxygen isotopic composition of a 15-cm push core (NA075-092b) obtained on a Telepresence-Enabled cruise to the Southeast Seep on Kimki Ridge offshore southern California. The seep core was taken at a depth of 973 m in the vicinity of a Beggiatoa bacterial mat and vesicomyid clams (Calyptogena) and compared to previously published data of living assemblages from ~ 714 m, four reference cores obtained at ~ 1030 m, and another one at 739 m. All of the reference sites are also from the Inner Continental Borderland but with no evidence of methane seepage.
No endemic species were found at the seep site and most of the taxa recovered there have been reported previously from other seep or low oxygen environments. Q- and R-mode cluster analyses clearly illustrated differences in the faunal assemblages of the seep and non-seep sites. The living assemblage at Southeast Seep was characterized by abundant Takayanagia delicata, Cassidulina translucens, and Spiroplectammina biformis, whereas the non-seep San Pedro Basin reference assemblage was comprised primarily of Chilostomella oolina and Globobulimina pacifica. Density and species richness were lower at the seep site compared to the non-seep site, reflecting the harsher living conditions there. The dead assemblage at the seep site was dominated by Gyroidina turgida compared to Cassidulina translucens at the ~ 1030 m non-seep site and Cassidulina translucens, Pseudoparrella pacifica, and Takayanagia delicata at the 739 m non-seep site. Density was three times lower at Southeast Seep than at the non-seep sites of comparable water depth but species richness was ~ 30% higher. Stable carbon isotopic values were considerably depleted in the seep samples compared to the non-seep samples, with a progression from lightest to heaviest average δ13C values evident at the seep site reflecting microhabitat preference and vital effect: the deep infaunal species of Globobulimina, the shallow infaunal species Uvigerina peregrina, the epifaunal species Cibicidoides wuellerstorfi, and the shallow infaunal but aragonite-shelled species Hoeglundina elegans. The δ13C values downcore among each benthic species indicates ongoing fluid seepage through at least the last 3800 cal yr B.P. at Southeast Seep. Besides the continual local seepage, evidence from δ13C values of planktic foraminifera in the seep core suggest two pulses of methane (at 3000 and 3700 cal yr B.P.) were released that were large enough to influence much of the water column. Paired benthic and planktic foraminiferal stable oxygen isotope records provide evidence that there were no paleoenvironmental changes such as increased bottom-water temperature or changes in oxygen isotopic composition of bottom and pore waters during this 3800-year record to induce the methane releases. Instead, Southeast Seep appears to be the result of local faulting providing pathways for fluid to flow to the seafloor at a fault stepover or transpressional bend in the regional strike-slip system.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2018.01.011","usgsCitation":"McGann, M., and Conrad, J.E., 2018, Faunal and stable isotopic analyses of benthic foraminifera from the Southeast Seep on Kimki Ridge offshore southern California, USA: Deep Sea Research Part II: Topical Studies in Oceanography, v. 150, p. 92-117, https://doi.org/10.1016/j.dsr2.2018.01.011.","productDescription":"26 p.","startPage":"92","endPage":"117","ipdsId":"IP-091301","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2018.01.011","text":"Publisher Index Page"},{"id":354758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kimki Ridge","volume":"150","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e571e4b060350a15d16d","contributors":{"authors":[{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":737319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":737320,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70197435,"text":"tm2A14 - 2018 - Monitoring riparian-vegetation composition and cover along the Colorado River downstream of Glen Canyon Dam, Arizona","interactions":[],"lastModifiedDate":"2018-06-06T10:52:17","indexId":"tm2A14","displayToPublicDate":"2018-06-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2-A14","title":"Monitoring riparian-vegetation composition and cover along the Colorado River downstream of Glen Canyon Dam, Arizona","docAbstract":"Vegetation in the riparian zone (the area immediately adjacent to streams, such as stream banks) along the Colorado River downstream of Glen Canyon Dam, Arizona, supports many ecosystem and societal functions. In both Glen Canyon and Grand Canyon, this ecosystem has changed over time in response to flow alterations, invasive species, and recreational use. Riparian-vegetation cover and composition are likely to continue to change as these pressures persist and new ones emerge. Because this system is a valuable resource that is known to change in response to flow regime and other disturbances, a long-term monitoring protocol has been designed with three primary objectives:
A protocol for the long-term monitoring of riparian vegetation is described in detail and standard operating procedures are included herein for all tasks. Visual estimates of foliar and ground covers are collected in conjunction with environmental measurements to assess correlations of foliar cover with abiotic and flow variables. Sample quadrats are stratified by frequency of inundation, geomorphic feature, and by river segment to account for differences in vegetation type. Photographs of sites are also taken to illustrate qualitative characteristics of the site at the time of sampling. Procedures for field preparation, generating random samples, data collection, data management, collecting and managing unknown species collections, and reporting are also described. Although this protocol is intended to be consistent over the long-term, procedures for minor and major revisions to the protocol are also outlined.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Biological science in Book 2: Collection of environmental data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A14","collaboration":"Prepared in cooperation with the Bureau of Reclamation Glen Canyon Adaptive Management Program","usgsCitation":"Palmquist, E.C., Ralston, B.E., Sarr, D.A., and Johnson, T.C., 2018, Monitoring riparian-vegetation composition and cover along the Colorado River downstream of Glen Canyon Dam, Arizona: U.S. Geological Survey Techniques and Methods, book 2, chap. A14, 65 p., https://doi.org/10.3133/tm2A14.","productDescription":"ix, 65 p.","numberOfPages":"79","onlineOnly":"Y","ipdsId":"IP-071203","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":354697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a14/tm2a14.pdf","text":"Report","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods 2-A14"},{"id":354696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a14/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114,\n 35.5\n ],\n [\n -111.5,\n 35.5\n ],\n [\n -111.5,\n 37\n ],\n [\n -114,\n 37\n ],\n [\n -114,\n 35.5\n ]\n ]\n ]\n }\n }\n ]\n}","publicComments":"This report is Chapter 14 of Section A: Biological science in Book 2: Collection of environmental data.","contact":"SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Semi-quantitative mussel surveys, conducted by the U.S. Geological Survey and the Delaware Riverkeeper Network in cooperation with The Nature Conservancy, were completed in the vicinity of the Columbia Dam, on the Paulins Kill, New Jersey, in August 2017 in order to document the mussel species composition and relative abundance prior to removal of the dam. Surveys were conducted from the Brugler Road Bridge downriver approximately 2,000 meters (m) to the Columbia Dam and downriver from the dam about 300 m to 75 m upriver from the confluence of the Paulins Kill with the Delaware River. Sixteen sections (average length=175 m) were surveyed by personnel snorkeling or SCUBA diving; 13 sections were upriver from the dam, and 3 were downriver from the dam. Mussels, as they were encountered by surveyors, were removed from the sediment, immediately identified to species, and replaced in their original collection locations. Habitat data were collected for each surveyed section. Upriver and downriver from the dam, river margins with dense vegetation were examined for mussels by personnel using snorkels in transects (approximately 25 meters) perpendicular to river flow every 50 m on both sides of the river. Only two species were found upriver from the dam, and those were present in relatively low numbers. Catch per unit effort is reported here within parentheses as the average across upriver sections in number of mussels per person hour of survey time: 42 Elliptio complanata (2.6) and 1 Pyganodon cataracta (0.1) were found upriver from the dam. No mussels were found in the dense vegetation either upriver or downriver of the dam by surveyors using snorkels. Significantly higher species richness and mussel catch per unit effort were found downriver from the dam than upriver, including 106 E. complanta (32.5), 27 Utterbackiana implicata (8.2), 1 Alasmidonta undulata (0.4), 2 Lampsilis cariosa (0.5), 6 Lampsilis radiata (2.1), 4 P. cataracta (1.1), and 1 Strophitus undulatus (0.4). The average habitat assessment score did not differ upriver and downriver from the dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181074","collaboration":"Prepared in cooperation with The Nature Conservancy","usgsCitation":"Galbraith, H.S., Blakeslee, C.J., Cole, J.C., and Silldorff, E.L., 2018, Freshwater mussel survey for the Columbia Dam removal, Paulins Kill, New Jersey: U.S. Geological Survey Open-File Report 2018–1074, 7 p., https://doi.org/10.3133/ofr20181074.","productDescription":"v, 7 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094047","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":354676,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1074/ofr20181074.pdf","text":"Report","size":"9.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1074"},{"id":354675,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1074/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Columbia Dam, Paulins Kill","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.0889778137207,\n 40.9203876084737\n ],\n [\n -75.06837844848633,\n 40.9203876084737\n ],\n [\n -75.06837844848633,\n 40.937896253014145\n ],\n [\n -75.0889778137207,\n 40.937896253014145\n ],\n [\n -75.0889778137207,\n 40.9203876084737\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
An area of great importance to resource management and conservation biology in the Klamath Basin is balancing water usage against the life history requirements of threatened Coho Salmon. One tool for addressing this topic is a freshwater dynamics model to forecast Coho Salmon productivity based on environmental inputs. Constructing such a forecasting tool requires local data to quantify the unique life history processes of Coho Salmon inhabiting this region. Here, we describe analytical methods for estimating a series of sub-models, each capturing a different life history process, which will eventually be synchronized as part of a freshwater dynamics model for Klamath River Coho Salmon. Specifically, we draw upon extensive population monitoring data collected in the basin to estimate models of freshwater productivity, overwinter survival, and migration patterns. Our models of freshwater productivity indicated that high summer temperatures and high winter flows can both adversely affect smolt production and that such relationships are more likely in tributaries with naturally regulated flows due to substantial intraannual environmental variation. Our models of overwinter survival demonstrated extensive variability in survival among years, but not among rearing locations, and demonstrated that a substantial proportion (~ 20%) of age-0+ fish emigrate from some rearing sites in the winter. Our models of migration patterns indicated that many age-0+ fish redistribute in the basin during the summer and winter. Further, we observed that these redistributions can entail long migrations in the mainstem where environmental stressors likely play a role in cueing refuge entry. Finally, our models of migration patterns indicated that changes in discharge are important in cueing the seaward migration of smolts, but that the nature of this behavioral response can differ dramatically between tributaries with naturally and artificially regulated flows. Collectively, these analyses demonstrate that environmental variation interacts with most phases of the freshwater life history of Klamath River Coho Salmon and that anthropogenic environmental variation can have a particularly large bearing on productivity.
","language":"English","publisher":"U.S. Fish and Wildlife Service, Arcata Fish and Wildlife Office","usgsCitation":"Manhard, C.V., Som, N.A., Perry, R.W., Faukner, J., and Soto, T., 2018, Estimating freshwater productivity, overwinter survival, and migration patterns of Klamath River Coho Salmon: Arcata Fisheries Technical Report TR 2018-33, x, 74 p.","productDescription":"x, 74 p.","numberOfPages":"84","ipdsId":"IP-088669","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":354933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354702,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/arcata/fisheries/reports/technical/2018/EstimatingFreshwaterProductivityOverwinterSurvivalandMigrationPatternsofKlamathRiverCohoSalmon.pdf"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1ab","contributors":{"authors":[{"text":"Manhard, Christopher V.","contributorId":203911,"corporation":false,"usgs":false,"family":"Manhard","given":"Christopher","email":"","middleInitial":"V.","affiliations":[{"id":36754,"text":"U.S. Fish and Wildlife Service, California Cooperative Fish and Wildlife Research Unit, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, USA","active":true,"usgs":false}],"preferred":false,"id":737180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Som, Nicholas A.","contributorId":203773,"corporation":false,"usgs":false,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[{"id":36713,"text":"Statistician, USFWS - Arcata Fisheries Program, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":737181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":737179,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faukner, Jimmy","contributorId":205405,"corporation":false,"usgs":false,"family":"Faukner","given":"Jimmy","email":"","affiliations":[{"id":37098,"text":"Yurok Tribal Fisheries Program","active":true,"usgs":false}],"preferred":false,"id":737182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soto, Toz","contributorId":205406,"corporation":false,"usgs":false,"family":"Soto","given":"Toz","email":"","affiliations":[{"id":37099,"text":"Karuk Tribe Fisheries Program","active":true,"usgs":false}],"preferred":false,"id":737183,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195691,"text":"ofr20181031 - 2018 - Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15 ","interactions":[],"lastModifiedDate":"2018-05-31T09:44:13","indexId":"ofr20181031","displayToPublicDate":"2018-05-31T09:15:00","publicationYear":"2018","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":"2018-1031","title":"Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15 ","docAbstract":"This report was written as a collaborative effort between the U.S. Geological Survey, SilvaCarbon, and Wageningen University with funding provided by the U.S. Agency for International Development and the European Space Agency, respectively, to address a pressing need for enhanced result-based monitoring and evaluation of delivered capacity-building activities. For this report, the capacity-building activities delivered by capacity-building providers (referred to as “providers” hereafter) during 2011–15 (the study period) to support countries in building measurement, reporting, and verification (MRV) systems for reducing emissions from deforestation and forest degradation (REDD+) were assessed and evaluated.
Summarizing capacity-building activities and outcomes across multiple providers was challenging. Many of the providers did not have information readily available, which precluded them from participating in this study despite the usefulness of their information. This issue led to a key proposed future action: Capacity-building providers could establish a central repository within the Global Forestry Observation Initiative (GFOI; http://www.gfoi.org/) where data from past, current, and future activities of all capacity-building providers could be stored. The repository could be maintained in a manner to continually learn from previous lessons.
Although various providers monitored and evaluated the success of their capacity-building activities, such evaluations only assessed the success of immediate outcomes and not the overarching outcomes and impacts of activities implemented by multiple providers. Good monitoring and evaluation should continuously monitor and periodically evaluate all factors affecting the outcomes of a provided capacity-building activity.
The absence of a methodology to produce quantitative evidence of a causal link between multiple capacity-building activities delivered and successful outcomes left only a plausible association. A previous publication argued that plausible association, although not a precise measurement of cause and effect, was a realistic tool. Our review of the available literature on this subject did not find another similar assessment to assess capacity-building activities for supporting the countries in building MRV system for REDD+.
Four countries from the main forested regions of Africa, the Americas, and Asia were chosen as subjects for this report based on the length of time SilvaCarbon and other providers have provided capacity-building activities toward MRV system for REDD+: Colombia (the Americas), the Democratic Republic of the Congo (DRC; Africa), Peru (the Americas), and the Republic of the Philippines (referred to as “the Philippines” hereafter; Asia).
Several providers were contacted for information to include in this report, but, because of various constraints, only SilvaCarbon, the Food and Agriculture Organization of the United Nations (FAO), and the World Wildlife Fund (WWF) participated in this study. These three providers supported various targeted capacity-building activities through-out Africa, the Americas, and Asia, including the following: technical workshops at national and regional levels (referred to as “workshops” hereafter), hands on training, study tours, technical details by experts, technical consultation between providers and recipients, sponsorship for travel, organizing network meetings, developing sampling protocols, assessing deforestation and degradation drivers, estimating carbon stock and flow, designing monitoring systems for multiple uses, promoting public-private partnerships to scale up investments on MRV system for REDD+, and assisting with the design of national forest monitoring systems.
Their activities were planned in coordination with key partners in each country and region and with the support and assistance of other providers. Note that several other organizations and institutions assisted the providers to deliver capacity-building activities, including Boston University, Conservation International, Stanford University, University of Maryland, and Wageningen University & Research.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181031","collaboration":"Prepared in cooperation with Wageningen University, the U.S. Agency for International Development, the U.S. Department of State, and the European Space Agency ","usgsCitation":"Peneva-Reed, E.I., and Romijn, J.E, 2018, Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15: U.S. Geological Survey Open-File Report 2018–1031, 35 p., https://doi.org/10.3133/ofr20181031. ","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088895","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":354567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1031/ofr20181031.pdf","text":"Report","size":"1.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1031"},{"id":354566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1031/coverthb.jpg"}],"contact":"Director, U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey developed a groundwater-flow model for the uppermost principal aquifer systems in the Williston Basin in parts of Montana, North Dakota, and South Dakota in the United States and parts of Manitoba and Saskatchewan in Canada as part of a detailed assessment of the groundwater availability in the area. The assessment was done because of the potential for increased demands and stresses on groundwater associated with large-scale energy development in the area. As part of this assessment, a three-dimensional groundwater-flow model was developed as a tool that can be used to simulate how the groundwater-flow system responds to changes in hydrologic stresses at a regional scale.
The three-dimensional groundwater-flow model was developed using the U.S. Geological Survey’s numerical finite-difference groundwater model with the Newton-Rhapson solver, MODFLOW–NWT, to represent the glacial, lower Tertiary, and Upper Cretaceous aquifer systems for steady-state (mean) hydrological conditions for 1981‒2005 and for transient (temporally varying) conditions using a combination of a steady-state period for pre-1960 and transient periods for 1961‒2005. The numerical model framework was constructed based on existing and interpreted hydrogeologic and geospatial data and consisted of eight layers. Two layers were used to represent the glacial aquifer system in the model; layer 1 represented the upper one-half and layer 2 represented the lower one-half of the glacial aquifer system. Three layers were used to represent the lower Tertiary aquifer system in the model; layer 3 represented the upper Fort Union aquifer, layer 4 represented the middle Fort Union hydrogeologic unit, and layer 5 represented the lower Fort Union aquifer. Three layers were used to represent the Upper Cretaceous aquifer system in the model; layer 6 represented the upper Hell Creek hydrogeologic unit, layer 7 represented the lower Hell Creek aquifer, and layer 8 represented the Fox Hills aquifer. The numerical model was constructed using a uniform grid with square cells that are about 1 mile (1,600 meters) on each side with a total of about 657,000 active cells.
Model calibration was completed by linking Parameter ESTimation (PEST) software with MODFLOW–NWT. The PEST software uses statistical parameter estimation techniques to identify an optimum set of input parameters by adjusting individual model input parameters and assessing the differences, or residuals, between observed (measured or estimated) data and simulated values. Steady-state model calibration consisted of attempting to match mean simulated values to measured or estimated values of (1) hydraulic head, (2) hydraulic head differences between model layers, (3) stream infiltration, and (4) discharge to streams. Calibration of the transient model consisted of attempting to match simulated and measured temporally distributed values of hydraulic head changes, stream base flow, and groundwater discharge to artesian flowing wells. Hydraulic properties estimated through model calibration included hydraulic conductivity, vertical hydraulic conductivity, aquifer storage, and riverbed hydraulic conductivity in addition to groundwater recharge and well skin.
The ability of the numerical model to accurately simulate groundwater flow in the Williston Basin was assessed primarily by its ability to match calibration targets for hydraulic head, stream base flow, and flowing well discharge. The steady-state model also was used to assess the simulated potentiometric surfaces in the upper Fort Union aquifer, the lower Fort Union aquifer, and the Fox Hills aquifer. Additionally, a previously estimated regional groundwater-flow budget was compared with the simulated steady-state groundwater-flow budget for the Williston Basin. The simulated potentiometric surfaces typically compared well with the estimated potentiometric surfaces based on measured hydraulic head data and indicated localized groundwater-flow gradients that were topographically controlled in outcrop areas and more generalized regional gradients where the aquifers were confined. The differences between the measured and simulated (residuals) hydraulic head values for 11,109 wells were assessed, which indicated that the steady-state model generally underestimated hydraulic head in the model area. This underestimation is indicated by a positive mean residual of 11.2 feet for all model layers. Layer 7, which represents the lower Hell Creek aquifer, is the only layer for which the steady-state model overestimated hydraulic head. Simulated groundwater-level changes for the transient model matched within plus or minus 2.5 feet of the measured values for more than 60 percent of all measurements and to within plus or minus 17.5 feet for 95 percent of all measurements; however, the transient model underestimated groundwater-level changes for all model layers. A comparison between simulated and estimated base flows for the steady-state and transient models indicated that both models overestimated base flow in streams and underestimated annual fluctuations in base flow.
The estimated and simulated groundwater budgets indicate the model area received a substantial amount of recharge from precipitation and stream infiltration. The steady-state model indicated that reservoir seepage was a larger component of recharge in the Williston Basin than was previously estimated. Irrigation recharge and groundwater inflow from outside the Williston Basin accounted for a relatively small part of total groundwater recharge when compared with recharge from precipitation, stream infiltration, and reservoir seepage. Most of the estimated and simulated groundwater discharge in the Williston Basin was to streams and reservoirs. Simulated groundwater withdrawal, discharge to reservoirs, and groundwater outflow in the Williston Basin accounted for a smaller part of total groundwater discharge.
The transient model was used to simulate discharge to 571 flowing artesian wells within the model area. Of the 571 established flowing artesian wells simulated by the model, 271 wells did not flow at any time during the simulation because hydraulic head was always below the land-surface altitude. As hydraulic head declined throughout the simulation, 68 of these wells responded by ceasing to flow by the end of 2005. Total mean simulated discharge for the 571 flowing artesian wells was 55.1 cubic feet per second (ft3/s), and the mean simulated flowing well discharge for individual wells was 0.118 ft3/s. Simulated discharge to individual flowing artesian wells increased from 0.039 to 0.177 ft3/s between 1961 and 1975 and decreased to 0.102 ft3/s by 2005. The mean residual for 34 flowing wells with measured discharge was 0.014 ft3/s, which indicates the transient model overestimated discharge to flowing artesian wells in the model area.
Model limitations arise from aspects of the conceptual model and from simplifications inherent in the construction and calibration of a regional-scale numerical groundwater-flow model. Simplifying assumptions in defining hydraulic parameters in space and hydrologic stresses and time-varying observational data in time can limit the capabilities of this tool to simulate how the groundwater-flow system responds to changes in hydrologic stresses, particularly at the local scale; nevertheless, the steady-state model adequately simulated flow in the uppermost principal aquifer systems in the Williston Basin based on the comparison between the simulated and estimated groundwater-flow budget, the comparison between simulated and estimated potentiometric surfaces, and the results of the calibration process.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175158","collaboration":"Water Availability and Use Science Program","usgsCitation":"Davis, K.W., and Long, A.J., 2018, Construction and calibration of a groundwater-flow model to assess groundwater availability in the uppermost principal aquifer systems of the Williston Basin, United States and Canada: U.S. Geological Survey Scientific Investigations Report 2017–5158, 70 p., https://doi.org/10.3133/sir20175158.","productDescription":"Report: ix, 70; Appendixes 1-2; Data Release","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080007","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":354478,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01CZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used to assess groundwater availability in the uppermost principal aquifer systems of the Williston structural basin, United States and Canada"},{"id":354477,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158.pdf","text":"Report","size":"97.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5158"},{"id":354510,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158_appendix_1.xlsx","text":"Appendix Table 1","size":"1.77 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5158 Appendix 1"},{"id":354511,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158_appendix_2.xlsx","text":"Appendix Table 2","size":"25.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5158 Appendix 2"},{"id":354476,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5158/coverthb2.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.3359375,\n 42.35854391749705\n ],\n [\n -97.734375,\n 42.35854391749705\n ],\n [\n -97.734375,\n 49.89463439573421\n ],\n [\n -109.3359375,\n 49.89463439573421\n ],\n [\n -109.3359375,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
South Dakota Office
U.S. Geological Survey
1608 Mountain View Rd.
Rapid City, SD 57702
**September 28, 2018: The purpose of a USGS Open-file report (OFR) is dissemination of information that must be released immediately to fill a public need or information that is not sufficiently refined to warrant publication in one of the other USGS series. As part of that refinement process, an error was discovered in one of the input data sets of the Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM) that this OFR was based upon. The error involved the assignment of storage properties to “phantom cells.”
Phantom cells are required for most variants of MODFLOW that use a structured finite-difference grid when individual stratigraphic layers are represented as separate layers. Using phantom cells is a common practice that allows separate model layers to be maintained without having to combine stratigraphic layers into equivalent model layers or to use an unstructured grid. Typically, phantom cell horizontal hydraulic conductivities and storage properties are set to a small number and vertical hydraulic conductivities are set to a number large enough to allow vertical flow between the vertically adjacent layers.
In the RGTIHM, the specific storage properties of the phantom cells for the upper (RGTIHM layers 3 and 4), middle (RGTIHM layers 5 and 6), and lower (RGTIHM layers 7 and 8) members of the Santa Fe Group were inadvertently assigned a value of 1 feet-1. The revision of these specific storage values to a small number (1.0 x 10-09 feet-1) required additional trial-and-error model calibration and a new sensitivity analysis. After calibration, the overall model fit remained similar to the fit described in the OFR, but the fit for many individual features such as project water available for diversions at the American Canal and Acequia Madre improved due to the reduction in flow coming from lower layers. Overall, there is still an average net depletion of groundwater flow, and the conclusions of the report are not changed. The revised average annual groundwater flow depletion simulated for the period 1953-2014 is -1,480 acre-feet/year for the entire model region, and -3,660 acre-feet/year for the portion of the model in the United States. The final version of the model will be the basis of the USGS Scientific Investigations Report that will supersede this OFR. An updated Model Archive of RGTIHM is available upon request to the USGS California Water Science Center.
The corrected version of the model WAS the basis for the USGS Scientific Investigations Report that SUPERSEDED this Open-File Report.**
Changes in population, agricultural development and practices (including shifts to more water-intensive crops), and climate variability are increasing demands on available water resources, particularly groundwater, in one of the most productive agricultural regions in the Southwest—the Rincon and Mesilla Valley parts of Rio Grande Valley, Doña Ana and Sierra Counties, New Mexico, and El Paso County, Texas. The goal of this study was to produce an integrated hydrological simulation model to help evaluate water-management strategies, including conjunctive use of surface water and groundwater for historical conditions, and to support long-term planning for the Rio Grande Project. This report describes model construction and applications by the U.S. Geological Survey, working in cooperation and collaboration with the Bureau of Reclamation.
This model, the Rio Grande Transboundary Integrated Hydrologic Model, simulates the most important natural and human components of the hydrologic system, including selected components related to variations in climate, thereby providing a reliable assessment of surface-water and groundwater conditions and processes that can inform water users and help improve planning for future conditions and sustained operations of the Rio Grande Project (RGP) by the Bureau of Reclamation. Model development included a revision of the conceptual model of the flow system, construction of a Transboundary Rio Grande Watershed Model (TRGWM) water-balance model using the Basin Characterization Model (BCM), and construction of an integrated hydrologic flow model with MODFLOW-One-Water Hydrologic Flow Model (referred to as One Water). The hydrologic models were developed for and calibrated to historical conditions of water and land use, and parameters were adjusted so that simulated values closely matched available measurements (calibration). The calibrated model was then used to assess the use and movement of water in the Rincon Valley, Mesilla Basin, and northern part of the Conejos-Médanos Basin, with the entire region referred to as the “Transboundary Rio Grande” or TRG. These tools provide a means to understand hydrologic system response to the evolution of water use in the region, its availability, and potential operational constraints of the RGP.
The conceptual model identified surface-water and groundwater inflows and outflows that included the movement and use of water both in natural and in anthropogenic systems. The groundwater-flow system is characterized by a layered geologic sedimentary sequence combined with the effects of groundwater pumping, operation of the RGP, natural runoff and recharge, and the application of irrigation water at the land surface that is captured and reused in an extensive network of canals and drains as part of the conjunctive use of water in the region.
Historical groundwater-level fluctuations followed a cyclic pattern that were aligned with climate cycles, which collectively resulted in alternating periods of wet or dry years. Periods of drought that persisted for one or more years are associated with low surface-water availability that resulted in higher rates of groundwater-level decline. Rates of groundwater-level decline also increased during periods of agricultural intensification, which necessitated increasing use of groundwater as a source of irrigation water. Agriculture in the area was initially dominated by alfalfa and cotton, but since 1970 more water-intensive pecan orchards and vegetable production have become more common. Groundwater levels substantially declined in subregions where drier climate combined with increased demand, resulting in periods of reduced streamflows.
Most of the groundwater was recharged in the Rio Grande Valley floor, and most of the pumpage and aquifer storage depletion was in Mesilla Basin agricultural subregions. A cyclic imbalance between inflows and outflows resulted in the modeled cyclic depletion (groundwater withdrawals in excess of natural recharge) of the groundwater basin during the 75-year simulation period of 1940–2014. Changes in groundwater storage can vary considerably from year to year, depending on land use, pumpage, and climate conditions. Climatic drivers of wet and dry years can greatly affect all inflows, outflows, and water use. Although streamflow and, to a minor extent, precipitation during inter-decadal wet-year periods replenished the groundwater historically, contemporary water use and storage depletion could have reduced the effects of these major recharge events. The average net groundwater flow-rate deficit for 1953–2014 was estimated to be about 8,990 acre-feet per year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181091","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hanson, R.T., Ritchie, A.B., Boyce, S.E., Galanter, A.E., Ferguson, I.A., Flint, L.E., and Henson, W.R., 2018, Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico: U.S Geological Survey Open-File Report 2018–1091, 185 p., https://doi.org/10.3133/ofr20181091.","productDescription":"Report: x, 185 p.; Dataset; Data release; Errata","numberOfPages":"200","onlineOnly":"Y","ipdsId":"IP-071162","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":354790,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1091/ofr20181091.pdf","text":"Report","size":"25 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":354791,"rank":2,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.3133/ofr20181091","linkHelpText":"- This Open-File report (OFR) was superseded by USGS Scientific Investigations report (SIR) SIR 2019-5120. The final model archive will be available on the national USGS archive site."},{"id":357946,"rank":4,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/of/2018/1091/erratum.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"}},{"id":363155,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J9NYND","linkHelpText":"Digital hydrologic and geospatial data for the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico"},{"id":354795,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1091/coverthb_.jpg"}],"country":"Mexico, United States","state":"New Mexico, Northern Chihuahua, Texas","otherGeospatial":"Rio Grande","publicComments":"This Open-File report (OFR) will be superseded by a USGS Scientific Investigations report (SIR) once the USGS Techniques and Methods report (T&M) documenting the numerical code is published. Once the SIR is released, the final model archive will be available on the national USGS archive site. For the interim archive for this model, please contact CaWSC for directions on downloading 916-278-3026.","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Nuclear research activities at the U.S. Department of Energy (DOE) Idaho National Laboratory (INL) in eastern Idaho produced radiochemical and chemical wastes that were discharged to the subsurface, resulting in detectable concentrations of some waste constituents in the eastern Snake River Plain (ESRP) aquifer. These waste constituents may pose risks to the water quality of the aquifer. In order to understand these risks to water quality the U.S. Geological Survey, in cooperation with the DOE, conducted a study of groundwater geochemistry to improve the understanding of hydrologic and chemical processes in the ESRP aquifer at and near the INL and to understand how these processes affect waste constituents in the aquifer.
Geochemistry data were used to identify sources of recharge, mixing of water, and directions of groundwater flow in the ESRP aquifer at the INL. The geochemistry data were analyzed from 167 sample sites at and near the INL. The sites included 150 groundwater, 13 surface-water, and 4 geothermal-water sites. The data were collected between 1952 and 2012, although most data collected at the INL were collected from 1989 to 1996. Water samples were analyzed for all or most of the following: field parameters, dissolved gases, major ions, dissolved metals, isotope ratios, and environmental tracers.
Sources of recharge identified at the INL were regional groundwater, groundwater from the Little Lost River (LLR) and Birch Creek (BC) valleys, groundwater from the Lost River Range, geothermal water, and surface water from the Big Lost River (BLR), LLR, and BC. Recharge from the BLR that may have occurred during the last glacial epoch, or paleorecharge, may be present at several wells in the southwestern part of the INL. Mixing of water at the INL primarily included mixing of surface water with groundwater from the tributary valleys and mixing of geothermal water with regional groundwater. Additionally, a zone of mixing between tributary valley water and regional groundwater, trending southwesterly, extended from near the northeastern boundary of the INL to the southern boundary of the INL. Groundwater flow directions for regional groundwater were southwesterly, and flow directions for tributary groundwater were southeasterly upon entering the ESRP, but eventually began to flow southwesterly in a direction parallel with regional groundwater.
Several discrepancies were identified from comparison of sources of recharge determined from geochemistry data and backward particle tracking with a groundwater-flow model. Some discrepancies observed in the particle tracking results included representation of recharge from BC near the north INL boundary, groundwater from the BC valley not extending far enough south, regional groundwater that extends too far west in the southern part of the INL, and no representation of recharge from geothermal water in model layer 1 or recharge from the BLR in the southwestern part of the INL.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1837A","collaboration":"DOE/ID-22246Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
A 3-year study was undertaken to evaluate the suitability of the available modeling tools for characterizing environmental flows in the middle Verde River watershed of central Arizona, describe riparian vegetation throughout the watershed, and estimate sediment mobilization in the river. Existing data on fish and macroinvertebrates were analyzed in relation to basin characteristics, flow regimes, and microhabitat, and a pilot study was conducted that sampled fish and macroinvertebrates and the microhabitats in which they were found. The sampling for the pilot study took place at five different locations in the middle Verde River watershed. This report presents the results of this 3-year study.
The Northern Arizona Groundwater Flow Model (NARGFM) was found to be capable of predicting long-term changes caused by alteration of regional recharge (such as may result from climate variability) and groundwater pumping in gaining, losing, and dry reaches of the major streams in the middle Verde River watershed. Over the period 1910 to 2006, the model simulated an increase in dry reaches, a small increase in reaches losing discharge to the groundwater aquifer, and a concurrent decrease in reaches gaining discharge from groundwater. Although evaluations of the suitability of using the NARGFM and Basin Characteristic Model to characterize various streamflow intervals showed that smallerscale basin monthly runoff could be estimated adequately at locations of interest, monthly stream-flow estimates were found unsatisfactory for determining environmental flows.
Orthoimagery and Moderate Resolution Imaging Spectroradiometer data were used to quantify stream and riparian vegetation properties related to biotic habitat. The relative abundance of riparian vegetation varied along the main channel of the Verde River. As would be expected, more upland plant species and fewer lowland species were found in the upper-middle section compared to the lower-middle section, and vice-versa. Vegetation changes within the upper-middle and lower-middle reaches are related to differences in climate and hydrology. In general, the riparian vegetation of the middle Verde River watershed is that of a healthy ecosystem’s mixed age, mixed patch structure, likely a result of the mostly unaltered disturbance regime.
The frequency of in-river hydrogeomorphic features (pool, riffle, run) varied along the middle Verde River channel. There was a greater abundance of riffle habitat in the upper-middle reach; the lower-middle reach included more pool habitat. The Oak Creek tributary was more homogenous in geomorphic stream habitat composition than West Clear Creek, where runs dominated the upper reaches and pools dominated many of the lower reaches.
On the basis of the period of record and discharges recorded at 15-minute intervals, five flows were found to reach the gravel-transport threshold. Sediment mobilization computed with flows averaged over daily time steps yielded just three flows that reached the gravel-transport threshold, and monthly averaged flows yielded none. In the middle Verde River watershed, 15-minute data should be used when possible to evaluate sediment transport in the river system.
Data from more than 300 fish surveys conducted from 1992 to 2011 were analyzed using two schemes, one that divided the river into five reaches based on basin characteristics, and a second that divided the river into five reaches based on degree of flow alteration (specifically, diversions). Fish community metrics and assemblage data were used to analyze patterns of species composition and abundance in the two approaches. Overall, native and non-native species were regularly interacting and probably competing for similar resources. Fish abundances were also analyzed in response to floods and other flow metrics. Although the data are limited, native fish abundances increased more rapidly than non-native fish abundances in response to large floods. The basin-characteristic reach analysis showed native fish in greater abundance in the upper-middle reaches of the Verde River watershed and generally decreasing with downstream distance. The median relative abundance of native fish decreased by 50 percent from reach 1 to reach 5. Using the reach scheme based on degree of flow alteration, nondiverted reaches were found to have a greater abundance of native fish than diverted reaches. In heavily diverted reaches, non-native species outnumbered native species.
Fish metrics and stream-flow metrics for the 30, 90, and 365-day periods before collection were computed and the results analyzed statistically. Only abundance of all fish species was associated with the 30-day flow metrics. The 90-day flow metrics were generally positively associated with fish metrics, whereas the 365-day flow metrics had more negative correlations. In particular, significant relations were found between fish metrics and the magnitude and frequency of high flows, including maximum monthly flow, median annual number of high-flow events, and median annual maximum streamflow. Native sucker (Catostomidae) populations tended to decrease in periods of extended base flow, and fish in the non-native sunfish family (Centrarchidae) decreased in periods of flashy, high magnitude flows.
A pilot study surveyed fish at five locations in the upper part of the middle Verde River watershed as a means to measure microhabitat availability and quantify native and non-native fish use of that available microhabitat. Results indicated that native and non-native species exhibit some clear differences in microhabitat use. Although at least some native and non-native fish were found in each velocity, depth, and substrate category, preferential microhabitat use was common. On a percentage basis, non-native species had a strong preference for slow-moving and deeper water with silt and sand substrate, with a secondary preference for faster moving and very shallow water and a coarse gravel substrate. Native species showed a general preference for somewhat faster, moderate depth water over coarse gravel and had no clear secondary preference.
Macroinvertebrate-variables index period, high-flow year, and collection location (upper-middle Verde River, lowermiddle Verde River, or Verde River tributaries) were found to be important explanatory variables in differentiating among community metrics. Overall richness (number of unique taxa), Shannon’s diversity index, and the percent of the most dominant taxa were all highly correlated, but their response to each macroinvertebrate variable was different. The percentage of mayfly (order Ephemeroptera) taxa was significantly higher in Oak Creek and the upper-middle and lower-middle Verde River reaches, locations which have higher flows and more urbanization than other reaches. When community metrics were related to hydrologic metrics, caddisfly (order Trichoptera) populations appeared to increase and mayfly populations to decrease in response to less flashy and more stable streamflows. Conversely, caddisfly populations appeared to decrease and mayfly populations to increase in response to greater flow variability.
Six locations along the Verde River were sampled for macroinvertebrates as part of a pilot study associated with this report—(1) below Granite Creek, (2) near Campbell Ranch, (3) at the U.S. Geological Survey Paulden gage, (4) at the Perkinsville Bridge, (5) at the USGS Clarkdale gage, and (6) near the Reitz Ranch property. A nonmetric multidimensional scaling ordination of macroinvertebrate assemblages showed that the Verde River below Granite Creek site was different from the five other sites and that the Perkinsville Bridge and near Reitz Ranch samples had similar community structure. The near Campbell Ranch and Paulden gage locations had similar microhabitat characteristics, with the exception of riparian cover, yet the assemblage structure was very different. The different community composition at Verde River below Granite Creek was likely due to it having the smallest substrate sizes, lowest velocities, shallowest depths, and most riparian cover of the six sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175100","collaboration":"Prepared in cooperation with The Nature Conservancy and Salt River Project","usgsCitation":"Paretti, N.V., Brasher, A.M.D., Pearlstein, S.L., Skow, D.M., Gungle, Bruce, and Garner, B.D., 2018, Preliminary synthesis and assessment of environmental flows in the middle Verde River watershed, Arizona: U.S. Geological Survey Scientific Investigations Report 2017–5100, 104 p., https://doi.org/10.3133/sir20175100.","productDescription":"Report: xii; 104 p.; 3 Tables","numberOfPages":"120","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084364","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":354141,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_table14.csv","text":"Table 14","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"Scientific Investigation Report 2017-5100 Table 12"},{"id":354142,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_tables12_14.xlsx","text":"Table 12 and 14","size":"25 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Scientific Investigation Report 2017-5100 Table 12 and 14 Excel file"},{"id":354139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigation Report 2017-5100"},{"id":354138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5100/coverthb.jpg"},{"id":354140,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_table12.csv","text":"Table 12","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"Scientific Investigation Report 2017-5100 Table 12"}],"country":"United States","state":"Arizona","otherGeospatial":"Verde River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 34.5\n ],\n [\n -112.5,\n 34.5\n ],\n [\n -112.5,\n 35.5\n ],\n [\n -111.5,\n 35.5\n ],\n [\n -111.5,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719